diff --git a/examples/automl_example/callibration.py b/examples/automl_example/callibration.py index 43d5dcd7c..1a9089a55 100644 --- a/examples/automl_example/callibration.py +++ b/examples/automl_example/callibration.py @@ -6,7 +6,7 @@ from fedot_ind.api.main import FedotIndustrial from fedot_ind.api.utils.data import init_input_data from fedot_ind.tools.loader import DataLoader - +from sktime.transformations.panel.signature_based import SignatureTransformer # sklearn-compatible interface class SklearnCompatibleClassifier(BaseEstimator, ClassifierMixin): diff --git a/examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb b/examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb index e35dfa2a3..93c164ec4 100644 --- a/examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb +++ b/examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "id": "366f5ea1-3ff8-4dd3-9fc3-583cdc133b5c", "metadata": { "pycharm": { @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 2, "outputs": [], "source": [ "path_to_datasets = PROJECT_PATH + '/benchmark/results/ts_multi_classification'\n", @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 3, "id": "44f3a1af-9b8d-49ea-9d23-8a386e3f2fcf", "metadata": { "pycharm": { @@ -49,6 +49,15 @@ "name": "stdout", "output_type": "stream", "text": [ + "-----------------------------------------------------\n", + "Result for dataset - AtrialFibrillation\n", + "old_metric - 0.267\n", + "new metric - 0.3333333333333333\n", + "new metric_tuned - 0\n", + "server metric - 0.1333333333333333\n", + "best_metric - 0.3333333333333333\n", + "-----------------------------------------------------\n", + "\n", "-----------------------------------------------------\n", "Result for dataset - DuckDuckGeese\n", "old_metric - 0.4\n", @@ -59,6 +68,15 @@ "-----------------------------------------------------\n", "\n", "-----------------------------------------------------\n", + "Result for dataset - EigenWorms\n", + "old_metric - 0.786\n", + "new metric - 0.8931297709923665\n", + "new metric_tuned - 0\n", + "server metric - 0\n", + "best_metric - 0.8931297709923665\n", + "-----------------------------------------------------\n", + "\n", + "-----------------------------------------------------\n", "Result for dataset - ERing\n", "old_metric - 0.926\n", "new metric - 0.9222222222222224\n", @@ -70,10 +88,19 @@ "-----------------------------------------------------\n", "Result for dataset - EthanolConcentration\n", "old_metric - 0.281\n", - "new metric - 0.4866920152091255\n", + "new metric - 0.3155893536121673\n", "new metric_tuned - 0\n", "server metric - 0.2965779467680608\n", - "best_metric - 0.4866920152091255\n", + "best_metric - 0.3155893536121673\n", + "-----------------------------------------------------\n", + "\n", + "-----------------------------------------------------\n", + "Result for dataset - Handwriting\n", + "old_metric - 0.408\n", + "new metric - 0.4682352941176471\n", + "new metric_tuned - 0\n", + "server metric - 0.3317647058823529\n", + "best_metric - 0.4682352941176471\n", "-----------------------------------------------------\n", "\n", "-----------------------------------------------------\n", @@ -85,6 +112,8 @@ "best_metric - 0.56\n", "-----------------------------------------------------\n", "\n", + "[Errno 2] No such file or directory: 'D:\\\\WORK\\\\Repo\\\\Industiral\\\\IndustrialTS\\\\benchmark\\\\results\\\\ts_multi_classification\\\\PhonemeSpectra\\\\metrics_report.csv'\n", + "PhonemeSpectra\n", "-----------------------------------------------------\n", "Result for dataset - SelfRegulationSCP1\n", "old_metric - 0.785\n", @@ -93,6 +122,24 @@ "server metric - 0.8464163822525598\n", "best_metric - 0.8532423208191127\n", "-----------------------------------------------------\n", + "\n", + "-----------------------------------------------------\n", + "Result for dataset - StandWalkJump\n", + "old_metric - 0.333\n", + "new metric - 0.0666666666666666\n", + "new metric_tuned - 0\n", + "server metric - 0.2\n", + "best_metric - 0.333\n", + "-----------------------------------------------------\n", + "\n", + "-----------------------------------------------------\n", + "Result for dataset - UWaveGestureLibrary\n", + "old_metric - 0.833\n", + "new metric - 0.846875\n", + "new metric_tuned - 0\n", + "server metric - 0\n", + "best_metric - 0.846875\n", + "-----------------------------------------------------\n", "\n" ] } @@ -132,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 4, "outputs": [], "source": [ "clf_comp['Fedot_Industrial_best'] = clf_comp.apply(lambda row: max(row.loc['Fedot_Industrial'],row.loc['Fedot_Industrial_finetuned']), axis=1)\n", @@ -147,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 5, "outputs": [], "source": [ "def categorize_dataset(metric):\n", @@ -169,13 +216,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 6, "outputs": [ { "data": { - "text/plain": "DTW_I 0.592154\nRISE 0.650269\nTSF 0.651077\nDTW_D 0.653808\nCBOSS 0.656500\nDTW_A 0.663680\nResNet 0.668462\ngRSF 0.675269\nTapNet 0.679450\nSTC 0.700885\nTDE 0.703115\nHC1 0.710692\nArsenal 0.715462\nInceptionTime 0.716700\nROCKET 0.724269\nmrseql 0.724600\nCIF 0.726000\nFedot_Industrial_best 0.728921\nDrCIF 0.732385\nMUSE 0.736350\nHC2 0.747846\ndtype: float64" + "text/plain": "DTW_I 0.592154\nRISE 0.650269\nTSF 0.651077\nDTW_D 0.653808\nCBOSS 0.656500\nDTW_A 0.663680\nResNet 0.668462\ngRSF 0.675269\nTapNet 0.679450\nSTC 0.700885\nTDE 0.703115\nHC1 0.710692\nArsenal 0.715462\nInceptionTime 0.716700\nROCKET 0.724269\nmrseql 0.724600\nCIF 0.726000\nFedot_Industrial_best 0.731862\nDrCIF 0.732385\nMUSE 0.736350\nHC2 0.747846\ndtype: float64" }, - "execution_count": 33, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 7, "outputs": [], "source": [ "not_stable_models = []" @@ -207,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "outputs": [], "source": [ "stable_models = [x for x in clf_comp.columns if x not in not_stable_models]\n", @@ -225,14 +272,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 10, "outputs": [ { "data": { - "text/plain": " Arsenal CBOSS CIF DrCIF DTW_A DTW_D DTW_I \\\nBasicMotions 1.000 1.000 1.000 1.000 1.000 0.975 0.725 \nCricket 1.000 0.986 0.986 0.986 1.000 1.000 0.958 \nLSST 0.642 0.435 0.573 0.556 0.567 0.551 0.490 \nFingerMovements 0.530 0.480 0.520 0.600 0.510 0.530 0.510 \nHandMovementDirection 0.473 0.189 0.595 0.527 0.203 0.189 0.189 \nNATOPS 0.883 0.861 0.856 0.844 0.883 0.883 0.817 \nPenDigits 0.983 0.908 0.967 0.977 0.977 0.977 0.973 \nRacketSports 0.901 0.882 0.882 0.901 0.842 0.803 0.849 \nHeartbeat 0.741 0.722 0.780 0.790 0.693 0.717 0.615 \nAtrialFibrillation 0.133 0.267 0.333 0.333 0.267 0.200 0.267 \nSelfRegulationSCP2 0.494 0.489 0.500 0.494 0.522 0.539 0.472 \nStandWalkJump 0.533 0.333 0.400 0.533 0.333 0.200 0.267 \nEigenWorms 0.901 0.618 0.916 0.924 NaN 0.618 0.595 \nPEMS-SF 0.827 0.983 1.000 1.000 0.734 0.711 0.740 \nDuckDuckGeese 0.460 0.380 0.440 0.540 0.500 0.580 0.300 \nERing 0.981 0.907 0.981 0.993 0.926 0.915 0.911 \nPhonemeSpectra 0.277 0.195 0.265 0.308 0.151 0.151 0.105 \nHandwriting 0.541 0.472 0.356 0.346 0.607 0.607 0.376 \nEpilepsy 0.986 1.000 0.986 0.978 0.978 0.964 0.630 \nMotorImagery 0.530 0.590 0.500 0.440 0.500 0.500 0.360 \nSelfRegulationSCP1 0.846 0.805 0.860 0.877 0.785 0.775 0.775 \nArticularyWordRecognition 0.993 0.990 0.983 0.980 0.987 0.987 0.953 \nFaceDetection 0.653 0.516 0.627 0.620 0.528 0.529 0.516 \nLibras 0.906 0.844 0.911 0.894 0.883 0.872 0.833 \nEthanolConcentration 0.460 0.361 0.734 0.692 0.316 0.323 0.304 \nUWaveGestureLibrary 0.928 0.856 0.925 0.909 0.900 0.903 0.866 \n\n gRSF HC1 HC2 ... RISE ROCKET STC \\\nBasicMotions 1.000 1.000 1.000 ... 1.000 1.000 0.975 \nCricket 0.986 0.986 1.000 ... 0.986 1.000 0.986 \nLSST 0.588 0.575 0.643 ... 0.509 0.637 0.587 \nFingerMovements 0.580 0.550 0.530 ... 0.560 0.540 0.510 \nHandMovementDirection 0.419 0.446 0.473 ... 0.297 0.514 0.392 \nNATOPS 0.844 0.889 0.894 ... 0.839 0.889 0.872 \nPenDigits 0.935 0.934 0.979 ... 0.832 0.983 0.941 \nRacketSports 0.882 0.888 0.908 ... 0.809 0.895 0.888 \nHeartbeat 0.761 0.722 0.732 ... 0.732 0.746 0.722 \nAtrialFibrillation 0.267 0.133 0.267 ... 0.267 0.067 0.267 \nSelfRegulationSCP2 0.517 0.461 0.500 ... 0.494 0.572 0.533 \nStandWalkJump 0.333 0.333 0.467 ... 0.267 0.533 0.467 \nEigenWorms 0.817 0.634 0.947 ... 0.817 0.908 0.779 \nPEMS-SF 0.908 0.983 1.000 ... 0.994 0.832 0.971 \nDuckDuckGeese 0.400 0.480 0.560 ... 0.460 0.520 0.360 \nERing 0.952 0.970 0.989 ... 0.859 0.985 0.889 \nPhonemeSpectra 0.224 0.321 0.290 ... 0.269 0.276 0.295 \nHandwriting 0.375 0.482 0.548 ... 0.194 0.595 0.288 \nEpilepsy 0.978 1.000 1.000 ... 1.000 0.993 0.993 \nMotorImagery 0.500 0.610 0.540 ... 0.550 0.580 0.500 \nSelfRegulationSCP1 0.823 0.853 0.891 ... 0.724 0.843 0.840 \nArticularyWordRecognition 0.983 0.990 0.993 ... 0.963 0.993 0.990 \nFaceDetection 0.548 0.656 0.660 ... 0.508 0.644 0.646 \nLibras 0.694 0.900 0.933 ... 0.806 0.900 0.861 \nEthanolConcentration 0.346 0.791 0.772 ... 0.487 0.445 0.821 \nUWaveGestureLibrary 0.897 0.891 0.928 ... 0.684 0.941 0.850 \n\n TapNet TDE TSF Fedot_Industrial_best \\\nBasicMotions 1.000 1.000 1.000 1.000000 \nCricket 1.000 0.986 0.931 1.000000 \nLSST 0.513 0.570 0.350 0.688000 \nFingerMovements 0.470 0.560 0.580 0.590000 \nHandMovementDirection 0.338 0.378 0.486 0.541000 \nNATOPS 0.811 0.839 0.800 0.961000 \nPenDigits 0.856 0.935 0.892 0.986000 \nRacketSports 0.875 0.836 0.888 0.908000 \nHeartbeat 0.790 0.746 0.741 0.780000 \nAtrialFibrillation 0.200 0.267 0.200 0.267000 \nSelfRegulationSCP2 0.483 0.500 0.483 0.500000 \nStandWalkJump 0.133 0.333 0.333 0.333000 \nEigenWorms NaN 0.939 0.740 0.786000 \nPEMS-SF NaN 1.000 0.983 0.954000 \nDuckDuckGeese NaN 0.340 0.220 0.660000 \nERing 0.904 0.963 0.881 0.926000 \nPhonemeSpectra NaN 0.246 0.137 0.222000 \nHandwriting 0.281 0.561 0.366 0.408000 \nEpilepsy 0.957 0.993 0.978 0.978000 \nMotorImagery NaN 0.590 0.480 0.560000 \nSelfRegulationSCP1 0.935 0.812 0.840 0.853242 \nArticularyWordRecognition 0.957 0.993 0.953 0.977000 \nFaceDetection NaN 0.564 0.640 0.877000 \nLibras 0.878 0.850 0.806 0.877000 \nEthanolConcentration 0.308 0.555 0.445 0.486692 \nUWaveGestureLibrary 0.900 0.925 0.775 0.833000 \n\n Difference % Metric dispersion by dataset \\\nBasicMotions -0.000000 6.261831 \nCricket -0.000000 1.619771 \nLSST 4.500000 11.632272 \nFingerMovements -1.000000 5.657759 \nHandMovementDirection -5.400000 21.047922 \nNATOPS -1.700000 4.559055 \nPenDigits -0.200000 4.494429 \nRacketSports -2.000000 3.546466 \nHeartbeat -1.000000 6.502006 \nAtrialFibrillation -13.300000 19.569692 \nSelfRegulationSCP2 -7.200000 4.979902 \nStandWalkJump -20.000000 20.396578 \nEigenWorms -16.100000 21.703985 \nPEMS-SF -4.600000 11.063032 \nDuckDuckGeese 4.000000 17.545343 \nERing -6.700000 4.616256 \nPhonemeSpectra -9.900000 21.489720 \nHandwriting -23.400000 20.371838 \nEpilepsy -2.200000 8.077395 \nMotorImagery -5.000000 10.244262 \nSelfRegulationSCP1 -8.175768 6.150232 \nArticularyWordRecognition -1.600000 1.395131 \nFaceDetection 21.700000 8.897818 \nLibras -6.700000 5.853756 \nEthanolConcentration -33.430798 23.028829 \nUWaveGestureLibrary -10.800000 6.456548 \n\n dataset_category \nBasicMotions Normal to solve dataset \nCricket Easy to solve dataset \nLSST Hard to solve dataset \nFingerMovements Normal to solve dataset \nHandMovementDirection Extraordinary Hard to solve dataset \nNATOPS Easy to solve dataset \nPenDigits Easy to solve dataset \nRacketSports Easy to solve dataset \nHeartbeat Normal to solve dataset \nAtrialFibrillation Extraordinary Hard to solve dataset \nSelfRegulationSCP2 Easy to solve dataset \nStandWalkJump Extraordinary Hard to solve dataset \nEigenWorms Extraordinary Hard to solve dataset \nPEMS-SF Hard to solve dataset \nDuckDuckGeese Extraordinary Hard to solve dataset \nERing Easy to solve dataset \nPhonemeSpectra Extraordinary Hard to solve dataset \nHandwriting Extraordinary Hard to solve dataset \nEpilepsy Normal to solve dataset \nMotorImagery Hard to solve dataset \nSelfRegulationSCP1 Normal to solve dataset \nArticularyWordRecognition Easy to solve dataset \nFaceDetection Normal to solve dataset \nLibras Normal to solve dataset \nEthanolConcentration Extraordinary Hard to solve dataset \nUWaveGestureLibrary Normal to solve dataset \n\n[26 rows x 24 columns]", - "text/html": "
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ArsenalCBOSSCIFDrCIFDTW_ADTW_DDTW_IgRSFHC1HC2...RISEROCKETSTCTapNetTDETSFFedot_Industrial_bestDifference %Metric dispersion by datasetdataset_category
BasicMotions1.0001.0001.0001.0001.0000.9750.7251.0001.0001.000...1.0001.0000.9751.0001.0001.0001.000000-0.0000006.261831Normal to solve dataset
Cricket1.0000.9860.9860.9861.0001.0000.9580.9860.9861.000...0.9861.0000.9861.0000.9860.9311.000000-0.0000001.619771Easy to solve dataset
LSST0.6420.4350.5730.5560.5670.5510.4900.5880.5750.643...0.5090.6370.5870.5130.5700.3500.6880004.50000011.632272Hard to solve dataset
FingerMovements0.5300.4800.5200.6000.5100.5300.5100.5800.5500.530...0.5600.5400.5100.4700.5600.5800.590000-1.0000005.657759Normal to solve dataset
HandMovementDirection0.4730.1890.5950.5270.2030.1890.1890.4190.4460.473...0.2970.5140.3920.3380.3780.4860.541000-5.40000021.047922Extraordinary Hard to solve dataset
NATOPS0.8830.8610.8560.8440.8830.8830.8170.8440.8890.894...0.8390.8890.8720.8110.8390.8000.961000-1.7000004.559055Easy to solve dataset
PenDigits0.9830.9080.9670.9770.9770.9770.9730.9350.9340.979...0.8320.9830.9410.8560.9350.8920.986000-0.2000004.494429Easy to solve dataset
RacketSports0.9010.8820.8820.9010.8420.8030.8490.8820.8880.908...0.8090.8950.8880.8750.8360.8880.908000-2.0000003.546466Easy to solve dataset
Heartbeat0.7410.7220.7800.7900.6930.7170.6150.7610.7220.732...0.7320.7460.7220.7900.7460.7410.780000-1.0000006.502006Normal to solve dataset
AtrialFibrillation0.1330.2670.3330.3330.2670.2000.2670.2670.1330.267...0.2670.0670.2670.2000.2670.2000.267000-13.30000019.569692Extraordinary Hard to solve dataset
SelfRegulationSCP20.4940.4890.5000.4940.5220.5390.4720.5170.4610.500...0.4940.5720.5330.4830.5000.4830.500000-7.2000004.979902Easy to solve dataset
StandWalkJump0.5330.3330.4000.5330.3330.2000.2670.3330.3330.467...0.2670.5330.4670.1330.3330.3330.333000-20.00000020.396578Extraordinary Hard to solve dataset
EigenWorms0.9010.6180.9160.924NaN0.6180.5950.8170.6340.947...0.8170.9080.779NaN0.9390.7400.786000-16.10000021.703985Extraordinary Hard to solve dataset
PEMS-SF0.8270.9831.0001.0000.7340.7110.7400.9080.9831.000...0.9940.8320.971NaN1.0000.9830.954000-4.60000011.063032Hard to solve dataset
DuckDuckGeese0.4600.3800.4400.5400.5000.5800.3000.4000.4800.560...0.4600.5200.360NaN0.3400.2200.6600004.00000017.545343Extraordinary Hard to solve dataset
ERing0.9810.9070.9810.9930.9260.9150.9110.9520.9700.989...0.8590.9850.8890.9040.9630.8810.926000-6.7000004.616256Easy to solve dataset
PhonemeSpectra0.2770.1950.2650.3080.1510.1510.1050.2240.3210.290...0.2690.2760.295NaN0.2460.1370.222000-9.90000021.489720Extraordinary Hard to solve dataset
Handwriting0.5410.4720.3560.3460.6070.6070.3760.3750.4820.548...0.1940.5950.2880.2810.5610.3660.408000-23.40000020.371838Extraordinary Hard to solve dataset
Epilepsy0.9861.0000.9860.9780.9780.9640.6300.9781.0001.000...1.0000.9930.9930.9570.9930.9780.978000-2.2000008.077395Normal to solve dataset
MotorImagery0.5300.5900.5000.4400.5000.5000.3600.5000.6100.540...0.5500.5800.500NaN0.5900.4800.560000-5.00000010.244262Hard to solve dataset
SelfRegulationSCP10.8460.8050.8600.8770.7850.7750.7750.8230.8530.891...0.7240.8430.8400.9350.8120.8400.853242-8.1757686.150232Normal to solve dataset
ArticularyWordRecognition0.9930.9900.9830.9800.9870.9870.9530.9830.9900.993...0.9630.9930.9900.9570.9930.9530.977000-1.6000001.395131Easy to solve dataset
FaceDetection0.6530.5160.6270.6200.5280.5290.5160.5480.6560.660...0.5080.6440.646NaN0.5640.6400.87700021.7000008.897818Normal to solve dataset
Libras0.9060.8440.9110.8940.8830.8720.8330.6940.9000.933...0.8060.9000.8610.8780.8500.8060.877000-6.7000005.853756Normal to solve dataset
EthanolConcentration0.4600.3610.7340.6920.3160.3230.3040.3460.7910.772...0.4870.4450.8210.3080.5550.4450.486692-33.43079823.028829Extraordinary Hard to solve dataset
UWaveGestureLibrary0.9280.8560.9250.9090.9000.9030.8660.8970.8910.928...0.6840.9410.8500.9000.9250.7750.833000-10.8000006.456548Normal to solve dataset
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" + "text/plain": " Arsenal CBOSS CIF DrCIF DTW_A DTW_D DTW_I \\\nBasicMotions 1.000 1.000 1.000 1.000 1.000 0.975 0.725 \nCricket 1.000 0.986 0.986 0.986 1.000 1.000 0.958 \nLSST 0.642 0.435 0.573 0.556 0.567 0.551 0.490 \nFingerMovements 0.530 0.480 0.520 0.600 0.510 0.530 0.510 \nHandMovementDirection 0.473 0.189 0.595 0.527 0.203 0.189 0.189 \nNATOPS 0.883 0.861 0.856 0.844 0.883 0.883 0.817 \nPenDigits 0.983 0.908 0.967 0.977 0.977 0.977 0.973 \nRacketSports 0.901 0.882 0.882 0.901 0.842 0.803 0.849 \nHeartbeat 0.741 0.722 0.780 0.790 0.693 0.717 0.615 \nAtrialFibrillation 0.133 0.267 0.333 0.333 0.267 0.200 0.267 \nSelfRegulationSCP2 0.494 0.489 0.500 0.494 0.522 0.539 0.472 \nStandWalkJump 0.533 0.333 0.400 0.533 0.333 0.200 0.267 \nEigenWorms 0.901 0.618 0.916 0.924 NaN 0.618 0.595 \nPEMS-SF 0.827 0.983 1.000 1.000 0.734 0.711 0.740 \nDuckDuckGeese 0.460 0.380 0.440 0.540 0.500 0.580 0.300 \nERing 0.981 0.907 0.981 0.993 0.926 0.915 0.911 \nPhonemeSpectra 0.277 0.195 0.265 0.308 0.151 0.151 0.105 \nHandwriting 0.541 0.472 0.356 0.346 0.607 0.607 0.376 \nEpilepsy 0.986 1.000 0.986 0.978 0.978 0.964 0.630 \nMotorImagery 0.530 0.590 0.500 0.440 0.500 0.500 0.360 \nSelfRegulationSCP1 0.846 0.805 0.860 0.877 0.785 0.775 0.775 \nArticularyWordRecognition 0.993 0.990 0.983 0.980 0.987 0.987 0.953 \nFaceDetection 0.653 0.516 0.627 0.620 0.528 0.529 0.516 \nLibras 0.906 0.844 0.911 0.894 0.883 0.872 0.833 \nEthanolConcentration 0.460 0.361 0.734 0.692 0.316 0.323 0.304 \nUWaveGestureLibrary 0.928 0.856 0.925 0.909 0.900 0.903 0.866 \n\n gRSF HC1 HC2 ... RISE ROCKET STC \\\nBasicMotions 1.000 1.000 1.000 ... 1.000 1.000 0.975 \nCricket 0.986 0.986 1.000 ... 0.986 1.000 0.986 \nLSST 0.588 0.575 0.643 ... 0.509 0.637 0.587 \nFingerMovements 0.580 0.550 0.530 ... 0.560 0.540 0.510 \nHandMovementDirection 0.419 0.446 0.473 ... 0.297 0.514 0.392 \nNATOPS 0.844 0.889 0.894 ... 0.839 0.889 0.872 \nPenDigits 0.935 0.934 0.979 ... 0.832 0.983 0.941 \nRacketSports 0.882 0.888 0.908 ... 0.809 0.895 0.888 \nHeartbeat 0.761 0.722 0.732 ... 0.732 0.746 0.722 \nAtrialFibrillation 0.267 0.133 0.267 ... 0.267 0.067 0.267 \nSelfRegulationSCP2 0.517 0.461 0.500 ... 0.494 0.572 0.533 \nStandWalkJump 0.333 0.333 0.467 ... 0.267 0.533 0.467 \nEigenWorms 0.817 0.634 0.947 ... 0.817 0.908 0.779 \nPEMS-SF 0.908 0.983 1.000 ... 0.994 0.832 0.971 \nDuckDuckGeese 0.400 0.480 0.560 ... 0.460 0.520 0.360 \nERing 0.952 0.970 0.989 ... 0.859 0.985 0.889 \nPhonemeSpectra 0.224 0.321 0.290 ... 0.269 0.276 0.295 \nHandwriting 0.375 0.482 0.548 ... 0.194 0.595 0.288 \nEpilepsy 0.978 1.000 1.000 ... 1.000 0.993 0.993 \nMotorImagery 0.500 0.610 0.540 ... 0.550 0.580 0.500 \nSelfRegulationSCP1 0.823 0.853 0.891 ... 0.724 0.843 0.840 \nArticularyWordRecognition 0.983 0.990 0.993 ... 0.963 0.993 0.990 \nFaceDetection 0.548 0.656 0.660 ... 0.508 0.644 0.646 \nLibras 0.694 0.900 0.933 ... 0.806 0.900 0.861 \nEthanolConcentration 0.346 0.791 0.772 ... 0.487 0.445 0.821 \nUWaveGestureLibrary 0.897 0.891 0.928 ... 0.684 0.941 0.850 \n\n TapNet TDE TSF Fedot_Industrial_best \\\nBasicMotions 1.000 1.000 1.000 1.000000 \nCricket 1.000 0.986 0.931 1.000000 \nLSST 0.513 0.570 0.350 0.688000 \nFingerMovements 0.470 0.560 0.580 0.590000 \nHandMovementDirection 0.338 0.378 0.486 0.541000 \nNATOPS 0.811 0.839 0.800 0.961000 \nPenDigits 0.856 0.935 0.892 0.986000 \nRacketSports 0.875 0.836 0.888 0.908000 \nHeartbeat 0.790 0.746 0.741 0.780000 \nAtrialFibrillation 0.200 0.267 0.200 0.333333 \nSelfRegulationSCP2 0.483 0.500 0.483 0.500000 \nStandWalkJump 0.133 0.333 0.333 0.333000 \nEigenWorms NaN 0.939 0.740 0.893130 \nPEMS-SF NaN 1.000 0.983 0.954000 \nDuckDuckGeese NaN 0.340 0.220 0.660000 \nERing 0.904 0.963 0.881 0.926000 \nPhonemeSpectra NaN 0.246 0.137 0.222000 \nHandwriting 0.281 0.561 0.366 0.468235 \nEpilepsy 0.957 0.993 0.978 0.978000 \nMotorImagery NaN 0.590 0.480 0.560000 \nSelfRegulationSCP1 0.935 0.812 0.840 0.853242 \nArticularyWordRecognition 0.957 0.993 0.953 0.977000 \nFaceDetection NaN 0.564 0.640 0.877000 \nLibras 0.878 0.850 0.806 0.877000 \nEthanolConcentration 0.308 0.555 0.445 0.315589 \nUWaveGestureLibrary 0.900 0.925 0.775 0.846875 \n\n Difference % Metric dispersion by dataset \\\nBasicMotions -0.000000 6.261831 \nCricket -0.000000 1.619771 \nLSST 4.500000 11.632272 \nFingerMovements -1.000000 5.657759 \nHandMovementDirection -5.400000 21.047922 \nNATOPS -1.700000 4.559055 \nPenDigits -0.200000 4.494429 \nRacketSports -2.000000 3.546466 \nHeartbeat -1.000000 6.502006 \nAtrialFibrillation -6.666667 19.569692 \nSelfRegulationSCP2 -7.200000 4.979902 \nStandWalkJump -20.000000 20.396578 \nEigenWorms -5.387023 21.703985 \nPEMS-SF -4.600000 11.063032 \nDuckDuckGeese 4.000000 17.545343 \nERing -6.700000 4.616256 \nPhonemeSpectra -9.900000 21.489720 \nHandwriting -17.376471 20.371838 \nEpilepsy -2.200000 8.077395 \nMotorImagery -5.000000 10.244262 \nSelfRegulationSCP1 -8.175768 6.150232 \nArticularyWordRecognition -1.600000 1.395131 \nFaceDetection 21.700000 8.897818 \nLibras -6.700000 5.853756 \nEthanolConcentration -50.541065 23.028829 \nUWaveGestureLibrary -9.412500 6.456548 \n\n dataset_category \nBasicMotions Normal to solve dataset \nCricket Easy to solve dataset \nLSST Hard to solve dataset \nFingerMovements Normal to solve dataset \nHandMovementDirection Extraordinary Hard to solve dataset \nNATOPS Easy to solve dataset \nPenDigits Easy to solve dataset \nRacketSports Easy to solve dataset \nHeartbeat Normal to solve dataset \nAtrialFibrillation Extraordinary Hard to solve dataset \nSelfRegulationSCP2 Easy to solve dataset \nStandWalkJump Extraordinary Hard to solve dataset \nEigenWorms Extraordinary Hard to solve dataset \nPEMS-SF Hard to solve dataset \nDuckDuckGeese Extraordinary Hard to solve dataset \nERing Easy to solve dataset \nPhonemeSpectra Extraordinary Hard to solve dataset \nHandwriting Extraordinary Hard to solve dataset \nEpilepsy Normal to solve dataset \nMotorImagery Hard to solve dataset \nSelfRegulationSCP1 Normal to solve dataset \nArticularyWordRecognition Easy to solve dataset \nFaceDetection Normal to solve dataset \nLibras Normal to solve dataset \nEthanolConcentration Extraordinary Hard to solve dataset \nUWaveGestureLibrary Normal to solve dataset \n\n[26 rows x 24 columns]", + "text/html": "
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ArsenalCBOSSCIFDrCIFDTW_ADTW_DDTW_IgRSFHC1HC2...RISEROCKETSTCTapNetTDETSFFedot_Industrial_bestDifference %Metric dispersion by datasetdataset_category
BasicMotions1.0001.0001.0001.0001.0000.9750.7251.0001.0001.000...1.0001.0000.9751.0001.0001.0001.000000-0.0000006.261831Normal to solve dataset
Cricket1.0000.9860.9860.9861.0001.0000.9580.9860.9861.000...0.9861.0000.9861.0000.9860.9311.000000-0.0000001.619771Easy to solve dataset
LSST0.6420.4350.5730.5560.5670.5510.4900.5880.5750.643...0.5090.6370.5870.5130.5700.3500.6880004.50000011.632272Hard to solve dataset
FingerMovements0.5300.4800.5200.6000.5100.5300.5100.5800.5500.530...0.5600.5400.5100.4700.5600.5800.590000-1.0000005.657759Normal to solve dataset
HandMovementDirection0.4730.1890.5950.5270.2030.1890.1890.4190.4460.473...0.2970.5140.3920.3380.3780.4860.541000-5.40000021.047922Extraordinary Hard to solve dataset
NATOPS0.8830.8610.8560.8440.8830.8830.8170.8440.8890.894...0.8390.8890.8720.8110.8390.8000.961000-1.7000004.559055Easy to solve dataset
PenDigits0.9830.9080.9670.9770.9770.9770.9730.9350.9340.979...0.8320.9830.9410.8560.9350.8920.986000-0.2000004.494429Easy to solve dataset
RacketSports0.9010.8820.8820.9010.8420.8030.8490.8820.8880.908...0.8090.8950.8880.8750.8360.8880.908000-2.0000003.546466Easy to solve dataset
Heartbeat0.7410.7220.7800.7900.6930.7170.6150.7610.7220.732...0.7320.7460.7220.7900.7460.7410.780000-1.0000006.502006Normal to solve dataset
AtrialFibrillation0.1330.2670.3330.3330.2670.2000.2670.2670.1330.267...0.2670.0670.2670.2000.2670.2000.333333-6.66666719.569692Extraordinary Hard to solve dataset
SelfRegulationSCP20.4940.4890.5000.4940.5220.5390.4720.5170.4610.500...0.4940.5720.5330.4830.5000.4830.500000-7.2000004.979902Easy to solve dataset
StandWalkJump0.5330.3330.4000.5330.3330.2000.2670.3330.3330.467...0.2670.5330.4670.1330.3330.3330.333000-20.00000020.396578Extraordinary Hard to solve dataset
EigenWorms0.9010.6180.9160.924NaN0.6180.5950.8170.6340.947...0.8170.9080.779NaN0.9390.7400.893130-5.38702321.703985Extraordinary Hard to solve dataset
PEMS-SF0.8270.9831.0001.0000.7340.7110.7400.9080.9831.000...0.9940.8320.971NaN1.0000.9830.954000-4.60000011.063032Hard to solve dataset
DuckDuckGeese0.4600.3800.4400.5400.5000.5800.3000.4000.4800.560...0.4600.5200.360NaN0.3400.2200.6600004.00000017.545343Extraordinary Hard to solve dataset
ERing0.9810.9070.9810.9930.9260.9150.9110.9520.9700.989...0.8590.9850.8890.9040.9630.8810.926000-6.7000004.616256Easy to solve dataset
PhonemeSpectra0.2770.1950.2650.3080.1510.1510.1050.2240.3210.290...0.2690.2760.295NaN0.2460.1370.222000-9.90000021.489720Extraordinary Hard to solve dataset
Handwriting0.5410.4720.3560.3460.6070.6070.3760.3750.4820.548...0.1940.5950.2880.2810.5610.3660.468235-17.37647120.371838Extraordinary Hard to solve dataset
Epilepsy0.9861.0000.9860.9780.9780.9640.6300.9781.0001.000...1.0000.9930.9930.9570.9930.9780.978000-2.2000008.077395Normal to solve dataset
MotorImagery0.5300.5900.5000.4400.5000.5000.3600.5000.6100.540...0.5500.5800.500NaN0.5900.4800.560000-5.00000010.244262Hard to solve dataset
SelfRegulationSCP10.8460.8050.8600.8770.7850.7750.7750.8230.8530.891...0.7240.8430.8400.9350.8120.8400.853242-8.1757686.150232Normal to solve dataset
ArticularyWordRecognition0.9930.9900.9830.9800.9870.9870.9530.9830.9900.993...0.9630.9930.9900.9570.9930.9530.977000-1.6000001.395131Easy to solve dataset
FaceDetection0.6530.5160.6270.6200.5280.5290.5160.5480.6560.660...0.5080.6440.646NaN0.5640.6400.87700021.7000008.897818Normal to solve dataset
Libras0.9060.8440.9110.8940.8830.8720.8330.6940.9000.933...0.8060.9000.8610.8780.8500.8060.877000-6.7000005.853756Normal to solve dataset
EthanolConcentration0.4600.3610.7340.6920.3160.3230.3040.3460.7910.772...0.4870.4450.8210.3080.5550.4450.315589-50.54106523.028829Extraordinary Hard to solve dataset
UWaveGestureLibrary0.9280.8560.9250.9090.9000.9030.8660.8970.8910.928...0.6840.9410.8500.9000.9250.7750.846875-9.4125006.456548Normal to solve dataset
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" }, - "execution_count": 37, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/benchmark_example/analysis of results/analysis_regr.ipynb b/examples/benchmark_example/analysis of results/analysis_regr.ipynb index 9cb510c07..7e7b7e21a 100644 --- a/examples/benchmark_example/analysis of results/analysis_regr.ipynb +++ b/examples/benchmark_example/analysis of results/analysis_regr.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "outputs": [], "source": [ "path_to_datasets = PROJECT_PATH + '/benchmark/results/ts_regression'\n", @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "id": "ef009804-6aa8-4bf0-911a-35c917e1238e", "metadata": { "pycharm": { @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "outputs": [], "source": [ "reg_comp = reg_comp[reg_comp['Fedot_Industrial_best']!=empty_result]" @@ -578,14 +578,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 5, "outputs": [ { "data": { "text/plain": " 1NN-DTW_RMSE 1NN-ED_RMSE 5NN-DTW_RMSE \\\nHouseholdPowerConsumption1 417.520861 534.266678 363.106601 \nAppliancesEnergy 5.739470 5.768104 4.504958 \nHouseholdPowerConsumption2 53.429559 76.024116 43.287550 \nIEEEPPG 23.987240 27.769715 20.452884 \nFloodModeling1 0.013817 0.018511 0.013124 \nBeijingPM25Quality 71.066726 73.245699 59.503839 \nBenzeneConcentration 4.402315 4.858189 5.053820 \nFloodModeling3 0.013074 0.017028 0.011562 \nBeijingPM10Quality 118.230571 119.246110 95.318785 \nFloodModeling2 0.011092 0.012949 0.012064 \nAustraliaRainfall 17.084390 17.477663 16.120195 \nNewsHeadlineSentiment 0.197916 0.199315 0.154958 \nNewsTitleSentiment 0.192523 0.192805 0.149818 \nLiveFuelMoistureContent 56.145576 56.685415 44.220169 \nBIDMC32SpO2 0.525750 2.508360 0.531987 \nBIDMC32HR 1.944837 6.544297 1.904489 \nBIDMC32RR 1.233577 2.136593 1.229752 \nCovid3Month 0.054553 0.053302 0.041270 \nHotwaterPredictor 1819.103151 1906.032299 1337.373398 \nSteamPredictor 42924.761750 55239.471520 34168.975130 \nChilledWaterPredictor 3350.285304 2224.276368 2420.350041 \nElectricityPredictor 666.809599 684.484545 469.378018 \nGasSensorArrayEthanol 0.342482 0.282927 0.268868 \nGasSensorArrayAcetone 0.317429 0.307368 0.259960 \nSierraNevadaMountainsSnow 63.580655 75.131205 53.707346 \nBitcoinSentiment 0.260328 0.264244 0.207365 \nEthereumSentiment 0.318791 0.333878 0.241217 \nCardanoSentiment 0.393083 0.421637 0.316565 \nBinanceCoinSentiment 0.484438 0.500171 0.372489 \nElectricMotorTemperature 8.840429 9.023510 7.604740 \nSolarRadiationAndalusia 0.823649 0.844081 0.645187 \nPrecipitationAndalusia 0.490903 0.503864 0.410596 \nAcousticContaminationMadrid 5.440282 6.591085 5.046155 \nVentilatorPressure 1.135626 0.983354 1.000072 \nOccupancyDetectionLight 185.122574 184.640670 155.141891 \nWindTurbinePower 276.399699 586.730016 238.769740 \nDhakaHourlyAirQuality 7.448274 6.442643 5.133465 \nDailyOilGasPrices 2.742105 2.822595 2.055256 \nWaveDataTension 21.337284 21.051859 16.516183 \nNaturalGasPricesSentiment 0.074277 0.074651 0.057354 \nDailyTemperatureLatitude 2.274194 2.930519 2.158074 \nMethaneMonitoringHomeActivity 0.693400 0.694371 0.540157 \nLPGasMonitoringHomeActivity 1.699540 1.696243 1.334805 \nAluminiumConcentration 382.557872 358.169450 324.538120 \nBoronConcentration 1.993851 1.861265 1.909409 \nCopperConcentration 2.481286 2.413716 1.934787 \nIronConcentration 88.669240 83.835078 77.742182 \nManganeseConcentration 121.990065 118.427219 101.108313 \nSodiumConcentration 659.380735 620.198671 453.954204 \nPhosphorusConcentration 22.153652 21.424030 24.305616 \nPotassiumConcentration 242.027142 221.960612 223.386480 \nMagnesiumConcentration 298.216850 228.529714 277.321165 \nSulphurConcentration 251.901880 251.159147 221.083366 \nZincConcentration 1.828631 1.889708 1.540422 \nCalciumConcentration 4236.151380 3027.549129 3446.114173 \n\n 5NN-ED_RMSE CNN_RMSE DrCIF_RMSE \\\nHouseholdPowerConsumption1 507.796826 6.045497e+02 1.657667e+02 \nAppliancesEnergy 4.470334 4.097874e+00 2.404670e+00 \nHouseholdPowerConsumption2 57.685928 5.152938e+01 3.063533e+01 \nIEEEPPG 23.655007 2.242689e+01 1.247458e+01 \nFloodModeling1 0.019009 1.723553e-02 9.462636e-03 \nBeijingPM25Quality 60.872956 9.988757e+01 4.712755e+01 \nBenzeneConcentration 4.240799 7.543093e+00 3.666043e+00 \nFloodModeling3 0.017738 1.096383e-02 6.438931e-03 \nBeijingPM10Quality 97.130645 1.275990e+02 7.057271e+01 \nFloodModeling2 0.013299 1.852107e-02 5.180100e-03 \nAustraliaRainfall 15.419262 1.000000e+10 1.000000e+10 \nNewsHeadlineSentiment 0.154884 1.196766e+01 1.455912e-01 \nNewsTitleSentiment 0.149231 2.308311e+01 1.405348e-01 \nLiveFuelMoistureContent 44.678597 4.147725e+01 4.061007e+01 \nBIDMC32SpO2 2.493010 1.717190e+00 4.198496e-01 \nBIDMC32HR 6.990050 5.229133e+00 1.270281e+00 \nBIDMC32RR 1.986163 3.536865e+00 9.154016e-01 \nCovid3Month 0.040783 6.759766e-02 3.977303e-02 \nHotwaterPredictor 1458.866739 1.174256e+03 1.246468e+03 \nSteamPredictor 38081.062240 3.061124e+04 3.113187e+04 \nChilledWaterPredictor 2200.831018 2.215134e+03 2.241064e+03 \nElectricityPredictor 493.478164 4.846374e+02 5.874044e+02 \nGasSensorArrayEthanol 0.265737 2.017014e-01 1.517741e-01 \nGasSensorArrayAcetone 0.262321 2.389416e-01 1.845477e-01 \nSierraNevadaMountainsSnow 66.961454 5.431997e+01 4.300014e+01 \nBitcoinSentiment 0.207243 2.462431e-01 1.970548e-01 \nEthereumSentiment 0.246046 2.716099e-01 2.234477e-01 \nCardanoSentiment 0.315416 4.370255e-01 2.973890e-01 \nBinanceCoinSentiment 0.383144 4.094964e-01 3.448162e-01 \nElectricMotorTemperature 7.833623 1.760786e+01 4.656284e+00 \nSolarRadiationAndalusia 0.655481 1.042590e+00 6.216146e-01 \nPrecipitationAndalusia 0.453094 9.854324e-01 3.851207e-01 \nAcousticContaminationMadrid 5.726287 6.712051e+00 4.119996e+00 \nVentilatorPressure 0.894525 2.163210e+00 6.166132e-01 \nOccupancyDetectionLight 157.236274 2.386848e+02 8.054537e+01 \nWindTurbinePower 466.186615 9.515196e+02 9.785142e+01 \nDhakaHourlyAirQuality 4.632155 8.680293e+00 3.171020e+00 \nDailyOilGasPrices 2.251424 2.150854e+00 1.594442e+00 \nWaveDataTension 16.544666 1.816305e+01 1.517911e+01 \nNaturalGasPricesSentiment 0.056623 6.482931e-02 5.968074e-02 \nDailyTemperatureLatitude 3.021106 5.427370e+00 1.950407e+00 \nMethaneMonitoringHomeActivity 0.541140 6.367615e-01 5.052408e-01 \nLPGasMonitoringHomeActivity 1.338572 1.339258e+00 1.262008e+00 \nAluminiumConcentration 306.064290 2.737297e+02 2.451929e+02 \nBoronConcentration 1.982375 3.608769e+00 1.762709e+00 \nCopperConcentration 1.953789 2.343641e+00 1.826204e+00 \nIronConcentration 72.378913 7.232745e+01 6.213210e+01 \nManganeseConcentration 98.898216 9.280130e+01 8.128308e+01 \nSodiumConcentration 444.132225 3.993961e+02 3.127763e+02 \nPhosphorusConcentration 23.357350 2.783047e+01 1.906393e+01 \nPotassiumConcentration 224.239115 2.614602e+02 1.676802e+02 \nMagnesiumConcentration 248.002202 2.983644e+02 1.608196e+02 \nSulphurConcentration 217.302684 2.465601e+02 2.060873e+02 \nZincConcentration 1.540109 1.496162e+00 1.429352e+00 \nCalciumConcentration 3095.929708 3.247370e+03 1.938747e+03 \n\n FCN_RMSE FPCR-Bs_RMSE FPCR_RMSE \\\nHouseholdPowerConsumption1 353.211342 110.606270 107.061828 \nAppliancesEnergy 4.452677 4.500813 4.419099 \nHouseholdPowerConsumption2 172.601053 41.344814 40.829072 \nIEEEPPG 7.190419 26.398372 26.395514 \nFloodModeling1 0.009510 0.020827 0.021319 \nBeijingPM25Quality 45.523481 61.336023 62.028248 \nBenzeneConcentration 0.596533 4.884306 5.241220 \nFloodModeling3 0.009331 0.018776 0.019448 \nBeijingPM10Quality 70.596483 92.628381 93.047781 \nFloodModeling2 0.004745 0.014591 0.013853 \nAustraliaRainfall 14.820553 14.822390 14.821999 \nNewsHeadlineSentiment 1.206852 0.141583 0.141565 \nNewsTitleSentiment 1.007102 0.136684 0.136614 \nLiveFuelMoistureContent 47.763911 41.467245 41.448372 \nBIDMC32SpO2 0.482227 3.252909 3.273603 \nBIDMC32HR 1.120176 13.043557 13.032991 \nBIDMC32RR 0.771526 2.952190 2.952411 \nCovid3Month 0.067899 199.920486 0.048775 \nHotwaterPredictor 1072.502657 1427.171255 1331.504687 \nSteamPredictor 29315.721000 32018.746920 31246.608530 \nChilledWaterPredictor 2215.939738 2364.754528 2332.364049 \nElectricityPredictor 609.970047 465.844347 455.941085 \nGasSensorArrayEthanol 0.306066 0.209465 0.207139 \nGasSensorArrayAcetone 0.294932 0.232775 0.244260 \nSierraNevadaMountainsSnow 36.415663 46.966215 47.103784 \nBitcoinSentiment 0.220598 0.215564 0.211556 \nEthereumSentiment 0.252702 0.235169 0.231336 \nCardanoSentiment 0.304449 0.342885 0.333628 \nBinanceCoinSentiment 0.366414 0.380741 0.383121 \nElectricMotorTemperature 6.593876 11.969364 11.979490 \nSolarRadiationAndalusia 0.670086 0.673903 0.657212 \nPrecipitationAndalusia 0.397996 0.415940 0.388695 \nAcousticContaminationMadrid 64.512385 5.664351 5.620265 \nVentilatorPressure 0.935243 2.225154 2.182645 \nOccupancyDetectionLight 129.605793 118.989563 114.206848 \nWindTurbinePower 1191.209810 891.458761 888.944783 \nDhakaHourlyAirQuality 10.265259 12.384106 12.368746 \nDailyOilGasPrices 2.069046 2.097964 2.052389 \nWaveDataTension 21.100020 14.107994 14.110484 \nNaturalGasPricesSentiment 0.099374 0.113517 0.112670 \nDailyTemperatureLatitude 6.156039 10.660307 10.700298 \nMethaneMonitoringHomeActivity 0.535491 0.589072 0.589213 \nLPGasMonitoringHomeActivity 1.297673 1.306738 1.306077 \nAluminiumConcentration 985.815086 277.958777 274.385013 \nBoronConcentration 5.724229 1.834673 1.814600 \nCopperConcentration 5.178149 1.900740 1.925904 \nIronConcentration 197.801726 75.611945 72.223676 \nManganeseConcentration 229.354966 98.033724 99.584134 \nSodiumConcentration 469.649236 399.916772 410.500936 \nPhosphorusConcentration 42.623989 26.104236 25.922738 \nPotassiumConcentration 497.219697 272.228356 262.982471 \nMagnesiumConcentration 1112.378767 324.661186 293.109564 \nSulphurConcentration 279.789674 201.620532 201.470538 \nZincConcentration 4.265433 1.436632 1.454666 \nCalciumConcentration 3539.993435 3094.429853 2983.270751 \n\n FreshPRINCE_RMSE ... ROCKET_RMSE \\\nHouseholdPowerConsumption1 1.040404e+02 ... 214.812814 \nAppliancesEnergy 2.053976e+00 ... 2.768555 \nHouseholdPowerConsumption2 2.939287e+01 ... 30.675485 \nIEEEPPG 1.011591e+01 ... 7.159721 \nFloodModeling1 8.750739e-03 ... 0.017304 \nBeijingPM25Quality 4.341873e+01 ... 67.005211 \nBenzeneConcentration 2.132254e+00 ... 2.217476 \nFloodModeling3 5.582533e-03 ... 0.017291 \nBeijingPM10Quality 6.650945e+01 ... 94.557863 \nFloodModeling2 5.027746e-03 ... 0.009274 \nAustraliaRainfall 1.000000e+10 ... 17.878905 \nNewsHeadlineSentiment 1.456120e-01 ... 0.146838 \nNewsTitleSentiment 1.406148e-01 ... 0.141759 \nLiveFuelMoistureContent 4.117283e+01 ... 44.509484 \nBIDMC32SpO2 3.407657e-01 ... 0.300322 \nBIDMC32HR 1.039876e+00 ... 0.656318 \nBIDMC32RR 6.882746e-01 ... 0.526479 \nCovid3Month 3.829917e-02 ... 0.042819 \nHotwaterPredictor 1.240377e+03 ... 1236.408458 \nSteamPredictor 3.101483e+04 ... 30813.803720 \nChilledWaterPredictor 2.479203e+03 ... 2517.901417 \nElectricityPredictor 5.826548e+02 ... 575.695827 \nGasSensorArrayEthanol 1.859832e-01 ... 0.213550 \nGasSensorArrayAcetone 2.010619e-01 ... 0.194288 \nSierraNevadaMountainsSnow 3.692058e+01 ... 68.547775 \nBitcoinSentiment 2.011734e-01 ... 0.268838 \nEthereumSentiment 2.258759e-01 ... 0.287077 \nCardanoSentiment 3.017266e-01 ... 0.306467 \nBinanceCoinSentiment 3.460515e-01 ... 0.387179 \nElectricMotorTemperature 4.650170e+00 ... 12.270972 \nSolarRadiationAndalusia 6.195325e-01 ... 0.644610 \nPrecipitationAndalusia 3.612930e-01 ... 0.345239 \nAcousticContaminationMadrid 3.281021e+00 ... 4.632819 \nVentilatorPressure 6.015145e-01 ... 0.852913 \nOccupancyDetectionLight 6.710728e+01 ... 111.422905 \nWindTurbinePower 4.465780e+01 ... 611.429184 \nDhakaHourlyAirQuality 1.784270e+00 ... 46.896156 \nDailyOilGasPrices 1.490442e+00 ... 2.275254 \nWaveDataTension 1.491209e+01 ... 15.143283 \nNaturalGasPricesSentiment 5.923535e-02 ... 0.090880 \nDailyTemperatureLatitude 1.782911e+00 ... 4.734260 \nMethaneMonitoringHomeActivity 5.305011e-01 ... 0.623893 \nLPGasMonitoringHomeActivity 1.295706e+00 ... 1.349483 \nAluminiumConcentration 2.699899e+02 ... 247.129053 \nBoronConcentration 1.880072e+00 ... 2.131067 \nCopperConcentration 1.845851e+00 ... 2.042072 \nIronConcentration 6.535273e+01 ... 59.652667 \nManganeseConcentration 8.462758e+01 ... 87.290996 \nSodiumConcentration 4.079309e+02 ... 1009.654912 \nPhosphorusConcentration 1.914935e+01 ... 23.400360 \nPotassiumConcentration 1.748586e+02 ... 209.230873 \nMagnesiumConcentration 1.679659e+02 ... 144.878976 \nSulphurConcentration 2.224243e+02 ... 272.034096 \nZincConcentration 1.589329e+00 ... 1.489652 \nCalciumConcentration 2.189777e+03 ... 2487.968147 \n\n RandF_RMSE ResNet_RMSE Ridge_RMSE \\\nHouseholdPowerConsumption1 239.492621 108.828881 2.035437e+02 \nAppliancesEnergy 3.986324 3.957921 4.695629e+00 \nHouseholdPowerConsumption2 39.071698 33.304921 5.771092e+01 \nIEEEPPG 21.655840 5.051279 4.750275e+01 \nFloodModeling1 0.018997 0.009046 2.095060e-02 \nBeijingPM25Quality 46.711947 44.166483 6.091479e+01 \nBenzeneConcentration 0.283166 0.302587 1.132592e+00 \nFloodModeling3 0.016680 0.007282 1.903150e-02 \nBeijingPM10Quality 71.712571 68.418040 9.235021e+01 \nFloodModeling2 0.009483 0.007550 1.416184e-02 \nAustraliaRainfall 15.191093 19.032231 1.000000e+10 \nNewsHeadlineSentiment 0.146199 0.147673 1.435795e-01 \nNewsTitleSentiment 0.141116 0.141528 1.388921e-01 \nLiveFuelMoistureContent 41.094026 47.347938 8.350272e+02 \nBIDMC32SpO2 1.851714 0.277936 3.357586e+00 \nBIDMC32HR 5.900761 0.768738 1.375567e+01 \nBIDMC32RR 1.943142 0.481028 3.017642e+00 \nCovid3Month 0.040183 0.124416 4.646435e-01 \nHotwaterPredictor 1310.439205 1145.645542 2.719383e+03 \nSteamPredictor 32219.241800 29429.960450 7.616053e+04 \nChilledWaterPredictor 2449.605008 2202.908042 4.346009e+03 \nElectricityPredictor 527.343198 589.427954 5.745584e+02 \nGasSensorArrayEthanol 0.254922 0.654548 1.848965e-01 \nGasSensorArrayAcetone 0.259360 0.548682 2.623136e-01 \nSierraNevadaMountainsSnow 52.216070 39.732449 5.227692e+01 \nBitcoinSentiment 0.199306 0.226852 2.324026e-01 \nEthereumSentiment 0.233020 0.255854 2.621682e-01 \nCardanoSentiment 0.295906 0.324041 3.881151e-01 \nBinanceCoinSentiment 0.350101 0.371933 5.139279e-01 \nElectricMotorTemperature 5.241818 5.911347 1.198277e+01 \nSolarRadiationAndalusia 0.630443 0.654886 7.698799e-01 \nPrecipitationAndalusia 0.394483 0.409187 7.985268e-01 \nAcousticContaminationMadrid 5.071709 5.127297 5.387562e+00 \nVentilatorPressure 0.671745 0.492728 2.072326e+00 \nOccupancyDetectionLight 100.189621 89.718024 2.249267e+02 \nWindTurbinePower 103.784273 1033.868527 6.220624e+04 \nDhakaHourlyAirQuality 5.571973 5.915342 1.245697e+01 \nDailyOilGasPrices 1.708196 1.938074 2.363609e+00 \nWaveDataTension 15.356970 18.332223 1.408012e+01 \nNaturalGasPricesSentiment 0.055981 0.143140 7.955113e-02 \nDailyTemperatureLatitude 3.116088 4.985807 1.003086e+01 \nMethaneMonitoringHomeActivity 0.525422 1.558559 5.887444e-01 \nLPGasMonitoringHomeActivity 1.303421 1.344633 1.304916e+00 \nAluminiumConcentration 293.337887 316.411353 2.370224e+02 \nBoronConcentration 2.315727 2.765997 1.802806e+00 \nCopperConcentration 1.951352 2.096999 1.819889e+00 \nIronConcentration 72.907851 73.217837 6.058250e+01 \nManganeseConcentration 93.774650 97.229102 8.461378e+01 \nSodiumConcentration 470.035579 478.427281 4.154721e+02 \nPhosphorusConcentration 23.611200 20.737713 2.274373e+01 \nPotassiumConcentration 227.961272 182.300959 2.112084e+02 \nMagnesiumConcentration 244.734176 166.348644 1.404055e+02 \nSulphurConcentration 223.069451 208.013712 2.136158e+02 \nZincConcentration 1.533721 1.638925 1.440314e+00 \nCalciumConcentration 2963.172908 3026.672964 2.103223e+03 \n\n RotF_RMSE SingleInception_RMSE \\\nHouseholdPowerConsumption1 1.992346e+02 116.202251 \nAppliancesEnergy 2.557591e+00 4.399413 \nHouseholdPowerConsumption2 3.759094e+01 34.585894 \nIEEEPPG 2.021281e+01 4.119866 \nFloodModeling1 1.798786e-02 0.010004 \nBeijingPM25Quality 4.283921e+01 42.008177 \nBenzeneConcentration 7.287375e-01 0.276406 \nFloodModeling3 1.568419e-02 0.010451 \nBeijingPM10Quality 6.631563e+01 66.519288 \nFloodModeling2 7.900159e-03 0.007653 \nAustraliaRainfall 1.000000e+10 15.299189 \nNewsHeadlineSentiment 1.494606e-01 0.148587 \nNewsTitleSentiment 1.441636e-01 0.142814 \nLiveFuelMoistureContent 4.133734e+01 47.022444 \nBIDMC32SpO2 1.644120e+00 0.314609 \nBIDMC32HR 4.884870e+00 0.765547 \nBIDMC32RR 1.711275e+00 0.550188 \nCovid3Month 4.169759e-02 0.074202 \nHotwaterPredictor 1.383306e+03 1162.325568 \nSteamPredictor 3.152469e+04 29823.924800 \nChilledWaterPredictor 2.437387e+03 2138.914979 \nElectricityPredictor 6.066627e+02 588.659847 \nGasSensorArrayEthanol 1.499463e-01 0.504130 \nGasSensorArrayAcetone 1.930651e-01 0.304125 \nSierraNevadaMountainsSnow 4.869493e+01 34.691728 \nBitcoinSentiment 2.014305e-01 0.224385 \nEthereumSentiment 2.294233e-01 0.257753 \nCardanoSentiment 3.052854e-01 0.357882 \nBinanceCoinSentiment 3.538278e-01 0.385787 \nElectricMotorTemperature 4.698074e+00 5.163396 \nSolarRadiationAndalusia 6.238755e-01 0.695240 \nPrecipitationAndalusia 3.690052e-01 0.388841 \nAcousticContaminationMadrid 5.126173e+00 4.429513 \nVentilatorPressure 5.685455e-01 0.406689 \nOccupancyDetectionLight 7.528684e+01 95.353950 \nWindTurbinePower 7.424638e+01 274.187508 \nDhakaHourlyAirQuality 2.702426e+00 5.476823 \nDailyOilGasPrices 1.559385e+00 2.149368 \nWaveDataTension 1.521209e+01 17.793210 \nNaturalGasPricesSentiment 5.858841e-02 0.341370 \nDailyTemperatureLatitude 2.677904e+00 1.780762 \nMethaneMonitoringHomeActivity 5.244137e-01 0.634288 \nLPGasMonitoringHomeActivity 1.289305e+00 1.385517 \nAluminiumConcentration 2.340929e+02 250.411638 \nBoronConcentration 1.885018e+00 1.801264 \nCopperConcentration 1.863294e+00 1.918499 \nIronConcentration 6.192114e+01 66.516998 \nManganeseConcentration 7.991396e+01 88.303869 \nSodiumConcentration 6.527057e+02 431.525344 \nPhosphorusConcentration 2.063641e+01 18.550513 \nPotassiumConcentration 1.862390e+02 177.300333 \nMagnesiumConcentration 1.665049e+02 135.343967 \nSulphurConcentration 2.150779e+02 210.114434 \nZincConcentration 1.509911e+00 1.659296 \nCalciumConcentration 1.783895e+03 2478.981119 \n\n TSF_RMSE XGBoost_RMSE \\\nHouseholdPowerConsumption1 2.761883e+02 227.507324 \nAppliancesEnergy 3.752866e+00 4.045985 \nHouseholdPowerConsumption2 3.601573e+01 38.955683 \nIEEEPPG 2.047200e+01 21.033698 \nFloodModeling1 1.102917e-02 0.019334 \nBeijingPM25Quality 6.167426e+01 42.147228 \nBenzeneConcentration 4.189510e+00 0.218806 \nFloodModeling3 8.546052e-03 0.017007 \nBeijingPM10Quality 8.465717e+01 66.474818 \nFloodModeling2 5.379332e-03 0.012971 \nAustraliaRainfall 1.000000e+10 16.282844 \nNewsHeadlineSentiment 1.422297e-01 0.142740 \nNewsTitleSentiment 1.371251e-01 0.137670 \nLiveFuelMoistureContent 4.074238e+01 42.553580 \nBIDMC32SpO2 1.275249e+00 1.674734 \nBIDMC32HR 3.986727e+00 4.975369 \nBIDMC32RR 1.417203e+00 1.812002 \nCovid3Month 3.895102e-02 0.044934 \nHotwaterPredictor 1.401285e+03 1424.823440 \nSteamPredictor 3.135775e+04 35238.808060 \nChilledWaterPredictor 2.286958e+03 3137.416197 \nElectricityPredictor 6.041935e+02 618.172663 \nGasSensorArrayEthanol 1.941129e-01 0.268107 \nGasSensorArrayAcetone 2.117950e-01 0.275739 \nSierraNevadaMountainsSnow 5.180834e+01 56.066891 \nBitcoinSentiment 2.097271e-01 0.208650 \nEthereumSentiment 2.344765e-01 0.252351 \nCardanoSentiment 2.998514e-01 0.324627 \nBinanceCoinSentiment 3.479485e-01 0.377766 \nElectricMotorTemperature 8.869247e+00 5.312691 \nSolarRadiationAndalusia 6.255512e-01 0.656600 \nPrecipitationAndalusia 4.023337e-01 0.401574 \nAcousticContaminationMadrid 4.602419e+00 5.255653 \nVentilatorPressure 1.848328e+00 0.704912 \nOccupancyDetectionLight 1.024337e+02 103.161750 \nWindTurbinePower 1.170780e+02 104.971863 \nDhakaHourlyAirQuality 4.813817e+00 4.946151 \nDailyOilGasPrices 1.684828e+00 1.716903 \nWaveDataTension 1.543533e+01 16.094344 \nNaturalGasPricesSentiment 5.479847e-02 0.060197 \nDailyTemperatureLatitude 2.151806e+00 2.942073 \nMethaneMonitoringHomeActivity 5.170912e-01 0.556360 \nLPGasMonitoringHomeActivity 1.280573e+00 1.383632 \nAluminiumConcentration 2.450510e+02 295.733413 \nBoronConcentration 2.038787e+00 2.895465 \nCopperConcentration 1.902307e+00 2.014370 \nIronConcentration 6.382449e+01 74.123936 \nManganeseConcentration 8.410939e+01 96.531621 \nSodiumConcentration 3.970369e+02 850.978348 \nPhosphorusConcentration 2.034701e+01 24.225030 \nPotassiumConcentration 1.838792e+02 230.535312 \nMagnesiumConcentration 1.930456e+02 237.257738 \nSulphurConcentration 2.131277e+02 309.202107 \nZincConcentration 1.508128e+00 1.662117 \nCalciumConcentration 2.343738e+03 3533.258208 \n\n Fedot_Industrial_best \\\nHouseholdPowerConsumption1 99.972746 \nAppliancesEnergy 1.924000 \nHouseholdPowerConsumption2 34.370000 \nIEEEPPG 29.105472 \nFloodModeling1 0.004626 \nBeijingPM25Quality 60.750000 \nBenzeneConcentration 1.637000 \nFloodModeling3 0.006690 \nBeijingPM10Quality 96.149556 \nFloodModeling2 0.006991 \nAustraliaRainfall 8.663000 \nNewsHeadlineSentiment 0.142000 \nNewsTitleSentiment 0.138000 \nLiveFuelMoistureContent 43.000000 \nBIDMC32SpO2 4.791000 \nBIDMC32HR 10.831000 \nBIDMC32RR 3.496000 \nCovid3Month 0.014000 \nHotwaterPredictor 1139.292599 \nSteamPredictor 29813.888024 \nChilledWaterPredictor 1022.114716 \nElectricityPredictor 439.365941 \nGasSensorArrayEthanol 0.206536 \nGasSensorArrayAcetone 0.227163 \nSierraNevadaMountainsSnow 29.778091 \nBitcoinSentiment 0.220253 \nEthereumSentiment 0.226023 \nCardanoSentiment 0.300261 \nBinanceCoinSentiment 0.373925 \nElectricMotorTemperature 4.206348 \nSolarRadiationAndalusia 0.633310 \nPrecipitationAndalusia 0.461973 \nAcousticContaminationMadrid 3.231754 \nVentilatorPressure 0.972656 \nOccupancyDetectionLight 71.678605 \nWindTurbinePower 46.843659 \nDhakaHourlyAirQuality 0.996620 \nDailyOilGasPrices 1.097276 \nWaveDataTension 15.578184 \nNaturalGasPricesSentiment 0.050170 \nDailyTemperatureLatitude 2.330602 \nMethaneMonitoringHomeActivity 0.510272 \nLPGasMonitoringHomeActivity 1.227710 \nAluminiumConcentration 293.353253 \nBoronConcentration 0.935570 \nCopperConcentration 1.778302 \nIronConcentration 80.240398 \nManganeseConcentration 91.551196 \nSodiumConcentration 464.905752 \nPhosphorusConcentration 19.546838 \nPotassiumConcentration 171.181679 \nMagnesiumConcentration 236.562421 \nSulphurConcentration 313.827026 \nZincConcentration 2.102851 \nCalciumConcentration 2511.154578 \n\n Fedot_Industrial_finetuned \nHouseholdPowerConsumption1 1.000000e+10 \nAppliancesEnergy 1.000000e+10 \nHouseholdPowerConsumption2 1.000000e+10 \nIEEEPPG 1.000000e+10 \nFloodModeling1 1.000000e+10 \nBeijingPM25Quality 6.431978e+01 \nBenzeneConcentration 1.000000e+10 \nFloodModeling3 1.000000e+10 \nBeijingPM10Quality 9.645160e+01 \nFloodModeling2 1.000000e+10 \nAustraliaRainfall NaN \nNewsHeadlineSentiment 1.000000e+10 \nNewsTitleSentiment 1.000000e+10 \nLiveFuelMoistureContent 4.622938e+01 \nBIDMC32SpO2 1.000000e+10 \nBIDMC32HR NaN \nBIDMC32RR NaN \nCovid3Month 1.000000e+10 \nHotwaterPredictor 1.000000e+10 \nSteamPredictor 1.000000e+10 \nChilledWaterPredictor 1.000000e+10 \nElectricityPredictor 1.000000e+10 \nGasSensorArrayEthanol 1.000000e+10 \nGasSensorArrayAcetone 1.000000e+10 \nSierraNevadaMountainsSnow 1.000000e+10 \nBitcoinSentiment 1.000000e+10 \nEthereumSentiment 1.000000e+10 \nCardanoSentiment 1.000000e+10 \nBinanceCoinSentiment 1.000000e+10 \nElectricMotorTemperature 1.000000e+10 \nSolarRadiationAndalusia 1.000000e+10 \nPrecipitationAndalusia 1.000000e+10 \nAcousticContaminationMadrid 1.000000e+10 \nVentilatorPressure 1.000000e+10 \nOccupancyDetectionLight 1.000000e+10 \nWindTurbinePower 1.000000e+10 \nDhakaHourlyAirQuality 1.000000e+10 \nDailyOilGasPrices 1.000000e+10 \nWaveDataTension 1.000000e+10 \nNaturalGasPricesSentiment 1.000000e+10 \nDailyTemperatureLatitude 1.000000e+10 \nMethaneMonitoringHomeActivity 1.000000e+10 \nLPGasMonitoringHomeActivity 1.000000e+10 \nAluminiumConcentration 2.962362e+02 \nBoronConcentration 1.013848e+00 \nCopperConcentration 1.758711e+00 \nIronConcentration 1.000000e+10 \nManganeseConcentration 8.893625e+01 \nSodiumConcentration 9.942167e+01 \nPhosphorusConcentration 1.961884e+01 \nPotassiumConcentration 1.720064e+02 \nMagnesiumConcentration 2.427799e+02 \nSulphurConcentration 3.160448e+02 \nZincConcentration 1.000000e+10 \nCalciumConcentration 1.000000e+10 \n\n[55 rows x 25 columns]", "text/html": "
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1NN-DTW_RMSE1NN-ED_RMSE5NN-DTW_RMSE5NN-ED_RMSECNN_RMSEDrCIF_RMSEFCN_RMSEFPCR-Bs_RMSEFPCR_RMSEFreshPRINCE_RMSE...ROCKET_RMSERandF_RMSEResNet_RMSERidge_RMSERotF_RMSESingleInception_RMSETSF_RMSEXGBoost_RMSEFedot_Industrial_bestFedot_Industrial_finetuned
HouseholdPowerConsumption1417.520861534.266678363.106601507.7968266.045497e+021.657667e+02353.211342110.606270107.0618281.040404e+02...214.812814239.492621108.8288812.035437e+021.992346e+02116.2022512.761883e+02227.50732499.9727461.000000e+10
AppliancesEnergy5.7394705.7681044.5049584.4703344.097874e+002.404670e+004.4526774.5008134.4190992.053976e+00...2.7685553.9863243.9579214.695629e+002.557591e+004.3994133.752866e+004.0459851.9240001.000000e+10
HouseholdPowerConsumption253.42955976.02411643.28755057.6859285.152938e+013.063533e+01172.60105341.34481440.8290722.939287e+01...30.67548539.07169833.3049215.771092e+013.759094e+0134.5858943.601573e+0138.95568334.3700001.000000e+10
IEEEPPG23.98724027.76971520.45288423.6550072.242689e+011.247458e+017.19041926.39837226.3955141.011591e+01...7.15972121.6558405.0512794.750275e+012.021281e+014.1198662.047200e+0121.03369829.1054721.000000e+10
FloodModeling10.0138170.0185110.0131240.0190091.723553e-029.462636e-030.0095100.0208270.0213198.750739e-03...0.0173040.0189970.0090462.095060e-021.798786e-020.0100041.102917e-020.0193340.0046261.000000e+10
BeijingPM25Quality71.06672673.24569959.50383960.8729569.988757e+014.712755e+0145.52348161.33602362.0282484.341873e+01...67.00521146.71194744.1664836.091479e+014.283921e+0142.0081776.167426e+0142.14722860.7500006.431978e+01
BenzeneConcentration4.4023154.8581895.0538204.2407997.543093e+003.666043e+000.5965334.8843065.2412202.132254e+00...2.2174760.2831660.3025871.132592e+007.287375e-010.2764064.189510e+000.2188061.6370001.000000e+10
FloodModeling30.0130740.0170280.0115620.0177381.096383e-026.438931e-030.0093310.0187760.0194485.582533e-03...0.0172910.0166800.0072821.903150e-021.568419e-020.0104518.546052e-030.0170070.0066901.000000e+10
BeijingPM10Quality118.230571119.24611095.31878597.1306451.275990e+027.057271e+0170.59648392.62838193.0477816.650945e+01...94.55786371.71257168.4180409.235021e+016.631563e+0166.5192888.465717e+0166.47481896.1495569.645160e+01
FloodModeling20.0110920.0129490.0120640.0132991.852107e-025.180100e-030.0047450.0145910.0138535.027746e-03...0.0092740.0094830.0075501.416184e-027.900159e-030.0076535.379332e-030.0129710.0069911.000000e+10
AustraliaRainfall17.08439017.47766316.12019515.4192621.000000e+101.000000e+1014.82055314.82239014.8219991.000000e+10...17.87890515.19109319.0322311.000000e+101.000000e+1015.2991891.000000e+1016.2828448.663000NaN
NewsHeadlineSentiment0.1979160.1993150.1549580.1548841.196766e+011.455912e-011.2068520.1415830.1415651.456120e-01...0.1468380.1461990.1476731.435795e-011.494606e-010.1485871.422297e-010.1427400.1420001.000000e+10
NewsTitleSentiment0.1925230.1928050.1498180.1492312.308311e+011.405348e-011.0071020.1366840.1366141.406148e-01...0.1417590.1411160.1415281.388921e-011.441636e-010.1428141.371251e-010.1376700.1380001.000000e+10
LiveFuelMoistureContent56.14557656.68541544.22016944.6785974.147725e+014.061007e+0147.76391141.46724541.4483724.117283e+01...44.50948441.09402647.3479388.350272e+024.133734e+0147.0224444.074238e+0142.55358043.0000004.622938e+01
BIDMC32SpO20.5257502.5083600.5319872.4930101.717190e+004.198496e-010.4822273.2529093.2736033.407657e-01...0.3003221.8517140.2779363.357586e+001.644120e+000.3146091.275249e+001.6747344.7910001.000000e+10
BIDMC32HR1.9448376.5442971.9044896.9900505.229133e+001.270281e+001.12017613.04355713.0329911.039876e+00...0.6563185.9007610.7687381.375567e+014.884870e+000.7655473.986727e+004.97536910.831000NaN
BIDMC32RR1.2335772.1365931.2297521.9861633.536865e+009.154016e-010.7715262.9521902.9524116.882746e-01...0.5264791.9431420.4810283.017642e+001.711275e+000.5501881.417203e+001.8120023.496000NaN
Covid3Month0.0545530.0533020.0412700.0407836.759766e-023.977303e-020.067899199.9204860.0487753.829917e-02...0.0428190.0401830.1244164.646435e-014.169759e-020.0742023.895102e-020.0449340.0140001.000000e+10
HotwaterPredictor1819.1031511906.0322991337.3733981458.8667391.174256e+031.246468e+031072.5026571427.1712551331.5046871.240377e+03...1236.4084581310.4392051145.6455422.719383e+031.383306e+031162.3255681.401285e+031424.8234401139.2925991.000000e+10
SteamPredictor42924.76175055239.47152034168.97513038081.0622403.061124e+043.113187e+0429315.72100032018.74692031246.6085303.101483e+04...30813.80372032219.24180029429.9604507.616053e+043.152469e+0429823.9248003.135775e+0435238.80806029813.8880241.000000e+10
ChilledWaterPredictor3350.2853042224.2763682420.3500412200.8310182.215134e+032.241064e+032215.9397382364.7545282332.3640492.479203e+03...2517.9014172449.6050082202.9080424.346009e+032.437387e+032138.9149792.286958e+033137.4161971022.1147161.000000e+10
ElectricityPredictor666.809599684.484545469.378018493.4781644.846374e+025.874044e+02609.970047465.844347455.9410855.826548e+02...575.695827527.343198589.4279545.745584e+026.066627e+02588.6598476.041935e+02618.172663439.3659411.000000e+10
GasSensorArrayEthanol0.3424820.2829270.2688680.2657372.017014e-011.517741e-010.3060660.2094650.2071391.859832e-01...0.2135500.2549220.6545481.848965e-011.499463e-010.5041301.941129e-010.2681070.2065361.000000e+10
GasSensorArrayAcetone0.3174290.3073680.2599600.2623212.389416e-011.845477e-010.2949320.2327750.2442602.010619e-01...0.1942880.2593600.5486822.623136e-011.930651e-010.3041252.117950e-010.2757390.2271631.000000e+10
SierraNevadaMountainsSnow63.58065575.13120553.70734666.9614545.431997e+014.300014e+0136.41566346.96621547.1037843.692058e+01...68.54777552.21607039.7324495.227692e+014.869493e+0134.6917285.180834e+0156.06689129.7780911.000000e+10
BitcoinSentiment0.2603280.2642440.2073650.2072432.462431e-011.970548e-010.2205980.2155640.2115562.011734e-01...0.2688380.1993060.2268522.324026e-012.014305e-010.2243852.097271e-010.2086500.2202531.000000e+10
EthereumSentiment0.3187910.3338780.2412170.2460462.716099e-012.234477e-010.2527020.2351690.2313362.258759e-01...0.2870770.2330200.2558542.621682e-012.294233e-010.2577532.344765e-010.2523510.2260231.000000e+10
CardanoSentiment0.3930830.4216370.3165650.3154164.370255e-012.973890e-010.3044490.3428850.3336283.017266e-01...0.3064670.2959060.3240413.881151e-013.052854e-010.3578822.998514e-010.3246270.3002611.000000e+10
BinanceCoinSentiment0.4844380.5001710.3724890.3831444.094964e-013.448162e-010.3664140.3807410.3831213.460515e-01...0.3871790.3501010.3719335.139279e-013.538278e-010.3857873.479485e-010.3777660.3739251.000000e+10
ElectricMotorTemperature8.8404299.0235107.6047407.8336231.760786e+014.656284e+006.59387611.96936411.9794904.650170e+00...12.2709725.2418185.9113471.198277e+014.698074e+005.1633968.869247e+005.3126914.2063481.000000e+10
SolarRadiationAndalusia0.8236490.8440810.6451870.6554811.042590e+006.216146e-010.6700860.6739030.6572126.195325e-01...0.6446100.6304430.6548867.698799e-016.238755e-010.6952406.255512e-010.6566000.6333101.000000e+10
PrecipitationAndalusia0.4909030.5038640.4105960.4530949.854324e-013.851207e-010.3979960.4159400.3886953.612930e-01...0.3452390.3944830.4091877.985268e-013.690052e-010.3888414.023337e-010.4015740.4619731.000000e+10
AcousticContaminationMadrid5.4402826.5910855.0461555.7262876.712051e+004.119996e+0064.5123855.6643515.6202653.281021e+00...4.6328195.0717095.1272975.387562e+005.126173e+004.4295134.602419e+005.2556533.2317541.000000e+10
VentilatorPressure1.1356260.9833541.0000720.8945252.163210e+006.166132e-010.9352432.2251542.1826456.015145e-01...0.8529130.6717450.4927282.072326e+005.685455e-010.4066891.848328e+000.7049120.9726561.000000e+10
OccupancyDetectionLight185.122574184.640670155.141891157.2362742.386848e+028.054537e+01129.605793118.989563114.2068486.710728e+01...111.422905100.18962189.7180242.249267e+027.528684e+0195.3539501.024337e+02103.16175071.6786051.000000e+10
WindTurbinePower276.399699586.730016238.769740466.1866159.515196e+029.785142e+011191.209810891.458761888.9447834.465780e+01...611.429184103.7842731033.8685276.220624e+047.424638e+01274.1875081.170780e+02104.97186346.8436591.000000e+10
DhakaHourlyAirQuality7.4482746.4426435.1334654.6321558.680293e+003.171020e+0010.26525912.38410612.3687461.784270e+00...46.8961565.5719735.9153421.245697e+012.702426e+005.4768234.813817e+004.9461510.9966201.000000e+10
DailyOilGasPrices2.7421052.8225952.0552562.2514242.150854e+001.594442e+002.0690462.0979642.0523891.490442e+00...2.2752541.7081961.9380742.363609e+001.559385e+002.1493681.684828e+001.7169031.0972761.000000e+10
WaveDataTension21.33728421.05185916.51618316.5446661.816305e+011.517911e+0121.10002014.10799414.1104841.491209e+01...15.14328315.35697018.3322231.408012e+011.521209e+0117.7932101.543533e+0116.09434415.5781841.000000e+10
NaturalGasPricesSentiment0.0742770.0746510.0573540.0566236.482931e-025.968074e-020.0993740.1135170.1126705.923535e-02...0.0908800.0559810.1431407.955113e-025.858841e-020.3413705.479847e-020.0601970.0501701.000000e+10
DailyTemperatureLatitude2.2741942.9305192.1580743.0211065.427370e+001.950407e+006.15603910.66030710.7002981.782911e+00...4.7342603.1160884.9858071.003086e+012.677904e+001.7807622.151806e+002.9420732.3306021.000000e+10
MethaneMonitoringHomeActivity0.6934000.6943710.5401570.5411406.367615e-015.052408e-010.5354910.5890720.5892135.305011e-01...0.6238930.5254221.5585595.887444e-015.244137e-010.6342885.170912e-010.5563600.5102721.000000e+10
LPGasMonitoringHomeActivity1.6995401.6962431.3348051.3385721.339258e+001.262008e+001.2976731.3067381.3060771.295706e+00...1.3494831.3034211.3446331.304916e+001.289305e+001.3855171.280573e+001.3836321.2277101.000000e+10
AluminiumConcentration382.557872358.169450324.538120306.0642902.737297e+022.451929e+02985.815086277.958777274.3850132.699899e+02...247.129053293.337887316.4113532.370224e+022.340929e+02250.4116382.450510e+02295.733413293.3532532.962362e+02
BoronConcentration1.9938511.8612651.9094091.9823753.608769e+001.762709e+005.7242291.8346731.8146001.880072e+00...2.1310672.3157272.7659971.802806e+001.885018e+001.8012642.038787e+002.8954650.9355701.013848e+00
CopperConcentration2.4812862.4137161.9347871.9537892.343641e+001.826204e+005.1781491.9007401.9259041.845851e+00...2.0420721.9513522.0969991.819889e+001.863294e+001.9184991.902307e+002.0143701.7783021.758711e+00
IronConcentration88.66924083.83507877.74218272.3789137.232745e+016.213210e+01197.80172675.61194572.2236766.535273e+01...59.65266772.90785173.2178376.058250e+016.192114e+0166.5169986.382449e+0174.12393680.2403981.000000e+10
ManganeseConcentration121.990065118.427219101.10831398.8982169.280130e+018.128308e+01229.35496698.03372499.5841348.462758e+01...87.29099693.77465097.2291028.461378e+017.991396e+0188.3038698.410939e+0196.53162191.5511968.893625e+01
SodiumConcentration659.380735620.198671453.954204444.1322253.993961e+023.127763e+02469.649236399.916772410.5009364.079309e+02...1009.654912470.035579478.4272814.154721e+026.527057e+02431.5253443.970369e+02850.978348464.9057529.942167e+01
PhosphorusConcentration22.15365221.42403024.30561623.3573502.783047e+011.906393e+0142.62398926.10423625.9227381.914935e+01...23.40036023.61120020.7377132.274373e+012.063641e+0118.5505132.034701e+0124.22503019.5468381.961884e+01
PotassiumConcentration242.027142221.960612223.386480224.2391152.614602e+021.676802e+02497.219697272.228356262.9824711.748586e+02...209.230873227.961272182.3009592.112084e+021.862390e+02177.3003331.838792e+02230.535312171.1816791.720064e+02
MagnesiumConcentration298.216850228.529714277.321165248.0022022.983644e+021.608196e+021112.378767324.661186293.1095641.679659e+02...144.878976244.734176166.3486441.404055e+021.665049e+02135.3439671.930456e+02237.257738236.5624212.427799e+02
SulphurConcentration251.901880251.159147221.083366217.3026842.465601e+022.060873e+02279.789674201.620532201.4705382.224243e+02...272.034096223.069451208.0137122.136158e+022.150779e+02210.1144342.131277e+02309.202107313.8270263.160448e+02
ZincConcentration1.8286311.8897081.5404221.5401091.496162e+001.429352e+004.2654331.4366321.4546661.589329e+00...1.4896521.5337211.6389251.440314e+001.509911e+001.6592961.508128e+001.6621172.1028511.000000e+10
CalciumConcentration4236.1513803027.5491293446.1141733095.9297083.247370e+031.938747e+033539.9934353094.4298532983.2707512.189777e+03...2487.9681472963.1729083026.6729642.103223e+031.783895e+032478.9811192.343738e+033533.2582082511.1545781.000000e+10
\n

55 rows × 25 columns

\n
" }, - "execution_count": 36, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -602,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 6, "outputs": [], "source": [ "reg_comp['Fedot_Industrial_best'] = reg_comp.apply(lambda row: min(row.loc['Fedot_Industrial_best'],row.loc['Fedot_Industrial_finetuned']), axis=1)\n", @@ -629,13 +629,13 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 7, "outputs": [ { "data": { "text/plain": "['RDST_RMSE', 'RIST_RMSE', 'Ridge_RMSE', 'CNN_RMSE', 'TSF_RMSE']" }, - "execution_count": 38, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -654,7 +654,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 8, "outputs": [], "source": [ "def categorize_dataset(metric):\n", @@ -676,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 9, "outputs": [], "source": [ "stable_models = [x for x in reg_comp.columns if x not in not_stable_models]\n", @@ -694,14 +694,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 10, "outputs": [ { "data": { "text/plain": " 1NN-DTW_RMSE 1NN-ED_RMSE 5NN-DTW_RMSE \\\nHouseholdPowerConsumption1 417.520861 534.266678 363.106601 \nAppliancesEnergy 5.739470 5.768104 4.504958 \nHouseholdPowerConsumption2 53.429559 76.024116 43.287550 \nIEEEPPG 23.987240 27.769715 20.452884 \nFloodModeling1 0.013817 0.018511 0.013124 \nBeijingPM25Quality 71.066726 73.245699 59.503839 \nBenzeneConcentration 4.402315 4.858189 5.053820 \nFloodModeling3 0.013074 0.017028 0.011562 \nBeijingPM10Quality 118.230571 119.246110 95.318785 \nFloodModeling2 0.011092 0.012949 0.012064 \nAustraliaRainfall 17.084390 17.477663 16.120195 \nNewsHeadlineSentiment 0.197916 0.199315 0.154958 \nNewsTitleSentiment 0.192523 0.192805 0.149818 \nLiveFuelMoistureContent 56.145576 56.685415 44.220169 \nBIDMC32SpO2 0.525750 2.508360 0.531987 \nBIDMC32HR 1.944837 6.544297 1.904489 \nBIDMC32RR 1.233577 2.136593 1.229752 \nCovid3Month 0.054553 0.053302 0.041270 \nHotwaterPredictor 1819.103151 1906.032299 1337.373398 \nSteamPredictor 42924.761750 55239.471520 34168.975130 \nChilledWaterPredictor 3350.285304 2224.276368 2420.350041 \nElectricityPredictor 666.809599 684.484545 469.378018 \nGasSensorArrayEthanol 0.342482 0.282927 0.268868 \nGasSensorArrayAcetone 0.317429 0.307368 0.259960 \nSierraNevadaMountainsSnow 63.580655 75.131205 53.707346 \nBitcoinSentiment 0.260328 0.264244 0.207365 \nEthereumSentiment 0.318791 0.333878 0.241217 \nCardanoSentiment 0.393083 0.421637 0.316565 \nBinanceCoinSentiment 0.484438 0.500171 0.372489 \nElectricMotorTemperature 8.840429 9.023510 7.604740 \nSolarRadiationAndalusia 0.823649 0.844081 0.645187 \nPrecipitationAndalusia 0.490903 0.503864 0.410596 \nAcousticContaminationMadrid 5.440282 6.591085 5.046155 \nVentilatorPressure 1.135626 0.983354 1.000072 \nOccupancyDetectionLight 185.122574 184.640670 155.141891 \nWindTurbinePower 276.399699 586.730016 238.769740 \nDhakaHourlyAirQuality 7.448274 6.442643 5.133465 \nDailyOilGasPrices 2.742105 2.822595 2.055256 \nWaveDataTension 21.337284 21.051859 16.516183 \nNaturalGasPricesSentiment 0.074277 0.074651 0.057354 \nDailyTemperatureLatitude 2.274194 2.930519 2.158074 \nMethaneMonitoringHomeActivity 0.693400 0.694371 0.540157 \nLPGasMonitoringHomeActivity 1.699540 1.696243 1.334805 \nAluminiumConcentration 382.557872 358.169450 324.538120 \nBoronConcentration 1.993851 1.861265 1.909409 \nCopperConcentration 2.481286 2.413716 1.934787 \nIronConcentration 88.669240 83.835078 77.742182 \nManganeseConcentration 121.990065 118.427219 101.108313 \nSodiumConcentration 659.380735 620.198671 453.954204 \nPhosphorusConcentration 22.153652 21.424030 24.305616 \nPotassiumConcentration 242.027142 221.960612 223.386480 \nMagnesiumConcentration 298.216850 228.529714 277.321165 \nSulphurConcentration 251.901880 251.159147 221.083366 \nZincConcentration 1.828631 1.889708 1.540422 \nCalciumConcentration 4236.151380 3027.549129 3446.114173 \n\n 5NN-ED_RMSE CNN_RMSE DrCIF_RMSE \\\nHouseholdPowerConsumption1 507.796826 6.045497e+02 1.657667e+02 \nAppliancesEnergy 4.470334 4.097874e+00 2.404670e+00 \nHouseholdPowerConsumption2 57.685928 5.152938e+01 3.063533e+01 \nIEEEPPG 23.655007 2.242689e+01 1.247458e+01 \nFloodModeling1 0.019009 1.723553e-02 9.462636e-03 \nBeijingPM25Quality 60.872956 9.988757e+01 4.712755e+01 \nBenzeneConcentration 4.240799 7.543093e+00 3.666043e+00 \nFloodModeling3 0.017738 1.096383e-02 6.438931e-03 \nBeijingPM10Quality 97.130645 1.275990e+02 7.057271e+01 \nFloodModeling2 0.013299 1.852107e-02 5.180100e-03 \nAustraliaRainfall 15.419262 1.000000e+10 1.000000e+10 \nNewsHeadlineSentiment 0.154884 1.196766e+01 1.455912e-01 \nNewsTitleSentiment 0.149231 2.308311e+01 1.405348e-01 \nLiveFuelMoistureContent 44.678597 4.147725e+01 4.061007e+01 \nBIDMC32SpO2 2.493010 1.717190e+00 4.198496e-01 \nBIDMC32HR 6.990050 5.229133e+00 1.270281e+00 \nBIDMC32RR 1.986163 3.536865e+00 9.154016e-01 \nCovid3Month 0.040783 6.759766e-02 3.977303e-02 \nHotwaterPredictor 1458.866739 1.174256e+03 1.246468e+03 \nSteamPredictor 38081.062240 3.061124e+04 3.113187e+04 \nChilledWaterPredictor 2200.831018 2.215134e+03 2.241064e+03 \nElectricityPredictor 493.478164 4.846374e+02 5.874044e+02 \nGasSensorArrayEthanol 0.265737 2.017014e-01 1.517741e-01 \nGasSensorArrayAcetone 0.262321 2.389416e-01 1.845477e-01 \nSierraNevadaMountainsSnow 66.961454 5.431997e+01 4.300014e+01 \nBitcoinSentiment 0.207243 2.462431e-01 1.970548e-01 \nEthereumSentiment 0.246046 2.716099e-01 2.234477e-01 \nCardanoSentiment 0.315416 4.370255e-01 2.973890e-01 \nBinanceCoinSentiment 0.383144 4.094964e-01 3.448162e-01 \nElectricMotorTemperature 7.833623 1.760786e+01 4.656284e+00 \nSolarRadiationAndalusia 0.655481 1.042590e+00 6.216146e-01 \nPrecipitationAndalusia 0.453094 9.854324e-01 3.851207e-01 \nAcousticContaminationMadrid 5.726287 6.712051e+00 4.119996e+00 \nVentilatorPressure 0.894525 2.163210e+00 6.166132e-01 \nOccupancyDetectionLight 157.236274 2.386848e+02 8.054537e+01 \nWindTurbinePower 466.186615 9.515196e+02 9.785142e+01 \nDhakaHourlyAirQuality 4.632155 8.680293e+00 3.171020e+00 \nDailyOilGasPrices 2.251424 2.150854e+00 1.594442e+00 \nWaveDataTension 16.544666 1.816305e+01 1.517911e+01 \nNaturalGasPricesSentiment 0.056623 6.482931e-02 5.968074e-02 \nDailyTemperatureLatitude 3.021106 5.427370e+00 1.950407e+00 \nMethaneMonitoringHomeActivity 0.541140 6.367615e-01 5.052408e-01 \nLPGasMonitoringHomeActivity 1.338572 1.339258e+00 1.262008e+00 \nAluminiumConcentration 306.064290 2.737297e+02 2.451929e+02 \nBoronConcentration 1.982375 3.608769e+00 1.762709e+00 \nCopperConcentration 1.953789 2.343641e+00 1.826204e+00 \nIronConcentration 72.378913 7.232745e+01 6.213210e+01 \nManganeseConcentration 98.898216 9.280130e+01 8.128308e+01 \nSodiumConcentration 444.132225 3.993961e+02 3.127763e+02 \nPhosphorusConcentration 23.357350 2.783047e+01 1.906393e+01 \nPotassiumConcentration 224.239115 2.614602e+02 1.676802e+02 \nMagnesiumConcentration 248.002202 2.983644e+02 1.608196e+02 \nSulphurConcentration 217.302684 2.465601e+02 2.060873e+02 \nZincConcentration 1.540109 1.496162e+00 1.429352e+00 \nCalciumConcentration 3095.929708 3.247370e+03 1.938747e+03 \n\n FCN_RMSE FPCR-Bs_RMSE FPCR_RMSE \\\nHouseholdPowerConsumption1 353.211342 110.606270 107.061828 \nAppliancesEnergy 4.452677 4.500813 4.419099 \nHouseholdPowerConsumption2 172.601053 41.344814 40.829072 \nIEEEPPG 7.190419 26.398372 26.395514 \nFloodModeling1 0.009510 0.020827 0.021319 \nBeijingPM25Quality 45.523481 61.336023 62.028248 \nBenzeneConcentration 0.596533 4.884306 5.241220 \nFloodModeling3 0.009331 0.018776 0.019448 \nBeijingPM10Quality 70.596483 92.628381 93.047781 \nFloodModeling2 0.004745 0.014591 0.013853 \nAustraliaRainfall 14.820553 14.822390 14.821999 \nNewsHeadlineSentiment 1.206852 0.141583 0.141565 \nNewsTitleSentiment 1.007102 0.136684 0.136614 \nLiveFuelMoistureContent 47.763911 41.467245 41.448372 \nBIDMC32SpO2 0.482227 3.252909 3.273603 \nBIDMC32HR 1.120176 13.043557 13.032991 \nBIDMC32RR 0.771526 2.952190 2.952411 \nCovid3Month 0.067899 199.920486 0.048775 \nHotwaterPredictor 1072.502657 1427.171255 1331.504687 \nSteamPredictor 29315.721000 32018.746920 31246.608530 \nChilledWaterPredictor 2215.939738 2364.754528 2332.364049 \nElectricityPredictor 609.970047 465.844347 455.941085 \nGasSensorArrayEthanol 0.306066 0.209465 0.207139 \nGasSensorArrayAcetone 0.294932 0.232775 0.244260 \nSierraNevadaMountainsSnow 36.415663 46.966215 47.103784 \nBitcoinSentiment 0.220598 0.215564 0.211556 \nEthereumSentiment 0.252702 0.235169 0.231336 \nCardanoSentiment 0.304449 0.342885 0.333628 \nBinanceCoinSentiment 0.366414 0.380741 0.383121 \nElectricMotorTemperature 6.593876 11.969364 11.979490 \nSolarRadiationAndalusia 0.670086 0.673903 0.657212 \nPrecipitationAndalusia 0.397996 0.415940 0.388695 \nAcousticContaminationMadrid 64.512385 5.664351 5.620265 \nVentilatorPressure 0.935243 2.225154 2.182645 \nOccupancyDetectionLight 129.605793 118.989563 114.206848 \nWindTurbinePower 1191.209810 891.458761 888.944783 \nDhakaHourlyAirQuality 10.265259 12.384106 12.368746 \nDailyOilGasPrices 2.069046 2.097964 2.052389 \nWaveDataTension 21.100020 14.107994 14.110484 \nNaturalGasPricesSentiment 0.099374 0.113517 0.112670 \nDailyTemperatureLatitude 6.156039 10.660307 10.700298 \nMethaneMonitoringHomeActivity 0.535491 0.589072 0.589213 \nLPGasMonitoringHomeActivity 1.297673 1.306738 1.306077 \nAluminiumConcentration 985.815086 277.958777 274.385013 \nBoronConcentration 5.724229 1.834673 1.814600 \nCopperConcentration 5.178149 1.900740 1.925904 \nIronConcentration 197.801726 75.611945 72.223676 \nManganeseConcentration 229.354966 98.033724 99.584134 \nSodiumConcentration 469.649236 399.916772 410.500936 \nPhosphorusConcentration 42.623989 26.104236 25.922738 \nPotassiumConcentration 497.219697 272.228356 262.982471 \nMagnesiumConcentration 1112.378767 324.661186 293.109564 \nSulphurConcentration 279.789674 201.620532 201.470538 \nZincConcentration 4.265433 1.436632 1.454666 \nCalciumConcentration 3539.993435 3094.429853 2983.270751 \n\n FreshPRINCE_RMSE ... ResNet_RMSE \\\nHouseholdPowerConsumption1 1.040404e+02 ... 108.828881 \nAppliancesEnergy 2.053976e+00 ... 3.957921 \nHouseholdPowerConsumption2 2.939287e+01 ... 33.304921 \nIEEEPPG 1.011591e+01 ... 5.051279 \nFloodModeling1 8.750739e-03 ... 0.009046 \nBeijingPM25Quality 4.341873e+01 ... 44.166483 \nBenzeneConcentration 2.132254e+00 ... 0.302587 \nFloodModeling3 5.582533e-03 ... 0.007282 \nBeijingPM10Quality 6.650945e+01 ... 68.418040 \nFloodModeling2 5.027746e-03 ... 0.007550 \nAustraliaRainfall 1.000000e+10 ... 19.032231 \nNewsHeadlineSentiment 1.456120e-01 ... 0.147673 \nNewsTitleSentiment 1.406148e-01 ... 0.141528 \nLiveFuelMoistureContent 4.117283e+01 ... 47.347938 \nBIDMC32SpO2 3.407657e-01 ... 0.277936 \nBIDMC32HR 1.039876e+00 ... 0.768738 \nBIDMC32RR 6.882746e-01 ... 0.481028 \nCovid3Month 3.829917e-02 ... 0.124416 \nHotwaterPredictor 1.240377e+03 ... 1145.645542 \nSteamPredictor 3.101483e+04 ... 29429.960450 \nChilledWaterPredictor 2.479203e+03 ... 2202.908042 \nElectricityPredictor 5.826548e+02 ... 589.427954 \nGasSensorArrayEthanol 1.859832e-01 ... 0.654548 \nGasSensorArrayAcetone 2.010619e-01 ... 0.548682 \nSierraNevadaMountainsSnow 3.692058e+01 ... 39.732449 \nBitcoinSentiment 2.011734e-01 ... 0.226852 \nEthereumSentiment 2.258759e-01 ... 0.255854 \nCardanoSentiment 3.017266e-01 ... 0.324041 \nBinanceCoinSentiment 3.460515e-01 ... 0.371933 \nElectricMotorTemperature 4.650170e+00 ... 5.911347 \nSolarRadiationAndalusia 6.195325e-01 ... 0.654886 \nPrecipitationAndalusia 3.612930e-01 ... 0.409187 \nAcousticContaminationMadrid 3.281021e+00 ... 5.127297 \nVentilatorPressure 6.015145e-01 ... 0.492728 \nOccupancyDetectionLight 6.710728e+01 ... 89.718024 \nWindTurbinePower 4.465780e+01 ... 1033.868527 \nDhakaHourlyAirQuality 1.784270e+00 ... 5.915342 \nDailyOilGasPrices 1.490442e+00 ... 1.938074 \nWaveDataTension 1.491209e+01 ... 18.332223 \nNaturalGasPricesSentiment 5.923535e-02 ... 0.143140 \nDailyTemperatureLatitude 1.782911e+00 ... 4.985807 \nMethaneMonitoringHomeActivity 5.305011e-01 ... 1.558559 \nLPGasMonitoringHomeActivity 1.295706e+00 ... 1.344633 \nAluminiumConcentration 2.699899e+02 ... 316.411353 \nBoronConcentration 1.880072e+00 ... 2.765997 \nCopperConcentration 1.845851e+00 ... 2.096999 \nIronConcentration 6.535273e+01 ... 73.217837 \nManganeseConcentration 8.462758e+01 ... 97.229102 \nSodiumConcentration 4.079309e+02 ... 478.427281 \nPhosphorusConcentration 1.914935e+01 ... 20.737713 \nPotassiumConcentration 1.748586e+02 ... 182.300959 \nMagnesiumConcentration 1.679659e+02 ... 166.348644 \nSulphurConcentration 2.224243e+02 ... 208.013712 \nZincConcentration 1.589329e+00 ... 1.638925 \nCalciumConcentration 2.189777e+03 ... 3026.672964 \n\n Ridge_RMSE RotF_RMSE \\\nHouseholdPowerConsumption1 2.035437e+02 1.992346e+02 \nAppliancesEnergy 4.695629e+00 2.557591e+00 \nHouseholdPowerConsumption2 5.771092e+01 3.759094e+01 \nIEEEPPG 4.750275e+01 2.021281e+01 \nFloodModeling1 2.095060e-02 1.798786e-02 \nBeijingPM25Quality 6.091479e+01 4.283921e+01 \nBenzeneConcentration 1.132592e+00 7.287375e-01 \nFloodModeling3 1.903150e-02 1.568419e-02 \nBeijingPM10Quality 9.235021e+01 6.631563e+01 \nFloodModeling2 1.416184e-02 7.900159e-03 \nAustraliaRainfall 1.000000e+10 1.000000e+10 \nNewsHeadlineSentiment 1.435795e-01 1.494606e-01 \nNewsTitleSentiment 1.388921e-01 1.441636e-01 \nLiveFuelMoistureContent 8.350272e+02 4.133734e+01 \nBIDMC32SpO2 3.357586e+00 1.644120e+00 \nBIDMC32HR 1.375567e+01 4.884870e+00 \nBIDMC32RR 3.017642e+00 1.711275e+00 \nCovid3Month 4.646435e-01 4.169759e-02 \nHotwaterPredictor 2.719383e+03 1.383306e+03 \nSteamPredictor 7.616053e+04 3.152469e+04 \nChilledWaterPredictor 4.346009e+03 2.437387e+03 \nElectricityPredictor 5.745584e+02 6.066627e+02 \nGasSensorArrayEthanol 1.848965e-01 1.499463e-01 \nGasSensorArrayAcetone 2.623136e-01 1.930651e-01 \nSierraNevadaMountainsSnow 5.227692e+01 4.869493e+01 \nBitcoinSentiment 2.324026e-01 2.014305e-01 \nEthereumSentiment 2.621682e-01 2.294233e-01 \nCardanoSentiment 3.881151e-01 3.052854e-01 \nBinanceCoinSentiment 5.139279e-01 3.538278e-01 \nElectricMotorTemperature 1.198277e+01 4.698074e+00 \nSolarRadiationAndalusia 7.698799e-01 6.238755e-01 \nPrecipitationAndalusia 7.985268e-01 3.690052e-01 \nAcousticContaminationMadrid 5.387562e+00 5.126173e+00 \nVentilatorPressure 2.072326e+00 5.685455e-01 \nOccupancyDetectionLight 2.249267e+02 7.528684e+01 \nWindTurbinePower 6.220624e+04 7.424638e+01 \nDhakaHourlyAirQuality 1.245697e+01 2.702426e+00 \nDailyOilGasPrices 2.363609e+00 1.559385e+00 \nWaveDataTension 1.408012e+01 1.521209e+01 \nNaturalGasPricesSentiment 7.955113e-02 5.858841e-02 \nDailyTemperatureLatitude 1.003086e+01 2.677904e+00 \nMethaneMonitoringHomeActivity 5.887444e-01 5.244137e-01 \nLPGasMonitoringHomeActivity 1.304916e+00 1.289305e+00 \nAluminiumConcentration 2.370224e+02 2.340929e+02 \nBoronConcentration 1.802806e+00 1.885018e+00 \nCopperConcentration 1.819889e+00 1.863294e+00 \nIronConcentration 6.058250e+01 6.192114e+01 \nManganeseConcentration 8.461378e+01 7.991396e+01 \nSodiumConcentration 4.154721e+02 6.527057e+02 \nPhosphorusConcentration 2.274373e+01 2.063641e+01 \nPotassiumConcentration 2.112084e+02 1.862390e+02 \nMagnesiumConcentration 1.404055e+02 1.665049e+02 \nSulphurConcentration 2.136158e+02 2.150779e+02 \nZincConcentration 1.440314e+00 1.509911e+00 \nCalciumConcentration 2.103223e+03 1.783895e+03 \n\n SingleInception_RMSE TSF_RMSE \\\nHouseholdPowerConsumption1 116.202251 2.761883e+02 \nAppliancesEnergy 4.399413 3.752866e+00 \nHouseholdPowerConsumption2 34.585894 3.601573e+01 \nIEEEPPG 4.119866 2.047200e+01 \nFloodModeling1 0.010004 1.102917e-02 \nBeijingPM25Quality 42.008177 6.167426e+01 \nBenzeneConcentration 0.276406 4.189510e+00 \nFloodModeling3 0.010451 8.546052e-03 \nBeijingPM10Quality 66.519288 8.465717e+01 \nFloodModeling2 0.007653 5.379332e-03 \nAustraliaRainfall 15.299189 1.000000e+10 \nNewsHeadlineSentiment 0.148587 1.422297e-01 \nNewsTitleSentiment 0.142814 1.371251e-01 \nLiveFuelMoistureContent 47.022444 4.074238e+01 \nBIDMC32SpO2 0.314609 1.275249e+00 \nBIDMC32HR 0.765547 3.986727e+00 \nBIDMC32RR 0.550188 1.417203e+00 \nCovid3Month 0.074202 3.895102e-02 \nHotwaterPredictor 1162.325568 1.401285e+03 \nSteamPredictor 29823.924800 3.135775e+04 \nChilledWaterPredictor 2138.914979 2.286958e+03 \nElectricityPredictor 588.659847 6.041935e+02 \nGasSensorArrayEthanol 0.504130 1.941129e-01 \nGasSensorArrayAcetone 0.304125 2.117950e-01 \nSierraNevadaMountainsSnow 34.691728 5.180834e+01 \nBitcoinSentiment 0.224385 2.097271e-01 \nEthereumSentiment 0.257753 2.344765e-01 \nCardanoSentiment 0.357882 2.998514e-01 \nBinanceCoinSentiment 0.385787 3.479485e-01 \nElectricMotorTemperature 5.163396 8.869247e+00 \nSolarRadiationAndalusia 0.695240 6.255512e-01 \nPrecipitationAndalusia 0.388841 4.023337e-01 \nAcousticContaminationMadrid 4.429513 4.602419e+00 \nVentilatorPressure 0.406689 1.848328e+00 \nOccupancyDetectionLight 95.353950 1.024337e+02 \nWindTurbinePower 274.187508 1.170780e+02 \nDhakaHourlyAirQuality 5.476823 4.813817e+00 \nDailyOilGasPrices 2.149368 1.684828e+00 \nWaveDataTension 17.793210 1.543533e+01 \nNaturalGasPricesSentiment 0.341370 5.479847e-02 \nDailyTemperatureLatitude 1.780762 2.151806e+00 \nMethaneMonitoringHomeActivity 0.634288 5.170912e-01 \nLPGasMonitoringHomeActivity 1.385517 1.280573e+00 \nAluminiumConcentration 250.411638 2.450510e+02 \nBoronConcentration 1.801264 2.038787e+00 \nCopperConcentration 1.918499 1.902307e+00 \nIronConcentration 66.516998 6.382449e+01 \nManganeseConcentration 88.303869 8.410939e+01 \nSodiumConcentration 431.525344 3.970369e+02 \nPhosphorusConcentration 18.550513 2.034701e+01 \nPotassiumConcentration 177.300333 1.838792e+02 \nMagnesiumConcentration 135.343967 1.930456e+02 \nSulphurConcentration 210.114434 2.131277e+02 \nZincConcentration 1.659296 1.508128e+00 \nCalciumConcentration 2478.981119 2.343738e+03 \n\n XGBoost_RMSE Fedot_Industrial_best \\\nHouseholdPowerConsumption1 227.507324 99.972746 \nAppliancesEnergy 4.045985 1.924000 \nHouseholdPowerConsumption2 38.955683 34.370000 \nIEEEPPG 21.033698 29.105472 \nFloodModeling1 0.019334 0.004626 \nBeijingPM25Quality 42.147228 60.750000 \nBenzeneConcentration 0.218806 1.637000 \nFloodModeling3 0.017007 0.006690 \nBeijingPM10Quality 66.474818 96.149556 \nFloodModeling2 0.012971 0.006991 \nAustraliaRainfall 16.282844 8.663000 \nNewsHeadlineSentiment 0.142740 0.142000 \nNewsTitleSentiment 0.137670 0.138000 \nLiveFuelMoistureContent 42.553580 43.000000 \nBIDMC32SpO2 1.674734 4.791000 \nBIDMC32HR 4.975369 10.831000 \nBIDMC32RR 1.812002 3.496000 \nCovid3Month 0.044934 0.014000 \nHotwaterPredictor 1424.823440 1139.292599 \nSteamPredictor 35238.808060 29813.888024 \nChilledWaterPredictor 3137.416197 1022.114716 \nElectricityPredictor 618.172663 439.365941 \nGasSensorArrayEthanol 0.268107 0.206536 \nGasSensorArrayAcetone 0.275739 0.227163 \nSierraNevadaMountainsSnow 56.066891 29.778091 \nBitcoinSentiment 0.208650 0.220253 \nEthereumSentiment 0.252351 0.226023 \nCardanoSentiment 0.324627 0.300261 \nBinanceCoinSentiment 0.377766 0.373925 \nElectricMotorTemperature 5.312691 4.206348 \nSolarRadiationAndalusia 0.656600 0.633310 \nPrecipitationAndalusia 0.401574 0.461973 \nAcousticContaminationMadrid 5.255653 3.231754 \nVentilatorPressure 0.704912 0.972656 \nOccupancyDetectionLight 103.161750 71.678605 \nWindTurbinePower 104.971863 46.843659 \nDhakaHourlyAirQuality 4.946151 0.996620 \nDailyOilGasPrices 1.716903 1.097276 \nWaveDataTension 16.094344 15.578184 \nNaturalGasPricesSentiment 0.060197 0.050170 \nDailyTemperatureLatitude 2.942073 2.330602 \nMethaneMonitoringHomeActivity 0.556360 0.510272 \nLPGasMonitoringHomeActivity 1.383632 1.227710 \nAluminiumConcentration 295.733413 293.353253 \nBoronConcentration 2.895465 0.935570 \nCopperConcentration 2.014370 1.758711 \nIronConcentration 74.123936 80.240398 \nManganeseConcentration 96.531621 88.936249 \nSodiumConcentration 850.978348 99.421667 \nPhosphorusConcentration 24.225030 19.546838 \nPotassiumConcentration 230.535312 171.181679 \nMagnesiumConcentration 237.257738 236.562421 \nSulphurConcentration 309.202107 313.827026 \nZincConcentration 1.662117 2.102851 \nCalciumConcentration 3533.258208 2511.154578 \n\n Difference % Metric dispersion by dataset \\\nHouseholdPowerConsumption1 3.909708 27.619601 \nAppliancesEnergy 0.124929 18.433573 \nHouseholdPowerConsumption2 -5.896093 17.004424 \nIEEEPPG -24.420600 22.156923 \nFloodModeling1 0.001733 18.458389 \nBeijingPM25Quality -19.597525 15.859396 \nBenzeneConcentration -1.353166 30.551632 \nFloodModeling3 -0.001495 19.130581 \nBeijingPM10Quality -31.046866 15.590057 \nFloodModeling2 -0.002827 18.713455 \nAustraliaRainfall 5.918424 50.361015 \nNewsHeadlineSentiment -0.000418 28.232985 \nNewsTitleSentiment -0.001332 28.232985 \nLiveFuelMoistureContent -2.297114 19.333145 \nBIDMC32SpO2 -4.337798 28.232985 \nBIDMC32HR -9.854445 28.232985 \nBIDMC32RR -2.897885 28.232985 \nCovid3Month 0.023356 20.412415 \nHotwaterPredictor -64.196147 12.936500 \nSteamPredictor -478.820649 13.690419 \nChilledWaterPredictor 694.206802 13.822995 \nElectricityPredictor 15.931446 10.011093 \nGasSensorArrayEthanol -0.083125 20.406379 \nGasSensorArrayAcetone -0.080460 20.400939 \nSierraNevadaMountainsSnow 3.898200 16.307300 \nBitcoinSentiment -0.022297 8.037369 \nEthereumSentiment -0.002475 8.248480 \nCardanoSentiment -0.015981 9.648634 \nBinanceCoinSentiment -0.036581 9.403395 \nElectricMotorTemperature 0.426586 19.286989 \nSolarRadiationAndalusia -0.022433 9.556290 \nPrecipitationAndalusia -0.112200 15.151995 \nAcousticContaminationMadrid 0.047353 18.912880 \nVentilatorPressure -0.573309 28.232985 \nOccupancyDetectionLight -4.393793 20.990736 \nWindTurbinePower -2.100970 20.287725 \nDhakaHourlyAirQuality 0.757061 24.939633 \nDailyOilGasPrices 0.377897 14.066854 \nWaveDataTension -1.439890 10.047456 \nNaturalGasPricesSentiment 0.004449 17.728011 \nDailyTemperatureLatitude -0.686876 27.059306 \nMethaneMonitoringHomeActivity -0.004836 13.400291 \nLPGasMonitoringHomeActivity 0.032966 6.714723 \nAluminiumConcentration -64.741656 15.202154 \nBoronConcentration 0.663200 15.770175 \nCopperConcentration 0.058802 13.179117 \nIronConcentration -23.977095 13.974437 \nManganeseConcentration -8.850760 13.050513 \nSodiumConcentration 87.929925 19.279354 \nPhosphorusConcentration -1.711847 11.639586 \nPotassiumConcentration -3.365540 13.569614 \nMagnesiumConcentration -104.817242 17.533404 \nSulphurConcentration -111.339416 10.492794 \nZincConcentration -0.693322 13.426782 \nCalciumConcentration -699.016390 14.667538 \n\n dataset_category \nHouseholdPowerConsumption1 Hard to solve dataset \nAppliancesEnergy Normal to solve dataset \nHouseholdPowerConsumption2 Normal to solve dataset \nIEEEPPG Hard to solve dataset \nFloodModeling1 Normal to solve dataset \nBeijingPM25Quality Normal to solve dataset \nBenzeneConcentration Extraordinary Hard to solve dataset \nFloodModeling3 Normal to solve dataset \nBeijingPM10Quality Normal to solve dataset \nFloodModeling2 Normal to solve dataset \nAustraliaRainfall Extraordinary Hard to solve dataset \nNewsHeadlineSentiment Hard to solve dataset \nNewsTitleSentiment Hard to solve dataset \nLiveFuelMoistureContent Normal to solve dataset \nBIDMC32SpO2 Hard to solve dataset \nBIDMC32HR Hard to solve dataset \nBIDMC32RR Hard to solve dataset \nCovid3Month Hard to solve dataset \nHotwaterPredictor Normal to solve dataset \nSteamPredictor Normal to solve dataset \nChilledWaterPredictor Normal to solve dataset \nElectricityPredictor Normal to solve dataset \nGasSensorArrayEthanol Hard to solve dataset \nGasSensorArrayAcetone Hard to solve dataset \nSierraNevadaMountainsSnow Normal to solve dataset \nBitcoinSentiment Easy to solve dataset \nEthereumSentiment Easy to solve dataset \nCardanoSentiment Easy to solve dataset \nBinanceCoinSentiment Easy to solve dataset \nElectricMotorTemperature Normal to solve dataset \nSolarRadiationAndalusia Easy to solve dataset \nPrecipitationAndalusia Normal to solve dataset \nAcousticContaminationMadrid Normal to solve dataset \nVentilatorPressure Hard to solve dataset \nOccupancyDetectionLight Hard to solve dataset \nWindTurbinePower Hard to solve dataset \nDhakaHourlyAirQuality Hard to solve dataset \nDailyOilGasPrices Normal to solve dataset \nWaveDataTension Normal to solve dataset \nNaturalGasPricesSentiment Normal to solve dataset \nDailyTemperatureLatitude Hard to solve dataset \nMethaneMonitoringHomeActivity Normal to solve dataset \nLPGasMonitoringHomeActivity Easy to solve dataset \nAluminiumConcentration Normal to solve dataset \nBoronConcentration Normal to solve dataset \nCopperConcentration Normal to solve dataset \nIronConcentration Normal to solve dataset \nManganeseConcentration Normal to solve dataset \nSodiumConcentration Normal to solve dataset \nPhosphorusConcentration Normal to solve dataset \nPotassiumConcentration Normal to solve dataset \nMagnesiumConcentration Normal to solve dataset \nSulphurConcentration Normal to solve dataset \nZincConcentration Normal to solve dataset \nCalciumConcentration Normal to solve dataset \n\n[55 rows x 27 columns]", "text/html": "
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1NN-DTW_RMSE1NN-ED_RMSE5NN-DTW_RMSE5NN-ED_RMSECNN_RMSEDrCIF_RMSEFCN_RMSEFPCR-Bs_RMSEFPCR_RMSEFreshPRINCE_RMSE...ResNet_RMSERidge_RMSERotF_RMSESingleInception_RMSETSF_RMSEXGBoost_RMSEFedot_Industrial_bestDifference %Metric dispersion by datasetdataset_category
HouseholdPowerConsumption1417.520861534.266678363.106601507.7968266.045497e+021.657667e+02353.211342110.606270107.0618281.040404e+02...108.8288812.035437e+021.992346e+02116.2022512.761883e+02227.50732499.9727463.90970827.619601Hard to solve dataset
AppliancesEnergy5.7394705.7681044.5049584.4703344.097874e+002.404670e+004.4526774.5008134.4190992.053976e+00...3.9579214.695629e+002.557591e+004.3994133.752866e+004.0459851.9240000.12492918.433573Normal to solve dataset
HouseholdPowerConsumption253.42955976.02411643.28755057.6859285.152938e+013.063533e+01172.60105341.34481440.8290722.939287e+01...33.3049215.771092e+013.759094e+0134.5858943.601573e+0138.95568334.370000-5.89609317.004424Normal to solve dataset
IEEEPPG23.98724027.76971520.45288423.6550072.242689e+011.247458e+017.19041926.39837226.3955141.011591e+01...5.0512794.750275e+012.021281e+014.1198662.047200e+0121.03369829.105472-24.42060022.156923Hard to solve dataset
FloodModeling10.0138170.0185110.0131240.0190091.723553e-029.462636e-030.0095100.0208270.0213198.750739e-03...0.0090462.095060e-021.798786e-020.0100041.102917e-020.0193340.0046260.00173318.458389Normal to solve dataset
BeijingPM25Quality71.06672673.24569959.50383960.8729569.988757e+014.712755e+0145.52348161.33602362.0282484.341873e+01...44.1664836.091479e+014.283921e+0142.0081776.167426e+0142.14722860.750000-19.59752515.859396Normal to solve dataset
BenzeneConcentration4.4023154.8581895.0538204.2407997.543093e+003.666043e+000.5965334.8843065.2412202.132254e+00...0.3025871.132592e+007.287375e-010.2764064.189510e+000.2188061.637000-1.35316630.551632Extraordinary Hard to solve dataset
FloodModeling30.0130740.0170280.0115620.0177381.096383e-026.438931e-030.0093310.0187760.0194485.582533e-03...0.0072821.903150e-021.568419e-020.0104518.546052e-030.0170070.006690-0.00149519.130581Normal to solve dataset
BeijingPM10Quality118.230571119.24611095.31878597.1306451.275990e+027.057271e+0170.59648392.62838193.0477816.650945e+01...68.4180409.235021e+016.631563e+0166.5192888.465717e+0166.47481896.149556-31.04686615.590057Normal to solve dataset
FloodModeling20.0110920.0129490.0120640.0132991.852107e-025.180100e-030.0047450.0145910.0138535.027746e-03...0.0075501.416184e-027.900159e-030.0076535.379332e-030.0129710.006991-0.00282718.713455Normal to solve dataset
AustraliaRainfall17.08439017.47766316.12019515.4192621.000000e+101.000000e+1014.82055314.82239014.8219991.000000e+10...19.0322311.000000e+101.000000e+1015.2991891.000000e+1016.2828448.6630005.91842450.361015Extraordinary Hard to solve dataset
NewsHeadlineSentiment0.1979160.1993150.1549580.1548841.196766e+011.455912e-011.2068520.1415830.1415651.456120e-01...0.1476731.435795e-011.494606e-010.1485871.422297e-010.1427400.142000-0.00041828.232985Hard to solve dataset
NewsTitleSentiment0.1925230.1928050.1498180.1492312.308311e+011.405348e-011.0071020.1366840.1366141.406148e-01...0.1415281.388921e-011.441636e-010.1428141.371251e-010.1376700.138000-0.00133228.232985Hard to solve dataset
LiveFuelMoistureContent56.14557656.68541544.22016944.6785974.147725e+014.061007e+0147.76391141.46724541.4483724.117283e+01...47.3479388.350272e+024.133734e+0147.0224444.074238e+0142.55358043.000000-2.29711419.333145Normal to solve dataset
BIDMC32SpO20.5257502.5083600.5319872.4930101.717190e+004.198496e-010.4822273.2529093.2736033.407657e-01...0.2779363.357586e+001.644120e+000.3146091.275249e+001.6747344.791000-4.33779828.232985Hard to solve dataset
BIDMC32HR1.9448376.5442971.9044896.9900505.229133e+001.270281e+001.12017613.04355713.0329911.039876e+00...0.7687381.375567e+014.884870e+000.7655473.986727e+004.97536910.831000-9.85444528.232985Hard to solve dataset
BIDMC32RR1.2335772.1365931.2297521.9861633.536865e+009.154016e-010.7715262.9521902.9524116.882746e-01...0.4810283.017642e+001.711275e+000.5501881.417203e+001.8120023.496000-2.89788528.232985Hard to solve dataset
Covid3Month0.0545530.0533020.0412700.0407836.759766e-023.977303e-020.067899199.9204860.0487753.829917e-02...0.1244164.646435e-014.169759e-020.0742023.895102e-020.0449340.0140000.02335620.412415Hard to solve dataset
HotwaterPredictor1819.1031511906.0322991337.3733981458.8667391.174256e+031.246468e+031072.5026571427.1712551331.5046871.240377e+03...1145.6455422.719383e+031.383306e+031162.3255681.401285e+031424.8234401139.292599-64.19614712.936500Normal to solve dataset
SteamPredictor42924.76175055239.47152034168.97513038081.0622403.061124e+043.113187e+0429315.72100032018.74692031246.6085303.101483e+04...29429.9604507.616053e+043.152469e+0429823.9248003.135775e+0435238.80806029813.888024-478.82064913.690419Normal to solve dataset
ChilledWaterPredictor3350.2853042224.2763682420.3500412200.8310182.215134e+032.241064e+032215.9397382364.7545282332.3640492.479203e+03...2202.9080424.346009e+032.437387e+032138.9149792.286958e+033137.4161971022.114716694.20680213.822995Normal to solve dataset
ElectricityPredictor666.809599684.484545469.378018493.4781644.846374e+025.874044e+02609.970047465.844347455.9410855.826548e+02...589.4279545.745584e+026.066627e+02588.6598476.041935e+02618.172663439.36594115.93144610.011093Normal to solve dataset
GasSensorArrayEthanol0.3424820.2829270.2688680.2657372.017014e-011.517741e-010.3060660.2094650.2071391.859832e-01...0.6545481.848965e-011.499463e-010.5041301.941129e-010.2681070.206536-0.08312520.406379Hard to solve dataset
GasSensorArrayAcetone0.3174290.3073680.2599600.2623212.389416e-011.845477e-010.2949320.2327750.2442602.010619e-01...0.5486822.623136e-011.930651e-010.3041252.117950e-010.2757390.227163-0.08046020.400939Hard to solve dataset
SierraNevadaMountainsSnow63.58065575.13120553.70734666.9614545.431997e+014.300014e+0136.41566346.96621547.1037843.692058e+01...39.7324495.227692e+014.869493e+0134.6917285.180834e+0156.06689129.7780913.89820016.307300Normal to solve dataset
BitcoinSentiment0.2603280.2642440.2073650.2072432.462431e-011.970548e-010.2205980.2155640.2115562.011734e-01...0.2268522.324026e-012.014305e-010.2243852.097271e-010.2086500.220253-0.0222978.037369Easy to solve dataset
EthereumSentiment0.3187910.3338780.2412170.2460462.716099e-012.234477e-010.2527020.2351690.2313362.258759e-01...0.2558542.621682e-012.294233e-010.2577532.344765e-010.2523510.226023-0.0024758.248480Easy to solve dataset
CardanoSentiment0.3930830.4216370.3165650.3154164.370255e-012.973890e-010.3044490.3428850.3336283.017266e-01...0.3240413.881151e-013.052854e-010.3578822.998514e-010.3246270.300261-0.0159819.648634Easy to solve dataset
BinanceCoinSentiment0.4844380.5001710.3724890.3831444.094964e-013.448162e-010.3664140.3807410.3831213.460515e-01...0.3719335.139279e-013.538278e-010.3857873.479485e-010.3777660.373925-0.0365819.403395Easy to solve dataset
ElectricMotorTemperature8.8404299.0235107.6047407.8336231.760786e+014.656284e+006.59387611.96936411.9794904.650170e+00...5.9113471.198277e+014.698074e+005.1633968.869247e+005.3126914.2063480.42658619.286989Normal to solve dataset
SolarRadiationAndalusia0.8236490.8440810.6451870.6554811.042590e+006.216146e-010.6700860.6739030.6572126.195325e-01...0.6548867.698799e-016.238755e-010.6952406.255512e-010.6566000.633310-0.0224339.556290Easy to solve dataset
PrecipitationAndalusia0.4909030.5038640.4105960.4530949.854324e-013.851207e-010.3979960.4159400.3886953.612930e-01...0.4091877.985268e-013.690052e-010.3888414.023337e-010.4015740.461973-0.11220015.151995Normal to solve dataset
AcousticContaminationMadrid5.4402826.5910855.0461555.7262876.712051e+004.119996e+0064.5123855.6643515.6202653.281021e+00...5.1272975.387562e+005.126173e+004.4295134.602419e+005.2556533.2317540.04735318.912880Normal to solve dataset
VentilatorPressure1.1356260.9833541.0000720.8945252.163210e+006.166132e-010.9352432.2251542.1826456.015145e-01...0.4927282.072326e+005.685455e-010.4066891.848328e+000.7049120.972656-0.57330928.232985Hard to solve dataset
OccupancyDetectionLight185.122574184.640670155.141891157.2362742.386848e+028.054537e+01129.605793118.989563114.2068486.710728e+01...89.7180242.249267e+027.528684e+0195.3539501.024337e+02103.16175071.678605-4.39379320.990736Hard to solve dataset
WindTurbinePower276.399699586.730016238.769740466.1866159.515196e+029.785142e+011191.209810891.458761888.9447834.465780e+01...1033.8685276.220624e+047.424638e+01274.1875081.170780e+02104.97186346.843659-2.10097020.287725Hard to solve dataset
DhakaHourlyAirQuality7.4482746.4426435.1334654.6321558.680293e+003.171020e+0010.26525912.38410612.3687461.784270e+00...5.9153421.245697e+012.702426e+005.4768234.813817e+004.9461510.9966200.75706124.939633Hard to solve dataset
DailyOilGasPrices2.7421052.8225952.0552562.2514242.150854e+001.594442e+002.0690462.0979642.0523891.490442e+00...1.9380742.363609e+001.559385e+002.1493681.684828e+001.7169031.0972760.37789714.066854Normal to solve dataset
WaveDataTension21.33728421.05185916.51618316.5446661.816305e+011.517911e+0121.10002014.10799414.1104841.491209e+01...18.3322231.408012e+011.521209e+0117.7932101.543533e+0116.09434415.578184-1.43989010.047456Normal to solve dataset
NaturalGasPricesSentiment0.0742770.0746510.0573540.0566236.482931e-025.968074e-020.0993740.1135170.1126705.923535e-02...0.1431407.955113e-025.858841e-020.3413705.479847e-020.0601970.0501700.00444917.728011Normal to solve dataset
DailyTemperatureLatitude2.2741942.9305192.1580743.0211065.427370e+001.950407e+006.15603910.66030710.7002981.782911e+00...4.9858071.003086e+012.677904e+001.7807622.151806e+002.9420732.330602-0.68687627.059306Hard to solve dataset
MethaneMonitoringHomeActivity0.6934000.6943710.5401570.5411406.367615e-015.052408e-010.5354910.5890720.5892135.305011e-01...1.5585595.887444e-015.244137e-010.6342885.170912e-010.5563600.510272-0.00483613.400291Normal to solve dataset
LPGasMonitoringHomeActivity1.6995401.6962431.3348051.3385721.339258e+001.262008e+001.2976731.3067381.3060771.295706e+00...1.3446331.304916e+001.289305e+001.3855171.280573e+001.3836321.2277100.0329666.714723Easy to solve dataset
AluminiumConcentration382.557872358.169450324.538120306.0642902.737297e+022.451929e+02985.815086277.958777274.3850132.699899e+02...316.4113532.370224e+022.340929e+02250.4116382.450510e+02295.733413293.353253-64.74165615.202154Normal to solve dataset
BoronConcentration1.9938511.8612651.9094091.9823753.608769e+001.762709e+005.7242291.8346731.8146001.880072e+00...2.7659971.802806e+001.885018e+001.8012642.038787e+002.8954650.9355700.66320015.770175Normal to solve dataset
CopperConcentration2.4812862.4137161.9347871.9537892.343641e+001.826204e+005.1781491.9007401.9259041.845851e+00...2.0969991.819889e+001.863294e+001.9184991.902307e+002.0143701.7587110.05880213.179117Normal to solve dataset
IronConcentration88.66924083.83507877.74218272.3789137.232745e+016.213210e+01197.80172675.61194572.2236766.535273e+01...73.2178376.058250e+016.192114e+0166.5169986.382449e+0174.12393680.240398-23.97709513.974437Normal to solve dataset
ManganeseConcentration121.990065118.427219101.10831398.8982169.280130e+018.128308e+01229.35496698.03372499.5841348.462758e+01...97.2291028.461378e+017.991396e+0188.3038698.410939e+0196.53162188.936249-8.85076013.050513Normal to solve dataset
SodiumConcentration659.380735620.198671453.954204444.1322253.993961e+023.127763e+02469.649236399.916772410.5009364.079309e+02...478.4272814.154721e+026.527057e+02431.5253443.970369e+02850.97834899.42166787.92992519.279354Normal to solve dataset
PhosphorusConcentration22.15365221.42403024.30561623.3573502.783047e+011.906393e+0142.62398926.10423625.9227381.914935e+01...20.7377132.274373e+012.063641e+0118.5505132.034701e+0124.22503019.546838-1.71184711.639586Normal to solve dataset
PotassiumConcentration242.027142221.960612223.386480224.2391152.614602e+021.676802e+02497.219697272.228356262.9824711.748586e+02...182.3009592.112084e+021.862390e+02177.3003331.838792e+02230.535312171.181679-3.36554013.569614Normal to solve dataset
MagnesiumConcentration298.216850228.529714277.321165248.0022022.983644e+021.608196e+021112.378767324.661186293.1095641.679659e+02...166.3486441.404055e+021.665049e+02135.3439671.930456e+02237.257738236.562421-104.81724217.533404Normal to solve dataset
SulphurConcentration251.901880251.159147221.083366217.3026842.465601e+022.060873e+02279.789674201.620532201.4705382.224243e+02...208.0137122.136158e+022.150779e+02210.1144342.131277e+02309.202107313.827026-111.33941610.492794Normal to solve dataset
ZincConcentration1.8286311.8897081.5404221.5401091.496162e+001.429352e+004.2654331.4366321.4546661.589329e+00...1.6389251.440314e+001.509911e+001.6592961.508128e+001.6621172.102851-0.69332213.426782Normal to solve dataset
CalciumConcentration4236.1513803027.5491293446.1141733095.9297083.247370e+031.938747e+033539.9934353094.4298532983.2707512.189777e+03...3026.6729642.103223e+031.783895e+032478.9811192.343738e+033533.2582082511.154578-699.01639014.667538Normal to solve dataset
\n

55 rows × 27 columns

\n
" }, - "execution_count": 41, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/pipeline_example/time_series/ts_classification/advanced_example.py b/examples/pipeline_example/time_series/ts_classification/advanced_example.py deleted file mode 100644 index 52b61620b..000000000 --- a/examples/pipeline_example/time_series/ts_classification/advanced_example.py +++ /dev/null @@ -1,65 +0,0 @@ - -from fedot.core.pipelines.pipeline_builder import PipelineBuilder -from fedot_ind.api.utils.data import init_input_data -from fedot_ind.tools.example_utils import evaluate_metric -from fedot_ind.tools.loader import DataLoader -from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels - -metric_dict = {} - -model_dict = { - 'eigen_basis_basic': PipelineBuilder().add_node( - 'eigen_basis', - params={'window_size': 10, 'low_rank_approximation': False}).add_node( - 'quantile_extractor', - params={'window_size': 50}).add_node( - 'rf'), - - 'eigen_basis_advanced': PipelineBuilder().add_node( - 'eigen_basis', params={'window_size': 10, 'low_rank_approximation': False}). - add_node('feature_filter_model', params={ - 'grouping_level': 0.5}).add_node( - 'quantile_extractor', params={'window_size': 50}).add_node( - 'rf')} - -datasets_bad_f1 = [ - 'EOGVerticalSignal', - # 'ScreenType', - # 'CricketY', - # 'ElectricDevices', - 'Lightning7' -] - -# datasets_good_f1 = [ -# 'Car', -# 'ECG5000', -# "Beef" -# ] -# -# datasets_good_roc = [ -# 'Chinatown', -# 'Computers', -# 'Earthquakes', -# 'Ham', -# 'ECG200', -# 'ECGFiveDays', -# 'MiddlePhalanxOutlineCorrect', -# 'MoteStrain', -# 'TwoLeadECG' -# ] - -if __name__ == "__main__": - OperationTypesRepository = IndustrialModels().setup_repository() - for dataset_name in datasets_bad_f1: - train_data, test_data = DataLoader( - dataset_name=dataset_name).load_data() - input_data = init_input_data(train_data[0], train_data[1]) - val_data = init_input_data(test_data[0], test_data[1]) - for model in model_dict.keys(): - pipeline = model_dict[model].build() - pipeline.fit(input_data) - features = pipeline.predict(val_data, 'labels').predict - metric = evaluate_metric(target=test_data[1], prediction=features) - metric_dict.update({f'{dataset_name}_{model}': metric}) - print(f'{dataset_name}_{model} - {metric}') - print(metric_dict) diff --git a/examples/pipeline_example/time_series/ts_classification/basic_example.py b/examples/pipeline_example/time_series/ts_classification/basic_example.py deleted file mode 100644 index 9833ef85c..000000000 --- a/examples/pipeline_example/time_series/ts_classification/basic_example.py +++ /dev/null @@ -1,25 +0,0 @@ -import matplotlib -from fedot.core.pipelines.pipeline_builder import PipelineBuilder - -model_dict = {'basic_quantile': PipelineBuilder().add_node('quantile_extractor', - params={'window_size': 10, - 'stride': 5}).add_node('rf'), - 'basic_topological': PipelineBuilder().add_node('topological_extractor', - params={'window_size': 10, - 'stride': 5}).add_node('rf'), - 'basic_recurrence': PipelineBuilder().add_node('recurrence_extractor', - params={'window_size': 10, - 'stride': 5}).add_node('rf'), - 'advanced_quantile': PipelineBuilder().add_node('fourier_basis').add_node('quantile_extractor', - params={ - 'window_size': 10}).add_node( - 'rf'), - 'advanced_topological': PipelineBuilder().add_node('eigen_basis').add_node('topological_extractor', - params={'stride': 20, - 'window_size': 10}).add_node( - 'rf'), - 'advanced_reccurence': PipelineBuilder().add_node('wavelet_basis').add_node( - 'recurrence_extractor', - params={'window_size': 0}).add_node( - 'rf') - } diff --git a/examples/pipeline_example/time_series/ts_classification/nn_example.py b/examples/pipeline_example/time_series/ts_classification/nn_example.py deleted file mode 100644 index 4c1af11fe..000000000 --- a/examples/pipeline_example/time_series/ts_classification/nn_example.py +++ /dev/null @@ -1,44 +0,0 @@ -import pandas as pd -from fedot.core.pipelines.pipeline_builder import PipelineBuilder -from examples.example_utils import evaluate_metric, init_input_data -from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels -from fedot_ind.tools.loader import DataLoader - -if __name__ == "__main__": - dataset_list = [ - 'Lightning2', - 'EOGVerticalSignal'] - result_dict = {} - pipeline_dict = {'inception_model': PipelineBuilder().add_node('inception_model', params={'epochs': 100, - 'batch_size': 10}), - - 'quantile_rf_model': PipelineBuilder() - .add_node('quantile_extractor') - .add_node('rf'), - 'composed_model': PipelineBuilder() - .add_node('inception_model', params={'epochs': 100, - 'batch_size': 10}) - .add_node('quantile_extractor', branch_idx=1) - .add_node('rf', branch_idx=1) - .join_branches('logit')} - - for dataset in dataset_list: - try: - train_data, test_data = DataLoader( - dataset_name=dataset).load_data() - input_data = init_input_data(train_data[0], train_data[1]) - val_data = init_input_data(test_data[0], test_data[1]) - metric_dict = {} - for model in pipeline_dict: - with IndustrialModels(): - pipeline = pipeline_dict[model].build() - pipeline.fit(input_data) - target = pipeline.predict(val_data).predict - metric = evaluate_metric( - target=test_data[1], prediction=target) - metric_dict.update({model: metric}) - result_dict.update({dataset: metric_dict}) - except Exception: - print('ERROR') - result_df = pd.DataFrame(result_dict) - print(result_df) diff --git a/examples/pipeline_example/time_series/ts_regression/advanced_regression.py b/examples/pipeline_example/time_series/ts_regression/advanced_regression.py deleted file mode 100644 index 6e9492366..000000000 --- a/examples/pipeline_example/time_series/ts_regression/advanced_regression.py +++ /dev/null @@ -1,16 +0,0 @@ -from fedot.core.pipelines.pipeline_builder import PipelineBuilder - -from fedot_ind.api.utils.input_data import init_input_data -from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels -from fedot_ind.tools.loader import DataLoader - -dataset_name = 'AppliancesEnergy' -train_data, test_data = DataLoader(dataset_name=dataset_name).load_data() -train_data = init_input_data(train_data[0], train_data[1], task='regression') -test_data = init_input_data(test_data[0], test_data[1], task='regression') -with IndustrialModels(): - pipeline = PipelineBuilder().add_node('quantile_extractor').add_node( - 'fedot_regr', params={'timeout': 2}).build() - pipeline.fit(train_data) - pred = pipeline.predict(test_data).predict - print(pred) diff --git a/examples/pipeline_example/time_series/ts_regression/basic_example.py b/examples/pipeline_example/time_series/ts_regression/basic_example.py deleted file mode 100644 index 702e86a11..000000000 --- a/examples/pipeline_example/time_series/ts_regression/basic_example.py +++ /dev/null @@ -1,80 +0,0 @@ -from fedot.core.pipelines.pipeline_builder import PipelineBuilder - -from examples.example_utils import calculate_regression_metric -from fedot_ind.api.utils.data import init_input_data -from fedot_ind.api.utils.path_lib import PROJECT_PATH -from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels -from fedot_ind.tools.loader import DataLoader - -model_dict = { - 'regression_with_statistical_features': PipelineBuilder().add_node('quantile_extractor', - params={'window_size': 5}).add_node('ridge'), - 'regression_pca_with_statistical_features': PipelineBuilder().add_node('quantile_extractor', - params={'window_size': 5}) - .add_node('pca', params={'n_components': 0.9}) - .add_node('ridge'), - 'regression_with_reccurence_features': PipelineBuilder().add_node('recurrence_extractor', - params={'window_size': 20}).add_node('ridge'), - 'regression_pca_with_reccurence_features': PipelineBuilder().add_node('recurrence_extractor', - params={'window_size': 20}) - .add_node('pca', params={'n_components': 0.9}) - .add_node('ridge'), - 'regression_with_topological_features': PipelineBuilder().add_node('topological_extractor', - params={'window_size': 20}) - .add_node('pca', params={'n_components': 0.9}) - .add_node('ridge'), - 'regression_pca_with_topological_features': PipelineBuilder().add_node('topological_extractor', - params={'window_size': 20}) - .add_node('pca', params={'n_components': 0.9}) - .add_node('ridge') -} -metric_dict = {} - -data_path = PROJECT_PATH + '/examples/data' -dataset_list = [ - # 'AppliancesEnergy', - # 'AustraliaRainfall', - # 'BeijingPM10Quality', - # 'BeijingPM25Quality', - # 'BenzeneConcentration', - # 'HouseholdPowerConsumption1', - # 'HouseholdPowerConsumption2', - # 'IEEEPPG', - # 'FloodModeling1', - # 'FloodModeling2', - # 'FloodModeling3' - # 'LiveFuelMoistureContent', - # 'BIDMC32HR', - # 'BIDMC32RR', - # 'BIDMC32SpO2', - # 'DailyOilGasPrices', - # 'ElectricityPredictor', - # 'OccupancyDetectionLight', - # 'SolarRadiationAndalusia', - # 'TetuanEnergyConsumption', - # 'WindTurbinePower', - # 'ElectricMotorTemperature', - # 'LPGasMonitoringHomeActivity', - # 'GasSensorArrayAcetone', - # 'GasSensorArrayEthanol', - # 'WaveTensionData' -] - -if __name__ == "__main__": - dataset_name = 'MadridPM10Quality-no-missing' - with IndustrialModels(): - _, train_data, test_data = DataLoader(dataset_name=dataset_name).read_train_test_files( - dataset_name=dataset_name, - data_path=data_path) - for model in model_dict.keys(): - pipeline = model_dict[model].build() - input_data = init_input_data( - train_data[0], train_data[1], task='regression') - val_data = init_input_data( - test_data[0], test_data[1], task='regression') - pipeline.fit(input_data) - features = pipeline.predict(val_data).predict - metric = calculate_regression_metric( - test_target=test_data[1], labels=features) - metric_dict.update({model: metric}) - print(metric_dict) diff --git a/examples/pipeline_example/time_series/ts_regression/multi_ts_example.py b/examples/pipeline_example/time_series/ts_regression/multi_ts_example.py deleted file mode 100644 index 280a83efb..000000000 --- a/examples/pipeline_example/time_series/ts_regression/multi_ts_example.py +++ /dev/null @@ -1,208 +0,0 @@ -import os - -from fedot_ind.core.architecture.settings.computational import backend_methods as np -import pandas as pd -from fedot.api.main import Fedot -from fedot.core.data.data import InputData -from fedot.core.pipelines.node import PipelineNode -from fedot.core.pipelines.pipeline import Pipeline -from fedot.core.pipelines.pipeline_builder import PipelineBuilder -from fedot.core.repository.dataset_types import DataTypesEnum -from fedot.core.repository.tasks import Task, TaskTypesEnum -from sklearn.decomposition import PCA -from sklearn.metrics import explained_variance_score, max_error, mean_absolute_error, \ - mean_squared_error, d2_absolute_error_score, \ - median_absolute_error, r2_score - -from fedot_ind.tools.loader import DataLoader -from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor -from fedot_ind.core.models.recurrence.reccurence_extractor import RecurrenceExtractor - -model_dict = {'regression_with_statistical_features': PipelineBuilder().add_node('data_driven_basis_for_forecasting', - params={'window_size': 10, - } - ), - 'regression_with_reccurence_features': PipelineBuilder().add_node('recurrence_extractor')} - -datasets = { - 'm4_yearly': f'../data/ts/M4YearlyTest.csv', - 'm4_weekly': f'../data/ts/M4WeeklyTest.csv', - 'm4_daily': f'../data/ts/M4DailyTest.csv', - 'm4_monthly': f'../data/ts/M4MonthlyTest.csv', - 'm4_quarterly': f'../data/ts/M4QuarterlyTest.csv'} - -forecast_length = 13 - - -def init_input(X, y): - input_data = InputData(idx=np.arange(len(X)), - features=np.array(X.values.tolist()), - target=y.reshape(-1, 1), - task=Task(TaskTypesEnum.classification), - data_type=DataTypesEnum.image) - input_data.features = np.where( - np.isnan(input_data.features), 0, input_data.features) - input_data.features = np.where( - np.isinf(input_data.features), 0, input_data.features) - return input_data - - -def prepare_features(dataset_name, - pca_n_components: float = 0.95, - feature_generator: list = ['statistical'], - reduce_dimension: bool = False): - train_data, test_data = DataLoader(dataset_name=dataset_name).load_data() - - train_target = np.array([float(i) for i in train_data[1]]) - test_target = np.array([float(i) for i in test_data[1]]) - train_input_data = init_input(train_data[0], train_target) - test_input_data = init_input(test_data[0], test_target) - - generator_dict = {'statistical': QuantileExtractor({'window_mode': False, - 'window_size': 10, - 'var_threshold': 0}), - 'reccurence': RecurrenceExtractor({'window_mode': True, - 'min_signal_ratio': 0.7, - 'max_signal_ratio': 0.9, - 'rec_metric': 'cosine' - }) - } - train_features_list, test_features_list = [], [] - for extractor in feature_generator: - extractor = generator_dict[extractor] - extracted_features_train = extractor.transform(train_input_data) - train_size = extracted_features_train.predict.shape - train_features = extracted_features_train.predict - - extracted_features_test = extractor.transform(test_input_data) - test_size = extracted_features_test.predict.shape - test_features = extracted_features_test.predict - - if reduce_dimension: - pca = PCA(n_components=pca_n_components, - svd_solver='full') - train_features = pca.fit_transform(train_features) - test_features = pca.transform(test_features) - train_features_list.append( - train_features), test_features_list.append(test_features) - - return train_features_list, train_target, test_features_list, test_target - - -def calculate_metric(test_target, labels): - metric_dict = {'r2_score:': r2_score(test_target, labels), - 'mean_squared_error:': mean_squared_error(test_target, labels), - 'root_mean_squared_error:': np.sqrt(mean_squared_error(test_target, labels)), - 'mean_absolute_error': mean_absolute_error(test_target, labels), - 'median_absolute_error': median_absolute_error(test_target, labels), - 'explained_variance_score': explained_variance_score(test_target, labels), - 'max_error': max_error(test_target, labels), - # 'mean_poisson_deviance': mean_poisson_deviance(test_target, labels), - # 'mean_gamma_deviance': mean_gamma_deviance(test_target, labels), - 'd2_absolute_error_score': d2_absolute_error_score(test_target, labels) - } - df = pd.DataFrame.from_dict(metric_dict, orient='index') - return df - - -def evaluate_baseline(train, train_target, test, test_target): - node_scaling = PipelineNode('scaling') - node_rfr = PipelineNode('lasso', nodes_from=[node_scaling]) - baseline_model = Pipeline(node_rfr) - input_fit = InputData(idx=np.arange(len(train)), - features=train, - target=train_target.reshape(-1, 1), - task=Task(TaskTypesEnum.regression), - data_type=DataTypesEnum.image) - input_predict = InputData(idx=np.arange(len(test)), - features=test, - target=test_target.reshape(-1, 1), - task=Task(TaskTypesEnum.regression), - data_type=DataTypesEnum.image) - - baseline_model.fit(input_fit) - labels_baseline = baseline_model.predict(input_predict).predict - metric_df_baseline = calculate_metric(test_target, labels_baseline) - return metric_df_baseline - - -if __name__ == "__main__": - dataset_list = [ - # 'Gazprom', - # 'AppliancesEnergy', - # 'AustraliaRainfall', - # 'BeijingPM10Quality', - # 'BeijingPM25Quality', - # 'BenzeneConcentration', - # 'HouseholdPowerConsumption1', - # 'HouseholdPowerConsumption2', - # 'IEEEPPG', - # 'FloodModeling1', - 'FloodModeling2', - 'FloodModeling3' - 'LiveFuelMoistureContent', - 'BIDMC32HR', - 'BIDMC32RR', - 'BIDMC32SpO2', - 'DailyOilGasPrices', - 'ElectricityPredictor', - 'OccupancyDetectionLight', - 'SolarRadiationAndalusia', - 'TetuanEnergyConsumption', - 'WindTurbinePower', - 'ElectricMotorTemperature', - 'LPGasMonitoringHomeActivity', - 'GasSensorArrayAcetone', - 'GasSensorArrayEthanol', - 'WaveTensionData' - - ] - ten_minutes = range(0, 3, 1) - one_hour = ['1hr'] - for dataset_name in dataset_list: - try: - os.makedirs(f'./{dataset_name}') - except Exception: - _ = 1 - - train_features, train_target, test_features, test_target = prepare_features(dataset_name=dataset_name, - reduce_dimension=False, - feature_generator=[ - 'statistical' - # ,'reccurence' - ]) - if len(train_features) > 1: - concatenate_train = np.concatenate(train_features, axis=1) - concatenate_test = np.concatenate(test_features, axis=1) - train_features.append(concatenate_train) - test_features.append(concatenate_test) - else: - concatenate_train = train_features[0] - concatenate_test = test_features[0] - - for train, test in zip(train_features, test_features): - metric_df_baseline = evaluate_baseline( - train, train_target, test, test_target) - print(metric_df_baseline) - metric_df_baseline.to_csv(f'./{dataset_name}/baseline_metrics.csv') - - for run in one_hour: - predictor = Fedot(problem='regression', - metric='rmse', - timeout=60, - early_stopping_timeout=30, - logging_level=20, - n_jobs=6) - model = predictor.fit( - features=concatenate_train, target=train_target) - labels = predictor.predict(features=concatenate_test) - metric_df = calculate_metric(test_target, labels) - metric_df.to_csv(f'./{dataset_name}/metrics_run_{run}.csv') - pipeline = predictor.current_pipeline - pipeline.show(f'./{dataset_name}/pipeline_structure_{run}.png') - predictor.history.save(f'./{dataset_name}/history_run_{run}.json') - path_to_save = f'./{dataset_name}/saved_pipelines_run_{run}' - pipeline.save(path=path_to_save, create_subdir=True, - is_datetime_in_path=True) - - _ = 1 diff --git a/examples/real_world_examples/industrial_examples/economic_analysis/oil_gas_prices.ipynb b/examples/real_world_examples/industrial_examples/economic_analysis/oil_gas_prices.ipynb index d63c6a47a..fb3233224 100644 --- a/examples/real_world_examples/industrial_examples/economic_analysis/oil_gas_prices.ipynb +++ b/examples/real_world_examples/industrial_examples/economic_analysis/oil_gas_prices.ipynb @@ -40,30 +40,10 @@ "outputs": [], "source": [ "import pandas as pd\n", - "\n", "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", "from fedot_ind.tools.loader import DataLoader\n", - "from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels\n", - "from examples.example_utils import init_input_data, calculate_regression_metric\n", - "from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder\n", - "from fedot.core.repository.metrics_repository import RegressionMetricsEnum\n", - "from fedot_ind.core.optimizer.IndustrialEvoOptimizer import IndustrialEvoOptimizer\n", - "from golem.core.tuning.simultaneous import SimultaneousTuner\n", - "import matplotlib\n", - "from fedot_ind.api.main import FedotIndustrial\n", - "matplotlib.use('TkAgg')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "The list of basic fedot industrial models for experiment are shown below. We using simple linear machine learning pipelines with 3 different feature generators: Statistical, Reccurence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." + "from fedot_ind.api.main import FedotIndustrial" ] }, { @@ -71,52 +51,13 @@ "execution_count": 2, "outputs": [], "source": [ - "model_dict = {\n", - " 'regression_with_statistical_features': PipelineBuilder().add_node('quantile_extractor',\n", - " params={'window_size': 5}).add_node('ridge')\n", - "}\n", - "experiment_setup = {'problem': 'regression',\n", - " 'metric': 'rmse',\n", - " 'timeout': 10,\n", - " 'num_of_generations': 5,\n", - " 'pop_size': 10,\n", - " 'logging_level': 40,\n", - " 'available_operations':\n", - " ['rfr',\n", - " 'ridge',\n", - " 'gbr',\n", - " 'sgdr',\n", - " 'linear',\n", - " 'xgbreg',\n", - " 'dtreg',\n", - " 'treg',\n", - " 'knnreg',\n", - " 'scaling',\n", - " 'normalization',\n", - " 'pca'\n", - " 'eigen_basis',\n", - " 'fourier_basis',\n", - " 'minirocket_extractor',\n", - " 'quantile_extractor',\n", - " 'signal_extractor'\n", - " ],\n", - " 'n_jobs': 4,\n", - " 'industrial_preprocessing': True,\n", - " 'initial_assumption': None,\n", - " 'max_pipeline_fit_time': 5,\n", - " 'with_tuning': False,\n", - " 'early_stopping_iterations': 5,\n", - " 'early_stopping_timeout': 10,\n", - " 'optimizer': IndustrialEvoOptimizer}\n", - "\n", - "dataset_name = 'DailyOilGasPrices'\n", - "data_path = PROJECT_PATH + '/examples/data'\n", - "experiment_setup['output_folder'] = f'./{dataset_name}/results_of_experiment'\n", - "OperationTypesRepository = IndustrialModels().setup_repository()\n", - "tuning_params = {'task': 'regression',\n", - " 'metric': RegressionMetricsEnum.RMSE,\n", - " 'tuning_timeout':10,\n", - " 'tuning_iterations':30}" + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" ], "metadata": { "collapsed": false, @@ -125,64 +66,31 @@ } } }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The list of basic fedot industrial models for experiment are shown below. We using simple linear machine learning pipelines with 3 different feature generators: Statistical, Reccurence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "outputs": [], "source": [ - "def evaluate_industrial_model(train_data, test_data, task:str = 'regression'):\n", - " metric_dict = {}\n", - " input_data = init_input_data(train_data[0], train_data[1], task=task)\n", - " val_data = init_input_data(test_data[0], test_data[1], task=task)\n", - " for model in model_dict.keys():\n", - " print(f'Current_model - {model}')\n", - " pipeline = model_dict[model].build()\n", - " pipeline.fit(input_data)\n", - " features = pipeline.predict(val_data).predict\n", - " metric = calculate_regression_metric(test_target=test_data[1], labels=features)\n", - " metric_dict.update({model: metric})\n", - " return metric_dict\n", - "\n", - "def tuning_industrial_pipelines(pipeline, tuning_params, train_data):\n", - " input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - " tuning_method = SimultaneousTuner\n", - " pipeline_tuner = TunerBuilder(input_data.task) \\\n", - " .with_tuner(tuning_method) \\\n", - " .with_metric(tuning_params['metric']) \\\n", - " .with_timeout(tuning_params['tuning_timeout']) \\\n", - " .with_iterations(tuning_params['tuning_iterations']) \\\n", - " .build(input_data)\n", - "\n", - " pipeline = pipeline_tuner.tune(pipeline)\n", - " return pipeline\n", - "\n", - "def evaluate_automl(runs = 5):\n", - " metric_dict = {}\n", - " model_list = []\n", - "\n", - " if 'tuning_params' in experiment_setup.keys():\n", - " del experiment_setup['tuning_params']\n", - "\n", - " if 'industrial_preprocessing' in experiment_setup.keys():\n", - " ind_preproc = experiment_setup['industrial_preprocessing']\n", - " del experiment_setup['industrial_preprocessing']\n", - " else:\n", - " ind_preproc = True\n", - "\n", - " for run in range(runs):\n", - " model = FedotIndustrial(**experiment_setup)\n", - " model.preprocessing = ind_preproc\n", - " model.fit(train_data)\n", - " prediction = model.predict(test_data)\n", - "\n", - " metric = calculate_regression_metric(test_target=test_data[1], labels=prediction)\n", - " metric = metric.T\n", - " metric.columns = metric.columns.values\n", - "\n", - " metric_dict.update({f'run_number - {run}':metric})\n", - " model_list.append(model)\n", - "\n", - " return metric_dict, model_list" + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=15,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "dataset_name = 'DailyOilGasPrices'\n", + "data_path = PROJECT_PATH + '/examples/data'" ], "metadata": { "collapsed": false, @@ -219,7 +127,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-01-11 17:08:08,298 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/DailyOilGasPrices\n" + "2024-04-03 16:08:27,693 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/DailyOilGasPrices\n" ] } ], @@ -234,8 +142,8 @@ "execution_count": 5, "outputs": [], "source": [ - "input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - "val_data = init_input_data(test_data[0], test_data[1], task=tuning_params['task'])" + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" ], "metadata": { "collapsed": false, @@ -270,7 +178,7 @@ } ], "source": [ - "input_data.features.shape" + "features.shape" ], "metadata": { "collapsed": false, @@ -293,12 +201,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", - "pd.DataFrame(input_data.features[1, 0, :]).plot(title='Crude Oil WTI close prices')\n", - "pd.DataFrame(input_data.features[1, 1, :]).plot(title='Crude Oil WTI traded volumes')\n", + "pd.DataFrame(features[1, 0, :]).plot(title='Crude Oil WTI close prices')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='Crude Oil WTI traded volumes')\n", "plt.show()" ], "metadata": { @@ -338,76 +246,801 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n" + "2024-04-03 16:10:18,852 - Initialising experiment setup\n", + "2024-04-03 16:10:18,855 - Initialising Industrial Repository\n", + "2024-04-03 16:10:18,971 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-03 16:10:19,445 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-04-03 16:10:19,478 - State start\n", + "2024-04-03 16:10:19,480 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-dz5roxue', purging\n", + "2024-04-03 16:10:19,482 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-er2nmzvc', purging\n", + "2024-04-03 16:10:19,490 - Scheduler at: inproc://10.64.4.217/22788/1\n", + "2024-04-03 16:10:19,491 - dashboard at: http://10.64.4.217:8787/status\n", + "2024-04-03 16:10:19,492 - Registering Worker plugin shuffle\n", + "2024-04-03 16:10:19,505 - Start worker at: inproc://10.64.4.217/22788/4\n", + "2024-04-03 16:10:19,506 - Listening to: inproc10.64.4.217\n", + "2024-04-03 16:10:19,506 - Worker name: 0\n", + "2024-04-03 16:10:19,507 - dashboard at: 10.64.4.217:55039\n", + "2024-04-03 16:10:19,507 - Waiting to connect to: inproc://10.64.4.217/22788/1\n", + "2024-04-03 16:10:19,508 - -------------------------------------------------\n", + "2024-04-03 16:10:19,508 - Threads: 8\n", + "2024-04-03 16:10:19,509 - Memory: 31.95 GiB\n", + "2024-04-03 16:10:19,509 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-y1km3sqr\n", + "2024-04-03 16:10:19,509 - -------------------------------------------------\n", + "2024-04-03 16:10:19,514 - Register worker \n", + "2024-04-03 16:10:19,516 - Starting worker compute stream, inproc://10.64.4.217/22788/4\n", + "2024-04-03 16:10:19,517 - Starting established connection to inproc://10.64.4.217/22788/5\n", + "2024-04-03 16:10:19,518 - Starting Worker plugin shuffle\n", + "2024-04-03 16:10:19,519 - Registered to: inproc://10.64.4.217/22788/1\n", + "2024-04-03 16:10:19,520 - -------------------------------------------------\n", + "2024-04-03 16:10:19,521 - Starting established connection to inproc://10.64.4.217/22788/1\n", + "2024-04-03 16:10:19,524 - Receive client connection: Client-85742180-f1bb-11ee-9904-b42e99a00ea1\n", + "2024-04-03 16:10:19,526 - Starting established connection to inproc://10.64.4.217/22788/6\n", + "2024-04-03 16:10:19,527 - LinK Dask Server - http://10.64.4.217:8787/status\n", + "2024-04-03 16:10:19,528 - Initialising solver\n", + "2024-04-03 16:10:19,608 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-03 16:10:19,608 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-03 16:10:19,611 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-03 16:10:25,976 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - 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r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
regression_with_statistical_features-5.64407421.5055364.6374063.2317812.051471-5.29541718.537842-1.248095
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" }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_baseline = pd.concat([x for x in metric_dict.values()],axis=1)\n", - "df_baseline.columns = list(metric_dict.keys())\n", - "df_baseline = df_baseline.T\n", - "df_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "best_baseline = df_baseline.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" + "metrics" ], "metadata": { "collapsed": false, @@ -416,10 +1049,28 @@ } } }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "At the end of the experiment we can obtain the desired metric values using `calculate_regression_metric` method. Now there are five available metrics for classification task:\n", + "- `explained_variance_score`\n", + "- `max_error`\n", + "- `mean_absolute_error`\n", + "- `mean_squared_error`\n", + "- `d2_absolute_error_score`.\n", + "- `median_absolute_error`\n", + "- `r2_score`" + ] + }, { "cell_type": "markdown", "source": [ - "## Could it be done better? Tuning approach" + "## AutoML approach" ], "metadata": { "collapsed": false, @@ -436,3037 +1087,705 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n", - "2024-01-11 17:08:37,555 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", - "2024-01-11 17:08:37,555 - DataSourceSplitter - Hold out validation is applied.\n", - "2024-01-11 17:08:37,558 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" + "2024-04-03 16:17:29,001 - Initialising experiment setup\n", + "2024-04-03 16:17:29,003 - Initialising Industrial Repository\n", + "2024-04-03 16:17:29,004 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-03 16:17:29,022 - State start\n", + "2024-04-03 16:17:29,031 - Scheduler at: inproc://10.64.4.217/22788/9\n", + "2024-04-03 16:17:29,032 - dashboard at: http://10.64.4.217:55185/status\n", + "2024-04-03 16:17:29,034 - Registering Worker plugin shuffle\n", + "2024-04-03 16:17:29,045 - Start worker at: inproc://10.64.4.217/22788/12\n", + "2024-04-03 16:17:29,046 - Listening to: inproc10.64.4.217\n", + "2024-04-03 16:17:29,046 - Worker name: 0\n", + "2024-04-03 16:17:29,047 - dashboard at: 10.64.4.217:55188\n", + "2024-04-03 16:17:29,047 - Waiting to connect to: inproc://10.64.4.217/22788/9\n", + "2024-04-03 16:17:29,048 - -------------------------------------------------\n", + "2024-04-03 16:17:29,048 - Threads: 8\n", + "2024-04-03 16:17:29,049 - Memory: 31.95 GiB\n", + "2024-04-03 16:17:29,049 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-153lpg33\n", + "2024-04-03 16:17:29,050 - -------------------------------------------------\n", + "2024-04-03 16:17:29,053 - Register worker \n", + "2024-04-03 16:17:29,055 - Starting worker compute stream, inproc://10.64.4.217/22788/12\n", + "2024-04-03 16:17:29,055 - Starting established connection to inproc://10.64.4.217/22788/13\n", + "2024-04-03 16:17:29,057 - Starting Worker plugin shuffle\n", + "2024-04-03 16:17:29,058 - Registered to: inproc://10.64.4.217/22788/9\n", + "2024-04-03 16:17:29,059 - -------------------------------------------------\n", + "2024-04-03 16:17:29,059 - Starting established connection to inproc://10.64.4.217/22788/9\n", + "2024-04-03 16:17:29,063 - Receive client connection: Client-857a9db7-f1bc-11ee-9904-b42e99a00ea1\n", + "2024-04-03 16:17:29,065 - Starting established connection to inproc://10.64.4.217/22788/14\n", + "2024-04-03 16:17:29,067 - LinK Dask Server - http://10.64.4.217:55185/status\n", + "2024-04-03 16:17:29,068 - Initialising solver\n", + "2024-04-03 16:17:29,108 - AssumptionsHandler - Initial pipeline fitting started\n", + "2024-04-03 16:17:29,801 - AssumptionsHandler - Initial pipeline was fitted successfully\n", + "2024-04-03 16:17:29,803 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.5 MiB, max: 0.9 MiB\n", + "2024-04-03 16:17:29,804 - ApiComposer - Initial pipeline was fitted in 0.7 sec.\n", + "2024-04-03 16:17:29,805 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", + "2024-04-03 16:17:29,813 - ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 15 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'dtreg', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "2024-04-03 16:17:29,838 - ApiComposer - Pipeline composition started.\n", + "2024-04-03 16:17:29,839 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-03 16:17:29,840 - DataSourceSplitter - Hold out validation is applied.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 106/106 [00:00<00:00, 603.88it/s]\n", - "100%|██████████| 27/27 [00:00<00:00, 731.68it/s]\n" + "Generations: 0%| | 0/10000 [00:00']\n", + "2024-04-03 16:17:36,176 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:19:56,027 - IndustrialDispatcher - 21 individuals out of 21 in previous population were evaluated successfully.\n", + "2024-04-03 16:19:56,122 - IndustrialEvoOptimizer - Generation num: 2 size: 21\n", + "2024-04-03 16:19:56,124 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-03 16:19:56,125 - IndustrialEvoOptimizer - Next population size: 21; max graph depth: 6\n", + "2024-04-03 16:20:03,661 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:20:53,008 - full garbage collections took 11% CPU time recently (threshold: 10%)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/106 [00:00.on_destroy at 0x000001B17545D1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861470264176\n", + "Exception ignored in: .on_destroy at 0x000001B1759295E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1857737900368\n", + "Exception ignored in: .on_destroy at 0x000001B17FB52C10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861689906832\n", + "Exception ignored in: .on_destroy at 0x000001B182CE35E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861689476272\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 33%|███▎ | 1/3 [00:00<00:00, 2.21trial/s, best loss: 7.546356685867857]2024-01-11 17:08:38,467 - build_posterior_wrapper took 0.000997 seconds\n", - "2024-01-11 17:08:38,468 - TPE using 1/1 trials with best loss 7.546357\n" + "2024-04-03 16:21:14,862 - full garbage collections took 12% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:21:25,667 - full garbage collections took 24% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:21:32,243 - full garbage collections took 26% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:21:37,849 - full garbage collections took 26% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:21:44,251 - full garbage collections took 26% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:21:53,145 - full garbage collections took 26% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:22:02,245 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:22:18,763 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:22:22,572 - full garbage collections took 27% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:22:34,246 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:22:43,814 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:22:43,818 - IndustrialDispatcher - 18 individuals out of 18 in previous population were evaluated successfully.\n", + "2024-04-03 16:22:43,993 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:22:50,636 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:22:58,559 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:23:11,557 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:23:14,623 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:23:14,639 - IndustrialDispatcher - 4 individuals out of 4 in previous population were evaluated successfully.\n", + "2024-04-03 16:23:14,642 - ReproductionController - Reproduction achieved pop size 22 using 2 attempt(s) with success rate 0.986\n", + "2024-04-03 16:23:14,644 - RandomAgent - len=24 nonzero=15 avg=-0.475 std=0.927 min=-3.023 max=0.594 \n", + "2024-04-03 16:23:14,645 - RandomAgent - actions/rewards: [(>, -1.0), (>, -1.0), (>, -1.0), (>, 0.0), (>, 0.0), (>, 0.0), (>, 0.0), (>, 0.0), (>, 0.0), (, 0.0), (>, 0.0), (>, -0.0053), (>, -3.0234), (>, 0.0041), (>, 0.5889), (>, 0.0022), (, 0.1716), (>, -0.8564), (, 0.0356), (, 0.5939), (, -0.188), (>, -1.6156), (>, 0.1699), (, 0.0)]\n", + "2024-04-03 16:23:14,648 - RandomAgent - exp=[0.2 0.2 0.2 0.2 0.2] probs=[0.2 0.2 0.2 0.2 0.2]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/106 [00:00']\n", + "2024-04-03 16:23:14,738 - IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "2024-04-03 16:23:14,739 - IndustrialEvoOptimizer - spent time: 5.7 min\n", + "2024-04-03 16:23:14,740 - IndustrialEvoOptimizer - Next mutation proba: 0.05; Next crossover proba: 0.95\n", + "2024-04-03 16:23:14,742 - IndustrialEvoOptimizer - Next population size: 34; max graph depth: 6\n", + "2024-04-03 16:23:20,363 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:23:27,385 - full garbage collections took 22% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:23:32,224 - full garbage collections took 22% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:23:49,188 - full garbage collections took 23% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:23:55,613 - full garbage collections took 23% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:24:06,998 - full garbage collections took 23% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:24:12,135 - full garbage collections took 23% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:24:28,265 - full garbage collections took 23% CPU time recently (threshold: 10%)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/106 [00:00.on_destroy at 0x000001B1866C5550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861949568560\n", + "Exception ignored in: .on_destroy at 0x000001B1856CFEE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861958713488\n", + "Exception ignored in: .on_destroy at 0x000001B1863405E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1860751192496\n", + "Exception ignored in: .on_destroy at 0x000001B1865EC160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862034091248\n", + "Exception ignored in: .on_destroy at 0x000001B18674A1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1857913817008\n", + "Exception ignored in: .on_destroy at 0x000001B189EFE9D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861348031632\n", + "Exception ignored in: .on_destroy at 0x000001B18A17DB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861996696176\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "100%|██████████| 3/3 [00:01<00:00, 2.16trial/s, best loss: 7.546356685867857]\n", - " 10%|█ | 3/30 [00:00.on_destroy at 0x000001B18C1FA1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861996734000\n", + "Exception ignored in: .on_destroy at 0x000001B18C37F9D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861975770960\n", + "Exception ignored in: .on_destroy at 0x000001B18C37FE50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861945401776\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 13%|█▎ | 4/30 [00:00<00:10, 2.50trial/s, best loss: 7.546356685867857]2024-01-11 17:08:39,808 - build_posterior_wrapper took 0.000997 seconds\n", - "2024-01-11 17:08:39,809 - TPE using 4/4 trials with best loss 7.546357\n" + "2024-04-03 16:26:08,935 - full garbage collections took 32% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:26:24,483 - full garbage collections took 32% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:26:29,610 - full garbage collections took 33% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:26:29,612 - IndustrialDispatcher - 18 individuals out of 18 in previous population were evaluated successfully.\n", + "2024-04-03 16:26:31,507 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:26:31,513 - IndustrialDispatcher - 0 individuals out of 14 in previous population were evaluated successfully. 0.0% is a fairly small percentage of successful evaluation.\n", + "2024-04-03 16:26:37,597 - full garbage collections took 30% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:26:42,276 - full garbage collections took 31% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:26:47,201 - full garbage collections took 31% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:26:51,894 - full garbage collections took 32% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:26:56,891 - full garbage collections took 32% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:01,485 - full garbage collections took 33% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:06,168 - full garbage collections took 34% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:10,823 - full garbage collections took 34% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:15,465 - full garbage collections took 35% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:20,128 - full garbage collections took 35% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:24,573 - full garbage collections took 36% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:29,126 - full garbage collections took 36% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:33,596 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:37,977 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:38,681 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:27:38,687 - IndustrialDispatcher - 0 individuals out of 13 in previous population were evaluated successfully. 0.0% is a fairly small percentage of successful evaluation.\n", + "2024-04-03 16:27:48,131 - full garbage collections took 49% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:27:56,770 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:28:05,660 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:28:14,185 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:28:22,597 - full garbage collections took 51% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:28:31,404 - full garbage collections took 52% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:28:40,342 - full garbage collections took 52% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:28:49,202 - full garbage collections took 53% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:28:57,772 - full garbage collections took 54% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:29:06,579 - full garbage collections took 55% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:29:14,868 - full garbage collections took 56% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:29:23,645 - full garbage collections took 56% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:29:32,142 - full garbage collections took 58% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:29:37,222 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:29:37,230 - IndustrialDispatcher - 0 individuals out of 12 in previous population were evaluated successfully. 0.0% is a fairly small percentage of successful evaluation.\n", + "2024-04-03 16:29:44,921 - full garbage collections took 45% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:29:51,649 - full garbage collections took 45% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:29:58,083 - full garbage collections took 45% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:04,838 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:11,501 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:18,212 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:25,411 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:32,653 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:39,509 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:46,453 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:30:53,359 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:00,543 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:01,989 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-03 16:31:01,994 - IndustrialDispatcher - 0 individuals out of 12 in previous population were evaluated successfully. 0.0% is a fairly small percentage of successful evaluation.\n", + "2024-04-03 16:31:02,429 - full garbage collections took 47% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:02,847 - full garbage collections took 47% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:03,246 - full garbage collections took 47% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:03,661 - full garbage collections took 48% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:04,053 - full garbage collections took 48% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:04,426 - full garbage collections took 50% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:04,787 - full garbage collections took 51% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:05,144 - full garbage collections took 51% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:05,538 - full garbage collections took 51% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:05,885 - full garbage collections took 52% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:06,240 - full garbage collections took 52% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:06,611 - full garbage collections took 53% CPU time recently (threshold: 10%)\n", + "2024-04-03 16:31:06,621 - ReproductionController - Could not achieve required population size: have 22, required 34!\n", + "Check objective, constraints and evo operators. Possibly they return too few valid individuals.\n", + "2024-04-03 16:31:06,624 - RandomAgent - len=25 nonzero=10 avg=-0.690 std=1.135 min=-3.770 max=0.331 \n", + "2024-04-03 16:31:06,625 - RandomAgent - actions/rewards: [(>, -1.0), (>, -1.0), (>, -1.0), (, 0.0), (, 0.0), (>, 0.0), (>, 0.0), (, 0.1035), (>, 0.3311), (, 0.0), (>, 0.0), (, 0.0), (>, 0.0), (>, 0.0), (>, -0.4141), (>, -0.3373), (>, 0.1862), (>, 0.0), (>, 0.0), (>, 0.0), (>, 0.0), (>, 0.0), (, -0.0023), (>, 0.0), (>, -3.7697)]\n", + "2024-04-03 16:31:06,628 - RandomAgent - exp=[0.2 0.2 0.2 0.2 0.2] probs=[0.2 0.2 0.2 0.2 0.2]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/106 [00:00']\n", + "2024-04-03 16:31:06,743 - GroupedCondition - Optimisation stopped: Time limit is reached\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/106 [00:00']\n", + "2024-04-03 16:31:06,756 - IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "2024-04-03 16:31:06,757 - IndustrialEvoOptimizer - spent time: 13.6 min\n", + "2024-04-03 16:31:06,762 - GPComposer - GP composition finished\n", + "2024-04-03 16:31:06,765 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-03 16:31:06,766 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-03 16:31:06,773 - ApiComposer - Hyperparameters tuning started with 1 min. timeout\n", + "2024-04-03 16:31:06,780 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/106 [00:00\n", + "2024-04-03 18:00:02,444 - OperationsAnimatedBar - The animation was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\history_animated_bars.gif\".\n", + "2024-04-03 18:00:02,446 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-03 18:00:02,535 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-03 18:00:02,544 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-03 18:00:02,640 - default - The figure was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\fitness_by_gen.png\".\n" ] - }, + } + ], + "source": [ + "industrial_auto_model.solver.current_pipeline.show()\n", + "industrial_auto_model.plot_operation_distribution(mode='each')\n", + "industrial_auto_model.plot_fitness_by_generation()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 45, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/106 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_scoremodel_params
regression_with_statistical_features0.4266621.8557821.3622711.0563720.8801410.4324584.1718150.265165{ridge: {'alpha': 2.1380306940047142}, quantile_extractor: {'window_size': 0, 'stride': 1}}
\n" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_tuned.columns = list(metric_dict.keys())\n", - "df_tuned.T.sort_values('root_mean_squared_error:')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "\"{ridge: {'alpha': 2.1380306940047142}, quantile_extractor: {'window_size': 0, 'stride': 1}}\"" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned.T.sort_values('root_mean_squared_error:')['model_params'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "best_tuned = df_tuned.T.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Even better? AutoML approach" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-11 17:23:04,362 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.3 MiB, max: 1.8 MiB\n", - "2024-01-11 17:23:04,364 - ApiComposer - Initial pipeline was fitted in 1.0 sec.\n", - "2024-01-11 17:23:04,365 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", - "2024-01-11 17:23:04,373 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 10 min. Set of candidate models: ['rfr', 'ridge', 'gbr', 'sgdr', 'linear', 'xgbreg', 'dtreg', 'treg', 'knnreg', 'scaling', 'normalization', 'pcaeigen_basis', 'fourier_basis', 'minirocket_extractor', 'quantile_extractor', 'signal_extractor'].\n", - "2024-01-11 17:23:04,397 - ApiComposer - Pipeline composition started.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 0%| | 0/5 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
run_number - 00.6072321.2713111.1275240.7995390.6400490.6072324.4971060.443824
run_number - 10.6072321.2713111.1275240.7995390.6400490.6072324.4971060.443824
run_number - 20.6072321.2713111.1275240.7995390.6400490.6072324.4971060.443824
\n" - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_automl = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_automl.columns = list(metric_dict.keys())\n", - "df_automl = df_automl.T\n", - "df_automl.sort_values('root_mean_squared_error:')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "for model in model_list:\n", - " model.solver.current_pipeline.show()\n", - " model.plot_operation_distribution(mode='each')\n", - " model.plot_fitness_by_generation()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Compare with State of Art (SOTA) models" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 49, - "outputs": [], - "source": [ - "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 50, - "outputs": [], - "source": [ - "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", - "df.index = df['algorithm']\n", - "df = df.drop(['algorithm','ds/type'], axis=1)\n", - "df = df.replace(',','.', regex=True).astype(float)\n", - "df['Fedot_Industrial_baseline'] = best_baseline\n", - "df['Fedot_Industrial_tuned'] = best_tuned\n", - "df['Fedot_Industrial_AutoML'] = 0" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 51, - "outputs": [], - "source": [ - "df.loc['min','Fedot_Industrial_AutoML'] = df_automl['root_mean_squared_error:'].min()\n", - "df.loc['max','Fedot_Industrial_AutoML'] = df_automl['root_mean_squared_error:'].max()\n", - "df.loc['average','Fedot_Industrial_AutoML'] = df_automl['root_mean_squared_error:'].mean()\n", - "df = df.T" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 58, - "outputs": [ + "data": { + "text/plain": " r2 rmse mae\n0 0.612 1.121 0.756", + "text/html": "
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r2rmsemae
00.6121.1210.756
\n
" + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auto_metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 46, + "outputs": [], + "source": [ + "predictions = np.vstack([test_data[1].flatten(),labels.flatten(),auto_labels.flatten()]).T" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [], + "source": [ + "all_prediction = pd.DataFrame(predictions,columns=['target','baseline','automl'])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 55, + "outputs": [], + "source": [ + "all_prediction.plot()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Compare with State of Art (SOTA) models" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 49, + "outputs": [], + "source": [ + "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')\n", + "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", + "df.index = df['algorithm']\n", + "df = df.drop(['algorithm','ds/type'], axis=1)\n", + "df = df.replace(',','.', regex=True).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [], + "source": [ + "df['Fedot_Industrial_tuned'] = metrics['rmse'][0]\n", + "df['Fedot_Industrial_AutoML'] = auto_metrics['rmse'][0]\n", + "df = df.T" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 52, + "outputs": [ { "data": { - "text/plain": "FreshPRINCE_RMSE 1.020426\nDrCIF_RMSE 1.046399\nRotF_RMSE 1.097089\nFedot_Industrial_AutoML 1.127524\nTSF_RMSE 1.172730\nRandF_RMSE 1.182821\nXGBoost_RMSE 1.272832\nRIST_RMSE 1.301506\nFedot_Industrial_tuned 1.362271\nRDST_RMSE 1.382981\nMultiROCKET_RMSE 1.454406\nSingleInception_RMSE 1.490343\nInceptionT_RMSE 1.542210\n5NN-DTW_RMSE 1.593567\nCNN_RMSE 1.602674\nFPCR-Bs_RMSE 1.607024\nResNet_RMSE 1.610736\nFCN_RMSE 1.636335\nFPCR_RMSE 1.637966\nGrid-SVR_RMSE 1.699988\nRidge_RMSE 1.713667\nROCKET_RMSE 1.856612\n5NN-ED_RMSE 1.905470\n1NN-ED_RMSE 2.129638\n1NN-DTW_RMSE 2.211915\nFedot_Industrial_baseline 4.637406\nName: min, dtype: float64" + "text/plain": "FreshPRINCE_RMSE 1.020426\nFedot_Industrial_tuned 1.032000\nDrCIF_RMSE 1.046399\nRotF_RMSE 1.097089\nFedot_Industrial_AutoML 1.121000\nTSF_RMSE 1.172730\nRandF_RMSE 1.182821\nXGBoost_RMSE 1.272832\nRIST_RMSE 1.301506\nRDST_RMSE 1.382981\nMultiROCKET_RMSE 1.454406\nSingleInception_RMSE 1.490343\nInceptionT_RMSE 1.542210\n5NN-DTW_RMSE 1.593567\nCNN_RMSE 1.602674\nFPCR-Bs_RMSE 1.607024\nResNet_RMSE 1.610736\nFCN_RMSE 1.636335\nFPCR_RMSE 1.637966\nGrid-SVR_RMSE 1.699988\nRidge_RMSE 1.713667\nROCKET_RMSE 1.856612\n5NN-ED_RMSE 1.905470\n1NN-ED_RMSE 2.129638\n1NN-DTW_RMSE 2.211915\nName: min, dtype: float64" }, - "execution_count": 58, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -3483,13 +1802,13 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 53, "outputs": [ { "data": { - "text/plain": "Fedot_Industrial_AutoML 1.127524\nFedot_Industrial_tuned 1.362271\nRIST_RMSE 1.887153\nRotF_RMSE 2.088333\nFreshPRINCE_RMSE 2.101253\nMultiROCKET_RMSE 2.137563\nTSF_RMSE 2.181236\nRDST_RMSE 2.200364\nDrCIF_RMSE 2.201153\nResNet_RMSE 2.404590\nRandF_RMSE 2.446314\n5NN-DTW_RMSE 2.502464\nXGBoost_RMSE 2.511297\nFPCR-Bs_RMSE 2.512605\nFPCR_RMSE 2.523426\n5NN-ED_RMSE 2.554152\nFCN_RMSE 2.559661\nInceptionT_RMSE 2.574168\nSingleInception_RMSE 2.633115\nCNN_RMSE 2.674200\nGrid-SVR_RMSE 2.800419\nROCKET_RMSE 2.870444\nRidge_RMSE 2.904762\n1NN-ED_RMSE 3.281798\n1NN-DTW_RMSE 3.408802\nFedot_Industrial_baseline 4.637406\nName: max, dtype: float64" + "text/plain": "Fedot_Industrial_tuned 1.032000\nFedot_Industrial_AutoML 1.121000\nRIST_RMSE 1.887153\nRotF_RMSE 2.088333\nFreshPRINCE_RMSE 2.101253\nMultiROCKET_RMSE 2.137563\nTSF_RMSE 2.181236\nRDST_RMSE 2.200364\nDrCIF_RMSE 2.201153\nResNet_RMSE 2.404590\nRandF_RMSE 2.446314\n5NN-DTW_RMSE 2.502464\nXGBoost_RMSE 2.511297\nFPCR-Bs_RMSE 2.512605\nFPCR_RMSE 2.523426\n5NN-ED_RMSE 2.554152\nFCN_RMSE 2.559661\nInceptionT_RMSE 2.574168\nSingleInception_RMSE 2.633115\nCNN_RMSE 2.674200\nGrid-SVR_RMSE 2.800419\nROCKET_RMSE 2.870444\nRidge_RMSE 2.904762\n1NN-ED_RMSE 3.281798\n1NN-DTW_RMSE 3.408802\nName: max, dtype: float64" }, - "execution_count": 57, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -3506,13 +1825,13 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 54, "outputs": [ { "data": { - "text/plain": "Fedot_Industrial_AutoML 1.127524\nFedot_Industrial_tuned 1.362271\nFreshPRINCE_RMSE 1.490442\nRIST_RMSE 1.501047\nRotF_RMSE 1.559385\nDrCIF_RMSE 1.594442\nTSF_RMSE 1.684828\nRandF_RMSE 1.708196\nXGBoost_RMSE 1.716903\nRDST_RMSE 1.772813\nMultiROCKET_RMSE 1.773578\nResNet_RMSE 1.938074\nInceptionT_RMSE 2.030315\nFPCR_RMSE 2.052389\n5NN-DTW_RMSE 2.055256\nFCN_RMSE 2.069046\nFPCR-Bs_RMSE 2.097964\nSingleInception_RMSE 2.149368\nCNN_RMSE 2.150854\nGrid-SVR_RMSE 2.203537\n5NN-ED_RMSE 2.251424\nROCKET_RMSE 2.275254\nRidge_RMSE 2.363609\n1NN-DTW_RMSE 2.742105\n1NN-ED_RMSE 2.822595\nFedot_Industrial_baseline 4.637406\nName: average, dtype: float64" + "text/plain": "Fedot_Industrial_tuned 1.032000\nFedot_Industrial_AutoML 1.121000\nFreshPRINCE_RMSE 1.490442\nRIST_RMSE 1.501047\nRotF_RMSE 1.559385\nDrCIF_RMSE 1.594442\nTSF_RMSE 1.684828\nRandF_RMSE 1.708196\nXGBoost_RMSE 1.716903\nRDST_RMSE 1.772813\nMultiROCKET_RMSE 1.773578\nResNet_RMSE 1.938074\nInceptionT_RMSE 2.030315\nFPCR_RMSE 2.052389\n5NN-DTW_RMSE 2.055256\nFCN_RMSE 2.069046\nFPCR-Bs_RMSE 2.097964\nSingleInception_RMSE 2.149368\nCNN_RMSE 2.150854\nGrid-SVR_RMSE 2.203537\n5NN-ED_RMSE 2.251424\nROCKET_RMSE 2.275254\nRidge_RMSE 2.363609\n1NN-DTW_RMSE 2.742105\n1NN-ED_RMSE 2.822595\nName: average, dtype: float64" }, - "execution_count": 56, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -3526,18 +1845,6 @@ "name": "#%%\n" } } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } } ], "metadata": { diff --git a/examples/tabular/requirements.txt b/examples/real_world_examples/industrial_examples/economic_analysis/requirements.txt similarity index 100% rename from examples/tabular/requirements.txt rename to examples/real_world_examples/industrial_examples/economic_analysis/requirements.txt diff --git a/examples/tabular/scoring_prediction.py b/examples/real_world_examples/industrial_examples/economic_analysis/scoring_prediction.py similarity index 100% rename from examples/tabular/scoring_prediction.py rename to examples/real_world_examples/industrial_examples/economic_analysis/scoring_prediction.py diff --git a/examples/tabular/scoring_train.csv b/examples/real_world_examples/industrial_examples/economic_analysis/scoring_train.csv similarity index 100% rename from examples/tabular/scoring_train.csv rename to examples/real_world_examples/industrial_examples/economic_analysis/scoring_train.csv diff --git a/examples/tabular/scroing_prediction.ipynb b/examples/real_world_examples/industrial_examples/economic_analysis/scroing_prediction.ipynb similarity index 99% rename from examples/tabular/scroing_prediction.ipynb rename to examples/real_world_examples/industrial_examples/economic_analysis/scroing_prediction.ipynb index 83d8929cb..31f1f02d9 100644 --- a/examples/tabular/scroing_prediction.ipynb +++ b/examples/real_world_examples/industrial_examples/economic_analysis/scroing_prediction.ipynb @@ -6,7 +6,10 @@ "## Imports" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -18,6 +21,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:46.840284200Z", "start_time": "2024-01-09T14:23:42.905260300Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -34,7 +40,10 @@ "## Opening Data" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -49,6 +58,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:46.862273700Z", "start_time": "2024-01-09T14:23:46.834260400Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -75,6 +87,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:46.895260400Z", "start_time": "2024-01-09T14:23:46.867262300Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -115,6 +130,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:46.926262700Z", "start_time": "2024-01-09T14:23:46.894259900Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -130,6 +148,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:46.969259500Z", "start_time": "2024-01-09T14:23:46.912261200Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -139,7 +160,10 @@ "Split data for validation" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -154,6 +178,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:47.066260100Z", "start_time": "2024-01-09T14:23:46.926262700Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -178,6 +205,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:47.122259200Z", "start_time": "2024-01-09T14:23:46.942259900Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -187,7 +217,10 @@ "## Experiments settings" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -206,6 +239,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:47.372265900Z", "start_time": "2024-01-09T14:23:47.330261600Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -215,7 +251,10 @@ "## Fedot (current master)" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -230,6 +269,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:23:49.032388300Z", "start_time": "2024-01-09T14:23:48.900388800Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -314,6 +356,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:18.131389400Z", "start_time": "2024-01-09T14:23:49.410176Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -339,6 +384,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:18.450391500Z", "start_time": "2024-01-09T14:33:18.133389300Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -364,6 +412,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:18.641098900Z", "start_time": "2024-01-09T14:33:18.452391700Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -392,6 +443,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:18.662099600Z", "start_time": "2024-01-09T14:33:18.643099800Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -428,6 +482,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:18.687102500Z", "start_time": "2024-01-09T14:33:18.660100500Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -452,6 +509,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:18.862100Z", "start_time": "2024-01-09T14:33:18.674101400Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -476,6 +536,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:19.238101600Z", "start_time": "2024-01-09T14:33:18.861099Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -485,7 +548,10 @@ "## Fedot with use_auto_preprocessing (current master)" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -500,6 +566,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:33:19.569648500Z", "start_time": "2024-01-09T14:33:19.240102100Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -604,6 +673,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:44:44.532792500Z", "start_time": "2024-01-09T14:33:19.571649Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -639,6 +711,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:44:44.787011300Z", "start_time": "2024-01-09T14:44:44.533803600Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -664,6 +739,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:44:44.869049900Z", "start_time": "2024-01-09T14:44:44.789038300Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -692,6 +770,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:44:44.882008Z", "start_time": "2024-01-09T14:44:44.871000100Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -728,6 +809,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:44:44.912006600Z", "start_time": "2024-01-09T14:44:44.886003900Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -752,6 +836,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:44:45.067037200Z", "start_time": "2024-01-09T14:44:44.900002900Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -776,6 +863,9 @@ "ExecuteTime": { "end_time": "2024-01-09T14:44:45.464663300Z", "start_time": "2024-01-09T14:44:45.070001Z" + }, + "pycharm": { + "name": "#%%\n" } } }, @@ -785,7 +875,10 @@ "outputs": [], "source": [], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } } ], @@ -813,4 +906,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption.ipynb b/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption.ipynb deleted file mode 100644 index c7f0c38f0..000000000 --- a/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption.ipynb +++ /dev/null @@ -1,4813 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "## Predict how much energy will a building consume with Fedot.Industrial" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Dataset published on Kaggle, aims to assess the value of energy efficiency improvements. For that purpose, four types of sources are identified: electricity, chilled water, steam and hot\n", - "water. The goal is to estimate the **energy consumption in kWh**. Dimensions correspond to the air temperature, dew temperature, wind direction and wind speed. These values were taken hourly during a week, and the output is the meter reading of the four aforementioned sources. In this way, was created four datasets: **ChilledWaterPredictor**, **ElectricityPredictor**, **HotwaterPredictor**, and **SteamPredictor**.\n", - "Link to the dataset - https://www.kaggle.com/code/fatmanuranl/ashrae-energy-prediction2" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2023-08-28T10:34:48.354623Z", - "start_time": "2023-08-28T10:34:39.594404Z" - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from fedot_ind.tools.loader import DataLoader\n", - "from examples.example_utils import init_input_data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "The list of basic fedot industrial models for experiment are shown below. We using simple linear machine learning pipelines with 3 different feature generators: Statistical, Reccurence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "from cases.utils import ts_regression_setup\n", - "dataset_name = 'ElectricityPredictor'\n", - "OperationTypesRepository, tuning_params, data_path, experiment_setup, model_dict = ts_regression_setup()\n", - "experiment_setup['output_folder'] = f'./{dataset_name}/results_of_experiment'\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Now we must download the dataset. It could be done by using `DataReader` class that implemented as attribute of `FedotIndustrial` class. This class firstly tries to read the data from local project folder `data_path` and then if it is not possible, it downloads the data from the UCR/UEA archive. The data will be saved in the `data` folder." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2023-08-28T10:35:13.321212Z", - "start_time": "2023-08-28T10:35:12.913025Z" - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-12 15:40:20,156 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/ElectricityPredictor\n" - ] - } - ], - "source": [ - "_, train_data, test_data = DataLoader(dataset_name=dataset_name).read_train_test_files(\n", - " dataset_name=dataset_name,\n", - " data_path=data_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - "val_data = init_input_data(test_data[0], test_data[1], task=tuning_params['task'])" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Lets check our data." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "(567, 4, 168)" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_data.features.shape" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Lets visualise our predictors." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" 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" 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "pd.DataFrame(input_data.features[1, 0, :]).plot(title='air temperature')\n", - "pd.DataFrame(input_data.features[1, 1, :]).plot(title='dew temperature')\n", - "pd.DataFrame(input_data.features[1, 2, :]).plot(title='wind direction')\n", - "pd.DataFrame(input_data.features[1, 3, :]).plot(title='wind speed')\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Next steps are quite straightforward. We need to fit the model and then predict the values for the test data just like for any other model in sklearn.\n", - "\n", - "At the `fit` stage FedotIndustrial will transform initial time series data into features dataframe and will train regression model." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "start_time": "2023-08-28T10:35:27.965798Z" - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current_model - regression_with_statistical_features\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 567/567 [00:10<00:00, 55.46it/s] \n", - "100%|██████████| 243/243 [00:01<00:00, 165.81it/s]\n" - ] - } - ], - "source": [ - "from cases.utils import evaluate_industrial_model\n", - "metric_dict = evaluate_industrial_model(train_data,test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "At the end of the experiment we can obtain the desired metric values using `calculate_regression_metric` method. Now there are five available metrics for classification task:\n", - "- `explained_variance_score`\n", - "- `max_error`\n", - "- `mean_absolute_error`\n", - "- `mean_squared_error`\n", - "- `d2_absolute_error_score`.\n", - "- `median_absolute_error`\n", - "- `r2_score`" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2023-08-28T11:01:34.941934Z", - "start_time": "2023-08-28T11:01:34.928460Z" - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": " r2_score: mean_squared_error: \\\nregression_with_statistical_features 0.251014 197808.184612 \n\n root_mean_squared_error: \\\nregression_with_statistical_features 444.75632 \n\n mean_absolute_error \\\nregression_with_statistical_features 257.159778 \n\n median_absolute_error \\\nregression_with_statistical_features 135.067006 \n\n explained_variance_score max_error \\\nregression_with_statistical_features 0.253476 1900.097811 \n\n d2_absolute_error_score \nregression_with_statistical_features -0.333187 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
regression_with_statistical_features0.251014197808.184612444.75632257.159778135.0670060.2534761900.097811-0.333187
\n
" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_baseline = pd.concat([x for x in metric_dict.values()],axis=1)\n", - "df_baseline.columns = list(metric_dict.keys())\n", - "df_baseline = df_baseline.T\n", - "df_baseline.sort_values(by='root_mean_squared_error:')\n", - "best_baseline = df_baseline.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Could it be done better? Tuning approach" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current_model - regression_with_statistical_features\n", - "2024-01-12 14:32:54,347 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", - "2024-01-12 14:32:54,348 - DataSourceSplitter - Hold out validation is applied.\n", - "2024-01-12 14:32:54,351 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 453/453 [00:02<00:00, 162.67it/s]\n", - "100%|██████████| 114/114 [00:00<00:00, 204.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-12 14:32:58,267 - SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [ridge, quantile_extractor]}\n", - "ridge - {}\n", - "quantile_extractor - {'window_size': 0} \n", - "Initial metric: [457.639]\n", - " 0%| | 0/3 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_scoremodel_params
regression_with_statistical_features0.251014197808.184612444.75632257.159778135.0670060.2534761900.097811-0.333187{ridge: {}, quantile_extractor: {'window_size': 0}}
\n" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_tuned.columns = list(metric_dict.keys())\n", - "df_tuned.T.sort_values('root_mean_squared_error:')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "\"{ridge: {}, quantile_extractor: {'window_size': 0}}\"" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned.T.sort_values('root_mean_squared_error:')['model_params'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "best_tuned = df_tuned.T.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Even better? AutoML approach" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-12 15:40:41,380 - Initialising experiment setup\n", - "2024-01-12 15:40:41,381 - Initialising Industrial Repository\n", - "2024-01-12 15:40:41,382 - Initialising solver\n", - "2024-01-12 15:41:19,986 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 6.7 MiB, max: 13.4 MiB\n", - "2024-01-12 15:41:19,988 - ApiComposer - Initial pipeline was fitted in 38.0 sec.\n", - "2024-01-12 15:41:19,990 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", - "2024-01-12 15:41:19,998 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 20 min. Set of candidate models: ['rfr', 'ridge', 'gbr', 'sgdr', 'linear', 'xgbreg', 'dtreg', 'treg', 'scaling', 'normalization', 'kernel_pca', 'eigen_basis', 'fourier_basis', 'minirocket_extractor', 'quantile_extractor', 'signal_extractor'].\n", - "2024-01-12 15:41:20,018 - ApiComposer - Pipeline composition started.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 0%| | 0/5 [00:00\n from fedot.core.data.data import InputData, OutputData\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\__init__.py\", line 4, in \n from fedot.api import Fedot, FedotBuilder\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\__init__.py\", line 1, in \n from fedot.api.main import Fedot\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\main.py\", line 14, in \n from fedot.api.api_utils.api_composer import ApiComposer\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\api_utils\\api_composer.py\", line 11, in \n from fedot.api.api_utils.params import ApiParams\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\api_utils\\params.py\", line 7, in \n from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\golem\\core\\optimisers\\genetic\\gp_params.py\", line 5, in \n from golem.core.optimisers.adaptive.mab_agents.neural_contextual_mab_agent import ContextAgentTypeEnum\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\golem\\core\\optimisers\\adaptive\\mab_agents\\neural_contextual_mab_agent.py\", line 4, in \n from golem.core.optimisers.adaptive.neural_mab import NeuralMAB\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\golem\\core\\optimisers\\adaptive\\neural_mab.py\", line 8, in \n import torch\n File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\torch\\__init__.py\", line 129, in \n raise err\nOSError: [WinError 1455] Файл подкачки слишком мал для завершения операции. Error loading \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\torch\\lib\\shm.dll\" or one of its dependencies.\n\"\"\"", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[1;31mBrokenProcessPool\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[11], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m metric_dict, model_list \u001B[38;5;241m=\u001B[39m \u001B[43mevaluate_automl\u001B[49m\u001B[43m(\u001B[49m\u001B[43mruns\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m3\u001B[39;49m\u001B[43m)\u001B[49m\n", - "Cell \u001B[1;32mIn[3], line 43\u001B[0m, in \u001B[0;36mevaluate_automl\u001B[1;34m(runs)\u001B[0m\n\u001B[0;32m 41\u001B[0m model \u001B[38;5;241m=\u001B[39m FedotIndustrial(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mexperiment_setup)\n\u001B[0;32m 42\u001B[0m model\u001B[38;5;241m.\u001B[39mpreprocessing \u001B[38;5;241m=\u001B[39m ind_preproc\n\u001B[1;32m---> 43\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 44\u001B[0m prediction \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mpredict(test_data)\n\u001B[0;32m 46\u001B[0m metric \u001B[38;5;241m=\u001B[39m calculate_regression_metric(test_target\u001B[38;5;241m=\u001B[39mtest_data[\u001B[38;5;241m1\u001B[39m], labels\u001B[38;5;241m=\u001B[39mprediction)\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\api\\main.py:150\u001B[0m, in \u001B[0;36mFedotIndustrial.fit\u001B[1;34m(self, input_data, **kwargs)\u001B[0m\n\u001B[0;32m 148\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mensemble_solver\n\u001B[0;32m 149\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 150\u001B[0m fitted_pipeline \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolver\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 151\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m fitted_pipeline\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\main.py:177\u001B[0m, in \u001B[0;36mFedot.fit\u001B[1;34m(self, features, target, predefined_model)\u001B[0m\n\u001B[0;32m 175\u001B[0m \u001B[38;5;66;03m# Final fit for obtained pipeline on full dataset\u001B[39;00m\n\u001B[0;32m 176\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhistory \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhistory\u001B[38;5;241m.\u001B[39mis_empty() \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcurrent_pipeline\u001B[38;5;241m.\u001B[39mis_fitted:\n\u001B[1;32m--> 177\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_train_pipeline_on_full_dataset\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrecommendations_for_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfull_train_not_preprocessed\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 178\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog\u001B[38;5;241m.\u001B[39mmessage(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFinal pipeline was fitted\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 179\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\main.py:531\u001B[0m, in \u001B[0;36mFedot._train_pipeline_on_full_dataset\u001B[1;34m(self, recommendations, full_train_not_preprocessed)\u001B[0m\n\u001B[0;32m 526\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m recommendations \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 527\u001B[0m \u001B[38;5;66;03m# if data was cut we need to refit pipeline on full data\u001B[39;00m\n\u001B[0;32m 528\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata_processor\u001B[38;5;241m.\u001B[39maccept_and_apply_recommendations(full_train_not_preprocessed,\n\u001B[0;32m 529\u001B[0m {k: v \u001B[38;5;28;01mfor\u001B[39;00m k, v \u001B[38;5;129;01min\u001B[39;00m recommendations\u001B[38;5;241m.\u001B[39mitems()\n\u001B[0;32m 530\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m k \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcut\u001B[39m\u001B[38;5;124m'\u001B[39m})\n\u001B[1;32m--> 531\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcurrent_pipeline\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 532\u001B[0m \u001B[43m \u001B[49m\u001B[43mfull_train_not_preprocessed\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 533\u001B[0m \u001B[43m \u001B[49m\u001B[43mn_jobs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mn_jobs\u001B[49m\n\u001B[0;32m 534\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\pipeline.py:194\u001B[0m, in \u001B[0;36mPipeline.fit\u001B[1;34m(self, input_data, time_constraint, n_jobs)\u001B[0m\n\u001B[0;32m 192\u001B[0m copied_input_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_assign_data_to_nodes(copied_input_data)\n\u001B[0;32m 193\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m time_constraint \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 194\u001B[0m train_predicted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopied_input_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 195\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 196\u001B[0m train_predicted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fit_with_time_limit(input_data\u001B[38;5;241m=\u001B[39mcopied_input_data, time\u001B[38;5;241m=\u001B[39mtime_constraint)\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\pipeline.py:111\u001B[0m, in \u001B[0;36mPipeline._fit\u001B[1;34m(self, input_data, process_state_dict, fitted_operations)\u001B[0m\n\u001B[0;32m 109\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Timer() \u001B[38;5;28;01mas\u001B[39;00m t:\n\u001B[0;32m 110\u001B[0m computation_time_update \u001B[38;5;241m=\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mroot_node\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcomputation_time \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 111\u001B[0m train_predicted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mroot_node\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 112\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m computation_time_update:\n\u001B[0;32m 113\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcomputation_time \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mround\u001B[39m(t\u001B[38;5;241m.\u001B[39mminutes_from_start, \u001B[38;5;241m3\u001B[39m)\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:196\u001B[0m, in \u001B[0;36mPipelineNode.fit\u001B[1;34m(self, input_data)\u001B[0m\n\u001B[0;32m 186\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Runs training process in the node\u001B[39;00m\n\u001B[0;32m 187\u001B[0m \n\u001B[0;32m 188\u001B[0m \u001B[38;5;124;03mArgs:\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 192\u001B[0m \u001B[38;5;124;03m OutputData: values predicted on the provided ``input_data``\u001B[39;00m\n\u001B[0;32m 193\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 194\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTrying to fit pipeline node with operation: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m--> 196\u001B[0m input_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_input_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparent_operation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mfit\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 198\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 199\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Timer() \u001B[38;5;28;01mas\u001B[39;00m t:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:273\u001B[0m, in \u001B[0;36mPipelineNode._get_input_data\u001B[1;34m(self, input_data, parent_operation)\u001B[0m\n\u001B[0;32m 271\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_get_input_data\u001B[39m(\u001B[38;5;28mself\u001B[39m, input_data: InputData, parent_operation: \u001B[38;5;28mstr\u001B[39m):\n\u001B[0;32m 272\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnodes_from:\n\u001B[1;32m--> 273\u001B[0m input_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_input_from_parents\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparent_operation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparent_operation\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 274\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 275\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdirect_set:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:299\u001B[0m, in \u001B[0;36mPipelineNode._input_from_parents\u001B[1;34m(self, input_data, parent_operation)\u001B[0m\n\u001B[0;32m 295\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFit all parent nodes in secondary node with operation: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 297\u001B[0m parent_nodes \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_nodes_from_with_fixed_order()\n\u001B[1;32m--> 299\u001B[0m parent_results, _ \u001B[38;5;241m=\u001B[39m \u001B[43m_combine_parents\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparent_nodes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minput_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 300\u001B[0m \u001B[43m \u001B[49m\u001B[43mparent_operation\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 301\u001B[0m secondary_input \u001B[38;5;241m=\u001B[39m DataMerger\u001B[38;5;241m.\u001B[39mget(parent_results)\u001B[38;5;241m.\u001B[39mmerge()\n\u001B[0;32m 302\u001B[0m \u001B[38;5;66;03m# Update info about visited nodes\u001B[39;00m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:390\u001B[0m, in \u001B[0;36m_combine_parents\u001B[1;34m(parent_nodes, input_data, parent_operation)\u001B[0m\n\u001B[0;32m 388\u001B[0m parent_results\u001B[38;5;241m.\u001B[39mappend(prediction)\n\u001B[0;32m 389\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m parent_operation \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfit\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[1;32m--> 390\u001B[0m prediction \u001B[38;5;241m=\u001B[39m \u001B[43mparent\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 391\u001B[0m parent_results\u001B[38;5;241m.\u001B[39mappend(prediction)\n\u001B[0;32m 392\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:196\u001B[0m, in \u001B[0;36mPipelineNode.fit\u001B[1;34m(self, input_data)\u001B[0m\n\u001B[0;32m 186\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Runs training process in the node\u001B[39;00m\n\u001B[0;32m 187\u001B[0m \n\u001B[0;32m 188\u001B[0m \u001B[38;5;124;03mArgs:\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 192\u001B[0m \u001B[38;5;124;03m OutputData: values predicted on the provided ``input_data``\u001B[39;00m\n\u001B[0;32m 193\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 194\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTrying to fit pipeline node with operation: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m--> 196\u001B[0m input_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_input_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparent_operation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mfit\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 198\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 199\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Timer() \u001B[38;5;28;01mas\u001B[39;00m t:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:273\u001B[0m, in \u001B[0;36mPipelineNode._get_input_data\u001B[1;34m(self, input_data, parent_operation)\u001B[0m\n\u001B[0;32m 271\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_get_input_data\u001B[39m(\u001B[38;5;28mself\u001B[39m, input_data: InputData, parent_operation: \u001B[38;5;28mstr\u001B[39m):\n\u001B[0;32m 272\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnodes_from:\n\u001B[1;32m--> 273\u001B[0m input_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_input_from_parents\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparent_operation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparent_operation\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 274\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 275\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdirect_set:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:299\u001B[0m, in \u001B[0;36mPipelineNode._input_from_parents\u001B[1;34m(self, input_data, parent_operation)\u001B[0m\n\u001B[0;32m 295\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFit all parent nodes in secondary node with operation: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 297\u001B[0m parent_nodes \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_nodes_from_with_fixed_order()\n\u001B[1;32m--> 299\u001B[0m parent_results, _ \u001B[38;5;241m=\u001B[39m \u001B[43m_combine_parents\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparent_nodes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minput_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 300\u001B[0m \u001B[43m \u001B[49m\u001B[43mparent_operation\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 301\u001B[0m secondary_input \u001B[38;5;241m=\u001B[39m DataMerger\u001B[38;5;241m.\u001B[39mget(parent_results)\u001B[38;5;241m.\u001B[39mmerge()\n\u001B[0;32m 302\u001B[0m \u001B[38;5;66;03m# Update info about visited nodes\u001B[39;00m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:390\u001B[0m, in \u001B[0;36m_combine_parents\u001B[1;34m(parent_nodes, input_data, parent_operation)\u001B[0m\n\u001B[0;32m 388\u001B[0m parent_results\u001B[38;5;241m.\u001B[39mappend(prediction)\n\u001B[0;32m 389\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m parent_operation \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfit\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[1;32m--> 390\u001B[0m prediction \u001B[38;5;241m=\u001B[39m \u001B[43mparent\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 391\u001B[0m parent_results\u001B[38;5;241m.\u001B[39mappend(prediction)\n\u001B[0;32m 392\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:200\u001B[0m, in \u001B[0;36mPipelineNode.fit\u001B[1;34m(self, input_data)\u001B[0m\n\u001B[0;32m 198\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 199\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Timer() \u001B[38;5;28;01mas\u001B[39;00m t:\n\u001B[1;32m--> 200\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation, operation_predict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparams\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_parameters\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 201\u001B[0m \u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 202\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfit_time_in_seconds \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mround\u001B[39m(t\u001B[38;5;241m.\u001B[39mseconds_from_start, \u001B[38;5;241m3\u001B[39m)\n\u001B[0;32m 203\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\operations\\operation.py:89\u001B[0m, in \u001B[0;36mOperation.fit\u001B[1;34m(self, params, data)\u001B[0m\n\u001B[0;32m 85\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_init(data\u001B[38;5;241m.\u001B[39mtask, params\u001B[38;5;241m=\u001B[39mparams, n_samples_data\u001B[38;5;241m=\u001B[39mdata\u001B[38;5;241m.\u001B[39mfeatures\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m])\n\u001B[0;32m 87\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_eval_strategy\u001B[38;5;241m.\u001B[39mfit(train_data\u001B[38;5;241m=\u001B[39mdata)\n\u001B[1;32m---> 89\u001B[0m predict_train \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_for_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfitted_operation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 91\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation, predict_train\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\repository\\industrial_implementations\\abstract.py:101\u001B[0m, in \u001B[0;36mpredict_for_fit\u001B[1;34m(self, fitted_operation, data, params, output_mode)\u001B[0m\n\u001B[0;32m 89\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpredict_for_fit\u001B[39m(\u001B[38;5;28mself\u001B[39m, fitted_operation, data: InputData, params: Optional[OperationParameters] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m 90\u001B[0m output_mode: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdefault\u001B[39m\u001B[38;5;124m'\u001B[39m):\n\u001B[0;32m 91\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"This method is used for defining and running of the evaluation strategy\u001B[39;00m\n\u001B[0;32m 92\u001B[0m \u001B[38;5;124;03m to predict with the data provided during fit stage\u001B[39;00m\n\u001B[0;32m 93\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 99\u001B[0m \u001B[38;5;124;03m for example, is the operation predict probabilities or class labels\u001B[39;00m\n\u001B[0;32m 100\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 101\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_predict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfitted_operation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moutput_mode\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mis_fit_stage\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\repository\\industrial_implementations\\abstract.py:55\u001B[0m, in \u001B[0;36mpredict_operation\u001B[1;34m(self, fitted_operation, data, params, output_mode, is_fit_stage)\u001B[0m\n\u001B[0;32m 52\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_init(data\u001B[38;5;241m.\u001B[39mtask, output_mode\u001B[38;5;241m=\u001B[39moutput_mode, params\u001B[38;5;241m=\u001B[39mparams, n_samples_data\u001B[38;5;241m=\u001B[39mdata\u001B[38;5;241m.\u001B[39mfeatures\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m])\n\u001B[0;32m 54\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_fit_stage:\n\u001B[1;32m---> 55\u001B[0m prediction \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_eval_strategy\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_for_fit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 56\u001B[0m \u001B[43m \u001B[49m\u001B[43mtrained_operation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfitted_operation\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 57\u001B[0m \u001B[43m \u001B[49m\u001B[43mpredict_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 58\u001B[0m \u001B[43m \u001B[49m\u001B[43moutput_mode\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moutput_mode\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 59\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 60\u001B[0m prediction \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_eval_strategy\u001B[38;5;241m.\u001B[39mpredict(\n\u001B[0;32m 61\u001B[0m trained_operation\u001B[38;5;241m=\u001B[39mfitted_operation,\n\u001B[0;32m 62\u001B[0m predict_data\u001B[38;5;241m=\u001B[39mdata,\n\u001B[0;32m 63\u001B[0m output_mode\u001B[38;5;241m=\u001B[39moutput_mode)\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\interfaces\\industrial_preprocessing_strategy.py:291\u001B[0m, in \u001B[0;36mIndustrialPreprocessingStrategy.predict_for_fit\u001B[1;34m(self, trained_operation, predict_data, output_mode)\u001B[0m\n\u001B[0;32m 290\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpredict_for_fit\u001B[39m(\u001B[38;5;28mself\u001B[39m, trained_operation, predict_data: InputData, output_mode: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdefault\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m OutputData:\n\u001B[1;32m--> 291\u001B[0m prediction \u001B[38;5;241m=\u001B[39m \u001B[43mtrained_operation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtransform_for_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpredict_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 292\u001B[0m converted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmulti_dim_dispatcher\u001B[38;5;241m.\u001B[39m_convert_to_output(prediction, predict_data)\n\u001B[0;32m 293\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m converted\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\core\\operations\\evaluation\\operation_implementations\\implementation_interfaces.py:46\u001B[0m, in \u001B[0;36mDataOperationImplementation.transform_for_fit\u001B[1;34m(self, input_data)\u001B[0m\n\u001B[0;32m 39\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_for_fit\u001B[39m(\u001B[38;5;28mself\u001B[39m, input_data: InputData) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m OutputData:\n\u001B[0;32m 40\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\" Method apply transform operation on a dataset for fit stage.\u001B[39;00m\n\u001B[0;32m 41\u001B[0m \u001B[38;5;124;03m Allows to implement transform method different from main transform method\u001B[39;00m\n\u001B[0;32m 42\u001B[0m \u001B[38;5;124;03m if another behaviour for fit graph stage is needed.\u001B[39;00m\n\u001B[0;32m 43\u001B[0m \n\u001B[0;32m 44\u001B[0m \u001B[38;5;124;03m :param input_data: data with features, target and ids to process\u001B[39;00m\n\u001B[0;32m 45\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m---> 46\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtransform\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\IndustrialCachableOperation.py:81\u001B[0m, in \u001B[0;36mIndustrialCachableOperationImplementation.transform\u001B[1;34m(self, input_data, use_cache)\u001B[0m\n\u001B[0;32m 79\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m predict\n\u001B[0;32m 80\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m---> 81\u001B[0m transformed_features \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_transform\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 82\u001B[0m predict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_convert_to_fedot_datatype(input_data, transformed_features)\n\u001B[0;32m 83\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m predict\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\transformation\\basis\\eigen_basis.py:70\u001B[0m, in \u001B[0;36mEigenBasisImplementation._transform\u001B[1;34m(self, input_data)\u001B[0m\n\u001B[0;32m 68\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m dimension \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(features\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m1\u001B[39m]):\n\u001B[0;32m 69\u001B[0m parallel \u001B[38;5;241m=\u001B[39m Parallel(n_jobs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_processes, verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, pre_dispatch\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m2*n_jobs\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m---> 70\u001B[0m v \u001B[38;5;241m=\u001B[39m \u001B[43mparallel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdelayed\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_transform_one_sample\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[43msample\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43msample\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mfeatures\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdimension\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m:\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 71\u001B[0m predict\u001B[38;5;241m.\u001B[39mappend(np\u001B[38;5;241m.\u001B[39marray(v) \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(v) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m v[\u001B[38;5;241m0\u001B[39m])\n\u001B[0;32m 73\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredict \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mconcatenate(predict, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\parallel.py:1952\u001B[0m, in \u001B[0;36mParallel.__call__\u001B[1;34m(self, iterable)\u001B[0m\n\u001B[0;32m 1946\u001B[0m \u001B[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001B[39;00m\n\u001B[0;32m 1947\u001B[0m \u001B[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001B[39;00m\n\u001B[0;32m 1948\u001B[0m \u001B[38;5;66;03m# reach the first `yield` statement. This starts the aynchronous\u001B[39;00m\n\u001B[0;32m 1949\u001B[0m \u001B[38;5;66;03m# dispatch of the tasks to the workers.\u001B[39;00m\n\u001B[0;32m 1950\u001B[0m \u001B[38;5;28mnext\u001B[39m(output)\n\u001B[1;32m-> 1952\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m output \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreturn_generator \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43moutput\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\parallel.py:1595\u001B[0m, in \u001B[0;36mParallel._get_outputs\u001B[1;34m(self, iterator, pre_dispatch)\u001B[0m\n\u001B[0;32m 1592\u001B[0m \u001B[38;5;28;01myield\u001B[39;00m\n\u001B[0;32m 1594\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backend\u001B[38;5;241m.\u001B[39mretrieval_context():\n\u001B[1;32m-> 1595\u001B[0m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_retrieve()\n\u001B[0;32m 1597\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mGeneratorExit\u001B[39;00m:\n\u001B[0;32m 1598\u001B[0m \u001B[38;5;66;03m# The generator has been garbage collected before being fully\u001B[39;00m\n\u001B[0;32m 1599\u001B[0m \u001B[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001B[39;00m\n\u001B[0;32m 1600\u001B[0m \u001B[38;5;66;03m# the user if necessary.\u001B[39;00m\n\u001B[0;32m 1601\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_exception \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\parallel.py:1699\u001B[0m, in \u001B[0;36mParallel._retrieve\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 1692\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_wait_retrieval():\n\u001B[0;32m 1693\u001B[0m \n\u001B[0;32m 1694\u001B[0m \u001B[38;5;66;03m# If the callback thread of a worker has signaled that its task\u001B[39;00m\n\u001B[0;32m 1695\u001B[0m \u001B[38;5;66;03m# triggered an exception, or if the retrieval loop has raised an\u001B[39;00m\n\u001B[0;32m 1696\u001B[0m \u001B[38;5;66;03m# exception (e.g. `GeneratorExit`), exit the loop and surface the\u001B[39;00m\n\u001B[0;32m 1697\u001B[0m \u001B[38;5;66;03m# worker traceback.\u001B[39;00m\n\u001B[0;32m 1698\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_aborting:\n\u001B[1;32m-> 1699\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_raise_error_fast\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1700\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[0;32m 1702\u001B[0m \u001B[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001B[39;00m\n\u001B[0;32m 1703\u001B[0m \u001B[38;5;66;03m# async callbacks to progress.\u001B[39;00m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\parallel.py:1734\u001B[0m, in \u001B[0;36mParallel._raise_error_fast\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 1730\u001B[0m \u001B[38;5;66;03m# If this error job exists, immediatly raise the error by\u001B[39;00m\n\u001B[0;32m 1731\u001B[0m \u001B[38;5;66;03m# calling get_result. This job might not exists if abort has been\u001B[39;00m\n\u001B[0;32m 1732\u001B[0m \u001B[38;5;66;03m# called directly or if the generator is gc'ed.\u001B[39;00m\n\u001B[0;32m 1733\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m error_job \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1734\u001B[0m \u001B[43merror_job\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_result\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\parallel.py:736\u001B[0m, in \u001B[0;36mBatchCompletionCallBack.get_result\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 730\u001B[0m backend \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mparallel\u001B[38;5;241m.\u001B[39m_backend\n\u001B[0;32m 732\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m backend\u001B[38;5;241m.\u001B[39msupports_retrieve_callback:\n\u001B[0;32m 733\u001B[0m \u001B[38;5;66;03m# We assume that the result has already been retrieved by the\u001B[39;00m\n\u001B[0;32m 734\u001B[0m \u001B[38;5;66;03m# callback thread, and is stored internally. It's just waiting to\u001B[39;00m\n\u001B[0;32m 735\u001B[0m \u001B[38;5;66;03m# be returned.\u001B[39;00m\n\u001B[1;32m--> 736\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_return_or_raise\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 738\u001B[0m \u001B[38;5;66;03m# For other backends, the main thread needs to run the retrieval step.\u001B[39;00m\n\u001B[0;32m 739\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\parallel.py:754\u001B[0m, in \u001B[0;36mBatchCompletionCallBack._return_or_raise\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 752\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 753\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstatus \u001B[38;5;241m==\u001B[39m TASK_ERROR:\n\u001B[1;32m--> 754\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_result\n\u001B[0;32m 755\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_result\n\u001B[0;32m 756\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n", - "\u001B[1;31mBrokenProcessPool\u001B[0m: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable." - ] - } - ], - "source": [ - "from cases.utils import evaluate_automl\n", - "metric_dict, model_list = evaluate_automl(runs=3)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Length mismatch: Expected axis has 8 elements, new values have 1 elements", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[12], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m df_automl \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mconcat([x \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m metric_dict\u001B[38;5;241m.\u001B[39mvalues()],axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m)\u001B[38;5;241m.\u001B[39mT\n\u001B[1;32m----> 2\u001B[0m \u001B[43mdf_automl\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(metric_dict\u001B[38;5;241m.\u001B[39mkeys())\n\u001B[0;32m 3\u001B[0m df_automl \u001B[38;5;241m=\u001B[39m df_automl\u001B[38;5;241m.\u001B[39mT\n\u001B[0;32m 4\u001B[0m df_automl\u001B[38;5;241m.\u001B[39msort_values(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mroot_mean_squared_error:\u001B[39m\u001B[38;5;124m'\u001B[39m)\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\pandas\\core\\generic.py:6002\u001B[0m, in \u001B[0;36mNDFrame.__setattr__\u001B[1;34m(self, name, value)\u001B[0m\n\u001B[0;32m 6000\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 6001\u001B[0m \u001B[38;5;28mobject\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__getattribute__\u001B[39m(\u001B[38;5;28mself\u001B[39m, name)\n\u001B[1;32m-> 6002\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mobject\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;21;43m__setattr__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 6003\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m:\n\u001B[0;32m 6004\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\pandas\\_libs\\properties.pyx:69\u001B[0m, in \u001B[0;36mpandas._libs.properties.AxisProperty.__set__\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\pandas\\core\\generic.py:730\u001B[0m, in \u001B[0;36mNDFrame._set_axis\u001B[1;34m(self, axis, labels)\u001B[0m\n\u001B[0;32m 725\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 726\u001B[0m \u001B[38;5;124;03mThis is called from the cython code when we set the `index` attribute\u001B[39;00m\n\u001B[0;32m 727\u001B[0m \u001B[38;5;124;03mdirectly, e.g. `series.index = [1, 2, 3]`.\u001B[39;00m\n\u001B[0;32m 728\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 729\u001B[0m labels \u001B[38;5;241m=\u001B[39m ensure_index(labels)\n\u001B[1;32m--> 730\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_mgr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mset_axis\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlabels\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 731\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_clear_item_cache()\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\pandas\\core\\internals\\managers.py:225\u001B[0m, in \u001B[0;36mBaseBlockManager.set_axis\u001B[1;34m(self, axis, new_labels)\u001B[0m\n\u001B[0;32m 223\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mset_axis\u001B[39m(\u001B[38;5;28mself\u001B[39m, axis: AxisInt, new_labels: Index) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 224\u001B[0m \u001B[38;5;66;03m# Caller is responsible for ensuring we have an Index object.\u001B[39;00m\n\u001B[1;32m--> 225\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_set_axis\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_labels\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 226\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxes[axis] \u001B[38;5;241m=\u001B[39m new_labels\n", - "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\pandas\\core\\internals\\base.py:70\u001B[0m, in \u001B[0;36mDataManager._validate_set_axis\u001B[1;34m(self, axis, new_labels)\u001B[0m\n\u001B[0;32m 67\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 69\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m new_len \u001B[38;5;241m!=\u001B[39m old_len:\n\u001B[1;32m---> 70\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m 71\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mLength mismatch: Expected axis has \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mold_len\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m elements, new \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 72\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mvalues have \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnew_len\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m elements\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 73\u001B[0m )\n", - "\u001B[1;31mValueError\u001B[0m: Length mismatch: Expected axis has 8 elements, new values have 1 elements" - ] - } - ], - "source": [ - "df_automl = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_automl.columns = list(metric_dict.keys())\n", - "df_automl = df_automl.T\n", - "df_automl.sort_values('root_mean_squared_error:')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Compare with State of Art (SOTA) models" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "from cases.utils import sota_compare\n", - "\n", - "sota_compare(data_path,dataset_name, best_baseline,best_tuned,df_automl)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.1" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} \ No newline at end of file diff --git a/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_electricity.ipynb b/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_electricity.ipynb new file mode 100644 index 000000000..184d02e1f --- /dev/null +++ b/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_electricity.ipynb @@ -0,0 +1,1413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Predict how much energy will a building consume with Fedot.Industrial" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Dataset published on Kaggle, aims to assess the value of energy efficiency improvements. For that purpose, four types of sources are identified: electricity, chilled water, steam and hot\n", + "water. The goal is to estimate the **energy consumption in kWh**. Dimensions correspond to the air temperature, dew temperature, wind direction and wind speed. These values were taken hourly during a week, and the output is the meter reading of the four aforementioned sources. In this way, was created four datasets: **ChilledWaterPredictor**, **ElectricityPredictor**, **HotwaterPredictor**, and **SteamPredictor**.\n", + "Link to the dataset - https://www.kaggle.com/code/fatmanuranl/ashrae-energy-prediction2" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-28T10:34:48.354623Z", + "start_time": "2023-08-28T10:34:39.594404Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", + "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", + "from fedot_ind.tools.loader import DataLoader\n", + "from fedot_ind.api.main import FedotIndustrial" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The list of basic fedot industrial models for experiment are shown below. We using simple linear machine learning pipelines with 3 different feature generators: Statistical, Reccurence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=15,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "dataset_name = 'ElectricityPredictor'\n", + "data_path = PROJECT_PATH + '/examples/data'" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we must download the dataset. It could be done by using `DataReader` class that implemented as attribute of `FedotIndustrial` class. This class firstly tries to read the data from local project folder `data_path` and then if it is not possible, it downloads the data from the UCR/UEA archive. The data will be saved in the `data` folder." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-28T10:35:13.321212Z", + "start_time": "2023-08-28T10:35:12.913025Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 15:54:52,114 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/ElectricityPredictor\n" + ] + } + ], + "source": [ + "_, train_data, test_data = DataLoader(dataset_name=dataset_name).read_train_test_files(\n", + " dataset_name=dataset_name,\n", + " data_path=data_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Lets check our data." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "(567, 4, 168)" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features.shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Lets visualise our predictors." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "pd.DataFrame(features[1, 0, :]).plot(title='air temperature')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='dew temperature')\n", + "pd.DataFrame(features[1, 2, :]).plot(title='wind direction')\n", + "pd.DataFrame(features[1, 3, :]).plot(title='wind speed')\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Next steps are quite straightforward. We need to fit the model and then predict the values for the test data just like for any other model in sklearn.\n", + "\n", + "At the `fit` stage FedotIndustrial will transform initial time series data into features dataframe and will train regression model." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "start_time": "2023-08-28T10:35:27.965798Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 15:55:08,487 - Initialising experiment setup\n", + "2024-04-04 15:55:08,503 - Initialising Industrial Repository\n", + "2024-04-04 15:55:08,632 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-04 15:55:09,341 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-04-04 15:55:09,375 - State start\n", + "2024-04-04 15:55:10,361 - Scheduler at: inproc://10.64.4.217/16700/1\n", + "2024-04-04 15:55:10,361 - dashboard at: http://10.64.4.217:57469/status\n", + "2024-04-04 15:55:10,361 - Registering Worker plugin shuffle\n", + "2024-04-04 15:55:11,358 - Start worker at: inproc://10.64.4.217/16700/4\n", + "2024-04-04 15:55:11,359 - Listening to: inproc10.64.4.217\n", + "2024-04-04 15:55:11,359 - Worker name: 0\n", + "2024-04-04 15:55:11,360 - dashboard at: 10.64.4.217:57470\n", + "2024-04-04 15:55:11,360 - Waiting to connect to: inproc://10.64.4.217/16700/1\n", + "2024-04-04 15:55:11,361 - -------------------------------------------------\n", + "2024-04-04 15:55:11,362 - Threads: 8\n", + "2024-04-04 15:55:11,362 - Memory: 31.95 GiB\n", + "2024-04-04 15:55:11,362 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-21a90yuv\n", + "2024-04-04 15:55:11,363 - -------------------------------------------------\n", + "2024-04-04 15:55:11,370 - Register worker \n", + "2024-04-04 15:55:11,371 - Starting worker compute stream, inproc://10.64.4.217/16700/4\n", + "2024-04-04 15:55:11,372 - Starting established connection to inproc://10.64.4.217/16700/5\n", + "2024-04-04 15:55:11,373 - Starting Worker plugin shuffle\n", + "2024-04-04 15:55:11,374 - Registered to: inproc://10.64.4.217/16700/1\n", + "2024-04-04 15:55:11,375 - -------------------------------------------------\n", + "2024-04-04 15:55:11,375 - Starting established connection to inproc://10.64.4.217/16700/1\n", + "2024-04-04 15:55:11,379 - Receive client connection: Client-9291e462-f282-11ee-813c-b42e99a00ea1\n", + "2024-04-04 15:55:11,380 - Starting established connection to inproc://10.64.4.217/16700/6\n", + "2024-04-04 15:55:11,382 - LinK Dask Server - http://10.64.4.217:57469/status\n", + "2024-04-04 15:55:11,382 - Initialising solver\n", + "2024-04-04 15:55:11,488 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 15:55:11,490 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 15:55:11,498 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-04 15:55:21,869 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [472.422]\n", + " 0%| | 0/100 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2rmsemae
00.161470.714209.547
\n" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## AutoML approach" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 16:47:57,482 - Initialising experiment setup\n", + "2024-04-04 16:47:57,499 - Initialising Industrial Repository\n", + "2024-04-04 16:47:57,500 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-04 16:47:57,520 - State start\n", + "2024-04-04 16:47:58,505 - Scheduler at: inproc://10.64.4.217/16700/9\n", + "2024-04-04 16:47:58,506 - dashboard at: http://10.64.4.217:58090/status\n", + "2024-04-04 16:47:58,507 - Registering Worker plugin shuffle\n", + "2024-04-04 16:47:59,504 - Start worker at: inproc://10.64.4.217/16700/12\n", + "2024-04-04 16:47:59,505 - Listening to: inproc10.64.4.217\n", + "2024-04-04 16:47:59,506 - Worker name: 0\n", + "2024-04-04 16:47:59,506 - dashboard at: 10.64.4.217:58093\n", + "2024-04-04 16:47:59,507 - Waiting to connect to: inproc://10.64.4.217/16700/9\n", + "2024-04-04 16:47:59,507 - -------------------------------------------------\n", + "2024-04-04 16:47:59,508 - Threads: 8\n", + "2024-04-04 16:47:59,508 - Memory: 31.95 GiB\n", + "2024-04-04 16:47:59,509 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-d9wa1bqe\n", + "2024-04-04 16:47:59,509 - -------------------------------------------------\n", + "2024-04-04 16:47:59,513 - Register worker \n", + "2024-04-04 16:47:59,515 - Starting worker compute stream, inproc://10.64.4.217/16700/12\n", + "2024-04-04 16:47:59,517 - Starting established connection to inproc://10.64.4.217/16700/13\n", + "2024-04-04 16:47:59,518 - Starting Worker plugin shuffle\n", + "2024-04-04 16:47:59,518 - Registered to: inproc://10.64.4.217/16700/9\n", + "2024-04-04 16:47:59,518 - -------------------------------------------------\n", + "2024-04-04 16:47:59,519 - Starting established connection to inproc://10.64.4.217/16700/9\n", + "2024-04-04 16:47:59,522 - Receive client connection: Client-f2ee50fe-f289-11ee-813c-b42e99a00ea1\n", + "2024-04-04 16:47:59,524 - Starting established connection to inproc://10.64.4.217/16700/14\n", + "2024-04-04 16:47:59,525 - LinK Dask Server - http://10.64.4.217:58090/status\n", + "2024-04-04 16:47:59,526 - Initialising solver\n", + "2024-04-04 16:47:59,574 - AssumptionsHandler - Initial pipeline fitting started\n", + "2024-04-04 16:48:03,787 - AssumptionsHandler - Initial pipeline was fitted successfully\n", + "2024-04-04 16:48:03,789 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 10.4 MiB, max: 12.7 MiB\n", + "2024-04-04 16:48:03,790 - ApiComposer - Initial pipeline was fitted in 4.2 sec.\n", + "2024-04-04 16:48:03,791 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", + "2024-04-04 16:48:03,798 - ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 15 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'dtreg', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "2024-04-04 16:48:03,821 - ApiComposer - Pipeline composition started.\n", + "2024-04-04 16:48:03,824 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 16:48:03,825 - DataSourceSplitter - Hold out validation is applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:00.on_destroy at 0x0000025403806940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559918432144\n", + "Exception ignored in: .on_destroy at 0x000002541105EF70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560086593040\n", + "Exception ignored in: .on_destroy at 0x0000025406B56F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560083699632\n", + "Exception ignored in: .on_destroy at 0x00000254069EC8B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558710190896\n", + "Exception ignored in: .on_destroy at 0x00000253BF6F30D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558677891088\n", + "Exception ignored in: .on_destroy at 0x00000253BF6BFEE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558716053808\n", + "Exception ignored in: .on_destroy at 0x00000254053D20D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559948696240\n", + "Exception ignored in: .on_destroy at 0x000002540F9B0C10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560060855280\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 16:49:09,644 - IndustrialDispatcher - 1 individuals out of 1 in previous population were evaluated successfully.\n", + "2024-04-04 16:49:09,687 - IndustrialEvoOptimizer - Generation num: 1 size: 1\n", + "2024-04-04 16:49:09,688 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-04 16:49:10,868 - IndustrialDispatcher - Number of used CPU's: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: .on_destroy at 0x0000025410E1FC10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558680841360\n", + "Exception ignored in: .on_destroy at 0x0000025403563A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560003265232\n", + "Exception ignored in: .on_destroy at 0x00000254059338B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559884599344\n", + "Exception ignored in: .on_destroy at 0x000002540F0135E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559865368464\n", + "Exception ignored in: .on_destroy at 0x00000253BF18C8B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558677120080\n", + "Exception ignored in: .on_destroy at 0x0000025403BE4D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559886040016\n", + "Exception ignored in: .on_destroy at 0x0000025405603790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559950005712\n", + "Exception ignored in: .on_destroy at 0x000002540EF49AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560087410608\n", + "Exception ignored in: .on_destroy at 0x000002540BFBB3A0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560024125776\n", + "Exception ignored in: .on_destroy at 0x000002540DD3C550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560053489360\n", + "Exception ignored in: .on_destroy at 0x000002540514B430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560088643120\n", + "Exception ignored in: .on_destroy at 0x000002540C1A2550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558676602192\n", + "Exception ignored in: .on_destroy at 0x0000025411084A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558710227568\n", + "Exception ignored in: .on_destroy at 0x000002540DD2B700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558717618192\n", + "Exception ignored in: .on_destroy at 0x000002540DB06670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560060019504\n", + "Exception ignored in: .on_destroy at 0x0000025408AF9790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559912196880\n", + "Exception ignored in: .on_destroy at 0x0000025403E835E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558855051184\n", + "Exception ignored in: .on_destroy at 0x0000025403E80160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559865388944\n", + "Exception ignored in: .on_destroy at 0x0000025405415940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559888905776\n", + "Exception ignored in: .on_destroy at 0x0000025406C4F160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559890412912\n", + "Exception ignored in: .on_destroy at 0x000002540C1A23A0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560029663664\n", + "Exception ignored in: .on_destroy at 0x000002540F8FDB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560059937584\n", + "Exception ignored in: .on_destroy at 0x000002540F351B80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560057356816\n", + "Exception ignored in: .on_destroy at 0x000002540F7661F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560057359504\n", + "Exception ignored in: .on_destroy at 0x0000025412860820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559967673008\n", + "Exception ignored in: .on_destroy at 0x0000025410A2D1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559973269200\n", + "Exception ignored in: .on_destroy at 0x00000254035CC4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558862539248\n", + "Exception ignored in: .on_destroy at 0x00000253C8191DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558675465104\n", + "Exception ignored in: .on_destroy at 0x00000254037EEF70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560112649392\n", + "Exception ignored in: .on_destroy at 0x0000025412A81310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559995014960\n", + "Exception ignored in: .on_destroy at 0x00000253C8191040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559971797296\n", + "Exception ignored in: .on_destroy at 0x000002540A9265E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559977735152\n", + "Exception ignored in: .on_destroy at 0x000002540F63A0D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560084983856\n", + "Exception ignored in: .on_destroy at 0x000002540F63ACA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560081500272\n", + "Exception ignored in: .on_destroy at 0x000002540D68E040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560113765424\n", + "Exception ignored in: .on_destroy at 0x000002540D9E6D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559893222288\n", + "Exception ignored in: .on_destroy at 0x0000025412BBAC10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559921069584\n", + "Exception ignored in: .on_destroy at 0x00000254020D5CA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559997600272\n", + "Exception ignored in: .on_destroy at 0x00000254035915E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560057858832\n", + "Exception ignored in: .on_destroy at 0x0000025403CE6700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559864220432\n", + "Exception ignored in: .on_destroy at 0x0000025407431A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559978083696\n", + "Exception ignored in: .on_destroy at 0x0000025406C75C10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560025230960\n", + "Exception ignored in: .on_destroy at 0x0000025408E3B280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559920855728\n", + "Exception ignored in: .on_destroy at 0x00000254020C41F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560107685808\n", + "Exception ignored in: .on_destroy at 0x000002540F766280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560088474896\n", + "Exception ignored in: .on_destroy at 0x000002540A703CA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560082772784\n", + "Exception ignored in: .on_destroy at 0x00000254069674C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560028486608\n", + "Exception ignored in: .on_destroy at 0x0000025412C97280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560081714800\n", + "Exception ignored in: .on_destroy at 0x000002540F7E3EE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560002761040\n", + "Exception ignored in: .on_destroy at 0x0000025404F9D820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560114858096\n", + "Exception ignored in: .on_destroy at 0x00000254035AA9D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559975253072\n", + "Exception ignored in: .on_destroy at 0x000002540F63A5E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559890105424\n", + "Exception ignored in: .on_destroy at 0x0000025406E499D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560000824496\n", + "Exception ignored in: .on_destroy at 0x000002540BC70B80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558671182224\n", + "Exception ignored in: .on_destroy at 0x000002540A248AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2560060202512\n", + "Exception ignored in: .on_destroy at 0x0000025413FA1310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558707013264\n", + "Exception ignored in: .on_destroy at 0x00000253BED76AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558708189584\n", + "Exception ignored in: .on_destroy at 0x00000253BED3B1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559856029584\n", + "Exception ignored in: .on_destroy at 0x0000025408A38040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559996212304\n", + "Exception ignored in: .on_destroy at 0x00000253C167A700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558748392304\n", + "Exception ignored in: .on_destroy at 0x00000253C177A1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558750588432\n", + "Exception ignored in: .on_destroy at 0x00000253C1894D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2559883848176\n", + "Exception ignored in: .on_destroy at 0x00000253C16C0B80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558753050128\n", + "Exception ignored in: .on_destroy at 0x00000253C1E3B040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558758653264\n", + "Exception ignored in: .on_destroy at 0x00000253C1B43670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558759208016\n", + "Exception ignored in: .on_destroy at 0x00000253C1CAD790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558759024560\n", + "Exception ignored in: .on_destroy at 0x00000253C1E1AA60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558703058736\n", + "Exception ignored in: .on_destroy at 0x00000253C227C8B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558671062096\n", + "Exception ignored in: .on_destroy at 0x00000253C2723C10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558755872368\n", + "Exception ignored in: .on_destroy at 0x00000253C2F04430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2558779961584\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 16:57:42,094 - IndustrialDispatcher - 7 individuals out of 21 in previous population were evaluated successfully. 0.3333333333333333% is a fairly small percentage of successful evaluation.\n", + "2024-04-04 16:57:42,128 - IndustrialEvoOptimizer - Generation num: 2 size: 7\n", + "2024-04-04 16:57:42,130 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-04 16:57:42,131 - GroupedCondition - Optimisation stopped: Time limit is reached\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [09:38']\n", + "2024-04-04 16:57:42,146 - IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "2024-04-04 16:57:42,148 - IndustrialEvoOptimizer - spent time: 9.6 min\n", + "2024-04-04 16:57:42,153 - GPComposer - GP composition finished\n", + "2024-04-04 16:57:42,157 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 16:57:42,159 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 16:57:42,169 - ApiComposer - Hyperparameters tuning started with 5 min. timeout\n", + "2024-04-04 16:57:42,172 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 16:57:53,096 - SimultaneousTuner - Initial graph: {'depth': 3, 'length': 3, 'nodes': [treg, scaling, quantile_extractor]}\n", + "treg - {}\n", + "scaling - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [477.156]\n", + " 0%| | 0/100000 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2rmsemae
00.087491.02212.539
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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 17:08:10,349 - OperationsAnimatedBar - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-04 17:08:10,450 - MovieWriter ffmpeg unavailable; using Pillow instead.\n", + "2024-04-04 17:08:10,451 - Animation.save using \n", + "2024-04-04 17:08:15,804 - OperationsAnimatedBar - The animation was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\history_animated_bars.gif\".\n", + "2024-04-04 17:08:15,806 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-04 17:08:15,833 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-04 17:08:15,839 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-04 17:08:15,921 - default - The figure was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\fitness_by_gen.png\".\n" + ] + } + ], + "source": [ + "industrial_auto_model.solver.current_pipeline.show()\n", + "industrial_auto_model.plot_operation_distribution(mode='each')\n", + "industrial_auto_model.plot_fitness_by_generation()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.vstack([test_data[1].flatten(),labels.flatten(),auto_labels.flatten()]).T\n", + "all_prediction = pd.DataFrame(predictions,columns=['target','baseline','automl'])\n", + "all_prediction.plot()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Compare with State of Art (SOTA) models" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')\n", + "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", + "df.index = df['algorithm']\n", + "df = df.drop(['algorithm','ds/type'], axis=1)\n", + "df = df.replace(',','.', regex=True).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df['Fedot_Industrial_tuned'] = metrics['rmse'][0]\n", + "df['Fedot_Industrial_AutoML'] = auto_metrics['rmse'][0]\n", + "df = df.T" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "CNN_RMSE 387.616535\nGrid-SVR_RMSE 390.641257\nFPCR_RMSE 399.459870\n5NN-DTW_RMSE 407.790548\nFPCR-Bs_RMSE 408.199670\n5NN-ED_RMSE 424.504623\nRandF_RMSE 456.359415\nFedot_Industrial_tuned 470.714000\nFreshPRINCE_RMSE 487.642605\nResNet_RMSE 489.719817\nFedot_Industrial_AutoML 491.020000\nRDST_RMSE 491.072419\nROCKET_RMSE 492.914183\nDrCIF_RMSE 494.923063\nFCN_RMSE 499.100408\nSingleInception_RMSE 499.449452\nInceptionT_RMSE 506.696071\nMultiROCKET_RMSE 506.770267\nRidge_RMSE 509.119169\nTSF_RMSE 510.156025\nRotF_RMSE 519.799244\nXGBoost_RMSE 526.421564\n1NN-ED_RMSE 529.096971\n1NN-DTW_RMSE 557.562623\nRIST_RMSE 560.257921\nName: min, dtype: float64" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('min')['min']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "Fedot_Industrial_tuned 470.714000\nFedot_Industrial_AutoML 491.020000\nFPCR_RMSE 522.998212\nFPCR-Bs_RMSE 537.381895\n5NN-DTW_RMSE 546.981708\nGrid-SVR_RMSE 553.666929\nCNN_RMSE 561.795243\n5NN-ED_RMSE 562.413046\nRDST_RMSE 575.720140\nRandF_RMSE 589.478468\nRidge_RMSE 627.513473\nROCKET_RMSE 629.215008\nRIST_RMSE 639.787936\nFreshPRINCE_RMSE 640.463800\nInceptionT_RMSE 645.057362\nDrCIF_RMSE 646.879703\nSingleInception_RMSE 652.585529\nResNet_RMSE 661.954851\nTSF_RMSE 663.258974\nMultiROCKET_RMSE 663.968054\nRotF_RMSE 670.442108\nFCN_RMSE 693.068654\nXGBoost_RMSE 693.574841\n1NN-DTW_RMSE 755.854409\n1NN-ED_RMSE 812.146002\nName: max, dtype: float64" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('max')['max']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[1], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mdf\u001B[49m\u001B[38;5;241m.\u001B[39msort_values(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124maverage\u001B[39m\u001B[38;5;124m'\u001B[39m)[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124maverage\u001B[39m\u001B[38;5;124m'\u001B[39m]\n", + "\u001B[1;31mNameError\u001B[0m: name 'df' is not defined" + ] + } + ], + "source": [ + "df.sort_values('average')['average']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_hotwater.ipynb b/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_hotwater.ipynb new file mode 100644 index 000000000..4c96fa1e5 --- /dev/null +++ b/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_hotwater.ipynb @@ -0,0 +1,2382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Predict how much energy will a building consume with Fedot.Industrial" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Dataset published on Kaggle, aims to assess the value of energy efficiency improvements. For that purpose, four types of sources are identified: electricity, chilled water, steam and hot water. The goal is to estimate the **energy consumption in kWh**. Dimensions correspond to the air temperature, dew temperature, wind direction and wind speed. These values were taken hourly during a week, and the output is the meter reading of the four aforementioned sources. In this way, was created four datasets: **ChilledWaterPredictor**, **ElectricityPredictor**, **HotwaterPredictor**, and **SteamPredictor**.\n", + "Link to the dataset - https://www.kaggle.com/code/fatmanuranl/ashrae-energy-prediction2" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-28T10:34:48.354623Z", + "start_time": "2023-08-28T10:34:39.594404Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", + "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", + "from fedot_ind.tools.loader import DataLoader\n", + "from fedot_ind.api.main import FedotIndustrial" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=30,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "dataset_name = 'HotwaterPredictor'\n", + "data_path = PROJECT_PATH + '/examples/data'" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we must download the dataset. It could be done by using `DataReader` class that implemented as attribute of `FedotIndustrial` class. This class firstly tries to read the data from local project folder `data_path` and then if it is not possible, it downloads the data from the UCR/UEA archive. The data will be saved in the `data` folder." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-28T10:35:13.321212Z", + "start_time": "2023-08-28T10:35:12.913025Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 12:27:32,955 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/HotwaterPredictor\n" + ] + } + ], + "source": [ + "_, train_data, test_data = DataLoader(dataset_name=dataset_name).read_train_test_files(\n", + " dataset_name=dataset_name,\n", + " data_path=data_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Lets check our data." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "(245, 4, 168)" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features.shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Lets visualise our predictors." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": "
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" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "pd.DataFrame(features[1, 0, :]).plot(title='air temperature')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='dew temperature')\n", + "pd.DataFrame(features[1, 2, :]).plot(title='wind direction')\n", + "pd.DataFrame(features[1, 3, :]).plot(title='wind speed')\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Next steps are quite straightforward. We need to fit the model and then predict the values for the test data just like for any other model in sklearn.\n", + "\n", + "At the `fit` stage FedotIndustrial will transform initial time series data into features dataframe and will train regression model." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "start_time": "2023-08-28T10:35:27.965798Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 12:28:47,939 - Initialising experiment setup\n", + "2024-04-05 12:28:47,945 - Initialising Industrial Repository\n", + "2024-04-05 12:28:49,424 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-05 12:28:51,241 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-04-05 12:28:51,306 - State start\n", + "2024-04-05 12:28:52,549 - Scheduler at: inproc://10.64.4.217/16120/1\n", + "2024-04-05 12:28:52,550 - dashboard at: http://10.64.4.217:59060/status\n", + "2024-04-05 12:28:52,551 - Registering Worker plugin shuffle\n", + "2024-04-05 12:28:53,818 - Start worker at: inproc://10.64.4.217/16120/4\n", + "2024-04-05 12:28:53,819 - Listening to: inproc10.64.4.217\n", + "2024-04-05 12:28:53,819 - Worker name: 0\n", + "2024-04-05 12:28:53,821 - dashboard at: 10.64.4.217:59062\n", + "2024-04-05 12:28:53,821 - Waiting to connect to: inproc://10.64.4.217/16120/1\n", + "2024-04-05 12:28:53,822 - -------------------------------------------------\n", + "2024-04-05 12:28:53,823 - Threads: 8\n", + "2024-04-05 12:28:53,823 - Memory: 31.95 GiB\n", + "2024-04-05 12:28:53,824 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-z6bcxv2k\n", + "2024-04-05 12:28:53,824 - -------------------------------------------------\n", + "2024-04-05 12:28:53,847 - Register worker \n", + "2024-04-05 12:28:53,849 - Starting worker compute stream, inproc://10.64.4.217/16120/4\n", + "2024-04-05 12:28:53,850 - Starting established connection to inproc://10.64.4.217/16120/5\n", + "2024-04-05 12:28:53,851 - Starting Worker plugin shuffle\n", + "2024-04-05 12:28:53,852 - Registered to: inproc://10.64.4.217/16120/1\n", + "2024-04-05 12:28:53,852 - -------------------------------------------------\n", + "2024-04-05 12:28:53,853 - Starting established connection to inproc://10.64.4.217/16120/1\n", + "2024-04-05 12:28:53,857 - Receive client connection: Client-eb6789f5-f32e-11ee-bef8-b42e99a00ea1\n", + "2024-04-05 12:28:53,859 - Starting established connection to inproc://10.64.4.217/16120/6\n", + "2024-04-05 12:28:53,860 - LinK Dask Server - http://10.64.4.217:59060/status\n", + "2024-04-05 12:28:53,860 - Initialising solver\n", + "2024-04-05 12:28:53,963 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-05 12:28:53,964 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-05 12:28:53,968 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-05 12:29:01,932 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [1236.897]\n", + " 0%| | 0/100 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2rmsemae
00.2021178.575717.016
\n" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## AutoML approach" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 12:44:50,707 - Initialising experiment setup\n", + "2024-04-05 12:44:50,713 - Initialising Industrial Repository\n", + "2024-04-05 12:44:50,714 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-05 12:44:50,735 - State start\n", + "2024-04-05 12:44:51,981 - Scheduler at: inproc://10.64.4.217/16120/9\n", + "2024-04-05 12:44:51,981 - dashboard at: http://10.64.4.217:59465/status\n", + "2024-04-05 12:44:51,982 - Registering Worker plugin shuffle\n", + "2024-04-05 12:44:53,252 - Start worker at: inproc://10.64.4.217/16120/12\n", + "2024-04-05 12:44:53,253 - Listening to: inproc10.64.4.217\n", + "2024-04-05 12:44:53,254 - Worker name: 0\n", + "2024-04-05 12:44:53,255 - dashboard at: 10.64.4.217:59468\n", + "2024-04-05 12:44:53,255 - Waiting to connect to: inproc://10.64.4.217/16120/9\n", + "2024-04-05 12:44:53,256 - -------------------------------------------------\n", + "2024-04-05 12:44:53,256 - Threads: 8\n", + "2024-04-05 12:44:53,256 - Memory: 31.95 GiB\n", + "2024-04-05 12:44:53,257 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-8dptmyn3\n", + "2024-04-05 12:44:53,258 - -------------------------------------------------\n", + "2024-04-05 12:44:53,264 - Register worker \n", + "2024-04-05 12:44:53,266 - Starting worker compute stream, inproc://10.64.4.217/16120/12\n", + "2024-04-05 12:44:53,267 - Starting established connection to inproc://10.64.4.217/16120/13\n", + "2024-04-05 12:44:53,267 - Starting Worker plugin shuffle\n", + "2024-04-05 12:44:53,268 - Registered to: inproc://10.64.4.217/16120/9\n", + "2024-04-05 12:44:53,269 - -------------------------------------------------\n", + "2024-04-05 12:44:53,270 - Starting established connection to inproc://10.64.4.217/16120/9\n", + "2024-04-05 12:44:53,273 - Receive client connection: Client-2742d2f1-f331-11ee-bef8-b42e99a00ea1\n", + "2024-04-05 12:44:53,275 - Starting established connection to inproc://10.64.4.217/16120/14\n", + "2024-04-05 12:44:53,277 - LinK Dask Server - http://10.64.4.217:59465/status\n", + "2024-04-05 12:44:53,277 - Initialising solver\n", + "2024-04-05 12:44:53,325 - AssumptionsHandler - Initial pipeline fitting started\n", + "2024-04-05 12:44:56,205 - AssumptionsHandler - Initial pipeline was fitted successfully\n", + "2024-04-05 12:44:56,208 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 4.3 MiB, max: 5.3 MiB\n", + "2024-04-05 12:44:56,210 - ApiComposer - Initial pipeline was fitted in 2.9 sec.\n", + "2024-04-05 12:44:56,211 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", + "2024-04-05 12:44:56,219 - ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 30 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'dtreg', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "2024-04-05 12:44:56,244 - ApiComposer - Pipeline composition started.\n", + "2024-04-05 12:44:56,250 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-05 12:44:56,251 - DataSourceSplitter - Hold out validation is applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:00.on_destroy at 0x00000289F9FFF550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791627608592\n", + "Exception ignored in: .on_destroy at 0x00000289F9F3E9D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791634387280\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 12:45:26,996 - IndustrialDispatcher - 1 individuals out of 1 in previous population were evaluated successfully.\n", + "2024-04-05 12:45:27,040 - IndustrialEvoOptimizer - Generation num: 1 size: 1\n", + "2024-04-05 12:45:27,042 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-05 12:45:28,011 - IndustrialDispatcher - Number of used CPU's: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: .on_destroy at 0x00000289FA351940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791631898928\n", + "Exception ignored in: .on_destroy at 0x00000289FA125430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791671405712\n", + "Exception ignored in: .on_destroy at 0x00000289FA6145E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791671403792\n", + "Exception ignored in: .on_destroy at 0x00000289FE0D6790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791671356272\n", + "Exception ignored in: .on_destroy at 0x00000289FA125DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791671354544\n", + "Exception ignored in: .on_destroy at 0x00000289FC6E0310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791671449136\n", + "Exception ignored in: .on_destroy at 0x00000289FA4F6790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791669433424\n", + "Exception ignored in: .on_destroy at 0x00000289FCD88E50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791633658576\n", + "Exception ignored in: .on_destroy at 0x00000289FE224550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791723567984\n", + "Exception ignored in: .on_destroy at 0x00000289F865D310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791726543184\n", + "Exception ignored in: .on_destroy at 0x0000028982083280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789614962992\n", + "Exception ignored in: .on_destroy at 0x00000289FFE1CCA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789614963568\n", + "Exception ignored in: .on_destroy at 0x00000289FA5548B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789615832592\n", + "Exception ignored in: .on_destroy at 0x00000289820639D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789616209808\n", + "Exception ignored in: .on_destroy at 0x00000289FE2369D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789619250832\n", + "Exception ignored in: .on_destroy at 0x00000289FCB0B940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791633333104\n", + "Exception ignored in: .on_destroy at 0x00000289FFB09D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789619235216\n", + "Exception ignored in: .on_destroy at 0x0000028982063700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789619769872\n", + "Exception ignored in: .on_destroy at 0x00000289FC771A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789619980112\n", + "Exception ignored in: .on_destroy at 0x0000028982096DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789620227984\n", + "Exception ignored in: .on_destroy at 0x00000289FC6E0A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791608443024\n", + "Exception ignored in: .on_destroy at 0x00000289836BB1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791694933136\n", + "Exception ignored in: .on_destroy at 0x00000289839410D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791695805104\n", + "Exception ignored in: .on_destroy at 0x00000289839E19D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789641525168\n", + "Exception ignored in: .on_destroy at 0x0000028982096670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791606141360\n", + "Exception ignored in: .on_destroy at 0x0000028984158160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789648497616\n", + "Exception ignored in: .on_destroy at 0x000002898408FD30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789616861808\n", + "Exception ignored in: .on_destroy at 0x0000028985370CA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789668847664\n", + "Exception ignored in: .on_destroy at 0x00000289FFB4F940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789616813808\n", + "Exception ignored in: .on_destroy at 0x0000028983978D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791723500048\n", + "Exception ignored in: .on_destroy at 0x00000289841DF670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789616862000\n", + "Exception ignored in: .on_destroy at 0x00000289838601F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791695431024\n", + "Exception ignored in: .on_destroy at 0x00000289841EE1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789616309808\n", + "Exception ignored in: .on_destroy at 0x00000289838AD790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789615914128\n", + "Exception ignored in: .on_destroy at 0x0000028983E73430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789620397616\n", + "Exception ignored in: .on_destroy at 0x0000028A12512670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791727732656\n", + "Exception ignored in: .on_destroy at 0x0000028A125124C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791727733904\n", + "Exception ignored in: .on_destroy at 0x0000028A1A0BB1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792190333168\n", + "Exception ignored in: .on_destroy at 0x0000028983FD8AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792435642256\n", + "Exception ignored in: .on_destroy at 0x0000028A4C0E0790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793165547920\n", + "Exception ignored in: .on_destroy at 0x0000028A55A91A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793166380656\n", + "Exception ignored in: .on_destroy at 0x0000028A55AEF1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793281233232\n", + "Exception ignored in: .on_destroy at 0x0000028A5C9873A0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791726590544\n", + "Exception ignored in: .on_destroy at 0x0000028A4C0C5700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791628166704\n", + "Exception ignored in: .on_destroy at 0x0000028A55A9CEE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792034112688\n", + "Exception ignored in: .on_destroy at 0x0000028A1C03E4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789618564816\n", + "Exception ignored in: .on_destroy at 0x0000028A5CA4F820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789646761008\n", + "Exception ignored in: .on_destroy at 0x0000028983C61160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789614938032\n", + "Exception ignored in: .on_destroy at 0x0000028A78105040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791669974480\n", + "Exception ignored in: .on_destroy at 0x0000028A78112550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792198647216\n", + "Exception ignored in: .on_destroy at 0x0000028A757561F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793699405424\n", + "Exception ignored in: .on_destroy at 0x0000028A32DC7B80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791726829168\n", + "Exception ignored in: .on_destroy at 0x0000028A55A46310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793699295472\n", + "Exception ignored in: .on_destroy at 0x00000289838E49D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789616926224\n", + "Exception ignored in: .on_destroy at 0x0000028A45999310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791726544528\n", + "Exception ignored in: .on_destroy at 0x0000028A5CA04F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789648497904\n", + "Exception ignored in: .on_destroy at 0x0000028A77E0F0D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793740803792\n", + "Exception ignored in: .on_destroy at 0x0000028A5C949550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792036413136\n", + "Exception ignored in: .on_destroy at 0x0000028A77E878B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791727689424\n", + "Exception ignored in: .on_destroy at 0x0000028A756F6310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793741679376\n", + "Exception ignored in: .on_destroy at 0x0000028A75672310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789619362288\n", + "Exception ignored in: .on_destroy at 0x0000028A780E1AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789650503504\n", + "Exception ignored in: .on_destroy at 0x0000028A7579A040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792165535056\n", + "Exception ignored in: .on_destroy at 0x0000028A5C88CD30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792161936656\n", + "Exception ignored in: .on_destroy at 0x0000028A1C03E4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793742463888\n", + "Exception ignored in: .on_destroy at 0x0000028A77E87E50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793740381904\n", + "Exception ignored in: .on_destroy at 0x0000028A527A6550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793160713680\n", + "Exception ignored in: .on_destroy at 0x0000028A5C88C4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793160653296\n", + "Exception ignored in: .on_destroy at 0x0000028A5553EEE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793746108016\n", + "Exception ignored in: .on_destroy at 0x0000028A1A0BBB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793281678576\n", + "Exception ignored in: .on_destroy at 0x0000028A52767B80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793281782384\n", + "Exception ignored in: .on_destroy at 0x0000028A7579AD30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789646520848\n", + "Exception ignored in: .on_destroy at 0x0000028A3893D4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792162054384\n", + "Exception ignored in: .on_destroy at 0x0000028A7857AE50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793748672560\n", + "Exception ignored in: .on_destroy at 0x0000028A785B9820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793748706192\n", + "Exception ignored in: .on_destroy at 0x0000028A786FA9D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793749249552\n", + "Exception ignored in: .on_destroy at 0x0000028A7839F160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792198626544\n", + "Exception ignored in: .on_destroy at 0x0000028A55AEFCA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793746833648\n", + "Exception ignored in: .on_destroy at 0x0000028A7860A670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793740235280\n", + "Exception ignored in: .on_destroy at 0x0000028A527A64C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793742151568\n", + "Exception ignored in: .on_destroy at 0x0000028A785AFB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791673944368\n", + "Exception ignored in: .on_destroy at 0x0000028A786654C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793848738320\n", + "Exception ignored in: .on_destroy at 0x0000028A7857F820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793749248208\n", + "Exception ignored in: .on_destroy at 0x0000028A7E6A6550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791670294928\n", + "Exception ignored in: .on_destroy at 0x0000028A7E6354C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793953367408\n", + "Exception ignored in: .on_destroy at 0x0000028A780E1040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793953677744\n", + "Exception ignored in: .on_destroy at 0x0000028A84A635E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793954807216\n", + "Exception ignored in: .on_destroy at 0x0000028A84974040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793955062768\n", + "Exception ignored in: .on_destroy at 0x0000028A787B2D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793108094000\n", + "Exception ignored in: .on_destroy at 0x0000028A4C0C5040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793112668944\n", + "Exception ignored in: .on_destroy at 0x0000028A77D5F700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791726562928\n", + "Exception ignored in: .on_destroy at 0x0000028A7839F5E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793956677936\n", + "Exception ignored in: .on_destroy at 0x0000028A84B74D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793956817488\n", + "Exception ignored in: .on_destroy at 0x0000028A84B365E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793748200880\n", + "Exception ignored in: .on_destroy at 0x0000028A7860A5E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789649136496\n", + "Exception ignored in: .on_destroy at 0x0000028A84A630D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794025827184\n", + "Exception ignored in: .on_destroy at 0x0000028A88EEE280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794026003984\n", + "Exception ignored in: .on_destroy at 0x0000028A77D5F0D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791604222640\n", + "Exception ignored in: .on_destroy at 0x0000028A7E6DAA60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793956955696\n", + "Exception ignored in: .on_destroy at 0x0000028A7E5EC310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789648526096\n", + "Exception ignored in: .on_destroy at 0x0000028A89DE5F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794042156976\n", + "Exception ignored in: .on_destroy at 0x00000289845C0550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789653324688\n", + "Exception ignored in: .on_destroy at 0x0000028984D5DAF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789665583536\n", + "Exception ignored in: .on_destroy at 0x0000028A8A1C9A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794027123120\n", + "Exception ignored in: .on_destroy at 0x0000028A8A250C10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793953262736\n", + "Exception ignored in: .on_destroy at 0x0000028A8A1921F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794069672656\n", + "Exception ignored in: .on_destroy at 0x0000028A786F74C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794151035504\n", + "Exception ignored in: .on_destroy at 0x0000028A89884D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791676086864\n", + "Exception ignored in: .on_destroy at 0x0000028A8BEB5550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2792582018032\n", + "Exception ignored in: .on_destroy at 0x0000028984F50310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793847000880\n", + "Exception ignored in: .on_destroy at 0x00000289FA420040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793849407696\n", + "Exception ignored in: .on_destroy at 0x00000289F9F769D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789615608912\n", + "Exception ignored in: .on_destroy at 0x00000289FA4463A0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794044985040\n", + "Exception ignored in: .on_destroy at 0x00000289FCC77430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793750185136\n", + "Exception ignored in: .on_destroy at 0x0000028A8BEB5280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2793746836336\n", + "Exception ignored in: .on_destroy at 0x00000289FA540DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791677501968\n", + "Exception ignored in: .on_destroy at 0x00000289FCD769D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791695058960\n", + "Exception ignored in: .on_destroy at 0x0000028A8BDCBE50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791674260528\n", + "Exception ignored in: .on_destroy at 0x0000028A8B4778B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789667707760\n", + "Exception ignored in: .on_destroy at 0x0000028A91BD5F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2789660094256\n", + "Exception ignored in: .on_destroy at 0x0000028A89928E50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2791697261360\n", + "Exception ignored in: .on_destroy at 0x00000289FE258A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794078698544\n", + "Exception ignored in: .on_destroy at 0x00000289F8B47940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794027123984\n", + "Exception ignored in: .on_destroy at 0x0000028A8BE4D310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794026003696\n", + "Exception ignored in: .on_destroy at 0x0000028A8B999B80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 2794072203312\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 13:03:50,131 - IndustrialDispatcher - 14 individuals out of 21 in previous population were evaluated successfully.\n", + "2024-04-05 13:03:50,187 - IndustrialEvoOptimizer - Generation num: 2 size: 14\n", + "2024-04-05 13:03:50,190 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-05 13:03:50,192 - IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "2024-04-05 13:03:50,194 - IndustrialEvoOptimizer - spent time: 18.9 min\n", + "2024-04-05 13:03:50,195 - GroupedCondition - Optimisation stopped: Time limit is reached\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [18:53']\n", + "2024-04-05 13:03:50,207 - IndustrialEvoOptimizer - no improvements for 2 iterations\n", + "2024-04-05 13:03:50,208 - IndustrialEvoOptimizer - spent time: 18.9 min\n", + "2024-04-05 13:03:50,211 - GPComposer - GP composition finished\n", + "2024-04-05 13:03:50,214 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-05 13:03:50,217 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-05 13:03:50,224 - ApiComposer - Hyperparameters tuning started with 11 min. timeout\n", + "2024-04-05 13:03:50,227 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 13:03:58,366 - SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [1214.155]\n", + " 0%| | 0/100000 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2rmsemae
00.2161168.245710.851
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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 13:14:59,231 - OperationsAnimatedBar - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-05 13:14:59,367 - MovieWriter ffmpeg unavailable; using Pillow instead.\n", + "2024-04-05 13:14:59,367 - Animation.save using \n", + "2024-04-05 13:15:05,640 - OperationsAnimatedBar - The animation was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\history_animated_bars.gif\".\n", + "2024-04-05 13:15:05,642 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-05 13:15:05,665 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-05 13:15:05,670 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-05 13:15:05,743 - default - The figure was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\fitness_by_gen.png\".\n" + ] + } + ], + "source": [ + "industrial_auto_model.solver.current_pipeline.show()\n", + "industrial_auto_model.plot_operation_distribution(mode='each')\n", + "industrial_auto_model.plot_fitness_by_generation()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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Neo79gUQOQRAE0XQmZSRnCrFq1SrH788++ywWLFiA1157DUNDQ/jBD36AOXPmAABeeOGFir/v6urCpz/9aXz605/GW9/6Vnz961/HddddhxNOOAG///3v0d3djWg02pR9b8Vz1IKqqwiCIIimQ31yDowdO3bg0ksvxYYNG3D33XfjJz/5Cb761a9i7ty58Hq9+MlPfoItW7bg/vvvx1VXXeX422XLluGvf/0rNm3ahHXr1uGBBx7AUUcdBQA477zz0NnZibPPPhv//Oc/sXXrVjz55JP4yle+gl27djVk31vxHLUgkUMQBEE0HYrkHBif+tSnkMvl8B//8R9YunQpvvrVr+LCCy9EV1cX7rjjDvzxj3/EokWL8IMf/ADXXXed42+9Xi8uv/xyHHvssTjttNMgSRLuueceAEAwGMSKFSswd+5cfOADH8BRRx2F888/H/l8vmFRl1Y8Ry0Ewzh4ZXUqlUIsFkMymWx5CI0gCOJg4uTv/QP70gXEAh6s/f+aV00zGvl8Hlu3bsX8+fPh9/snZB+I+hnt86r3+k2RHIIgCKLpWFPIKZJDtBASOQRBEETT4SJHJZFDtBASOQRBEETT0cl4TEwAJHIIgiCIpjMpZ1cR0x4SOQRBEETTUSldRUwAJHIIgiCIpmOP4FA0h2gVJHIIgiCIpmP34pAvh2gVJHIIgiCIpqLrBuy6hhoCEq2CRA5BEATRVNyRGxI505958+bhxz/+8UTvBokcgiAIorm4RQ2lq1rPHXfcgXg8PtG70XJI5BAEQRBNRXeJGjIeE62CRA5BEATRVNxl41RGPn4eeeQRvOUtb0E8HkdHRwfe+973YvPmzQCAJ598EoIgIJFIWNuvWbMGgiBg27ZtePLJJ/HZz34WyWQSgiBAEARceeWVAICRkRF86lOfQltbG4LBIN797ndj48aN1uPwCNADDzyAhQsXIhgM4oMf/CAURcGdd96JefPmoa2tDV/5ylegaVor35K6kCd6BwiCIIjpjTtyM1kiOYZhIKfmJuS5A3IAgiDUvX02m8Wll16KY489FplMBsuWLcP73/9+rFmzZsy/PfXUU/HjH/8Yy5Ytw4YNGwAA4XAYAPCZz3wGGzduxP33349oNIpvfOMbeM973oP169fD4/EAABRFwU033YR77rkH6XQaH/jAB/D+978f8XgcDz30ELZs2YJzzz0Xb37zm/GRj3xk/G9GEyGRQxAEQTSVyerJyak5nPK7UybkuVd9fBWCnmDd25977rmO32+//XZ0dXVh/fr1Y/6t1+tFLBaDIAjo7e21bufi5umnn8app54KALjrrrswZ84c3HffffjQhz4EACiVSvj5z3+Oww47DADwwQ9+EL/5zW/Q39+PcDiMRYsW4e1vfzueeOKJSSdyKF1FEARBNBW3qFG1ySFyphIbN27Exz72MRx66KGIRqOYN28eAGDHjh37/ZivvvoqZFnGKaeUhV5HRwcWLlyIV1991botGAxaAgcAenp6MG/ePCsaxG8bGBjY731pFhTJIQiCIJqKO5LjNiJPFAE5gFUfXzVhzz0e3ve+9+GQQw7BL37xC8ycORO6ruMNb3gDisWiJTYM2/taKpUatq88bcURBKHqbbquN+w5GwWJHIIgCKKpVKSrJoknRxCEcaWMJoqhoSFs2LABv/jFL/DWt74VAPCvf/3Lur+rqwsAsHfvXrS1tQFAhVfH6/VWGIOPOuooqKqKVatWWekq/lyLFi1q1stpKZSuIgiCIJqKe4E/WUTOVKGtrQ0dHR249dZbsWnTJjz++OO49NJLrfsPP/xwzJkzB1deeSU2btyIBx98ENdff73jMebNm4dMJoPHHnsMg4ODUBQFCxYswNlnn40LLrgA//rXv7B27Vp84hOfwKxZs3D22We3+mU2BRI5BEEQRFNRXSpnshiPpwqiKOKee+7B6tWr8YY3vAGXXHIJfvjDH1r3ezwe3H333Xjttddw7LHH4pprrsF3v/tdx2Oceuqp+OIXv4iPfOQj6OrqwrXXXgsA+NWvfoUTTzwR733ve7F48WIYhoGHHnqoIh01VREM4+A92lKpFGKxGJLJJKLR6ETvDkEQxLRk00AaZ/xohfX7g195C46eGWv5fuTzeWzduhXz58+H3+9v+fMT42O0z6ve6zdFcgiCIIimolG6ipggGi5yNE3DFVdcgfnz5yMQCOCwww7DVVdd5XB9G4aBZcuWYcaMGQgEAjjjjDMcHRYBYHh4GOeddx6i0Sji8TjOP/98ZDIZxzYvvfQS3vrWt8Lv92POnDlW+I0gCIKYPExW4zEx/Wm4yLnmmmvw85//HD/96U/x6quv4pprrsG1116Ln/zkJ9Y21157LW666SbccsstWLVqFUKhEJYsWYJ8Pm9tc95552HdunVYvnw5HnjgAaxYsQIXXnihdX8qlcKZZ56JQw45BKtXr8YPf/hDXHnllbj11lsb/ZIIgiCIA2CylpAT05+Gl5A/88wzOPvss3HWWWcBMB3dd999N5577jkAZhTnxz/+Mb71rW9Z7u1f//rX6OnpwX333YePfvSjePXVV/HII4/g+eefx0knnQQA+MlPfoL3vOc9uO666zBz5kzcddddKBaLuP322+H1enH00UdjzZo1+NGPfuQQQwRBEMTE4jYau9NXBNEsGh7JOfXUU/HYY4/h9ddfBwCsXbsW//rXv/Dud78bALB161b09fXhjDPOsP4mFovhlFNOwcqVKwEAK1euRDwetwQOAJxxxhkQRRGrVq2ytjnttNPg9XqtbZYsWYINGzZgZGSk6r4VCgWkUinHP4IgCKK5uCM57morgmgWDY/kfPOb30QqlcKRRx4JSZKgaRq+973v4bzzzgMA9PX1ATBbQNvp6emx7uvr60N3d7dzR2UZ7e3tjm3mz59f8Rj8Pt4Qyc7VV1+Nb3/72w14lQRBEES9VKSrJljjHMRFxVOKRnxODY/k/OEPf8Bdd92F3/3ud/j3v/+NO++8E9dddx3uvPPORj/VuLn88suRTCatfzt37pzoXSIIgpj2TJYBnZIkAQCKxeKEPD8xPhRFAVA5VmI8NDyS8/Wvfx3f/OY38dGPfhQAcMwxx2D79u24+uqr8elPf9qagNrf348ZM2ZYf9ff34/jjjsOANDb21sx6EtVVQwPD1t/39vbi/7+fsc2/Hf7lFU7Pp8PPp/vwF8kQRAEUTduo7E2QaEcWZYRDAaxb98+eDweiCJ1UZmMGIYBRVEwMDCAeDxuidP9oeEiR1GUigNHkiRrcNf8+fPR29uLxx57zBI1qVQKq1atwpe+9CUAwOLFi5FIJLB69WqceOKJAIDHH38cuq5b01IXL16M//3f/0WpVLJU3vLly7Fw4cKqqSqCIAhiYqgsIZ+Y/RAEATNmzMDWrVuxffv2idkJom7i8XjNoEW9NFzkvO9978P3vvc9zJ07F0cffTRefPFF/OhHP8LnPvc5AOZBdvHFF+O73/0uFixYgPnz5+OKK67AzJkzcc455wAwh4a9613vwgUXXIBbbrkFpVIJF110ET760Y9i5syZAICPf/zj+Pa3v43zzz8f3/jGN/DKK6/gxhtvxA033NDol0QQBEEcAJOpT47X68WCBQsoZTXJ8Xg8BxTB4TRc5PzkJz/BFVdcgS9/+csYGBjAzJkz8YUvfAHLli2ztrnsssuQzWZx4YUXIpFI4C1veQseeeQRR9vmu+66CxdddBHe+c53QhRFnHvuubjpppus+2OxGP7+979j6dKlOPHEE9HZ2Ylly5ZR+ThBEMQkYzKJHMCcBUVjHQ4OaHYVza4iCIJoKo+u68MXfrPa+v2mjx2P/3rjzAncI2KqQ7OrCIIgiElBZQn5Qbu2JloMiRyCIAiiqVQ2AySRQ7QGEjkEQRBEU3GXkFMkh2gVJHIIgiCIpjJZmgESBx8kcgiCIIim4k5PTXR1FXHwQCKHIAiCaCru9BSJHKJVkMghCIIgmoo7PUUih2gVJHIIgiCIplJRQk6eHKJFkMghCIIgmgqVkBMTBYkcgiAIoqlMtrEOxMEDiRyCIAiiqVCfHGKiIJFDEARBNBV3eorSVUSrIJFDEARBNBV35IaMx0SrIJFDEARBNBVNd/9OIodoDSRyCIIgiKai6brrdxI5RGsgkUMQBEE0FWoGSEwUJHIIgiCIplKRriJPDtEiSOQQBEEQTcVtNKZIDtEqSOQQBEEQTUXVSOQQEwOJHIIgCKKpVDQDpHQV0SJI5BAEQRBNhUduvLLo+J0gmg2JHIIgCKKp8A7HXkl0/E4QzYZEDkEQBNFUeMdjjyQ4fieIZkMihyAIgmgqvGTcSleRxiFaBIkcgiAIoqnoFZ4cfbTNCaJhkMghCIIgmopqpavIeEy0FhI5BEEQRFOx0lWWyJnIvSEOJkjkEARBEE2Fp6t8lK4iWgyJHIIgCKKpVKSrKFtFtAgSOQRBEERT0V0ih0rIiVZBIocgCIJoKu4ScpXSVUSLIJFDEARBNBX3WAfSOESrIJFDEARBNBXNNdZBowGdRIsgkUMQBEE0FRrQSUwUJHIIgiCIpqJX9MkhkUO0BhI5BEEQRFOxSshlc0AniRyiVZDIIQiCIJqKNbtKkszfyZNDtAgSOQRBEERT4UZjHslRKZJDtAgSOQRBEERT4bOqfNQMkGgxJHIIgiCIpsJnVVnVVZSuIloEiRyCIAiiqWiusQ4qDa8iWgSJHIIgCKKp8OyU1fGYIjlEiyCRQxAEQTQVPqvKQ31yiBZDIocgCIJoKnxWFXU8JloNiRyCIAiiqdDsKmKiIJFDEARBNBUuaiiSQ7QaEjkEQRBEU6mI5JDIIVoEiRyCIAiiqVgl5BTJIVoMiRyCIAiiqeiuSA6VkBOtgkQOQRAE0VT4rCry5BCthkQOQRAE0VQs47EVyQEMiuYQLYBEDkEQBNFUdFckB6BoDtEaSOQQBEEQTcVdQm6/jSCaCYkcgiAIomnougGuZzySYN1OkRyiFZDIIQiCIJqGPWJD6Sqi1ZDIIQiCIJqGXcz4JMn6mc+zIohmQiKHIAiCaBp2kSPb0lUqqRyiBZDIIQiCIJqGPV0liQIEofJ2gmgWJHIIgiCIpqHbIzmiAFkU2O0TtUfEwURTRM7u3bvxiU98Ah0dHQgEAjjmmGPwwgsvWPcbhoFly5ZhxowZCAQCOOOMM7Bx40bHYwwPD+O8885DNBpFPB7H+eefj0wm49jmpZdewlvf+lb4/X7MmTMH1157bTNeDkEQBLGf2NNVkihAZKEcSlcRraDhImdkZARvfvOb4fF48PDDD2P9+vW4/vrr0dbWZm1z7bXX4qabbsItt9yCVatWIRQKYcmSJcjn89Y25513HtatW4fly5fjgQcewIoVK3DhhRda96dSKZx55pk45JBDsHr1avzwhz/ElVdeiVtvvbXRL4kgCILYT7jIEQRAEARIFMkhWojc6Ae85pprMGfOHPzqV7+ybps/f771s2EY+PGPf4xvfetbOPvsswEAv/71r9HT04P77rsPH/3oR/Hqq6/ikUcewfPPP4+TTjoJAPCTn/wE73nPe3Dddddh5syZuOuuu1AsFnH77bfD6/Xi6KOPxpo1a/CjH/3IIYYIgiCIiYN7b3iaiosc8uQQraDhkZz7778fJ510Ej70oQ+hu7sbxx9/PH7xi19Y92/duhV9fX0444wzrNtisRhOOeUUrFy5EgCwcuVKxONxS+AAwBlnnAFRFLFq1Sprm9NOOw1er9faZsmSJdiwYQNGRkaq7luhUEAqlXL8IwiCIJoHj+TwNJUlciiUQ7SAhoucLVu24Oc//zkWLFiARx99FF/60pfwla98BXfeeScAoK+vDwDQ09Pj+Luenh7rvr6+PnR3dzvul2UZ7e3tjm2qPYb9OdxcffXViMVi1r85c+Yc4KslCIIgRoOLHC5uJIGLnAnbJeIgouEiR9d1nHDCCfj+97+P448/HhdeeCEuuOAC3HLLLY1+qnFz+eWXI5lMWv927tw50btEEAQxrbFETkUkh9JVRPNpuMiZMWMGFi1a5LjtqKOOwo4dOwAAvb29AID+/n7HNv39/dZ9vb29GBgYcNyvqiqGh4cd21R7DPtzuPH5fIhGo45/BEEQRPPQmfdGkpwiRydPDtECGi5y3vzmN2PDhg2O215//XUccsghAEwTcm9vLx577DHr/lQqhVWrVmHx4sUAgMWLFyORSGD16tXWNo8//jh0Xccpp5xibbNixQqUSiVrm+XLl2PhwoWOSi6CIAhi4uBpKR7JKZeQk8ghmk/DRc4ll1yCZ599Ft///vexadMm/O53v8Ott96KpUuXAjBLCC+++GJ897vfxf3334+XX34Zn/rUpzBz5kycc845AMzIz7ve9S5ccMEFeO655/D000/joosuwkc/+lHMnDkTAPDxj38cXq8X559/PtatW4ff//73uPHGG3HppZc2+iURBEEQ+wnvhyOyCA4f7UDpKqIVNLyE/OSTT8a9996Lyy+/HN/5zncwf/58/PjHP8Z5551nbXPZZZchm83iwgsvRCKRwFve8hY88sgj8Pv91jZ33XUXLrroIrzzne+EKIo499xzcdNNN1n3x2Ix/P3vf8fSpUtx4oknorOzE8uWLaPycYIgiEkEL6KSXcZjSlcRrUAwjIP3SEulUojFYkgmk+TPIQiCaAJrdiZwzs1PY1Y8gKe/+Q6c8aOnsGkgg7sveBMWH9Yx0btHTFHqvX7T7CqCIAiiafB+ONxwLJPxmGghJHIIgiCIpmEZj0Wn8Zg8OUQrIJFDEARBNI2KZoDUJ4doISRyCIIgiKZh9cnhJeQkcogWQiKHIAiCaBq8H47o8uTQgE6iFZDIIQiCIJqGrrumkJMnh2ghJHIIgiCIpqG5Ijmi6LydIJoJiRyCIAiiaajWgE7zd5mpHCohJ1oBiRyCIAiiaVjGY5GMx0TrIZFDEARBNI2KEnIW0aEBnUQrIJFDEARBNA13JEfi6SoSOUQLIJFDEARBNA1VY8ZjgYsc83YqISdaAYkcgiAIomlwMSNTx2NiAiCRQxAEQTQNvWKsg3nZIZFDtAISOQRBEETT4JEcK13FjMckcohWQCKHIAiCaBru6ioqISdaCYkcgiAIomm4RQ7NriJaCYkcgiAIomlU9Mlh/1MJOdEKSOQQBEEQTcMSOcyTw7051AyQaAUkcgiCIIimYRmPKZJDTAAkcgiCIIimwcVMRZ8c8uQQLYBEDkEQBNE0NN3834rkULqKaCEkcgiCIIimoemmypEESlcRrYdEDkEQBNE0tIoBnbxPzoTtEnEQQSKHIAiCaBpczFSUkJMnh2gBJHIIgiCIpmGlq0R3CTmFcojmQyKHIAiCaBqW8VhwdTwmjUO0ABI5BEEQRNPgaSnZNbuKjMdEKyCRQxAEQTQN3vHY3QyQSsiJVkAihyAIgmgaqmusg0zGY6KFkMghCIIgmobV8VhyGo81iuQQLYBEDkEQBNE0rNlVgrtPDokcovmQyCEIgiCahjWFnF1tSOQQrYREDkEQBNE0LOOxO5JDnhyiBZDIIQiCIJqG5iohl8iTQ7QQEjkEQRBE09B15+wqkdJVRAshkUMQBEE0DdXVJ4dKyIlWQiKHIAiCaBpWCbkrkqNqJHKI5kMihyAIgmgaFSXkAhmPidZBIocgCIJoGprLkyPR7CqihZDIIQiCIJpGLZFDkRyiFZDIIQiCIJpGpchx3k4QzYREDkEQBNE0eBWVZDUDNC87JHKIVkAihyAIgmga7hJyagZItBISOVMUwzCw7K+v4LZ/bpnoXSEIgqhJZQm5eTuJHKIVkMiZouwczuHXK7fjx//YONG7QhAEUROrhNxqBig6bieIZkIiZ4qilFQAQK6kTfCeEARB1IY3/St7cszbqYScaAUkcqYohZIOwAz5UtiXIIjJimU85ukqJnZUOm8RLYBEzhSloOrWz0XbzwRBEJMJdwk5T1dRJIdoBSRypigFtZymKmokcgiCmJxwLSO5jcfkySFaAImcKQpPVwEUySEIYvKi6ub5yZpdJVIJOdE6SORMURzpKorkEAQxSWEaxyohpz45RCshkTNFcaSrKJJDEMQkpebsKhI5RAsgkTNFIeMxQRBTAavjsStdRRqHaAUkcqYohRJFcgiCmPzULiGn8xbRfEjkTFHyDk8ONQQkCGJyUlFCLrFIDmkcogWQyJmi2KurChTJIQhikqK7PTnceEwl5EQLIJEzRSHjMUEQUwHuyeHiRrQZjw0SOkSTIZEzRSHjMUEQUwEesZEk3vFYsO4j8zHRbEjkTFGo4zFBEFMBvUYkB6AycqL5NF3k/OAHP4AgCLj44out2/L5PJYuXYqOjg6Ew2Gce+656O/vd/zdjh07cNZZZyEYDKK7uxtf//rXoaqqY5snn3wSJ5xwAnw+Hw4//HDccccdzX45kwa7J6dEIocgpjy6bmDl5iEkldJE70pDsUrI2dWGix2ARA7RfJoqcp5//nn83//9H4499ljH7Zdccgn+9re/4Y9//COeeuop7NmzBx/4wAes+zVNw1lnnYVisYhnnnkGd955J+644w4sW7bM2mbr1q0466yz8Pa3vx1r1qzBxRdfjM9//vN49NFHm/mSJg2UriKI6cUzm4fwsV88i2X3vzLRu9Iw7EM4JVefHIDMx0TzaZrIyWQyOO+88/CLX/wCbW1t1u3JZBK//OUv8aMf/QjveMc7cOKJJ+JXv/oVnnnmGTz77LMAgL///e9Yv349fvvb3+K4447Du9/9blx11VW4+eabUSwWAQC33HIL5s+fj+uvvx5HHXUULrroInzwgx/EDTfc0KyXNKkg4zFBTC/2JHMAgF0juQnek8ZhFzF8+rhE6SqihTRN5CxduhRnnXUWzjjjDMftq1evRqlUctx+5JFHYu7cuVi5ciUAYOXKlTjmmGPQ09NjbbNkyRKkUimsW7fO2sb92EuWLLEeoxqFQgGpVMrxb6pij+RQCTlBTH3491gpTp++V3YRQ+kqYiKQm/Gg99xzD/7973/j+eefr7ivr68PXq8X8XjccXtPTw/6+vqsbewCh9/P7xttm1QqhVwuh0AgUPHcV199Nb797W/v9+uaTDimkJMnhyCmPLyLea6ojrHl1MEuYqyOx6IAQQAMg0QO0XwaHsnZuXMnvvrVr+Kuu+6C3+9v9MMfEJdffjmSyaT1b+fOnRO9S/sNpasIYnoxLSM5RqXIAcrRHJ08OUSTabjIWb16NQYGBnDCCSdAlmXIsoynnnoKN910E2RZRk9PD4rFIhKJhOPv+vv70dvbCwDo7e2tqLbiv4+1TTQarRrFAQCfz4doNOr4N1Uh4zFBTC/49zg3jURONeMxUC4jVymSQzSZhoucd77znXj55ZexZs0a699JJ52E8847z/rZ4/Hgscces/5mw4YN2LFjBxYvXgwAWLx4MV5++WUMDAxY2yxfvhzRaBSLFi2ytrE/Bt+GP8Z0J08DOgliWsEXLtmiOm06AatV0lWALZJDIodoMg335EQiEbzhDW9w3BYKhdDR0WHdfv755+PSSy9Fe3s7otEo/vu//xuLFy/Gm970JgDAmWeeiUWLFuGTn/wkrr32WvT19eFb3/oWli5dCp/PBwD44he/iJ/+9Ke47LLL8LnPfQ6PP/44/vCHP+DBBx9s9EualDgiOeTJIYgpD09B64b5/fZ7pAneowOHixhBAARbJEe2jXYgiGbSFOPxWNxwww0QRRHnnnsuCoUClixZgp/97GfW/ZIk4YEHHsCXvvQlLF68GKFQCJ/+9Kfxne98x9pm/vz5ePDBB3HJJZfgxhtvxOzZs3HbbbdhyZIlE/GSWg6lqwhiemH/TueK2rQQOdyTYx/lANjmV02TiBUxeWmJyHnyyScdv/v9ftx88824+eaba/7NIYccgoceemjUxz399NPx4osvNmIXpxwFSlcRxLTCXjGplDS0jbLtVIFHakTBKXIkiuQQLYJmV01RKF1FENML+/dYKUyPMnIuYiSRRA4xMZDImYIYhkHpKoKYZtijs9OljLymyBFI5BCtgUTOFMQduaFIDkFMfewLl+kicngfHIrkEBMFiZwpiHuMA0VyCGLqY2/wmStNj3QVLyGXanlyyHhMNBkSOVMQu0ERIJFDENOB6RjJsYzHNSI51CeHaDYkcqYg9hUfQOkqgpgO2BcrSmF6iBydvaSKEnL2K3U8JpoNiZwpCKWrCGL64YzkTI90FU9HuUvIZTaSnCI5RLMhkTMFoXQVQUw/7BFapTQ9IjkaC+W4jcfUDJBoFSRypiB5V7rKHdkhCGLqYV+8TJchnVqNdJXErjyUriKaDYmcKUhFJIc8OQQx5XE0A5w2IqeW8ZjSVURrIJEzBeFhbZ9sfnyUriKIqY9jrMN08eTUKiEXnPcTRLMgkTMF4empiN8DAChRJIcgpjRmF/Np2PHYGKOEnDw5RJMhkTMF4SIn6jfnq1IkhyCmNqpuwB7UmC4ih6ejKkvIzd/Jk0M0GxI5UxA+4ybCRI6qG5TbJogpjLt4YPoYj6tHcmSJxjoQrYFEzhTEna4CyHxMEFMZdzQ2O8k9OZsGMlhywwrcv3bPqNuVxzo4b+eRHEpXEc2GRM4UpCxy5IrbCIKYeri7mE/2SM4/N+7Dhv40/jaGyOEihjf/43BPjqqRyCGaC4mcKQg/IYZ8ZZFDvhxiMpFUSvjH+n5Ko9aJuy3EZPfk8P3Lj9G0sJyuct4uk/GYaBEkcqYg/ITo94jw8jJySlcRk4jr/r4Bn//1C7jn+Z0TvStTAnckdvKLHDOdVq/Iqeh4LHBPThN2jiBskMiZgvATok+W4JOoVw4x+djQlwYAPP7awATvydSgMl01uT052QKP5Ix+3rEiOe4+OXysg07nLaK5kMiZgtibAXqpISDRbAZeA/74GWDg1br/ZG8qBwBYtXWIKmjqgH9/Q14JgDm7ypjEqRzuGcqNFckxqpeQl0XO5H2NxPSARM4UJG+lqyR4KJJDNJvVdwDr7gVe/G1dm+u6gf5kAQCQzqtYtyfZxJ2bHvDobFvICwAwjLGjJBMJHyA6VrpKr5GuskQOaRyiyZDImYJUjeRokzuHT0xhksxXU8zWtfmwUnR4xJ7ZPNSMvZpW8O90PFhuCzGZRzsohfo8OWqtdJVA6SqiNZDImYKUPTn2dBUtiYgmkWJlwqVcXZv3JfOO31eSyBkTXkwQ8EjWTLrJbD4uV1eNLlKsEnKpVrqqCTtHEDZI5ExB+AnR55Hglai6imgylshR6tp8LxM5vI/T89uGab7aGNiLCYLMlzOW32Ui4emqMT05YxiPqYScaDYkcqYgZDwmWoZaBDL95s91R3LM7d50aAfaQ14oRQ0v7Uo0aQenB0VbdDboNcVhtjB501W8+kvTjVEFbM0ScmoGSLQIEjlTEPuqj0QO0VQyfQDYhahOkcMjObPiAbzp0HYAwDObKGU1GtbCxSOWIzmTOF3FS8iB0aM5lsip5cmhSA7RZEjkTEHsnhwfGY+JZpLcXf55nOmq3pgfiw/rBEDm47Golq6azJ4cu7AZzXzMRUyt6qrJ1BF7Mu0L0ThI5ExB+BRyn0cse3IokkM0g5Rd5NQbyTG3mxHzY/GhHQCA1TtGxqzEOZjhIscriQjYeuVMVuyVX/li7XPP2CXkk0NYfP2Pa3HqDx5HMlea6F0hGgyJnClIkdJVRKtIjT+Sw6ureqN+HNYVQnfEh6Kq4987Rpqxh9MCK5LjKXtylEnqydF0w1FVlVdrizGrhHySNwN88vV96EvlrU7dxPSBRM4UpFoJOU0hJ5pCyjZluo5IjmEYVrpqRiwAQRBw6mFmNOdZSlnVxF5MEJjk6Sq3B2c07xCP5Ez2jsd59hrSeYrkTDdI5ExB7CZFD5WQE80kuav8cx0iJ6GULMHdE/MBABYzkUO+nNpYbSFkyRrtMFlLyN1NCuvx5NRuBjjxIscwDCs1mM5PzugZsf+QyJmC2E+IlK4imoojkqOY8wZGgUdxOsNe+GTzYn0qMx+v2ZmY1GXRE0mhSgl5PR2PVV3Fc3ufQ06tzy/VCJSCK5IzishRxyghnwwip6QZ1n6kKJIz7SCRMwXhJ0Q/GY+JZmP35BgaoI1+Eehjgzl7Y37rtjntQcxuC0DVDbywnXw51eDRWe8401V/2/w3nP/383HrS7c2df/suPdrtK7HtYzH8iQyHtvTbRTJmX6QyJliaLphpaZ8crkFPHWUJRqOWgQyAygCWOX3oQiMaT62ysejAcftvMrqmc2DTdjRqY+jGaCHiZzC2CJnZ9qcK7Y9tb15O+ciVxpHuoqdliZzCbliez0pqq6adpDImWLYIzbU8ZhoKum9AAz8uq0Nn5/Rg9/GImP6cvos07HfcfvJ88ymgOv3pJqyq1OdcnWVhKCPpavq8OQoqik608XWVQVVRnJGMR4b1ZsBco+OOglEjj2Sk6JIzrSDRM4Uo2Ar1/TJIs2uIpoH8+O8GIoAAPbIcv2RHJfI4dO1M+TJqYrTk8M7Ho/9Xins80gVWyces+Py5JivSxQFjORHcOe6OzGYG7TSVZMikuMQORTJmW7IE70DxPjgJ0NJFCBLVEJOOPnDCzuRL2n41OJ5B/5gzI+zQZYAaMgJwpiRHHsjQDthHp1gF0jDMPCF5V+AT/bhprffBMG10j/YsBp8ypKVyqnHk5MtZQEAqULrRE5lumq02VXm/7Io4J4N9+Bna36GZCGJkPg+8/5J4MmxR6LIkzP9IJEzxShXVpnihtJVBEfVdNx17/0Q9RLOfuNSxFj0ZL9J7UZSFNEvmBcBRRTrEDnVIzk8BcMjOUO5QazcuxIAsDO5FXPjhx7Yvk5xrI7HsgiPMQ6Rkx0AAKTyw83bORfu/RotkmM3Hg/nzH0czg8jyjTtZEhXKQ7j8TSN5Ky/HxBE4Kj3TvSetBxKV01SckUNr+xOwnCtdOxNwwASOUSZbL6IX8vfxd3e7yKdShz4AyZ343VvWSiZkZza6SrDMGyeHKfxOGRVDJkiJ53cYd239pnrDnxfpzjVppDXU0KupM1oW1pVoButOQe4DdGFOkrIRUGw/ENKSYHE0uyTIV1lF2nT0nhczKL0p8+h9KfPAmphovem5ZDImaQs++sreO9P/oWVrgZq9kF+AMiTQ1jksinEBAV+oYRisu/AHzDlFDmKOHq6KpVXrVVxb7R6JCfL7s9k+6371m55BOh75cD3dwpjX7wEffWXkCuaedEyAGRKmabtn+M5xxPJsQZ0Arn0XgBANrNnUjUD3N8S8sHcIPZm9jZjlxqKlt2HD8zoxId726HnkxO9Oy2HRM4kZduQmWvfPJh13G7vdgxQJIcok1fKvoxitgHpi9RubPB6rV9zgjhqJIdHceJBj9XrhRNm0YmiqqOk6Uhn91n3rfV6gHu/aJasH6Q4qqss43Ed6Sq9/J61qsJKGVcJORc5IpSkWeauJHeCrc0mhcjZH+OxYRj42J/egw/8+d3I1TnTbaIYSe/GNq8Hm7xex+LiYIFEziSFVzC4c8QVnhxqBkgwCtnyRU7NNKDpXmrPuCI53HTsjuIAcIgepaAhkyuLnNe9HigDrwBP/eDA93mKYp9CHvTwdFUdkRzd1uOlReZjLr4iLDqXG814bJWQl6NOil60Ssgng/HYHonKl/S6eo4VSwr69Bwy0DA8uKGZu3fAZJhvCwAU5eAbrUIiZ5LC8/GpnHPVVJGukildRZgUbZEcTTnASI5ahJoZwCZP/Z4cHsmZGQ9U3Oe1tTvIFlVkcuX90wUBr/i8wL9uAHY+X3ufDAN46lrg1QfG+2omPVZ1lafc8ThX0sb0rCiwRSFaVEbOF2DtYTPKN2okRysbjxXdXLApegmyNJnSVc5zbD0pKyVXbmqZUfaNsuXEk7btX7aFBvXJAomcSUq2xlRcnq7yU7qKcFHKpTEoieiTJOjKAUZy0nuxwyOhIJZPEWNVV9WqrOKEfGXzcbrg9AasPeQkwNCB+75Y+zn2vYb8k9+H/vBl43klU4JyF/NynxxgrB40KvIoi4RUoTV+C15C3h6qQ+TwAZ2iYEWdFEO1Ijn6JIvkAPWZj+0REWWSC4d0rryv2dzBN1aFRM4khQ8ydHfgJOMxUYtiLo2PzOzFB2bNQPFAw9KpPXid+XFmhmYCABRBgFEcO5Izo0q6CoBVNZQpaMiw1IoM82K3tmMOEOoGhjYBW56qvkupXfjPOTPx38HJOZ17fzEMw/G9Dnhsqb1RUla8WomTyrUmFcH3qT04tsjhkShZFKCAixzd6gWkak0QOUObgX//BtDrO07c73E9kZysMnWEQzpf3r9sITFxOzJBkMiZhOi6YX3x3KuKvNU0jCI5hBMlP4wBWUZaEpEsDoz9B6Nhq6x6Y/cbAQCGIKAwSgXPnmTlcE47ViSnoCJTMv1DJ3jMcQ8vDb8Ko+doc8MaF41tya1ISBJe9Mp1X8CmAiXNsIa7+zwiRFGwhM5o5mPFlTpMKwf4mdcJLyHnkZx6ppCLggCFlbgrMMCyVc2J5Dz0NeD+i4AtT9a1uVuk1WM+VmzCITPJhYN9/5QWRfsmEyRyJiE5RwdOd7qKV2GQyCGc5PIJ6+ds6QBXl7bKqjd2vdG6WRmlgqdWjxwOj+RkixrS7AJ9UnAWvKIXI4UR7PCwSq5idSGVZa9PEQUYhdaUS7cC+6gWHpnlKSt3JZMdt8hJKa0Zfsr3qezJGWUKORMxgmAgx4WNAGiGWRXWFE9OwhxainR9bRQqIzl1iJwpJBzSNkN6toWdsScLJHImIVmbEa4iXVVypqt8ZDwmGLliwvpZUQ/wZJbcjQ0sknNU+1HwCzyykK35J31jeHL4aIdsQUVGM6M+7b44FnUsAgCsldjFplBdSClF82KiCQIKk9wHMR7cQ3eBcjWae06UHT7SgdOqrsdcFHTwSM4o0SYuYgw4hYOKvOP+xu4gE3s1jqOKzYvuSM7Y6aqcTdhkWzg3bH9I26Kv2Rb1UppMkMiZhNg7itYyHpdLyM2T4cEQyfnNym24dcXmid6NSUu+VD6p5/TaYqQeksnt6JdNUbKgbQGConlBU9Tqj5vOl5BmPrJaIodHJ7JFFRlWThz2x61I0VqDGY5rRXJsUaTpVAprH+nAZ3iFWNRrNAHhXpW3ynjMz09tzJNjj0S54SKGixpOyWAip9HpKk0Fcgnz5zr7BvF0FR+fVpfx2PbeZ2ocr5OFtC3ip5QO7LwwFSGRMwmxT2quXUJ+cKWriqqOK/+2Ht9/6DWMZA/epnGjUVDLJ1vFOLAGZa9ndgEAZvnaEPFGEBDNqE6uRuVTf8q8aEX8shWxcROyDelMs3LiiL/d8vys1diFo0YqKmu7mGRblJppBe7vNFCO5Iw22iGbdwq9VvXJ4fvUEa4/kqPpzuNGY5HGhgegcyMoCAZ2yPK4IzkdIR+AOkvIbceiO2042choZYGZVSf3vjYDEjmTEHv4NFfSHM2p7J1RAcDDHHyqbkyKOTDNIlNQrRNmYjrOl2kARa28SlOEA5tR83rRTH0siM4HAAQk8wLgrujh7LX8ONWjOEDZeJwtqsgY5jEeDnZakZyNpRSyglA7kmMLtduNn1OdcnS2XFUVtPXKqYWSc6an0jWibI2G71M7EwV5Va+YscfhkRpVc36mGvOMaXqDVY4yhGWdHThrzkysy+6u60+4SOuJmq+nLuOxPQU0yYVDWi+fCxR19AG70xESOROAYRj458Z92JeufiHKjtKcqlCjugqY3r6crC26NW0nBR8gBa18AlOEA3iP1CI2sHTCwk6z4ikomeIlp1U/Zss9cqqbjoFyCiZbUJERzGM1HOxCd7AbM0IzoMMwmwLW8uTYokjZ6SRySpWRHMt4PFp1FTNit2msErMFFzBzLIcpXLgnR9MN6zY3fOFlaM7PSyslrb9tKMqgVRW4OV9ftI+Lth7W+qCuSI4temOPlExGeNQUALKTfF+bAYmcCeCF7SP45C+fw+V/ebnq/e4pv/Ycca10lf2+6choKTzCpGiUT2AZ0dj/Muv0XutCsbD7OABAQDYvAEqNk+RYPXIAW3VVQUWa+R/CoW4A5QqutT5f7UiOXcRNoyoReyNATsD2XtWCV5v1skhQsgUXMHtqipeQA0C+hi+HR3K0kvPzKqkJAEDDg8/ZQQwzn2K6zjRSOZJjHrt1eXJs0RulhvCfLPCoKQBktYMv1U8iZwLYNaI4/nfjPrE5Ijk1mgEC09uXY39P6h2id7BRMson24QoAvs5cVhN7LDGORzRvhAAEJSDAIBcjZOkla6Kj52uyueSKDGXZzg8A4BN5Ph9NSM5WbX8+rItLNstaAX892P/jWueu6Y5j18qG485oTqGdPKqnhkqaxxqqDXTRo2Cl4/LooCgVwLr6Yd8jf3kzf5UV7VfUTU/Y7XB6So9O2ge+wAydaaRuMeoO1K/Jydri5pl9cl9PkoL5WNCmeT72gxI5EwAGfYlqvVlcqer7Bd19xRyQRAOiq7HGUpXjUlRKAuQEUms2VRvLHYMrkNBFBEwBMyJzAEABDymyFH0WiLHPOmP7slhk8jz5UnIoaAzkvOSzwujVrrK9tyj9etpNE/seAJP7noSd792d1NERPk7XfbkWMbj0Tw5LOLFIzkaDOSanLLi6bOAV4IglJsW1uqVw/vkuEVOiVUCNtqSk8zsgc4EdL1ppIp0VaGOSI4tepM1JvH5qJRHmpeNAcgaB18UnETOBMBLbTM1QtEVfRvs6Sp2MvHbTIp8BVia1pEc2yBCSldVpWjrRZIURcDWHHA8vD60HgCwQApBFFhzOk8YQO2VYF8dnhzuM9GK5sDAkG5Aks2I0ZHtR8InepCQJGyvYaDN2kROK/t93Lf5PgCAZmjINyElZEVnpUpPzugdj833oF3TIDMx0ewhnTyVzv1Vfs/oBmnuuSm5jMcFFslptCdnJFsW0OkagtxOSSt7jCzjcR3nl5xuFzmTt/u2nk8gI9pFzvS9RtSCRM4EwCM5mUL18HJd6SpP+aM7GCaRU7pqbIpC+WQ7Ikkw9nNI5+vpHQCAI/xd1m0BbwgAkKtxQu9jJeS9o3hy+IVRV82qoDDKJ1+P5MERkUMAAJuM6tEI+8XE3QivWfRn+7Fyz8qmPq87Ogs4/Uu1yDDPiaYHEGUhkaaLHBZl5iLMb0VyxilyWCVgo/vkDNlGW6TqSM3YxVnZeFxHJMdu5kVzU4QHgpIZgGGL5CiYvteIWpDImQB4BEfTjaorIPeJzX5Rd8+uAmxDOqdxJMdpPCaRUw27yFEFAalM/yhb12ZD3rxQLGSiAwCC3ggAQIFWkWPQdQNJ9pm0hTw1H5enq3TVFF8R1+kn7m8DAKS1AlBN/NtO0K0qhX1gywPQbavfZjR+K1brk+MZO13FRU5RDyHKFjjN7pXD9ydgiRxzn2tGctjnWHD5Y4rMRN7wSI59GCXGjrBwL5EoAJ1hXkI+trdJsZn6lUksctJZ52iLrFBjw2kMiZwJIGOLzGSq+HKyo7QZdxuPgXIk52CprqrHGHgwUhCdn/++1J79epwdLF10aNsR1m0BJnJyggCozpSNUtIsTRLxjSZyzGPW0E3TcFh0bhv2xQDArLxSKytW7KvQVpTCGoaBv27+q+O25kRyKr/T/L0a1XjMfCGKFi1Hcpotcli6ikdyuNipFcnheriomSJHZAdKTuNRqMYKhOFi2ZCeEYSqx5Edy2PkkRDxy9Y+jdafCGBin5ETBaj65DwnuYe2ZkUB0Eq44+mtOPOGp6wmnvVgGMaUXEg3XORcffXVOPnkkxGJRNDd3Y1zzjkHGzZscGyTz+exdOlSdHR0IBwO49xzz0V/v3PVuWPHDpx11lkIBoPo7u7G17/+daiq80B68sknccIJJ8Dn8+Hwww/HHXfc0eiX0xTS9gt2lXA0DwnzL52zhLxKJOcg6HpM6arR0TQdedcqbSizf1OpU0xMxGOzrduCTIAoogi4uh5zoS6JgrWyrwZPwRiG6ccIi17H/REWycmIYmUZuWE4VqGtKNt9efBlbE1uhV/yY0bIrALLNMELVK1PDi8hH63jMX8P0noMES5ylP2L3tVLOV3FPDlyfekq3sOpnUWc8mzfG93AdMQWMcqIYs3u2ZyykVpG0CtBYv6VsRZS7rSPsp/+t2aTVkz/W7thvq6SIKCUG8G9a/bg9f4Mnt1S/3iUr9yzBm+6+jEMT7GO8w0XOU899RSWLl2KZ599FsuXL0epVMKZZ56JbLa8Arrkkkvwt7/9DX/84x/x1FNPYc+ePfjABz5g3a9pGs466ywUi0U888wzuPPOO3HHHXdg2bJl1jZbt27FWWedhbe//e1Ys2YNLr74Ynz+85/Ho48+2uiX1HDs0ZtqXyZusuWVKs7qqkpPjucgq64i43EluVwGiuhUOYn9mO9kGAZSrOQ0Guq1bufVVTlBAFz9RzIFPqJBtmYvVcOK5MA8F0Qkp38nzKJFaVGoLCMvZpEVy8d8tg5T6YHy101mFOeMQ85AN6sCa4rIYQsXewl50FOH8ZhV9ShaBGH21ecXtWbBIxyVkZzq5x6ViZi8bkYMuljjwjwz7qqNFDmGgWHN3itKAMaIbNlfjyAIVReW1XCnqJRsc9/3/SXDumJ3Cz7rNkUZRJq9vnqGkXJWbh7EcLaIdXsm99R1N9WHzBwAjzzyiOP3O+64A93d3Vi9ejVOO+00JJNJ/PKXv8Tvfvc7vOMd7wAA/OpXv8JRRx2FZ599Fm9605vw97//HevXr8c//vEP9PT04LjjjsNVV12Fb3zjG7jyyivh9Xpxyy23YP78+bj++usBAEcddRT+9a9/4YYbbsCSJUsa/bIaiv2CXTVdxe7vifrxen/G1fG4drqKIjkHL7lM0gxFA5B1EaqoI1FIjP9xilmoTKhEIzOt23mfHEUUKiI5/ERZa2YVh3tyIJp/H2aPyYkwkVMtklPMDVv7BTS/30dBK+DhrQ8DAM4+/Gzc8cqvADQ7XVVZXeVOXdtRDA0QgKIeRlgzt081eaaXvYQcKJ+HaqV3eAl5gYmaDhZx4u0AGmo8LmYwbOsJkxbFMedX5WzpKgCI+j1IKKVRL/6GYUBxafmMMgDgqP3b7yaSYueAuOSDT82hIAjI5gYtD914/I38PelPTe7mh26a7slJJk3V197eDgBYvXo1SqUSzjjjDGubI488EnPnzsXKlWYVw8qVK3HMMcegp6fH2mbJkiVIpVJYt26dtY39Mfg2/DGqUSgUkEqlHP8mAofIqdKTgZ/YrEhOroTn+57H237/NuT8KwA4T4i+g8J4bJ/MTpEcN4Vs2kwlAQhrpnhIlMZ/fKcypo9HNgwEQuXvX0A2S8NzglgZyalT5PDoBERztR1mZekc/nu1NIN7IKfS5H4fT+x4AulSGjNCM/Af+3YitOWf5r4103hcpU/OaJGcHEuZFPUgZJ1FIPLDNbdvBAo7d/FKubE8OTxdlWdRp07RjCjk2O8NTVcpQxiRyu9hThShjpFGyrmM1FYkZ5SFVKmUKy8EWPQ8m2vu+76/ZFjTzIjkR4ilrDLKkPX66j2X5kuadZwOpKfWaIimihxd13HxxRfjzW9+M97whjcAAPr6+uD1ehGPxx3b9vT0oK+vz9rGLnD4/fy+0bZJpVLI5apXXlx99dWIxWLWvzlz5hzwa9wf7AdWtRUDz3vzniPJvIIrnr4Cw/lhaLHlALTqkRxt8vZrOFCyVF01KnklBYWdeEOGGRFJ7cfAxlRmLwAgqhsQPOV0UtCRrnJ5cthnE/XXNh0DgCyJ8MkiDMlcxfPIDSfsNUVOukokJ5tzipxm9/vgvXH+K1eC+JfPI8xec7YJIqdaJMea2F7Dk6PpGvICTwWFIGumvym1H9G78cAjOVYJuTxGdRUTMTkWeeuSzVYECmsH0NB0VXbIbIJpv2mMyBZ/f3kkh4uc0S7+iu1Y7GLCYbLOUkuz4zXiCSLIWjYks0NWb6B6o+L292OAIjllli5dildeeQX33HNPM5+mbi6//HIkk0nr386dOydkP+zRm+rpKmckZ1B+ALsz5kRdQU5DCm+o3ifHFckZzA3i9ldux1Bu/N6MyYa9C3TaNpGcMMkrCeRZJCdomAbelD7+FRcvOY0Yzng8j+SY6SpnJIf3FQn7y5GcgdQu/Pm5HyNfdG4b8slQRba9L+64L+Lh6apKT457pZwVmvf592f7sHL3MwCAs7e/BIgehFiaJdOESEm1YgKrhLxGJMc+OymnRSBorPy5yZ2gK0vIR/fklCM55ve3g33mOVt1UsOiOcqQNbeKkx5D5ORdHiMu1EfrlaOw86lPNxAVWD+jJkfQ9pcMS6+GPWEEBfM1jmTL+1pvJMf+ftQaLD1ZaZrIueiii/DAAw/giSeewOzZ5SqN3t5eFItFJBIJx/b9/f3o7e21tnFXW/Hfx9omGo0iEKjeddXn8yEajTr+tZqSpjtOCNW6HvOoRW/MD9Hbj0LocQDA0R3mRGhv/Pm6+uT8bv1vccPqG3D3q3c19kVMAO73qVa36IOVdLYsZEOSaZJNG+M356aYcTUqOC8WznSVM5KTrpKuuvmRL+DKV3+JB5/5vmPbkE9CSTQvLGF/3HEfj+RU8+QorpVyFqjaS+dAyak5/M8Dn4QOAyfk85jTewLwxX8hzE6V2SZU0VSbXcUvugVVryro+RRs2TCgy1EYuhlpSzW5E/S401XsM+Il113+Tva7UbHNgaJlB6y5VSJ7yEx+9AUeF5F+K11lipzRihsUJriDhoEQa4OQ3c85cfvDtY+8hs/f+TzUOgpN+GT6iDeKEBNkCVuT0Hqj4nYxNJ6y88lAw0WOYRi46KKLcO+99+Lxxx/H/PnzHfefeOKJ8Hg8eOyxx6zbNmzYgB07dmDx4sUAgMWLF+Pll1/GwEC5BHb58uWIRqNYtGiRtY39Mfg2/DEmK+7Ijftibe/R0B3xwjfjXkDQcfrs03HFKVcBAKTwa0iVyiuUcrrKebIY2G76CPax/6cyFQ0SKWXlgK8kJQPwyabISQnjT1+mWEVWVCinnp54bQAfuHk1ADOSYxTd1VXOlgcAsIeF9HcNO9tHhLwyikzkRAIdjvu4JyddzZPDLiK8FDYnCjBKjT3ZFrQCLn7kfDyX70NI13HZoecCn3sU6D4SIeYlyTQhHVTQKosJeIk2UD1llWWpyIBuIBhph8Z8WOkqTRJ/+a+t+NJvV6PUgOpLt/GYp6tq98lh6SrmH+pkA1nNdgfmbY2KyibT5blVMZWnnRKj/o2VfuPG4wD/u1EiOUxwByGURU4LZ6nd/vRW/OPVAWzaN7ag5fO7Ir4YgiKPUpW9evXOAXSkqw72SM7SpUvx29/+Fr/73e8QiUTQ19eHvr4+yycTi8Vw/vnn49JLL8UTTzyB1atX47Of/SwWL16MN73pTQCAM888E4sWLcInP/lJrF27Fo8++ii+9a1vYenSpfD5zJPNF7/4RWzZsgWXXXYZXnvtNfzsZz/DH/7wB1xyySWNfkkNxS1q3AeZPbe9ZmQ55OA2GLoHXznu65gRPASqMg+CYOCRbQ9Y29VKV6XYKiY5HdJVBedJlCqsnGRyCQBAwBDg9ZgiJykYgDa+iBc3rkalcg+bp17fh5GMefHQBAEl1wndiuTYRE6SVdPsc0Vggl4JebbMDgc7HfdZ1VVCFU8OM1B2CeZ+qYKAYgN9ECWthEsf/yqeGXoZAV3Hz/xH4Ogzrga4mZtFsppxMavWJ8fvEcGLyaqZj5W8uR8hQ0ckEoeqmV6XVJX+QT9/cjMefqUPL+8+8GiDu4TcP4ZBmntueOSmM1KO6kNkFVYNEjkjzE/m00R4NfNYzIwxrd6drrIiOaOmq8zjLgARIcm8HrVqYGy+pFmZgKQy9jmQz++K+NvKQt22r/uTrhpI55s+7b6RNFzk/PznP0cymcTpp5+OGTNmWP9+//vfW9vccMMNeO9734tzzz0Xp512Gnp7e/GXv/zFul+SJDzwwAOQJAmLFy/GJz7xCXzqU5/Cd77zHWub+fPn48EHH8Ty5cvxxje+Eddffz1uu+22SV8+7j6o3L/zcLAoZ/Dzl24EABT2/SdCUhcKqoZS4mQAwL2b7rXazdcUOawzbUprTQv8ZqHrhs3cOrYx8GAkxyqp/IYIn99M6SYkERhnGD1tVWOUU74jShHQy5Ed9wmdRycjtnRVghlLB10VXiGvBIWddSIhZ+EAT1cVRQFF1wpcYTOZOm29dbIN6glT0kv42lNfw4o9T8Ov67g5peKE/7oNsJWshzzm+9GM6iruyRElFb98+ZfYOLIRgiBY0YVqvhxuxA7pOqKxNhQ0ZjZ3TcTWdcP8/FDfRXEseEQ1ZqSA+5ZiTtasds1Xqewse21UqOytjMXnWcNEBdEUZI1KVw3nzOPBp3khs+M1PcYsL3e6qp7zi8K+I0FBQogdj5kWzVLjnyUAqwx8NDJgC5BAB0KyKXKyxfK+7o/xOF/Sqzaxnaw0vE9OPQrP7/fj5ptvxs0331xzm0MOOQQPPfTQqI9z+umn48UXXxz3Pk4kY3lL+O+h7ieRLCYhFGegNPxmpPIl+GQJauoYoPdv2JXZhef7nscpM04pe3Jc1VUpvQgIQLIF3WGbiX1+z8x4AKm+NKWrXORY0zOfISPu7wDSQEKUYORGIIQ6xvjrMty4GvWErNsSSgmABNkQoAoGcsUU4ra/KaerykIoIRgABAy6xi+0eVS8qptXvLCt2SAAhOTyc6YLSdj3mlc1RSQf/KqBvCAgqwyive5XVpufr/k5Ht/5OLy6gRv7B3Hyub8FQlVSaUbaShM1El5dtTn7HH73yo+xcu9K3HbmbQh4ZWSLWlWRw30hAR3obItipxY3HwsGCloBPhZhSOZKVqSknoviWFjpne2/waf3/R3vEzYB+ErVSI4lXsTyhTkYm4OAbiAtCfCJGeQRg6Y1SOSwKKSs+iGyvkHJwuiflxWZ8piXwqjlyRklksOEU1CQEZKDQAHIumZzNYuETajW83mmDB2AhEiwCyG2cFFsi956F4tuMTSQyo9ZTTlZoNlVLcbdF6ciksMHxvl3AQC6cm+BDxpSSslc8RleiMoJAIA/b/wzgFEiOayiIdnkniLNhq8eRQHoipSH6BFl8uzi6xdktDFDb1EUkMuOb7QDN65GbeXdCXYy9Rrmcaa4ohn8BMiNx4VCCjnWmHDQNSSxTUqjwFNAoW7HfZIoWR4Hd5qBX0RCUgBB5stxm5H3l3/vfQ4A8LXhEZx6zCeABf9ZsU3YaxYpZNTGmy65yCnktgMANgy8BMMwrBRKVU8Oe+0+XcCMmB8ZPWbNhbLPrxoe58p/LLgoWJ56Fv/2+/GouI+9hioiR3dGbDyGAU9kJoIsAh0UTUHdqEjOCD9mtCAEnZ0nxoiwWM0AveYxWVcJOVsIBEUPQmwx0KqBseOK5Og6WJYZkVBPuQ2EbV+VolaXV8t9vp1KZeQkcloM//LwDvxuIzK/oMdFM798jfJTbPB/Bif9diEOu+N4vF/8JwK5UwEA/9j+DyTyiZrVVSlBd/w/VcnYKjpigbFLPA9GCpp5MvfBi5g/BA/7yBPpXeN6nLRVjRGzbkuyE6us8+62TpHDPx/uyUkktlv3jYgCtFxZsITEhPUzT0/ZCVu+AZcnh4mcoBxAiJ22lAaV7SZHNgMA5gW6gf+8quo2IZ8pcpoxGJR/b430egBAQsthKD9kEzlVIjnMAO3VRXSEfMgKEWt+VdqWTrTPGWpcJMfASt1Ml6VZOqRqJIeJHD8TM0FdB3wRqyldUDI/40aVkI+w41JXw9A1M41UzYhtJ2ebXQWUo5GjixzzuxYUvQjxisAWRcvtKccxo9mFlDkiBUAk3IsQM/bnDee+Vmtj4sZ9vp1K5mMSOS2GXxB4RMKdruIntCJb/bSxFJSol+DJD+IbnnsQMWbgyPYjUdJLeHDrg7bqqrKYKekls3EbzAZuJW3qioKs7SIaDYxd4nkwUtDNk7lf9CLk8yDEUkKJ7PgGNvLeOtFAm3Ubj+RwkaPU6HjMV8GJ5A7rPl0QMDz0mvW7n4mcgG5AEp1l6gAQYQbfVMnp+1FYBCXkCSHEytuzY5hK6yXJBFT8hM8C3mDVbcJ8eGgTxknwKEiulLBu2zSycVSRw6vNPIaEkE9C0WMb0mnzoQxlGixyCirC3p3YxT46XsGXrxbJYRGagMhEgWEAtqZ0finr2O5AGWJpmKIWg6Ezr8wYotTq++OqrhrVeMyiQyHZhxCLeCotmKUGACN2kTOGOCnlhspR00C7JcjyLt9WPb4ct+ibSmXkJHJaDL8gzGDdjN0K2Wx6pyHLKlDu7v42jsnfhj+f9jCK/i70CiN4p/EsPrDAHGh678Z7LZFTsEVyUq7maclMX1NeTyuwIjk+uZwzp0iOgwITJ37Rj5BXQoB5EhLKONNV7CIeZeXdum5YF0fBiuRU75MT8ZmfTYI1ruQMDm+0fvYKzLRpVD/18HlWGZfHIctWyiFvGEHegG2M4Yv1YBgGEnzqetuhNbcL+c33owgdRa2xFzReXZXRysJu88DLVhl5rlStK7r52iVdQtgnQ/fHEOUix2baHq9RdTQMw4BS0tAdec66LSEAgFE1ksMjNH4WsQkaAETREqk+yfyM1UZ4crQSRlhaPq/GIQqm+Mgao7/mHEsFnvjqNcDPFiPKRo6MGsmxRxV9ZsQz2+RZapxErv7PM20754c9YQSZICtg9OKXqo/FzrchJrwpkkPUhF+weTfjTEF1mLWzBRWClIPBojCewBykEcRedGHn4ecBAM4t3o+3zz4dALAxsRG8+bE9XZVMOtMUqXGmLSYTvHw85JPrnhJ8sFFkIWifFEDQJ1sltIlxtg9IGbyHjVnenc6rVs89gc1HUtTqfXJ4umrEJaj3JbdZPwuGKXICtUQOD/+7vC986njQG7H6fShjDF+sh5yaQ4lX/sTm1twuFCwbkRs9pJMvTtI2Q+jGgbVWL5qqkRyWmhF12RRD/rglcpJKOXrXyHRVQdXNYyG8ybotLYkII1u14zEvH/fxSA673ASYSPVK5uvVGxHJUYatuVWK1o4g66ycHcOPyD1Gs3b+DRhYj/Z9LwAwj+lape0Kiw4F5KD1PJkW+R7HYzxOZ8zjIGQwvxsTZHmXT66ecykXQod1m99PEjlETfjB0stEjm44T2LZggavZF4IopoGOWKW2abyKrYc8mEUDA8WqBvRPriJ/b0Og60+7CInld3reN5k2rm6nkpY6SqfZKWrqITcSRHmiSoghxDySvCwNv8j4zTnppl/K8oqn+wrR4GV5eZs/gN7eT8XoMmcs7R7KFUW2ILBqsD06oWdfLRD2uVx4FPHQ95ouTdJA7r7Jpkx22MYCERrz7KTAm0I8NEODe4qzNNVSZtXYnNyczldVajiyWEpQ0H3IuyTEfAHENJMtZaymc3tIudAFwbZggqIOQwGEo7bo/JQ1WaAPJLjFVnkg0VwuEiV2TT6hvTJUYYwzOdW6VGE/WbdXXYMP6JS1CBAh6fIIozJ1637anlVFHZsBuWglcZU0BrfY2IckbkMG2kRYZd5LsjyrvekniIOHjk/rIuJHEpXEbXgF4TOsA8SM4XZfTlKUUVUMi8SbboBT8j8sqbzJaSlGO7V3gwA8K661XL2l2CuaO2enJRrNe3+3eLhbwJ3fXjcTeNaid14HK1jSvDBSEEw36OgJ4KgV4bIRE5ijD4hdkpa2ccVZZ1p7R4A6GYjPsXmc8jYKn94dVUi5xRW+2wpM003j1WPUV3khNlqM+PyOGQNHs2LWyKnERGVRGIbACCm6xAC8dob+mMIM5HT6EgOX5wkbKmVTbl+BDy8mq1SQFh9WXQvQj4JEb8MH0sn2uc1NTKSoxQ1yKHXYQjAocUSYswvGJYGq4oc7rXhEZsAEzn885MaKnIGrUhOWI7B7zUjb1nBAPTanb/zRQ0RKIChQwMgD75mNWWsdY6xRI43hFCQiSk0xlc0FiPjiOSkWC+lMIuchZgg4804y5Vk9UdyDqdIDjEWfHUQ9ctVD7JsUUNYNv00bRAdRtuCquN27d3mhq89gDhzy6tM5NhLAVOuRmnJao3TdB147lZg46PAvlcb8Oqag5UOIU9OTXiePeiNIOSTANbmPzGOCzLvjC0YBiIRU+TYV45lkVO+jR/PHkmwLg6JYsLxuIO2KijVMPdH1r2oRoSVv6cN1TGbKstC7KFAHEHefbgBvUkSKdMkHTdER/O/CvwxhNjFuJENAQ3DYOkq3exQzcgYKgTZjC4oVTw5WZ7O031WGlfW+AKg/H4PNTCSkytp8EbMCrC3KTm08SopeQS5klbRI417bWQWaQ6K5mceZCJHbGAzQC0zgBFmso152+Dn6dYqc9DsKCUNbUIG/9vZgf+cMxOJgVfK59xaIodFFYPeCILsebKiYDVnbSbJcYicDIviRljkLBQwBVmOHeaz29i8s7o8OSxd1WUurCmSQ9TE7l/gK1976iVbUOGXEwCAuOB1tBkvlDS8bszBa8ETAUNHvGieJIpsdexIV7m8GMlqg+pywwBbISMxMRPZ6yFrNx4HKt8zAiiI5mcf9scQ9MrQVVMAJ8bR7Tqd2WM+hm5AZNVV9hOpznqP5GwmS3sjQIGJhAS7qMwwzJX1oK1SqqAzYcIey02EpRkyogDYOrNm2Yk56G83G7AByDagN0mSjQKIiWM0NvPHEDIaH8nh0VdRVKy5S3NK5vubF8zPo5qpl5ey60YAPllE2C9bwjFlm6810sBITqZQhCdsVsqdpnsQY91+/VISugGUXAZi7rWRJLfIYd20GzjWIZnebfkY2/xxRAL8OBIrJtrbyRU1tCGDFUE/9skyXsjsRMxnPk6tc4yi8wVF1NHrKVcc/3Gxbk8Sd63aXveYhPEYydPsOIiwzykY7AJgzp8ToGF2W/XiFzeGYVjb8EhOtqhVzBOcrJDIaTFpKyrhsUSOPV1l9+S0yX7bqkK1DIr/7PgwACCeYY24DPOi4hA5rkGCqWqD6jK2ypvk5BU5VauryHjsoCBwkdNmTvrWmcjR6w8r85Rm1DAAkVdnVYocxZZWSbsaAQJlYXW4z7zQDOrlVV/eYGZTrTyewY5Vqm1fges6FCZyQsFOBFmaNteA3iRJlkqLidX3x8IXRdiK5DRuThH/Tsdlcz9Cuo5FBfNCljXM72RV4zFrCSEgAEEQTJHJ3lN7Cbk9XZWts/FbLV4ZfAm6VEBE03Fc+9FoYxdPj2w+X86VsuLihUdsgmweWog1pdMbKHK42d2viWgLBtAWMPsaKaIIrcZoE003o2gxIYkkS3W9Kgs4wmOmeWqdY/hE9aAvBp+/HRITKNlclWj5GPzPva/gf+99BS9sr887l7D2SUdR1WsORgXK/ZLCTFSG2Kw4VRAQErKWyBmrHUdB1S0B2xP1T7kKKxI5LSbDLgoRf/mCbV8xKEUVkmye3OOecHmWSq5knRC3tS0GOhagTTVPEnndPMk4SshdPUSS1bwZ9m64iR2V9wMwSgWseeo7yA68UvdrbDR243E5sqVOqSFxzSbPvsmxUBv8soSianpbRsZR9ZFi1RhR22nBLnI01nskp5cf0xrOaRc5TFgdHp0PABiEbnm+8kzwqFp5NpadsI8Zj22TyI1iBgpbpYeCXVZvkmwDSrmTzL8St42xqIo/hhD35Iwx2Xo88PLxqGzuR5tu4DB24VKKm9j/VUrImdAUWKl02CfD4CLHFmmyixzgwBYH/973DADgLbkc5JnHIc4iahLrd1NwXXB1a0YVq4xjF9sAe691wdyXRoicYVZR5tM8iAc86AiUm1lmstXFBxdlYaksMDb4vFgomkb5mpEcFtEL+dsgeIMIsv3PZgerbj8au0dM0b8nMXZU0jAMJJQiOmf/Au2HXQ0IxVGjOVzkRNj7HfSXe19FPRmrV9tYkRyethME0xfZHTWPs6nSK4dEToux+0t4ya3dxZ8tahBY/4g2b8xxUeeq3euRgTd9EXFm/MtppoCxG4+TrAKknW2TrJaXzti+/LYutXaeXnUDPrntj/j+o18e3wttIPYScp6u0nSj6gr3YEQvFSwREAt1QhQFaIJpvEyOo+ojzVaiEcEmWGzVVaphntwUWwmqu7IKABJMWC3oOgYAMCiJMJiA4k3T8qVAVZHKq6vMdJV5ks4pg1YqJxTosPp9jNUDpR4SLOIZY2MbauINIcx2NzPOsvzR4N/ZmIf58AQPFnjjAICR4jYA1SM5OfChnuZ7EfXL0HXzYsa7/OaKmnUhl1mRw4GkrF5JrAJg+nEw41jE2ecgyuz5XCKHl5AbXOQwLxUXqRozyzeihHyYfSYe1YdY0IN4IGB1/c7U6BXF04DcHgAAr3o9OMwwF3y1Lv4K804F/XFAEMDlcVYZn8jRdAPDWXNBMJgZW7CbIxg0lMKbUPKm0enbNOrnmbG6l5vvtyTJ8LPPpMOfrdvfyCM9YZ8MURQscUSRHKIq9u6wlifHka5SobIcdtzf7ujAySM1PlkCjvovxNnKUlHNqI0jXcVmGc1mVSxVJ5HbVzg1PDnr960FAGxRD7zx2v5iF4YBj2SdsMmXY5JXUsiy96Q9YubdddFsPZAQBaDObte8gWRULJuCudFRFgUr+pKzGSzT+UqRww20h3W+wdxeFKGMbAUAZJgwKaghR+SRY/XJsUVyFHbxEAwDAU/Q6veh6Af++fMIZ8y2yq2KICAk8EVJY8ZJAOXoR8hjfofjog+HRQ4BAPSrAwD06mMdmHiVmMiJ+D0oMh9WiqXxhtgF1CuJ6GGr7/0VOXsye9Cf3wrRMPCWXB6YcRzirCRZk83zlbtXDo/QGCxiw2cncZFaEs3X1YhmgCOsBFzQgogHvIj6PQhwkVNDlFojHTzl9OOALCOubwFQ25DLU6dB5vsJMwN2tprvcRQSShE8iDWUGVswjChF+OQRS/B3enaPHsnRuMgpC/gQe76YL1fXnC7zftYglIkifixNFfMxiZwWoukGsuyLVTOSU1BRksyDqi3YZUVyiqpuhZp9sggEO9DGviG54qC1DYef6GazCqxUNW9GHemqXazfznjSHo0maxM5giCMWf3QbNYNrsOVz1yJoQau6A+EfDaFLKssaQ+bJ15DNg2ReVFELlPfaIdUwQzbR6WyP4V7AGa1BVDUzYuUIsASThlXuqqUT5mpJgAzOxZawzT3sa7HGWZ0z+uRqsbFMDte7VUxWVYKGzQAQRCsfh8KDjySZ410YH6F0QgzT0lD01XsO+uVzQttmxzEnI4j4dUNFKBC8IxUGI91Q7cqZCSPKc7CPhkF3RQPKSYkR7LsPBLyWDPfql0UhzIFvOvHK3Dris0193PFrhUAgOMKBXjhB9rmo411gS6J5mO6IzlsDQZN5EZd87MNsjlgJTYSohHVVcPM3K5rYbSFPKyk3jwO0zV6RfH99Xhcs9j07ezvKt+rUimPEhMZAVZZxfv/jPe4sFe+DdURyUkoJbR5yv3OAvKQo9rKDe81FWYViwAQYCM1It5c3ZEc90Kmm0Vy9lEkh3CTtfcUqVFCrhQ15CTzy9cWnoGIT7YqWweZ2vd5RECUEJdD7HHNlaWjTw5LC8wNmCv6ZLVVrz1dlRu2Vs52drMV0rAAR0lvK7EbjwFMeNfjXz53Hf688c94+LU/TMjzu0mnh6GygyTsNY+JgDcKmX1eSVszvtHg06ujcnl+E6/mmNseRMlgU4xFAWCjHdK26ioASCbNC4RgGIiEZ6CLVS0NJrcBhoEMi0DktFjVCAUPrWeEciQnyyJMIfCUlXlhzzagAVuCVSnFQj1jbhtinpJmGI9FNvog7o1C7lyAQ1mFlejrd5w3AOcUaa/XFLURv4wC82Ep0FHSS1Ykpz3kG1XkrNwyhNf60vjT6trHybN7nwUAnKbksNd/OCCKiLFqnTxblLlNsCpTOSoTM0GWiiz3azHv1w/8Y8Qwe09KahSxgAdhvwwv6xuUqSE+uNeJ2wM4u4UEvChVNeQqNnNxkAnj0H6KnEFb9IZ/VqORUEqIyuWFqUdOjB7JYQvTKIs4AeVO4wFZqbtSNW21PTGPIS5yyJNDVMBXvV5JhE+WEKlSXaUUVWQk81vfFpkFURSsVfI+LnJk80vVxluKl6qkq9gBPic2z/y9WufPrCtX7a6wMgzsZl1Yc6IApVqvnRbAT/Jc5FQzbLeSvYPrAAADO5+ZkOd3k7RVyfEGkWGfjAjvDsBKw8cizXxcERZNAcrpqkM6gijp5kVeEUSAddy1Ijl8OCfrOxMzTA9ABxPig+ndQDFjem0AZLV4xXBaoJyuKooCimwFzi8eQbBmcgHezbaulzUqSfY9iYVnjblt2MP68zRQ5PDvrM59eP440HEYDmMiR/L1V0RyeAm7aBiQvKZgiPg9yOhls226mLYEakfIa4mcaguDQbYiHy2asJdFdA8vljAQWmjuK+uKnWVipSKSw0Q2T0sFmKmcG2ALLK2pHqjKMQyMcK+X2oZ40Gy94dG5HaB6dRV/XzXJmcrf4JUxX9iLdKHyvcopzPtjGPCwBUWYpXez42i8CTjf73o8OSNKEQFP2fcjyJlRozA8ahoOlKOUfvC5Ybm6Jq6b95eLZQCgO0qeHKIG7hk/1Tw5mWIOeXYhiDOBwi/qg2nzi8CbrsVYT5GkWllCnmYnkNkdR5rbCIDu7mqccYkcV8pKzQygTyofIiMjtcPZzYQbj/n7Vc+k4GYywCqEBvPjr6ZoBqmsGenw6gZk0Xxvgl4ZYdbmfySzt+bfOh6HXTyjvnIOn6er5rYHYfASclGwRI67hDzBxofE2amli5loB5V+IJ80vTYACnq4atVQSC5XOaVZ52SFXaRCrKydN2BTBAHGgXTq1jXLPxQfZW6Ve98yauP65PCRDiXWMC8e6AQ6DsfhRS5y+ioiXlnW9yWkG5CC5mcV8ctIGRGrK3O6mLYuom0hr/Wdqbby5xfYYaUItUaJ+SDzRXVqGoaiR5n7GjGFYZp10HVXV/GHKrIFVpB5qbiXhVcEHrDxuJDCMDtnKlo74gGzPYek8b5B1cUHF2VFybxYHxUyX89rXi8WCruqRnJ4VDFoGFbzyKAlcsYnfofGG8nJleCRy6k3VVZqR3IMwxrRErH18vExj6Ys5RztOEarVHWnq3oizJNDIodw4y63dStpTTeg6eYJXTIMRNiJlx9cVrqKiZw2FmJPa3kAOlTdgK4bKKlFqz3/nN4TAAC6ICDrHu2QHcQGjwfPt7NVrEvk9O15HpqtC+zIBPXS4dGCkM+8yPFp1xORrtJ0zSyJBjDYwBX9gZBhYsBvlD+rkE9CgHkSqna7rkKKpW6iLEKo64bV8Xhue8jqeJwTyukqLtx5q4Nk1vT/xARz204mSAbzwygpQ8gxkWPofku82pFEyQr/Z1jlU5alTEMC69zK0iSqUI727A+6MoQk75IbO2TM7cPMwNmIJoQcXkKeF7l/pheIzsYClY1E8O2uiOQolkdJh8xKpSN+GUmEEGXKIlVIWeXj9khOdZFjnlcMwzXGg6HpmiXouzQdqRgTOdHZAMwhnSJKNfvkFJgICnCRw4admt4WFQfQusdEGcIIW4wV1RjiQVPkCLw5Yo2Ox1w8Kuy9f1PnGwEA2zwy5kvbqnpylDwXObbvmmxe9LPj7IRtj94M1+PJyRYB2dZYUy7UFjlqHml27o7YUrGyYR4Hgpi3riuqblQdsMopR3JYuopHcihdRbixVwkBsBmPzYNIKaqIsblVcV2HwLwHXHFbU3095kUgHpkJANBhAHxIp6YjmTXFjGAY6OhYCB9T6am0TaQYBozsAL7Y240LYhIGJKmijHz3wMuO34cnYJJ5UdUtr1FlJKf16aqh1A6rumFQO/CxAo2AiwG/bbJ30CvDp5nHzUiuvohTmoX8ra7DRdWq/jAjOWysgyhWiBxrAjkTVG2sdX8nm4G1r5RBNmszQGv+qpEcAAgzgZRhERweueDN5AL+clpGGWfZrp1MYrv1WcZsU8ZrEWIRrozWuJM79+RkWUonHpkFiCIOC5nfbcM7jKJWckRpFZYyCeoG/H7TJxX2yUgYYURYJCeVH7bSVe11ihz3z5yRwgh0Q4dgGAipIvLxwwEA0egcCOzcEpaGa1ZX5a2Sa/N8FgyUIwuCmD/wPjnZIQyzZn6GFkY84IUkCpAM3jeo+veUizKeblvQthAdUhCGICDs21E1jaOw1GnIdunkXq3xjhmxR2+yRa1qZ2s7iVwJqlwW2FlJqylyjFw5ahoJVoocCHkEvZI1P3G0qHjKFcnpYpEce1uTyQyJnBbi9i+4PTlKUUOENQWLGwLADlJ+Uef4WSTHE+61wtOCbIbQi5qOFBMjYd2A6Isgxs4hyZTNm5FPIm2UMChL0ABs8Hoqysh3jWx0/D6crTHks4nYK3D25rbiteHXJnR+1YCtKeKQPjHpMjdZJgZ8bIwCAAS9EmQWrk+4ul/XglflRAOsxw5b1fs9IrqjPkvkqIKAEksBpKzopPmZJFg4P8a8QZ0sQjKk55FmVV4+HQAkZKpEcgAgwgRSmnkc+LTxkGjeLkse+K0us/tf4cZN0gED8EnVx0zY4d2YG9GEkGOmqwyk2Jm4jUVvZ7YdjoCuwxB1iN4h5wWRvcc+XbCim7IkouSJIspFTrbfka6K2WbgudmXGb3KZx8Tru2ajo3GHAT85kVO9gbN7tgAovK+iou0WTWlo8AupDxNJQdi8LH9DIqZAxY5WnYfEuxcCS1kXYxlmAIwXSPylitq8KGIJH/vo7NwZHQeAED176t6flHYcc8rqoByB+fxihy3D2eslNWIUoQil7dJygaSuerHopLttwR82F9OP4tM5GhCnnXKHntIJ38feFVr1C9b2YSB1ORPWZHIaSEZZmSLuCI5fMWQKagIsuGc7SgLG35R5/BIDsLdiLEoB68QKKo6UmnWnh8ABAFRZjZL2kVKdh/6pfJzbPF4KtJVuzPOyM3wBBiPuQD0ygYuXP55fOKhT0BifS3GakfeDPqHX7d+HhEMlCaB0MmxtJkPdpEjQ2R9beqdRJ5iabgoy+Hzbse87whsQzW5TybjMiUmWWopzlI7nfHDAAD7BCCTNI+voMGna9eI5LASdj4IM8tW4iG53CU5yK6L423AZof7h2K29200rJETDWynUFR1BKEgzdIt8bjZJVrsPNwyH4u+fsfFJMtSdKbIKX+HQ34vAjpbmWcHHMbj6Bgl5JxqkZx9rKKoS9OwXp+HgLf8fsXZZxmWRpBX3SXkBiCWn4+LHMh+09MCICCmDriEPJneZc2tinhjEJmokkVWfapXFwK5koY4Mlaqqy08C0d1HwcA6PcVUMpXpp/4cR+wNcwMMaN+Vh3fBd/dG2esMvKkUkJaLkfLSoKAhFI9XZtm1gTZAPy2lhCGZor5ksGjttyrVfuYdntyBEEo98pJT/6UFYmcFpJ2R3KYeOERHqWgwSeZF6S47cC0N1oDyp4chLrQpvNeDzaRw8QMP3lHWRlvyt75MzOAfrl8strsrRQ5u1hzK5mdg0YOwP+wv/DKqnAwi0QhgYJWwB7VLGediEjOvmQ5pWcIAkbq7EHTTPLMeO5DWQyHvBKgmqJgpDS2V0DTNaSZzSASNqtmeLfjeNADrywi4PFBYsdCjoscVwp2hAmuuM8UBF3MtzEoS8gwgRhgEadqnhwACHv4CtyMTvIVsl3k8HSBcgA9a7joj9uaH44Gn/2Tgw5Nb0yYvqDqaJfNY0gwDMRYeg/th1nmY9HX7zB5ZllkzmOIDpET9svwa+Z7m84NWn1YRktXGYYxZrpqMMf9OBrWG4cg5C0/Z5xFNHxyAnlXJEfVDUhiznptfhYhhCBYnpaAlDHF0AEwnDYj1AFNRHuwfIx4WKPETI3O2EpRQ5uQwggztLcF2rGw93gAwAafB3O1XRXpGIUd30HR/l1jHbjHMScOKPfJ4bbHsSI5Q0oGGXbK9jBhqBaqd6pPs3N9BII1OBcANC5yYD53uVJ1lFJ0lycHsJeRUySHsFHhyeHpqqIKXTeQLaqQ2bC7NluvEr4K41giJ9yNOIvkyB7zZFJUdSTZSSnKjJp8WnDSHtrPDqBfsokcjwdQBoEiC7kaBnYzz8mRkrlSGR5niWQj4Okqf6BsuNuU/ReAiSkhH3CVYw8Ob6yxZevIMRHgQ/liHfTJ0DVzJZuswyibsZkmY8zrxU2o8SALUwdk+FikQGE+GfcqL8m6rMbZBa2D/T8iikiMbAMA+JkYqzXFmM/a4W3psyqbYm2bLxViAl6pMxVXjQRroRCzfddGI8wMz+Y+NabCqqBqlg8vZgiQ2AXXXmFlipzyijnHvoceXbKGJQJwlE2ncsOW8Xg0kZMtag4vTbVSZp6u6tI0bDd6ELQ9ZxtLIfqkFPJqpScnIDBRYBgQWAk5AARZzyOfmLG8hvvLSJYP5yw3PQQAWTa9W9kaTSNzRRVRMYEir2b1xXFUu2mq3ujxYIG4veIco7AKxKBNGHOvVnacUV0euTmk3Tz+xiojT+ZNMezXdcwpmftllKr7JPmIlrArSqmq5n4X4IzAvjy0Fm//w9vxwJYHKh+rSlfzchk5RXIIGxWeHPa/YQBKSYNSVK20U9zWirsyksMO3FA32pjI8bChnkVNL7fnZz4DfhJ3TCLPDqJftqWrvF4YQLlXjjKM3ezL/4bYoQCA4QaWztYL9214fGWBtSv3GgTP8IRUV/W7WrcPJra0fB/cFHRTDPiEsq8k5JVQ0sxjaKSOFSaP8gV0HR4WsUiydEc8YJ4YYwEPPEzk5Iopx/wwvspLsC6r8aCZ8mrztUGCGfXakTUFop9dGN1N7kqajr3JnNX1mBt8Fbb/IW+5f09Q5NGg/RfeyTz3D4XH2NLEG2i3VtDZYoNETklHmKWo4zafBzoOx+FWr5w+Z7qKCUxJlx2RnKi/XDadyI9YgqZ9lD45g2l3yqR2uqpT1bDH6HCkq2LMdCtLmSqdmQ0ERPO8FNQNQC4fn9zT4pOyBx7JYZ+jR/VaghwAvKwbdLZajzCY6aqwbH6f/YaAoCeIOZE5CAoSCqKILt+WigiHJXJsHi4+ZiQ7jjRmvqRZi94jekzxN5QpoqgV8czuZ5BXq4iHkhlp79J0dLLvkGBU90nyisuI6Fwg50tsyC7337Hv7dqhpzGYG8Rj2x+reKxyM0CbyJlCZeQkclqINcyQnZh8smibw1RCpqDBYI2p2uwTYys8OexjC3Ygxgx8AU8CAEtXsdVtlIX3o6zTaKpoa4rlSldlRIFVWJlfpNy+VzHE7j+242gAwPA4w7GNgK/2vR7nvCBP9KWJMR67olmDqYkpq7dTYJ+LX7J5VnwyCkzkJOs4+aZYyD+iGwATEwl3JMfvgcfy02Qczfy4AZYP54yzlJckSmhn1VJb2UDGgMiaCrrSVV/741osvvpxa0ZWmhl8+QqZpwUAICjwaFD1Rm/1kOTDOX2x0Tfk+GOW0T9TRwqwHgqqjgDrfcKjIgCAcDcOZyZR0TuIvany81nNAHWPY/q7WTZtPkaikLIalMcD5QhHuqA6jL7u9FTVdBXrs9SlmSInaEtXtXHhKSsVqR1NN+BnE8qDQDkvg7LI8Yi5A/bkDLM0uqAFELdFcgJs7ERWMKp2a1eKmjWcM848NqIgYiHrEi/7dldUcOaYPywol+0EITY2IWvUn8Lk77NXEjGv04xQDmUKuOvVu/CFf3wBv1r3K8f2um5A0k0PWbchoUNiE+CFEavXkp1UnouccsRJ0w0olshxdiofzJufcb9SmX5PVUtXWWXkJHIIG2lXusrubs/kVSgFFSWJr57LJa0Rt8jh6SpJRhtLRfkk82RfUHWk2Ik/ylao/CSetJ+YXekqANjikS2Rs7tvjfnchoBDWMnoRMyv4hdSn2R+CbtV83c5umZC0lX7mKCYyYYKDtbZaK+Z5FlXap9YTruEvBJyqimUR2qsZO2kWeomasC6GPFGgDEmcmIBD2TWKj9XUqxVrlc2O3jDMJDgzfUis63H7mRN9LZ5zMcJsP10R3Ke32oKWaXIjMdstclFDp9ZBQAhVk6uHIDYSLC/tX/XRsUfQ4iJHC40DpSipsPDfXg2zxEEAd1t8xHRdEAwsDOz1brLSpXpXkfqKOKXYVhmc/bagh7IkuhIedujOW5RY5+nxNnHjvGA6kUOfsdzxlmzR13KVe2T4xW5yHFeakIswiCJijNd9fxtwCP/M64RMiMl8/0ztDDiwfJFPeA304sZUQSqREbyJc1679tskZkj280Gqll/oiKSk2XRxaDdH8bFFOrfZ56q6gh70REy93koW8T6ofUAzPl4dtIFFT4WdeqSfOjmE+A9qapm8gxbjEVsYiyTV1Fik+r5gNfyaAfz+OpPOX2ZhmFUjHUA7JEcSlcdnJTywJanKm4up6vKB4tVYVVQkS1qKPDhnLYGTu4Scp8tAhNnQkYSzRB2UdWtCeRRlvLinZFTdm9GZp8VyYmwSM8Wm/l49/BrAIBZcgjtrAx4WACMRgyaGQfW8FLBDJl/OJ2BbBiQ/H1IaS3u26OVMMAu4os8pnAcrLMHTTPh+fWApyxygl4ZGc0UOTlBQEEbfcXF01VRW8rEXl0FmCJHYiJHKWUrGgFqhTSS3N8Qn2c9TicT2Vs9MttPVo1iiwTlihr2JM0TZqFkvg7elp4P4gzZIi4hJu4PRGwk2YUvZvPajIo/hjC7IGcaZMIvlDRIbG5VmyfquE9oPxwLSubFsD+3zbpd4d9j3eeI5ET8HsuHxcum29kF1COJln/HflHk5eM9bGXuTl8B5XSVrrKxDHaRw6IYmlSo2ifHK5qRj4DrUhPk7QDEfDldpWvAo/8LPHszMPBqxX7Ugs+tUtWIw5MTZinTrChCq2JQV4q2914qf3eOnPEfAIBdPh1K0hlBVpjICdn9YazHkiIYo3YOtsNNxp1hHzrC7L3PFLA9ZRqJt6W2ObZPKiXILOrU44mgiwlzQ85UTdvz+Wphm98slS+hwI4PPvfNXEAbSGnm93+omIRqm3OYK2lW5M/hyYlQJOfgJTcC/Ogo4DfnAEnnRdhtPDZ/Lnc9VgoqsnxulW2WTs10FYA2duI3RFufHGZE5Z1ro6zrbNJ+ocsOWCXkp8w4BQAzHzORsyu5DQAw29+J9jbTk1MQBeRaXEbOL4RFJAAACwslvDlnnmiM4IstbUalDG+2Sn2PiswDAAwWWl9x5qYAlgbylNM5IZ+ElNYOiZ10E2OUWqdcZnUAVg+OsvHYA4m1hc+pSsUE8nRqh1XKG2PpKgDoCpgiIsUihyEmELM2D8e2obJYSRfZhVrQAU1F1jC/E3wwJ1BOFyg1Gr2NiWEgyUqLrYqmsfBFrEhO5gBK1+0UVB0Ga/AWt6WoAQAdh2MBMx8n1PIKmxuxdd2HoCtdVdJMATmk5wBBRbstslHNfMxFzZG9psAazBYdF2rDMDDIJnwXWGTQnq6Ks8+2IFU2htMMAx4mcoKCc6HGPS2iWCinz5I78bBPxC9iURiuSk87uuEUUzyNXtDiDk9Oeyhu/Zx1z+mDKawFmc0Ms6VCj+wxK6xe9Xog7nOKLYWdQwM2f1iInV81QUC+zkaRg/ZITtjLbitgR5qdf9O7UNLKn9OIUgRY64wuXzu6WIO/kpyvGsnhI1occ+hyJRR083eFZQ6jfhkQc8jzifAAhmwFKjyKI4mCQ9yS8fhgJtAG9BwNGDrw79847spUcanb01WZgoo0O47abLN0apaQA4izUKkqlqurUuyLyKfPxljfE7s3I5sdsC7Yi2cuBsDKyJnxeHfOPCnMCs9GINgJPzsRDbV4flWGpTTSgrni6tU0vCtjfoE90Zdaaj7mjQADBnBIZA4AYGgCzNhuCnzKs82sHvTKSCNsebYSY3iH0jyHbwvblyM5ZZEjsOodRc1VtERIsD44Yd2ARypfXDtcIoJ7GOx9crYNlt/HkTyb9i2KQDFjDeLkaQEACMq8Adt+jlgoZpEUeWpt7OGcAABRQphPnG6gyNHY7KQ2d9qs4zAcwUROXthlRTwy7Puta34EPa50VbEDAV3HiKAiMOu3iAfLPphqvXJ4umphr3mRL6q6Y5ZespBEiUXU0moXBMFsDsnhEWdF1Kp6cmTWiT3oMsBa6R6byNH2vY7/r7MdN7XHsXVgbdX3a3NiM95y91vw1ce/ikQ+AYxswwiLSiildrTZRF3cH4CHv2fVRE5Js4ZzttmihIfHD4dkmKJcGVnt+BuFp05tC4pAyFZ1l6tv0WOlq0I+dIbM79xwbtiKTGqGhp22DvWJXAklVpTSE+pGF0sHK1L1aem8MjFiE2+pXAmKTeQYuo6o3wPR6yymGLC1GrHPprOXovP5VSNKqaonaDJBIqcZnPgZ8/9//xqwDRCsFskpdz0uIZ1PsHku5eGcQGUJudc2NDPOTjIF0fzSlDQdKZ17KcwvXyxslgTzZm8A0M8qEiJyCMd0HgPAjOTwFdRuluee1b4AgiCgjfW1GEnWXmE1g2xBBYQSMoL5mnoPX4J3KDn4dAOibxBrbB2Im80+1gG6R/CgK2qKnMEGtvjfXwrMcxOyjTsI+SToEBFl3qFEevRJ5JZZ3WZedntyon4Zgm7+nNPy1sWQzxIrD+d0er06o865UHwqsr1PzhabyBnKmif9jCAChZS16gzaRAAflqns7/ufHbC65MbrjeQACAl8UTI8xpb1UVDLKeq4LUUNAOg4HEcUWS8VXx+GFW7EZr4ZIWQ1vgPMiG9Oi+Om/n3wGYAceQ1bpZ+hyAzc1UQOv9jObgtY6Sx7yoqnqmKahgG9C0GP5LjY8fcuKxlVPTmiJXKcvYjKIqdkGY/39L1ozTbbmdhU9f16du+zSJfSeHzn4/jQX9+Ptb85yxrpkNU6rGMVMFMxQXbKS1cRpUpRszyQdoHplbyYoZsiem/mNeffsIVi0FYOL/qiVoRPqTPSzavYOm2RnJGS8zu6NVn2YSWUInKyua9d4VnoYIvgtKRXjeSk2fciYvOxpfIlKGxSvS4IyOcTiPhlSB6XyMnstv1N5cIcYL2z2HVo3ySvsCKR0wyOeh8Q7ADSe4CNj1o3W6rYdsDYux4XcmZ6y6/rCERnWtvYDzCfLDpOMm3sJJMTVAC6GclhHoYoy0lHo+ZKNSkC0EpAMYs+ZursCXVjXmweBAhISpLZ1Ti1F7uZ92RWtymA2tkKdtj2BWgF2YIGQTaN1H5dR+yo9yPYeSTeppirmsd2PjranzeUftYIsFsOojNmdqYdFCZ+FZNjn1XYlu7gKYV6J5GnWA4/avMaJKqUkPOhh4pWrGiJkGCzqeKi84TYZRPsABBlK1+7J2erTeQMpljFoShCS+2xLnwh24qZl5Pv94iF7CCSrAzdPnV9LMJ84vQBNCG0U1R15CTzGGpzR5Q6bA0BPSlsGTIvoDnWiFAQnaXvYTak8035Aq4aFmHoHgxoa3DJk5egqBWrp6syZW9IJ/NZ2M3H9m7He4xOBLyuix0T+xlJQK7k/Cx0wyZybOXjQNnTootFK5KzlZluAWBXuvp5Zpdtfl5ffhCfiQrYwQzt5tyqssiJ+mX4WcuDTK5SlOaKGvISqwYMOH1Zcz1munVP0Sk8FBbVCtqPGUlGiEeM6hU52XK6ivumdNn5t1tTZZEznCkgxbodd8fno6vNLAZRJAGD2cpBwWmdX2vK54RUTkVOL3+/s8o+RAMeRLzO97rP1vuLR8rdxS+CIKCL+3JI5ByEyD7guPPMn18wSwENw6goIQdsfoa8Ci1vfqFiOgCxvBr2yZKVovJ7nKvkWMw8yRgCAClnihx20YsyARRloc2cKKKUHXCUj/eEZiAgBzCLeYC2eD0wtjyFXcwkOpu15W8TWXjSPcm8yWQKKkSPKXJ6VQ1C2zzguI/jPVlT5DzT91hFjr5Z8EaAXb42dHYsAAAoggClQT1T9pcc+xZHgu3WbTx/HlXN//e6RnS4SbFKowgTD4ZhWOmqtlA5XcXnV+W0QrkTKjuGE+wEHxPLFR0A0BlyXkDawizFYfPk2EVOocgGdIoCFNvQ2FDA/vrMi4xi7J/IUdN7y6MUbKvdsQixdF7G3o7hAFBLRaSt2UlznXcG2hCOz8NM1vjtpQEzqsCN2KLoFGcRv4yEYUYY3pnei/m73wZZ8GLFrhW49MlLEfVXGo/tIodX+dgjOfZux3uMDqtVACcanW0N6SypzlSNphsQWIQ5KDmPiSDziuiiaqXhttjMtrtd/ag4u9hxfHFGxZJMFqogQGVrPkMNOaqrwn4ZPmaUr2YUz5U0KExgtruieYewRcyw4GwZkWPnmqDP6Z/izQ2zVcRUNfj73hHywe+REPHJVtqIv5/2SM6AkkCRHSdd7QsQjs+Hn0WPBqoIwjQvEbdFqJK5EgzICPKoU24QEb+MsNe5ABpgfkygeo8cjruMPFVM4Zv//CYe2fZIPW9ByyCR0yx4ymrTP4CR7ciVNGuic9jhyWGjHQoqtBIbx2BUfiw81Gz34wCAJzKjPKRTUqAUFeRZpIeLm4g/Zn1xksmd5twqS+SYK5bDmJjZ7PEgufkfyLLV80yW6mpnK6+hXOuNxzLrkdOjaUB8LnDsh7E4V0BY15Eo7sOagTUt2ZcBduLtDvYiGDsEAfa+D01kQ0BdQ45/3qFO62aPJMIri2grmSf97WNMkOfVOFFv2RTMS3vtkRyd9WHJ6aWyaOeRHHaCb7NFgwCgM9Dp+L0twtJVRdUyudpFjqGZF8SCKCLJ0qOyYcBr8wsFrS6z+9dGIJUqvx9R7zgiOdwLVKxcPe8P3sJIeXZS7JDKDWafjAWsKeAGNhZDYaXKguTs7xPxe/CqMRdrhSPhNQr4S+lO/D//6fBJPjy16ykUPaafzllCboqQzrAXnbzKxx7JYV6WLt4I0LXIkoMd1uRzWXP6XjTdgMFFjr08HuV0jyqUj7MthbKw2a1Wbw2wm0VTF2aG8UOhB986/mJ4RA90NQxDCzkiORG/B14mctJVCgQKxZI1GNXty1o404xgD8tF5GwRR8U1UZ0TtsaM1OfJsRuP+f+i1xSUxxbM+7bZRE4/W1zGNA3+2FwIgTg6WSPYTLry/JPh1VO2ykHe78aa+5YbdnhyFrDUqL2re7nbsTOSA5QrrPYx8/FtL9+GB7c8iJ+++NN63oKWQSKnWXQcBsx/GwAD+PevrdC+KMBxorAbjzWVV7hUztLh29krqwAAoW7ENbayk7LI5UxVLhgGwizlJQoiIsxTk0rvNiM5LI/dw1z6h8bNCqrNXg9273oaANAleOFnVSzt7ELQ6vlV2YKKqMd8Tb06gFAnEOnFRv/JWMyqrF4efLkl+zLAqkx6onMA2YtOFkDaN4GjHfRCGlnmy2iPdjvuC3kleIpxAMC27BieHG5WZydvnqryyqJlNI36yyJHMUqVxmPm64nbzI5ApcjpCrKKQMNcTSeVkjWCYH5nCNDLq/6BFB/qCUealpeTK+NowGYnyU7kEUGCLFauUmvB0yyZBvXJ8Zb6rXRc3Bapsph9snXx2ZHZDN3QrfSkzHrUcMI+GTpEnG98C0943waPoOET62/D6RIbLArzOOUXO3vX3c6IrZTZHslhoqJDN7APcUeFDQBAlBBn5xYZzgWQKXKYh8UlfINMTJdEDbphAKUcttiicruhAqozSmcYhpWumh2eBeHTD+Ajx56P373rfihbvgrA2Q8o7JMh67xFhzMio+sGPKUUEkxgtruiaEfOOdHcD4+EbTvL0URugg+6PquQwCNGCdTDkC2CBgAdYR+8XlMknq6YC46tya3WIiCTNQVPl6YBoS5AENDGG3PmXEUFmoo0O0YiNp8XF7cBoxx1ivhlFD2moDwxb+7TgK0tBo/WVovkzIiZwnX7kIKh3BDuee0eAMDO9E7LBzYZIJHTBB7e+jBW7FoB7cRPmze8+Buk2YHrdqlb6apCCaqRAFAZ7gfKZeT2HjnmA5RHO4hSFkWFnbwNA6ItDx5jX8JkZi+L5JjPy0XOYTEzkrPF48EutuqZZTuJtrPwbKvnV2UKKoIe8+TZK4esRnUvtr8H85lfYesokZSCqjk6vO43hoEBVqra3Wa+V53MhDpoC+82lFyiog2Bm1R6EDp7T9ojzuqcoFeGUjDD8NsLo4fRU+wCE2UjHeyVVfx4jQU90JgAUQytLHK48ZiJwJgrlB+QAwjbSog7glGr+W22oGErKx/vifpwWFcYgGidiPuzpsANuZvJsXLyLMZIVWol4PHvAdtXOm5OMv9Qte/aaIS5F6i0n1VdLjyauUKXDFjjLBzMPtEqI+/LbUVezVtl+rLX+XnzC9FQQcQ3jf/GtaWPAACO32t6XYZUM93F01XcMOqVRUR8MrrCvCmdzXjM0kNxIQQdoqN8nBNnn42MIUf5uWYY0EVejeQSOX4ucgxTDA1usvooAcBujwwj6bx4jxRGkDNKEAwDMw87EwiZr1824jC0iDnWwmbEjtjGXCQLzshbQdURF5JWW4O4K6U6l33HhyUJ27ealV4lNY8ie+/5sFbr9bDqsWwdaUxdNyxRz0VOe9ADgUVU3pLLQTAMpEsZDLHocT5vvhedugiw1h9tbFZdvujy2xVSZmUigLBd5LDvKx+Qq+QT8HuAjGx+RsezVHS/7TVUm1vFOXa2+Rmu3jGCO9bdgRyLBuuGbvX7mQyQyGkwqq7iuuevw9LHluJdr/4cP++agb7cIMSNZp7SHfZzeHJgCohqs3SsSI4rXYVgJ+IsXByShlAsmIIgagiOzWICn0S+zxQ5PJLDvgT2dNVudrKZFSr3OmljYc8RdT/7kuwn2YIGibW977WVEO/sehu6S+Z7sW3gpep/my/hzqu/hNt+fEUDdmQfBtiqr7vD7IjaySqRBmuYJA+Uy+45E//5pzMxsrd6OS0AJNLlVVdH0Jl2CfkkDBTNz3UQqmMIp5s0i4hEeE+lnHOkA2BeRFWdjWQwNGQKziF/CXZsVJRCo9z1GABivqhV+qwUVWwdNPdrfmcIM+Om6AiwVWo/W1VWiBx7y/7RePlPwIprgb9+2XGz5R9yXXzHIswimpkGVdXJurkfcTgLCix6jsFhLFuSUncgwyISomFA9rpSJrZZeAOZIn6mnY3B996O41T+Xq4DUK7G4b6QrrAPgiDYIjmV6aoQS41VRHIAxFnk2SulUNTKolPTDKiieVwFXOe0EGtQWmAiZ6h/LVKSBP5xZkQRqaHXHX/DozjdmgZv5xHW7eV+Ts4IuN8jQTTM21KuY18pqohK3AMDxLyu1J83ghgzLW/vfxEAkFPK6bSgKzrJDemKS0xVI5UvWSk6bjoOhxToogrRMHBYsYRZrLP7NraAKqmmGO4QyqK8TTSP3YLq9C+VcsNWdDAaLH8XrUgOb4NQTGKw0AdDMGfWHdpxHABgQM9bYrXaBHLOyfPMz/CVvl24m0VxQiyduyU58TP9OCRyGkxezeNd89+FmC+GPqUfPwt7sGTOTFyz8QYIUtZRPg7Y0lUFFapgXiTcXzigticHkow2tkoOyiMolsyLQgzO5+HDOpO5IafxmEVy5jOj3ZAsYZ3X/OLNYiksoGzMa/X8qkxBhcbCqb3hcsVZKBjEvoJ5Ad+WrS4ytry6GmcKf8Z/ZX6OdN+B9ffRR7ZjHxOG3Ww/OtiJe1AZqPl3+0tBGcTfBQV9soTH1vxfze1GMuZz+3UDXtn5mQe9MrZq89DJ+lhsT1R/DwzDQIr5PKJM9Lq7HQOmINcMU+TkDL3Sk8Ob6wWdaTOgnLLyQYBH8lhN7DIFFVv3mZGc+Z1hKwTuZ2kGvqoMulJKQavLrDDqCICRjQ/jyz1duLfYDwyXT7xJllZwp9bGwppT1KDvgQjzAlUtRQ0AkL1oCx0B2TCgCgVsGjRbJgQNA56g8zwR8EhWJIO/JaFjz8ERJ3weAV2HYhQg+gZsIqfsxzH/59VVtkgOi+r6JXORU1XksJS2LKUdXY81w4DKezi5KtiCLBKXF8201tb+NQCAmaIPnawFwa596x1/Y6WqSirQcbh1+0iWGeSDlRdiCWwOmmtxphQ1BNlg1Bhs099t9LC/3ZcyvzcKa5InGwY8PqdoC1qG9LFFDn/fo34ZXnY+l33mvsxQNXi8YcxjZnNeYVXUzefulMvP2+aJAwAKcEaPMrbiEHtnZv65l+e+pbCLpYNnqSp8vW8BAORgIM2ispbxOFAZyZndFkB3xAcx/iQKWh7HhubgjIR5/dkyCQYXc0jkNJiwN4yvn/x1PPahx3DNW6/ByR1vgC4IeFrKob37XofpGHCWkBcFPpyzciVcM12F8iRgv5xAsWSelKKu5lsxtpJO5keQy/Qh6YrkhDwhzGDPuzJgPt7szkXW37czY95QC+dXGYaBbFGFIpsn3d74fOu+iN+DRMHMow9pOaSrnFx2bn0WH57Vi0/M6sbQ07cc0L6MDL0GVRAgGEAnC1V3+cyVzFCdFRXjYfO2J6Cxlf2K/hdqbpfMms8dqJK1Cfkk7DY6MZeJnG19qys3ApBTc1aFSpQJuESOC5bycSQIAiSZNxMzKjoeJ1hbgrit2zGns92sRuMlrfxvlKJm9cg5tDOEGTHzgulhnZX7NfM7ERKcxzPvmaMKAoq1Vs+6jt8PrMI/gwH8XzwGbCpPWE6wtKs7tTYWfP8zemOaUAqC+X2NSYGa2+gzTsShLGW1tt/8DIO6Aa8v6NjOPgsPMEVPwCvBc/wncEyBtwNYb13suC+kw/KF8M675raGYWCQiQNRMs8T7hJyAGhj5xZRyjgaAuq6gZLIBghXiBzz88sJgKrr2JIw/UKH+jsxSzIfb7dLlO9Oly/IdpFT7udUKRRlsBEhrtlV+ZKGgJQw91+ofE0AMNPLopqqmQ7iIidoGI5howAQYp9fto7moG4/DlAuHz+kVAKOeh/mM7M5r7AqsiqvLtvx2uE39y8vOJ8zzVKxAQMOv5llPLZSaxnsZFHwWSUdqfaTEWX+zn72GNWGc3IEQcCxh4jwtD0LAPjytpdxaNF8bRTJOQjwST6859D34Pb33o2bYZ70jeg6BHzOk6PVSE0pWU3B2sOVK+FoLeMxyi3JZTGDosaGc9oqUQAgyrwEqWISA1lT6QdErzW3CgAOZb4cXlo7i6WwAKCd9ToZEVs3v0opajBQQF4yl6W97Qut+6IBGTu0Q6woxbYqvpitQy8gJ4oYkGU8s/M+QN3/1ffAsNmcrF30wMNOEp0sYjFYakw5sZ0Nu5+1fn7WyKJQpWMrAKRYh1W/UZnqCHplGBAxg53ot9dI66VYVEM2DARYd2x3t2OOl41kyAlA2t5Dwzacs431TrHDIzkRlu7hEYFsQbUqq+bZRI6oMZHDGx1KzgtY0OaJyNboTaLvfRF/9Znvy26PjOSm5dZ9SXYxirm8FWPBy9jHM3F6VFgn75hcO6IkzfkPq8JqDTPZ+3VULJgAZ6NRngpBx2E4zmdGYmaHXkRScaar3JEcfnu2lEWOlavrslmpGaoWyWGfqSHnHCJHMwwUWVfpoKtMn/c80gUBqpbFFua9OjR6CGaxxdZuV9uDXUMbzNegC0CkXPJd7udUeSH2SOZ5L6M7jbBKUYOH9d9qq+HLms/ac2SEBAqqhhxbzASrfNdCbG6cUodXy11ZBQAFw3z9c1QVOOaDViRnW3IbNN1A3up2XH7dXew9UETneY03Poy4Lu+8MzKf+6aUMtZ7Gil5MSj1mBWsAAYSpriq1QyQUwz/A4Ko4g15FadmUjiMifEtw69V3X4iIJHTAt562jLML5ZQEg3IwsOO+/jBM5QtWE3BOm2NADk101Uo9/kQ5CyKGmvqJjtXebw0OFnKWN2Oe/3tDh/AYcxrwpltmyTdxuZXlQQBGabym03W1iMnrOsId5Tz8FG/BxuN2ZjHTv7bqqwcBgrbrJ/vCUnQ19233/syYDUCLId/OyPm5zS4v6MFRuF120kiJ4p4fu2dVbfL5hIAAF+VtgP8gtQhmReNbTVCyCkmeqO6DiEQB1C+cLSFXD4HLxc5AooF88Qb9skw8ikkmQ8gNorI4ebaEIsIZAuaNdLB9OSYK2KjZB7vfSxy6e6zIss+a9RILZHz73X3YJenfOF7bc8qs2JHK1lzq+LBnqp/W4sw86ZlUP8wxtHQ2cy50Xr1RA5fbFVYrR0xL0o+HQj5Ki889hV3u+2zO/7QJQCAXGAA6UIJum7Y0lU+9r+5fTpvzqHijQDDuo6kaF5Qq6Wr2lh0SxMLjq7Hqm6gwKuRXCXXfpunxSgNY4tqnrcO7Twas1g0cXfe2aV4F4tqzA50OiIp1fxjHJn1EsrCGYHOlTRIsikw21znSs6CbvN8mPIUsG1QgVJgqdMql02eFsrW4dXi6cCOUHkhquTNqNWMkgDMPx3z2du4dWSjOY6BdTueGS+fk2fEzZYDaUlFyeaFSrNWAxFXl2kelQmxcv5sScEuFh3zlKJIqF50s/PIAKsYHa2EfEAZwCsp02v63yPDwIw34tCIuU/b0rug6RPfKBUgkdMShPlvxdt1M5ozpD/qODnazYIZFrHoss2t4phVJ8C8jkqjJE9v6VIeRYNNIHcZ/WLsJJNSc+hnkYeegDNidGis7MGRIVh+HQAIBDsQYBeVkRblWzMFFTJrOd6rqmaPHEY04MEOoxtz2Ul160BlGfmAWC533+r14F+rb97vfeFVPt32cDErOx3cz4Z0o/G6woWH+Z6v2L686nZZZkT1GZUXH+57CUum6NiWqxENytiey2OKCR7JiblWx372+hVBhMZETsQvI5PeBZWPJKkyJmEGW4G2s0hIkDWV2zaURbaoQRSAue1B9ET9EATAYFUxg3yop1y52uaXJqVGuvCve/7p+H29pAG7ngOyg9ZIh5h7lEIViqqOz9/5Ai7/y0sIsu0NAVY1yf5iGAZKbG5VvIpZm+PvmIuugnlB5BdRryE6hnNy7Ctuu8h54wkXQjAM9HkEvEF6Gem8in0sYnOYsAfKLW9GZPXNkJmnZzhbtBoBdqoa+mCKu2rpKr7vRbnk9ORoutWoMhhwihzR47f6TAWKu7CFHb6HzjgZs9l5aFfJaRbezbxvsyPO82OtqCMA+PgwWDgvuLmiBkiVwzntzO05DgDQ7zGwqW+oLHKEagsKFjGqQ+RY4jJS/nwGC2bUSix1AJKM+dF5AIA9Sj/2ppJIs8HNM9rK5+jZXWbKLi0ZGM6Wj8V0v+nbso90KGm61Xwz4mVRJy2HXeycoBY7kc6X0MPSbn0sklM2Hjs/d8MwcN1z16BklHB8Po85uTA2n3E7Zs46BV7dQNFQscfWb2ciIZHTIoTOi+HTDWyTVbz04i+t23l4WYSKFO93Ep9X8fdLju7Boxefhq8tWVhxX4yFfktSEQWDNXVz5cB5WD6p5tDPVrE9rgZYh9nSU71yqMKM187CtMMNmF+1YXgDVuxaMeo22YKGuMf8ovRoBmBL40X8MjRIiKmm6Ns25JwWnM5ksMtjfqk9efMk/OviXqBv/3rq7OONAG0VZ528zFQwGtp12dB1bIB5sjyv62QAwIrcbhha5cpIYQZBr1F58eEVTILH9MNs17JVow88kmMPbydqrI6jAfOiURQFGHzSsV+2hnMGDMPqrWTnHXPfgS8f92V85fivAChHIV7ZbV445rQH4ZXNBoadYZ/VK4eXx4eqrLZD7His1oBNyfTjUcN8b07rPgkA8KrXa/pysgOWJy3muvhW41+b9uEfr/bj7ud2YsOgByLvXeK6CI+XgqqjKPHURaWPyUIQYBjzHTd5dAlhX6WwjVRLVwGIRGbgcOZ1OT70BJK5EgbTBQSQx9xX/x9O9yVw5br/Qwfrbj2YKWAfG+rapWnogylO3R2PASDOolt5UTXFA6Okq5avLFglLcib0gWUVzDATPPzOxdhFoso70YJYEKopJewl0V7Znc4z4GjeXJ8XpZedLUaUIoaNMn8jrXViKLNYSNt+mUZe3ast4mcyu9aiAklpQ6v1lDGGckxDAN7i6ZQTxVmQtV0dHQdjYimQ4eB5/v/DUMAJMNAe1vZi9TRcTgkw4AhCNiRYNF1XcPalLkInWvb1t4Aknc1z6p57GRNF9PFmUjlVfSwtiEDrK9WrY7Hv3/lDjy8/e+QDANfHingc8XL8Ow+D6SZb7Si65PFl0Mip0UMiIdhfsY8of7x3z+1Bnf6ZBEeSUBMKvc7icUrO58KgoCFvRF4pCqeHBbezUsacgJr6uYyVPI5Vim9YKUAeiLOtAKvsAKclVWcdvblHj7AkmnDMPCVx7+CpY8txcv7aouOTEFF2GN+eXulgCNEzY3YKLBUjCt/v2XjauxgpfCJvR+GYACrAn5sWPnj/dpn3giw25aKaWdmWlUQkGjguIv+vn8jKYqQDAPnve178BkGdksiNm96sGLbHBMaPlSe4PlKX5GOhmgYUARgsIq3J63wCp/yiSxZpbrK/L0sNvySeYIM+WQk2QDQat26AcAv+/GlN34JC5mviqfSXtljXjjmd5YjlDNifuia04gbqtJWga+os1VEzvI1tyInipirAR8/9vMAgPU+r9mBPLvPSq3VM9LhkVfKn+1tLwyV5xTlD8yLVShpyLEVent01qjbpnzHIGJLSci6VLVnTa1IDgAc1308AMAX3IJUOo3BdB7f9dyO33pTyIkiHvFJOC5gfo5DmSL2jZgXqU5DxC6FD2mtjJa0MeGflXTkbROpVZtXLVCl0aHVlE43U7PtEBHzxTCr51gAwB5Jgp4xv/992T7oAHy6js6uNzgeZzRPjt/Humu7DstcSYNaZTin43X52xFib3lf/xqry7V7ojoAhNhxlK1L5Dir2gaUAeShQTIM7CkcihGlBKFnkWU+fnHgeQBAh6ZBipajpFJkBtrZwmfPEBMU/evwFPtuvW3B2da2KVuRQJh1m96lKVBgQDAM9JfmI50voZsJ1oHcIAzDsDUDLL/ml7f8Hdes/hEA4JKkgp2H/QAbjdlYvX0EmHEcDuUip0Y1Z6shkdMiMnkVrw2/HwDwiFRC8rmfA+AVER7EmLs+rOnweKrniGsRZ2kTRTSgsGndUddJJcZMailBqOiRY23ji6GLDaqb3bag4nnaRXPlMZwdfdjjWPQr/djDVgqPbqs9YDNbUOFlIx16XRcj7lFK5c2Lw45iwhFNWbf1KeiCgKgGhHAoIul5AIDf7F0BjPfilE+hn4W7e2yrI0+ww2rEODj8etU/3R9e3/4UAGCeISEWmYmTJTMqt+K1P1Zsm9PMkLu3SgkyFxJ7xFmYNUqFVYpFqaK2HH7C6j3iPKHHA0ErkuETFfg9IjySiBF2TLRVuQBUg1+gdw6bkUe3yFH1oGv7SpHDJ4JnC5Wf5307zPTe2eHDsKjDrBLc7vEg0/8y0L/e6nRbrV2DHVXTsXx92YO2fEseYXacZQ+wdUBRSSDJoredsUofk52h2HFYYBuAKeqeinYUQG1PDgAcf9i7AQDr/SLkjQ/hrZlHcLz/WTweMt/rvChijtdsmrgvU8AgKy/u8oQsMbpoZuUIjHjU9IlkJQHZQnkfDS0BwBQmMiu9t8N7Ie2D+f4eKpufRW9kNiTDQFEUMLjPTL3w8vFZqgah03lucs9YsxNkF+2sIEC3dVDOFVXk+fR313BOjiAImAHWeiOzEQrrteOeqA7YRA7G9qFYnhzmhdrBfDEzVRWv6/PM+7sXWRGRDSNmdWW3pgFh2zlb9qGdPR330Gzf+BC2eT2QAZw66y3WpjySEwt4EGJG8Y26+d3r0TRs12YhlVPRzWYY9heTyBbLo4j4cZXY8jj+35OXQBWAMwo6PvWhv6D3uP8EADy/bRjoORqHMgvBZtbuYKIhkdMiMgUV6dwRmC3EURBFPPDCT8yOtjDVdUQyL+YRvUpDsDGIMQ+PIQADzLzs7lUSY6mppCRa3Y57Q7340+pduPmJTVYag4934AM77fCZRCOK0xAIXQOK9TcJtI9hWL714ZoGzmxRhSGbnpNel0GUr1j7iofCYxgoQMdem/jaMWJ+weYYYRzeHcHeIfME/1DQh8HVt9e9rwCA5C4MsOhXt739uyCgg32FBkcaF5p9nc3iOoKF2t/W+yYAwFPDlSeNHCux9gm+ivusXjRFAfOYCNq2t7IcPcUiIVGbubeWJycW8Frl6l4hWz751RjOWQt32sMpcgJQNafICVWZL2Xv92FnZ3onXiiNQDAM/NcRH0Sbv83yBL3m8wIv/aHuSM5z24YxopTQFvTgv944EyXICLLXn6lR8VYvWqrfEltto6WrAOQ7j8HhthlKou6pajwOjxbJ6TkBgBnRan/pp7jc+CVujznf16K0DgCL5HBDuhSDUtQQ8kqWN9BO1BYRHmGjPQBAV01hFDAASFWiLKwfzmbWIuLQkBmRlkUZvazP1+595v7sGjGjArNUFWh3RpmtdgeBSvERYe+rIQjI2gomciUNWR5FG+W9n8uOD0XbjSwbxOs2wQO2Dtx1pK2t6ir2+ewYNPsBzSmp2GDMMSM93UdhPquw2p03zy0dmmAOf7YR03l03Uwtrtj1JADgRP8Mqzs3YC8Fl62RKHw8yCxVRz/akC6U0MMsCwNa3oriyKIAv0eE/urfcPnfv4i9koi5uojvnHsfhJnH4fi5bRAFYNdIDn2KgPlMNG4d3jDme9EKSOS0CLOniICTZn0cAPAHvwjj/2/vvMPbqu7//7pX21ree8TO3glxNgmEBAh7lBTCCoEWaEOB0pbS0lI6GC0tXwo/WroYbdmbQlkZhASyyXKGM53heC9ZtjXv+f1xr2TZkh0nzWjS+3oenodIV/LR0dU5n/MZ78/njwGqkWPThKkcCRJID4fJlRd1ZTdrL3d189K4NJeyR5aj4SqnMY373tzEYx+XU1apbhRzB89lVPoozi06N+7vRPpXNcYsZAB8cA/8phjq+nZTlx1aHf3/Qx21bG3YmvA6rz9EwKRu4tndQmsmg4zNZGCXyKcwUmHV3NnQrjqgCYdZchmQ4UDxFdGPNIKSxCtb/t6rgFwcLQeiQoAZSV1Pfema8dDgORD3sqOlXOvGPFhLwJw+Sm0PskEK0qKVskfwCU2aX47XWYl4ctoCIYosqkt+X1O8x6lV04xxalUXQogec3LcNhNmLcxgMnREc0BatOTf5D4qCHffoGONnNxkKwGlu0JuvMfFrm2c7d3UbP9V9g8AJvn8ZA+5BIChqUMB2Go2E6jZ3KkIa4k3nmL5WAtVnTssizvOUb14Vm0fa+tI3Cm7r4Q8tTRq91XKYfR6UlPcOAKdCbKSYk5Yzt1buCrfkY9FOAhJEvuDB2gyKvzLoc7zlTnqqX+PoR4Qak6O5uEzkgzAiDx3l7YJEYz29Ki+iifGkJDCMUZOAqyaIXNACyuXpHZ6aPK0KsaDzer9XqmtEfkYIamrlzqaeJyguspld2OK9n/qHFu7P0SrNn3JMVWk3SnWDochUzNNPs2TkyDnLKLgfFgFbmI6kGuenH2aEGJG2EI7VvV5dz79UD+P0IQ63SL+IOPUHmvuqAYhWNqmeoXOivHiQGcFmstmirbUiKCKHkp4OkJkpavh5CZJoaGtM+dOAv625F6W2yxYkHj8whdwpmnaVxYjQ3PU39HafY30T1Vzqva0VR2TCsT/FN3IOUG0aqewM3MuwCab2WM2sW7jc9BUgdNqxGJQN5ok0YPyaW/Y00nuVq7nitGRAHBp1ntYkmjSFtZDDZaovPjKPeqCNrNoJi9e9CL9NF2cWFK1H3KX/lXhoCqdH/LB1vf6NNwt1WqM2aolFX7aQ+VQa0eQVq2vSnaC8JnLZmSfyKaf5h6tqO1sf1Atq2PsnzqC/pnqQu6W1HDha0Y/vorlfRorgL9xD83dGppGSNcSYuuPMIQXVIJ8UvEJbQkaPZYHmwEYnK2evHOzRjNAkVVRyQ3PdR2blkRuNcSHOCMhofZAmH6aB2pft9wl6JS8j1Tk+YIKgZD63XSXynfZTFi0Td4kt0c9B009NOfsCbu5ZyMn223DH+76Pt11VqBTZbY9JgFYEQrv7lVzly6z5IC2oA9NU42cbRYzLZr3REbC2ct4FUXw8RZ1Y5w9IptBWU7OHZYV7Wzt/Q+NnDZPTEVagnBOLJlOKx2+zs1YUSyJS8h7SDwGNfziNqjzsMFi4Q+uTBQJxmeP5xvjfwDAFrOB/nIFDV4/dVoemi+k/u5HF/QwRouT5Eg5f4z6eDis/gZtCXRlAKzdBB6Ls8ZG/z9PE9qs1PL/DmoVnfmWrgZOKKxEk2MT5eQ4raZoDpUnJrwY9Hk7m3MmqGaNUKh5NtrNHXi0asLuHdWhU/cnKEm9Nqf0hzp7vkVycvZruStOST2INHgDIEkUu7rmZrqlRCFb9XfjCdTjrSljnaajdtawuV2ui2jkuKymqEEWIduk/rvVF8SdOgiLti4faNH6IFpNhOu285xVfe/7J/2EwVrlWYTSItVIX1vRRFHuRGQh8IpgVIbgZKIbOSeISJ+fDIebC0suBuBFhw0W/hyn1YjRoCW10bPyaY8YTHEJn65upxOr0Yo1xqi2ILN5f2eSXMTI6Y1ULQTWGKvqWbURIifpimUJXtUVRShsaVU1Z27wqJ/504qPE1r8Tb4W/NrHysoYFve8y2oiiJE8rYfLXi1+39Hexj6T+n7jBpzFAM3FXtc4ihzJTJPBwLKyfx52rBFqNVe5BRlXt7BJumY81veg1dITT371JN9b+j1+s+Y3XR73tTewTxNQG1R0TvTx6W71hLW0W1m0D/U7tBrjPSiRkFCbP0SRlpdSkaCBoEdTtY14NZq0RE6TQYrzFrhtJkyKJkUv+aOegxZtQ+xLIi901VsxG2Vy3Z33fa7bii/crQ+XNd7T0aky2xkqXVu9lkNBDw5F4Zx+F0Qfj+TlbLVYaNaqBt0GK3KCcuAIGw82U+3x4bAYmdJfTWD99tn9MWlGTq2nvsfX9oUWLcRgUtQmpr2R6bRQ5Rsa/XePRk4vOTkA2Vb1lP2+PYN3XOrfvGXELeQnF5OLkZAk0d++knpvgHqtHLrKq26+o/OTEw9OkqJh9nZfZ5K2UNR1wdrDNmPplttSkjuJBS9+xcVPLSPLpnqeK7Uy9oNa6Cyvm0d3W5V639lMhrjQKqhGn007/3ljwuyhjiqCEQPTGR+aj1CgJTnXm8J4NcmApASVfkkx6QFtvfSvijTmNMpSdLz72lVD2mHqB3Tm7BRkjMAQsy66TfHeTIchGQBP2MOX214lJEn0E0YKU7seCiPhKrfNFBW0jJCZlK9dE0Jy5ZKpRQVqtN5hTquR7Tveo9Ug4xASlwy8Mm4cpVofq7X7GjHnjVVFDfnvqLDSjZwTREQC32kxMnfotchILLQnsXTPvxmulINRNRxsCaz1vuCm8wcuC4E9gf6Hi86NJcvkYPXeTn2R1XsbD9utO1JF0RSrHhpr2BxYrYqt9cI+zz68IoRVUbipxYNFUdjvPciOBGGU+nbVO5IcDmNLi/fkRDbYTJO6wFRoYmHrty3Fa5AxCsGY4qkM0Dw5FfU+ZqaplRuf1/bcKqE7tVpH3UyTI66JYromrV7fPYTXCzVtNby0VQ2pfLj7fdqDnZv0ropFKJJEiiLISO8UZzxr0OUALA82EIpRVfVriY5WU7xHoosnJ3cCAAcJEux20vRoeT0RXY3OfBxz3Od1WY2YNINaNnREk1+btA0gOUEVTSJik2aL0+zIMWGQnGQb7Uo3IyfB+ybFiJpFeG/XuwCc723HNvC86OMRI2evyUiVFq5N1Ag3lo+2qBvrjCGZWLVy/LGFKSRp+U9bD/1nCfieDvX9k5TEarKxZLosbA4PJUfbPMJK0mHDVWkJjJx+9uEA7LYEEXIImyhgSu4UJElioqMfACb7Tuq8rbRqoZfyJnXuI12nE+HUwuz+QKexr0SNnMQh+Ng8siQB9Z4kPthcRVmlh3BYXb8qtbLxSq1Sq3tBxEdb1O9gxpAMjAkqT51WI1bNKPfG6CkFOlQvhVUR2Hop9CjMGQdAjVGmXTvcJZnjDxRGqzvqmW7r6Nn4bYhRO5YkCUUoHNCKB+xOtWQ9YgiZs0ZEG3UCpFjilfCdWuuJVtHG0io1afwsZ3HcdZ5ouMoYbakRISdZndNWXxAMRjK176tRa7XhsppYc1Bd58fZcrq0iohQ2k89hGw95MGbMoziiPJxAv2yE41u5JwAhBDRZoYOq5HBqYO5YdiNAPwiPZXzGv6I0DQbrIbecwR6IraNg1OAbIi/EWP7WWWYk9l0UF04zAaZVn+IbVWeuNfEkqp5hxpj1UNjwz6hDjj0Va/vUVatPj8kEMQ19AqmdKifO1HIyt+uxuOzQkrXqoLIZ3Cqn9ksqae7vZpCatk+9QeZFzRgNlmiGiz+kMKwPLWscjkdKK19U26u9aoLaUaCvIl0zfBrCPVdM+UvC+8moLUF6FACLN6/OPrcjspVAAySk7oYGKOGXIVbEXhkmU1bXo4+7os0QExQJRTx5LQHQmTmjcemKIQkiUO1XROYW8ORhoHqZtZTZRWoJ0Gj5skwyH4cWluSFu3U31cF4Vghu9hQFaheC9GtusreLRcKOlVm28Pqydcf9rNIu48uCcqQOyZ6bbotnQxbBgJYY1VzKtyWnjdtIUQ0H2f28K6JqRlJWm+llsbohnQ0eDVXvjVBrkV3MpxWKkQ2Z3uDOMMKSYHEm3rEeDTIUsJy72LXIITS+fgA86XR+2xCwVkAVCd5aGiPNH5VOBjIJs1uJj+lZ2+TQ5Mw6Ah2GhKKJkzao5ETk6ReLNt466tO8bgqr2bkiCDegJdmrWdeftbo6DVCCD6MfEcj4gUoQfVsWTQjpzVGaiAYVH/77h5CaREyXAVYhFB/N9oanZQgCR6TDbvoXYEbYvJxNI2c2tZD+CW1pYora6J2jXZPZQ6LJh+rr4n3OLls6udulgIs86vf2VkJ8imjOTlWU9xvqTBbDRNGyswjgoDNWn6P02pkdWsFAONzJiT8XDluG3nJNhQBG2oVSjRNpj099Ms7kehGzgnAH1IIhtUfQGQRWjB2AYX2XGqNRl63HsKqNYuzmft2Eu6Oy9B5KnX18MN1y52LqY0UQoog121l6gDVsj9cyCpV0+9pkrT+VeEgYv9KNpvN/NqVpwZODhOyKtv/GQAjhAkm3sa5beopZmECIycYqAAgXRhBjr9VZwxWTza7GtUff63w0x5sZ1+zKgyYi7oYGWSJEm0jtZhL1VOj0cC2spd6HSsAbfXUagt+lj2+CiNdM/zq+6B0ihAc/PiHvNmknm4mawbe+1v+Hr2kvEnVDBls79raw2g0c6Z2kntv2yvRx31ab6ekBBt2bOsE2WynUPPA7IvpiwXgEZFTniYY2YuCrMtmwqAZOVIkXCVEdBNK1JwzEbFeiH7djByTQSbd3q2hYyIjR6seadfmfvnB5XjDHWSFQozNPxO6iVlGvDkrbOrmmmzt+bdWXtNKRUM7ZqPM2YO7JZsnaWOTfby74eg1o3zeCgBM4vCSES6rEYvRQP+64SzeV0U9oxJeFzH8s13WLt6xCKlJNsId6j2rBFIZ7u5MUJ04dA4AO80GcoTqFUgPK9SSyqh8d5xXLxa7pM6pL9QZDg0rkcq/xHmGlpg8smJLepe5XFWp3ovVBol99WqFVUo4jD1zePSaXbVe9tS1YTbIzBgcf3+Aeqg0aZ4yb4zUQDCkVQMmENGMRZZk8oT6O6jUvHndO6oDIEnYNUd4Wy8Nexu69a3aV6nOc25YIUlTMI6IBZI5NFpGDpCZHO+hSXGoa3KDQaFJBqeiMGbY1+Ou80S7iZuw2tKiMhB2RSEzfwygKnv7gmGyNI9uixZ6zJdq+cqoXj9h0BU9fraIN2dNRSMlmir1f0M3ct3IOQF4Y0o/IxuPzWjjF9MeBuBNp4NN2qkrKYFLsi/E6n24e+iq64pxywaC6g05qSSNyf37ZuRE+leFJAlPWxUc2oAIePleZgb/TDPwjtPe1bOTgLIG1Yswwt0f8sdzFjaMQrC7ZQ+7u4lHBbTuv6ly4oqd84ZnY5AlvmzIISUcadS5l0NB9XX51s6Ewv7RkJWfyTbVgPi8F40eABp2w19nUatVMGWm9I+7JF1Tp64/nD5GOAT/upM/7nyNkCQx2ZrD/bmzAFjRtI067fRXrrVzGJwWn4M0Z8RNALznP0RtnWrIRcpA7VrPqVgieS8dwTBhRVBkVBfnirqunhyPpgbrijTn7KUXkNtmQtY2BskQUI2cimU0acZWcmp8WDERsUJ2Jenx32+u2xHtTaV+vngvWpIWbooIsP17j5pwPLutHXnIRXHXR5KPyy3qBtNbS4eIAOD0gRlxuS+R0lwh+3ln/VEaOY178Xeor/XJvQsBgpo0nOmycF/oG0zw/5Fqa/yGB1CS4eCRK0fy2zmjEz7vtpkIecYghIS/9nwynZ1rQoYzjxJMCEnC4VYrIFMUiTCGnpOONezab9SnxCSB07uRY47JI3PIOTS0BUizmzEbZPY0OjALgSJJrNVEMPODIUjt/NyR7+jMgekJeyuB6oUwakZOS4zUQCisySb0MLZYCs3dk+ATe9vt2nYarbrze2H9P+HLp2DZ7+CzX9Nv0xNcLi8nR5N73lelejoKZRvpTtVQbIh4B+3pFEuqV8WqKKSmxQu0psXodgFMVSyYktK69LKCmHCV1YhkNKmd1IGCkIIjJTeqs9rqC5GplYC3htQ5Sg4tpU2WcQkpLuE4lkhezkdl1RSkqcboHp+eePw/gTdGbTL2dDUuaxxzB14FdHb+TrIldrseDre1M87qSiBWBV1zEGpaVaNoYkkqk0rU1646TF6OxZaKXYs7NzXthYplrLFaqNJOOO85HIhe8nKCSpByv7oAjMifCrIB18DZUY9G95CVX6jhpzRT4hN3qt3M5JI09oiczq69tZuoNKhu8gEZY6LXRpKPd9V6mVY4E4Bl3v2qxk8iDqyGv86Cpr3U2tRFLTOBdlB6qrrItMgSgZ46EDftg39eyZ7NL/G+Q13Yv3PO7yiadCejfH4U4N/bXkYoCjs0g2pQ3pS4txk38gbOUIwEJYm/L/8ZQLQ3kDOm4WGE2M25IximSNOKqfB0ltoHwgF82i3p1Db92Jyc7pgMMkbtZIsUxGExsuXzh6Oy/FkJDMFEdMnJyUhg5CRbsWnJrBYhEuYBRLxXbSJIW7CNpQeWAHBByARDL427flhqV8PR3UtFU2QDPX94vCHk0MIVihxk48EWdtcdRXuHsjej1T2S3HPfqlgynVYEMh4ccdVpscydUBg9uHTHbTMRbJ6It/yXhFpHd+mEDTDRqW6kO+1a/klIfb7HpGMNu0E1BHx0FiWEhfq77p5gHMES432ublHn+coz8hhfnALIZIXV+Vl1SPV25EkWMHWGzDpDVT17Dx1mI7LWB60lJiE4KFSvjstw+EKPQlvXeyBRpR+AXQvLvX1gES9+cjcL/1RK2YffJfDJT2DRL+Czhxm37y88Yf4Dv9h1Jbz/XQ5oulVFSVnRkvKItwdgoFZhlR8K4UyPX38caYXR8n2A6anDWLeviTN+8SnfeGEtbdoBOzbxGDpbauQbbMgGOfp79PiCZDlUT1+L0Fo+hNYDUGrN7jVR/7xhWbisRsprWvlXuTpnDYRoSSDWeSLRjZwTQDQfJ0E1xN3jf0B6TLKxy9G78mlPxIr/uRKIVQFdKoN216v/P7E4jWE5LhwWI62+PuTlaCGPxpZ9KBXLecfZOfYNVgsHRAAOrU/42l2NO/AjcIYVCvurXZEZNDsmZLWwy/XtkroopfZi+F04Mgc/ZrK0rtVlB76gWpvmsQM7q5Miyce7ar1MG3G9eq1JpqEivn+W2PIOvHAJdDRC7lhqc9QKi0x7vJfNlVyCUTsVNTR1kzFXFFj1J/jDZNi7lD+kpqJIEjMKZjAyYyRkDuUSs7pAv1/+OlXVX9EqSxiFoKTo7PgPK0ncMkh1Rb/WvJVmbxXtEb0XR7yRYzHKRGzqdn+IYi1pc19Md+cG7dQpCYFTkx3oLScHwKjlXyhykEJfOb/ROihfnD8j2m38cCTFiAF2z8kBNcZv1Ywcew/h14ioWbsIs3j/YvwiRFEwyLKmiTyxdH9cxV7EkxOhJ7XjjQea2V7dikGWmDU03sixayd5yaQa/O8ejTen7K2oRo5F7lseXqazM9ycqLKqL0QrkDRvXKQDeYSJhepvxq/dOHJQXUt6SzoGSDJrjVvxRx8La/9vSaDhBGCJSZZfeVD1rn5tXD7TB6qehOSwOrZ1kbBJjHGxv6GdrVUeDLLEuQm+owiyLGFA/QxNMXpKQc0YcxkPL3lQ2E1Oo3tH9QhZWkn8pw0bebRqEd91m5ibl81ZxcXcN6iUhSMv5LOUi6lQMvHSwYqyF1mr5b0UJvePGpxev9oFHqB/8igerqvnkboG7Gnxej6O5AzSNa+NLARTiy/gVx9spdUfYuG2Gq796yoa2wIxicfqGCNep3ztcBzJ32r1haJhsSZJXQd2C9UzPiE7cT5O9PO7rLxw8wQcFiPvHsohS0ua3tvNc3yi0Y2cE0BEF8FhTXAaNSVx1YD7AZAFuFIGHdXfSI7RxXEmKHEEcMdUqPgDbrJcForSkjAaZMZr8dTD5uVoobAmzwFa9q9kYZK6gDkMWr6Iw95jXk7ZXtVTMzwYRsrS+s/0P4cZ/hAGIShvKme/p7P5Z6usLpLpzvheXhHOG56FLIHRry5WC2tWIiSJtFCYIcWduhuxRk6GI4ehkg0hSXyx9ZUu73dg+3vMWvkjJuSlc2Fxf27My2Nbi7qJZ9rijRzJaCZd837Va9Lq7cF27v7oG1z991LuX/MozyXJvF00mo+TrEhI3DH2jujrZ4+9FaMQbA8288FWNUeoWBgwWxJX/kyb+H0GhwQdssTflvww+niyK8HYJKkzLycQpkhzNVdo1VRCCB5e9RAAAwNBJG3x7i0nB8CkJYwKOYjnwO/5ymrFisxdk36c8PpEpNnNnDssi0tH5yasAspxW6N6NEn0YOTYIhurwoc73gRgtredF0MzeWLhTh54d0sXz2RWUlZU6wm6lrv7gmHe3VDJNX9ewWVPfwHA5JI0UhKMzaHNU1hTF39nwyGUcJiKfZ/THhMS2VnTSmVzAu9e7Tba67ayRPvtOA19894eUyNHo7uRUzp0TjRfA0CEnOSn2KJehp5wmNXNsl3q9EKE0KqEelDBtphU484oBHW+AkbkuRiS7WL6INXIMfhs2nuq1+fHeFIjVVWTSlITfkexpGg6P4sCldRpKtV+ST1YJSrL7k5B2pAu/zaZkhNe931DJt9vaOK6llbO7QhSIqUiQkl4CfNBsJbvesu4072di4tSmVGYz605WZRZ1HntX3g2TosRs+bdi4SsvO6BXOJtJy9gxGCNXxPcSRbcIXWCRvv9lCljWL+/GatJJjnJxMYDzVz1zJfUtmo9Da1djZwCzWsTqcrzdATJ0qo6Gw0K6VI9ZZoxP37w5Yedq7GFKTw3fzwdphTytFth5/6+a5IdD3Qj5wTQmycH4IysyXQcvJa2Q9eQYU8+qr+RGtP7xtlDaaw7piWBCLqZVJIWTSaMhKxW7uk5aQ4gRVuwGg6tYaFV7XfjkvO4beQCAN532PHtSdxdvExLshthzYRI9ZfFQXLRNMb7VNf2PZ/dw7aGbQghaNI2kcxui0ws6Q4LE4vTCAXUTb5KC/fkB01IMcnKxel2JElNwKvz+pmWruYsfF7XWQ0mQgEe/uIBao1GOmSZAwRZX7+JjlAHsiRT4EzsZUvXlFvrW/YRVIJ876NbWFSziq1SkPecDh5PTeEBWY1vzy6ezaAYQzZ5xNeZFlAXkWcPqkbg4F6SzyWjiVvy1HDbyw3q2GUhcCfw5ECnx6TNH6IofyoAtQaJdk8lz295ns8OLsWsCH5Z38CVz5axZHttVCcnuYfNw6wlmZoMXp5HLa+f1/8KshMkZvf4OSSJv9xYypNzxyZMaM1x2zCH1bHbpcTVORFRs0ZJsKJWnYt0TwEtljwkCf6xch93vrIev9a3S5KkLt4ctyYU+K+Nh5j0yCLuemUDK/c0IkswY3AGj1w5MvHf1bxVPkltdbC/sZ3fvnEtl3y2gMkvT+GKf0zgjtfncvnzv+LMx95jzjNf8o+V+zorsTa/wcsuB00GA0ogjTzTxD7NWaar01hIVD7eF1zdjRxn1+/Ybc+gONR5jT+UcthQFYBDC+k0GsJ8WfklAEGtj57ZkDinzp5eCkCWzwoY+doZ6oY7JNtJptOCEuj6d/OSO0OhH/VQ+ZaIOi5jhC9AhyT4/Wf3qp9LO0D1RfKgQBPljDD3ue1844U1/H1FBTWezoKD9JKZzGvzc9+Ar/PgnM/YV3E/3p0/oa3iW7gCM8mx5yKkAJKxHQmJIlcRM/Om8f0RtzJ+yBVIkhTVNmrw+gmEFJ7ZpRrUBw2JBQtdNhNpmhf7HJHEw5+rh9T5U4t54/bJ5Lqt7Klr6+wmblPXqvGyHauiUJo9Xn08xpOTlqE29A1LEoOdi+mQZVKExIBu89AT4/ul8rd5pVGF7k+2fEmoW47QieTojgM6R0RECNCZwJMTeTzUqlZLJB3l4pWW0pmU5jAldn+7NMluSRgQYTsTizvj9hEjZ/XeBsKKSCjfDpBmskPQS2PNZpZpkvCX9L+Mq4dfwBPrH6LSBBtqvmJSOBjXr6asVc0FGZHRrTJk8AXcsfBztlvtlDeVc+0H13L14DkEZQlJCLIzE282ES4clcOyDwuBzlyTHKnr4mU1GShISWJ/Yzu7a9uYNvTr/LluJV/STtBbi8mRyeLFP2K5MYxJCP4040mMthTq2uuo66ijn6tfXEuHCOkGK9BOXetBHvz0DpY3lmFVFH5AGo3DLmKXr47dzbsJizB3jr2z64uNZi7Nnc6Shi/walMeaefQE+dO+ykFr3zKAU3vJUkR2K2JDRLVk+OnPRDG7c4nRRE0yRJvl73AEztfBeCHjU1YfOms93iZ//yaaBJiT54cs+YpXGcXhCQTGRi5eeK9vY75SMlJtmJUTIA/2ogT1JwBm8mAySCTpG1QPkkCBEP8AT7xzeb7lwwm1W7mu69u4INNVbS0B3nmhnE4LEaGpQ7ji0rVU+M2u9lV6+X7r2/EH1LIcVu5enwBXy8tIDe5l3JpTWekDcH5I7JZu+UjXu7YApKEIknsUjrY1V6GnF1GdtpC1uy5jzUVTfz8vS3MHp7Fw/Vv8JzWM8pfNxNrWt+ammYcA0+O1WTAbJQJhBQkCVKT4u+bIimf3VSonzGYzpkFh/d2ZDqKyK0Occhk5LaFtzGzcCaNsubJ6cGznJk8gva936Y8mILJIHHZGNVTI0kS0wdlULEzk9jfdH6mum5Ut/j4an8zoBYfHI7WpCKG146krLCcd+vXcU3VWtplddNPTYr3gHYnJ2MERq2MHKDRn8TCbbUs3FbLYx+V8/aCKQzIdMK078HUu0E28PS/t9HcHqRfmgOPbyCVu4sYW3gVptbdNHf4eHX+ZYwvih97msNMtcfHoWYfTy7aycJdSWw3Pshdl89MODaTQWZmUwrn+ndQ4JjJg9WtOK1GbpteQnKSmTe/PYUb/7aanbVqqC7iybtn+sN8p+xNTGPUljER48fjC2KyZ5OmKGorG7daCVpqyew1H6c7UwakszFzIJ8HNlEfrGLV3kamDuhbKPtYo3tyjgP/9+kOfvZuWVSbIDbxOBGxxs/RLl7paZ2nnCRzMgDBsMLKPQ3Rk2xEF0QJuQGZiSWdhsDwXDUvx3OYvJwULa/nK6PCJqsFSUjcMuYqbEYbg53TAPh3kikuL6cj1MFuTfRqRHE3HYdBsxntD/D2/gOcmzedkAjx4nZVCyYtrOBKT1xJEuH84VkcCg6M5sYA5NniDYVoyKrOy8iic0gR0CrLbNz8D9ob9/DogQ8BmJ85mfFF5zA2cyzn9TuP64Zex9S8qT3+/TQtt+DPlYt5r/pLDELwWzmHr1//MbdP/CG/Peu3vH3Z27x3+XvkJ+iTM33KvbhiTjqDDnNiMtrTuTllTPTfNtG1WimWqCcnoN6DRVp+xK93vIyCwoXeNiZ1pJI87yVum16CzWSItvVKSbABAli0k3lk0b9z6DySehFUOxpy3TZkRf37SZq+07p9jUx+eBEXPbmMlo4gSd3yf6Z4YZN1PHPGFXDxqFyeu2kCSWYDy3fVM+/Z1bQHQtEyclAPA/e8tgF/SGHawHSW//Ac7p41qFcDB8Ch5WZ5JcFlo7NJzXmFkCQxHTuLpjzGrW2DmdsYIjsUwmsKMG3o3xme5ySkCPaXfcHLopEWgwG3IYeQZwwWY9+W4awYT05Pa0lfiGx0qUnmhFo7Oc7Oe70llMOoPnhyrM4s3qis4pqWdgxILNq/iP1m9Z6z9JD3YpAlwr5CRNjJjMGZXRSapw/KwBPolFEwCEG2Jsz3yVbVizOuKKXLnPSE02ri9bZruaBD/Y09+tn3o80505y5vb0UAKPBRK6Wi2gQgje+fS73zh7MwEwHrf4QC15cT0dAS/6VDRxsaue5LysAeOCSYfzjlgm4rEbW72+hsSkdxZdPrjux4RgJC/7knc0s3FaLxShz+43XM2lMYskAgL3yKGZ7O/j9IbWiKWLggOoRff32ycwens3XS/M7q9BKzsZ06VOgCRs6o56cIEgSmZr/Y6tdNVQnaB6fI6G0/2QA2qwdJ83AAd3IOebUenz88bPdvLBiHzN/t5S31x+MahT0tDDFPn60nhyzxY5D2yht5lSCYYVbXljLNX9eyfV/XYXHF+SMzDMYk3Ym/vqzSXdYupTuGg1yVOcgNi+nIxDmtTUH2KWdBCI5DSts6kYw0DIk6uGYP1rV2fjEnkTd1q5JxNv3LSUsSWSEwmT1n9V18O58yB5FuhLm8eUv8tv6FlK0z5IRlLDbes8HyHRaSS0YpZaYapRkjYu7LmLk7K71YpANTNXkzD+vWMifPryVaqOBXEWmWflWr/onze0BlmyvjfZ3StdEAmu0Fgs/I52z5r7XpRKkN8ypJZxv6lwEYts59MSl0x4kU0vssypqa4RERIyfbVUeFEXQT1NoFhIUB4IMaZyAdcEy0vqfwY8uHMqyH87gtrNKuGR0bvR+6I4tpgnnEGHm0vF3JrzuPyHDaYlWxVglMweb2rn17+toC4TZUePl7lfWY+1m5DS3TOLGqf2xab+hMwem8/I3J+G2mVi3r4nb/rGO/u7OUOE7a5vYdLAFl9XIY1eN7tF72R27pgUUkiQOHvgdu21hbIrC7AEPsjk0lt/tn89fah/jfve5GIVgg1LBvInrePXWSVxo/YIXXOpBYYh1DiBjMfVtGY7NyTnadQI6jZzu+TgRcnMvJkkRWBRBTbCYkXmH9+RI7gLMioH7G+t5/eAhJnR0hnHs5sT3Uex8XzWuq/E/bUA6DcHOXLzsUBijVj5+JKEqUA+RPixMSL8Vm6KwMdBAlUn92+nunptzxlKgafokCRhZkMK3zx7AS9+cRIbTQnlNKw+825lc+7tPdhAIKUwqSWXG4EyG57r5xy0Tu6zzifLQANK1x+u9Aawmmb/NG89ZgxJ7kCN84JzDSP9fWRgcRbrDzPypXQ+FyUlmnrlhHL+5KrGsAKil5dCZP5qlfd7IQWbC4PhWDoejpN8MAKplQUdH02GuPn7oRs4xJtNl5bn54ynJsFPv9fPdVzfy9BJVuTdR4jGoVrRRlpCk+Jj5keDSSi6THP344Rub+HyHqlGwpqKJuX9eidcnUWq9h2DzBCaVpMblQsSWkgNsPtjCxU8t4943NzH7ic/5zUfbcVq7bizXau5OgPP6T8YVttEmy3y4p6sGTTTpWLJR1SHz+Y46Vu5p6KyAKb0ZUMMO57e28M7BQ3y7qZnpDbk9buCxzBxdTFZQvc4kBKMGzYi7JraMHGB6oWpsfdC+j78H1YXTUHcBf1texV2vbOD7r2/sPKFpLNley6zHP2f+82u46MllrK1oJCPG5X2nSOaKaz8A85F5Ni4boc5jXliQnt5zDlIEc8YgrjaqC7Qj3PP8RE66v/monFn/txShqK+xKAJz5eUMve5pMlOSo9enOyz86IKhPDV3bLSVQXdsMToh3xl66xG5sfuKQZZokFWDRLYP4BsvrKWhLUD/DDsWo8yS8jqeWnowKqU/2udnoXIhN0zqmqQ+uiCZ5+aPJ8lsYNnOeh59r5YzMsdR6BjA88vUhfeXl48g2314j0CEpBh9nScOqt6/4fX9eL08lR+9pbr3v3FmCWdf+QR3awn5v9n6LKm2vQTSN9FqkLH5XSzfqHoRLMa+GSzHIvEYYowcZ+KNNislHXnf9Vj2X01OekGf/pbFmcplgV/xe+k6Bg68iL/6rPxfTR13NLSQn5nYaI/kgaTZzZw9uGvYKMVuJi97YFSyIl8yg2ygweuPrk+9lY7HEvGUH0q+gG8YuhoMWan9+vQeBRZ1bUzqdBaT4bTw+2vGIEvw+rqDvLHuIGWVLbytVdvdf+Gw6Bo7uiCZ5+ePx2ExMiTb2eNvKxKStJkMPHvTeM4ceHgPiMtmok3rebhgxoCjujcinpzy6lZ8wTCZMZWH6QoU55Qe8Xumpg8lWRFYhaCq6uQpH5/yOTlPP/00jz32GNXV1YwePZqnnnqKCRN6L3U73kwdkM6Hd03jr8v28tTinbRrG6Wzh5vPbJT59ddG0REMJ5Ri7yvBpq+RZdrCm9sG8um2SgyyxI8uGMIfP9vNlkMevv7Miugpd2JJvI5G1MjZ08BTi3by+0U7CSmCJLOB9kCYP3y2mxkZPtB+dw5F5tJBnf2BZEmm1D2Nxd5P+Fyq5sZwECEb2XSwhc8r1oIJQp4UJj/S2cZgZJ6be84dxNnjbkIaOQeC7RDy0VzdyFsvrKLJVsQdHJ7ZI7L5arUd7D6KAyEKCgfHXdM/s6uRM2XkDcjbn6NW22T6ex1saJxGvzQ1d+eNdQfZfLCFp687gxy3lYf+vY2XVqnVX5IEO2u9XPXMCm4bNozc4GIuMqbyjbkfRl3A3alr9VNW2cKmgy1srmxhT72XYTkuZgzO5KzB1/GHthoy04awqbKFT7bU8OnWGqpaOihMS6Jfmp1+aXZKMuxMLEkjL9nGtFH3w5Kb8Ad6zln62SXDyHBYeH3tAfbUtbGv6SwysyvxNp3JzLOvjn7nR0Jm2pkM2PtPCjrsTC395hG/vq/YzJfjr7XxefUE6ppaSXdY+MctE1m1t4HvvrqRp5bspmgQ+ICC1iwGThiRsNLmjMIU/nJjKfOfW8PHW2u4Ysxd7K9sIqx0cPGonGguSF8xmO0kKQrtsoxPkhgcgi8abibQoB4qSjLsfO+8wSDL3HDla6x4eQZfmOD7H95MbZLWYqD+AjTpkj6Hq1KSzBhliZAijjrxGDqNnEhrge6kOyxU+dR76qphyX16T5vJwHZRSLmvkM0dmcyelsNLS9azq7ad37sSJ80OzXHyq8tHMDzXlfAgc/bgLJZUSLRZIN/sZm1FI99/fSNhRTA810VBat8OEpENfE99G7eWPsabK+ZxyGhAFoLUlH59eo9CZz749pPUzS8wpX86d88axOOf7uAn72ymv3aQumxMLiO7ld2X9kvli/vO6fX7vnp8AVUtPuZNKWJcUd/U7yPfZ16yjWsn9txRvTcKUlUj6ZOtNZz92GdckNdZvFJqyexV7bonJFnmnQteJCV9KLLx8KKLx4tT2sh59dVXueeee3jmmWeYOHEiTzzxBOeffz7l5eVkZh6dcvCxwmI0sGDGAC4bk8sv/rWVz8rrGNev55v2a+P65jbtDW/wTGobx7OrRl1sH71yJHNKCzhnSCY3/G01e+o7hbomFcePZUSuC7vZgMcX4nefqg0zLxqZw68uH8GaikYefG8LVa2uqJFzpmUQpm7JxfMn387iTz9hjdXEn95+kTcPFLOjxsvA/g2ARIN3AAZZoigtieoWH5srW5j//BrOKEzmrlmDGJHrJtmVQVOLk52ikoIeEmq7k+WykksBsJPBfhuGBLkGkXBVtcfH35bvZX9DGwOCFnaYA1gUwb66W/jB+YO5dXoJayuauPOV9ZTXtHLZ/1tOqsPMgUa1HPjmqcXcdlYJj3+yg1fXHuBPW/sx0P4rdpYUcddbOzFoXrl2f5g6r596r5+6Vn/U2I1lT10b729Sy2FH5k2jrtVPteeLLteUVXooq+yaJ9U/w05JhoMvan9Hdnoq3+lhXtIdFh64ZBj3nDeIt746yPNfVLDn4ALOHZbFbdN7T3DuCXtKf3Yu+iV7zNaE83ysyHensWHfDOpQDwJ/vnEcuck2rhibz+aDHp79Yi8TvTIV1gDlLRfz5Jk9525NHZDOU9eO5dsvfsXbG9QeSVkuC7+6fMSRD0yScAhoR61se3Dcvdzdks6uWi+yBL+dMzp6Upft6Tw062muWnw7ewwGQGaQbOea827kB2+oXp++GjmyLJHhtFDV4js2npwewlXpMQKBh1M6jpCbbGNcUQrr9jVFk3LVQIEDYw9hQEmSuL6b5y2W6YMyWL/TBJYQit/NnD+tQAhVXuChK3ovRogl8nnf2XCIdzbAFckDOJSzl8yQ0ufNt3/GKKj7klQ5/hC6YMYAVu9tZPmuerYc8mA2yHz/vPhDVuxYeqIkw8GTc8f2ek13xhWlsGhbDT+6cEifvYLdmTOugEBY8Iclu6hq8VEuW0BTNpiYIPTfV9Kyew6RnShOaSPn8ccf55vf/Cbz588H4JlnnuGDDz7g2Wef5b777jvJo1PJT0nizzeWEgorCZP8jiWxp6EfnD+YOaVqyXNJhoPXb5/M9X9bxZ66NtLs5uiGH4vRIDOhOJUl5XU4LUZ+ftlwrhibhyRJnDc8m6kD0vnD+y383fs2ADeeMS/uPcbkDmRgwMxOc4C/tv4WmwsGOqFai4HfN+c2hvcfhcWoup7/9PkeXviygq/2NzPvWVVKXpLUkyHQq7JrdyYMmMN1GxawPe36hM+7bSYynBbqWv388v2tAAxJLoWcLxnmm8QD374mOi+T+6fxwZ1ncufL61m5p5G2xg5y3VZ+O2c0U7Qkul9fNYorz8jjR29vZmcd7Nzce8NPSYL+GQ5G5bkZme+mX5qdr/Y3saS8lrJKD5srW7TPbOCswRmcNyybwdlODjZ1UFHfRkVDG9uqPGw40MzuujZ217UB1i7NLnvCYTFy4+R+XD+xiF11XkrS7Ql7G/UFt82ElySyrIdvLPmfEJsA/OuvjeSMws7cjh9fOIRtVR5W7b2bLJooGjOL/JTeT/bnD8/msatGcc9rGwH4zVWjowmaR4oLmVrgWlM2I8bcyI0dFTzw7hYWzBjQZZwAaf2m8/CAa7ltzysISWLB4LmcU1pIWIGXVu+PC9X0RrbbSlWL77CbZW+MzHPz9vpKxhQmJ3w+1vgZfRgRwAgGWeKN2yezo8bLvzdX8VFZNeU1rXHvdySMKUgm1zOCbL6gvGYsQqi5Oz+9eNgRff6LRuawbGcd1S0+Wn0hFrbO52vSk6S4Dx8WjjDpjFv5Sf0WxpZcEPecQZZ44poxXPj7ZdS2+rlpar8+e5mOBbdNL2HuhML/6J6QZYkbJhXx9dJ8Xl1zgI+W5wKqEd5bv6pTAUl0lwU9RQgEAiQlJfHGG29w+eWXRx+fN28ezc3NvPvuu3Gv8fv9+P2dipwej4eCggJaWlpwuY6u+/d/E5f9v+VsPNjCTVP68bNLhsW5GBu8fh79cDvTBmVw6ejEVQU7alp566tKrp9UmHjTCIf4v7+WYjQY+c7NqxM2znz27R/wRMuHiG5/f6hi4LX5G+Kur/X4+MNnu3lv46G4rs6Xjs7t88lGCMHKsh0MH1CMK0FLAoBnl+/l9XUHyU+xUZJhpzgtifw0A1OK8xJu+qGwwt+W76Xe6+eOcwYmXEj8oTAflVXT1BZAEaAIQVgR2MwGMhwW0p0WMhwWMl2WHqugaj0+vtzdgNtmYnL/tB5j9qCK9X2xu57Pd9Sx4UAzN07ud9Ru6qNh08FmLv1/XzAiz8X735l23P7O8p31fOPva/jWWQO4a1Z8T6wGr5/Lnv6CWo+f974zlSHZffsNr9rTQCCsMG1g7wmdvbFo4Q9ZWbmM7176Ekkp/RBCcKjFR14vlVkffXw3de01XH/pP5EMR3fi/mJXPR+VVfPjC4dGQ89HihCChrZAj8aHEIKbn19DS0eQV2+bjOkoD2d76ry0+kJ99gYlYsFLX7F4014cTjePXjmSmb2oGx8JgZDSp1y/I2FHTSufbq3h5qnFR/3d/LdQWb2Nyz+cQ74w8NZN67tojv234PF4cLvdh92/T1kj59ChQ+Tl5fHll18yefLk6OP33nsvS5cuZdWqVXGvefDBB/n5z38e9/jpYuRsr/awrcrDZaMTb9jHjHBI7fDcU5xWCJoq1+Bpq6Ej1EFHqANfOMDQ/ueT7O59Mw6FFVo6gjS1B/D4QgzLcfW64euceIQQvL7uIMNyXIzoQ+XNf0Jvmk0ALR1BWtqDFKaduJOzzomjusXHp1uruWR07lF73XSOjgOH1mC3pZGacnRh7eONbuQkMHJOd0+Ojo6Ojo7O/wJ9NXJO2Zyc9PR0DAYDNTVd8yBqamrIzk5cWmixWLBYjm8egY6Ojo6Ojs5/B/99gbY+YjabGTduHIsWLYo+pigKixYt6uLZ0dHR0dHR0fnf5JT15ADcc889zJs3j9LSUiZMmMATTzxBW1tbtNpKR0dHR0dH53+XU9rIufrqq6mrq+OBBx6gurqaMWPG8NFHH5GVdWwy8HV0dHR0dHROXU7ZxONjQV8Tl3R0dHR0dHT+e+jr/n3K5uTo6Ojo6Ojo6PSGbuTo6Ojo6OjonJboRo6Ojo6Ojo7OaYlu5Ojo6Ojo6OicluhGjo6Ojo6Ojs5piW7k6Ojo6Ojo6JyW6EaOjo6Ojo6OzmmJbuTo6Ojo6OjonJac0orH/ykRHUSPx3OSR6Kjo6Ojo6PTVyL79uH0jP+njZzW1lYACgoKTvJIdHR0dHR0dI6U1tZW3G53j8//T7d1UBSFQ4cO4XQ6kSTpmL2vx+OhoKCAAwcO6O0ijiH6vB4f9Hk9PujzenzQ5/X4cKrNqxCC1tZWcnNzkeWeM2/+pz05siyTn59/3N7f5XKdEjfLqYY+r8cHfV6PD/q8Hh/0eT0+nErz2psHJ4KeeKyjo6Ojo6NzWqIbOTo6Ojo6OjqnJbqRcxywWCz87Gc/w2KxnOyhnFbo83p80Of1+KDP6/FBn9fjw+k6r//Ticc6Ojo6Ojo6py+6J0dHR0dHR0fntEQ3cnR0dHR0dHROS3QjR0dHR0dHR+e0RDdydHR0dHR0dE5LdCPnOPD000/Tr18/rFYrEydOZPXq1Sd7SKcMjzzyCOPHj8fpdJKZmcnll19OeXl5l2t8Ph8LFiwgLS0Nh8PB1772NWpqak7SiE9NHn30USRJ4u67744+ps/r0VFZWcn1119PWloaNpuNkSNHsnbt2ujzQggeeOABcnJysNlszJo1i507d57EEf/3Ew6H+elPf0pxcTE2m43+/fvzy1/+skufIn1eD8/nn3/OJZdcQm5uLpIk8c4773R5vi9z2NjYyHXXXYfL5SI5OZlbbrkFr9d7Aj/Ff4jQOaa88sorwmw2i2effVZs2bJFfPOb3xTJycmipqbmZA/tlOD8888Xzz33nCgrKxMbNmwQF154oSgsLBRerzd6ze233y4KCgrEokWLxNq1a8WkSZPElClTTuKoTy1Wr14t+vXrJ0aNGiXuuuuu6OP6vB45jY2NoqioSNx0001i1apVYs+ePeLjjz8Wu3btil7z6KOPCrfbLd555x2xceNGcemll4ri4mLR0dFxEkf+381DDz0k0tLSxPvvvy/27t0rXn/9deFwOMTvf//76DX6vB6ef//73+L+++8Xb731lgDE22+/3eX5vszh7NmzxejRo8XKlSvFsmXLxIABA8TcuXNP8Cc5enQj5xgzYcIEsWDBgui/w+GwyM3NFY888shJHNWpS21trQDE0qVLhRBCNDc3C5PJJF5//fXoNdu2bROAWLFixcka5ilDa2urGDhwoPj000/FWWedFTVy9Hk9On74wx+KM888s8fnFUUR2dnZ4rHHHos+1tzcLCwWi3j55ZdPxBBPSS666CJx8803d3nsyiuvFNddd50QQp/Xo6G7kdOXOdy6dasAxJo1a6LXfPjhh0KSJFFZWXnCxv6foIerjiGBQIB169Yxa9as6GOyLDNr1ixWrFhxEkd26tLS0gJAamoqAOvWrSMYDHaZ4yFDhlBYWKjPcR9YsGABF110UZf5A31ej5b33nuP0tJS5syZQ2ZmJmPHjuUvf/lL9Pm9e/dSXV3dZV7dbjcTJ07U57UXpkyZwqJFi9ixYwcAGzduZPny5VxwwQWAPq/Hgr7M4YoVK0hOTqa0tDR6zaxZs5BlmVWrVp3wMR8N/9MNOo819fX1hMNhsrKyujyelZXF9u3bT9KoTl0UReHuu+9m6tSpjBgxAoDq6mrMZjPJycldrs3KyqK6uvokjPLU4ZVXXuGrr75izZo1cc/p83p07Nmzhz/+8Y/cc889/PjHP2bNmjXceeedmM1m5s2bF527RGuCPq89c9999+HxeBgyZAgGg4FwOMxDDz3EddddB6DP6zGgL3NYXV1NZmZml+eNRiOpqamnzDzrRo7Ofy0LFiygrKyM5cuXn+yhnPIcOHCAu+66i08//RSr1Xqyh3PaoCgKpaWlPPzwwwCMHTuWsrIynnnmGebNm3eSR3fq8tprr/Hiiy/y0ksvMXz4cDZs2MDdd99Nbm6uPq86R4QerjqGpKenYzAY4ipSampqyM7OPkmjOjW54447eP/991myZAn5+fnRx7OzswkEAjQ3N3e5Xp/j3lm3bh21tbWcccYZGI1GjEYjS5cu5cknn8RoNJKVlaXP61GQk5PDsGHDujw2dOhQ9u/fDxCdO31NODJ+8IMfcN9993HNNdcwcuRIbrjhBr773e/yyCOPAPq8Hgv6MofZ2dnU1tZ2eT4UCtHY2HjKzLNu5BxDzGYz48aNY9GiRdHHFEVh0aJFTJ48+SSO7NRBCMEdd9zB22+/zeLFiykuLu7y/Lhx4zCZTF3muLy8nP379+tz3AszZ85k8+bNbNiwIfpfaWkp1113XfT/9Xk9cqZOnRoncbBjxw6KiooAKC4uJjs7u8u8ejweVq1apc9rL7S3tyPLXbcng8GAoiiAPq/Hgr7M4eTJk2lubmbdunXRaxYvXoyiKEycOPGEj/moONmZz6cbr7zyirBYLOL5558XW7duFbfeeqtITk4W1dXVJ3topwTf+ta3hNvtFp999pmoqqqK/tfe3h695vbbbxeFhYVi8eLFYu3atWLy5Mli8uTJJ3HUpyax1VVC6PN6NKxevVoYjUbx0EMPiZ07d4oXX3xRJCUliX/+85/Rax599FGRnJws3n33XbFp0yZx2WWX6aXOh2HevHkiLy8vWkL+1ltvifT0dHHvvfdGr9Hn9fC0traK9evXi/Xr1wtAPP7442L9+vVi3759Qoi+zeHs2bPF2LFjxapVq8Ty5cvFwIED9RLy/3WeeuopUVhYKMxms5gwYYJYuXLlyR7SKQOQ8L/nnnsuek1HR4f49re/LVJSUkRSUpK44oorRFVV1ckb9ClKdyNHn9ej41//+pcYMWKEsFgsYsiQIeLPf/5zl+cVRRE//elPRVZWlrBYLGLmzJmivLz8JI321MDj8Yi77rpLFBYWCqvVKkpKSsT9998v/H5/9Bp9Xg/PkiVLEq6n8+bNE0L0bQ4bGhrE3LlzhcPhEC6XS8yfP1+0traehE9zdEhCxEhI6ujo6Ojo6OicJug5OTo6Ojo6OjqnJbqRo6Ojo6Ojo3Naohs5Ojo6Ojo6OqclupGjo6Ojo6Ojc1qiGzk6Ojo6Ojo6pyW6kaOjo6Ojo6NzWqIbOTo6Ojo6OjqnJbqRo6Ojo6Ojo3Naohs5Ojo6Ojo6OqclupGjo6Ojo6Ojc1qiGzk6Ojo6Ojo6pyW6kaOjo6Ojo6NzWvL/AQwkTQqdVa6wAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.vstack([test_data[1].flatten(),labels.flatten(),auto_labels.flatten()]).T\n", + "all_prediction = pd.DataFrame(predictions,columns=['target','baseline','automl'])\n", + "all_prediction.plot()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 13:19:45,408 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-05 13:19:45,432 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-05 13:19:45,440 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "industrial_auto_model.solver.return_report()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 13:19:50,131 - OperationsKDE - Visualizing optimization history... It may take some time, depending on the history size.\n" + ] + }, + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "industrial_auto_model.solver.history.show.operations_kde(dpi=100)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Compare with State of Art (SOTA) models" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')\n", + "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", + "df.index = df['algorithm']\n", + "df = df.drop(['algorithm','ds/type'], axis=1)\n", + "df = df.replace(',','.', regex=True).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "df['Fedot_Industrial_tuned'] = metrics['rmse'][0]\n", + "df['Fedot_Industrial_AutoML'] = auto_metrics['rmse'][0]\n", + "df = df.T" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "CNN_RMSE 867.167248\nFCN_RMSE 886.301979\nInceptionT_RMSE 958.396510\nSingleInception_RMSE 961.233445\nResNet_RMSE 973.711924\nRDST_RMSE 986.749722\nRIST_RMSE 1032.905074\nFPCR_RMSE 1047.799053\nROCKET_RMSE 1049.727695\nFreshPRINCE_RMSE 1051.735216\nMultiROCKET_RMSE 1065.140487\n5NN-DTW_RMSE 1071.163827\nRandF_RMSE 1076.629639\nDrCIF_RMSE 1080.966688\nFPCR-Bs_RMSE 1099.364430\n5NN-ED_RMSE 1110.793732\nXGBoost_RMSE 1155.910532\nFedot_Industrial_AutoML 1168.245000\nTSF_RMSE 1178.412908\nFedot_Industrial_tuned 1178.575000\nRotF_RMSE 1189.173860\nGrid-SVR_RMSE 1246.184958\n1NN-DTW_RMSE 1396.816565\n1NN-ED_RMSE 1615.919446\nRidge_RMSE 2286.355459\nName: min, dtype: float64" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('min')['min']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": "Fedot_Industrial_AutoML 1168.245000\nFedot_Industrial_tuned 1178.575000\nRDST_RMSE 1223.537234\nFCN_RMSE 1261.023121\nRIST_RMSE 1286.172802\nResNet_RMSE 1314.236132\nCNN_RMSE 1329.048575\nROCKET_RMSE 1381.935767\nSingleInception_RMSE 1403.745725\nDrCIF_RMSE 1426.485773\nInceptionT_RMSE 1432.379154\nFreshPRINCE_RMSE 1467.946007\nMultiROCKET_RMSE 1479.726986\nRandF_RMSE 1529.707449\nFPCR_RMSE 1566.918068\nRotF_RMSE 1597.388873\nXGBoost_RMSE 1620.479639\nTSF_RMSE 1624.255525\n5NN-DTW_RMSE 1652.839501\nFPCR-Bs_RMSE 1667.648841\n5NN-ED_RMSE 1825.271162\nGrid-SVR_RMSE 1934.918113\n1NN-DTW_RMSE 2101.344987\n1NN-ED_RMSE 2322.456606\nRidge_RMSE 3337.671272\nName: max, dtype: float64" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('max')['max']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "FCN_RMSE 1072.502657\nRDST_RMSE 1142.764246\nResNet_RMSE 1145.645542\nInceptionT_RMSE 1156.251420\nSingleInception_RMSE 1162.325568\nFedot_Industrial_AutoML 1168.245000\nRIST_RMSE 1172.270167\nCNN_RMSE 1174.255738\nFedot_Industrial_tuned 1178.575000\nROCKET_RMSE 1236.408458\nFreshPRINCE_RMSE 1240.376659\nDrCIF_RMSE 1246.467591\nMultiROCKET_RMSE 1252.545137\nRandF_RMSE 1310.439205\nFPCR_RMSE 1331.504687\n5NN-DTW_RMSE 1337.373398\nRotF_RMSE 1383.306131\nTSF_RMSE 1401.285094\nXGBoost_RMSE 1424.823440\nFPCR-Bs_RMSE 1427.171255\n5NN-ED_RMSE 1458.866739\nGrid-SVR_RMSE 1587.147748\n1NN-DTW_RMSE 1819.103151\n1NN-ED_RMSE 1906.032299\nRidge_RMSE 2719.383329\nName: average, dtype: float64" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('average')['average']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_steam.ipynb b/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_steam.ipynb new file mode 100644 index 000000000..ef45536c8 --- /dev/null +++ b/examples/real_world_examples/industrial_examples/energy_monitoring/building_energy_consumption_steam.ipynb @@ -0,0 +1,2330 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Predict how much energy will a building consume with Fedot.Industrial" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Dataset published on Kaggle, aims to assess the value of energy efficiency improvements. For that purpose, four types of sources are identified: electricity, chilled water, steam and hot\n", + "water. The goal is to estimate the **energy consumption in kWh**. Dimensions correspond to the air temperature, dew temperature, wind direction and wind speed. These values were taken hourly during a week, and the output is the meter reading of the four aforementioned sources. In this way, was created four datasets: **ChilledWaterPredictor**, **ElectricityPredictor**, **HotwaterPredictor**, and **SteamPredictor**.\n", + "Link to the dataset - https://www.kaggle.com/code/fatmanuranl/ashrae-energy-prediction2" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-28T10:34:48.354623Z", + "start_time": "2023-08-28T10:34:39.594404Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", + "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", + "from fedot_ind.tools.loader import DataLoader\n", + "from fedot_ind.api.main import FedotIndustrial" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=30,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "dataset_name = 'SteamPredictor'\n", + "data_path = PROJECT_PATH + '/examples/data'" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we must download the dataset. It could be done by using `DataReader` class that implemented as attribute of `FedotIndustrial` class. This class firstly tries to read the data from local project folder `data_path` and then if it is not possible, it downloads the data from the UCR/UEA archive. The data will be saved in the `data` folder." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-28T10:35:13.321212Z", + "start_time": "2023-08-28T10:35:12.913025Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 17:56:37,331 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/SteamPredictor\n" + ] + } + ], + "source": [ + "_, train_data, test_data = DataLoader(dataset_name=dataset_name).read_train_test_files(\n", + " dataset_name=dataset_name,\n", + " data_path=data_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Lets check our data." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "(210, 4, 168)" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features.shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Lets visualise our predictors." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "
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" 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" 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LBk0kEsH06dMBAAsWLMCbb76Jn/3sZ3jggQeyjl24cCEAYMeOHTjuuOPQ1taWJSI6eFCK1ba1tVkePEHYgRk0STJoCAKA2qDZfXgQmGXt9bxBkyaDJi+V4SC23Lm0ZJ9dbthufZBOpxGLxXSf27x5MwBg3LhxAICOjg58//vfR3d3N1pbWwEAL7zwAurq6uSwFUEUi5GMRiBFBg1BAAAODfAeGmsNFI8MxvHuvl75d/J85kcQBNNhn1LS3NyMYDAoOyAYBw8e9JQzwpIoeOXKlVizZg127dqF9957DytXrsSrr76KK664Ajt37sT3vvc9bNq0Cbt27cKf/vQnXHnllVi8eDHmzp0LADjvvPMwe/ZsfOUrX8E777yD5557DrfeeiuWL19OISWi6CgeGuviR4IoR1Qemh5rot612w+Bd8qQPVM+RCIRLFiwAC+99JL8WDqdxksvvaRKCio1lkzD7u5uXHnllThw4ADq6+sxd+5cPPfcczj33HOxd+9evPjii/jpT3+KwcFBTJw4EcuWLcOtt94qvz4YDOKZZ57B17/+dXR0dKC6uhpXXXUV7rzzTse/GEHkI5YxaMhDQxASKoPmiDUPDR9uAmhelRsrVqzAVVddhZNPPhmnnnqqfK+/5pprSj00GUsGzYMPPpjzuYkTJ2L16tV532Py5Mn4y1/+YuVjCcIVRhKSZ4Y0NAQhwRs0e48MIZUWEQwIeV+XTotYs+2w6jGqQ1NefOELX8ChQ4dw2223oaurC/Pnz8ezzz6bJRQuJd4P3hGES7CQkyhKC3LAxMJNEOUMr6FJpETs7x3GxMaqvK/bcqAfhwdiqIoEMa6+AjsPDVKWUxlyww034IYbbij1MHJCzSmJUctIUqmTQYsvQSgemmhIujXsNikMZuGm049rRkUme4Ycn0SxIYOGGLWwkBNA8X6CGIonMRCTikzOn9gAQKpFY4Z9R4cBACeOr0NAkDydlOVEFBsyaIhRCws5AaSjIYjDx6R+ehXhAD7RXg/AfKYTM17CwQBY5Jbq0BDFhgwaYtSi8tCkaPElRjeHBkYAAC21UUxtlnQzZmvRMONFECBr0cjrSRQbMmiIUQuvoaFaNMRoh+lnWmqimNwktTww7aHJ2C4BQVBCTmTP6DIasr9K9R3JoCFGLTEu5ES7SWK0Ixs0tVFMkQ2aIVNaGHYDCwhAUDZoaE7xhMNhAMDQkLX6Pn6EfUf2nYsFpW0ToxY+5EQaGmK0wxs07Q0VCAUExJJpHDw2gnH1lYavTcsGjQCBNDS6BINBNDQ0oLu7GwBQVVUFQSivUhGiKGJoaAjd3d1oaGhAMFjcflFk0BCjlhHy0BCEDKtB01JTgVAwgImNVeg8PIhdh4dMGDTSz4AgyIX4aE5lw/oeMaOmXGloaChJjycyaIhRi6oODS2+xCiH99AAwOQmyaDZ3TOIjuOaDF+b5kJOTENDDppsBEHAuHHj0NraikQiUerhuEI4HC66Z4ZBBo2PeXvPUew6LIn2KsJBfHpWKyoj5dcSvlBEUcSbu47iuJZqNNVkNz+lkBMxGvlgfx+aqqNoq69QPa41aCQdzSFTmU7MeAkEBMpyMkEwGCzZTb+cIYPGp+w7OoRl961T7YJu+PR0fHPprNINymO8u68Pn39gPc4+vhUPXX1K1vMUciJGG3uPDOHS/3odU5ur8fxNi1UaDq1BMynT8mCviSaVbP4IgkB1aIiSQVlOPmXf0WGIIlAdCeL4tloA0s6LUOjOLNDbu4/pPq/20FDaNlH+fLC/D8m0iO3dA9h5aEB+XBRFRUOTMWiqo5IHgTf8c5HWyXIie4YoNmTQ+JRjI1KJ8hlja3HbxbMBmO+7Mlpgu8ZDx2K6dREobZsYbfDho1e3HpL/3zecQCJTXLK5JgIAsvfGjKeFFwWz11F/NKLYkEHjUwZikqCstiIk14zYc2QIyRR5GhjMiBlJpOUeNTzqwnq0+BLlD18ojzWUBJRwU31lGNGQ5JkJWiiQp6pDk7mrUMiJKDZk0PgU5qGpiYbQVleBSCiAZFrE/t6REo/MO/ALMVuwGam0KO9I2e8EUe7sOqx4aDZ2HsFwXDLqtfoZAAhYMEz4OjTUnJIoFWTQ+BRm0NRWhBAICJjcyHqvmCtVPhrgF2KtQaPVBZBBQ4wGmIcmIADxZBobOnsA8DVoOIOmwJATy3KiKUUUGzJofAoLodREpdLSVnuvjAZUBs0AGTTE6GYkkcL+PsmDe95sqejZ6oyORtdDI3ta8r+37KEJKK+jOUUUGzJofMqxEUVDAwBTmqx1xx0NiAYhp5GkepUmDQ1R7rD069poCJ89aTwARUdjZNCYEfeKnIcmSGnbRIkgg8anDHAhJwCY3EweGi38DjF/yInE1ER5wzY7k5urcPr0JoQCglwJWN+gkX6a6ZzMjBdB1W2bDBqiuJBBw/Hs+wfw+QfW49+f26r7/FA8iZufeAfPfdDl2hhWvbIDP/jL3/Mex4uCgWwPjSiK+PFzH+K/136ket3qbYfwxV+sx/+5bx3+z33r8G/PbPFsO/vOw4P4x8c24f2PC6uvY0VDk0x58xwQhFOwzc7kpmrUVYTxycljAAD/8MibeOlDqbeQSkNjQQvDNg8BwdrrCMJJqFIwR99wAm90HkF1jvYBf3j7YzyxaR9e23EY580e63in1AN9w/hxxpi6fvE0NOuU62ccizEPjaShkVO3e4aQSov4YH8fVr2yEwBwyfx2tNZKZc7vfvZDfLC/X36ft3Yfxec+OR6faK939Ls4wdPv7Mdf3utCRTiIez4/3/LrVSGnLA2N2iND8X6i3GEJA2zzc/4n2vBG5xHsPKR4dWdlinQC1kTBfMiJeXZoThHFhgwaDuZu1d78GCzefKBvBNu7BzBzbK3ucYWyhqsLMRhLGho0LORUkwk5jauvQDgoIJ5Ko6t/RBb7AcDabYexbMEEdB8bkY2Zn31xPn61fjfe2n0Uq7cd8qRBw2rqFFow0MhDE9N6aGjxJcocNo9YAsGVHZMxY2wNBjObo7b6Spw4XlkH5BYGJuaGqlJwgFUKpjlFFBcKOXG01EheDO3ND5BSHNftOCz/zhsMTsEXuhrOU278WEwtCg4FA5g4Rtp57T48qHov9v+126Txnzi+DpfOH49L5rdLz7vwXZyAiREL1QUZ1aHhi+pJx9LiS5Q3iodGMmhCwQAWzWjB+SeOw/knjsP8iQ2q462EjngNjVwpmGRpRJEhg4aDeWgOD8SzdiWbdh/FYFy5CfIGgxMkU2ms3a4YTMNxY4NGFgVHFSfb5Iwr+d2P+/C3vb3y42u3H0IqLcpjPnNmi+rnpt1H5awpL8H+BIcH4gWNj8/O6BmMq1zg2pATaWiIciaeTOPjo8MAlJBTPgqpQxMUBK7CMM0poriQQcPRlOlhkkqLODoUVz3HjIF5mV3MG51HMBTPLqdfKJv39spCX8DYoBFFUREFV/AGjbTz+vUbe5BKi5jaXI3aihCODiWweW8v1m5nBk2rfPyUpiok0yLW7exx7Ls4Bb8gFhJ24l3e2r8p1aEhRhP7jg4hLQKV4aAqk8kIK1oYUVWHRnqMDBqi2JBBwxEOBtBYLRk1Wh0NM2iuPn0yJoypRDyVxoaPnDMCtB4fo5BTLJmWNR9MFAwoOy928z/7+FZ8anozAODnr+zA0aEEaqMhnDSpQX4N89I47XFyAn49LMSg0XrZ+LBTloeGDBqijFH0M1WmkxmsdM1m00dQVQqmOUUUFzJoNLC0Rf7md7B/BH8/0A9BABbPaFGMAAe1J1YMGuadEQSgKqxkZLFaNIwzZypjZWmZZ0xvRjio/NnPnKV8F6+J+PidYSEtHbQ2itqgoTo0xOhBq58xg7Vu29m9nEhDQxQbMmg0yJlO3M2PZR/NGV+Pppqo416NwwMxvLtPqrUye1wdAOOQE9OT1ERD8m4IUC9WFeEATp3aiMWZsTKYAcM4bVoTIsEAPu4dVqVvegF+Id1TiIdGNPDQJCnLiRg9MA/NlGbzBo0ccjJh0PB1aCjLiSgVZNBo0DNotGLa06c3IxQQsKtnyDAD59hIwtSkfi0jBp49rk4W9hp5aFgfJ14QDADjGyrlxeS0aU2oCAfR3lCJmWNr5GO0Bk5VJISF0xpV39Mr8KeuEA+N9tTzYUSqQ1M+pNOinHrMc7B/BNsPHsP2g8fQ3Z+7C72kSXNGFJ9Oi/L8dIuBWNJUKrUoitjTM4TtB4/h7wekcg1mBcEAb5jkP5avQyP4oA5N35C5tZnwF2TQaNAaNKIo4rVMujYzaGqiIZw8RaqyyWcm8fzlvQOYc8fz+PUbe/N+JnuPM2e1oDITQjL20GQLggEgEgpgfEOlaqz8/2e01sjP87DnX9vuLYPGrihYu7PkjVRtHRovL76EMf/fU+/hlO+/iB3dx+THXt9xGAt/8BLO/ckanPuTNTj1By+pyi7wPLpxD+bc8Tyefd9+BfB/f34r5tzxHP6256jt99Lj7T1HMeeO53DPC9vyHvuLNR9h8Y9fwbk/WYONnUcAKIkDZigk5CQI4LKcTH9UUXlr1xGc9L3n8R/P5z+HhL8gg0aDrKHJ7OYPHYuhdyiBgADMndAgHze1WfJ6HBmMZ70HAPz2TcmQWb2tO+9nbjsoLcQnTWxAZaZKsRkNDS8IZvzDGVNw6pRGXDp/vPzYl0+bjBPH1+GGs6frvt/UjBs613cpFbyR0dU/kjeVXYthyIkK65UNGzuPYCiewp/eOSA/9qfN+wEAVZGgvEl4+t39uq9nWrjfvZV/82FEOi3id2/tgygqXlenWbvtMEQR+O1be/N6GP6YOQe1FSE0Vkdw8uQxqoSAfFjJctLT0HhVFLzlQD/SIvBhV3/+gwlfQZWCNWg9NKw30vgxlYiEFPuP6Wr1Ju1IIiVnQOXzLIiiKIdTpjZX443MTspMyKkmmv3nu/qMqbj6jKmqxyY3VeOZbyzK+X5Mh2MmVl5MtOvoniNDqtLs+WBfpyIcwEgibZjlRB4a/9I3JIWLVm87hBXnzoQoKjWX7v/yAqRFEVc//KYsfNdm+bCw8fqdPRhJpFAR1m99ko8tB/pxeEC9bjgNG+uhYzH8/cAxzG6v0z2uu38EWzKJDK9+8yw0GVQdz0UhIadgQPB82jbbzNCcLz/IQ6Mh26DRzw6QdyE6k2Jj5xHEkmn59UY7qaNDCRwbSUIQgImNVYqHxoQouLbCGXtU+S6OvJ1jaM+bVR0N+9uMrctUgB4gUXC5IYoieoel+fDuvl4cGYxj28EBdPWPyML406Y1IRoKYH/fCHZ0D6hen06L2H1EMj6GEym8tavwUBGvQXOr6z0/B4w0b2syHiKWyFAI1grrZTen9KrBwDYzHh0eYQMyaDRo+zkpHWrVYrqAQZyYT+ceSaTRrdNKgcEWqHF1FagIB+XdoZFBI1cJdsig8WplT+14rN4k2N9GNmh0Qk6RjKuN0rb9yUAsKd84RVGqis3CvEwYXxEOYuG0JgDZRkBX/wjiSeVvbyZEnAv+vd3z0CjvazRWbSJDIVgR96rq0HhcQ8O8315b7wj7kEGjgWloeocSiCVT8sKU00OjMym0C82uw7lvxIrBJL1/lRkNjUHIqRC86iJmdSyimVCf1ZsEC6Exg6ZvWPqbAsourToqnW/y0PiT3iF1dtLqbYd0b+a5Si1ovX6FZvr1jyTw9m7Fu3N4IOZ4tlP/SAI9nM7trV1HdT8jlRa5quCFGzRWDBNeQ8NCVWYysUrBCBk0ZQsZNBrqK8MIB6UJ2TMQzzI4GLlqNOw9MoSdhwYRDAhyszcjHc2uw6w+hOQBspLlpCcKLgTBozsqFnJiomWrHhr2+jFVyt/08IB0Q2CLWlVEMgpT1MvJl/QNqw2aV7cewpudkmFx1qxW+XF2Y9/4kbplCZubJ01qQEAAth0cwP7eYcvjWLejB8m0iGnN1WjKVBt3OuzEajE110SVliU6mVvv7utF71ACdRWhrIaTVrBST0ZJ21Y8O141GNhmxqshMaJwLBk09913H+bOnYu6ujrU1dWho6MDf/3rX+XnR0ZGsHz5cjQ1NaGmpgbLli3DwYMHVe+xZ88eXHTRRaiqqkJraytuvvlmJJPu1m2wQiAgoDnjpek+FsNuZnBoQ045BHNrMjujT05qwNwJ9QCMtR9ag8lclpNSWM8JvLqjSmsMGmb8WX19QBCyKkCPZMIM7BySh8afMA/NtOZqVEWCODIYRzyVxqTGKtWcPa6lGuMbpJYlGz86Ij/O5ua8CQ3yzX9NAV4a5tlZPLNFDk8XUmrACEXPV2VY3JM9tmhGC0LBwveshRXWU5pTei3JgBGTPTQlHgjhOJau9gkTJuCHP/whNm3ahLfeegtnn302Lr30UnzwwQcAgJtuuglPP/00nnjiCaxevRr79+/HZZddJr8+lUrhoosuQjwex7p16/DLX/4SjzzyCG677TZnv5VNmI5mW9cxHIspgl2eXKJgpp85c2aLbKQYemh61AaT7KExU1jPMVGw9NNrOyp2aqe1SOdxf9+wHDKy8vqAIGSJvdmixkJOXvvuhDl6hyWPW3NtFKcf1yw/fubMFlU2kyAISpsPXrzLbVhY01arYSdRFGUj6MxZLXJ4upBikEYo/ZiqVd9F60FxQj8DcJ5bC2nbgqCsjV6dUiwhwGsbOMI+lgyaiy++GBdeeCFmzJiBmTNn4vvf/z5qamqwYcMG9PX14cEHH8Q999yDs88+GwsWLMDDDz+MdevWYcOGDQCA559/Hlu2bMGjjz6K+fPn44ILLsD3vvc9rFq1CvF47hoosVgM/f39qn9uwnbzb+6SdnJMsMujt3uJJ9Ny1+ozZ7bKRkpBHhou5LS/dxg/f3UHejPdop0WBSvN5Bx5O8dg57a5JoqaaAiiCOw9ooQDEqk0Hn69M2dbBLbIBgPZ2WsjskFDHho/wzw0DZVhVVsPvZu5nleDzc3JzYqRsHb7YXz36Q9w59NbsFHTgPb9j/vw32s/UoUrdh4awMe9w4iEAjhtapOykdHxKB7ok+by0Rw1n3qH4lj1yg4c6MsOezEt3pSmKrllyb6jw/jXJ9/Hd5/+AN99+gPc8acP8M7eXgDZVcGtUkhzyoAPmlMqWU7eHB9ROAX7I1OpFH7zm99gcHAQHR0d2LRpExKJBJYsWSIfc/zxx2PSpElYv349AGD9+vWYM2cOxo4dKx+zdOlS9Pf3y14ePe666y7U19fL/yZOnFjosE3Bbn5vZUR+etU19XYhO7oHMBBLoq4ihE+016k8NHpx6L6hBI5mFuTJBh6aX6z5CHc/uxWPbtgNgKsUHHVGQ6M0k/PWBBdlg0SQw06shDsA/Gr9bnz36S349+e35ni99FPPQ8MWNRZyIg2NP2EamoaqMM6a2YKAINUd6jiuKevYMzItSzoPD2J3ppwCX5Zhzvh6NNdEMBBL4uHXd+Gh1zvxL0+8o3qP2/74Pv7tz3/Hup2KduXNTKr3KVPGoDISlPVwehuZVa/swN3PbsWv39yj+33uX/0RfvzcVvz32s6s52QPTXM1qiIhnJb5jr9+Yw8efn0XHn59Fx5ZtwtpEThhXB3a6iuMT14eAhZCR/xctVKQrxSwzSJN+fLD8hb/vffeQ0dHB0ZGRlBTU4Mnn3wSs2fPxubNmxGJRNDQ0KA6fuzYsejqkkqKd3V1qYwZ9jx7LhcrV67EihUr5N/7+/tdNWrYza+T7Yias/uf6O1C4pm0nNqKMAIBARMbKyEIUoioZzAua3MYu49I799aG5XFqXpp2yyFnNXQcCvk5LXeJiyTWhAELJzaiPc+7sPa7Ydw8bx2AMArW6Vssq4+/T49bEEVeA3NgHQsczuTh8bfMK9lQ1UEExur8ODVp6A6EpL/rjysZcmGj45gzbZDOO8TbRhJpBEMCHIftAe+cjJe/vAghuNpPPR6Jw70jSCdFuX5/nFGMMwLhw9m+kRNapSMbqNQ8/aDA1mv53nlQ+ma1ustxWtoAODfLj0R//v2PiQ1JQcCgoCL5o7TfX8rWBH38nVorBTkKwVs7nttvSPsY/mOOGvWLGzevBl9fX34/e9/j6uuugqrV692Y2wy0WgU0WhhxaEKgRk0DH0PjfST34Ww/7MJHQ0F0V5fiY97h7G7ZzDLoNFLCddL22YeGVYArJ+Jgh0urOc1EZ8cMsroH/77tU5ZMzCcSMniTqajyPX6gJA75CR7aKgOjS9hIaf6Sslb+Wkus0mPM2e2YsNHR7B62yHMHCtVnR7foFQBXzB5DBZMHoNESjJoUmkRR4fiaKqJIp0W5Sw5vqYR+z+7xpjBwdp1sDAyoBg5h3RqUx3oG8bWTBuUhMZ9MBRPyvWsJmcMp0lNVbjp3JmG39cOQQuhaL4OjeBRjy+DspzKF8shp0gkgunTp2PBggW46667MG/ePPzsZz9DW1sb4vE4ent7VccfPHgQbW1tAIC2trasrCf2OzvGC7RoDA+9DrV6NRpE7gYqv5a5n3Xi6bsPZxft09PQDGQMGBa6ct5D400NDW+QnDKlERXhAA72x7D14DFs/OiI7BHT1iJh5Ao5iaIoL2rMgCQPjT/p5UJOZmA6mnU7e7At4/HUFs0EgHAwgMZM+jXzkB4diss3QSODpqEqIhtYe44o8344nkJXxpujZ9Dw2VXs2mYwQ2hMVRj1Jr+rXYyqoWtR1aHxaKFOBmU5lS+269Ck02nEYjEsWLAA4XAYL730kvzc1q1bsWfPHnR0dAAAOjo68N5776G7Wyk898ILL6Curg6zZ8+2OxTHMOOh0avRIAvjOItGcT9nx9NlD02z8v5MQ5NMi0hkFjVmwBwZjKOrf0S+Udc6pKGxUm+imPBCw4pwEB2s2uvWQyphZ+9wQnfsuh6agZjclgJQQk60W/MnfbIoOGLq+BPG1aKlNoqheAr/u2kfgOyimQxtqj/fOkPv//xGSC8hgDdu+Ncz+Gs6kdQaNPr1sNzESvYjX4fGq1mTDLmwHs35ssOSQbNy5UqsWbMGu3btwnvvvYeVK1fi1VdfxRVXXIH6+npce+21WLFiBV555RVs2rQJ11xzDTo6OnDaaacBAM477zzMnj0bX/nKV/DOO+/gueeew6233orly5cXNaSUj2yDJnsHp1cWnK/FwFAWNh0PjU5bBd49zcJOLOQEAO9/LIligwEBFWFn6iJ6VcTHp4IC6iwVfvGPJ9NZzSb51wcCAlpqlPYHMe7YGjJofA0LN5r10AiCIF9HmzPZQHrzG8gOU+p5Zfj/8+uG3kaGN26Yp5CRTKWxluvQndB4aLTlHYqBlexHfu3zatYkg1oflC+WYhbd3d248sorceDAAdTX12Pu3Ll47rnncO655wIAfvKTnyAQCGDZsmWIxWJYunQpfv7zn8uvDwaDeOaZZ/D1r38dHR0dqK6uxlVXXYU777zT2W9lE17rwgt2ecyGnEx5aLhdVyQYQECQ3ncknkJdRVhO0waktFFACjdpuwYXilcrBbPxMA/SmbNagae3YMNHPUiLQCggQIS0mPYOx1EZqVS9nt0TAoKA5lppBz+SSMu7Y5YRA5BB41e0GhoznDmzBb/PeGcAAw+NxqDp7leMGKZnEUVRfr61Vs9Do2xk+DVgJJHGsVgSdZlq35v39qo2LloNTWk8NMr6wguj9dCrQ+PVOSVraMigKTssGTQPPvig4fMVFRVYtWoVVq1alfOYyZMn4y9/+YuVjy061dEQqiNBDMZTORc7pUYD56ER9Tw0rMiW2kMzEEvicObGOonbdQmCgMqw9NlD8RTSaRED8WyDxqkqwYCHKwVrPF5TmqowqbFKdt0vmDwGOw8N4vBADL1DCYyrVxs0vIFZFQmhJhrCQCyJvUel11eEgwgFJIOGNDT+g++0bdZDAwCfmt4sbxoA/SxGQMdDM5DtlTkWS8ohTH4jpO+hUa8Bh47FZIOGeRwrw0EMJ1JZGhpti5RiwNsvaVFEALkNGl6vxooTe9EDIooil+VU4sEQjkO9nHLAFrNc7mg55KSnoeEMmkmZCsN9wwk5xRRQFrqm6oi8qDH49geD8aRq4r2/n3lonBMGejXmrQ058eECQKrKym5kesJg5fXSG7C/6b4jnEET9PZuksjNSCItd8puqDKnoQGAMdURzMu0ORAEYMKYHAZNjaK7AtRhpqF4CoOxpPxYbTSkChfrJQNovbT8+ymtE6Rqx9qQU0k8NJxFk2968KJgo8a9pSaeSsvrKc358oMMmhzIKZjN+guIXshJqUyrLASVkSDG1knvder3X8KsW/+KWbf+FZ9d9ToAtXeGfw0gGTTabroH+5UF1Cm8n+WknE9tB+WGTKihTyd1W2tgshvU3qNSDZCKUEB+TlvLg/A+TD8TCgiojgTzHK2GXUft9ZVZVcAZRhoa9ruefgZQatLs7xuWRajMuGHd49lrewZieHeftFE553ipLhdv0IwkUjiQyY7K5TF2A1XIKY9xoujV+Owo98bGuPPpLfjMf65VNRw1gtfaedHgcpPDAzGc8x+v4r5Xd5Z6KK5BBk0OOqY1IRQQcLpOxVGAr9HAeWjSSoiDhy2e8VQasaT0j8XI9Uq08x23+bg6j1M1aABlJ+a1mLJWQwMAp09vwqTGKsybUI8T2upMeWiYC5zddPbyHpoAeWj8itz2oCpsWU92ybx21ERDWHJC7ro1eQ2aAcWgadYYNM01ETTXRCCKkj4mlkxhf6adAWuCyV779wNS7ZlpzdUYP0YKmyaSyvV4dCgOMaMZG1OklG0gO+RkhKr1QRHrWj3z7n68/3E/Nu/pNXV8jKvv5bUQu9u8vfsodh4axJ/f21/qobiGc3fFMmPFebPw9bOmq9zIPHKYhpsUfHEpnh8tm4sbl8zMWhQiwQBa67LLk+sZNA1VYdVN26kaNICXKwVnG4hVkRBe/pczIWSyKeoz6bpMS8Ejaj00zKDJaGii4aBsLJGGxn8UIghmTGupwebbzjXsRs2n+vM/mf7GyEMjCAIWz2jBH/72MVZvO4TmmihEEaiOBHHCuDps7Dwivx/LfpraXI1wZjy8h4YZN5FQwLFEADOoPTS5j+PXDV5DU4z1hI1rV88QTp+e/3i+YOlom/LsuyfLuOcDGTQG5DJmAD4zKHelYP7Y9ga1YNXM5w4nUghnQk7t9ZUQALn3k6OiYI9mJWg1MAz+JmTkoeFbHwC8hyYTcgoHZA3NaNutlQN9w0rbg0IwMmYAJUTZO5RALJmSjZepzdXYeWhQMmh0atAwzpyVMWi2HsLJk8cAkDQwWs8Pr48JZ67HBBeviaekG1E4z3idxmzIiZ86AQFFrRTMxqWXRarHaA45sWKtWn1WOUEhpwLRKwuul7ZdCHyDStbTpbYipBIEOikK9nradsBgV2qsoVH/PdhNhzU0rAgFEaQsJ9/Cd9p2g/rKsGxgHOgdka+b2e31AIw9NICUTSUIwJYD/Xhjl9SmY0pzVZZBoxTYrFI8NFzIKc55aIqJKuRkMD94w0BQVQp2bWhZn63XCFSPEZWHZnTNeeah0ZYEKCfIoCkQo5CT0Q3YDHz7A1aDprYipCqq5WTIifcoeSnsJGo0MHoYeWhyhZwYFeEAaWh8DAszutUKIBAQ5FRs1uU9HBQwvaUGQH6DpqkmijnjJePnd2/uBZDfQ8OMFn4XzVK4I0X20ARNZjnxcycgSMJg6TXuzyn22XqNQPUYGcUhp6E4CzmRh4bQoBty0snKKYTKsGSsSB4ayaCpiao9NE6GnPidmJdu7KkcISee+ky4wUgUzETP2QYNaWj8TK/FtgeFwK6ZLRmDpqUmitY6RVtjZNAAiuifhYqnNFWp0sHTaVG+GU9pUjw0fB0aZtwU20MjmAw58U8VO22bfcSunkFTm7ERrqWEl9a6YsCMuUQZf28yaApEz60qh5xsntXKiPQGw/EUjsmNKMOY2syHnJwzaAST4r9ik+Yq/eaChRv0RMFZIScdg4Y8NP6lz2Lbg0JgxseW/RmDpjaq6vFkpKEBsrMYJzdVyxWFewZi2N83jFgyjVBAwPiGSkVDw3tokqXx0AD6nmgtaY0oWKkU7OrQMp8hffZIIi1XbzaCb/o76kJO5KEhcqHnVtXr5VQIvIaGhZxqKkKqIn9uVAoGvDXJtQaJHuxm1jeUraFJaQyixuoI+D9NRTgge2+oDo3/4NO23SLLQ1MblR/r6h9Bz0B22wOe+RMbVJuPKU3V8nWYFoG/ZdKNJ4ypRCgYkI0WXufAvDXhUPEynBh6WkEtKoMmUNxmt/xn7zqcX0cTS47etO3RkOVEBk2B6LlVHdPQqNK2laymKS6Jgq3UmygmbChBQw+NUdq22iAKBwNo5DJioiHOQ1PGk7xcsZO2bRZmvBzoG5F/5zUwaVGqNtxYrR/2CgUDWDRDqv5bEQ6gtTaKUDCApszxb2XEwiyczEJOqbQob5BK6aHRC61rUWc5CbqNe92CH5cZHU05amg+7OpHpwljTs5yKuPNGxk0BSJPdO7a0KsUXAiVEUVDwyoF11WE0FAVlhdvJxdxs/Umio05DY10HobiKdXuC9BP++bDTryGxmtFBYn8KH2c3NfQyL/XRNFUo/68puqIYQo4CztNaaqWPYJMbPzmrqOZ5yTva5jTybCwE/tZ7LRtQNkMGBknWXVoiqih4YdlJtOJT9suhzk/GEvis6tex+cfWJ/32NHgoaE6NAWiN2lzVQq2SmWmAzRv0NRkumvfcclsvP9xP45vq7X3IRy8QeMlLYmZkFNtNCQXOusbTqC1VqkdpOcxa6mN4sMuqTKrlOVE3bb9CgszupW2DWRrY1pqo4iGgqpCl8059DOMS+ePx9auAZw1S9HTsOvwwy4plKV4aJRrNZFKoyIcVDw0RRYFA3wT3tzHaOvQBEyEqZyCn7dWPTReyugslCODcYwk0hhJxPJ2RJcNmrQIURSLWqSxWJBBUyB6DR1zVQq2Cp+23c/StqPSov25kybgcyfZevss+DngpUkup10bTFKpWnAYR4cS6BtKoLVWqbysbX0AqG9QlOXkbwrptG2VLA9N5veWmqhs0OTKcGJUhIO47eLZuu+j7fgdDvAeGjHzs5SiYDMhJ3UdmmI1u9WuVZY9NGUw5/nKx6k8HdGHOEF0IiUiUgJNlttQyKlA9IrRMRemkebDDHLIKZ7CANPQOJjVpIUPkXlpkus1p9SDhRy0OhptHRpAE3IKBUhD41NiyZS8QBcjbVv7O/94PoPGzPsyD00gIMjzkRkypfTQyHoYI4NG45lWspzcnVPa99/dM5R3Q1ZurQ/4rK18oSTeO1WuSRBk0BSIXnNKx9K2VZWClTo0buHVtO2UyRAe0xNpa9FoWx8AuTU05KHxF6xqryA4W8JAizac1FIjeQBtGzTc+wYEKcuJwcJOzJCJZ25UpdDQmMlY0oZ2AybCVE6gnbIDsSR6BrOzHXn4mzrg/0ynYQtGyrDGQ1OOkEFTILqVgp1O247zomB3u+wWM9XSLHoeFj2UasHqxUxPg5NTFOzzhW200cdlOBmFJO1SHQ2hmuvp1lwreYN4gyRXDRoj+OuwvaES0ZDyGdoGlaX00AR0PNFatJ7UYs0pfjPJNjX5UrdzJQ74Fd5IyXe+VcZPmdaiIYOmQPQmesrkDTgfrLDeQCwpu9XdDDkBXDaDhya46ZCT3M/JRMhJpaFRQk7l6oItV2T9jIuCYAYzPmqiIVRlwsFOhpz4cgwAsmrRlDLLyUyjSSWbEKqfbhsL/PtPa5HO4a48wmBeQwN4a70rBLWHJo9BEzd/rF8hg6ZAAkYhJ9tZTtKiyaqQAu6GnABvNqhMmwzhNeRof6BnEPE3kijnoUmL3vJOEcbINWhcTNlm5NPNFGLQ8IX4+IKZQG4PTbQkHhrpp5nWB2wu6YXj3YA3slgV9Xxdt7UhJ79PeSsaGt74KdeO22TQFIhefQZZ82G7Do3kfuYXMrfdzXIauocsGm2l31zIGpphiyGnUFBO25Y+zzvfnTCmtwgp2ww+s0n7GJC7SrDhe9Yo2XhaDw2rCBzPqkNTukrBxmnb6o2DmTCVE/Dvf1ymYWg+D82wxqDx+5wfMqmhEUVRY9D4+3vnggyaAtETvjldKZjhpuiRUaxUSyuIZkNOOTpu64UA6yvD8o2hIhxAkLtJlKsbthxh4cUxLqZsM5ghk9NDwxknZqmrDMmhpZwemsyGJuYBDY1xyEn6yaZZsZpTph3w0HhpvSuEEZMamlgyrbpXkYaGUKHf+sCptG2tQeP+ol3MYlhmMVNYD+D6OWVpaLJDVoIgYFZbLQKCJMYMeTRlnTCGdbkek6PlgJPMaqsDAMwYWyM/NmFMFaoiQYyti6Ku0vqGQxAEzGyrQTAgYHZ7neo5rYYmXlINjfTTTB0axUMjPV5MUfCkRsko7Mq0qMiFVkPjd+mcWQ0NH5oCytdDQ4X1CkRPRCtnOTmUts1wWz8DFK92hBVkj1cei0bu55RDQ6MtdPjINaeiZyCOsXUVqlgyeWj8A6sKqw3XuMHnT56AWW21OHG8YnjUREN45hufQkU4WHAhzYevPhU9gzFMGGOsoUmUslKwiY2OVjtoJkzlBPyYqqNKuxgjys1Do85cMjBoNN+7XJMgyKApkIDOpHWqUnA4KBXXYsZFcQwa6aeXhLFm0+BZP6csDU0ODU5zTVSuL8J707xkzBHGsKqw2nCNG4SCASyYPCbr8WktNTpHm4dvdMkj16FhomCPVwrWat2KtTnie+fxpS6MYOE7hu+znOLmNDRag6ZcPTQUcioQvYnuVKVgQRBUXppiaGi82KTRdMgpR2E9MyHAQEDpDlyuu5ZyQxRF2UMzuQgemmKT5aFJlc5DI4ecTKVtZwyaImU58esDWy+TadEwg0dr8PjeQ2NSQ6P93qShIVToxYmdStsG1Doat2vQAPrdw0uNWZE1S9s+NpJUTVSlDo3x58jtD8hD4wu6j8UwnEghGBAwvqEy/wt8BjNctGnbJakUbKmwHlQ/i5W2HRAEVESUc2MUdhrRFtbz0HpXCKY1NFkhp/Jc68igKRD9LCdn0rYBtY6mtgghJ73u4aXG7Pms4ww+1swTULxN+UKAVC3YX7BqsOMbKkvitXAbJcuJiYKln14NOWXVoSlS2jZfODMSDMiG1IhB2KncNDRDJuvQaD00cfLQEDx6xaPM1k0xgzrkVIQsJw+mbZsNOYWCATksx7c/MP36jIqbDBp/oISb3NfPlIIsDU3GqxAuZcjJQpaTmerCTsDePxgQIAiCXMU5l4dGFMXsSsE+n/NmG04OZYWc/P29c0EGTYEI5Rpy8tB1bqWuj1yLhkvdlndwef4g1KDSXzBBcDEynEpBtoamdB4aM1lO2jo0QW6+uZlkoG25UME19dVDKwgGyqBScMKchkbrmSINDaFCt5eTQ5WCAa2HpoiiYA/d1LULlhEsdbuPEwab7QVFGhp/Ue4emkjO5pTFrxQsr3MmRMHaOjSAu3OKz3IClB54uTKdYpx3piKc8cr63KIx258pK8upTNc6MmgKRK87tVOVggGNh2YUpm2LoqjE5i14aI4WEHJiBmi5umHLjdHjoREzP1nadjDna9zCXC8njSiYm3Bu3je1621lHg8NezwUEGSj0Ush9kJQeWgsaGjIQ0Oo0C2s51DaNqA2aIrS+sBjlYL5cZgxEKMh6XzxbuVcdWi0kIfGP/Ap21Oay9NDI/dySmo9NCUQBZtYF3LVoZFe596c4rOcAOStRcPCLhXhoPK9fD7nC61DU66bNyqsVyCCjivWrEfADMUXBXvrps4vhGYMGr2dpNmQk6KhKc9dSznRMxjHQCwJQUBWhd1yIavbdgmbU5rr5aQODQeLZNBo19t8GhqWsl0RDsjeX48sdwVTcOuDMl3ryENTIGzS6qVt260UDKgNmtEYcuIXQsHEVRrU2XGZ1eCE5F2oN747kRvWfLC9vlK+gZUbuTU0pUjbln4arQvZWU7Kc65qaDL3ZEVDk89DI70gGgoWLRPLbUwX1tNqaHQE0uUAGTQFIu9cdNK2g06IgosdctL5PqWE30CYCeEFdMJGZjVNQdLQ+IZdh8tbEAwYaWhKWYcm9zGiZp4Fi6ahURtSVZk1U5vRw1BCTgGwU+nnTUwylVbVkzFav7LStn1uyOWCDJoC0avP4GjatqqwXvFCTl65zq2GnIKyQaY8xv4e+QxMqkPjH3bLPZzKUxAMKAZN3BMemvzeS20BTJWGxsU5lZI/V/o9X8hpmNfQ6HjY/caIxstiFDLXGnnUy4lQoVefwdFKwZyHpjrqvmtdr1BgKVGFnEycTv2Qk/Qz35+D6tD4h11yl+0y9tBkRMEsLMBuPqVofRAw4cnQzjN+vrm5nogaD40iCta/sccyN/VKzqDxike6ELIyl6iXkzWD5q677sIpp5yC2tpatLa24rOf/Sy2bt2qOuass86CIAiqf1/72tdUx+zZswcXXXQRqqqq0NraiptvvhnJZBJ+Qq8+gxuVgqsiQYSKsJDJolqP3NStZjnpecyYxyWfpikULI94+mhgNHhoeA2NKIpKt22ve2i4SsFy4VFXs5ykn0GtQZMz5CS9QMpykh7zygauELRGihkNDQvLlWsdGkvijNWrV2P58uU45ZRTkEwm8a//+q8477zzsGXLFlRXKwvMddddhzvvvFP+vapK2U2lUilcdNFFaGtrw7p163DgwAFceeWVCIfD+MEPfuDAVyoOelk1blQKLoYgGPBepWDesDKjSQrq7LhM16ERyEPjF3aVeco2oNbQ8KGBknhoTDSt1Vv3goKAJFdLyg20on9FFKy/OVZpaEwUDPQ6VlKxmfFTVxHGUDxVth4aS3fLZ599VvX7I488gtbWVmzatAmLFy+WH6+qqkJbW5vuezz//PPYsmULXnzxRYwdOxbz58/H9773Pdxyyy244447EIlEsl4Ti8UQi8Xk3/v7+60M2xX06jOkNDsVO7DdRjHaHgDeqxSs1tDkP14v5KQVK+ZCqUNTnpPc72z4qAfPf3AQyXQafZnWFpMay9+giWtEn9ESZjkZeVrYtOE9odKcE13OclJr5PKmbWcej3IhJ48sdwWR3UE7fx2ausoQuvrLd/Nma4b09fUBABobG1WPP/bYY2hubsaJJ56IlStXYmhoSH5u/fr1mDNnDsaOHSs/tnTpUvT39+ODDz7Q/Zy77roL9fX18r+JEyfaGbYj6AnfnKwU3FQjGXZjaytsv5cZvJe2rfzfTBq8kuXEv4c5UXBQ57WEd7jpt5vx0Oud+J/1uwEAExsr5UaE5QirN5NIpVXptaXw0OhVRNeS0vHQFCOkk7tSsP5EZiLailBQNyvSbwxpPFFmWh+wmmbxMk3bLnhVSKfTuPHGG3HGGWfgxBNPlB//0pe+hMmTJ6O9vR3vvvsubrnlFmzduhV/+MMfAABdXV0qYwaA/HtXV5fuZ61cuRIrVqyQf+/v7y+5UaMVvgUgmL6BmuG0qU34wefm4JQpY2y/lxm8tmOxGr4zKqyXtw5NkArreZVjIwkc6BsBAHztzOMQCgg454TWEo/KXZhWJsF5aIIBwZF1xSpmQtFacS7/fzenlNYjnq8ODXu8Ihzw3AauELSZS2ZaH7ASIOW61hVs0Cxfvhzvv/8+XnvtNdXj119/vfz/OXPmYNy4cTjnnHOwc+dOHHfccQV9VjQaRTQaLXSorsB7DdhkZ54aJ9adQEDAlxZOsv9GZj/PY6r/lEXjMKgjXjTb+iBIaduehbU5aK6J4NsXHF/i0RQHWUOTFOWddCmqBANcyMmwUjA7VsegKUal4Iw3KG8dmmS5ZTlp07bze2jqMh6acq25VZAP84YbbsAzzzyDV155BRMmTDA8duHChQCAHTt2AADa2tpw8OBB1THs91y6Gy+iLh4lqn46USm42LBFwSs7FjY3zZ5L/cJ65jRNTENTrnFlP6N01i7frCYtehqaUhTVA8yFnLSGBWBOe2MXWUMjmNPQxPgsJ495pAvBkoZG46GhOjSQLuobbrgBTz75JF5++WVMnTo172s2b94MABg3bhwAoKOjA++99x66u7vlY1544QXU1dVh9uzZVoZTUvRqLThZKbjYeK6Xk0Vvl50sJ68JogmFXXKadvmKgLWoNDQlTNkGzIWcdD00Jgwhu2g3PeabUwZ0kwj8hjabK9eGTBTFLA0NhZwghZkef/xx/PGPf0Rtba2seamvr0dlZSV27tyJxx9/HBdeeCGamprw7rvv4qabbsLixYsxd+5cAMB5552H2bNn4ytf+QruvvtudHV14dZbb8Xy5cs9F1YyIqATcnIybbvYeG3HYjZDiaEsoMpjZr08zBgiD433YHVnpowiDw3zxiRTSsipVB4aMxsdPQ2NvMFwU0OjyXKSNTR5Wx8EdTV3fkP7PXNpaEY4kXRdZUZDQx4a4L777kNfXx/OOussjBs3Tv7329/+FgAQiUTw4osv4rzzzsPxxx+Pf/mXf8GyZcvw9NNPy+8RDAbxzDPPIBgMoqOjA1/+8pdx5ZVXqurW+AG1QaMOOTmR5VRsvFYpWNbQmDVoNAsvvzPMm+XECutRmpPn2NVT/r2btIQ5UTDz0IRL5KEJmrjx64nvhSJoaLQbyHwemmE+bbsMvLJmNTS84cM8NIkyXesseWjyuQ8nTpyI1atX532fyZMn4y9/+YuVj/YcqpBT5kJKWfQqeAnvVQo2l6HEYBtYtkCpKw0bv1auQ+ONr05wjIbKwFp4DU3MIx4ao6VfT3yvnY9ukNJoFvPXoWFp2wHPeaQLYSihDTnpGynsfERCAbmWUbkaNNTLqUAMQ04+PKteqxSsnMvCspz4hTRvyIkK63mSoXgSB/ulgprl3LtJi1pDI2YeK62Gxkjcq1uHpgjNH9kUl1sfmAw5VUaCulmRfmMk44mS20zk8tBktDaV4aB8bZVreN2Ht15vENDJckrJQlb/eWj0RLWlxGqRQq0L2UqlYcpy8iZ7jkjhpvrKMBqqsiuIlysRrvVBKTttA/r1nbQY1qEpQpaTnLad8dDEk2ndmztfWE+v95vfYIZbTcRYF8NCU5XhIEIB8tAQOdCGafysofFa2rZV41DrQua/huk6NBRz8hS7Dpd/Z209lDo06ZKLgoM6YnsteuJ7tp64mrado7AeoF+LJsaJgsshs5FVRGap2Dk9NFxjStlDU6ZrHRk0NtDeRGUXqA/TnPS6h5cSsynXDG0aZtqCKJg8NN5kNOpnAEUAHPdQ2rZxYb3suRqUQ04uamg0mx6+15Ve2IlP2y5GSMxtWCipRq7+a2zQVPAemjJd68igsUFAkxnkZKXgYqNUzizxQDJYTtvWhMx4gybfW5TDbq0ckTtrjzoPjaKh8UqlYOMsJ+knv3FQsg5dG5q8RrDPFQTBMNOJv7GXRZaTydoysoYmElTavFDIidCiLQvu60rBmSF7JeRk3UOTeR37W3DzNX/IiTw0XmS0emhYeCktKuX6S+WhUco55D5GV0NThDIQemJkI2GwnOXE9XLytYZG258pl4aGCznxNY7KETJobKB1W7JrxGztFC/htR2L7E42adFoRYhqUbC5kBNlOXkL1vZgSvNo89Aoy/JALJn1WDExE4pmz/HTrBhlIPQ0i0YeGr6wXjlkOQ1lvmNNNI+GJq60fAgFSRRM5EA7Kfyctu21ugyWs5w0ITMrWU5KyMnaGAn3GEmksL9vGMDo89DwxstQrLQeGjPZQMbNKV0bmu7nVoSl86T10IiiKNf0qQgHPVemohBGEhoPTY4vM8SlbbOQU6JMN28+vPV6Bzn/vxwqBXtsx2K1jUS2KFh5Ll8IkDw03mPf0SGIorT7bKoePSnbgFovwzw00ZJVCs5/49cLDxejm7XcnJL74KpMCrPWoGHGDMCynKT/e8UjXQhmNTRy/Z1wEOEAhZyIHGgbsKV0Kmb6hYBGg1Jq7NahYX8TMxlnLG2bNDTegaVsT26q8qUmzQ6CIMhGDdtdlyzkZEILo7eRK0YrFb1q4izkNKIJOfEhKL5SsFc0g4Uga2ii5jQ0vCi4XLttW2p9QKjR7l70xHF+wWsuWOsaGumnoqFRP24Em+R+3q25hSiKeKPzCKa31qCpxv3mset2HEZX/whe23EYwOhqSskTDgaQSKUwyEJOJasULP001NDo1aEpgoZG25wSACpyiIJZ1/YxVWGEggHPaQYLQS6sl68ODSusx9ehKVNvNBk0NtDWaFBuwiUbUsF4rVKw5ZCTtvWBhYwzynLKzTv7+vCFX2zA2ce34qGrT3H1szbtPoIv/fdG1WOjTRDMkDwyKQwyD43fQk4msqPsolfaoTKjoRnSeGje6DwCADhlSqPqNX6d8nxbDCXklMtDw2loyjzkRAaNDbK9Av710HgvbVv6WXDrAws1gYKC/3drbsFSpz8+Ouz6Z73/cT8AoLU2iuPH1aE2GsIXT5nk+ud6ERZiGsxoaErdnNK49YH0U78OTTHStrOznLSVgplBc+pUyaAx00Xcy4yoOmgzUXCuOjRcpeAyb05JBo0NtGXB9Sa2XyhG3QgrWK3po3iYpN+tFOYjD01u+ocTAHI3/HMSFhb43EnjsfLCE1z/PC8TydxxB0uc5WSqsF46e64Wo1KwnmdIrkPDeWhSaRFv7JIMmoVTmzKv8dZ6ZxX2/QKCYsTl09BUhIMIB5SijeWID4Mj3kG7C9Er9OQXilHZ0wopWdRr7vhcrQ/M1ARiGhqvCKK9RF8RDRpWd2a0pWnrwXbSLORUMg+NKVFw5lhuqmkzQN1AL8upIpytodnadQzHRpKojgRxwrjazPi8td5ZZZjLXMqnAWTht0quDk1aLM/1jgwaG2hrNFClYOewKrDO/bfI/1rFQ+PT1c1F+kekG6o2a8QNmIdmtLU60EMJOaUyv5dmTTGz0THOcnJvbHpi5Eodg+aNzh4AwIIpjfINXa4s7pH1ziqqzCXWXDfHyR7RyXICyrMWDRk0NtBOWnZ9UKVg+7BzaTrkpNXQsF2jCXdZyGPf3Uv0DUkemqFEyvVGg3uPZDw0zeShydLQhIJGh7uGmY2OnoC/GM1ulSwn5bGqSLaGRgk3NRZ1fG7CQk6qVOxcGhrOoAlzGSvlKAwmg8YGucrt+1MU7C3Vv+VeTjb+FlSHJjf9I5JBk0qLrtau2N87jERKRCQUwLi6Ctc+xy8wjwy7GZXMQ2Mi5KSXUVgMTZ6eF7dC0/qAlR0AFEGwenyuDc9VhvkwEtuQ5dLQxLPDUwAZNIQGbY0G+Sbqw7PqtUrBVjQwgE6WkwWDiDw0uWEGDeCujobpZyY1VpmuPVTOaAvplU4UbCZtW32s9H/pZ9GznDIeGqYb+ejwIA4PxBEJBTB3Qn3W+Lyy3lmF19DkS2rQM34ACjkRGrQ1GnxdKdhjE7zQXk7a8J+ZkBW7gZbjjsUuTBQMZKfCOgnpZ9RoPTKlS9uWfhoX1svePChZTq4NTXeN0GpomHfmpIkNiHJhO69t4KxiRUPDd9vmq1CX43pHBo0NtCGn8qgU7I2L3IqoF8gW+VnKciIPTU76h5Py/7XFyuyw8aMerPjdZvQOxQEo9W4ow0nCex6a/HVoeM+anEVUlCwn5TFtHRpm0PD6GX58XlnvrMJnLjEPTa5UbD5tG4BsAJVj6jYZNDbIlVlTos2ULbzWcbrgbttyLyf2eP7Xyt/dp4ubm6hCTg4aND9/dSf+8PbH+OW63QCAXZmQE3loJLQemZL1cjITckpnb+SK4fHV08lpWx+8/3EfAOCkyWNUr/XaemcVPnMpbJC23d0/gpFEGoIA1FdJFYWVfk4+/fIG+PDW6x20WU7sgqK0bfvo1ZgwQpvlZKX1QShPDHq0kk6LcmE9wFkNzcH+EQDA6m3dAMhDo8U7Hhrpp5Gxr1eHRlsXyg3kED/3wZWcKDidFrEnkzk3TZM557X1zipsc1GRR0OzetshAMDc8fWoy7RIYNdWOa53ZNDYQJv6J1cK9qNB47Gwi9WQU0ATs7ci0FaMofLbsdhhMJ5U7cyd9NAcHogBADbv7cXRwbgsCh6tzSi1aHs3lcpDo1RDN6Oh4T00xahDk63dUdK20zg0EEMsmUYwIKC9oVL1Wq+td1bhdTFGGhpm0Jw5s0V+LJQnROVnyKCxgdatqqe69wveS9uWfloOORWgZyr3hm2FworqMZzy0CRTafQMStqZtAj879v7EEumEQoIaG+glG0gWxQcLZGHRtaaGNz7dOvQFMFg0NPJ8aJg5p1pb6jIMgi9tt5ZZVhHQ5NMiyrDM5UWsXa71LX+zFmKQSN7aMpwvSODxgbaWgZWvQpewmsuWMt1aLJaH2Qet9DLya+7NbdgRfUYThk0RwbjquyXX22QdDQTG6vkSq6jHa9paMyEnIQSaWj4z+Xr0PClALSUTZZTOKgyfvk17J19vegbTqCuIoR5Exrkx5mGphwro9PqYQOtEFW+ifqwjoaZhauYWNfQSD/Z+JWGeWZeSwaNHrwgGHCu/UH3sZjqd6WHEwmCGV7R0LBhGG109DzTxTAYWMSEXyMqOVEw02XpGTReK1NhFaVScEj1/XldzOqtUrhp0YwW1UaBhZziSX9+dyPIoLGB1quhp/b3C15zwert+ozIZVxSt+3C4QXBADAUT+Y40hqHMvqZ6a01qAgrSxDpZxS0Bk2pKgULJtYFvZCTmdfZRe9zWcgJALYdPAYAmNSYfV35XUMzJHtoAnLIHFB/Hz39DMCLgslDQ3BoJ62vKwUXISvBClZDTjlFwVSHpmD6hrUhJ2cWwEMZD82EMZXomNYkP04eGoVwSFNYr8R1aIzmRlon26gYzR/ZmPjPrVAZNAMAcnlovLWBs8oI18tJz0NzdDCOd/b1AgAW5zJoSEND8PBuVVEULQtZvYTXXLBWixRmN6ekkJOWdFrM0sVon2fNEAH3RMHMoGmpiap2j+ShUdBqaEpVKdiMYWKY5eSqKFj9WYA0l5nxt8sg5OS1DZxV+GJ5fDuDZCYOt3bHYYgicHxbLdrq1UJ7qkND6BLgJjs/3/1o0Hitcqbe7ssIrQbIinGpiOS88d3d4pu/fwcnf/8FfHRoQPf5/3hhK0684zls3tsLIDvk5FTrA9mgqY3izFmt8uPkoVHwioZG6/nUQ68OTTGaP+aqBs5St9mYJ+lcV9qiqH6DrxQcCAhZvbPW5gg3AZA7bpfjekcGjQ34suD8xPBjHRqvVc606u3KznIyLypWQk4e+fIusXlvLxIpEe9lqqfypNMifvvmPogisG6nlOqpDTk5raFpqY1iSlMVLj91Ii6e104eGo5sDU1p07aNvJd63tRiNKfM5YXldTT1lWHUV4azXhvUbID8BmsZMqY6AoArPZE530x4f1xrTdZry9lDEyr1APyM4lZVTwzBh2ai39O2tSEzPcFgLoJlvGPhYeGmQ5osIwDYcqBfLna3v3cYgJLlNKYqjKNDCQzHndXQtNRGIQgC7rpsriPvW07wImBBgCqsUEzMhKL1DAulOWURNDQai4Y3aHJ5/cx4nrwMH7YFMhu3lHJO4klprurVLwqRhobQgy8Lzk8MP3povJa2LVr00GizFlIWum0HTexC/Y4oiugdzm3QsIwIANjfK7UlYI0px9ZJMXinQk6HNYsxkQ0fYooEAyVrp2Kma7aeN7UYzSnlyuwaY48XBk/U0c8A/s5yGowlMZgJObXUSnNI63WJp3IbNGGqFEzoEeB2IfzE8KOGxmuqf6tVl4Oa8Vvx8ARHgYZmIJaUr9H8Bk3GQ5MxgJhB41jIifPQEPrwIaZSCYIBc4ZJSmeuafvcuYHioVE/zmrRAPqCYP41ftTQME9qVSSI6qgUZNFmajIPjZ72il1biTJc78igsQEvfOMnhg/tGVng7NeQkzZTyVrrA//u1szSy2U3MQ0Lo38kgbd3H5V/14ac2jIGjRNZTsPxFI5lMqnIoMmNyqApkSAYMHfjl+caN1nl1xVBQ6NNHFCFnHIYNIrB5b85r7ch0IbNZYMmGIQWOQmCPDQEDy984+etrysFe+SmbjXkpM1akN3gJv4WvDHkFYPOaXiBr9ZDs25HD5JpEe2Z9M7+kSSOjSQUD009M2jsL4Bsd1kRDqAmShK+XPAamlIJggFznpa0Tng3UASDIVetKT7klMtDY6ZHlVfR6meA7E1ZLCltPow8NKNeQ3PXXXfhlFNOQW1tLVpbW/HZz34WW7duVR0zMjKC5cuXo6mpCTU1NVi2bBkOHjyoOmbPnj246KKLUFVVhdbWVtx8881IJp1xZxcTPuSULpuQkzcucrnqsskrVLvjsuLh4QWXHrHnHEflodEYNCzctPTENtRVSEbGgb4R2QhiHhonWh90awTBhD4Rz3ho8tdr0ZtrygbJvbHl6tfGh5xyaWj8nOXEZwkysjQ0RqJgpqHxozWXB0szZfXq1Vi+fDk2bNiAF154AYlEAueddx4GBwflY2666SY8/fTTeOKJJ7B69Wrs378fl112mfx8KpXCRRddhHg8jnXr1uGXv/wlHnnkEdx2223OfasikSvk5EMHDZf2XOKBZChUQ2On9QFQnuXAAaB3OC7//8hQXHY3i6KINVzNivaGSgDAnp4hWXg4tk5aOIcS9jcdzJhqJkGwIbxXplRtDwBz9Vr0DIti9HLKlbZdlfHQSN3bK3Vf67WsTivohZyyNDSp3Bqacs5ysuTzffbZZ1W/P/LII2htbcWmTZuwePFi9PX14cEHH8Tjjz+Os88+GwDw8MMP44QTTsCGDRtw2mmn4fnnn8eWLVvw4osvYuzYsZg/fz6+973v4ZZbbsEdd9yBSCSS9bmxWAyxmLKr7O/vL+S7Og4fpklxk8uPO0+vieSs1qHhjUveY2ZOQ6PfC8UP7O8dxpN/+xhfXjgZ9VXZ9TYYvIdGFKWO1611Fdh5aAAf9w4jGgrgtGlNGN9QiQ+7jmFrpg8OALTWZkJODqRty7tLMmgMCfNZTqFsHUSxMBNyMqpDU4zWB9oQP/PQTBhTmTP87+csJ72Qk7YfXcxQFJxfQ7Np9xH8+o29uODENpxzwlhnBl4EbPky+/qkAl2NjY0AgE2bNiGRSGDJkiXyMccffzwmTZqE9evXAwDWr1+POXPmYOxY5SQtXboU/f39+OCDD3Q/56677kJ9fb38b+LEiXaG7Rj8pLWq+fAaXqsUbKWODKDeHfIeMyutDwD/ZTo9+FonfvzcVjz0eqfhcdoieSz0s+GjIwCAU6Y0oiIclHe0Ww5Im4bqSBC1mTCUE2nblOFkDt4rEymhh8ZKyImfa8XQ0ORac5mGZpJBoUavZXVaQd9DI93Ks7KcdPRX7Ni4gYfm+Q8O4veb9uEv73U5M+giUbBBk06nceONN+KMM87AiSeeCADo6upCJBJBQ0OD6tixY8eiq6tLPoY3Ztjz7Dk9Vq5cib6+Pvnf3r17Cx22o/A1GnKV4fYLSky5xAPJoCySJj00KoPGWl8t3qBJeeUEmGQg02+JtSvIBassymCeks7DUrj4+LZaAJANmg8zBk1dZVguJT+cSNl20ZNBYw6vaGishJz0ejm5qaHJ1R6FhUjZNa1HMZpnuoWehob30IiiqNShCet4aEL5PTRyp+5Z2a0TvEzBaQbLly/H+++/j9dee83J8egSjUYRjXpvAeRrNLDJ5VN7xnNp22yumc0Y48XDUtaZvjta97XcIX7z0LDxvv9xH0RRzGkA9mqaUjLDYnemgd/kZmk3294ghZeYoVNXEUZFxqBJpaWFMmojBEIGjTnUGpoSZjmZ8Nwq6dPc6wJKwoRb5BL+f/GUSWiqieLMGblvxl5LgrCC3hziw0iJlBIxiOqkbefr5dTVN4IPu45BEIBF05udHLrrFDRTbrjhBjzzzDN45ZVXMGHCBPnxtrY2xONx9Pb2qo4/ePAg2tra5GO0WU/sd3aMX9ALOfkxZRvwYtp2YXVoAKX7udnXC4Igi+r8tsCx/lM9g3Ec6BvJeRyrEszOh2LQDAEApmRKxI/PeGjYZVBfGVbV9RixqaMhDY05PFOHxkzato6Ghv23GL2c9LKcLpnXbqgp89p6Z5Z0WpRLHzTn0NDEOc+LvihYnRGlhSUJzJvQIPeK8guWZoooirjhhhvw5JNP4uWXX8bUqVNVzy9YsADhcBgvvfSS/NjWrVuxZ88edHR0AAA6Ojrw3nvvobu7Wz7mhRdeQF1dHWbPnm3nuxQdZReSu6+IX/BaTDlXjYlc8Mel0qKl1gdAtqjOL/ARMr2mkwzWx4nV5Th0LIZ0WsTuI8ygYR4adVZIXWUI4WBA3gHaLa53mDw0poiEvFGHxlQvJ525pq3c7QbsvQtJwvDaemeWvuEEEplJ31SjGBu8hobpZ4DC6tCsNujU7XUszZTly5fj0UcfxeOPP47a2lp0dXWhq6sLw8NSZdH6+npce+21WLFiBV555RVs2rQJ11xzDTo6OnDaaacBAM477zzMnj0bX/nKV/DOO+/gueeew6233orly5d7MqxkBN+J1ooI1YvwNXW8gNXFSq2hsV5pWE579JmGhu8Q/r6BQcPStqe3SrqCQwMxdPWPIJ5MIxwUMC5TPK+1NqrydtVVSLtcJrS00/5AFEUKOZnEMx4aKyEnnTo0pchyMkMxKhm7AfNwNlSFVaFflYcmY9AEA4LuuTGqQ5NMpbF2uz/1M4BFg+a+++5DX18fzjrrLIwbN07+99vf/lY+5ic/+Qk+85nPYNmyZVi8eDHa2trwhz/8QX4+GAzimWeeQTAYREdHB7785S/jyiuvxJ133unctyoS/O5FrsXg15BT5krwigtWWazMHa8KOaVFS60P+Nf7rQ4N//cy8tAwDc2MsTUAJA/Nrox+ZuKYKrk2RSgYkAvpAZIoGFDKydvx0PQPJ2V3ONWhMcYrvZyULKfcx8jh9iJXCrYaluYpxvjcQC9lG1C3MzAqqicdm9tD886+XvSPJFFfGca8CQ1ODbtoWBIFm9m9V1RUYNWqVVi1alXOYyZPnoy//OUvVj7ak/A1GqyGSLyG10RyVg0SflFL8VlOJlc7Nsm9YtCZRWXQ7NMXBvOdtme0SgbN4WMxWT8zuUldTbW9oQIfZ/o5yQZNRhhsJ3X70ICk8amrCKnK0xPZeM6gMeGhUbU+KIKGRi6+WZCHxltZnWbJ5eEMcR4ao7YHgCIg1tPQrN4qeWcWzWj25eacejnZgK/R4HeDphjdca1gNeQkCILKjVyoBsdvGhp+vLmEwSMJZdc2g4WcOA/NZE29Dl5HU6/10NgQBXdTuMk0vBETDpVuTWGeW6shJ15f6BbMa1TImstOr1dC7GbJZdAEOQ1NzKAGDcB129ax5vysnwHIoLEFX6MhVyt7v+C9SsHWzycv9FNqY5h7rV87bmvHqxd2YvqZUEDA5GbJG3MslsSHB6RqwFOyPDSKQcN6OzEPjVZDMxhL4qJ71+LOp7fkHSvpZ8zDGzF6HZOLhRnxrF4dGl5f6BZ2an95rZCoWXJlCfIeGqO2B+pj1ZuTnoEY3s2sH2TQjEJ4JT9VCnaWQhYruZy5xdYHgLrjtp9g463OGBzvf9yH4XgKP3r2Q/z1vQMAFP1MQ1UYtdEQKjLFtt7efRSAUoOGoTJo8mho/ranFx/s78eTf9uXd6yKQVOR50gi7BUPjYlKwXJ4mLubFGODVMimh6H0fnNyRO6TM+Sko6HJHXLS19C8tuMwRBE4YVwdWuv8OUcLLqxHqIVlVgq5eZGgCfFfMZHdyRbOZ1AnBGjWHpIXBJ8aNHMm1GPDR0ewsfMI3tz1BjZ8dATNNRFcMGecbNDUV4YhCAJaaqPYe2QYx2KSt2WKNuRUryxmLORUlUNDw8JWfcMJpNOi4d+LatCYh+8AHy1lYT0TIaeUjoamGM0pc1UKNoPXsjrNkk9Dw6dt5yqAmasODdPP+NU7A5CHxhZyyCldBpWCPeqhsXI+eS+LXgdgs6/1E2y88yeOAQC80XlE7s90eCCO3qE4+jIhp4YqqW6FtqndeE3tGXXISZu2rTZoWKXhtAgM5EnpppCTeQRBkDUQpaxDI5gJOeloWYpR58WOV9xrWZ1myaehSfIampwhp+xKwem0iDXbyaAZ1QR1NBt+9dB4bYJb7bYtHSv9TInWRcG54spehy1Kn2ivk6+9uoqQ3FDyo8ODSsgp423hF8PxDZVZC5865JTR0OQIOe3KZEoBSvG+XJBBYw2WjeKJOjQmmlOq6tAUYYOQkr3i1l/rtQ2cWfT6OAE5PDQ5RcFKeIqx5UA/Dg/EUR0JYsHkMY6Pu1iQQWMDflL4PcvJa5Uz7WhoRL71gek6Nv5M22bnqToaxKXz2jG+oRKPX3ca5oyvBwB8dGhQTtlmpeD5xVCbsg1IBtGiGc04cXydXJNGTtvO4aEBsvtFaSGDxhrhUOk9NGZCR3qekmJqaAqpFOy1rE4zJFJpHBmUvK256tAkUmnEU8Zp26xEBd9tm2U3nT69uaQGtF1IQ2MDVWG9Mgk5eSWmXFDIiRP6WW99IP30m4aGCfuCgQDu+cJ8uQ7N1OZqrNvZg87DA/K5aKhkISdFI6PVzwDSOfvVtQtVNW0qI9kemnRalGvZAEo2VS4Ok4bGEsyQ8Ua37dzH6G3mggH3PSDMmVpIlpPXsjrN0DMgza9gQMCYKnWPJT0PTb46NLyHphz0MwB5aGyhFgVLjxUyubwAu6GnPDLBCwo5qTQ01jIgZA+Nzyptse/JFjRmgExrkQrofXRokNPQmPPQMHhjsFJHQ3Pw2IgcrweMPTTJVBo9bHdJHhpTMA1NSQvrcZmDudCba7L2xsUIrh2vuB+bUzIPZ3NNJEsIzWto8lUKlrOcMt+9fySBTXukjEcyaEYxAc4j4PeQk2AiVl5MCknJ5N3jVisNh0ws3F6ELUra7zmtRfK8dPIaGh2DRs9Do4eehmbX4SHVMSy0pceRwThEUfp7Nvqsg2+p8IKGJmjCcyuHnLjJWtwsJ+uvVTzSTo7IXVilbb0NAQs5pUyJgtVZTut2HEYqLWJaSzUmNube4PgBCjnZgM1fkdfQ+FQUXIzuuFZghpUVkbVeb63RUoeGLWiMac2KQcNSr+t1RMFTms0tYHqtD3j9DAD0DeUOObEqwU01Ud8K54uNF0JOypzKfYx+c0r1c25QiBeX4cf5nquPE6A2UsxWCmbhar9XB+YhD40N+GJ0/q8U7C3VfyGCP/2Qk9UsJ298f7OkcnhoJoypQjgoIJZM4+8H+gEoadtj66KZ10jHmUFpfcB5aHo0HhqDkBPVoLFO2ENp20Y3fr02JUo43r2x2an9JRTB4HIaJeSU26Axo6FRam5lPDQ7ewAAi8vAoCEPjQ0UK78cKgVLP70ywQvZfQV1NE3mNTTsb+mvtG3ZQ5MVUxcwuakaO7oH0D8i1Ydhadvj6ivxT+fMQFN1xHSTSKX1QbaHprU2iu5jMcOQE2U4WecT7XXY3n1MbihaCvjLSq/xKaBvVBdDo2KrUrAPu20fzWwYGmuyQ7YqDU3e1gdKLydRFHGgVwplzRxb6/iYiw0ZNDYoq5ATm+AeuZ+LNjQ0Kk2T2W7bzEPjM1FwyiA0N61ZMmgYTEMDACvOnWnpc5iHhg85MQ/NvIkNeGHLQWMPDRk0lvnRsrn4zsWz5eKGpYC/rtIiENSZTnpztRjNH3N5J83gtTIVZlDqSWUbNLKGJpW/UjDfbbt/OCkbQM06hpLfoJCTDQIUcnKNQsqa814mq2n0fq1DkzQyaFrUO3u9hdAs2rRtURRlD838iQ0AIGdT6UEGjXUCAaGkxgygDiPlmhuGzSldXE9sVQr2YZaTNluRJ8iFzPOHnBQNDRMa11eGcxpAfoI8NDbQ6+7s17TtQBmkbStepkJaH0g//aehkRYvbcgJUITBgGTYserBhaBN2z40EMNQPIWAIIVGANLQlCP8ZZVrs6Nbh6YIadv2PDTST69s4MygrfjNw1c6z7RoM0jbznho0mlZrF8uGw0yaGxgp9S+1+DTGHPFyotJIfHxALcrtC4K9qeHxsiTxVK3AWkHZiccqs1yYgX1xo+plBdD0tCUH7znL9e9X958qLptu+/xVcLK1l/Lb378grbiNw+f1CDky3LKnDBRBA72Z1LBy2SjQQaNDfhS+4VUtvUS/I1fFEv/PQpxJ/OpmFY1OH5M4wRyi4IBYCrnodHb1VlBm+W067AUbprSVC1nT/UNJXIaw4fJoPEl/PzL5b3Vq/nEjIyiGDSkoUEwqBQGZX+PaNg4ywkA9vfmrm3jR0hDY4MA51b1e3PKoImFq5gUoqHhMxdSsoFpTRTsO4PGIHW1sTqi1KCpsif44zU0kn5G8tBMaqySjaV4Kp3VvJJBHhp/wk+f/CEn5bHiZDlJPwtZc81UQPYSoigaamhCehqaPHVoAOBA3zCA8pmXZNDYQBXisBHP9QICdyV4Ia5cSMhJ0DEwrRbW84KG5shgHN984h28tetI3mONspwEQZDDTk55aNIiEEumsatH8dBURYJyXF5PRzMcT+FYJrBfLgvnaIHf6Ig59DB6dWiCsvfataHJ134hSy6foeoHhuIpJDIZmMYGTdp0pWAAcsp2ucxLMmhsUFZp29yq4IXU7YJCTpymyWrISSkdXvov/8KWLvx+0z48+Fpn3mONspwAJeyktwhaoZKrVzOSSMkemslNVRAEAfUZNzgzaJ7/oAsr//AuhuMpuSllNBRAbZSi3H7CTMhJbzPH6wvdgDdECknECBbBg+QkTD8TCQZUc5FhpfUBv1Z83Jvx0JSJhoYMGhvw1TDLJW0b8K+HRpXllLFLzBqYXvLQDMaksM1IjvANI50WZcMv16J+0qQxANR6mkIIBQOyC/voUALbDh4DAByXKfrGDCbWcfvu57bi12/sxR83f6zKpCi12Jywht2Qk1trCT9NC/GKCz7T0PRm2orUV4V155BcWC+VP+QkCILsUT3QV14eGtou2YCPE5dLpWDAGwZNqgDBn50sJy/t2Fihq3zGFb/7DeVI9bj8lImYPa4Wc8Y32B5XRTiAeCqNV7d2I5ZMo72+Qk4NZyGtvqEEUmkRezIenNXbDsmi4XJZNEcTgiAgILDSFObr0PD6Qjfg52khXnFVwcC06HnPep9Byjag3/ogalAFPBwMIJFKoS/j+SmXuUkeGhuomyH6W0OjnuAlHAgbQwEGol5dIPNZTkrp8FLDFqR8xhX/fFCvhCskz8qCyY2ONDhkwuC/vt8FADhzVou8W1Q8NAkc6BuWjbLXth9WhIdl4tYebeQzTvQyPN1uLcC/byG2iJn6Ol6ChZxyhY7l5pTptNL6wKAHmDYrkgwaQiV8SxUQIvESXgs5yRoYC1eourCexTo0Qe/UpWAGTV4PDW/QFMGQZrH7NzNiZb47L6+h2c01rTwWS+KFLQcBlM+iOdrIFz6Sw57c4scuR7c8nvxY7GQ5Af7IdGLatPoc1b55DU2+SsGAOtMpGBAwxmYWpFcgg8YGfCdav6dt88P2wgR3qtu2+dYH3tHQyCGnlLGrjB9rMa67yogUoRZF6fNOn94sP8draFgGFIN18yWDxp/kqymjWynY5W7bdjU02rpbXqfXIGUbUGtoYklJe5erUjCgrkXTVB3x7X1LCxk0NlCFnPyeti0Inuq4ze7lVjwPfJaTVQPTS3VozIac0sU2aLhCXQsmjVH1GeI1NMxDo212RwaNPykk5OS2KFilobGR5aR9L69SiIbGyEPDa+7KaV6SQWMDNilEUX9S+w3++5Qaveqj+VDaNxQgCmYx6DxekWIQMxlySqoWdVeHBEDR0ACSfoZH9tAMJeQqwl88ZZLqGNLQ+JN8xomhKLgIaduFXPteS4LIh1wl2IyGRu62bRRyUk4AGTQEAHVHWb+HnABvdaAtqJeTvEuB5To0Ya4DbamxKgoOBYSipENXhpWkSF4/AyiViHuH47KH5uQpY+TGlUB5LZyjCaNGjmrDQqcOjUtrScqmd1KlGSz9HiYvLOSUq+K3SkOTMuGh4TQ05bTRIIPGBuUUcgLgqZCTXvXRfAQ5A1OpImru9XIHWk94aKQYuNm07WKlnDIPTXNNBLPH1ameY67wo4MJ7D6iVBHmDR8yaPyJUm8r+3pM5fASul0pmJ8ahRjzqqxOD6x3+TDqtA0oGppEMi1XFDbKcuJFweU0L8mgsQEvfPN72jbAZwmVeCBQ9CFWdl/qLCfpMbN/DzbB4x4waOQspzxjSaUUD00xYBqaxTNasowo5grvPDyIkUQawYCA8WMqVQZNcxntBEcTRo0ccxkWbnt72XpbqEfca0kQ+egzmbbN91IzznIqz5ATFdazgSKWE32ftg24H/e2gr2Qk/XWB8yg8YKHxmphvWKkbAPABXPG4W97evGVjslZz7EOwGzsE8ZUIhwMYMHkMbho7ji01ERRYVDoi/AuRutCrnowbnt7C1kfeFgSBK9/9DJGnbYBJeQ0GDdn0PCbIDJoCADqSev3SsGAcay82BQSclIXOsw8ZnLFi/hSQyMdl6uontN8elYrPj2rVfe5es3OcXKTVEE4FAxg1Zc+6frYCPeQ55WOrc8vFby3xO3CeikHQvwBQcg0FnZqVO5h1kPD1g4gT2E90tAQWlQhJzbBfOyiMYqVF5uCejlxO0mrIcBwKLMgeMFDYzHLqVghJyNqoyHV32pKU1XpBkM4inkPjV6WkztjcmIDGfSQR9qIkURKDiVpNw6MoKYCaSQUMNwMlmvIiQwaG5RvyKnEA0FhGho+y8lq6wMvhpzMZjl5wSsYCAhyzyZA8dAQ/sfI28I/pleHxu0sJztZpV5KgjCiP+OdCQaEnN3qtZuaqIF3RjqeRMGEBn5CUNq2sxTSy0nlobF4s2cTPOGhkFM+4yrlIQ8NoM7AIA9N+aCsc9nP5arYy1+SogsGgxN1v7yUBGEE6+NUX6nfaRvIvu/k693GPDQV4QBqchhJfoQMGhvYuYF6EW9paKwvWHKJ9gJaH0RCXkrbtuih8YhBw7vDyUNTPhhtdHLVoeFvsG5skOxmOQHeSoIwIl/KNqBuZQCYMWik51tqo0WpYVUsLBs0a9aswcUXX4z29nYIgoCnnnpK9fzVV1+dUZAr/84//3zVMUeOHMEVV1yBuro6NDQ04Nprr8XAwICtL1IKAryGphwqBbtcO8IKdrptpzgNjdkFTwk5lf7LW21O6TUPjSAAExsrSzwawimUdcF8HRr+JumGw7eQ9UGLXPzPCwueAb1DrKiegUGj0dAYVQkGFFFwOQmCgQIMmsHBQcybNw+rVq3Kecz555+PAwcOyP9+/etfq56/4oor8MEHH+CFF17AM888gzVr1uD666+3PvoSo5dVU6wUWjfwVsipNHVo/OShSTqgI3ASpqFpr69ENEQp2uWC2ZCTkMND44YHxJEsJwNDzUuwkJORh8ZyyClzfDnpZ4AC0rYvuOACXHDBBYbHRKNRtLW16T7397//Hc8++yzefPNNnHzyyQCA//zP/8SFF16If//3f0d7e3vWa2KxGGKxmPx7f3+/1WG7Au+ylLNqPHJzKQQvieQKqkPDeWh8XYcmUymY1dPJ5RIuRDjtJvWZBXdKM+lnygkzISftJcj/7sZ6YrcODcBVFi/9lDdEbkyZo+0BkO2lzWfQsBBVuRk0rmhoXn31VbS2tmLWrFn4+te/jp6eHvm59evXo6GhQTZmAGDJkiUIBALYuHGj7vvdddddqK+vl/9NnDjRjWFbhu9CWw4aGrdrR1iBnU9rdWiUEKDV1gesZkMiWfrVjU8dNwo7KR4ab0jhxtVXAABmtNaWeCSEkyhNa/WynKSf2nUv4HbIKTNF7GU5eWe9M0Lu42RFQ5Mny4kZRxPHlNfmw3F58/nnn4/LLrsMU6dOxc6dO/Gv//qvuOCCC7B+/XoEg0F0dXWhtVVdnCsUCqGxsRFdXV2677ly5UqsWLFC/r2/v98TRg3voaG0bWdha4yVEF5QJQqW/m86yynI6tCU/svzxbFSaRG5CuzKlYK9Yc/gi6dOQkU4iAvnjCv1UAgHMQ456Xum+XnnpijYVh2azLzxQojdiHydtoFsDU0+D811i6ZhfEMlPvfJ8fYH6CEcN2i++MUvyv+fM2cO5s6di+OOOw6vvvoqzjnnnILeMxqNIhr1nmtMzqopt0rBHpjgqQIWrHJofZBMpVU3DiMPDevl5BUPTX1lGFedPqXUwyAchg/laskV+uE9J25oVJTGrIW/R0D2PDkxIvcwo6HRnv98GraW2mhZzlXXV8Jp06ahubkZO3bsAAC0tbWhu7tbdUwymcSRI0dy6m68SoCLwZaDhsZo4So2hWSN8d22C299UFqDRlupOGXgMfJSpWCifOE3blpybeRUzR9d2CCJDnhovLTeGWFGQyMIgmodyOehKVdc/9b79u1DT08Pxo2T3NAdHR3o7e3Fpk2b5GNefvllpNNpLFy40O3hOEqAiy0rqvtSjsgeXtqxWDVIAG2WU2GtD0qdth1LqA2ahEHVLzkTzMdeQcL7GGto9OcZa/4oHeP8mJjdb+faNzLUvISsoTEIOQFqHc1oNWgsh5wGBgZkbwsAdHZ2YvPmzWhsbERjYyO++93vYtmyZWhra8POnTvxrW99C9OnT8fSpUsBACeccALOP/98XHfddbj//vuRSCRwww034Itf/KJuhpOXKbu0bS5kU2rEAm7WAqcBKrT1QTyVNswscpssD40pUbB/rznC+wicJ1qL0kQ2+zm5+aNXKwVzrWu8jJnCegDT0Uh/pHytD8oVy9/6rbfewkknnYSTTjoJALBixQqcdNJJuO222xAMBvHuu+/ikksuwcyZM3HttddiwYIFWLt2rUoD89hjj+H444/HOeecgwsvvBCf+tSn8Itf/MK5b1UkVIX1PFa1tRC8VCm4EI+XKuRkMesszAXj8xW0c5O4JsvKaCxeS9smyhOjdcGoHoybzR+dyCr1UhKEEWZCToB6HYiGR6dBY9lDc9ZZZxmKvJ577rm879HY2IjHH3/c6kd7Dr45JVUKdhZl51dolpO1vwcLOQGSMDhcoh1OLGldQ0MGDeEmRpWCjcT3bO65k+WkHlsheMkjnYtEKo1jsSQAMx4aLuREHhrCKuUWclJcy6Wd4Or+MOZfxy9QVvUlvAGTSHrJQ5NbQ5PKPEeiYMJNzIScdD00Lm6QUvKGxY6HRvrp5UrBrNM2ANTlM2hIQ0MGjR0CKs2G/wvreSXkxNtTVnZg/N9Dzr4w+XreKDAS4rqNVkNjmLadOdTPYU7C+xitC0bZnW62UlFaoxT+Hn7IcmIp23UVobxrIV+LhgwawjKyR6BMWh+4GfO2Ar8AWgo56bWiMPlyQRCUasElTN3O8tAYhJzIQ0MUA6MK4kbzzM0N0mjR0BwbkcJNtRXG3hlAvfmLBEdnLzUyaGzAuyzLKW271BM87UDIqRCXdDjjsvVSyMlod5sqAyE64X0CBhsdo4KiAQNDyC5OdNvmyzx4laGMfqYmml/uym9sRqsoeHR+a4cI6oU4/Bxy8khdBv7jrZxPFkKWspysvz7EpW6XilimMSXDSENDhfWIYiCXQ9DV0JjJcnJ+TE40p/RKiN2Iwbi0HlRF83tcVBoaEgUTVuFFtOUQcnIz5m0FfoGxpKHhsjGstj4AvNH+oBAPDWU5EW4SNLjxG9WhcTPJwImSBX7IchqKSx6a6kh+D02QNDRk0NiBn0vJMgo5lXrDwov0rDi8AioDU/2YGSKZldtIt+I2Wu+QUeViuTmlj72ChPcxCjkZ1qFx0eNbSFkHLV4JsRsxGMt4aCImPDTU+oAMGjvwuwN5t+zjm4tXdiwid0+3FHKSx19Y1lk45IWQkwUPTcbY4V3NBOE0gsGN38gTGjAIVdnFCWPeK0kQRsgeGhMaGlVhPTJoCKvwuwMWpvC1hsYjMWW1KLjQLKfM6y1c4V4MORnWoSmDUgGE9zHytBh5Qo08O3aRDSkbdzDBI+udEYV6aMigISyjCjmlyqBSsEdCTk5kORXkofGgQWNGQ0OiYMJN+IroWowqcjNjw406L0ahLrMEPeKRNsKKh4YK65FBYwvdkJOPby6yiK/UdWi4RdJKjFxdubkQUXAmbbuUBo2FwnpK6wOaxoR7GGlNlAJ3uT00blTidSJt203N4N8P9KPz8KDt9xnMGDTmPDScKJjq0BBW4ScTqy7rZ/e/V0JOhabA8wXACim8JXfc9lAdGiOBspLp4eqQiFGOUT0Zo7kqN4t1YX/gRJaTW72mhuJJfO7nr+P/3r/Odo2boUzIyVyWE3loRue3dgh+DrMbj5/Ttr1SaKrQGhN8llMhRhHz0BjpVtymkDo05KEh3CRgcONXQk7Z88xNjYoTdWiMKiDboW84gZFEGocH4ujqH7H1XrKHxkwdGjJoyKCxA6+wV0TBpRqNfbySxlioO1kxyArMcvKphoY8NISbGIVmlLma/ZybGyQnBPFuiZb5SuO7euyFnYbi5j00vIaGRMGEZfjJlHRApFZq3CxVboVC+7TwIbOUgVgxF3IvJy+FnEwZNDSNCfcwuvEbbRzc3CA5qaFxenx8c9tdh4dsvddgrEANDRk0hFX4ueSE6r7UGLmWi4ntkJMqbdv8m7AdTinr0GSJgg00NNT6gCgGykYh+zll86H3OveSDBypFOzSesd7eHc75aGxWIeGWh8QlhEEQZ4U5RRyKn3atvSz8JCTf1sfZBfWyz0WJxZ1gsiHsYdG+qmnoXGzN5xRurhZ2LxxOgvLyZCTtSwnCjmNzm/tIGyys520n28ubu6orFBoXyy5Dg3nobFSSTTiAYPGSsgpSQYNUQQCBloYI2+qUtfKvTo09jw07oecdvfYCznJWU5Uh8YUo/NbO4hs0FDatmMYubGN4EutG2Vf5ELx0HhHQ2PkDpdrgPj4miO8j1HIiRkreoaF0pzS+TEVWtqBx61WLwluDu/qGbRl0Fnx0PBaumiI6tAQBcCuIbZb9vO9RXHBlnYcBYecONe4suCZf304VPrCeizkxL46eWiIUmPkuTUKObmVFs2PxZ4oWPrp9Pj4OTuSSKP7WKyg90mlRYwkpPXAVJYTpW2TQWOXcgo5KTsqb4ScrHbSZcYlb5D4NW27MiztsJIGY2H6GmpOSbiJkdbEKOQkGwwurCeO1KFxKW1bK+zfVWDFYNb2ADBXh4b9nYIBwdf3ITuQQWMTdsOk5pTOUWh9laD8t1DGX5hBU8KQU+Y6qsrsyMykbfv5miO8j1GBPHPNKZ0fkzOVgt0ZnzYzsVAdDWtMGQoIprKWWGHQ0ZrhBJBBYxtt6p+fby5eqRRst/UB79Ww1m07k7adLL2HhsXMqTklUWoCBloY0SD042ratkGoyyzsvu9m2jZQeKYTr58x8z2ZhiYaHr239dH7zR2CCcuSBQpZvYR3KgUXZhyySc97NQrx0JSy9YHWoDHlofHzRUd4HqOQU8pAO+hWWjT/uXacEW41z9QaNIV6aKxkOAHKxoY8NETBaG+Yfr65uFUK3CqF1pgIBpwxaEpaKThl3kNDhfWIYlBoyMmt5o+AsWfILEqWkyNDkmEhazYtnfDQmIGtf6NVEAyQQWObLIPGxyEnOXzmEYPGanw8qNEzAQW2PvCAKJhpaIzG4kQtDoLIh1HIybAOjZzl5PyYPJ3llDlRk5uqAUgemkK8QEwUbNZDI2toyKAhCkU7kf18bwnILuLSjqPQtG05hb5gUXBGQ+OBtG0rGhoyaAg3Mbrxm9HQuJPlpP6MQnAry4ltQqY1V0MQgIFYEj2Dccvvw0TB5j000gJIISeiYLQTys83F2Un5o3CelbXKm2RQ+kx868PecBDE0uqFzEzGhoKORFuYlTx16hnmpsh7LQDGhrBpfHFMxuqqmgI7fWVAArr6SR7aEzUoAGUdSAaHp1F9QAyaGyT7aHx783FK2nbdns58WnXVgzMiBfStlkdmswiljIYixNud4LIh1yfqsA6NO5kOdm/9oMuaWhYyCkcFDCluQpAYV23ZQ+NyZAT+z5R8tAQhaLdmfj53uKdSsGFlfTXFyZaCDmVuFKwKIpZomBTHhoqrEe4iFH2o9Hmw1UNTWaK2knbZku3W1lOkWCA09HY8dCY87iESEMDc6YfkZNyCjl5r1Kwtddpz73VP0WpKwUn00rLBsWgyT0WpTr16F3ACPdhG369G3/pNDQOpG271csppWw0JjVmPDQFpG4Pxln42WzIKaOhGcUGzej95g5BISfnKVgUbPNvUepKwXxBPzOVgqk5JVEMjDY6Rno3uUu3R0NOrnXblkNOAYzLaGi6+kcsv89QjGU5mfPQnDG9CbPH1eHS+e2WP6tcIA+NTbQhJz8bNG6p/q0iL1YWzW27KfQsy6lUHhreoGGLmJGGhppTEsXA6MafMth8aKuoO4kjGhqX1js2L8PBAFpqowCAwwU0qLTqoZncVI2//PMiy59TTpCHxibZN9ESDcQB5B1V6ZJ8Mp9fmOchK+Rk8epmHppStT5g+pkg17vF0ENDBg1RBFhYxzhtW+d1cnaU82Niew4v1qFh60c4KMgGzaECDBqlDs3ozVqyChk0NimvkJNXPDTST6uCv2wNTWEhJyMjwk3YQhgJBriqxwYaGjJoiCJgpIUx8pQYZUfZRfSwhobN2VBA8dAciyUxnPG4mEWpQ0OBFLOQQWOTchIFe61SsNVTqV1TC9fQlMZDw2rQREIBOWOBmlMSpcaoK7VRHRojz45dlMQBD2poMq1TIqEAaqMhRDMi3cMD1rw0VrOcCDJobKO9afrYQcM1ayvtOArt06INUVn9W8h1aEoUcmJVgiOhgJyxkDSqQ0MeGqIIBA1CM8Z1aNzLcmJ7DjvXvpxW7nSWk+yhESAIStip22LYyWodGqIAg2bNmjW4+OKL0d7eDkEQ8NRTT6meF0URt912G8aNG4fKykosWbIE27dvVx1z5MgRXHHFFairq0NDQwOuvfZaDAwM2PoipUKr0/DzzcUtF6xV5Pi41V5OdkNOmTo08RJnOUWCAdnrYqY5pZ+vOcL7GGUrsYd0RcEu1qEx0u6Yxajpph1YliTz+BaqoyEPjXUsGzSDg4OYN28eVq1apfv83XffjXvvvRf3338/Nm7ciOrqaixduhQjI0ra2hVXXIEPPvgAL7zwAp555hmsWbMG119/feHfooRovQL+1tBIP0uvoSlssdIaQFZv9MwrUuosp2jInIam0CaeBGEFOeSk15wynTv042aWkxNVsoMuaXwSnCgYAFpqMgaNxZCT1SwnooC07QsuuAAXXHCB7nOiKOKnP/0pbr31Vlx66aUAgP/5n//B2LFj8dRTT+GLX/wi/v73v+PZZ5/Fm2++iZNPPhkA8J//+Z+48MIL8e///u9ob8/OoY/FYojFlIuhv7/f6rBdQzuRfWzPeK5SsN2Qk9X7fKm7bbMsJ15DYyRQTnJZUQThFkbpzUrNqOzXKSFsbzandCvEzjYhtj00FuvQEA5raDo7O9HV1YUlS5bIj9XX12PhwoVYv349AGD9+vVoaGiQjRkAWLJkCQKBADZu3Kj7vnfddRfq6+vlfxMnTnRy2LbQTmQ/FznzSqVgIze2Edl6psJCTka6FTdRe2jMa2hIFEy4iZHn1shTEnDJAwI4U7LArRB7XK4UXLhBk06LGEqQh8Yqjho0XV1dAICxY8eqHh87dqz8XFdXF1pbW1XPh0IhNDY2ysdoWblyJfr6+uR/e/fudXLYtrCr2/ASXgk5sQXGqoZGq2cqtPVBPJV2ZVeZjzgnCg6bWGypOSVRDIyygYy0LG5lEUnvaV9D49Z6xzenBAozaEaSKXljRx4a8/jC9ItGo4hGo6Uehi5aL4DVm7CX8FylYIun0q6eKcwVtUimRXlBKhZ8yMmMhoaaUxLFwEgUbJQ+Ladtu5LlVNimh8etult8c0qgMA0Ny3ASBKAiRAaNWRz10LS1tQEADh48qHr84MGD8nNtbW3o7u5WPZ9MJnHkyBH5GD+RXVivNONwAjd3VFYoNORkv7CecnwpdDSxBJflZKEOjZ/DnIT3UTwZ2c8ZaVncLNTpiIbGpcroiRwhJyvtD1iGU1U46OtNcrFx1KCZOnUq2tra8NJLL8mP9ff3Y+PGjejo6AAAdHR0oLe3F5s2bZKPefnll5FOp7Fw4UInh1MUyirk5GIzOSsUXlhP6y2z9nreQ8OKYxWTmMpDY9woM50W5UWdRMGEm5ipFKxXsVfRqDg/Jicas7pVSDRhEHIyG8qmGjSFYflsDQwMYMeOHfLvnZ2d2Lx5MxobGzFp0iTceOON+Ld/+zfMmDEDU6dOxXe+8x20t7fjs5/9LADghBNOwPnnn4/rrrsO999/PxKJBG644QZ88Ytf1M1w8jplWSm41HVobGhDggFBcUdbfD0vro2XwEOjaGiCeevQ8ItwyKrlRhAWKLgOjYuaPCXUVfh7KL2mnNbQqOvQNGdCTvFUGv3DSdRXhfO+B9WgKQzLBs1bb72FT3/60/LvK1asAABcddVVeOSRR/Ctb30Lg4ODuP7669Hb24tPfepTePbZZ1FRUSG/5rHHHsMNN9yAc845B4FAAMuWLcO9997rwNcpPuWUtu2VSsF23MlBQUAKhRk0giA1hYyn0obaFbdQFdbLk7bNGzpkzxBuYmSYGNWhcctgAJypki1nYTldKTilTtuuCAdRVxFC/0gShwZGTBk0VIOmMCyfrbPOOsvwAhUEAXfeeSfuvPPOnMc0Njbi8ccft/rRnkSrx/SznsErlYLlzIkCbtSBAIBMD7hC/hThoIB4qjQhJzltO8xXCtY3rPi/EXloCDdRQk7ZzxnVoXG3OaV6bIXgViVjufUBd3NoqY2ifySJ7mMxTG+tzfseVIOmMGgltIl2QvlaQ+ORtG2jXV8++PNfyN8iHFJSt4tNPJVpThnMX4eGv0mQPUO4iZG416gIZtAlgwFwKstJ+ul4lhNrTskJi6ymbpOHpjBoKbRJOYWcgh4JObF7eCHeLv41hbzeavuDGx5/G1f89wZHvFp8Yb2QnLadw6BJkYeGKA6GISfDOjSZY1ywaJyoQxN0KQmCrR1qD40kuTBr0MgaGvLQWILMP5vw6v6AYK+dfalx00VsBTuN5/gdWyF/ikhmETJj0KTSIp559wAAoPPwIKa31lj/QA6+23YwT/iPN3R8rEMnfIBS8Tf7OaM6NG5mTTqR5WTUo8oOWg0NYL0WjZzlRB4aS9DWziZ2QxxewjMhJ5tZTgw7Iadc6dI8sWRK/v/unkHLn6VFXxSsv9ryjSn9bEQT3kfp8WbUyyl3HRp30raln3aufdeaU7Isp0DhISfKcioMMmhsojJofL5Vll2wJRYF21ms1H8P658dttCgkhXCA4BdPUPWP0wD3/qAhZHSov7fI+lAlgdBmEEw2OiIsmGd/TrvZzlJPx1P22bNKUNqUTBgQUNDdWgKggwamwRUHoESDsQBvFIpWFmsrL+W/xsU5KGxYtAklWOc8NDEdFofAPo6Gqah8XNWHeEPjDwtzIGot/lgD7mT5eRALycXsjpFUVQqBZOHpuiQQWMTuzdQL+GVSsGiQyGnQjw8VjQ0fMjJSQ9NlCusB+gvuOwmQZ22CbcxDjmVKMtJLu1gvw6Nk+PjNx8RHQ3NYbMaGspyKggyaGzCT2S/75a9UinYqZBTIT0bWf+VuIk6NE57aOI6omBAX0fD6tMEqTEl4TJGISejOjRGLRPswqaEnU2k3DzTwQ0cvxHSCzn1DMblbtxGUB2awiCDxib8hPK5PeOhSsGFu5Nti4KteGg4Dc2+o8O2G1ryBg2fIaHrocl8lN+NaML7GFXUNfKm+ibLyVGDRr+cQmN1BAFBWluPDMbzvs8ga05JHhpLkEFjE/6m63eBppKe6Y3CeoWcT6c0NGZaH/Ahp1RaxMdHhy1/Hg8r5hcJBlTfQ09Dw8bn92uO8D5GG52UnLat97rMMV6tQ+NCFpbKQ8N5T4MBAU2ZsFO3CR3NUCbkRB4aa5D5Z5PRnLZ9eCCGHd0DOG1aU95jd3QP4N19vYbHTGupwfyJDbZ6OdmvQ5MRBVsMOQHArp5BTGmuRiyZwqZdR7FwWpPK4Phgfx+aqqNoq6/QvlXm/aRFLBoKQBAEhAICkmlRt1qwE1keBGEGo9CM0VwN5tCo9AzEsPvIED45aYzu5/UNJbB6+yE5NHPCuDqcMK5OdYwzlYKdz8JKyoLg7HIKLTVRHDoWw9Pv7se2g8cwc2wtThxfLz8/FE/ilQ8PIZZM4WD/CADy0FiFzpZN+NRgv9cDUcR/5o5f8bt3sGbbIfz2+tOw0MCoGUmksOy+degbThi+nyAA6759tq1OukGbBibz0JhpfcB7aABgd0YY/B/Pb8Mv1nyEH3xuDr60cBIAYE/PEC79r9cxraUaz924WPda4UNOgPT3SKbFHBoaMmiI4mBUcNO4UrB+SOeGx/+G9R/14DfXn6a7Gbr1j+/j6Xf2y79XhAPYdOu5qOZSmJ3p5ST9dNKDpFdUjzG2LootB4AHVn8EQJrnr99ytqyvuef5bfjv1zpVr6mtoFu0Fehs2UQlQvV5AE8wiJXrcaBXCrE8v+WgoUHz5q4j6BtOoDYawkmT9Xdlb3T2YCSRxoG+EXvdtgOC7v/NEipQQwNIHhpRFPGX96Tqwe/s7ZUNmg/29yGZFrHt4AB2HtKvKqw1aMLBAGLJdA4NDWU5EcXBqDmlUofGnIbmyGAcGzp7AADPfdCVZdDEk2m88mE3AGDh1Ea8uesIRhJpHB2KqwyalAMhJ6MeVYWi1/aA8Y+fno5gQEA8JeL9j/twZDCOtdsP4bJPToAoinhuSxcA4KRJDaitCOO4lmqc0FaX9T5EbsigscloDjmNZDwUq7cdwncMjlu99RAA4MI54/Cj/zNX95gl96zGju4BjCRSqiq4VuE9H7ZCThbr0ACSh+ajw4PYl9HS7OIyn/i07tXbDhkbNEHFQwPkqEPjgMudIMxgVCDPqA6NnoZm7fZDsndl9bZDWa95e89RDMSSaKqO4NfXnYb5dz6P/pEkRjSbB9kzZOP6dyOtnImCIzq721OmNOKUKY0AgB8/9yFWvbITq7dJBs2uniHsPTKMSDCAR69dqDLeCPP43KdQesqpDo3VSsFskdnRPYB9R3PXYWEL15mzWnIeUxGWLsVYIs112zY1DBXq3lp2CuuZb31QGZaEe7t6BmXjDVBCUNL/FeNGbyEHlDBXNHMumPeFPDREKTEqkGemDg3/Mv7a/+jQIPYeUa8b7PnFM1sQCAioyMytkYQ6vOtE2rYbrV6MPDQ8Z85sBQCs2XYIqbSI1Vslr9QpU8eQMWMDMmhsoqoU7POzabXQFL/IrNl2WPeYj3uHsb17AMGAgDOmN+d8r4qQsnDZCjmpPGaWXy7XjrDioZk5VvK27D0yhFcyCxMAdPWPYDiTrcB7azZ+1JO1QPPvp/XQ6I2FeW38bkQT3sdoXTCqQ6MNYafTorxOMG2I1rhnG4IzZ0qbH2bQaPVqTqRtG6WjF4qRhobnpEkNqI2GcHQogfc/7lM2fTNzb/qI/Pj8Flx6yivkZC2mzGtIVm/r1j1mTWainjSxAfWV4ZzvJe/EkilbKZnqVhR2PDTmNTSTm6oRCQaQSIl4bcfhzGdLx+zJ7ECZtyYgSIbLho96st5Pq6Ex9NCwSsFUWI9wGaNKwUZ1aIKa9WTLgX4cHoihKhLEP5wxFYDaoOnuH8GWA/0QBGDRDGnzwzy32pBTyoYXl+FG3S25MWUegyYcDMgbvOe3dGF9Zj1gnhuiMMigsYmqDo3fDRoLlTNTaVGVCfT6jh5dI4DtuBbn2XnwC5ed3VdQpaGx/vpIASGnqkgQExsrAUiL47j6Cjkdc1fPIEYSKRzok9Iwz5vdBiB7ZyqKyvmUDRq5Jo5BLye/uwUJz2NUT8YoI1Eb0mHX/OnHNePc2WMBAOt2HJYN+TXbpc3AnPH1cs2WnCGnzFDsZPkFDTYMhZKUPTT5x8VC8I+8vgsjiTTa6ipkby9RGLQa2sRu3RMvYSXkxLuAa6IhDMSS2LT7qOqYRCqN1zMei3yu1GiYDzmxRbKQwnr2ss6YxyOeNB9yioYCmNJULT++eEaL/PvunkHZS1NbEcJnT2oHAJXWBpAMKGZHRoPSuTDjoSEHDeE2gsG6wOx+40rB0u+8lm72uDo010QwGE/J68armXAtv1YooWj1fLTT741h1NKhUNimJGRio8E2eaxv0+KZzb4v/VFqyKCxifoG6u+L0UpMmV9gPn285CbVeh3e3n0Ux2JJNFZHMIcrIKUHv3DZK6zH/b9I3baj4SAmcwbNmbNaMKWpCoCU3bTrsKSfmdJUjdOnNyMYEPDR4UHs4UTDvLeLr0OTayyKKJimMOEuSjaQQchJ5zLk15P+kQTezhguZ86QBL+LZkg39NUZYeza7dmbn6jsuVV7aGSD3sblb/S9CoUV1guH8g9sfEMlZnDZjhRusg/JqW1STllOVlT/bIEJBwWcfXwLnn5nPx7bsBtvdh6Rj2ElvhfNaM6bXlnBLVyiDQ2N/V5OmTCPmZBTQqnsO2FMVP78M6Y3y2Lg3T2D2N0jGTuTm6pQVxHGgklj8MauI7jmkTcwpioifR7fpVdj0OgZmLIomOwZwmXkdcEg5KTrock8JIoi1u3oQTItYlpzNSZljP0zZ7bgyb99jMc37sa6nYelWlUVIcyf2CC/B6+tU31uunAvrjK+wtK2E6k0/vUP76Ezs1GpjARxy/nH48Tx9fLmI2LSdXrmzBZs7x5AQAA+ZZA0QZiDDBqb2NVseAkrlYKZQVMRCmLxjBZUhAPoH0niLU3YCQCWfqIt7/sp2QxpWzVW7DYLLaQOTTQUkMu4L57RjPrKMKY0Zzw0h4ewq0nx0ADA0hPb8MauI9h5aBCAukt3e32F/Hdg4S89DU2aPDREkTDMcjJIn2bzNyWKqnRsxuKZLagMB9E/ksS7+/oAAOeeMFbWjgG8hkZbh0b6WYospzXbDuGJTftUj01q3IPvf24OEhbn5QVzxuHB1zuxaEYL6qtyJ00Q5iCDxiaCTc2Glygk5BQNB9FUE8Uz3/gUdnQPZB1XXxnBadMa874f76GxFXKy3fogo6GxZNAEceL4ejx342K5TxMLQe3vG8b2gwOZxyQj56qOyZjRWoOhTEddnnnc7pQtiikdbxEzcvwe5iS8j1HXbGMPDVtPlGxHvhZVY3UET3/jU9jRfQyAdL13HKeuHFwR0g85GX2uWQqtQ8OMsyUnjEVDVRi/37QPgzFpLicya4KZkBMALJg8Bn/950UYV1dpaQyEPmTQ2KSc0ratiOSYC5gZItNbazG9tbbgz2YampjNtG27rQ/YQmTOQ5MJOWXOwaw25fs3VUdksfTbeySv1ZRmycgJBQN5s74ARRRs5KEhg4ZwG6Mbv9JTKft1zHuyu2cQQ/EUIqEATpuqNlimt9boVs1myJ5brYbGgZCr1UKiDGbQfP7kCeg+FsPvN+3DcGZ8rO9a2MK8PJ7aGziGz30KpcduZVovYUUkJ4ecws60t+ddy3Yaz9kNOYUDFtK2E0rISYsgCLJHhhkk7HezKK0PchfWI4OGcBvjwnq5tSzs0hzK6MkWTm1EZcTaeiF7bpPaLCf12AqhEA3NrsOD2N0zhHBQwOnTm1GV+T7DmbUgbrIODeEOdNZtIqg8NCUciANYStvOTGC24NiFDznZ0dDYbn1QQKXgaEh/keZTuasiQbRkamuYhWlo9FsfSJ/t99pHhPcxKrhp2G1b82AhVXBz1aFRspzsp23rtXTIBfPOnDy5ETXRkNz2ZDiuDjlRwcvSQAaNTcoxbduShybHzdwqenVoCqoUbLf1gSVRsJLlpAfvkZncVG1ZNM6K5ullXDEjJ0gLJ+EySrZSdrVgwzo0msfOMujllovchfXsVwo2qoCcC21fugrZQ6MOOek1pyTch866Tfjr1u9ZTkYLlxZFQ+OxkJNjrQ/MVApWN5PUwntoplgMNwFKHN4obZuaUxJuw88j7aVoVIeGXxvHN1TiuBbrVXCjsihY2WCIolKEsphZTrFkCut3shYFkkFTJXtopPWQrRvkoSkNZNDYxK5HwEsYLVxaRpwOObGFy64o2LHWB+Z7OeUKOfEeGiYItkLQSBTsQHM+gjADv0nQem+NNDT8Y4tnthQ0H/U8NPx0KKaG5q1dRzGcSKG1NorjMwkATBPE1kOzzSkJd6AsJ5sI5RRy4safSouG34ctMFEXPDT2NDT20uittT4wDjnxRkwhHhqlDg2JgonSwV9iWQZN5tLUM6z5xwrtIq0U1lPmAO9RKWSNkF/Lf6+0mPO99h4ZwkgihWfe3Q9A+i5s3WcaGlaCgQya0kIGjU14z6Lfs5yMFi4tsofGIQ0Nn55Z2jo0hRTW0z8HrbVRVIQDGEmkVa0RzGKooUmRQUMUB5XnVjMtjOrByAUiAwJOn96U9bwZKnRaH/Brk53LP6jxPAWQ/WaPvN6JO57eonqMr6VTqdXQyFlONC9LARk0NrGr2fAS/ATPp5NT0radz3Ky0/rAbiuKsEGHay35NDSCIODq06fi7d1HVeXcTY/FTHNKMmgIl9He+HmM6tBMb63BKVPG4JOTx6CuorAquHJ9qhwGjb0sp/whdlb5vCoSREU4iClNVfj0LKXnUiXnWU6nRbkgJ3loSgMZNDYpx7RtIH8qo2ui4GTaVrdtfoGz1frATMgpYRxyAoBvX3C89UFkMNLQpEgUTBQJwcBzazRXK8JBPPG10219tl7rA6c0NEaGGuNQph/dD5fNxSXz2rOe5+vqjCRTsocmRAZNSaCzbpOgzRCHlzBauLQ4XocmpIj/WISlEMGr7SynTB2auJUsJ4fCblqUOjS5u23b0RAQhBnMhZzc+WylsJ7ioVFpaBxofQAYGDQDkkGTq4YUH3IfjqcsN6cknIUMGpuoQhw+v7kEVQtXPg2Ns3VoVL2cbJQ1D9oUaZvV0CRTadlzYuShsQMbv14KOXloiGKhWhdyhZxcug71spxEhzQ0Ko90jvWOeWhaavUNmkBAkNeu4USKS9umW2spoLNuk9Gbtu1syIllS6VFJcOoMFGw8n97rQ+MDRq+eWUuDY1d5OaUBnVo/G5EE97HyHObKpaHJqGf5WRHQ5NvvRtJpHBsRMpeymXQALyOJkVZTiWGzrpN+BuK3wWaRvUmtDheh4Z7n6GEtIgUoqFxKuSUz6CJcQusW1VBjZpTkoeGKBaCIORsE2BH72aGaMi4Do2dz1VpaHTmGPPOREIB1FXklpsqqdu8QUPzshQ4vhLfcccdmQmg/Dv+eEUYOTIyguXLl6OpqQk1NTVYtmwZDh486PQwiobaI+D/i1jurJsv5JR0tg5NJBiQF03WzK4QDU3QpseMrxRsVC2Z6WdCAcE193LQhIYmaKfdMEGYhM0r7ZRgl6Zb+kG5nEMyLc/HtEMZfvk0NLx+xmhtl1O340rIiTw0pcGVs/6JT3wCBw4ckP+99tpr8nM33XQTnn76aTzxxBNYvXo19u/fj8suu8yNYRQFu3VPvIbZ6plOh5wEQZD1OEOxVGYs1t8naNdDwy1ERqnb+YrqOUHISENDlYKJIpKrz5vo8nXIe27ZJsIpIbKR5wnIr59h8LVomIeGPKelwZW07VAohLa2tqzH+/r68OCDD+Lxxx/H2WefDQB4+OGHccIJJ2DDhg047bTTdN8vFoshFovJv/f397sx7IJQhZzK4BoOBAQgLeZP25YL6zl3Q68IBzCcSMlVNwvxePGvsdP6AJDCTrl2WkoNGncynADF+6Jbh4Z6xhBFRL7xp7UhJ/XzTsNvmEYSKVSEg0qGnwMfGhAEpLjeUDymDRqun5PcnNLFjQ6RG1fO+vbt29He3o5p06bhiiuuwJ49ewAAmzZtQiKRwJIlS+Rjjz/+eEyaNAnr16/P+X533XUX6uvr5X8TJ050Y9gFYbeQm9cwHXJy2EPDvxerulmYh4b7f0EeGuU1iaSBh0bu4+S+h0bPU5R0cFEniHwEcoWcDCoFO0E4GJC9rmwTZad5rRa2RuhtGswaNPy6xdaMEIWCS4LjZ33hwoV45JFH8Oyzz+K+++5DZ2cnFi1ahGPHjqGrqwuRSAQNDQ2q14wdOxZdXV0533PlypXo6+uT/+3du9fpYReMKuRUBm7GXLFyLcxD4YZBk7BR1t+uhob/zLiBMLgoIScDDQ27kZBrmygGbF5kF9aTfrp5/5Yb12Y2Oop+zP61z5aLfBoaI6r4kFOaRMGlxPGQ0wUXXCD/f+7cuVi4cCEmT56M3/3ud6isrCzoPaPRKKJR44uqVJRT2jagfJ/8ISdnWx8A2cZBQWnbAXsGpiAIiAQDiKfShplObhfVA8x5aPyeWUf4g1whJ9FlDw0gbXQG4yk5EUHJrLL/3rKhpjPVCwk5Udp2aXH9rDc0NGDmzJnYsWMH2traEI/H0dvbqzrm4MGDupobP1BuISejHQuPmyEn7VisEFRpaAobB9td6TWFZMgeGpdq0ADGzSnTZNAQRSRXsoDbdWiA7PYHTmU5AbnFzkABouB4imtOSQZNKXD9rA8MDGDnzp0YN24cFixYgHA4jJdeekl+fuvWrdizZw86OjrcHoorlF3ISd6xFLfbNpDt7bHtoSnQoglnPEWGIaciamj0C+tJn08GDVEM2HWmLWXA1gk3S1ZENR230w5qaNj0sZXlFJYCHcOJFNeckuZlKXA85PTNb34TF198MSZPnoz9+/fj9ttvRzAYxOWXX476+npce+21WLFiBRobG1FXV4dvfOMb6OjoyJnh5HX42HE53FvMpG2Losg1p3Qyy0ltHBVys+YXuUJTSc20PyhGyElpTmlUh6YMLjrC8+S68Tsp0M1Fhaa4npLlZP+9AzkMNVEUTWtoKiNK6wNqTllaHDdo9u3bh8svvxw9PT1oaWnBpz71KWzYsAEtLS0AgJ/85CcIBAJYtmwZYrEYli5dip///OdOD6NoOHED9RKBHOI/nngqLS9kTqYta709BYWcuHWk4JCTXP+ltKLgcDC3h4YMGqKYMA9MruaUbq592vYHTmZWKVlO6sf7R5JyC5ZCNDRuVQ8njHHcoPnNb35j+HxFRQVWrVqFVatWOf3RJSGg0mz4/+Yi78QMXDR8XxVnPTQOhJwcKHTIQk6mPDTF0NAYiIIpy4koBsEcWhO369AAfLXgTMjJwerEQo7vxcJNtRWhvDpBVdo21YcqKWRG2qRcKwUbaYJjGdevIDi7E9EuHPYNmsLGwbc/yIWioSlClpOeKNhBYSRB5EOuT5Wjl5Ob+kFtx20nr322fGk3cGb1MwBQFcloaCjLqeS4Uil4NMFraMrhGjaTts0Lgp30SmVraKy/h6r1QYELHluMvvLgRgQEAUs/0YZ7Lz9JdUwxQk5GGpqkjVo9BGEVIYe2TtHQuPfZ2pBTysG0bX4D9y+/ewc9gzH8/6482bR+BlBraKg5ZWkpg1twaSk7D03mijDS0LghCAaywzd2u20XamydNKkBgOShiSXT+NM7+7PCT4oouDRZTrKGpgyuOcL75C6sV4Q6NBpRsOhC2vbRoTj+9+19eHXrIby166glD42q9QGlbZcU8tDYpOzStuUdi5GHxvkaNEC2KLiQRZK/wRd6s//+Z0/EN86ejmRKxKK7XwEA9A0n0Mzt1orRy4llSuhpaFIUciKKSK6WKE4WuctFNKsODRuTAwZNxu7oPDwoP7Z62yH5/6YMmkzIaSiupG2ThqY0kBlpE94QL4d7ixxyyq2HVUJOThs0WRoa6++hLnRY2DgEQcC4+kpMbKxCXYW0WPUOJVTHMB1RqT00tHASxUCvnIMoio4aF7mQQ05J59O22abno0MD8mOrtx0qyEMzmGmqC1CWU6kgD41NhDILOZmpFDzi0s3cK4X1eBqqIugfSaJvOK56vBghp6CcPp7boCmHa47wPnrlHPglwu3WB0C2KNipbtsA8BHnofn7gX7ZQ21KQ5MZX/+wsumhOjSlgc66TcpNQ2OmUrBrIScHspycaH3A01AVBqDjoSliLye95pSyh4a6+hJFQC/kxBs3roqCQ5qQU2Y6OKKhCTAPzaDq8Q+7jgEwG3KS5mD/iOKhIVFwaaDV0CbBMjNozFQKHpE7bbvsobGb5eTA36O+MpdB434vJyMNDTWnJIqJ3rrA/9/dtG1pHsQS2uaUzrU+2N83DACY1lKtet6KhoYPDYdpo1ES6KzbhJ9T5eBlNGrWxvCyKJhfWJ242TdURQAAvcNaDU3xQk56GhpqTkkUE711Qe2hKULIiWloZEG8/ffW1t26qmOK6nkrGhpGMCCURYKIHymDW3BpKbtKwazQlIFBw3ZKTjamBJwqrKf835GQU8ZD0zeUS0NTmsJ65KEhioleOQe1hsa9z9bWoRFd0NAwLp3fjtpMIkBAAJqqrRs0FG4qHWTQ2MTpEEepUXYsJgrruVyHppBF0ukQoKyh0XpoSlxYj3o5EcXEEx4auTmlc5/JR4aaqiNoqIpg0YxmAEBjddTU/NKugxRuKh105m3CX+/lFHIyTtv2rijY6Syn3Boa93s5hUw0p6ReTkQxkA0abl3gDRpX69CEcmU52X9vfgM0qakKAHDmTKmRclt9fu8MIHnmeS9N2MVNDmEMpW3bpNzStnP1bOFRKgV7T0Oj9tDYHpIJDY2bIScSBRPeQG9d4I2botShkbOcnLv2+fV7SpMkCP7sSeOx7eAAzprVYvp9KiNBDGcMLtpklA4yaGxSbiEn9n3MhJyc9k44keXEv8YJTVNuDU0RC+tRc0qixHgi5MS6bcsdvu1/Jj9/Jmc8NNFQEN/5zGxL76Py0JSDq96n0Jm3iROVab2EYCXk5ElRsNNZTrk0NO57aOTCerrNKdOqYwjCTZTCespjRatDk1kXYprmlE70MePHzTw0hVAZ4Q0ampOlggwam5RbLydTIaeitT4oIOSk8pjZHpJs0BwdzJHlVCINDXuImlMSxUA35CR7StzN8FRCTurmlE5ob/k1hnloCoE8NN6AzrxNnBahlppcXXV53Oq2nd36wPp7OJ3lVF8paWj6R5Iqw6IYvZyULCc9DQ15aIjioSQL8GnbxWm/oe227WTbj4COhqYQeIOG2h6UDjrzNlGHnPx/czFTWC9WrMJ6Bdys+Z2iE38OluUEqHu1FCPkxNI/RTG7FQU1pySKiaKtUx5TGlO6+9mKhsb5btvse9VVhGRvbCHwIacIzcmSQQaNTdSajRIOxCH00jO1uFWHJhAQVF1q7Yec7C8skVAA1ZnFiulokqm07DVx1UPDLYxaL41ch6YMjGjC+wgGomC3C4qydSaVFpFIpR3OcpJ+TmmutvU9yEPjDejM26TsKgVnvoJRpWC3RMGAWpNSUMgpwP/fmb+HnLqdyXSKc4ppVzU0Ad6gUT5TFEVFQ0MhJ6IIyOuCTnPKYnloAGntcbQOTeZNJtsINwEkCvYKZNDYpFxDToZp2y7VodG+ZyEGYsDhkBPAFdfLeGhYtgUAlUfJaYIBfQ8Nf1Mhg4YoBkF5XVAeEx0M/RjBe0FHEml5s+WkhmaKDUEwoF63SBRcOujM26TsQk5yQ8Tcx7hVhwZQh7EKuVm7UReIxdb7MtWCmX4mFBBcdS+HuDQOvhZNkgwaosiwuX4slpQfc1Kca4QgCLJRI3lo4NjnjquvAADMm9Bg632qImTQeAEqrGeTcstyMpe27aKHhgtjFXKvVqXRO2zQsJBTMYrqAZKxIgjSTjiXhyZEfWOIIjCxUfJg7D0yJD+maGjc//yKcBCxZBqxZMpRDc2tF83GsgUTcNLEBlvvo07b9v99wK/QamiTcgs5WakU7IaGhjeS7BbWc8p5wVK35ZCTXIPGvQwnhlwtmDdo+IJmNIOJIsBSmnf1DMqPOekpyQff/sBJQ6oyEsQnJ42xrX/kNTQkCi4ddOZt4oZHoJQolYLNpG27G3Iq5HSqQk6OiYLVDSqVPk7uTx+5WjAXA+TDT+ShIYoBKzq3u0fx0IhFbL/Bd9z2Yqd53kPjpq6OMIbOvE2crkxbapQ6NLmPKZYouJCUZH4tcSzkxPo5yR6a4oScAMVgyemhKYNrjvA+zEOz98iQ3HajWHVoAL64XrpoYmQrqDw0NClLBhk0NuHnVDm0PmDh31waGqkWhPScGwZNNGQv5CS4EHLK1tC4X1SPoVctmN+hlkOpAML7tNVVIBoKIJkWsb93BEDx6tAA6vYHTmY5OYVKQ1OEjQ6hD515m5RbyClfpWAmCAY8GnJy4e+RraHJeGhcrEHDCOv0c0p60OVOlDeBgCCHnZiOplh1aABFrzaSdLYOjVOo0ra9NLBRBhk0NinXtO1cISeVQeOiKLjQhnf8Td4p+zIrbbvEGpo0VQkmSgArPrc7Y9AUM/SjaGiUSsFe2kBS2rY3oDNvE94YLwf3P/s+3f0xbD94TL6JM1g/lUgw4EqIjXloCr1Zu5FGP6YqR5ZTEUJOehoa5qGhWD1RTKbIHhpJGFysOjQAUKFXh8ZD1z9lOXkDqkNjE0FQaoV4acdQKMwj8NDrnXjo9U5UR4J4+ZtnYWydVICKeWjcCrcwr0+h55Jf45xrfaBoaNJpsaiiYH0NjWRQeWlBJ8ofrYem2HVoAHWWk5cuf3WWk4cGNsogU9IB2M23HEIA5584DuMbKtFYHUEkGMBgPIWX/t4tP+9mUT3+fQs9lUEXRMGs9UFaBAbiSa4OTRGynHQ0NCz6RB4aopgotWgkD00p6tDEkumipoubhTw03oDOvAOwm6iH5lfBnDmzBa9/+2y8/Z1z8Y2zpwMAVm/jDRp3Om0z2PsW7KFRaWic+YNUhIPyuPqGEpyGpniF9fjmlOz/XlrQifKHiYL39AwhlRZLV4fG61lOZNCUDDrzDsDmVbmFAM6c1QIAeH1HjyxKjbnYaRtQFq5CF0k3spwAoIFlOg0lihxykj4jyRXTY7YNGTREMWlvqEQ4KCCeSqOrf0T20BQ75FRMz5BZqPWBNyCDxgECsoemvC7kE9vr0VgdwUAsibd3HwXgblE9QEnPLPRUqkXBToxIQtbRDMc5UXAxCuvppW2Th4YoPsGAIPd02n14kEufLqYoOO3JtO1KynLyBHTmHYDdWLw0wZwgEBCweEYzAGD1tkMAihByCtkMObnUW4vpaCQPTfF6OeUrrEcQxYTX0ZSkDk3C2eaUThENBeRNGBk0pYPOvAOUa8gJUMJOikFTHFFwoafSjV5OAO+hSchht2J4aJTCelwvJw8u6MToQOnpNFiaOjTJNBfq8s71LwiCHHYKUcipZJTUoFm1ahWmTJmCiooKLFy4EG+88UYph1Mw5RpyAoBFMySD5oP9/eg+NiJ7aNwSxNrV0LjRbRtQNDR9Q8UNORl5aCjLiSg2fNftkrU+kA161z/WEsygoeaUpaNkZ/63v/0tVqxYgdtvvx1vv/025s2bh6VLl6K7uzv/iz0Gu+mUQ9q2luaaKOaMrwcArN12mPPQuJvlVOgiqW4W6oKHhg85FbGwHi8K9mKWBzE64LtuF7MejNKcMiVnV3nt+mc6GvLQlI6SFda75557cN111+Gaa64BANx///3485//jIceegjf/va3VcfGYjHEYjH59/7+/qKONR9sQntsfjnGmTNb8N7Hffjv1zpRE5UmrVdDTvwi5+Tfoz5j0KzZfkhuzlmMOjTMQPvft/fhnX29AIB9R4cB0MJJFB/mofno8CAe27gHQHFDTtsPDuDQsVjRPtcKzENDGprSUZIzH4/HsWnTJixZskQZSCCAJUuWYP369VnH33XXXaivr5f/TZw4sZjDzUtdRVj1s9z49PFS2OnvB/rx5i4p26mpOuLKZzVm3rfQcxkQgJpoCKGAoEqltMu4eqlS8raDA+g8LFVKZS0R3IR5htZuP4yHX9+Fh1/fhRe2HARQvtcb4V3Gj6lEVSSIeDItX4e1Fe7vi5tqpLnW1T+CD7uOAQDqKr11/bMxNnhsXKMJQRRztFV2kf3792P8+PFYt24dOjo65Me/9a1vYfXq1di4caPqeD0PzcSJE9HX14e6urqijTsXm/f2YnfPIC6dP77UQ3GNJ97aK3fZrQwH8YVTJqGlNurKZz35t32Y1lyDeRMbCnr9uh2HMRhP4dzZYx0b00gihcc27sGRQek6bKqO4ksLJ7nmqWLsPTKE/317n6o5JSCFNy+e144ZY2td/XyC0PL6jsNYt/MwgOJdh+m0iMff2IMDfZJ3srYijC8tnOQpo377wWN4a/dRfOHkiWWZIOIU/f39qK+vd+X+7QuDRoubJ4QgCIIgCHdw8/5dkpBTc3MzgsEgDh48qHr84MGDaGtrK8WQCIIgCILwMSUxaCKRCBYsWICXXnpJfiydTuOll15SeWwIgiAIgiDMULIspxUrVuCqq67CySefjFNPPRU//elPMTg4KGc9EQRBEARBmKVkBs0XvvAFHDp0CLfddhu6urowf/58PPvssxg71jkhJ0EQBEEQo4OSiILtQqJggiAIgvAfZScKJgiCIAiCcBIyaAiCIAiC8D1k0BAEQRAE4XvIoCEIgiAIwveQQUMQBEEQhO8hg4YgCIIgCN9DBg1BEARBEL6HDBqCIAiCIHxPySoF24HVAuzv7y/xSAiCIAiCMAu7b7tR09eXBs2xY8cAABMnTizxSAiCIAiCsMqxY8dQX1/v6Hv6svVBOp3G/v37UVtbC0EQHH3v/v5+TJw4EXv37h31bRXoXKih86FA50INnQ8FOhdq6HwosHOxZcsWzJo1C4GAs6oXX3poAoEAJkyY4Opn1NXVjfqLj0HnQg2dDwU6F2rofCjQuVBD50Nh/PjxjhszAImCCYIgCIIoA8igIQiCIAjC95BBoyEajeL2229HNBot9VBKDp0LNXQ+FOhcqKHzoUDnQg2dDwW3z4UvRcEEQRAEQRA85KEhCIIgCML3kEFDEARBEITvIYOGIAiCIAjfQwYNQRAEQRC+hwwagiAIgiB8Dxk0HKtWrcKUKVNQUVGBhQsX4o033ij1kFznrrvuwimnnILa2lq0trbis5/9LLZu3ao65qyzzoIgCKp/X/va10o0Yne54447sr7r8ccfLz8/MjKC5cuXo6mpCTU1NVi2bBkOHjxYwhG7y5QpU7LOhyAIWL58OYDyvjbWrFmDiy++GO3t7RAEAU899ZTqeVEUcdttt2HcuHGorKzEkiVLsH37dtUxR44cwRVXXIG6ujo0NDTg2muvxcDAQBG/hXMYnY9EIoFbbrkFc+bMQXV1Ndrb23HllVdi//79qvfQu55++MMfFvmb2CfftXH11Vdnfc/zzz9fdcxouTYA6K4hgiDgxz/+sXyME9cGGTQZfvvb32LFihW4/fbb8fbbb2PevHlYunQpuru7Sz00V1m9ejWWL1+ODRs24IUXXkAikcB5552HwcFB1XHXXXcdDhw4IP+7++67SzRi9/nEJz6h+q6vvfaa/NxNN92Ep59+Gk888QRWr16N/fv347LLLivhaN3lzTffVJ2LF154AQDwf//v/5WPKddrY3BwEPPmzcOqVat0n7/77rtx77334v7778fGjRtRXV2NpUuXYmRkRD7miiuuwAcffIAXXngBzzzzDNasWYPrr7++WF/BUYzOx9DQEN5++2185zvfwdtvv40//OEP2Lp1Ky655JKsY++8807V9fKNb3yjGMN3lHzXBgCcf/75qu/561//WvX8aLk2AKjOw4EDB/DQQw9BEAQsW7ZMdZzta0MkRFEUxVNPPVVcvny5/HsqlRLb29vFu+66q4SjKj7d3d0iAHH16tXyY2eeeab4z//8z6UbVBG5/fbbxXnz5uk+19vbK4bDYfGJJ56QH/v73/8uAhDXr19fpBGWln/+538WjzvuODGdTouiOHquDQDik08+Kf+eTqfFtrY28cc//rH8WG9vrxiNRsVf//rXoiiK4pYtW0QA4ptvvikf89e//lUUBEH8+OOPizZ2N9CeDz3eeOMNEYC4e/du+bHJkyeLP/nJT9wdXJHROxdXXXWVeOmll+Z8zWi/Ni699FLx7LPPVj3mxLVBHhoA8XgcmzZtwpIlS+THAoEAlixZgvXr15dwZMWnr68PANDY2Kh6/LHHHkNzczNOPPFErFy5EkNDQ6UYXlHYvn072tvbMW3aNFxxxRXYs2cPAGDTpk1IJBKq6+T444/HpEmTRsV1Eo/H8eijj+If/uEfVF3uR9O1wejs7ERXV5fqWqivr8fChQvla2H9+vVoaGjAySefLB+zZMkSBAIBbNy4sehjLjZ9fX0QBAENDQ2qx3/4wx+iqakJJ510En784x8jmUyWZoAu8+qrr6K1tRWzZs3C17/+dfT09MjPjeZr4+DBg/jzn/+Ma6+9Nus5u9eGL7ttO83hw4eRSqUwduxY1eNjx47Fhx9+WKJRFZ90Oo0bb7wRZ5xxBk488UT58S996UuYPHky2tvb8e677+KWW27B1q1b8Yc//KGEo3WHhQsX4pFHHsGsWbNw4MABfPe738WiRYvw/vvvo6urC5FIJGuBHjt2LLq6ukoz4CLy1FNPobe3F1dffbX82Gi6NnjY31tvzWDPdXV1obW1VfV8KBRCY2Nj2V8vIyMjuOWWW3D55ZerOkz/0z/9Ez75yU+isbER69atw8qVK3HgwAHcc889JRyt85x//vm47LLLMHXqVOzcuRP/+q//igsuuADr169HMBgc1dfGL3/5S9TW1maF6p24NsigIWSWL1+O999/X6UZAaCK686ZMwfjxo3DOeecg507d+K4444r9jBd5YILLpD/P3fuXCxcuBCTJ0/G7373O1RWVpZwZKXnwQcfxAUXXID29nb5sdF0bRDmSCQS+PznPw9RFHHfffepnluxYoX8/7lz5yISieCrX/0q7rrrrrLqdfTFL35R/v+cOXMwd+5cHHfccXj11VdxzjnnlHBkpeehhx7CFVdcgYqKCtXjTlwbFHIC0NzcjGAwmJWtcvDgQbS1tZVoVMXlhhtuwDPPPINXXnkFEyZMMDx24cKFAIAdO3YUY2glpaGhATNnzsSOHTvQ1taGeDyO3t5e1TGj4TrZvXs3XnzxRfy///f/DI8bLdcG+3sbrRltbW1ZSQXJZBJHjhwp2+uFGTO7d+/GCy+8oPLO6LFw4UIkk0ns2rWrOAMsEdOmTUNzc7M8L0bjtQEAa9euxdatW/OuI0Bh1wYZNAAikQgWLFiAl156SX4snU7jpZdeQkdHRwlH5j6iKOKGG27Ak08+iZdffhlTp07N+5rNmzcDAMaNG+fy6ErPwMAAdu7ciXHjxmHBggUIh8Oq62Tr1q3Ys2dP2V8nDz/8MFpbW3HRRRcZHjdaro2pU6eira1NdS309/dj48aN8rXQ0dGB3t5ebNq0ST7m5ZdfRjqdlg2/coIZM9u3b8eLL76IpqamvK/ZvHkzAoFAVvil3Ni3bx96enrkeTHarg3Ggw8+iAULFmDevHl5jy3o2rAlKS4jfvOb34jRaFR85JFHxC1btojXX3+92NDQIHZ1dZV6aK7y9a9/XayvrxdfffVV8cCBA/K/oaEhURRFcceOHeKdd94pvvXWW2JnZ6f4xz/+UZw2bZq4ePHiEo/cHf7lX/5FfPXVV8XOzk7x9ddfF5csWSI2NzeL3d3doiiK4te+9jVx0qRJ4ssvvyy+9dZbYkdHh9jR0VHiUbtLKpUSJ02aJN5yyy2qx8v92jh27Jj4t7/9Tfzb3/4mAhDvuece8W9/+5uctfPDH/5QbGhoEP/4xz+K7777rnjppZeKU6dOFYeHh+X3OP/888WTTjpJ3Lhxo/jaa6+JM2bMEC+//PJSfSVbGJ2PeDwuXnLJJeKECRPEzZs3q9aSWCwmiqIorlu3TvzJT34ibt68Wdy5c6f46KOPii0tLeKVV15Z4m9mHaNzcezYMfGb3/ymuH79erGzs1N88cUXxU9+8pPijBkzxJGREfk9Rsu1wejr6xOrqqrE++67L+v1Tl0bZNBw/Od//qc4adIkMRKJiKeeeqq4YcOGUg/JdQDo/nv44YdFURTFPXv2iIsXLxYbGxvFaDQqTp8+Xbz55pvFvr6+0g7cJb7whS+I48aNEyORiDh+/HjxC1/4grhjxw75+eHhYfEf//EfxTFjxohVVVXi5z73OfHAgQMlHLH7PPfccyIAcevWrarHy/3aeOWVV3TnxlVXXSWKopS6/Z3vfEccO3asGI1GxXPOOSfrHPX09IiXX365WFNTI9bV1YnXXHONeOzYsRJ8G/sYnY/Ozs6ca8krr7wiiqIobtq0SVy4cKFYX18vVlRUiCeccIL4gx/8QHWT9wtG52JoaEg877zzxJaWFjEcDouTJ08Wr7vuuqzN8Wi5NhgPPPCAWFlZKfb29ma93qlrQxBFUTTvzyEIgiAIgvAepKEhCIIgCML3kEFDEARBEITvIYOGIAiCIAjfQwYNQRAEQRC+hwwagiAIgiB8Dxk0BEEQBEH4HjJoCIIgCILwPWTQEARBEAThe8igIQiCIAjC95BBQxAEQRCE7yGDhiAIgiAI3/P/Byyf801DPX7UAAAAAElFTkSuQmCC" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "pd.DataFrame(features[1, 0, :]).plot(title='air temperature')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='dew temperature')\n", + "pd.DataFrame(features[1, 2, :]).plot(title='wind direction')\n", + "pd.DataFrame(features[1, 3, :]).plot(title='wind speed')\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Next steps are quite straightforward. We need to fit the model and then predict the values for the test data just like for any other model in sklearn.\n", + "\n", + "At the `fit` stage FedotIndustrial will transform initial time series data into features dataframe and will train regression model." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "start_time": "2023-08-28T10:35:27.965798Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 17:56:51,696 - Initialising experiment setup\n", + "2024-04-04 17:56:51,703 - Initialising Industrial Repository\n", + "2024-04-04 17:56:53,307 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-04 17:56:54,558 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-04-04 17:56:54,600 - State start\n", + "2024-04-04 17:56:54,603 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-4t8xz7kq', purging\n", + "2024-04-04 17:56:54,856 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-fvvww1k6', purging\n", + "2024-04-04 17:56:55,040 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-urqkfd4z', purging\n", + "2024-04-04 17:56:55,042 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-xaj0ck2b', purging\n", + "2024-04-04 17:56:55,101 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-21a90yuv', purging\n", + "2024-04-04 17:56:55,103 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-2ixtl4xk', purging\n", + "2024-04-04 17:56:55,164 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-d9wa1bqe', purging\n", + "2024-04-04 17:56:55,226 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-ft3zbiem', purging\n", + "2024-04-04 17:56:55,666 - Scheduler at: inproc://10.64.4.217/24964/1\n", + "2024-04-04 17:56:55,667 - dashboard at: http://10.64.4.217:59996/status\n", + "2024-04-04 17:56:55,668 - Registering Worker plugin shuffle\n", + "2024-04-04 17:56:56,685 - Start worker at: inproc://10.64.4.217/24964/4\n", + "2024-04-04 17:56:56,686 - Listening to: inproc10.64.4.217\n", + "2024-04-04 17:56:56,687 - Worker name: 0\n", + "2024-04-04 17:56:56,688 - dashboard at: 10.64.4.217:59997\n", + "2024-04-04 17:56:56,689 - Waiting to connect to: inproc://10.64.4.217/24964/1\n", + "2024-04-04 17:56:56,690 - -------------------------------------------------\n", + "2024-04-04 17:56:56,691 - Threads: 8\n", + "2024-04-04 17:56:56,692 - Memory: 31.95 GiB\n", + "2024-04-04 17:56:56,693 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-dbd5knyt\n", + "2024-04-04 17:56:56,694 - -------------------------------------------------\n", + "2024-04-04 17:56:56,721 - Register worker \n", + "2024-04-04 17:56:56,723 - Starting worker compute stream, inproc://10.64.4.217/24964/4\n", + "2024-04-04 17:56:56,724 - Starting established connection to inproc://10.64.4.217/24964/5\n", + "2024-04-04 17:56:56,725 - Starting Worker plugin shuffle\n", + "2024-04-04 17:56:56,726 - Registered to: inproc://10.64.4.217/24964/1\n", + "2024-04-04 17:56:56,727 - -------------------------------------------------\n", + "2024-04-04 17:56:56,728 - Starting established connection to inproc://10.64.4.217/24964/1\n", + "2024-04-04 17:56:56,732 - Receive client connection: Client-94e646ac-f293-11ee-a184-b42e99a00ea1\n", + "2024-04-04 17:56:56,734 - Starting established connection to inproc://10.64.4.217/24964/6\n", + "2024-04-04 17:56:56,735 - LinK Dask Server - http://10.64.4.217:59996/status\n", + "2024-04-04 17:56:56,736 - Initialising solver\n", + "2024-04-04 17:56:56,847 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 17:56:56,848 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 17:56:56,850 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-04 17:57:04,616 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [29673.657]\n", + " 0%| | 0/100 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2rmsemae
00.06630481.78611798.198
\n" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## AutoML approach" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 18:10:11,659 - Initialising experiment setup\n", + "2024-04-04 18:10:11,666 - Initialising Industrial Repository\n", + "2024-04-04 18:10:11,667 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-04 18:10:11,687 - State start\n", + "2024-04-04 18:10:12,681 - Scheduler at: inproc://10.64.4.217/24964/9\n", + "2024-04-04 18:10:12,682 - dashboard at: http://10.64.4.217:60366/status\n", + "2024-04-04 18:10:12,683 - Registering Worker plugin shuffle\n", + "2024-04-04 18:10:13,683 - Start worker at: inproc://10.64.4.217/24964/12\n", + "2024-04-04 18:10:13,684 - Listening to: inproc10.64.4.217\n", + "2024-04-04 18:10:13,684 - Worker name: 0\n", + "2024-04-04 18:10:13,685 - dashboard at: 10.64.4.217:60369\n", + "2024-04-04 18:10:13,686 - Waiting to connect to: inproc://10.64.4.217/24964/9\n", + "2024-04-04 18:10:13,686 - -------------------------------------------------\n", + "2024-04-04 18:10:13,686 - Threads: 8\n", + "2024-04-04 18:10:13,687 - Memory: 31.95 GiB\n", + "2024-04-04 18:10:13,687 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-oxp4dt57\n", + "2024-04-04 18:10:13,688 - -------------------------------------------------\n", + "2024-04-04 18:10:13,694 - Register worker \n", + "2024-04-04 18:10:13,695 - Starting worker compute stream, inproc://10.64.4.217/24964/12\n", + "2024-04-04 18:10:13,696 - Starting established connection to inproc://10.64.4.217/24964/13\n", + "2024-04-04 18:10:13,697 - Starting Worker plugin shuffle\n", + "2024-04-04 18:10:13,698 - Registered to: inproc://10.64.4.217/24964/9\n", + "2024-04-04 18:10:13,698 - -------------------------------------------------\n", + "2024-04-04 18:10:13,700 - Starting established connection to inproc://10.64.4.217/24964/9\n", + "2024-04-04 18:10:13,703 - Receive client connection: Client-6fee878c-f295-11ee-a184-b42e99a00ea1\n", + "2024-04-04 18:10:13,706 - Starting established connection to inproc://10.64.4.217/24964/14\n", + "2024-04-04 18:10:13,707 - LinK Dask Server - http://10.64.4.217:60366/status\n", + "2024-04-04 18:10:13,708 - Initialising solver\n", + "2024-04-04 18:10:13,755 - AssumptionsHandler - Initial pipeline fitting started\n", + "2024-04-04 18:10:15,779 - AssumptionsHandler - Initial pipeline was fitted successfully\n", + "2024-04-04 18:10:15,780 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 3.1 MiB, max: 5.2 MiB\n", + "2024-04-04 18:10:15,781 - ApiComposer - Initial pipeline was fitted in 2.0 sec.\n", + "2024-04-04 18:10:15,783 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", + "2024-04-04 18:10:15,790 - ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 30 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'dtreg', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "2024-04-04 18:10:15,815 - ApiComposer - Pipeline composition started.\n", + "2024-04-04 18:10:15,817 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 18:10:15,818 - DataSourceSplitter - Hold out validation is applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:00.on_destroy at 0x000001B0E4F82AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859266964528\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 18:10:40,585 - IndustrialDispatcher - 1 individuals out of 1 in previous population were evaluated successfully.\n", + "2024-04-04 18:10:40,629 - IndustrialEvoOptimizer - Generation num: 1 size: 1\n", + "2024-04-04 18:10:40,630 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-04 18:10:41,907 - IndustrialDispatcher - Number of used CPU's: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: .on_destroy at 0x000001B0E4BB8A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859269749520\n", + "Exception ignored in: .on_destroy at 0x000001B0E535D700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859270911536\n", + "Exception ignored in: .on_destroy at 0x000001B0E8A14DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859329820592\n", + "Exception ignored in: .on_destroy at 0x000001B0E8AB6E50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859330684368\n", + "Exception ignored in: .on_destroy at 0x000001B0E8911F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859296615568\n", + "Exception ignored in: .on_destroy at 0x000001B0E6B49EE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859273171184\n", + "Exception ignored in: .on_destroy at 0x000001B0E933C700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859264171920\n", + "Exception ignored in: .on_destroy at 0x000001B0EAB45040>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859364141552\n", + "Exception ignored in: .on_destroy at 0x000001B0EAC230D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859364865008\n", + "Exception ignored in: .on_destroy at 0x000001B0E535D670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859305045424\n", + "Exception ignored in: .on_destroy at 0x000001B0E7223160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859307083408\n", + "Exception ignored in: .on_destroy at 0x000001B0E7046DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859327718480\n", + "Exception ignored in: .on_destroy at 0x000001B0EAB06F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859213774256\n", + "Exception ignored in: .on_destroy at 0x000001B0EAC14C10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859423344752\n", + "Exception ignored in: .on_destroy at 0x000001B0EDEF2DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859234503536\n", + "Exception ignored in: .on_destroy at 0x000001B0E73295E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859307038256\n", + "Exception ignored in: .on_destroy at 0x000001B0EE4B2550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859424975536\n", + "Exception ignored in: .on_destroy at 0x000001B0EDF35310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859415804016\n", + "Exception ignored in: .on_destroy at 0x000001B0EE491B80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859270927248\n", + "Exception ignored in: .on_destroy at 0x000001B0EE491550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859488150224\n", + "Exception ignored in: .on_destroy at 0x000001B0E75BCF70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859272318384\n", + "Exception ignored in: .on_destroy at 0x000001B103171550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859266887184\n", + "Exception ignored in: .on_destroy at 0x000001B104460160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859419118160\n", + "Exception ignored in: .on_destroy at 0x000001B0EE8B3F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859363207472\n", + "Exception ignored in: .on_destroy at 0x000001B0EA6029D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859428079728\n", + "Exception ignored in: .on_destroy at 0x000001B0EE112280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859417186832\n", + "Exception ignored in: .on_destroy at 0x000001B0EE1FF1F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859291833040\n", + "Exception ignored in: .on_destroy at 0x000001B0EE1B95E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859415990320\n", + "Exception ignored in: .on_destroy at 0x000001B0EDEDAD30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859415710192\n", + "Exception ignored in: .on_destroy at 0x000001B0EE6A6AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859797909936\n", + "Exception ignored in: .on_destroy at 0x000001B105CC6670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859420319984\n", + "Exception ignored in: .on_destroy at 0x000001B0EDE05AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859306654288\n", + "Exception ignored in: .on_destroy at 0x000001B11A1A5700>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859364653456\n", + "Exception ignored in: .on_destroy at 0x000001B1030FAB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859772262896\n", + "Exception ignored in: .on_destroy at 0x000001B0EE7B99D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859427528112\n", + "Exception ignored in: .on_destroy at 0x000001B11A6231F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859326689616\n", + "Exception ignored in: .on_destroy at 0x000001B10312AA60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859488418960\n", + "Exception ignored in: .on_destroy at 0x000001B0EDFC8820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859304494448\n", + "Exception ignored in: .on_destroy at 0x000001B119A431F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859791418640\n", + "Exception ignored in: .on_destroy at 0x000001B1044605E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859416744368\n", + "Exception ignored in: .on_destroy at 0x000001B0EE6A6820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859266761040\n", + "Exception ignored in: .on_destroy at 0x000001B11992D4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861117849744\n", + "Exception ignored in: .on_destroy at 0x000001B0EE15B430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859260587248\n", + "Exception ignored in: .on_destroy at 0x000001B1044ED940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859488150800\n", + "Exception ignored in: .on_destroy at 0x000001B11A623DC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861636089296\n", + "Exception ignored in: .on_destroy at 0x000001B0EDE7DEE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861124228656\n", + "Exception ignored in: .on_destroy at 0x000001B179FA8E50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859259159152\n", + "Exception ignored in: .on_destroy at 0x000001B16D229AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859791494480\n", + "Exception ignored in: .on_destroy at 0x000001B179FA83A0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859792017712\n", + "Exception ignored in: .on_destroy at 0x000001B0EADAF0D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861636262672\n", + "Exception ignored in: .on_destroy at 0x000001B105D120D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859792339568\n", + "Exception ignored in: .on_destroy at 0x000001B0EE81B280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859359851120\n", + "Exception ignored in: .on_destroy at 0x000001B18555F280>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862063500432\n", + "Exception ignored in: .on_destroy at 0x000001B0F2296EE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862066613872\n", + "Exception ignored in: .on_destroy at 0x000001B185DBC940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861767728752\n", + "Exception ignored in: .on_destroy at 0x000001B18BED4790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862068384272\n", + "Exception ignored in: .on_destroy at 0x000001B185526AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862068853520\n", + "Exception ignored in: .on_destroy at 0x000001B18BFD8A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859793399024\n", + "Exception ignored in: .on_destroy at 0x000001B18BF511F0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859327648272\n", + "Exception ignored in: .on_destroy at 0x000001B179E550D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862067764368\n", + "Exception ignored in: .on_destroy at 0x000001B18BBC84C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862069166704\n", + "Exception ignored in: .on_destroy at 0x000001B18C1CEA60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862071701200\n", + "Exception ignored in: .on_destroy at 0x000001B105CDECA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862072276624\n", + "Exception ignored in: .on_destroy at 0x000001B18C24B310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862073766512\n", + "Exception ignored in: .on_destroy at 0x000001B18FCCAA60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862082928688\n", + "Exception ignored in: .on_destroy at 0x000001B18FF969D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859264834800\n", + "Exception ignored in: .on_destroy at 0x000001B1931DC940>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1860151307408\n", + "Exception ignored in: .on_destroy at 0x000001B18BC2E550>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859358988592\n", + "Exception ignored in: .on_destroy at 0x000001B193274160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862157526576\n", + "Exception ignored in: .on_destroy at 0x000001B193274CA0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862074580400\n", + "Exception ignored in: .on_destroy at 0x000001B191142AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861010165808\n", + "Exception ignored in: .on_destroy at 0x000001B18FF96AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862165401008\n", + "Exception ignored in: .on_destroy at 0x000001B18BCA4430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862157718896\n", + "Exception ignored in: .on_destroy at 0x000001B193618820>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862070950768\n", + "Exception ignored in: .on_destroy at 0x000001B1920CFD30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862071698992\n", + "Exception ignored in: .on_destroy at 0x000001B1913B15E0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862068633200\n", + "Exception ignored in: .on_destroy at 0x000001B1932EA8B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862192955280\n", + "Exception ignored in: .on_destroy at 0x000001B1913CF790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862074108112\n", + "Exception ignored in: .on_destroy at 0x000001B1910CCB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859795325168\n", + "Exception ignored in: .on_destroy at 0x000001B193618F70>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862194679216\n", + "Exception ignored in: .on_destroy at 0x000001B1931CE670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862171251504\n", + "Exception ignored in: .on_destroy at 0x000001B1932EA4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861645167760\n", + "Exception ignored in: .on_destroy at 0x000001B18CA94310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862162144592\n", + "Exception ignored in: .on_destroy at 0x000001B1931CEDC0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862157960848\n", + "Exception ignored in: .on_destroy at 0x000001B191FDFC10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862071948176\n", + "Exception ignored in: .on_destroy at 0x000001B18BF369D0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862063915152\n", + "Exception ignored in: .on_destroy at 0x000001B1938F9D30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862191410416\n", + "Exception ignored in: .on_destroy at 0x000001B1931DCD30>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859259683440\n", + "Exception ignored in: .on_destroy at 0x000001B193617C10>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862075008752\n", + "Exception ignored in: .on_destroy at 0x000001B193B7B8B0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862200239376\n", + "Exception ignored in: .on_destroy at 0x000001B193D614C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862201654608\n", + "Exception ignored in: .on_destroy at 0x000001B193CEE310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862201668016\n", + "Exception ignored in: .on_destroy at 0x000001B193BADB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859291834192\n", + "Exception ignored in: .on_destroy at 0x000001B193D61AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862201669936\n", + "Exception ignored in: .on_destroy at 0x000001B193FC6A60>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862237108144\n", + "Exception ignored in: .on_destroy at 0x000001B193CEE4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862068474672\n", + "Exception ignored in: .on_destroy at 0x000001B193CEE790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862201033520\n", + "Exception ignored in: .on_destroy at 0x000001B1961E9160>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862242601840\n", + "Exception ignored in: .on_destroy at 0x000001B196508AF0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862243070192\n", + "Exception ignored in: .on_destroy at 0x000001B196323790>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862243073072\n", + "Exception ignored in: .on_destroy at 0x000001B19655FB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1861009476272\n", + "Exception ignored in: .on_destroy at 0x000001B193D013A0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862158892560\n", + "Exception ignored in: .on_destroy at 0x000001B1966BBB80>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862191500432\n", + "Exception ignored in: .on_destroy at 0x000001B19352BEE0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1859232056784\n", + "Exception ignored in: .on_destroy at 0x000001B196323E50>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862195022032\n", + "Exception ignored in: .on_destroy at 0x000001B193CFB670>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862246980944\n", + "Exception ignored in: .on_destroy at 0x000001B19686A4C0>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862247638384\n", + "Exception ignored in: .on_destroy at 0x000001B196BEC430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862192689488\n", + "Exception ignored in: .on_destroy at 0x000001B1966BB430>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862196802928\n", + "Exception ignored in: .on_destroy at 0x000001B19610C310>\n", + "Traceback (most recent call last):\n", + " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1862240332080\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 18:22:10,261 - IndustrialDispatcher - 21 individuals out of 21 in previous population were evaluated successfully.\n", + "2024-04-04 18:22:10,331 - IndustrialEvoOptimizer - Generation num: 2 size: 21\n", + "2024-04-04 18:22:10,332 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-04 18:22:10,333 - GroupedCondition - Optimisation stopped: Time limit is reached\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [11:54']\n", + "2024-04-04 18:22:10,343 - IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "2024-04-04 18:22:10,345 - IndustrialEvoOptimizer - spent time: 11.9 min\n", + "2024-04-04 18:22:10,349 - GPComposer - GP composition finished\n", + "2024-04-04 18:22:10,351 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 18:22:10,353 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 18:22:10,357 - ApiComposer - Hyperparameters tuning started with 18 min. timeout\n", + "2024-04-04 18:22:10,361 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-04 18:22:18,119 - SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {'max_features': 0.8345240113828605, 'min_samples_split': 16, 'min_samples_leaf': 2, 'bootstrap': False}\n", + "quantile_extractor - {} \n", + "Initial metric: [27366.716]\n", + " 0%| | 0/10 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2rmsemae
00.07330382.70411669.433
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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 12:27:29,064 - OperationsAnimatedBar - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-05 12:27:29,205 - MovieWriter ffmpeg unavailable; using Pillow instead.\n", + "2024-04-05 12:27:29,206 - Animation.save using \n", + "2024-04-05 12:27:36,258 - OperationsAnimatedBar - The animation was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\history_animated_bars.gif\".\n", + "2024-04-05 12:27:36,259 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-05 12:27:36,283 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-05 12:27:36,289 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-05 12:27:36,372 - default - The figure was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\fitness_by_gen.png\".\n" + ] + } + ], + "source": [ + "industrial_auto_model.solver.current_pipeline.show()\n", + "industrial_auto_model.plot_operation_distribution(mode='each')\n", + "industrial_auto_model.plot_fitness_by_generation()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.vstack([test_data[1].flatten(),labels.flatten(),auto_labels.flatten()]).T\n", + "all_prediction = pd.DataFrame(predictions,columns=['target','baseline','automl'])\n", + "all_prediction.plot()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 12:27:36,530 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-05 12:27:36,551 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-05 12:27:36,557 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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dayshoursminutessecondsmilliseconds
Data Definition (fit)00000
Data Preprocessing0000319
Fitting (summary)00302231
Composing001156613
Train Inference0004927
Tuning (composing)00180532
Tuning (after)00000
Data Definition (predict)00001
Predicting00038189
\n
" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "industrial_auto_model.solver.return_report()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-05 12:27:36,716 - OperationsKDE - Visualizing optimization history... It may take some time, depending on the history size.\n" + ] + }, + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "industrial_auto_model.solver.history.show.operations_kde(dpi=100)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Compare with State of Art (SOTA) models" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')\n", + "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", + "df.index = df['algorithm']\n", + "df = df.drop(['algorithm','ds/type'], axis=1)\n", + "df = df.replace(',','.', regex=True).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "df['Fedot_Industrial_tuned'] = metrics['rmse'][0]\n", + "df['Fedot_Industrial_AutoML'] = auto_metrics['rmse'][0]\n", + "df = df.T" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "Grid-SVR_RMSE 11281.82599\nInceptionT_RMSE 11878.07728\nSingleInception_RMSE 12076.96550\nResNet_RMSE 13190.55995\nCNN_RMSE 13713.22384\nFreshPRINCE_RMSE 15800.64934\nTSF_RMSE 16224.53735\nFCN_RMSE 16378.93547\nRotF_RMSE 16392.91292\nDrCIF_RMSE 16633.91380\nMultiROCKET_RMSE 17089.05903\nRandF_RMSE 17464.55070\n5NN-DTW_RMSE 18346.81641\nROCKET_RMSE 18707.73368\nXGBoost_RMSE 19038.79598\nFPCR_RMSE 19539.47673\nFPCR-Bs_RMSE 21007.47494\nRDST_RMSE 26308.46309\nRIST_RMSE 27093.64905\n5NN-ED_RMSE 27117.07595\n1NN-DTW_RMSE 28780.40955\nFedot_Industrial_AutoML 30382.70400\nFedot_Industrial_tuned 30481.78600\n1NN-ED_RMSE 39583.25526\nRidge_RMSE 61596.26590\nName: min, dtype: float64" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('min')['min']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": "Fedot_Industrial_AutoML 30382.70400\nFedot_Industrial_tuned 30481.78600\nRDST_RMSE 34281.04902\nRIST_RMSE 36322.93964\nFCN_RMSE 39837.12211\nFPCR-Bs_RMSE 41198.13017\nFPCR_RMSE 41433.05618\nMultiROCKET_RMSE 42408.38274\nFreshPRINCE_RMSE 42978.62092\nROCKET_RMSE 43075.96694\nResNet_RMSE 43183.68355\nCNN_RMSE 43662.79467\nDrCIF_RMSE 44117.73585\nRandF_RMSE 44124.97270\nSingleInception_RMSE 44206.86270\nInceptionT_RMSE 44422.43750\nTSF_RMSE 44700.12218\nRotF_RMSE 44880.77119\nGrid-SVR_RMSE 46582.29835\n5NN-DTW_RMSE 47398.24409\n5NN-ED_RMSE 49043.31025\nXGBoost_RMSE 50528.90423\n1NN-DTW_RMSE 53184.73404\n1NN-ED_RMSE 68682.91455\nRidge_RMSE 94765.01744\nName: max, dtype: float64" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('max')['max']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "FCN_RMSE 29315.72100\nResNet_RMSE 29429.96045\nInceptionT_RMSE 29704.37021\nSingleInception_RMSE 29823.92480\nFedot_Industrial_AutoML 30382.70400\nRDST_RMSE 30462.40230\nFedot_Industrial_tuned 30481.78600\nMultiROCKET_RMSE 30576.84618\nCNN_RMSE 30611.23737\nROCKET_RMSE 30813.80372\nFreshPRINCE_RMSE 31014.82503\nDrCIF_RMSE 31131.86635\nFPCR_RMSE 31246.60853\nTSF_RMSE 31357.74909\nRotF_RMSE 31524.69267\nFPCR-Bs_RMSE 32018.74692\nGrid-SVR_RMSE 32105.48137\nRandF_RMSE 32219.24180\nRIST_RMSE 32431.66763\n5NN-DTW_RMSE 34168.97513\nXGBoost_RMSE 35238.80806\n5NN-ED_RMSE 38081.06224\n1NN-DTW_RMSE 42924.76175\n1NN-ED_RMSE 55239.47152\nRidge_RMSE 76160.53072\nName: average, dtype: float64" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values('average')['average']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/examples/real_world_examples/industrial_examples/enviroment_monitoring/air_quality_madrid.ipynb b/examples/real_world_examples/industrial_examples/enviroment_monitoring/air_quality_madrid.ipynb index ee0f64f82..640aa956e 100644 --- a/examples/real_world_examples/industrial_examples/enviroment_monitoring/air_quality_madrid.ipynb +++ b/examples/real_world_examples/industrial_examples/enviroment_monitoring/air_quality_madrid.ipynb @@ -46,17 +46,6 @@ "from examples.example_utils import init_input_data" ] }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "The list of basic fedot industrial models for experiment are shown below. We using simple linear machine learning pipelines with 3 different feature generators: Statistical, Reccurence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." - ] - }, { "cell_type": "code", "execution_count": 2, diff --git a/examples/real_world_examples/industrial_examples/equipment_monitoring/motor_temperature.ipynb b/examples/real_world_examples/industrial_examples/equipment_monitoring/motor_temperature.ipynb index e106f7fed..b4d3c0c3b 100644 --- a/examples/real_world_examples/industrial_examples/equipment_monitoring/motor_temperature.ipynb +++ b/examples/real_world_examples/industrial_examples/equipment_monitoring/motor_temperature.ipynb @@ -6,7 +6,10 @@ "## Predict permanent magnet synchronous motor (PMSM) rotor temperature with Fedot.Industrial" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -16,7 +19,10 @@ "Link to the dataset - https://www.kaggle.com/datasets/wkirgsn/electric-motor-temperature" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -26,114 +32,95 @@ "ExecuteTime": { "end_time": "2023-08-28T10:34:48.354623Z", "start_time": "2023-08-28T10:34:39.594404Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], "source": [ "import pandas as pd\n", - "from functools import partial\n", - "\n", "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", "from fedot_ind.tools.loader import DataLoader\n", - "from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels\n", - "from examples.example_utils import init_input_data, calculate_regression_metric\n", - "from golem.core.tuning.sequential import SequentialTuner\n", - "from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder\n", - "from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The list of basic fedot industrial models for experiment are shown below. We are using simple linear machine learning pipelines with 3 different feature generators: Statistical, Recurrence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." + "import matplotlib\n", + "from fedot_ind.api.main import FedotIndustrial" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "outputs": [], "source": [ - "model_dict = {\n", - " 'regression_with_statistical_features': PipelineBuilder().add_node('quantile_extractor',\n", - " params={'window_size': 5}).add_node('ridge'),\n", - " 'regression_pca_with_statistical_features': PipelineBuilder().add_node('quantile_extractor',\n", - " params={'window_size': 5}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge'),\n", - " 'regression_with_recurrence_features': PipelineBuilder().add_node('recurrence_extractor',\n", - " params={'window_size': 20}).add_node('ridge'),\n", - " 'regression_pca_with_recurrence_features': PipelineBuilder().add_node('recurrence_extractor',\n", - " params={'window_size': 20}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge'),\n", - " 'regression_with_topological_features': PipelineBuilder().add_node('topological_extractor',\n", - " params={'window_size': 20}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge'),\n", - " 'regression_pca_with_topological_features': PipelineBuilder().add_node('topological_extractor',\n", - " params={'window_size': 20}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge')\n", - "}\n", - "\n", - "dataset_name = 'ElectricMotorTemperature'\n", - "data_path = PROJECT_PATH + '/examples/data'\n", - "tuning_params = {'task': 'regression',\n", - " 'metric': RegressionMetricsEnum.RMSE,\n", - " 'tuning_timeout':10,\n", - " 'tuning_iterations':30}" + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The list of basic fedot industrial models for experiment are shown below. We are using simple linear machine learning pipelines with 3 different feature generators: Statistical, Recurrence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "outputs": [], "source": [ - "def evaluate_industrial_model(train_data, test_data, task:str = 'regression'):\n", - " metric_dict = {}\n", - " input_data = init_input_data(train_data[0], train_data[1], task=task)\n", - " val_data = init_input_data(test_data[0], test_data[1], task=task)\n", - " with IndustrialModels():\n", - " for model in model_dict.keys():\n", - " print(f'Current_model - {model}')\n", - " pipeline = model_dict[model].build()\n", - " pipeline.fit(input_data)\n", - " features = pipeline.predict(val_data).predict\n", - " metric = calculate_regression_metric(test_target=test_data[1], labels=features)\n", - " metric_dict.update({model: metric})\n", - " return metric_dict\n", - "\n", - "def tuning_industrial_pipelines(pipeline, tuning_params, train_data):\n", - " input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - " pipeline_tuner = TunerBuilder(input_data.task) \\\n", - " .with_tuner(partial(SequentialTuner, inverse_node_order=True)) \\\n", - " .with_metric(tuning_params['metric']) \\\n", - " .with_timeout(tuning_params['tuning_timeout']) \\\n", - " .with_iterations(tuning_params['tuning_iterations']) \\\n", - " .build(input_data)\n", - "\n", - " pipeline = pipeline_tuner.tune(pipeline)\n", - " return pipeline" + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=15,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "dataset_name = 'ElectricMotorTemperature'\n", + "data_path = PROJECT_PATH + '/examples/data'" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Now we must download the dataset. It could be done by using `DataReader` class that implemented as attribute of `FedotIndustrial` class. This class firstly tries to read the data from local project folder `data_path` and then if it is not possible, it downloads the data from the UCR/UEA archive. The data will be saved in the `data` folder." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-08-28T10:35:13.321212Z", "start_time": "2023-08-28T10:35:12.913025Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -141,7 +128,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-10-12 15:37:31,970 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/ElectricMotorTemperature\n" + "2024-04-04 13:46:46,637 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/ElectricMotorTemperature\n" ] } ], @@ -153,14 +140,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "outputs": [], "source": [ - "input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - "val_data = init_input_data(test_data[0], test_data[1], task=tuning_params['task'])" + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -169,7 +159,10 @@ "Lets check our data." ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -186,10 +179,13 @@ } ], "source": [ - "input_data.features.shape" + "features.shape" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -198,7 +194,10 @@ "Lets visualise our predictors." ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -208,7 +207,7 @@ { "data": { "text/plain": "
", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -216,7 +215,7 @@ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -224,7 +223,7 @@ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -232,7 +231,7 @@ { "data": { "text/plain": "
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" 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CggIAATH39u3b8eSTTw7YprS0FEAgKGpqasJVV10FAOjt7QUAnlliGI3GkA64cHbt2oWioiLdB0GxoCCJIAiCiITFEmjm6e3thcPhSPPeJBcWD7D3HA9J626rra1Fa2sramtr4fP5sGvXLgDAuHHjkJubCyCgQ1qxYgWuueYaAMC6detQVlaGqqoq7N69G/fccw8WLlyIuXPn8uddvXo1Jk6ciLKyMmzduhX33HMPli5digkTJgAAZs2ahaKiItx8881Yvnw5HA4Hfvvb3+Lo0aNYsGABAODtt99GY2Mjzj//fNjtdmzYsAE//elP8cADDyTrcKQMl4+CJIIgCEIek8mEwsJCNDU1AQCcTicMhuwyHRYEAb29vWhqakJhYSFMpvjnwyUtSFq+fDnWrl3L/z7rrLMAAJs2bcLs2bMBADU1Nejo6ODb1NfX47777kNjYyOGDBmCm266CY888kjI89bU1GDZsmVobW3FqFGj8OMf/xhLly7l95eWluK9997Dj3/8Y1x66aXweDyYPHky3nrrLUybNg1AIKpcuXIlli5dCkEQMG7cODz33HO4/fbbk3U4UkaoBQAFSQRBEEQozPqGBUrZihY2PwZhMBQmk0RnZycKCgrQ0dHB9U/p5uZXPsPmA80AgJdvnIF5k7PLB4ogCILQBp/PB4/Hk+7dSAoWiyVqBknp9Ztmt2UZUp8kF5XbCIIgiAiYTKaESlGDAd34JBHaQMJtgiAIgtAGCpKyDDcJtwmCIAhCEyhIyjJCM0m+KFsSBEEQBBENCpKyjJAgibrbCIIgCCJuKEjKMkiTRBAEQRDaQEFSlkGaJIIgCILQBgqSsgwXmUkSBEEQhCZQkJRluKjcRhAEQRCaQEFSFiEIAmmSCIIgCEIjKEjKIjy+0AkzFCQRBEEQRPxQkJRFhLf8kwUAQRAEQcQPBUlZRHjmiDJJBEEQBBE/FCRlERQkEQRBEIR2UJCURQwIkqjcRhAEQRBxQ0FSFuH2hc5qc1EmiSAIgiDihoKkLKLfExoUUZBEEARBEPFDQVIWMaC7jYIkgiAIgogbCpKyiIHCbV+ELQmCIAiCiAUFSVkECbcJgiAIQjsoSMoiWJBkNhpC/iYIgiAIQj0UJGURLHOUZzcH/qYgiSAIgiDihoKkLIIFRbkUJBEEQRBEwlCQlEXwIMlmCfxNmiSCIAiCiBsKkrIIFyu32QKZJI9PgN8vpHOXCIIgCCJjoSApi3B5Ai3/TJMEUDaJIAiCIOKFgqQsggVEuRQkEQRBEETCUJCURTBNUo7NPOA2giAIgiDUQUFSFsECIpvZCKvJGHIbQRAEQRDqoCApi2ABkdVshNVMQRJBEARBJAIFSVkE0x/ZTJIgiTRJBEEQBBEXFCRlESGZpGC5zeWhIIkgCIIg4oGCpCxCttzm86Vzl4gspM/tw0sfHcahpu507wpBEERSoSApi3Bx4baJB0ku0iQRGrN+TwN+9t5+PP/BgXTvCkEQRFKhICmLcMmU20i4TWjN6R43AKC9153mPSEIgkguFCRlEUykbTVRdxuRPPrcXgBAP+ndCILIcihIyiLc3oD+KFSTRBcyQlt63YHPWb+H9G4EQWQ3FCRlEVLhto0ySUSS6AsGR30UJBEEkeVQkJRF8HIbaZKIJNIXzCSRvQRBENkOBUlZBB9LQmaSRBKhchtBEIMFCpKyCBpLQqQCFiRRuY0giGyHgqQsIsQnyUQ+SURy6POw7jYfBEFI894QBEEkDwqSsgjKJBGpgGWS/ALg8VGQRBBE9kJBUhYR2t1mCtxGmiRCY5hwG6CSG0EQ2Q0FSVmEy0eZJCL59EqCJBcFSQRBZDEUJGUJgiCImSRy3CaSiDRIItdtgiCymaQFSU899RQuuOACOJ1OFBYWKnpMY2MjFi9ejKFDh8LpdGL+/Pk4ePBgyDaHDx/GNddcg7KyMuTn5+P6669HY2NjyDaff/45Lr/8chQWFqKkpAR33HEHurtDJ5bX1tZiwYIFcDqdKC8vx4MPPgiv15vQe04nUm2I1EzS5aWVPqEtbCwJQOU2giCym6QFSW63G9dddx3uuusuRdsLgoCFCxfiyJEjeOutt7Bz506MHDkSc+bMQU9PDwCgp6cHc+fOhcFgwMaNG/HJJ5/A7XbjyiuvhN8fWNHW1dVhzpw5GDduHD799FO899572LNnDxYvXsxfy+fzYcGCBXC73diyZQvWrl2LNWvWYPny5Zofh1Qh1R7ZyEySSBKCIKDXI80kUZBEEET2Yk7WEz/++OMAgDVr1ija/uDBg9i2bRu++uorTJ48GQDw0ksvobKyEn/84x9x22234ZNPPsGxY8ewc+dO5OfnAwDWrl2LoqIibNy4EXPmzME777wDi8WClStXwmgMBAqrVq3C1KlTcejQIYwbNw7vv/8+9u7diw8++AAVFRWYPn06nnzySTz00EN47LHHYLVatT8gSUYaDFnJTJJIEi6vH9KufwqSCILIZnSjSXK5XAAAu93ObzMajbDZbPj444/5NgaDATabjW9jt9thNBpDtrFarTxAAgCHwwEAfJutW7diypQpqKio4NvMmzcPnZ2d2LNnT9R97OzsDPnRC6ysZjYaYDQaSJNEJAVpZxtA5TaCILIb3QRJ1dXVqKqqwrJly9DW1ga3242f/exnOHnyJOrr6wEA559/PnJycvDQQw+ht7cXPT09eOCBB+Dz+fg2l156KRoaGvCLX/wCbrcbbW1tePjhhwGAb9PQ0BASIAHgfzc0NETcxxUrVqCgoID/jBgxQvPjEC98JEkwOCIzSSIZ9IYFRSTcJggim1EVJD388MMwGAxRf/bv3x/XjlgsFrz55ps4cOAAiouL4XQ6sWnTJlxxxRU8K1RWVoZ169bh7bffRm5uLgoKCtDe3o6zzz6bbzN58mSsXbsWzz77LJxOJyorKzF69GhUVFSEZJfiYdmyZejo6OA/J06cSOj5tETqkST9lzJJhJZIRdsANQYQBJHdqNIk3X///SECaDnGjBkT987MmDEDu3btQkdHB9xuN8rKyjBz5kycc845fJu5c+fi8OHDaGlpgdlsRmFhISorK0Ne94YbbsANN9yAxsZG5OTkwGAw4LnnnuPbVFZW4rPPPgt5bdYhV1lZGXH/bDZbSKlPT7giBUmkSSI0pDe83OamIIkgiOxFVZBUVlaGsrKyZO0Lp6CgAEBAzL19+3Y8+eSTA7YpLS0FAGzcuBFNTU246qqrBmzDSmivvPIK7HY7Lr/8cgDArFmz8NRTT6GpqQnl5eUAgA0bNiA/Px+TJk1KyntKNm4fZZKI5BMeJJFwmyCIbCZp3W21tbVobW1FbW0tfD4fdu3aBQAYN24ccnNzAQR0SCtWrMA111wDAFi3bh3KyspQVVWF3bt345577sHChQsxd+5c/ryrV6/GxIkTUVZWhq1bt+Kee+7B0qVLMWHCBL7Nr3/9a1xwwQXIzc3Fhg0b8OCDD+Lpp5/mfk1z587FpEmTcOONN+LnP/85Ghoa8JOf/ARLlizRbaYoFlIjSQCwkQUAkQTCM0f99PkiCCKLSVqQtHz5cqxdu5b/fdZZZwEANm3ahNmzZwMAampq0NHRwbepr6/Hfffdh8bGRgwZMgQ33XQTHnnkkZDnrampwbJly9Da2opRo0bhxz/+MZYuXRqyzWeffYZHH30U3d3dqK6uxssvv4wbb7yR328ymfDOO+/grrvuwqxZs5CTk4Obb74ZTzzxhNaHIWWImiRT8F8qtxHaQ+U2giAGEwZBEGiMd5x0dnaioKAAHR0d3LcpXXywtxG3vbod00YU4q0lF+LLk+246tefYEiBHVuXXZbWfSOyh3XbT+DBP3/J//7exWOw7IqJadwjgiAI9Si9fuvGAoBIDCbcZmU20iQRySDcF8lFFgAEQWQxFCRlCW5f4OJls4T6JFGQRGgJldsIghhMUJCUJQwQblsC2iQXaZIIDRnQ3UY+SQShC/72RR0+PtiS7t3IOihIyhIGmElKMkkkOyO0grX851hNIX8TBJE+WrpduOf1nfje/26Hz0/ney2hIClLiGQmCVCHG6EdvUHH7aKcwBDoPtIkEUTaae91QxCAHrcPde196d6drIKCpCyBm0mycps0SCJdEqERrNxWEgySKJNEEOmnzy2e44+09KRxT7IPCpKyhEjlNul9BJEoTKjNMkkuCpIIIu1Iu06PNnencU+yDwqSsoTwIMloNMBsNATuo3IboREsk1TsZOU2CpIIIt30SgZPHzvdm8Y9yT4oSMoSwjVJ0t8pk0RoBcskFfNyG322CCLdSMveVG7TFgqSsgQWCNmCY0kACpII7en1hAq3SZNEEOlHas1xtIXKbVpCQVKWIAZJkkxSUJfkoiCJ0IjesEwSldsIQhvcXj/+b8dJNHb2q36s9Ht4qq0PLvIv0wwKkrKE8O42gIbcEtrDhdtOJtymzxZBaMF7expw/7ov8LP39qt+rNT53i8AJ1pJl6QVFCRlCeHCbenvVG4jtCI8k+T2+cm8jiA0oLEjkEFq7nKpfmz4eKAjzaRL0goKkrIEWeE2zW8jNEYUblv4bZTaJ4jE6XIF9H7ho3+U0BtW9j5K4m3NoCApS5Art9kok0RoiNfn558zVm4DaMgtQWhBTzBIiuf7xB4TdH2hIElDKEjKEtzB1bxsuY00SYQGSMWhOTYzD8j7KQgniITp7g8GSXE0Q7AgaUxZLgAKkrSEgqQsgXySiGQjXa3azEbYLcEgiTrcCCJhut2s3OaNseVAWGA1aUg+AAqStISCpCxBTrjNPJMoSCK0gGklnFYzDAYD7JbA54vKbQSROCyTFI8miQdJQwNBUlOXC90u9cEWMRAKkrKEqD5JVG4jNICdvB3WQHDEgiQSbhNE4mihSarMt/Ph08com6QJFCRlCUx3ZKNyG5Ek+oJu285gkOQIBkk0moQgEodlfrx+QfU5m2WS7BYTRpXmAKCSm1ZQkJQl8HKbaeBYElrpE1rAM0kWlkkKfL6o3EYQiSMtj6n9TomlcBNGU5CkKRQkZQmZYCbZ2e9BR68n3btBxIn0RAwANpZJoiCcIBJGGiSxGYlKYc0TDgqSNIeCpCxBNkjSkZmk3y/giv/5F+Y8vxke0khlJH0S4TZA5TaC0ApBELgmCVAv3mYdcQ6LCWOCQdIRCpI0gYKkLIGJs0O72/QTJHW5vDjV3ofmLhfaetzp3h0iDtiJ2x5ebiMLAIJICJfXD49PHO+jttzWJ2mqGF0WzCQ1d0MQaGRQolCQlAUIgiDRJOnTTFK6Sursp9bUTIStVp3h3W0UJBFEQvSEteurzSSxhYrDYsLI4kCQ1NnvRRvJGxKGgqQsQBoE6bXcJq23d/XTFzcT6QvTJInlNgqSCCIRwj2N1BhKenxiFsppNcFhNWFogR0AcLSlW7udHKRQkJQFSIMgvVoAhAZJlEnKRPo88j5JVG4jiMQID5LUlNuk3z/2nWQltyPNpEtKFAqSsgBpECRXbtODmaQ0nUxOsJnJwO42NpYk/Z8vgshkusMWjmoWHv1h44IAUIebhlCQlAWwcpvFZICRjYGGvjJJPVRuy3gid7dRJokgEqHHHb8mSapHMhgC5/9RJYEg6dhpCpIShYKkLEBOtC39Ww9BUrdL/NJTuS0z6fWEm0lSuY0gtCD8nKim3CaOCzLz28ZQuU0zKEjKAuQ8kqR/6yFIou62zKcvvLuNlXOp3EYQCdHjCg2K4sokWcXz/+jSXACBTJLfTzYAiUBBUhbgihAk2XRkAUDdbZlP+IBb9i+V2wgiMbpdoedENY7bvAxuETNJw4scMBsN6Pf40dDZr81ODlIoSMoCIgdJgYuY3jJJVG7LTHrDNElUbiMIbegOyySp6m5jJq9WcW6nxWTEiGInAOAYibcTgoKkLCCiJkmn5TbKJGUm4T5JLAinTBJBJAbrbjMFG2/UlNtErWDo+X80jSfRBAqSsgA3H0liCrldT47bJNzOfFgJIHwsCVkAEERisEVkSY4VgLpMUn9YhpdBNgDaQEFSFsAyRbZw4baJCWvTv9KnclvmQ47bBJEcmGazPN8GQJ3jtnS4rRQKkrSBgqQsIGZ3mw4ySVIfEDKTzEzCzSTtFCQRhCawc2JZLguS1HS3Bc7vDmtokDSGgiRNoCApC3D7Al+oAZkk1qKtA00SdbdlNoIgRBxL0q+DzxdBZDI8k5QXmLmmphmiL0ImaVQwSDrR2guPDhbKmQoFSVlAJphJkk9SZuPy+iEE7VbCHbfV6CcIghgIOz+W5cWTSQrN8DIq8+2wW4zw+gWcbOvTaE8HHxQkZQGRym1SnyRBSK+hmNQsze31w+WlC2smIT1pO8KF215f2j9fBJHJMJ0mC5LiGXBrD8skGY0GPp7kaEu3Frs5KKEgKQuI5JPE/hYEwJtm19VwHRKJtzMLJg61mY28TdkWPCkLgj50bwSRqTDNJg+SVJTbwrWCUmg8SeJQkJQFuGL4JAHpLbkJghBSbgMoSMo0wjvbgFANRL+bgiSCiAdBELhPUnme+u62/jCtoBQadJs4FCRlARG720z6CJJcXj/PZOXZAnoWEm9nFuFu2wBgMRkQTCqhn8qnBBEX0vMjyyT1e/yKZ67xcUGWgUES2QAkDgVJWQArddjCzCTNJiO/iKWzHCLNIlUUBLo3KJOUWYTPbQMAg8FANgAEkSBSKUJp0AIAUF5y65P5bjJYue0oldvihoKkLCBSJkl6WzozSUy07bCYUOCwAKBMUqbRF3TbDtc9OGh+GxGBHcfb8Lt/HSFRfwzYIjLHaoLTaoIhuLBV2uHGrTlkM0m5AIC6jn7qQo0TCpKygKhBkin9XklspZRjMyPPzsptlEnKJNgJO7yDRswkkSaJEPH7BSz5f5/jv9/dh38fa0v37ugadi7MsZlhMBhUW2tEyyQVOS18YXq8lbJJ8ZC0IOmpp57CBRdcAKfTicLCQkWPaWxsxOLFizF06FA4nU7Mnz8fBw8eDNnm8OHDuOaaa1BWVob8/Hxcf/31aGxsDNnm888/x+WXX47CwkKUlJTgjjvuQHd3aAukwWAY8PP6668n9J7TRaSxJIA4zy2tmaSgCDHXZkKenWWSKEjKJCJ10Nj4/DZapRIiu062o6GzHwBQ30EePdFgi8jc4AKSfcfYrMRYRNMkGQwGbipJJbf4SFqQ5Ha7cd111+Guu+5StL0gCFi4cCGOHDmCt956Czt37sTIkSMxZ84c9PQE/nN7enowd+5cGAwGbNy4EZ988gncbjeuvPJK+P2BIKCurg5z5szBuHHj8Omnn+K9997Dnj17sHjx4gGvuXr1atTX1/OfhQsXavX2UwofcGsa+N9p08FoEsokZT5y3W0AzW8j5Fn/VQP//XS3O417on9YuS032NTCMkJKy239noFNFVLYeJIjJN6OC/mjqgGPP/44AGDNmjWKtj948CC2bduGr776CpMnTwYAvPTSS6isrMQf//hH3Hbbbfjkk09w7Ngx7Ny5E/n5+QCAtWvXoqioCBs3bsScOXPwzjvvwGKxYOXKlTAaAwHCqlWrMHXqVBw6dAjjxo3jr1lYWIjKykoN33V6YMaMcuU2my40SZIgibrbMhJxtRp6yiDhNhGOIAj4hyRIau2hICka3WFBkjP4HVNabouWSQKowy1RdKNJcrlcAAC73c5vMxqNsNls+Pjjj/k2BoMBNpvYAWC322E0GkO2sVqtPEACAIfDAQB8G8aSJUtQWlqK8847D6+88kpMgaHL5UJnZ2fIjx7Qv3BbPAlQJikziTT6gLtukyaJCLKvvgu1rb3879MUJEUlPEhSk0mSm6kYDgVJiaGbIKm6uhpVVVVYtmwZ2tra4Ha78bOf/QwnT55EfX09AOD8889HTk4OHnroIfT29qKnpwcPPPAAfD4f3+bSSy9FQ0MDfvGLX8DtdqOtrQ0PP/wwAPBtAOCJJ57AG2+8gQ0bNmDRokX4/ve/jxdeeCHqPq5YsQIFBQX8Z8SIEUk6GuqIZCYJSIfcpm+l3x3sbguU24KaJBdlkjIJNkSTym1ELN7bE8giMfuR1h6X6ufYU9eBxas/w566Di13TZcwI0meSeJBUuyFpLQhJ1aQdIyCpLhQFSQ9/PDDsoJn6c/+/fvj2hGLxYI333wTBw4cQHFxMZxOJzZt2oQrrriCZ4XKysqwbt06vP3228jNzUVBQQHa29tx9tln820mT56MtWvX4tlnn4XT6URlZSVGjx6NioqKkOzSI488ggsvvBBnnXUWHnroIfzwhz/EL37xi6j7uGzZMnR0dPCfEydOxPVetYYLty2Ru9v0kUkyUSYpQ5HzSQLE0SRkAUAwmB7p0upyAPGV29ZtP4mPaprx152nNN03PdITJtxW090mN1MxHCbcPt3jRkcvLU7VokqTdP/998sKoKWMGTMm7p2ZMWMGdu3ahY6ODrjdbpSVlWHmzJk455xz+DZz587F4cOH0dLSArPZzHVF0te94YYbcMMNN6CxsRE5OTkwGAx47rnnou7bzJkz8eSTT8LlcoWU86TYbLaI96WTaMJtqw6E26IPiJhJ6qQgKaOIJNy2m8kCgBA50tyNmsYumI0GXHfOCHywrymucltLdyD7pFS8nMl0STSbgLpyG1ucWCUzFcPJtZlRnmdDU5cLR0/3YLqzUIO9HjyoCpLKyspQVlaWrH3hFBQUAAiIubdv344nn3xywDalpaUAgI0bN6KpqQlXXXXVgG0qKioAAK+88grsdjsuv/zyiK+5a9cuFBUV6TIIioUSTZL+fJJoRZNJiJmk0FOGw0oWAITI+j0BO5ZZY0t4mSeeTBLriBsMwXd4dxtbiCjJzrIyeKQsEmN0aU4gSGrpxvQRhQns7eAjad1ttbW1aG1tRW1tLXw+H3bt2gUAGDduHHJzAy6g1dXVWLFiBa655hoAwLp161BWVoaqqirs3r0b99xzDxYuXIi5c+fy5129ejUmTpyIsrIybN26Fffccw+WLl2KCRMm8G1+/etf44ILLkBubi42bNiABx98EE8//TT3a3r77bfR2NiI888/H3a7HRs2bMBPf/pTPPDAA8k6HElFiZmkPsptYpDUTZmkjKKXCbfDzSTNpEkiRJgeaf6ZlSjOsQIA2ns98Pr8MMtkuiPBMkmD4XM1oLvNqry7rS84WDo8wxvOmLIcfHq0FUdbeqNuRwwkaUHS8uXLsXbtWv73WWedBQDYtGkTZs+eDQCoqalBR4cozKuvr8d9992HxsZGDBkyBDfddBMeeeSRkOetqanBsmXL0NrailGjRuHHP/4xli5dGrLNZ599hkcffRTd3d2orq7Gyy+/jBtvvJHfzywCli5dCkEQMG7cODz33HO4/fbbtT4MKUGc3abP7japcDufzCQzkkjCbbIAIBh17X344kQ7DAbg8kkVKHJaYTAAggC09Xr48FYlsBLdYPhcsfNjPN1tvQozSSOKnQCAk20UJKklaUHSmjVrYnokhbfc/+AHP8APfvCDqI95+umn8fTTT0fd5tVXX416//z58zF//vyo22QSLg/TJA38ouhKkyQRbvd5fPD4/LCoWF0S6SOScJv9PRjKIkR03g9mkc4ZWYTyvICVS6HDgrZeD1p73IqDJK/Pj7beYJCUxq7cVNEdlB7kcJ8kVm6LvZCM1f7PKHYGsnok3FYPXaGyAC7c1quZpFtMJ7MTAUAlt0yiL4JhHft8UXcbwUpt8yaLBr2s5HZahQ1Aa68bbP08GIaysgHgbAGpSrgdw0iSUegMZPBZ8Ekoh4KkLEDvmiSpcNtiMvIvNJXcMgdxdhs5bhMDOd3twmdHWwGEBkklOYHskRrxtnSMyWDIUHaHdbex75ia7rZYmaQCR1Af1keZJLVQkJQFKHLc1kG5jdXc2YqpkzrcMgaufYhkJpnGIJxIPx/sa4RfAM4cls/1L4CYSVITJDHRNjBIym2RuttU+CTFyiQV5QQySVRuUw8FSRmOIAi6F273SITbAMhQMgOJPJYkGCQNgrIIEZn3ggaS8yeHzsIszg2W21QMuQ3JJGX550oQhChjSWKfH/sjfC/DKZRkkmKN3yJCoSApw5FmiOTLbYEvT7p8kgRB4JqkHFtgX/hoEsokZQQenx8eX+DEGnF22yBY8RPydPZ78Mmh0wACrf9SShLOJGV3htLl9cPnD3y3mOO2Mx5NUqwgKahJ8vnFoIxQBgVJGY40QyTnuM1GlaQrk9Tr9nERZni5jTJJmYFUlB2x3EaapEHLpv1NcPv8GFeei3HleSH3xVduk2qSsvtzJT0Hsq42NWaSzL/MHqPcZreYeKWhnUpuqqAgKcOJFSRx4XaaNElMj2Q0iBdU5pVEK5rMgK1WTUbDgM8YzW4jIpXagPi6205LMkl9Hl9Wl4ekek1jcKyIw6JCuB1hXJAcLJtEQZI6KEjKcFgZzWIy8C+ZFFGTlJ6LmHQukcEQ2D8aTZJZ8M42i4n/HzJ4uW0QdCERA+lz+/BRTTOAgaU2IL7uNmm5TRDS23SSbLpdoVIEQMzWKnPcVibcBoAiJ9MlkQ2AGihIynB4Z1sEU8Z0z24L72wDqNwmxe31636lHKmzDaBy22Dnnweb0efxYVihA5OH5g+4P55yW/hA3GwOwMNF24BUk+SNeW4QLQBi+0IXOCiTFA8UJGU40YwkgfSbSYZ7gACicLtzkAdJTV39OOe/N+DBP3+Z7l2JSrSUPvkkDW7WfyXOagvPMgJASbC7ra3XA79f2WIgvBMumz9bzFBXGiSxxYhfQRZNqQUAICm3kVeSKihIynCieSQB6TeTDG//B8QTwmAvt+051YnOfi834dMr4kiSgatVFiR5fALv0iEGB26vHx/sawQgX2oDxBKPzy+gQ8HFWRAENHeH6peyOUji0wjskkySJOCJVXJTagEASGwAVGT1CAqSMh5WRrOZ5b8k6TaTFMtt4v5RuS0A0wYoEWimE3G1OvB0IV3BZvPFjBjItiOn0dnvRWmuDWdXFcluYzUb+fc9vIwmR7fLyxd0OYNgLiA7B+ZIFiBmk5EvbmOdG1gpPFZ3G0CZpHihICnDiZlJ0ku5zTqw3DbYM0lMG9CnwDQunbBBm+EjSYBQA1PqcBtcbD8WyIDOnlAGk0zTCEONVxJr/3daTSgMZqGy+XPFF5H20O+W0vltfcEAUll3WzCTRJokVVCQlOFwTVIk4Xbay20Da+75lEkCIAmSdN7m3BvFsM5oNPBAnDJJg4vjrb0AgHHluVG3E8XbsW0AWPt/Sa6Vf96y+XMlJ9wGlI8m6YvSVBEOyyR1UHebKihIynCUZpLS3d0mJ9we7EES02j4hfT9/yghlheL2OGm3/dAaE9tMEiqksxqk6M4aAOgpNzGMkmluTZuL5HNmaRIQZLS0SS8u01JuY262+KCgqQMR3G5LU2apG4Z4Tb5JAVo7xUvGko8UdJFrCBJ9ErS73sgtOeEwiCJl9sUzG9jHkklOTbYgzpLVxZ/rlh3W06ETFJvjPeudCwJABQEM0ltvZRJUgMFSRmOK2gSKTfcVnp7+sttA4XbPW7foO6Ikgoo9bxa7uWrVXkvFrIBGHz0uLw86zMiViaJDblVkEli7f9ledJyW/ZmKFl3W16YJskZ/K7FLLepyiQF/h+UdBkSIhQkZTgxzSSDA27TJtx2Ry63AYN7NIk07a3nDjcqtxHhnGgLZJEKnRZuUhgJNcJtNr6kJMfGO3b1vIBIFLnuNkCZcDva4Gk5inLEcpueNZB6g4KkDCeWmWS6y21ymiSr2cgzXIO55CZd0em53BbNcRug+W2DkeOnlZXaAHWu2y0S4fZgKONG6m4ThduRF5HS75siC4BgJsnrF9Cj4/ON3qAgKcNxc5+k6EGSz58esz+57jaAvJKAME2Sji8EvbE0SdTdNuhgeqRYpTZAOuRWnXB7MGQoYwu3I3+n+oP3GQ2Rz/9S7BYjvx60ky5JMRQkZTiuGMJt6ZcnHSU3OeE2QB1u/jAH4lhdLOkkZrltELRqE6Eo7WwDpENuY1sAhGaSsj9DySYSDAiSLLGDJOlIErmRMOEYDAbqcIsDCpIyHKXdbdJtU4mccBugDrculxfSxJ6+y23Rh2iyLiQKkgYPaoIkJtxu7XHH1MJw4bbEAiCbu9vY+S9Sd1u0AFHNcFtGERlKqoaCpAxHNJOUX+WbjQawRYbLl/qTjZwmCaByW3i6W8/C7d4YHTSidiR7yyJEKCxIGqkokxS4MHt8ArqiNGq4vX6eXS0JKbfp97uRCIIgaoPCu9tY4BNt8SQuXpRfxgv4aBIqtymFgqQMJ1YmyWAwpNV1W24sCQDk2Qb3aJLwlZyeSwpMPErlNgIIlIpPtvYBUKZJsltM/LMTzSuJCbtNxkBZKNsbAvo9fq4TjeiTFE2TxIbbRrDmkIPKbeqhICnDYT5JkYIk6X2pdnX2+vz8NSMJtzsHayYpzKskM8ptEbrbWLnNq9/3QGhHQ2c/3D4/zEYDhhTYFT1GiXib6ZGKc6wwGg0S/63szFCyBaTBADjDsrRiuS3y+ZF9L+0K2v8Z4mgSCpKUQkFShhOru016X6ozSUyUCEQWbg9Wn6RMKrfFdtxm7crZeTEjQmGltmFFDpgj+LOFo8QrSXTbDmyb7eU2aZbdGDYgWIlwWzSSVH4ZZ0Nu2xR0GhIBKEjKcGKZSUrvS3WQxIwkrSbjgEzXYBduh6/k9FxS4BYAEdL6/GJGmaRBgRrRNkPJkFvRbTvQDZfts9vYSJLwLDsAOIPyBCUWAE4Vwu1CrkkanOfdeKAgKcOJZSYpvS/VhpKiaHtgBoKE2+HlNn0eB0EQJF00NLuNUD6zTYqSIbei23ZoJsmV5eU2ufOjaCYZTbgdNHlVYCTJYIaSpElSDgVJGY6Scps1TeW27gidbUBqgyS3148dx1vTNppFDnaSYll2vZbbpHqQWOU2CpIGB/FkkkpyYw+5ZUaSJbksk5TdGUpuJGkfONZFNJOM5rjtD9lWCaImicptSlGepyN0SSwzSel9qdckRU4ni2aSyVvRtPa48cfPavHq1mNo7HThzovH4uErqpP2empgLbjleXY0dPbrtqQgPUlHWrEOBmdkQkTNSBKGktEkTJNUGgySbKzcptMFRKJE8pADlGWS+uLKJFF3m1ooSMpwYlkAAKImKdXdbZE8koDkZpIONHZh9SdH8ebnp0Le87GWHs1fK146giepyoJgkKTTCwHvoLEYB4hLGTYqtw0q1IwkYSjpbjvNM0lhwu0szSR1RVlEcgsABWaSSobbMphPUhsFSYqhICnDEc0k9adJijSSBNB+LInfL2DzwWa88vFR/OtgC7/9zGH5OKMiD29+fgpdLv2cGJhwcmihHbtO6LfcJp6II58qBsP4CCJAt8vLA52qEhXlNhWZpLLwcluWZijFTJJcuS22cFtcwCgPkpjjdkdfwP1cyTiTwQ4FSRkOEzVGL7cFvkTpK7dFFm53alBu8/kFLHppC3adaAcQ0PnMnVSJ//raaJw7qggf1TTjzc9PobNPP+JoZgEwpMABIPqKMZ1I50NFgsptgweWRSpyWpAvo6WJhJJyW3gmiQdJOl1AJIrY3SZTbrOI52yfX4BJJosbTyaJaZI8PgG9bp/sApYIhY5QhqOouy1dFgAR3LYBMUjqdnnh9wsRSzlKqGvvw64T7TAagFsuHI3FF4wKKQVoGZBpBbMAYGZ8er0Q8A6aKCdiO+9C0ud7ILQjHtE2IA65PR3BAkAQBLG7LZhJyvZymyjcHnh+lH7fet1ennmX0hfD5FUOh8UEq8kIt8+P9j4PBUkKoO62DEedmWRqTzbRNElsFSoIiWdR2MmmOMeKR74xaYBWIj8oVuzUiTeIIAhcODm0kGWS9JPlkhLLSBLIfj8bQqT2tHo9EiAOue33+GU7tjr7vPD4AiM6WGmOfa48PgHeFEsFUkG07l+b2chnbkb6XvXFmKkoh8FgEOe39VKHmxIoSMpwRDPJyF8UW5p9kuSEiTazERZT4CyQaIcb0zXJrbYAMSDr7PfGnEKeCnrcPniDM5tYJknvwu1oJ2KyABg8xJtJyrGaeLb7tIwNQEswi5RnM/PPk1Rr068j+w6tYOfHPJnzo8Fg4CW3SOeGeDJJAHW4qYWCpAyHBT62KNb06fNJiizcNhgMmom3u4OCbLlgDADyHYHbfX5BFwJptoKzmY1cq6HXIElJJok0SYOHeIMkg8EQVbzd0hVs/w+6bQOh2fFsDMCjZZKA2OLteDJJgCjepiBJGRQkZTiKxpKk3SdJ/kus1WiSrij2/kDgJGIOap70oEtiJ6dCp0Wc0eTx6SLLFQ4rjUTrbrNJym16fA+EdnC3bRWdbYxo4m3WMccCKSAQWGWzm3t3lEw7ILEB0DiTxMttZCipCAqSMhxVPkmpLre5o6+U2MmhM+FMUmQBJBA42Yq6pPRrf3iQ5LDyE5wgpN7HSglKXH2lZRE9vgdCG3x+ASfb+gCozyQB0b2S+HDbXGvI7dlcyo02uw2IbSippBQuB5Xb1EFBUgYjCIKq2W2pnoEUK52slaGkqEmKnO3I11GHG1vBFUgySYA+S259PJMUu9wGZO+cLQJo7OyH2+eH2Wjg1hVqKIky5JaNJGFu24xsLuX2xFjcxRpN0q/Aw0wOcTSJ8nPhtiOncVRHZryphIKkDEa6atfzgNtIKyWtRpOwFZmcAJKhpw43MZNkgdlk5Jk+PXol9SpI6VtMRu7jQh1u2QsbRzK8yCHr2xOLaENuT3eHtv8zsjmT1BVjEckzSRHee9yZpKAmqS2KZ5WU46d7cMNvt+G7v/sUfv/gK6dTkJTBSIMefWqSIgu3Ae0ySbHKbYC0wy39QRJbwbEVnYOn1VNXCtxT14H/fmcvH48SCRa4OS3RV6t2c/ZqR4gA8YwjkRJtyK3oth1abmPi7WwLvgVBiNrdBgAOS2ThtiAIonBbbXcb1yQpOxcebOyGXwBOtfdhb32nqtfKBihIymCkQU/UIEmFmeRXpzrwsWSsRyJ0xxBu52uUSWKBTyQLAEDscNOHJilwkWArOlF7kLog9lcfHsTvPj6Kv+w8GXU7Jd1tgHiizlbjP0LsbBsZh2gbiCHc5m7bYeU2a3aW2/o8PrCkTKxMklyQJD0e6i0AgqNJFGqS6jr6+O+b9jepeq1sgIKkDIYFPRaTIapjtU1FJum/1vwbN73yKV/ZxYt0pRQrk9SdaCYphgAS0C4g0wJWbisIlgB5h1sKM0knWgMnvmPBEkoklDhuA4DNHHtqOZHZxNv+z4gm3JbrbgMAuzk7y21sAWkwRF6AOKNkmKWZNfXlNnXdbafaxSDpowPNql4rG6AgKYNR0v4PKNckubw+NHW54BeAho7+hPbN5fVzw8RUlduiCrcdoqFkummPUG5LpSapPrg6PH46uhhTqe5BbNXOrhU/IZJokKTWJwnIXjd3vrCzmiMOmXVEySSxxYvVbFStDytQ2d1W1y5eC3bWtg06p24KkjIY0Ugy+gVMqSZJ+qVR0/kgBwtcAPnZbYBYHtPMAiBqJomV29KfSeqQWAAAsVt9tabf40NbcB+Ot0bPJFG5jWDUJqhJilRu6/f4uIi5NEe+3JZtcwGZXjOajjJ6uS0+0TYgyST1ehT5mtVJMkl+AfinRnKMTCFpQdKxY8dw6623YvTo0XA4HBg7diweffRRuN3Ro9D+/n4sWbIEJSUlyM3NxaJFi9DY2BiyTW1tLRYsWACn04ny8nI8+OCD8HpDL7QfffQRzj77bNhsNowbNw5r1qwZ8ForV67EqFGjYLfbMXPmTHz22WcJv+9UojiTZBInSkejTbJCSNRDg5XaHBZTxJWO1maS0TRJeToSbrM0NztZ2WOMH9CaekmW8GRrX9SOFSXdbYCkLELltqykq9/Dg5v4M0mBAKjb5YVLEkyzUpvFZODaQQb7XGVbJqkrOCUg2oBZR5TzAtMvxlq8yMEct90+v6LjyoKk80YXAwA+qhlcuqSkBUn79++H3+/Hyy+/jD179uD555/HqlWr8KMf/Sjq45YuXYq3334b69atw+bNm1FXV4drr72W3+/z+bBgwQK43W5s2bIFa9euxZo1a7B8+XK+zdGjR7FgwQJccskl2LVrF+69917cdtttWL9+Pd/mT3/6E+677z48+uij+PzzzzFt2jTMmzcPTU2Z8wFwKTCSlN4fy0xSusJrSzClGssjCRAzP1r5JEXNJOlKuB2qSXKmuNxWL1kZun1+NHRGLq32KfRisWf5xPbBDtOwFedYoy5GopHvMHPne+m5hrf/59gGlJ5sWeqTxDNJ0YKk4HdOLpDhWsE4MklOq4nPzYy1GPb4/GgMnh++M7MKAPDPA82DygogaUHS/PnzsXr1asydOxdjxozBVVddhQceeABvvvlmxMd0dHTg97//PZ577jlceumlmDFjBlavXo0tW7Zg27ZtAID3338fe/fuxWuvvYbp06fjiiuuwJNPPomVK1fyLNWqVaswevRoPPvss5g4cSLuvvtufPOb38Tzzz/PX+u5557D7bffjltuuQWTJk3CqlWr4HQ68corryTrkGgOW40pDZJSWW4TTwKRv8TcJ8mVaGmPdbfp3wJAEIQBmiQWgKTKAqA+TG92PIp4u1eBmSQg9bPJrosZESDRUhsQcL4vYuLtbmmQxDrbrAMe48hSn6RY8yaB6OW2eNv/gcD/Q4FD2fy2xs5++IVAtWL+mZXIsZrQ0u3GV3Udql83U0mpJqmjowPFxcUR79+xYwc8Hg/mzJnDb6uurkZVVRW2bt0KANi6dSumTJmCiooKvs28efPQ2dmJPXv28G2kz8G2Yc/hdruxY8eOkG2MRiPmzJnDt5HD5XKhs7Mz5CedKC+3sSAp+okmtNyWWCYpVmcbIOqEEskkeXx+fmGOnknSh5lkv8fP/9+YBYAjxRYA9ZKWXkD0v5FDcbmNCWyp3JaVnEhQtM2QE283BzNJ4W7bQBYLtxVkkkQzSZnutjiNJBlKO9yYaHtIoR02swkXjisFAHxUM3i63FIWJB06dAgvvPACvve970XcpqGhAVarFYWFhSG3V1RUoKGhgW8jDZDY/ey+aNt0dnair68PLS0t8Pl8stuw55BjxYoVKCgo4D8jRoyI/qaTjJK5bYA4gDRWd5t0VZGoJklJuU103PbGPRhVah+gzEwyveU2dlIyGw3ICZ4ExSG3qdm3uvBMUmvkDjdx9IHCTBKV27IS9hmpKlY/jkSKnHg7WiYpWzOU7LylRJOkdSYJEOe3xfJKYnqkocExNLMnlAMYXLok1UHSww8/DIPBEPVn//79IY85deoU5s+fj+uuuw633367ZjufapYtW4aOjg7+c+LEibTuj5K5bYByM0npiUupG2skYo0kAcTymM8vxL1SZMGY3WKEJUpGTdQkKevoSBZ8JInTyvUXqe5uY/YOY8pyAEQut3l8fnh8gWMVy3E7m2dsEUBta/yDbaXIeSWJbtsDM0nsc5V93W2xbUvEMrycJinRTFJwNEmMIIl5JA0tZEFSGQBg54l2xWNNMh11k/EA3H///Vi8eHHUbcaMGcN/r6urwyWXXIILLrgAv/nNb6I+rrKyEm63G+3t7SHZpMbGRlRWVvJtwrvQWPebdJvwjrjGxkbk5+fD4XDAZDLBZDLJbsOeQw6bzQabbeAXOV2woMcWK5OkUJMkLbcpdWONhJK2fKc10Pnm8wvo7veqHtQISEXb0cWkLJPkDQZk8byWFohBkri/jhQHSWx1OHN0CY4090Qst0lXsErLbdmmHSECiOW2nISeR27IrTi3TS6TlK3lNpZJijI4WoEFQDzdbYCaclvgXDGs0A4gECxNqMhDTWMX/nmwGVdPHxbX62cSqjNJZWVlqK6ujvpjtQY+7KdOncLs2bO5ANtojP5yM2bMgMViwYcffshvq6mpQW1tLWbNmgUAmDVrFnbv3h3ShbZhwwbk5+dj0qRJfBvpc7Bt2HNYrVbMmDEjZBu/348PP/yQb5MJKA2S4hFuK3VjjUSsuW1AQEDIgqh4y2BKjCQBMSAD0tvh1sHa/x2SIMmS4u62YCbp/DEBfWAkryQWtJmNhpjZymweRDrY8fkFnGwLBklxjiRhsCG3IeU27rY9cAFqy9LPlbiIjLy4iybcVqoVjITqcluhWGZl2aTNg0SXlDRNEguQqqqq8Mwzz6C5uRkNDQ0hmp9Tp06hurqaZ4YKCgpw66234r777sOmTZuwY8cO3HLLLZg1axbOP/98AMDcuXMxadIk3Hjjjfjiiy+wfv16/OQnP8GSJUt4lufOO+/EkSNH8MMf/hD79+/Hiy++iDfeeANLly7lr33ffffht7/9LdauXYt9+/bhrrvuQk9PD2655ZZkHRLNUVxuYxYAqfRJckef28ZI1Cupqz92ZxsQCMi4oWQaO9zkMkmpLLf1ur28c3Hm6BK+T3LdjEpHkgAUJGUzDZ398PgEWEwGVObbE3qu4tyB3W3NEdy2gewt44qjlCJ/t5SMJXHEKINHQmooGQ0m3A4NkgK6pM2DxAogaTWHDRs24NChQzh06BCGDx8ech/ThHg8HtTU1KC3V1zJPv/88zAajVi0aBFcLhfmzZuHF198kd9vMpnwzjvv4K677sKsWbOQk5ODm2++GU888QTfZvTo0Xj33XexdOlS/PKXv8Tw4cPxu9/9DvPmzePbfOtb30JzczOWL1+OhoYGTJ8+He+9994AMbeeUdvd5vUL8PuFiHPeQjNJAe1OJMv8WCgRbgNMvN0Xd4ebkrIeI99hQVuvJ63z29q4R5JYWmB+KKmY3caySLk2MyoL7CjNtaKl243a072YMrwgZNtehW7bgMQQM8suZoQ4umZ4kVP1CIxw5LrbIs1tA6Sfq+wKvvkiMsriTjquKPxczLvbrPHlOQqCmiSl5TZpkHTOqCLk2sw43ePG7lMdmDaiMK59yBSSFiQtXrw4pnZp1KhRA0S0drsdK1euxMqVKyM+buTIkfj73/8e9blnz56NnTt3Rt3m7rvvxt133x11Gz2j1kwSCGSf7Eb5i570xOX2Blrr403nKhFuA0BegoaSSowkGbzDLY3ltnC3bUDirJuCAKOetfQWBDICVcXOQJDUOjBI6lMx+oA0SdnLCQ08khjh3W1+v8B/L5PJJGXr54qdtyKNbAJE4bYgBM71dsn3MFELgKLg+SeacLuz38PHxQwtFDOIFpMRF44rwfo9jfiopjnrgySa3ZbBxBMkRSq5+fzCgDJUIrokJT5JQOLlNp5JilFuAyQdbmnMJIlz2+TKbanIJAVWhpWSIAmQtwEQdQ+xj222mv4RopHkSA2CpJKw7rb2Pg98wZING5chhXe3xZAKZBo9Cs5b0gAoXJckWgDEWW4LZrKjaZJYFqnIaRnQ6HJJsOS2aRBYAVCQlMGI5bYYA24l5bhI4u2OPg9YUq9IYb06GsrLbYlmkgL7mK9gVEKeLf2GktG62+QEmlrDym3M96SqJNCtVCtjA9Cn0G0bEMsiLiq3ZR1atf8DYiapo88Dj8/PO9sKHBbZxV6q5xqmCiUyAZOkYSK8FJ+4BUDs7ja5Uhvj4qB4+4uT7QMGFmcbFCRlMErNJA0Gg+iVFMFQkom28+xmlAT9ShIJkpSMJQm8HjOUjDOTpKbc5kisk04L2EmpQLJqZkFIKrIwLJM0JJg+Z9mBWpkON3WapOxs1Sa0GUnCCPiDBX5v63VL3LYHZpEASbkty0xKlWopIzV1JGoBwOZGRjvHn5IRbTOGFDhQXZkHQQD+dTC7u9woSMpg3D5ls9uk20TKJDFjsOIcKy8FJTKahJfbYqSD8+yJBS5dasptdh1lkuQsAFKYSWKapJHBlm45Q0k1q1XqbstetBpJAgSyI6ys1trjlrhty/vPZePnShAExZpNZwThOsss2RPMJLm8/ojHVvRIkndZZ11um/Znd8mNgqQMRqlPEqAgSJI4QYup2FSU2ywh26t+nX5lPkmAZH5bOjVJfZHLbX3BLpZkIgq3g+W24IWvvqNvwGdDzWqVxpJkJ139Hl5OGZHgSBIGF293u3m5Tc5tGwgdS5JOp3wt6fP4wDrnYy3uIpXiWZNHvJmkXJsZ5mCnYluExbBYbpO3fWB+Sf882JLVVgAUJGUwqoKkGKNJ2BelyGlRPCE6Goq72xL2SVLT3cZGk6Sx3MYzSdJyW2gXSzKp6wg98ZXl2eCwmOAXxBEEDDXCbbs5tUN6idTASm3FOVa+oEkU6WiSlihz24DQTEm2iLfZws5oiJ2ljTSapE+Fh5kcBoMhpldSNE0SAMwYWYQ8mxmtPW58eaojrv3IBChIymCUmklKt2ElunDaeZBkVWxZHwm/X0CPO7bjNpC4cFup4zaQ/kxSv8fH0+YFMhYAQHJLbt0uLz/OlcFMksFgEDvcTod2uKnRJLGTdbbN2BrsaFlqY0i9kk4Hx5PIuW0DgF1ybsuWkluXJMsey4cuciYpMeE2EFuXJGckKcViMuJr40sBZHfJjYKkDIZ1EsUykwRiu2639ohlIKWW9ZGQjteIleHJ58LtRM0kY69y061JYq9rNIj+UED0LhYtaQhmkfLs5pD/lxERxNvqutuyU2A72KlNQpAkzSQ1dwUWYqV58pkks8kIiykQSGRLU4DSLDsgHU0SobstzkwSIA657ZBZDHt9fjR0BoKkSJokQCy5fXQge8XbFCRlMKoySTHKbSyTVCzNJMUZJLGTgNEgXjwjoVW5TV0mKT3lNqbxKnBYBriep6LDja8MC0JPeky8HW4DoOZEzMptHp8Ab4QOSiLzSEaQJB1yGyuTBIifrWwZTaKmI9dplRdu92uQSSqMkklq6nLB5w+MoomkFwOAi88IiLe/PNnO9WXZBgVJGYxSM0kAsFmUaZIKc6yKLesj0a0inZyrkU+SKguANGWS2iXi+HBS0eEWbiTJ4B1uYZkklhF0KjgRSwOp/izRjhBi12MyMknS7rayCJkkALCn0CIjFShtagHE2WzS84LH54fHFxBKxyvcBsTzkJzrNtMjVRbYI46xYvdPHJIPQQD+maVWABQkZTBKZ7dJt4nskxT4ohRJym2JZpKUBC55CZTb3F4/DxQVZZLsoiYpHZ0yLFsn7WxjpMJQkhtJhnWrsAtgeCapT0UmyZaF2hFC25EkjOJgZuJ0txst3QoySVnmwaVGR+mUOS9Ij0O8FgBAdENJ1sQRnnWW49LqQMnt77sbYmyZmVCQlMEoNZOUbhOr3FbktHIfE7nJ8EpQs1JiJwq3L7JfRyR6JLYBSl6Llds8PiEtnTKs3Cb1SGJEMo3TkvD2f0aVRJMkDR57eQdN7GNrMBh4oJRt7siDFZ9fwMm2wMWSZRu1gJXbTrX38Yt/pO42QFpuy47PlVIPOUB+ZFF/8JgZDco6myMRTXvKSvPR9EiMq6YNAwB8VNPE/fayCQqSMhg1miRbjCApRLidsCZJWWcbAORazdyBV202iQVjDosJFgXZtByrCSxznI6SW0eUcpszmFZP5mq5LkK5bXiRE0ZD4LWbJboCFuwoKbcBkg43Em9nBTUNXfD6BVjNRlTky3vlxAMrt7EAzGY2Rs06i52T2VHGVWOAK5dhlpq8xpIzRCPaeT5W+7+UCZV5mDw0Hx6fgHe+rIt7f/QKBUkZjOiTFPsiFq27TRAEUbidY+Xt6X0eX1yrN7HcFnu/jEYDcoMrKrWGkqyVX8nJBghkO9JpA8BHkshkkuwpKLc1dMgLt61mI88uSUtuaiwAgOwT2A523g5e8GafUQZTFF2KWlgmiVGaa4t6seceXFmWSVIl3JYpt8U73JZRGEV7qiZIAoBrzgpkk/7v81MJ7ZMeoSApg9HKTLLb5YVXMok7z2bmJ8V4Mi7dKtLJQPwdbtxtW8HJJvy1OtJgKNkmM9yWwccPJNECgI8kkXHQlRtPorbNONu0I4MZQRDwt12BIOmq6UM1fe6iAUFS5FIbIDadZEu5TU13m1xDh/i9TOzyHS2TdCqG23Y4V00fCqMB2HWiHUeauxPaL71BQVIGw8oa6swkBwZJ7EtitxhhD6ZwudFYHEGSmpUSEL94u1tF2pohFW+nmg6ZuW2MSK2+WtHZ7+HHa0jBwBNflYxXUh8fS6Ls+GbjnK3Byue1bTjV3occqwmXVVdo+twWk5G73wOR57YxHJbsylB2s+Hfisptwe42yXeKjwuyJJhJijJZIdbctnDK8+z4+hkBAfdfdmZXNomCpAxGVXdblHIbm81UJNHKJNLh1qNCuA0kkElS0SXCSKehJEtry2mSkl1uY6W2AodFNuipKpEJktSW27LsYjaYeSuYRZo7uTIhw8JISAOj8PJbOPYIQ14zlW5X4Nyj5PzI/dNkMkn2BP9fInW3dfV7uJfcEIVBEiCW3P6y81RWzXKjICmDUWcmGfhCyZXb2noHBklMlxRp+GE0ulUItwExyFFr8tipIm3N4F5JaTCUZAFnQdRyW3IuBGxlKJdFAoCRxTkAxNEkfr8g0T5QuW0w4fX58e6X9QC0L7UxiiWBUWle9EySPcvKbayxRYlMgAu3PeL5ShxJktjlm52H+j2hncX1kgWVmnPr3EmVyLWZcbKtD9uPtyW0b3qCgqQMxe8XuKFYohYA7OJdlCNevBMZTaJGuA0AufGW23iQpHzwZlozSWkst3E9UoQgSSy3BYIp6XgRpZkkB5XbsoJPDp/G6R43inOs+Nq40qS8hjRIipVJYp+rbJkL2KUi0+6U0SSJ44ISK7dJtadSu5dTKkXbDIfVhCvOrAQAvPn5yYT2TU9QkJShSLVFiQ645W7b0nJbAq7b3e5Ulds8IY9XQjq729iJKB3lNlG0LX/iY+W2lm4XelzekP2wK+ieBMSySLZczAYrb+0KaEr+Y0qlImuNeJAGRmUxM0nZVW5T190WtAYJCZISH0kCBLp92YJNWjEQ9UjqbR+uOTtQcnt3d33WLJYoSMpQQoIkBSeyaD5JbVyTJGY4Yk2Ijkb8miR1mSQ1c9sYYiYpteU2j8/PNVSymaQkl9vquYOu/ImvwCH6Y9W29oaciKONJZCSbRezwUi/x4f39zQCAK6ePixprxOaSYoeJNmyTOumqrtNzidJZRk8GgUyHW5q2/+lnD+6BEML7Ojq9+LDfU0J758eoCApQ5EGO4laAIgjSaSZpNR1t+Xzclt8FgDxaZJSm0mSprPzZcttyTWTZJmkyihjBkZKOtzimTJOwu3MZ+P+JnS7vBhW6MCMqqKkvU5IkBTDAsCRZcF3j4quXDmfpH6NMkmAfIMOH4QdR5BkNBqwMCjgzpaSGwVJGYq0s02J62o0CwA54XYimqR4hduqM0mJWACkWJPETkL5drOsMZ+4YkxOhosNt42USQLE+Vy1p3vFkSQqTsTZJrAdjDBvpG9MG6I4gxgP0sCoNIYFQDZ9rgRBkMgRYn+3WJDk9vnhDZ67RWsODYIkPoJKLLfFq0liXBssuW0+0IzTEgf/TIWCpAzFpWJum3Q7xcLtBDRJaoXbLEhS67jNzSTtKoTbjvgH6iZCR5T2f0CyWk5CuU0QhJiaJEBiKNnao7r9H6ByW6bT0efBxppAieTqackrtQFAcbDEZjCElvnlyCafpF63D2w8Yp6ChhNpJpeV2bgFgBaZpCjltng0SQAwrjwPU4YVwOsX8PYXmT+mhIKkDEXNcFtALMnJ+STJC7dTqEmyMTG12kxSULitptxmT0+5rT2K2zYgP+1bKzr7RCF2ZZQZXMwGoLa1T/VIEiC7LmaDkfV7GuD2+jG+PBcTh+Ql9bXKg2LtslwbzDE0lXozKZUOgVYLWwgaDWKGLBpWk5FnntnCRdNMUtBQkkkufH5BHF8UZyYJELNJb2aBsSQFSRmKGiNJIHomqU3OTNIZ2Y01FqkeS6Kq3OZIj3CbeyTJ6JEAccWYjCxMfWdgZVjktETVGHFDydM9cYlD2UmfutsyE7bqv2ra0IQGpyqhujIP984ZjyeunhxzWz2V246f7sE5//0BHvnrV3EFS90SvaaSY2wwGAbYAPTFoReMBFu0sUx3c5cLXr8Ak9GA8rz4hxpfOW0oTEYDvjzZgUNNmT2mhIKkDIW18isut5miaZICF/BiOU2SSu2Ox+fn2aqUjSVRJdxOjwVAe5T2f0ASJCUhk1TfzjySoq8MmVfSybY+HoCq8WKhclvm0tTVj08OtQBInoGkFIPBgHvnnIH5Zw6JuS3PJHnT/7n6cF8TTve48b/bjuO3/zqi+vHxNJuE6xVFM0nty21Mj1SZb09oqHFprg0X8zElmS3gpiApQ9FKk9Tv8fEvXWGIJinwe7fLC49MYBWJHomuKNk+SfFYALBt3V5/SlemHb0DbRaksDlMySi31XVEd9tmVObbYTUb4fULOBwcUhlfd1v6L2aEOt79sh5+AZg+ohAjS3LSvTsh2JNsj6GGffWd/Pen/7EfH9Woa3NX09nGCF9AxdN5Golwqxe1M9uiwUpuf91ZF/eYkte2HceSP3yO975qSHh/4oWCpAwl3nJbuCaJfTnMRkOItifPbgHLBqvJJrHsjtVkVBzAscCl3+NXHJC5vWLGSokAkpFrNfP3lcpsEs8kKSi3JaJ5kKOBi7ajB0lGowEjigInx5qGLgCif5MSyAIgc/mbpNSmN/T0udrXEAiSxpblwC8A/98fd6qaeq/GbZvhCCu39WuYSSriDTqhQdLQOEXbUuZMrECezYxT7X349GhrXM/xeW0b3v2yHseC45LSAQVJGUq8wu3wTBIbblvotITUyE1GA2+XV6NL6uHt/8q/wNLUs9KSW3dIxkr5axklwWAqdUni3Lbo5TZA+4tBncJyGyCW3PazIElNJslMs9sykdrTvdhZ2w6jAfjG1Njlr1TDx5Kkudzm9flxoDEQEK367gzMGFmErn4vbn91u+IsuFoPOWDgyCItM0liuS1wHUjESDIcu8WE/5gS+DzFW3JjetniCOfNVEBBUobCtEVKjCSByANu22U62xjhXyAldMexUjKbjPxEoPRkw2r7DospZndMOOnQJcXMJElWhVp7JdUrLLcB4KWWlqC/iUOFJomdtKncllm8/WUgi3TB2FKUR+l+TBd8cHKay21HWnrg9vqRYzVhbFkuXvru2ajMt+Nwcw/ufX2XopJSPDrK8NEkWo0lAcTuNlGTlHhnmxRWcvv77oa4zgut3J6GgiRCJS5PnJqksHKWnGibIefGGot4VkqAekPJrjjmtjHSYSgpBqPyQZLJaOABr9aZGF5uU5FJYsTjkyRnM6EVv/vXEXznd9vSMnsvGxEEAX8NtmnrsdQGSIXb6S23MT3ShMo8GIPdX7+5aQZsZiM+3N+E5zYciPkc8QRJ4aNJRAuAxAbcAuJYkj6PD/0en6aaJAA4d1QxSnKs6HZ5eQlfDTyTlKNcUqE1FCRlKKozSRHKbW1RLt4FYfVqJaj1SGKo7XDriqP9nyGOJkl9uS1SkAQkp8NNEAQu3FaiM2CGknyf1GiSzMkV2Hp8fvzPBwfxyaHT+HBfY1JeY7Cxv6ELB5u6YTUZMS84wV1vsCDJ5xdUNZFozb76wEV+4pB8ftvU4YV4etEUAMCvNx3CO19GN09kGXA150dneHebhpmkPJsZrImts88jOVdoEyQZjQZUBLOTrSoqEgw5e5pUQ0FShqJWkxQxSIryIRQzSckttwHqO9y427bK1wHSm0kqcET+sof7oWjzuh6ucapQUEoJzySp0T04rEE/myRpRz4/3sY/X1+c6EjKaww2WBbpkuqyiB5e6UZquphOvRvLJEmDJAC45qzhuOPrYwAAD6z7AnvqIn822SJSTQZcOr9NEATRAkADTZLRaOBSi1PtfXwxp4Vwm8FG0LR2qwuS3F4/F7oXU7mNUIvq7jaJT5K0e4qV2wpl0pmi0Vg85TZ1X2C1maTuOFppGanWJPn8As9aKckkaRkksXEkJTlWRWMMRiRQbrOZk6tJ+ufBZv77Fyfbk/Iag4ntx1rxyidHAQDXnJXcMSSJEJhPGfg9nXo3MUga6Eb+0PxqXHxGGfo9ftzx6g40dfbLPkd83W1BexCPL6SpQ4sgCRAXw3uD7y/PblY16ikWLMBhTUJKYQtLo0Fc2KYDCpIyFFZuU5tJkj4WED+ImmmSghd4pW7bDLWZJHayUdP+z8iP07wyXqQZq2irdaYx0PJCwEXbCleGdospZHRJPJqkfo9fcxsDAPjngRb++566zrSWXjKdU+19uPO1HfD4BPzHlErMnaTPUhsQMJ7kHW5psgE43e1CU1egmWFCZf6A+01GA371n2dhdGkOTrX34ZurtuJYy8C29YS629y+kEyaFuU2QNQl7a0LBEla6ZEYrEqhttzWKhm8nsxhy7GgIClDUWsmKdUuSUtubb2Ry23xaJLiLbflqxVuB4OphDRJKSq3seOXazPDEiXzF+6HogV1wUxSZb7yE1+VRJcUT3cboL14u6Xbhd2nAmUMh8UEt9cflxCUCGhbbl+7HS3dbkwcko9nrpuW1ouQEtLt5s4sMUaWOCMGOAVOC9bech6qip2obe3FN1dtwVenQktviQm3vVyXZDUbE3LElhKeSdJKj8QoyYmv3MYyT+nsbAMoSMpYxHKbstWEtCwXGiRFFhTHo0mKt7uNbd/lUlhui8Pen8E1SSnKJIl6pBjTzsMEmlpQH4c5nFSXFI9PEqB9WeTjg4Es0sQh+ThnVBEAKrnFgyAIeHDdl9hb34mSHCt+e9MMTbqkkg37bKWr3MZLbTJZJClVJU78+a5ZmDQkHy3dbvznb7ZhyyExA9od9JGLJ5PU6/ZpaiTJYJqk/UFhupZ6JEAMctRmktp6IndepxIKkjIUFujYFEySBgICPXNw5SEtt/FMkky0XpSjXpMUv3CblcAUCreDr5OfiCYpxZmkaHokQDwZankhUNP+zxgpCZLUnIzNJiP/jGltiPnPAwE90tfPKMXU4QUAgC9JvK2aX288hHd318NiMmDVjTMwvMgZ+0E6wG4VS7npgGVZqmX0SOGU59nxp++dj1ljStDt8mLx6n/j3S/rAQDdwfNbPN1tfW4fzzKrWbzEolBiAwAkMZOkUpPEy21pbP8HKEjKWJj7rFLhNiDvui12t8lYAIQZjSkhntlEgKhJUprd6U7AAkB8rdQESR0K2v+BZJXblBtJMqTlNrUn42SURfx+Af8MZpIuHl+GqcMLAVAmSS3vfdWAZ4NePk9efSbOHVWc5j1SDreX0OBzJQiC6sHd+2Xa/6ORZ7dg9S3n4oozK+H2+XH3Hz/H/249xicSqOluYyXvXrdP0/Z/RmFYx63WmiQm3G5TGSSJHkmUSSLiQK0FgHRb9livz8+DElkLgDgct3t4Olndl1jta3Xyclv8wu2UZZKYF1WU9n8gud1tqoKkkHKbuiA0GUNu99Z3oqXbBafVhBmjijB9RCEA4EBjl+bu5NnK/oZO3PfGLgDA4gtG4T/Pq0rvDqmE2QBo8blau+UYpj3+PtbvUTY01ePz41BTYBzJJIVBEhD4Lvz6hrPxnZlVEATgkbf2oCHY9RbX7DaPD70atv8zwhdvWmeSWJBzWm0mSQceSQAFSRmLWjNJYOCQ244YXVeFDlG741M4xZmX21ReXCvyAhdxVh6K/ToaCLdTpUnqY3PbUltuEwSBB0lqTnzSKfBOlcGulhczBmv9nzWmBDazCRX5dlTk2+AXAl1uRHRae9y4/dXt6HX7cOG4EvxkwcR075JqtBx5s+1IYNjquu3K5okdbu6G2+dHns2M4UXqAgiT0YD/Xngm7p0zPuT2+LrbvOhPRiYpRUFSR59HVUcqk4JQJomIi4QyScEPKvsQ5tvNsvPPpIGT0qxLvMLtimCmo6nTpWj77jhM2RipzyRFn9vG0Lrc1trj5p+T8nyb4scV51hx58VjcevXRqv2J0lGuU3UI5Xx26axktuJds1eJxtp6Xbh+/9vB0609mFkiRO//vbZqmcd6gG7hh5cbC7hlsMtiobm7pPokaRDwJViMBhw75wz8OTCM2EwBM6NbKGmBO7E7/FpOtyWIT3PGw1ARZ7yc4USCp1W7nOlRrqhl0yS/tsaCFnUmklKt2VeI20xhgeaTUbk2czocnnR1utW1IoZ71gS5s3T5fKix+WN+fiEHLeDJwWX149+j0+RyWIisIxdrC+7VHugBSyLVJpr40aPSnn4iuq4XlNrP5tulxc7jrcBAC6WBkkjCvH+3kZ8cZLE2+G09bjx3p4GvPtlPbYcboFfCFyYf3vTOWlvp44XqQdXorCyT6/bhx3H2nDBuNKo2zM9UnWMzrZY3Hj+SEwfXgiDAaq+j3I+SVpmkqTnpcp8u+ZBtMloQKHDgrZeD1p73ChTGITpJZNEQVKGotZMMrCtKeSxSubiFDgt6HJ5FXslxdvdlmMz84CsobMfY8tyo26fyOy2PJsZBgMgCIHnSXaQxC0AUlxuE0ttqZvsrnW5bevh0/D4BFQVOzGqVCwD8g43Em8DCATi7+9pwDtf1uOTQy3wSsrjU4YV4Ef/MRFnVMTuzNIrWmrdWrrEbPVHB5pjBkl7I4wjiYcpwc+tGpwWcfHE3n8yutsA7UttjKIcKw+SlMIsANId2FOQlKGoNZOUbsuyUCz1KdfZxih0WnCyrY93aEVDEATuuB2Pf1F5vg1dzV40digIkuIs6wEBO4Rcmxld/V509nsUr2zihVsApNgnibltVyqY2aYVWpfbpK3/UqYOKwQAHD/di7YeZVnObEQQBPzoL1/hzztOwOMTA6NJQ/KxYOoQfGPqkBCNWabCgu9EP1f9Hl+IF9vmmmb86D+ia7TEwbbpCTKl5TbWGKOpcFvSUJKsIKkkx4ojzT2qgiS2bbp9kihIylC4T5KKtK3NFBoktUZx22awL1B7X+wPt8vr5wLvHJWCXwCoLLDjcHMP7wCJ/Do+/h7inTGUb7cEgqQU6JJEC4AY5TaNNUl17epF24miZVkEEEXbF59RHnJ7gdOC0aU5ONrSgy9PdYSU4gYTNY1d+ONntQCACRV5WDB1CBZMHRJzkZFpaPW5Ynoks9EAvyCgprELde19Eb8jzV0utHS7YDAAEyrTEySxrJEgiCUoNs9NC/LsYmY9WecKcX6bMs2ptLSYtT5Jx44dw6233orRo0fD4XBg7NixePTRR+F2R7/Y9vf3Y8mSJSgpKUFubi4WLVqExsbGkG1qa2uxYMECOJ1OlJeX48EHH4TXG7r6/uijj3D22WfDZrNh3LhxWLNmTcj9jz32GAwGQ8hPdXV8Oox0kFAmyRf48LEvXLSLdwFvzY8dTHRLVmhqu9sAcUp9YwzxNltNAfFlkgBRl5SK+W3icVZWbtMqC9MQh0dSomhZFjnW0oPjp3thNhowa2zJgPtZyW0wi7fXfxU4N15aXY71S7+OH1w2PusCJEBcQCT6uWoJjsYoz7NhWtBKgmUr5djfECi1jS7JSZszuVR/xLIrDqt2l26j0cDF28OSVJoXgyRli1J2zrSYDHGf47UiaUHS/v374ff78fLLL2PPnj14/vnnsWrVKvzoRz+K+rilS5fi7bffxrp167B582bU1dXh2muv5ff7fD4sWLAAbrcbW7Zswdq1a7FmzRosX76cb3P06FEsWLAAl1xyCXbt2oV7770Xt912G9avXx/yWpMnT0Z9fT3/+fjjj7U9CEnEHYeZ5IByW4+CcpuKIbdMtO20muKaBVXJg6TomSTmyu20muKeX5SfIkNJv180rlNabuvTKpPE5ralMkgya1MWAcQs0oyRRbInStbhNph1SczrZ/6Z+h1QqwVaad2YHqk0z4bZwezkRzWRg6R9Kpy2k4XRaODv/3QwE6OlcBsQqwl6ySRJO9vi6SjUkqSFaPPnz8f8+fP532PGjEFNTQ1eeuklPPPMM7KP6ejowO9//3v84Q9/wKWXXgoAWL16NSZOnIht27bh/PPPx/vvv4+9e/figw8+QEVFBaZPn44nn3wSDz30EB577DFYrVasWrUKo0ePxrPPPgsAmDhxIj7++GM8//zzmDdvnvjmzWZUVio/ubhcLrhc4n9yZ2f6PFriEm6HlduijSRhsOyHEofaeEXbDJZJiuWV1JXA3DaGOJokuZmkLpcXTEObn2ILAKZJSmW5jQV6Li2CpOAK/+IJ8qW0aSMCmaRdJzogCELMk+mhpi48+/4B3D/3DIwrz1wRM+NEay/21nfCaADmTKxI9+4kFa0ylKzcVpprw8UTyvD8BwfwyaEWeHx+2eHTXI+UYGdbojitZvR73DjdzTJJ2l66b7toND7c1ySbsdUCFoS1KrQAiDZ4PdWk1DCjo6MDxcWRrfB37NgBj8eDOXPm8Nuqq6tRVVWFrVu3AgC2bt2KKVOmoKJCPCnMmzcPnZ2d2LNnD99G+hxsG/YcjIMHD2Lo0KEYM2YMvvOd76C2tjbq/q9YsQIFBQX8Z8SIEcreeBIQNUnxm0mKwm0FmiQFTtg9cQxvlMKDpBiZpEQ8khjikNvkZpKYHslhMcXsomPpfC2yMH6/gMaOwAUhLeU2b2LaEbfXjy2HTwMAvj5ePkiaNKQAJqMBLd0u3skXjSff2Yd/fNWANVuOJbRveoFlkc4bXZz2Nulko7UmqTTXiqnDClCcY0WXy4vPgzYT4ezTsLMtEdgCitkXaJ1J+s7MkXhl8blJKymW5MaZSUqzHglIYZB06NAhvPDCC/je974XcZuGhgZYrVYUFhaG3F5RUYGGhga+jTRAYvez+6Jt09nZib6+wOp65syZWLNmDd577z289NJLOHr0KC666CJ0dXVF3L9ly5aho6OD/5w4cULZm08CWphJisLt6N1tABRZAIgeSfF9gSu5oWSMIIm3/8f/BeKu20kWbjPBeyw9EhDqh5Iop3vccPv8MBjE4DMV8HJbgu9h+/FW9Lp9KM21RhwF4bCaMCHY1h6r5Hb8dA82BzNTx0/3JrRveoEFSfMmZ3epDdCua5JpkkpzbTAaDbhofKBr8iMZXZLbK44jmTg03ZmkwPtnti1aWgCkguKcQAexYk2STua2AXEESQ8//PAAwXP4z/79+0Mec+rUKcyfPx/XXXcdbr/9ds12PhGuuOIKXHfddZg6dSrmzZuHv//972hvb8cbb7wR8TE2mw35+fkhP+kiLjPJARYAsYXb7D41wu14RNuAqElq6nLBH2UMSldwJEk8RpKMvBRlkthxkxv7Eo70QhDt/SuBldrKcm2yZYRkYdfI6+mfBwIDbS8aXxZV38ZKbrFMJf/wqZglPna6J6F90wPNXS5sD2Y/5g6KIEkbTVKzpNwGALODpdzNMrqkQ03d8PoF5NvNGJrCbKwcLChi/lfJ9nbTGtbGrziTpKDKkSpUX2Xuv/9+LF68OOo2Y8aM4b/X1dXhkksuwQUXXIDf/OY3UR9XWVkJt9uN9vb2kGxSY2Mj1w5VVlbis88+C3kc636TbhPeEdfY2Ij8/Hw4HPL6jMLCQpxxxhk4dOhQ1H3UC4lqkgRBEMttUVKaajRJ8Y4kYZTmWmE0BE4ELT0ulOfJn5i423ZC5TaWSUquJol7JKnIJAFAv9eXUOq7rj3Y2ZZCPRIgGR+RYLmN65FitPZPHV6IP352ImqHW7/Hhze2i1nfuvb+iBqUTOGDfY0QhIBRpNZT2/WIZt1tEuE2EAjCgYBhZFNnP8olWVdRtJ2fdvFweFCUcZkkXm5zK9IPZnQmqaysDNXV1VF/rNbAGzt16hRmz56NGTNmYPXq1TAao7/cjBkzYLFY8OGHH/LbampqUFtbi1mzZgEAZs2ahd27d6OpqYlvs2HDBuTn52PSpEl8G+lzsG3Yc8jR3d2Nw4cPY8iQIeoOSBrw+wVuHKcmSLJZxCCpy+Xlq5LomiTW3RZbk5SocNtsMvIVHtPTyJGIkSSDC7eTrkkKZuscsb/sUp1BouWqIy2BbMnoEmdCz6MWng1LYP+buvq5y/HXxkd3Q2YdbrtPdkTMvv19dz3aej0YWmCHw2KCzy/gVFtf3PunB8RSW3YLthnJ0CQF/rVxK4nNYSU3FiRFKvemkvCgSEszyVTAMkkenxBiFRMJJR5+qSJpSykWIFVVVeGZZ55Bc3MzGhoauG6IbVNdXc0zQwUFBbj11ltx3333YdOmTdixYwduueUWzJo1C+effz4AYO7cuZg0aRJuvPFGfPHFF1i/fj1+8pOfYMmSJbDZAhfYO++8E0eOHMEPf/hD7N+/Hy+++CLeeOMNLF26lL/2Aw88gM2bN+PYsWPYsmULrrnmGphMJnz7299O1iHRDLdkkrIa4TY3k/T5eaQeS1BcIMkkxSoBMeF2vEESIOqSoom3ExlJwkjVkFs+3FZBJkna6ptoh9vhpkCQlGrPHObfomRwaCT+FSy1nTksnwfNkTijIhd2ixFdLi+ORiijvbbtOADghplVqCoOBI3HWzNXl9TV78GWQwFR+2DQIwGSclsCnytA1CSVST5XLFsZrkva35Bep20p4VllrYXbycZhNfF9VuK6ndGZJKVs2LABhw4dwocffojhw4djyJAh/Ifh8XhQU1OD3l7xhPX888/jG9/4BhYtWoSvf/3rqKysxJtvvsnvN5lMeOedd2AymTBr1ix897vfxU033YQnnniCbzN69Gi8++672LBhA6ZNm4Znn30Wv/vd70La/0+ePIlvf/vbmDBhAq6//nqUlJRg27ZtKCvTv3OvS1LKiHcsSZuCkSSAqKXxCwix85ejx80yPPF/gSsUeCUlMtyWwYXbSTaTZOW2WHPbGA6NBKqHmwOC07HlqQ2StJjWLrpsx/4umk1GTB4a2VRyT10HPq9th8VkwPXnjkBVMLN2PIN1SZtqmuH2+TGmNAfjUvz/my60yFC6vX4uG5AG30yX9PHBFniDC1BBEHTT2QYMzBxlWiYJkHolxQ6SxO629AdJSfNJWrx4cUzt0qhRoyAIodkJu92OlStXYuXKlREfN3LkSPz973+P+tyzZ8/Gzp07I97/+uuvR328nnFLg6Q4hdtK3LaBwNgTp9WEXrcP7b3uqALkRMttgDJDSdECIIHutlRnkhSU24DAirGt15PQxUAQBDFISnEmKdEuJL9fwL8OBjJJkVr/w5k2vBA7jrfhy5MduPbs4SH3vbYtINieN7kS5Xl2jGSZpAzucGOltrmTK9OulUkVWvgkMSNGs8RhGgh8fgocFnT0efDFyXbMGFmM5i4XTve4YTRAF4OBB5TbMiyTBASCpFPtfcoySb36mNsGpNgnidAGLto2GVWdJFlA5fL6ucZISTpTqet2osJtAKjID6zwohlKalFuK0iVJkmFBQAgHXIb/8WguduFrn4vjAZgZJo0SfFqR76q60Brjxu5NjPOHlmk6DFih1t7yO2d/R78decpAMB3zx8JABhZGhj2mqlBUr/Hh4/2B/SY2e6yLUUL/y1mxFicYw3pmDSbjFz7xrrcmCZudGmOLjrJwjNHmSbcBsRrzekYQZIgCGjrid1UlCooSMpA4vFICmwfdEP2+rlfhZKLdwGzAYiRdTkWFAuXJ+DLo8RQko0lSUi4Hcwk9Xv8IZk5rWlXWNZkiOW2+MuATI80otiZ8hN8oq3am/YHLlKzxpYo7j6bGhRv76nrDPm//Mvnp9Dn8WF8eS5mjg6Y2IqZpMwst2053IIetw+V+XZMHVaQ7t1JGex74ZYM0VZLePu/lNlhuiTutK2DUhsAOMMG2uohcFMLC5LaYgRJPW4fTwRktSaJSB7xB0micLtdRfeAkg43l9fHV1/TgxeteBANJSN3t7FyWyKZJOlju5KYTWKp5QKF5TYtMknpKrUBiZdFWCnp8knKu7ZGlTiRbzfD7fXjQGPg4iYIAhdsf/f8kTzjyjJrta29CXtRpQM20Hbu5Iq45iNmKiz4BuJvCghv/5fC9G9fnuxAS7eLD7bVTZAkyRwZDeoadvSCUk0SC6JsZqMuyoqZd6SJuIwkAakmySeZjRM7w6HEK2lvXSc8PgHFOVaMKI7ft6VSQSaJBUn5CQRJJqOBC7+TJd7+f58ex5GWHhgM4ILhWGjhui0GSTlxP0e8OBIot9WeDswiMxkNqmaRGQwGPtGdldw+PdqKg03dcFpNuObsYXzboYUOmIwGuLx+NHUpM7bTC16fHxv2BYKkwdLVxmANAUD8pVzRbXvggqU8385b/f91sFlX7f9AaLnNYTFlpBZNcZAkkYLo4X1SkJSBuH2BC6jqTJJpYHdbLOF2YJvYmqRdwc6i6SMKE/pgs1JdR58nYjaCjyWxJVavFofcap9J2nbkNB59KzBL8IG5ExQb/mnR3Xa4OT3t/0BimSSWRZoZxywy5nXDOtxYFunq6cN4aRUALCYjhhcF/i8yreS2/XgbWnsCzRPnjY48AzMbMRoN/HwX73eDeSSVRbCVYIOU39/TyL9D1Tpo/wdCM0laD7dNFUqDJN7ZpgPRNkBBUkbiimO4rXR7ablNycWogA+5jR0kTUug1AYEskMsUIgk3tZCuA2Ijt1ai7dPtPbirtd2wOsXcOW0ofj+7LGKH6tJua0pPe3/gFgW8foFeHzqVvzvBYOkeATJ7HP35ckONHX1472vAs/13fOrBmxblaEdbiyIvGxieUa7hccLmwsYbym3JYomCRB1Sev3NMDnF1DotPDMdroJDZIy8/9eqXC7TcW1KRVk5tEe5LgS1SSpFG4X8SG3kT/cbAU/vapQ1T6FYzAYohpKuryiqC8R4TYgzSRpV27rdnlx29rtaOv1YMqwAvx80VRVmbVEy219bh9OBUeSpDOTBKi7mDV19mMHm0U2KY4gKVhuO9DYhTWfHIPXL+DsqkLuoSSF6ZKOt2ZOJkkQBLy/Z3CW2hiJ6t14kJQnf/E9e2QR8mxmMKnaxMr0jyNhSLNH4SLuTIELt2NMb2jlnW0UJBFxkrBw26tSuM00SREySW09bhwLrsqnDU+844bZAMh5JXVL9EMJB0kaZ5L8fgFL/7QLNY1dKMuz4Tc3zVBt+sacdeMtKRxpCWSRipyWtKzEbGYj2HVFjXZk/d5AAHBWVSEPktVQkW9HRb4NfgH47b+OAABunDVSdttRJZlnA7CnrhOn2vtgtxgV+0dlG44Ehye3dDFNknwmyWIy4sJx4hgcvYi2gdBMkj0D2/8BSbmtW5lwu1hhR3CyoSApA4lbuB2iSVIeJPFyWwTtzq6gWHZ0aY4ijVMsorluc8NKqwmmBLt7tDaUfG7DAWzY2wir2YiXb5yBIQXqBexstdzrji+7lU49EhDIBNriKIusD5bH5ieQJWElN49PQJHTgivOlJ/DmInlNlZqu/iMsox0W9YC0c09XuF29HIbIOqSAP3okYBQ80iHJTMv28wYssvljWq7wue2USaJiJdEM0md/V5+olFi1iUKt+VXAF9IRNtawDvcZIbcaqVHArQdcvv2F3X49aZDAIAV10zB2VXKjBDDEctt8V0IuB4pTUESoH5ie3uvG1uPJD6LbJrk83f9uSMiesmM5JmkzCm3iQNtB2epDUjMg8vr8/OLb9QgSTIKRy+dbUBod1v4HLdMocBh4QvbaCU3Pc1tAyhIykiYJketcJsFSSwbYzYaFJWsYlkA7NI4SIqWSeJBUoKlNkBSbktQk7T7ZAceWPcFAOCOr4/BohnDYzwiMjxIitNMUpzZlvr2f4Za1+0P9jXB5xdQXZmHUaXx7zfrcDMYgO+cJ19qA8RMUme/N6r3l1442tKDA43dMBsNuKxauTVCtpHIyJvWXjcEIeAxFO3iO7TQgdsvGo2F04fqttymB++geDAaDVzfGq3DTW/dbZkZkg5y4s0khQdVhU5lPhSFku42QRBCHiMIAs8kTdMqk1QQu9yWyNw2hhaZpKauftz+6na4vH7MnlCGh+ZXJ7RPYrktPt1FusttgPqLGetES3TMxszRJfiPKZUYX54X1ZfKYTWhIt+Gxk4Xjp/u1aREnExYFmnW2BLFg5KzkURG3jA9UnGONWaZ/scLJqnfuSQjFWtncrm1yGlFS7c7apCkt+42CpIykETNJBlKR2WwTJLXL6Db5Q0JUGpbe9HW64HVZMREjWr40UaTdLsCAU2eFuU2DTRJr22rRUNnP8aU5eBX3z4rYZ2UMwELAL9fwJE0um0z1HQh9bi8+OfBwCiIRIMkq9mIF78zQ9G2I4tz0NjpwrHTPZoF98nifTbQVoULeTaSSLlNiR5Jz4SbSWYqSmwAeHebThYvVG7LQPiAW7WZJFPol0vph9BuMfEsVLhXEiu1TRqaD5tZmy8v625r6nRBEEJHR2habnMk7rh9sjUg/r1uxogQ08J4cSbQwXOqvQ8urx9WiWFiOmAXMyWZpI9qmuH2+jGqxIkJKZy2zjJNtToXbzd19WNn8Dt2eRzWCNmEWq2blEwPkqxmI8zBBVgmDrdlxJrfJgiC7jJJFCRlIK7gSUJtUDIgk6RiwnIkXdLO2nYA2umRAKA8L5BJcvv8A9KyLEjSMpOUyOw2NtqiXGYeVDwkUm5jeqRRpU6Y02g2yNzFX916LOZ8NGYgOe/MypR60vBBt636DpI+3NcEQQhYa8RjjZBNJOKTxIKkEpmRJJkCyyZl4nBbRqxMUme/lw8wVuLhlwooSMpAXHFmkgaW25SfMAojuG6zWVlaBklWs5HPV2oMG3TLh9smOJIE0MZMkummKjRy5uU+SXEFSenXIwHA/XMnwG4x4pNDp/Hap8cjbtfv8WFjcBZZIq3/8TCyNDM63HipbRB3tTES0SSd7o7d2aZ3WAYpmzNJ7PYcq0k3wSAFSRlIvMJtk9EQoplRI1gtkHHddnv92FMXGASpZZAERO5w69bSAsCuhXA7mEnK1+bkK3a3xZ9JSneQNLo0B8uumAgA+Onf9+Foi3wgsuVwC3rcPgwpsCc8zkYtIzPAK6nb5cUnhwPWCJcPcj0SkFh3W3OGl9sAcQGVycLtWPPb9OaRBFCQlJHEK9wOf4xS4TYAFDoGDrndV98Jt9ePQqeFj3rQikjibVYay9NQk9Tr9qmeMwYEMiGs/FiRp00myZGAmaQ4sy197f+MG88fiQvGlqDf48f9b+ziKXQprKtt3uRKGBMUvKuFfV6bulxxj4BJNv88IOq1xqdhDp/eSEy4zTJJ+rn4qoWdG7JDuD3QAw/Qn0cSQEFSRhJvJin8MWqidTlNEiu1TRteqLmehAdJYUNuRQuAxIMkqfi7Kw7xdnMwi2Q1G3nAlSji6AV/TD1POHoptwEBT5RfXDcNeTYzPq9tx8v/PBxyv9fnx4a96ZtFVui0cp+sWp3qklip7fJJFbqZIZZOHAlZALC5bZmbSWLnPC3OfelCLLfJZ+/15pEEUJCUkcRrJgmEBUlqNElOpkkS06S7kiDaZlRGKLdp6bhtNhmREwxK4rEBaOpieiSbZhcxqd6g36t8xdzR6+Hi1DE6CJKAgIB7+ZUBz5nnNxzA/oZOft9nx1rR1utBcY4V546Kz508UZhx5TEd6pI8Pj827m8CQHokhhbC7bIMLrf9f5eOx7fPq8JFGTy7L5ZwW2+dbQAFSRlJQpmkeMttzoHlNq2dtqVUFsgPuRWF29qsphIxlGSi8nKNSm2AOJ8KUNfhdjg42LYy367ZsdGCb84YjjkTK+DxCVj6py/4Z5fNart8YkXaOvGY87YebQA+O9qKzn4vSnKscY+4yTbiLbf5/QK/KGeyJulr40ux4topyNHR91stPJPU6x5g7wLozyMJoCApI0lEkyTNPqkRbheGDbnt6PXgSFCQmwwzvnKuSQqtXWtpAQBIDSXVl9uaggGcVu3/QKBMxX2G1ARJOtIjSTEYDPjptWeiyGnBvvpO/OrDg/D7BazfE+xqS9BAMhGYLul4q/4ySawUednE8oQNSrMFnklSkWEFAucsponLZAuAbIAFST6/IHvOFTVJ+mj/ByhIykhcwSDJFsc06NByWxw+ScFMEtMjjSxxJiU1GqncpuVYEkBqKBlHJimoc9Cq/Z/BbQBUrJj1pEcKpzzPjqeumQIAePGjQ1i79RgaOvuRazPjgnEladsvcdCtvjJJgiBIXLap1Mbg3W0qhfas1FbotMCSRv8wIuDtxzLdrTJzE6m7jdAEMZOkvsuBBUkGQ2Aqs1J4d1vQAoCV2pLVus2CpNYeN1ySlWO3ho7bQGKjSZqCWa4yjcWgjjgMJfXS/h+J/5gyBFdPHwq/ADz+9l4AwKXV5Zq5tMeDXm0A9tR1oq6jHw6LCV8bX5ru3dEN8fokcdF2BpfasglmYtwq0+HGM0lUbiMSIV4zSUAs0eXbLaq0IAVhmqQvkqhHAgKrPvb+WDDi8vq4aF0L4TaQmCZJFG5rm0lyWNXbAOg9SAKAJ646k4+cAdJbagPETNKp9r64LCCSxfvBUtvXzyjVjaGeHnDEWW4TPZL0c+EdzBTnBM4BzOBTCmWSCE3QwgJATakNkHa3eSAIgijaripUvQ9KMBgMA0pu0jb9HKtWmaRguS0uTZK2I0kY3FBSYSbJ4/Nz8bHeNElSCpwWPL1oKoCAo+7FZ6S3S6c8zwab2QifX0Bde19a90UK0yMN9llt4XDhtupyW+aLtrOJ4uC1p02m3KZHn6TMlckPYtzBlVQiwm01om1ALLe5fX4caOzG6R43LCYDJg3JV70PSqnIt6G2tZcbSkpLbVqJWVkmKZ75bUnLJKl0Fj5+uhdevwCn1cQDS71yyYRyvPpf5yHPbk57l47RaMDIEicONHbj2OlenllKJydae7GvvhNGA3BZdXm6d0dXiMJtleW2LHDbziZ4JinMBsDnF3hjEHW3EQnhTqTcFnyM2kjdaTXBYgoEJpsPBPxbJg7JT2o5INxQUuv2f0A6mkRdJsnl9aEtWHrUOpMkltuUBUnSUlsmmA5+/YwynKWTtvaq4kBgVKsTrySWRTp3VLGuSg56wBGvcLsrOdpBIj5Yh2H4/LaOPg+YK4BehtsCFCRlHC6vj/vzlMRxErUGhbJqP4QGgwEFQRuAj2qaASRPj8SIVG7TSo8ESLrbVAq3udu2yaj5F1ptuU0MktKfCck0uA2ATsTb7++lgbaRYN28/V6frMdOJFgmKZ7zJaE9LEsUnklibtv5drOuuhD1syeEIr461QG314+SHGtc89JYiS6edCYLBv59rBVA8jrbGJUFLEgKnORYSSw5mSR1QVKjpLNN6+yNw6LOAuBwk37b//UO+w4d00GQ1Nbjxr+PtQEA5tJA2wGwrLUgiNl0JZAmSV+URBhyq0e3bYCCpIyDnURnjCyK6+LMuoviCbCYLsnjC6zikiXaZpSHDbnVcm4bg3e3qRRuNwf1SOX52p94nfGW22gIqmqYDqlWB4aSG/c3wecXUF2ZhxHF2g6Mzgakg1373cqDpNPdmT+3LZsoypEvt/G5bToLkki4nWFsD2Zxzh1VHNfjv3/JOEwfUYivx9FZJC0r5dvNGJ1koWt4uS0pQVKCmaQKDUeSMBy83BY7cBMEISPa//UK80qqbe2FIAhJ03T5/AKMBkR9fqZHoiySPBaTESajAT6/gH6vDwWIXeYWBEGSSdLXxXewEml+mx49kgAKkjIKv1/AjuOBTNI5cQ4FzbWZ49Y7ME0SEBhFYkzyuIRKiXBbEARRk6RhuS3PHp8mqSmJmSQ13W3N3S509XthNMSXHRzsDCtywGQ0oN/jR1OXS/NOxROtvXht23H8afsJeLx+3HrRGNx+0egBjvH9Hh82Hwho/UiPFBm72Yget0/x/LbOfi8vzVG5TR8UR8ok6dAjCaAgKaM40tKNtl4P7BYjJg8tSPnrS72Vki3aBsQAxOX1o7PPK5nbpp1QmpXbetw+eH1+xQabzCNJ64sqoK7cxvRII4qdZDwYBxaTEUML7TjR2odjLT2a/H/6/QL+dagFr245ho01TZBqjH/14UH879ZjWHLJOHz3/JH8/+yTQy3o8/gwtMCOyUOTZ6uR6TisJvS4fYr1eky0nWcz0/dDJ7AgiQW77P9Fjx5JAAVJGQXTI00fURhX+3+iFKY4SLJbTCh0WtDe60FDZz+6XdoLt6Wlu65+r+JVTGMS24odKrrbqNSWOKNKcnCitQ/HW3sxc0z8s+Q6+jz4846T+N+tx0KE4F8/oww3zxoJl9ePZ96vwZHmHvz3u/vw+4+P4p7LxuObM4bj/T3MQLIiI2wc0gUbY6N0NAkfSUJ6JN2QbzfDbDTA6xfQ1uvGkAIHAKC1R38eSQAFSRkF6yo7Z2R8eqREKXCGlttSQWW+XQyS+rXXJFlMRjitJvS6fejs9ygOkpo6k2MkCaib3Ubt/4lTxXRJCXS4vb+nAfe8votnOPJsZnzznOG48fyRGCMJYOdOqsCbn5/C8x8cQH1HPx5+czde/ucRLlqlUlt0uOu24kwS6ZH0hsFgQFGOFc1dLpzuFoMksbtNPx5JAAVJGcX2Y4npkRKFdbcNL3KkrL5fkW/H/oYuNHb0J0WTBATE271un6oOt6au5IwkAQCnVbkFwOFmav9PFO6V1Bp/kPT6v0+gz+PD2LIc/NfXRmPh9GGyjuJmkxHXnzsCV00fite2HceLHx3G0ZbA/2G+3YzzRqdnAZQp8CyrynIb6ZH0RUkwSJLaAPDuNsokEfHQ1NmP2tZeGAzA2SPTEyTNGluCs6oKce1Zw1L2mpUSG4Aul/aaJCBgKNnQCbT3DZwlJIfb6+df6GQESQ5rYLWsqNzWRO3/icJsAI7H6botCAK+PNkBAPj5N6dhhoLvp91iwm0XjcG3zh2B3398FG/8+wS+c/5IXZno6RF7sNzmoiApo2GBkHR+m159kihIyhC2B7vaqivzedt6qinNteEv378wpa/JfJ0aJeU2LR23AWB4UWB+V63CTAKbKm4xGZKy6mFmkr0xLAD63D6cCg5mpUxS/CTqut3Q2Y+WbhdMRvWzDPPsFtw75wzcO+eMuF57sMHntynVJFGQpEuKg+XP090DM0lq54omG1q2ZAj/5v5I+ph5lSoqCkSvpGTMbgMCwl0AONqsLJPA9Ehlubak2CCw7rZYF4IjLYEsUpHTorvVVybBNEkdfR60y0wmj8UXJwJZpDMq8ng5iEgOdpXDn5u7gpqkPPp+6Anmus2yRx6fn8sp9HYuoyApQxD1SINLsxBSbgsaPmop3AaA0UHRM9OGxILrkZIg2gakFgDRM0mkR9IGp9XMuxTjySbtPtUOAJg6LPW2HIMN9cJtyiTpkfD5bSxYMhiAAoe+hNsUJGUA3S4v9tQFVqvnpEmPlC4quKGkKymO2wAwpjQYJCnUpLBMUjL0SIC4Wo7V3cb1SBQkJcyoBMTbTI80ZTgFScmGym3ZQUmw3NYaLLe1Bdv/Cx0WmJJsUqwWCpIygF217fALwLBCB4YWOtK9OymFDblt6XbxmXFal9tGB4Ok2tO98CoYnMkySclo/wfETJLL64ffH3nauTizjdr/E6WqmH0G1Im3BUHA7lOBIGkqBUlJR40bfWAkCQuS9FXCGeywTBJz2dbr3DaAgqSMYPvxoD/SINMjAYE5PhZT6Moix6ptkFSZb4fNbITXL+BkW1/M7RuTnElySt5ftIsBldu0I17x9sm2PrT3emA1GTGhMi8Zu0ZIYOU2Jd1tAUdnGkmiR5gmqTWs3Ka3uW0ABUkZwWDVIwGA0WhAuWSIbK7NrLlY2mg08GySEl2SqElKzonXJnFTj1Ry8/j8OEJu25oRb5D0xcl2AED1kDzuBk0kD7HcFjtIYm7bDotJ1rOKSB9FYfPb2nQ6tw2gIEn3eH1+fF4bCJIGW2cbo0ISjGitR2KoCZIaO5Mr3DYaDbysEOlisKeuEy6vH4VOC+/OIuKHeyW1qiu37WZ6JBJtpwQ13W2ne9hIEv1deAc70u42v18Q57YNpkzSsWPHcOutt2L06NFwOBwYO3YsHn30Ubjd0Vts+/v7sWTJEpSUlCA3NxeLFi1CY2NjyDa1tbVYsGABnE4nysvL8eCDD8LrFTuB6uvrccMNN+CMM86A0WjEvffeK/ta69atQ3V1Nex2O6ZMmYK///3vCb9vrdlX34Vetw95djPOKB+c6XymSwK01yMx1ARJzV3JLbcBsYfcbg9aQsyoKkqKDcFgY2Qw0GzsdCky8WQw0TbpkVKDGuE2b/+nUpvuYBkjvxCw3uBz2wZTJmn//v3w+/14+eWXsWfPHjz//PNYtWoVfvSjH0V93NKlS/H2229j3bp12Lx5M+rq6nDttdfy+30+HxYsWAC3240tW7Zg7dq1WLNmDZYvX863cblcKCsrw09+8hNMmzZN9nW2bNmCb3/727j11luxc+dOLFy4EAsXLsRXX32lzQHQCOaPNGPk4L0YhpTb0pxJ8vj8vG01WcJtQNrhJm8DsCNoLjpjkGYXtabQaUF+8LPFBPGx8PsFfMVF24XJ2jVCghoLAOps0y8Wk5FXBU73uHU7tw1IYpA0f/58rF69GnPnzsWYMWNw1VVX4YEHHsCbb74Z8TEdHR34/e9/j+eeew6XXnopZsyYgdWrV2PLli3Ytm0bAOD999/H3r178dprr2H69Om44oor8OSTT2LlypU8SzVq1Cj88pe/xE033YSCAvkV3i9/+UvMnz8fDz74ICZOnIgnn3wSZ599Nn79619rfzASgIm2zx2EeiSGNJOk9UgShtIgqaXbBUEAzEZDUlPDLJMkl9UQBIE7sM+ooiBJCwwGAx8n8q+DLYoec+x0D7pcXtjMRoynsTApQU13GwVJ+kYq3tbr3DYgxZqkjo4OFBdHvtjv2LEDHo8Hc+bM4bdVV1ejqqoKW7duBQBs3boVU6ZMQUVFBd9m3rx56OzsxJ49exTvy9atW0Nehz0Pex05XC4XOjs7Q36SiSAIomh7kPkjSamUZGzyklxuq+voi7pKbQrqkcrykuO2zXBGGeR5sq0PzV0uWEwGTBtRmLR9GGxcWl0OANi0v0nR9qz1f/LQfJhp5lpKYBlWl4JyGwuSyqj9X5cUSYIkvc5tA1IYJB06dAgvvPACvve970XcpqGhAVarFYWFhSG3V1RUoKGhgW8jDZDY/ew+pUR6nmjPsWLFChQUFPCfESNGKH69eDjR2ocmuhiGlLWSpUkqzrEi326GIETvcEp2+z/DEUWTxLKLk4cW8IsGkTiXBIOk7cdbFY0nYeNIqNSWOni5zauku42NJKFMkh6RzSRlQ5D08MMPw2AwRP3Zv39/yGNOnTqF+fPn47rrrsPtt9+u2c6nmmXLlqGjo4P/nDhxIqmvx/RIU4YN7ouhtLstWZokg8GA0cFW+qMtkTUpyR5JwuBlBbkgibKLSWF4kRMTKvLgF4DNB5pjbs/GkVBnW+qwR/lehEPlNn1TLOlw03N3m+orzv3334/FixdH3WbMmDH897q6OlxyySW44IIL8Jvf/Cbq4yorK+F2u9He3h6STWpsbERlZSXf5rPPPgt5HOt+Y9soobKyckDXnPR15LDZbLDZUveFE00kB68eCQjXJCXP72R0iRNfnGjHkSi6pGSPJGEwQ0m5chsTbQ9Gc9Fkc+nEctQ0dmHj/iZcPX1YxO18fgFfnQqU26eNoCApVfDuNiWZJAqSdA3LGtV39KEnGPRmRSaprKwM1dXVUX+s1sAbPXXqFGbPns0F2EZj9JebMWMGLBYLPvzwQ35bTU0NamtrMWvWLADArFmzsHv3bjQ1ibqBDRs2ID8/H5MmTVL8PmbNmhXyOux52OvogX9TxgBAIGBgwVGyym0AMLo0kEk6Fi1IYpmkvCRnkiKU2zr6PKhp7AIAnD3IPxfJgOmSPqppjjqi5nBzN/o8PuRYTfxzQyQfu1m5BUBLN7MA0N+FlxDLbYeCMyhNRgPvMNUTSdMksQCpqqoKzzzzDJqbm9HQ0BCi+Tl16hSqq6t5ZqigoAC33nor7rvvPmzatAk7duzALbfcglmzZuH8888HAMydOxeTJk3CjTfeiC+++ALr16/HT37yEyxZsiQky7Nr1y7s2rUL3d3daG5uxq5du7B3715+/z333IP33nsPzz77LPbv34/HHnsM27dvx913352sQ6KKth43//DMoIshF28nNZNUFrvDTZzblmRNEi8rhFoA7DrRDkEAqoqdSQ/UBiNnjShEodOCjj4Pdp5oj7gd80eaPKxAdwM5sxm2eOiPUW7r9/j4QGzSJOkT1snGxisVOa0wGPT3XUraFWfDhg04dOgQDh06hOHDh4fcJwiBoZ0ejwc1NTXo7RWFss8//zyMRiMWLVoEl8uFefPm4cUXX+T3m0wmvPPOO7jrrrswa9Ys5OTk4Oabb8YTTzwR8hpnnXUW/33Hjh34wx/+gJEjR+LYsWMAgAsuuAB/+MMf8JOf/AQ/+tGPMH78ePz1r3/FmWeeqfWhiAtWUhlbloMSShdjQmUeDjZ180GkyWCMAhsALtxOcpAUqbttR1CnNtizi8nCbDLi4jPK8NauOny4rymi9caXwXEkU0mPlFKUCrebg4sZq9mYtI5YIjFKghk+9n+lR48kIIlB0uLFi2Nql0aNGsUDJobdbsfKlSuxcuXKiI8bOXJkTHfs8OeV47rrrsN1110Xc7t08G/yRwrhqWumYPEFo5KaVRsVDJJaut3o7PcgX8aTKd3ltu1kIpl0Lq0ux1u76rBpfxMevqJadhvutD2Iu07TASu3eXwCvD5/ROsFrkfK0Wd2ggCKc0IXmnr0SAJodptuYR1MVGoLUOCw4JxRxUk94eXazCgLpubldElen5+ffJOdSZLrbvP6/NgVLAHR5yJ5XHxGGUxGA2oau3CidaAdhMfnx976gGibMkmphS0eAKDfG1mXxPVIVGrTLeGdbHr0SAIoSNIl/R4fT+dTJim1RHPePt3jhiAEBIYlOakvt9Ecv9RQ6LRyJ/NNNQONJQ80dsHt9SPPbsbIEhounEpsZvGSFc30lTrb9E9xmKBej51tAAVJuuTLkx3w+ASU5troJJximC7pSPPAIInpkUpzrUkX6zqCFgDSctuOYAn2bBpqm3QunRjoctso4769WzLUlko5qcVgMPBAKZpXUksXC5L0eeElgByrCVZJuVSPHkkABUm6RJzXVkQn4RQzKkomiY0kSUVXmVy5jemRSLSdfJgVwJbDpwcMGf4iGCRNGVaY6t0iIJbcXFHE22wINWWS9IvBYAgpsVEmiVCM1yeg0GkZ9CaS6YCV246dlskkdQUySclu/wfEcluvR7xA7yDRdsoYX56L4UUOuL1+bDl0OuQ+5rQ9bTjpkdKBEq+kZiq3ZQTSwEiv3W0UJOmQH1w2Hp//5HJ89/yqdO/KoIPbADT3DOiQFIfbpiCTZA3NJJ1q70N9Rz9MRgOmU0dV0jEYDDyb9KGk5Nbv8aGmIWDmOYWCpLTAbADk3OgZvNxGwm1dUyLNJFG5jVCD0WiAzTx457Wli6oSJwwGoMvl5R0yjFQZSQIS4XYwSNoe9EeaNCSfjywhkgsLkjbtb+IBc01DFzw+AcU5VgwrdKRz9wYtfDSJIuG2Pi+8RIDikEySPv+vKEgiCAk2s4lf/MJ1SeLcttRpknqDF4LPj5MlRKo5f0wJHBYTGjr7ecv/l6eYHolE2+lCDJJiWwCUUblN1xRTJokgMg+uSwoPklKYSQovt22nobYpx24x4cJxpQCAjfsCJbcvgz5VpEdKH7HKbW6vHx19HgCkSdI7lEkiiAyE2wCEBUmNKcwksZKay+tHZ78H+4KZDMokpRZWctsY9EvazTJJwwvTtUuDHkeMctvpnsBixmw0oMChTzEwEYAJt61mI5cY6A0KkggiDNFQspvf5vMLXOeQkkySRTxhbD18Gn4BGFbowJAC0sGkEhYk7TrRjpNtvTjQGBBtT6VMUtpg5TZXhCCppStQaivJtZKfmM5hwu1inQ63BShIIogBjC7LBRCqSTrd7YJfAIwGpGTgsN1iBDtn/OtgMwDKIqWDygI7Jg/NhyAAL350GH4BKM+zoSI/+dlEQh7uIRYhSNp2JGDZMLIkecOwCW0YGzzXjinT7/8VtckQRBijS5hXUi/8fgFGo4HrkUpybUl32wYCLegOiwm9bh8+PtgCgPRI6eLS6nLsqevEuu0nAABTqdSWVmxRhNuCIODPO04CAK6ePjSl+0WoZ0JlHv665EKMKNJvhpwySQQRxrAiBywmA9xeP+o6+gAATSk0kmSwFfOx04Ehq5RJSg+s5ObxBWwAqNSWXphwW06TtKeuEzWNXbCajfjGVAqSMoHpIwpTkp2PFwqSCCIMk9HAU/Ws5NaYwpEkDOnE8xyrCRMqaKhtOpg2vDDE9I5MJNNLtHIbyyLNnVRBom1CEyhIIggZRofNcGNu26nMJEm7Pc6qKoLZRF/XdGA0GjB7Qjn/e8owCpLSSSSfJLfXj7d2nQIALJoxPOX7RWQndNYlCBnCgyQ2ty0VI0kYDomzNpXa0stlEwNB0vAiB3nvpBlWbgvvbttU04S2Xg/K82y4KOhvRRCJQsJtgpBBD5kkh0Vcw5BoO73Mm1yJ+y4/g4JVHRCp3MZKbdecNYyyroRmUJBEEDIMCJK6UmckyWCGkkYDaKhtmjEZDfjBZePTvRsEpN1tYpB0utuFTcFBxFRqI7SEwm2CkIG5bp9o7YXb6+eZpPIUThVnwu0JlfnIs5MIlSAAeU3SW7vq4PULmDq8AGdQgwOhIRQkEYQMZXk25FhN8AvA8dM9aOZu2ynMJAUvBudQiYcgOHLltv/7PFBq+yZlkQiNoSCJIGQwGAwYFcwm7TjeBp9fgMEAlOambgjjNWcPwzkji3DDzKqUvSZB6J1wn6R99Z3YU9cJi8mAK8kbidAY0iQRRARGl+ZgT10nPj3aCgAoybGlVBB6wdhSXHAXdekQhBQ+u80bKLf9X1CwPWdiBR+YShBaQZkkgogA0yWxWVCp1CMRBCEPL7e5ffD4/Pgr80Y6m0pthPZQkEQQEWDltvqO1I8kIQhCHl5u8/rwzwPNaOl2ozTXiosnlKV5z4hshIIkgogAswFgpLL9nyAIeWxm0QJAHGY7DBbyRiKSAH2qCCIC4UESZZIIIv0wa4x+jx8f7gt4I1FXG5EsKEgiiAgUOq0olghBy1LY/k8QhDxMuA0Abp8fk4bkY+KQ/DTuEZHNUJBEEFEYVeLkv5NwmyDSj90cetmiLBKRTChIIogojC7N5b+n0kiSIAh5zCYjLCZD4HejAVdPJ28kInlQkEQQURhTJuqSKJNEEPrAHhRvX1JdjpJc+l4SyYOCJIKIglS8XUZBEkHogjx7wAeZvJGIZEOO2wQRBTYsc1ihg1qMCUInLL9yEvbVd+HySRXp3hUiy6EgiSCiMK48F7/8z+kYXuSMvTFBEClh/plDMP/MIeneDWIQQEESQcTg6unD0r0LBEEQRBqg+gFBEARBEIQMFCQRBEEQBEHIQEESQRAEQRCEDBQkEQRBEARByEBBEkEQBEEQhAwUJBEEQRAEQchAQRJBEARBEIQMFCQRBEEQBEHIQEESQRAEQRCEDBQkEQRBEARByEBBEkEQBEEQhAwUJBEEQRAEQchAQRJBEARBEIQM5nTvQCYjCAIAoLOzM817QhAEQRCEUth1m13HI0FBUgJ0dXUBAEaMGJHmPSEIgiAIQi1dXV0oKCiIeL9BiBVGERHx+/2oq6tDXl4eDAaDps/d2dmJESNG4MSJE8jPz9f0ubMROl7qoOOlHjpm6qDjpR46ZupI5HgJgoCuri4MHToURmNk5RFlkhLAaDRi+PDhSX2N/Px8+rKogI6XOuh4qYeOmTroeKmHjpk64j1e0TJIDBJuEwRBEARByEBBEkEQBEEQhAwUJOkUm82GRx99FDabLd27khHQ8VIHHS/10DFTBx0v9dAxU0cqjhcJtwmCIAiCIGSgTBJBEARBEIQMFCQRBEEQBEHIQEESQRAEQRCEDBQkEQRBEARByEBBEkEQBEEQhAwUJOmQlStXYtSoUbDb7Zg5cyY+++yzdO+SbvjnP/+JK6+8EkOHDoXBYMBf//rXkPsFQcDy5csxZMgQOBwOzJkzBwcPHkzPzuqAFStW4Nxzz0VeXh7Ky8uxcOFC1NTUhGzT39+PJUuWoKSkBLm5uVi0aBEaGxvTtMfp5aWXXsLUqVO5g++sWbPwj3/8g99Pxyo6Tz/9NAwGA+69915+Gx2zUB577DEYDIaQn+rqan4/Ha+BnDp1Ct/97ndRUlICh8OBKVOmYPv27fz+ZJ73KUjSGX/6059w33334dFHH8Xnn3+OadOmYd68eWhqakr3rumCnp4eTJs2DStXrpS9/+c//zl+9atfYdWqVfj000+Rk5ODefPmob+/P8V7qg82b96MJUuWYNu2bdiwYQM8Hg/mzp2Lnp4evs3SpUvx9ttvY926ddi8eTPq6upw7bXXpnGv08fw4cPx9NNPY8eOHdi+fTsuvfRSXH311dizZw8AOlbR+Pe//42XX34ZU6dODbmdjtlAJk+ejPr6ev7z8ccf8/voeIXS1taGCy+8EBaLBf/4xz+wd+9ePPvssygqKuLbJPW8LxC64rzzzhOWLFnC//b5fMLQoUOFFStWpHGv9AkA4S9/+Qv/2+/3C5WVlcIvfvELflt7e7tgs9mEP/7xj2nYQ/3R1NQkABA2b94sCELg+FgsFmHdunV8m3379gkAhK1bt6ZrN3VFUVGR8Lvf/Y6OVRS6urqE8ePHCxs2bBAuvvhi4Z577hEEgT5fcjz66KPCtGnTZO+j4zWQhx56SPja174W8f5kn/cpk6Qj3G43duzYgTlz5vDbjEYj5syZg61bt6ZxzzKDo0ePoqGhIeT4FRQUYObMmXT8gnR0dAAAiouLAQA7duyAx+MJOWbV1dWoqqoa9MfM5/Ph9ddfR09PD2bNmkXHKgpLlizBggULQo4NQJ+vSBw8eBBDhw7FmDFj8J3vfAe1tbUA6HjJ8be//Q3nnHMOrrvuOpSXl+Oss87Cb3/7W35/ss/7FCTpiJaWFvh8PlRUVITcXlFRgYaGhjTtVebAjhEdP3n8fj/uvfdeXHjhhTjzzDMBBI6Z1WpFYWFhyLaD+Zjt3r0bubm5sNlsuPPOO/GXv/wFkyZNomMVgddffx2ff/45VqxYMeA+OmYDmTlzJtasWYP33nsPL730Eo4ePYqLLroIXV1ddLxkOHLkCF566SWMHz8e69evx1133YUf/OAHWLt2LYDkn/fNCT8DQRAZwZIlS/DVV1+F6B+IgUyYMAG7du1CR0cH/vznP+Pmm2/G5s2b071buuTEiRO45557sGHDBtjt9nTvTkZwxRVX8N+nTp2KmTNnYuTIkXjjjTfgcDjSuGf6xO/345xzzsFPf/pTAMBZZ52Fr776CqtWrcLNN9+c9NenTJKOKC0thclkGtDJ0NjYiMrKyjTtVebAjhEdv4HcfffdeOedd7Bp0yYMHz6c315ZWQm324329vaQ7QfzMbNarRg3bhxmzJiBFStWYNq0afjlL39Jx0qGHTt2oKmpCWeffTbMZjPMZjM2b96MX/3qVzCbzaioqKBjFoPCwkKcccYZOHToEH3GZBgyZAgmTZoUctvEiRN5iTLZ530KknSE1WrFjBkz8OGHH/Lb/H4/PvzwQ8yaNSuNe5YZjB49GpWVlSHHr7OzE59++umgPX6CIODuu+/GX/7yF2zcuBGjR48OuX/GjBmwWCwhx6ympga1tbWD9piF4/f74XK56FjJcNlll2H37t3YtWsX/znnnHPwne98h/9Oxyw63d3dOHz4MIYMGUKfMRkuvPDCAbYlBw4cwMiRIwGk4LyfsPSb0JTXX39dsNlswpo1a4S9e/cKd9xxh1BYWCg0NDSke9d0QVdXl7Bz505h586dAgDhueeeE3bu3CkcP35cEARBePrpp4XCwkLhrbfeEr788kvh6quvFkaPHi309fWlec/Tw1133SUUFBQIH330kVBfX89/ent7+TZ33nmnUFVVJWzcuFHYvn27MGvWLGHWrFlp3Ov08fDDDwubN28Wjh49Knz55ZfCww8/LBgMBuH9998XBIGOlRKk3W2CQMcsnPvvv1/46KOPhKNHjwqffPKJMGfOHKG0tFRoamoSBIGOVzifffaZYDabhaeeeko4ePCg8P/+3/8TnE6n8Nprr/FtknnepyBJh7zwwgtCVVWVYLVahfPOO0/Ytm1bundJN2zatEkAMODn5ptvFgQh0A76yCOPCBUVFYLNZhMuu+wyoaamJr07nUbkjhUAYfXq1Xybvr4+4fvf/75QVFQkOJ1O4ZprrhHq6+vTt9Np5L/+67+EkSNHClarVSgrKxMuu+wyHiAJAh0rJYQHSXTMQvnWt74lDBkyRLBarcKwYcOEb33rW8KhQ4f4/XS8BvL2228LZ555pmCz2YTq6mrhN7/5Tcj9yTzvGwRBEBLPRxEEQRAEQWQXpEkiCIIgCIKQgYIkgiAIgiAIGShIIgiCIAiCkIGCJIIgCIIgCBkoSCIIgiAIgpCBgiSCIAiCIAgZKEgiCIIgCIKQgYIkgiAIgiAIGShIIgiCIAiCkIGCJIIgCIIgCBkoSCIIgiAIgpDh/weEqVB3yzny8QAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" @@ -240,7 +239,7 @@ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -248,7 +247,7 @@ { "data": { "text/plain": "
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cMkDREVFfsIaFiIiIFI8JCxERESkeExYiIiJSPNawEBERkeJxhIWIiIgUjwkLERERKZ7HNI6z2WwoKSmBwWAYkLbfRERE1H+iKKK+vh4xMTEddqi/kMckLCUlJZ02TyMiIiL3UFRUhEGDBnX7fY9JWAwGAwD7D2w0GmWOhoiIiHqirq4OcXFxjr/j3fGYhEWaBjIajUxYiIiI3MylyjlYdEtERESKx4SFiIiIFI8JCxERESmex9SwEBERuSNRFGGxWGC1WuUOxSXUajU0Gk2/W44wYSEiIpKJ2WxGaWkpmpqa5A7Fpfz8/BAdHQ2dTtfnczBhISIikoHNZkN+fj7UajViYmKg0+k8rvGpKIowm804d+4c8vPzMWTIkIs2h7sYJixEREQyMJvNsNlsiIuLg5+fn9zhuIyvry+0Wi0KCwthNpvh4+PTp/Ow6JaIiEhGfR1xcCfO+Bk9/yoRERGR22PCQkRERIrHhIWIiIgUjwkLERER9drrr7+OxMRE+Pj4YNKkSdizZ49L348JCxERuZ3c8ga8uTkPdS2tcofilT7++GM88cQTePrpp7F//36MHj0aV111FcrLy132nlzWTEREbmfZxiPYlluB/2aX4v37JiLYv+8NyZRCFEU0t8rT7dZXq+5VD5gXX3wRCxcuxL333gsAWLVqFb766iv84x//wJIlS1wSIxMWIiJyKyaLFXsLqgAA2WdqcceaXfjggUkIN+hljqx/mlutGPGnb2V576P/7yr46XqWEpjNZmRmZmLp0qWO51QqFWbPno2dO3e6KkROCRERkXs5VFwLk8WGID8tIgx65Jytx+2rd6K0tlnu0LxCRUUFrFYrIiMjOzwfGRmJsrIyl70vR1iIiMit7MqrBABMSw7Dk1cNw/y3d+NURSNuW70THz4wGXEh7tk11lerxtH/d5Vs7610TFiIiMit7Mq3JyyTB4cgMcwfHz80GfPf3o3Cyibcumon1i2chOTwAJmj7D1BEHo8LSOnsLAwqNVqnD17tsPzZ8+eRVRUlMvet19TQitXroQgCFi8eLHjuTVr1mDmzJkwGo0QBAE1NTWXPI/VasUf//hHJCUlwdfXF8nJyXjmmWcgimJ/wiMiIg9jttiQWVgNAJg8OBQAMCjYD588NAUpEQEoq2vB7at34nhZnZxhejSdTodx48bhxx9/dDxns9nw448/YsqUKS573z4nLHv37sXq1asxatSoDs83NTVhzpw5eOqpp3p8rueeew5vvvkmXnvtNRw7dgzPPfccnn/+ebz66qt9DY+IiDzQoeIatLTaEOqvQ0rE+VGUSKMPPn5wMkZEG1HRYMYda3bhUHGNfIF6uCeeeAJvvfUW3nvvPRw7dgyPPPIIGhsbHauGXKFPY08NDQ2YP38+3nrrLfz5z3/u8D1ptGXz5s09Pt+OHTtw/fXXY968eQCAxMREfPTRRy5vQkNERO5ld759ddCkwSGdluGGBujx0cLJWLB2D7KKanD/e/uwY8kV0Kq5vsTZbr/9dpw7dw5/+tOfUFZWhjFjxuCbb77pVIjrTH36r7ho0SLMmzcPs2fPdkoQU6dOxY8//ogTJ04AAA4ePIht27Zh7ty53b7GZDKhrq6uw4OIiDzbrlP2+pVJSaFdfj/QT4sPHpiEAL0G5+pNyDvXMJDheZXHHnsMhYWFMJlM2L17NyZNmuTS9+v1CMv69euxf/9+7N2712lBLFmyBHV1dUhNTYVarYbVasWzzz6L+fPnd/uaFStWYPny5U6LgYiIlK3VasO+go71K10J0GswItqIPQVVOHymDqlRxoEKkVyoVyMsRUVFePzxx7Fu3Tr4+Pg4LYhPPvkE69atw4cffoj9+/fjvffew1//+le899573b5m6dKlqK2tdTyKioqcFo+3++euQtzw+nb2NCAiRTlUXIvmVitC/HUYEnHxVUAjYuxJypGS2oEIjQZAr0ZYMjMzUV5ejrFjxzqes1qt2Lp1K1577TWYTCao1b1fy/3kk09iyZIluOOOOwAAI0eORGFhIVasWIEFCxZ0+Rq9Xg+93r27GirVqs15OFPTjHe3F2Dp1cPlDoeICMD56aCJiSFQqS7eRj49NhAAcKSE5QKeolcJy6xZs5Cdnd3huXvvvRepqan4wx/+0KdkBbCvLFKpOg72qNVq2Gy2Pp2P+u5MTTPO1NhHVv61/wx+d9UwFqwRkSJICcvkwSGXPDatbYTlWEkdbDbxkgkOKV+vEhaDwYD09PQOz/n7+yM0NNTxfFlZGcrKypCbmwsAyM7OhsFgQHx8PEJC7L9ks2bNwo033ojHHnsMAHDttdfi2WefRXx8PNLS0nDgwAG8+OKLuO+++/r9A1Lv7GvbnwMAKhpM2JJzDrNHuK7qm4ioJ1qt5/uvTLpI/YokJSIAOo0K9SYLiqqbkBDq7+oQ+8wbeo4542d0+kfnVatWISMjAwsXLgQAzJgxAxkZGdi4caPjmLy8PFRUVDi+fvXVV3HLLbfg0UcfxfDhw/G73/0ODz30EJ555hlnh0eXIG0optPYfzU2ZLI2iIjkl32mFk1mK4L8tBgWabjk8Vq1ynGcUqeFtFotAPssg6eTfkbpZ+6LfvcAvrDfyrJly7Bs2bKLvqagoKDD1waDAS+99BJeeuml/oZD/SRV4D86Mxkv/XASPx4rR2WDCaEBrBciIvnsPtXWfyXp0vUrkrQYI7LP1OJISS2uHhntyvD6RK1WIygoCOXl5QAAPz+/Tr1l3J0oimhqakJ5eTmCgoL6XDoCcC8haqe2qRU5Z+sBAHdPTsBPx8txqLgWn2eV4P7LkmSOjoi82aX6r3QlzbFSSJkjLAAce+9ISYunCgoK6vc+Q0xYyCHzdBVEERgc5o+wAD1uHTcIh4prsWFfEe6bluhxmT8RuQeL1eaor7tY/5ULjYhR/kohQRAQHR2NiIgItLa2yh2OS2i12n6NrEiYsJDD3rbpoPGJwQCA60bH4pmvjuF4WT2OlNQ5lgkSEQ2kwyV1aDRbEeirRWrUpetXJMOjDRAE4Fy9CeX1LYgwOK9/mLOp1Wqn/FH3ZFyvSg572/bomJBoX80V6KfFlW0rhDbsY/EtEcnD0X+lF/UrAOCn02BwmH11kJJHWahnmLAQAKCl1YpDxfaOkFLCAgC3jo8DAPznYAlMFqsssRGRdzvff6Xn00GSNGla6Aw73ro7JiwEwL5k0Gy1ISxAj4RQP8fzl6WEITrQBzVNrfjhqGcXhRGR8lja7R80KenSDeMulB6r/MJb6hkmLATgfP+ViUnBHYpr1SoBN42NBcCeLEQ08I6U1KHBZIHRR4Ph0b3fxDDNDQpvqWeYsBCA8/Ur4xM6f4K5dZx9WmjriXMoq20Z0LiIyLvtzj9fv6LuQ3t9aWnz6aom1LV45iocb8GEhWCzidjX1vK6ff2KJDHMHxMTQ2ATgX/tLx7o8IjIi+061fvlzO0F+ekQG+QLADjKURa3xoSFcKK8HvUtFvjr1Bge3fWSwVvGDwIAfJpZ7BX7XhCR/Kw20TH629eEBQBGuEEDObo0JizkuCGMTQiGppudmeeNjIafTo38ikbHBmRERK50tKQO9SYLDH2sX5Gc73jLlULujAkLnW8Y10X9isRfr3HsxbFhH6eFiMj1HP1XEvtWvyKRCm85JeTemLCQo+X1hLYOt925dZx9WujLQyVoMltcHhcReTfH/kGDe7+cuT1phOVkeQNaWtlPyl0xYfFyxdVNKKltgUYlYEx80EWPnZgUgoRQPzSarfg6u2xgAiQir2S1idjTh/2DuhId6INgPy2sNhEn2jZ4JffDhMXLSQ2Z0mID4ae7+NZSgiDglrH2URb2ZCEiVzpWWof6FgsMeg1G9KN+BbDfu6RpocNnOC3krpiweDmpYdyEhItPB0luyLA3kduTX4VmM4dWicg1DhTVAAAyLrIYoDdYeOv+mLB4OUfC0sOW13EhfggL0MEmgkOrROQyx0vtIyFSotFfabHseOvumLB4sZomM06cbQAAjO/hCAsApEbZbyDHy/gPn4hc43iZ/QNRalTXvaF6S0p8jpfVwWpjLyl3xITFi0n9VAaH+yM0QN/j10k3kGOlHGEhIuez2UTHCEt/+q+0lxTqDz+dGi2tNpw61+CUc9LAYsLixaQK/IldtOO/GOkGcqyUIyxE5HzF1c1oNFuhU6uQFObvlHOqVILj3sVpIffEhMWLSSuExvcyYUmNlkZY6timn4ic7ljbdPOQyABonVBwK2HhrXtjwuKlWlqtOFRcA+DSDeMulBIRAI1KQF2LBaXcvZmInOx4qVS/4pzpIEka9xRya0xYvNTBohq0WkVEGPSID/Hr1Wv1GjWSwwMAsPCWiJxPuq90txlrX0m9WI6UcHTYHTFh8VL72gpuJySGQBB6v0fH8GgW3hKRa5xfIeTcEZYhkfbR4drmVpypaXbqucn1mLB4Kan/yvheTgdJUll4S0Qu0GS2oKCyEcD5ejln0WvUGBJpPyenhdwPExYvZLWJyCw4P8LSF1wpRESukFNWD1EEwg16hPWi3UJPOepYzrDw1t0wYfFCOWX1qDdZEKDX9Lkp0/C21+VXNHL3UyJyGmc3jLtQOgtv3RYTFi+Uedo+upIRH9TnPTrCDXqE+LNFPxE5l7Mbxl2ILfrdFxMWL3S07R/qyLZ/uH0hCIKj8PY4C2+JyEmOuXiEZXi0EYIAlNW1oLLB5JL3INdgwuKFjjnpE4xUwX+UdSxE5ASiKDruT85eISQJ0GuQGGrvnstRFvfChMXLWG0icto+wfQ3YZFez14sROQMJbUtqG+xQKMSkBIR4LL3GcE6FrfEhMXLFFY2ornVCh9t//foaL8JIpswEVF/SfUrKREB0Glc9+eJLfrdExMWLyM1ehsWaYBa1fuGce0NiQyAuq0JU1kdW/QTUf+4eoWQROp4y+ls98KExcs4q34FkFr0+3c4LxFRXznqV1y0Qkgyou38+RWNaDJbXPpe5DxMWLyMMxOW9udhi34i6q/zBbeuHWEJN+gRbtBDFM+P6pDyMWHxMs5OWKRKfo6wEFF/tLRakV9hb8k/wsUjLO3f4ygLb90GExYvUtNkRkmtvdbEWXt0SOfhpxQi6o+TZxtgE4EQfx3CDc5vyX8haaUQ61jcBxMWLyJN2wwK9oXRR+uUc0qfUk6da2CLfiLqs2Nl56eD+rKDfG9xhMX9MGHxIs6eDgKACIMewX5a2ET7JyQior6QOma7qmHchaQRluNldbDa2JbBHTBh8SKuSFjsLfpZx0JE/SM1oBzupOnqS0kM9YevVo2WVpujdoaUjQmLF5GGXEc4+YbgKLxlx1si6oP2LfldtenhhdQqwVGDxzoW98CExUtYrDacaJuyGRHd900Pu8JNEImoP8rrTahuaoVKgEtb8l+IdSzuhQmLlzhV0QizxYYAvQaDgn2dem7HlFBZHVv0E1GvSaMrg8MD4KNVD9j7cqWQe2HC4iXaN2RS9bMl/4VSIuwt+muaWnG2jtu1E1HvDFRL/gtxhMW9MGHxEkddOD/so1VjcBhb9BNR3xwf4PoVSWqUESoBqGgwobye+6EpHRMWLyH1YHHVDSE1moW3RNQ35+9PAzvC4qtTO3at5yiL8jFh8RLSP0ZX3RCk83JPISLqDZPFirxz9gUBA9WDpb0R3LnZbTBh8QLn6k2oaDBBEIBhLpojHt52oznOf/RE1At55Y2w2EQYfTSIDvQZ8PdnHYv7YMLiBaS6kqRQf/jpNC55D2mq6VRFI1v0E1GPSQ3jUqONA9KS/0JcKeQ+mLB4gYFoyBRp1CPITwurTURuOVv0E1HPSCuEBmKH5q5I75tf0Ygms0WWGKhn+pWwrFy5EoIgYPHixY7n1qxZg5kzZ8JotGfLNTU1lzxPYmIiBEHo9Fi0aFF/wqM25xMW1xW0CYLgmBbiJxUi6qn2LRfkEG7QI9yghyhy13ml63PCsnfvXqxevRqjRo3q8HxTUxPmzJmDp556qlfnKi0tdTy+//57AMCtt97a1/CoHVevEJKksuMtEfWSdH9KlWmEBWAdi7voU8LS0NCA+fPn46233kJwcHCH7y1evBhLlizB5MmTe3y+8PBwREVFOR5ffvklkpOTcfnll/clPGqnfQW+qxMWboJIRL3RfkHA0MiBa8l/IdaxuIc+JSyLFi3CvHnzMHv2bGfHA7PZjA8++AD33XffRQuwTCYT6urqOjyos5NnG2CxiQj01bq8At+xUogt+omoB3LapmASXbggoCc4wuIeep2wrF+/Hvv378eKFStcEQ8+//xz1NTU4J577rnocStWrEBgYKDjERcX55J43F37+hVXV+APiQyASgCq2aKfiHpAWiE00A3jLiSNsBwvq4PVxg9bStWrhKWoqAiPP/441q1bBx8f13xaf+eddzB37lzExMRc9LilS5eitrbW8SgqKnJJPO5Omh929g7NXfHRqjE43D6sy463RHQpjvoVGRrGtZcY6g9frRotrTbkVzTKGgt1r1cJS2ZmJsrLyzF27FhoNBpoNBps2bIFr7zyCjQaDazW/vXfKCwsxA8//IAHHnjgksfq9XoYjcYOD+psIFYItSdV+rOOhYguRe4VQhK1SnAsGmAdi3L1KmGZNWsWsrOzkZWV5XiMHz8e8+fPR1ZWFtTq/m0LvnbtWkRERGDevHn9Og/ZiaLoGOkYqE3FzhfecqUQEXXPbLE5ejYN9KaHXWEdi/L1qsrJYDAgPT29w3P+/v4IDQ11PF9WVoaysjLk5uYCALKzs2EwGBAfH4+QkBAA9sTnxhtvxGOPPeY4j81mw9q1a7FgwQJoNPIVX3mSsroW1DS1QqMSMGSAKvClueAjJbUD8n5E5J5yyuphttoQ5KfFoGBfucPhSiE34PROt6tWrUJGRgYWLlwIAJgxYwYyMjKwceNGxzF5eXmoqKjo8LoffvgBp0+fxn333efskLyWNNyaHB4AvaZ/o189lRZzvmtko4ldI4moaweLawAAI2MDZWnJfyGOsChfv4cyNm/e3OHrZcuWYdmyZRd9TUFBQafnrrzySi6FdTI5tmyPMPgg3KDHuXoTjpfVYVxCyIC9NxG5j0NtCcvoQUGyxiFJjTJCJQAVDSaU17cgwjDwGzHSxXEvIQ92dAD2EOpKumNaiJ9UiKhrh4rt08ajBrl+BWNP+OrUSArzB8BRFqViwuLBBmLTw66kxdhvQEfO8B89EXXWZLbgxFn7CPDouCB5g2lnRNu9i3UsysSExUM1mS2OfgIDn7C0jbCUsvCWiDo7UlIHm2jf5T3SqJypF9axKBsTFg+VU1YPUQTCAuw7kQ4kaYTlRFkDWq22AX1vIlK+g0U1AIBRCqlfkXClkLIxYfFQchTcSuJCfGHw0cBsteHk2YYBf38iUjZH/UqsMupXJNIIS35FI5rMXOWoNExYPJRUvzJChoZMgiA43pf9WIjoQtIKoVEKql8BgHCDfURaFIHjZWx+qTRMWDyUXAW3EkfhLeeCiaid2qZWFFQ2AVDeCAvAOhYlY8LigURRdHw6kC9h4T96Iuos+4x91DU+xA/B/jqZo+mMdSzKxYTFAxVWNqHBZIFOo8LgcH9ZYkiLPf+P3sbt2omojdThVin9Vy7EERblYsLigaRPMMOjjdCq5flPnBweAJ1GhQaTBaermmSJgYiUR2kdbi8kjbAcL6uDlR+2FIUJiwc63JawjIyVbwdUrVrl2DKedSxEJFFah9sLJYb6w1erRkurzdHLipSBCYsHynYkLPLeENK4czMRtVNe34LS2haoBCBdgQW3AKBWCUiNlj5s8d6lJExYPIwoio4RFrlvCCO4UoiI2jlUZL83pUQEwF/f7713XSad9y5FYsLiYU5XNaGuxV5wOzRy4JvGtZfGTRCJqB1H/xWF1q9I0tum06UPf6QMTFg8jDQ/PDzKIFvBrWR4++3a61pkjYWI5Hew7f40WqH1KxKpj9ThM7UQRRbeKgUTFg+jlOkgwL5d++DwAAAcZSHydqIous0Iy9BIA7RqAXUtFhRXN8sdDrVhwuJhlFJwK2HhLREBQHF1M6qbWqFVny9qVSqdRoVhbascOS2kHExYPIiSCm4lrGMhIuB8w7jh0UboNWp5g+kBqfD2MD9sKQYTFg+ipIJbCfcUIiJA+f1XLpQWK9Wx8N6lFExYPIijw22UATqNMv7TSiMs9mSqVeZoiEguB4tqACi/fkWSHnN+pRALb5VBGX/VyCmyFTYdBABBfjrEBvkC4N4cRN7Kajs/Xa3UlvwXGh5thFoloLLRjLN1JrnDITBh8SiHFVZwKxnBOhYir3bqXAMazVb46dRIiQiQO5we8dGqkdK2ypGFt8rAhMVD2Atu7QmBkkZYAK4UIvJ2Uv+V9JhAqFWCzNH0nLTrPAtvlYEJi4coqmpGbXMrdGrlFNxKpMJbTgkReSep/8pINym4lThWCrHwVhGYsHgIqX4lNVo5BbcSaYTlZHkDWlqtMkdDRAPtoJutEJJIo9UcHVYGZf1loz5TYsGtJDrQB8F+WlhtInLK6uUOh4gGkNliw7G20VV3KbiVSPV3pbUtqGhg4a3cmLB4CKUW3AKAIAjsx0IdvL4pF4vXH0CzmSNuni6nrB5mqw2BvlokhPrJHU6vBOg1GBzmD4D3LiVgwuIBRFFUXEv+C7HwliTZxbV44dscfJ5Vgr99lyN3OORiBx37BwVCENyn4FZyvoEc711yY8LiAYqrlVtwK+HSZpI8/+1xx///x/Z87D9dLWM05GqH2iUs7iidH7YUgwmLB5BaXg9TUIfbC0lTQsfL6mC1sWukt9qeW4GfT1ZAqxYwY2g4bCLw+08PwWTh1JCnOt+SP0jeQPoonS36FUOZf92oV5RccCtJCvOHr1aNllYbTp1rkDsckoEoinjuG/voyvxJCXj59jEIC9Aht7wBr/2UK3N05ArNZitOltv/vbtbwa2k/fYitU3cXkROTFg8gJILbiVqlYDhbVvKc1rIO319uAyHimvhp1PjsStSEOyvw/+7Ph0A8MbmPA65e6AjJbWw2kREGPSICvSRO5w+CfLTYVCwfXuRI6X8HZUTExY35w4FtxL2NPBeFqsNf/3WXmD7wPTBCAvQAwCuHhmNuelRsNpE/P7TQ2i12uQMk5zsoJtPB0mkBnJHOC0kKyYsbk4quNWqBQyNUvYeHWksvPVaGzKLcaqiESH+OiycntThe8uvT0OgrxZHSuqwZuspmSIkV5AKqke7acGtJJ0t+hWBCYubk0ZXhkUZoNeoZY7m4tr3YuF27d6j2WzFSz+cAAA89osUGHy0Hb4fYfDBn64ZAQB4+YeTyC1nc0FPYLOJ2JFbAQCYkhwqczT9w6XNysCExc25y3QQAAyJDIBGJaC2uRVnaprlDocGyLs7CnC2zoTYIF/Mnxzf5TE3jY3FzGHhMFtt+P2nh7iSzAMcKalDdVMrAvQajI4LkjucfpGmhE5VNKLRZJE5Gu/FhMXNHXaDFUISvUbt6BMjLXUkz1bb1Io3N9tXAD3xy6HdjgIKgoC/3DgSAXoN9p+uwbs7CgYwSnKFn3PPAQAmDw6FVu3ef2rCDXpEGvUQReBYKae05eLev0Vezp0KbiXjEoIBAPsK2CzMG7y5JQ91LRYMizTghozYix4bE+SLJXNTAQAvfHschZWNAxEiuci2k/bpoOlDwmSOxDnO79zMD1tyYcLixoqrm1HTZC+4HRalzA63FxqfaE9YMgurZI6EXK2stgVrt+cDAJ68ahjUqku3Zb9rYjwmDw5BS6sNyzYecXWI5CLNZqvjQ8llHpKwOOpYuGhANkxY3JiU6Q+NVH7BrUQaYTlSUocmM+eCPdnLP56EyWLD+IRgzBoe0aPXqFT2qSFBADblnEN+BUdZ3NHu/EqYrTbEBvk6Ng90d1KLfo6wyIcJixtzt+kgAIgN8kWU0QcWm4isohq5wyEXyTvXgE/2FQEA/jA3tVeb3g0OD8DlQ8MBAB/uLnRJfORa0nTQZSlhbrnhYVekOsGT5Q1oaeVWEnJgwuLG3KEl/4UEQcA4aVqIdSwe64NdhbDaRFyRGoEJiSG9fv38SQkA7P1b+MfB/WxrW87sKdNBABAd6IMQfx2sNhE5ZVx6LwcmLG5KFEW3aMnflQlS4W0hExZPldn23/b6MTF9ev0VqRGICfRBTVMrvj5c6szQyMXK61pwvKweggBMS/GchEUQBEfzSzaQkwcTFjdVXN2M6qZWaFTuU3ArGd/2iXv/6WrY2G/D47S0WnG0rTBxbHxwn86hVgm4Y6K9Z8sHu047LTZyPWl0JS3GiBB/nczROBd3bpYXExY31b7g1kfrHgW3ktQoA/x0atS3WHCCXU09zqHiWlhsIsINesemcX1xx4Q4qFUCMgur2fvCjZxfzhwucyTO59hTiCMssmDC4qbcseBWolGrkBEfBADYyzoWjyPtHzM2PqhfBZcRRh9cOSISAPDhbo6yuANRFB0jLNM9aDpIIu0pdLy0nht1yoAJi5s6cLoGADDSTTcVG5dgnxbKLGA/Fk+zv61+RVrC3h9S8e1nB86wJbobOHG2AeX1JvhoVY7iek8SH+IHg48GZqsNJ882yB2O12HC4oaazVZHUaO7bio2IZGFt55IFEXsb0um+1q/0t7U5FAkhfmjwWTBf7JK+n0+cq2fT9rb8U9MCnWb3lC9wcJbeTFhcUNSU6aYQB+3bcqUER8MlWAvHj5b1yJ3OOQkxdXNqGgwQasWnLLcXqUScFdb8e263YXc5Vvhfj7pudNBEkcdCxvIDTgmLG7I0ZRpiPs2ZQrQa5AaZf+kwn2FPIdUvzIiJtBpxeC3jBsEnUaFIyV1bDaoYCaLFbvzKwEA04d6cMLCFv2y6VfCsnLlSgiCgMWLFzueW7NmDWbOnAmj0QhBEFBTU9Ojc505cwZ33303QkND4evri5EjR2Lfvn39Cc9jOYra3LwKf7xjWoh1LJ5Cql8Z21ZU7QzB/jpcMzIaALCOxbeKlVlYjZZWG8INegyLdK9WC70hFd4eKall4e0A63PCsnfvXqxevRqjRo3q8HxTUxPmzJmDp556qsfnqq6uxrRp06DVavH111/j6NGj+Nvf/obgYM8r2uqv8nrPacrEnZs9jzPrV9qbP9k+LfTFwRLUNJmdem5yDk9sx9+VwWEBMPpo0NJq43L7AdanhKWhoQHz58/HW2+91SmpWLx4MZYsWYLJkyf3+HzPPfcc4uLisHbtWkycOBFJSUm48sorkZyc3JfwPNp2D2rKJDWQO1paxxUgHqDJbMHRthv4WCesEGpvbHwwUqMMMFls+Nf+M049NznHz+0SFk+mUgn8sCWTPiUsixYtwrx58zB79mynBLFx40aMHz8et956KyIiIpCRkYG33nrroq8xmUyoq6vr8PAG528K7j0dBNg3QowJ9IHVJuIgaxPc3qHiWlhtIiKNesQE+jj13IIgYP5k+xJnFt8qT3Wj2bFqxpP2D+qO9GErk6scB1SvE5b169dj//79WLFihdOCOHXqFN58800MGTIE3377LR555BH85je/wXvvvdfta1asWIHAwEDHIy4uzmnxKJUoiu26SHrGTWFc2z98Lm92f+cbxgW7ZErghjEx8NOpcepcI3aeqnT6+anvtudVQBSBYZEGRBqdm6wqkWOEpbCKyfMA6lXCUlRUhMcffxzr1q2Dj4/zfiltNhvGjh2Lv/zlL8jIyMCDDz6IhQsXYtWqVd2+ZunSpaitrXU8ioqKnBaPUp0stzdl0mtUTmnKpQTjuRGix9hfWAPA+fUrEoOPFjdkxAJg8a3StF+56A1GDwqCRiXgbJ0JxdXNcofjNXqVsGRmZqK8vBxjx46FRqOBRqPBli1b8Morr0Cj0cBq7ds28NHR0RgxYkSH54YPH47Tp7u/Ken1ehiNxg4PTydNB01MCnG7/YO6IyVe+wurYeVGiG5LFEUckEZYEoJc9j7zJ9mLb789XIZz9SaXvQ/1nCiK56eqvSRh8dWpkda2vJnTQgOnVwnLrFmzkJ2djaysLMdj/PjxmD9/PrKysqBW9+2P6LRp05CTk9PhuRMnTiAhIaFP5/NUUhdJT5kOAuwbIfrr1GgwWZBTxo0Q3dXpqiZUNpqhVQtIi3HddhFpMYHIiA+CxSbik32eP6rqDvIrGnGmphk6tQqTkkLkDmfAjE9gW4aB1quExWAwID09vcPD398foaGhSE9PBwCUlZUhKysLubm5AOBIcKqqzv9HnTVrFl577TXH1//zP/+DXbt24S9/+Qtyc3Px4YcfYs2aNVi0aJEzfkaPYLJYsfuU/Rp6QsGtxL4Rov0ffib/4bstqX4lzYkN47oj7S/04e7THJVTAKkv1NiEIPjpNDJHM3DGc6XQgHN6p9tVq1YhIyMDCxcuBADMmDEDGRkZ2Lhxo+OYvLw8VFRUOL6eMGECPvvsM3z00UdIT0/HM888g5deegnz5893dnhua39hDZpbrQgL0CE1yrOaMo3nvkJuT6pfGYjaqmtGRSPQV4szNc3YnFPu8veji3O043fzRpa9JW3umHO2HrXNrTJH4x36nQ5v3ry5w9fLli3DsmXLLvqagoKCTs9dc801uOaaa/objsfalmufDrosJQwqlWc1ZRrftnMzP6m4r/YrhFzNR6vGbeMH4a2f8/HPXYWYNTzS5e9JXbNYbdiV19aO34OmqnsiwuCD+BA/nK5qwoHT1Zg5LELukDwe9xJyE+er8D3vU8yY+CCoBOBMTTNKa1lx726azBYcb6s/cmXBbXt3tU0LbTlxDkVVTQPyntTZweIa1JssCPLTurR2SamkaSEW3g4MJixuoKbJjENtO4N6YhfJAL0Gw6O5EaK7OlhkbxgXHeiD6EDfAXnPpDB/TB8SBlHkEmc5bT1h/yA1LTkMag8b+e0JaVqI962BwYTFDezIq4QoAkMiAhDl5A6iSsFPKu5rIKeD2pOKbz/ZVwSTpW8tFah/zm/E6nkfpHpCms7OKqrhRogDgAmLG/CGHgfnO95ypZC7kfqvZDhxh+aemD08AlFGH1Q1mvHN4bIBfW8C6lpakdW2pYYn35suZkiEfSPE5lYrN0IcAExY3IBUcOvJn2ImtA2tHiut50aIbkQUxfM7NA9w92WNWoU7Jtq35PhgV+GAvjcBO/MqYbWJGBzmj0HBfnKHIwuVSnD83nNayPWYsChcYWUjiqqaoVULmJQUKnc4LhMd6IvYIF9YbaLjUxspX2FlE6oazdCpVUiLGfhu03dOjIdaJWBvQTWOl/ET7kDyxEaWfcHp7IHDhEXhpOmgjPhg+Os9uymT1MNjbwGnhdyFVL+SHmuEXjPw20VEGn1w5Qj7smaOsgwsT1652BvjEs5PZ3MjRNdiwqJwjt2ZPXB10IWkBnL8pOI+5Cq4be/uyfbi28/2n0EDpxMHRFFVEwoqm6BRCZg82Hva8XdlTBw3QhwoTFgUzGoTsSPP8wtuJdIfvUPFtTJHQj3l2KFZxt3DpyaHYnCYPxrNVnx+4IxscXiT8yO/QTD4aGWORl6+OrVjOpQftlyLCYuCHSquQV2LBUYfDUYNCpI7HJcbHO4PAKhtbkV1o1nmaOhSGk0WR92InCMsgiDgrrZdnD/YVchh+QFwvn7Fu6eDJO2nhch1mLAomDQdNC3FO5oy+ek0iDLa+8zkVzbKHA1dysHiGthEIDbIV/b+QLeOi4OPVoXjZfWOaSpyDatNxPZc7xn57YkJbCA3IJiwKNjPXnhTSAyzL48sqGDConQH2pYzD3T/la4E+mlx7agYAMA/d7L41pU6jPzGel87/q603wixroUbIboKExaFajBZHA25pqd4z7BrUlgAACCfCYviSfP1ck4HtScV3/43uwyVDSaZo/FcUv3K1OQwaNT8EwKc3whRFM8n8uR8/G1TqN2nKtFqFREf4of4UO9pypTUNsLChEXZRFF0JNRyFty2NzouCCNjA2G22rAhs1jucDyWY+XiUO8Z+e0JRz8WtmVwGSYsCrU5x17UNsPLbgqJofbC2wLWsChafkUjqptaodeoMCJ64BvGdefuyfbi23W7C2GzsfjW2RpMFkeN0AwW3Hbg2AiRK4VchgmLAomiiM0nygEAM4dGyBzNwJJWCuWfa+RqDwXbeaoSgH1UQ6dRzm3kutGxMPpoUFTVjJ+Ol8sdjsfZlVcJi01EQqgf4kK8Z+S3J9pvhGjhRoguoZw7DTmcqrC349epVZia4rnt+LsSF+IHlQA0mq04xzoExdqRZ09YpiUrawTQV6fGnRPtoyzvbMuXORrPw3b83ZM2QmwyW3GstF7ucDwSExYFkqaDJiaFwE/n2e34L6TXqBET5AsAKKhokjka6orNJmKnlLAoMKH+9dREqFUCdp6qxNES7i/kTI6Vi160EKCnOmyEyH4sLsGERYE257RNBw3zzptCUljbtFBFg8yRUFdyztajqtEMP51akQ0NY4N8MSc9CgDwj+0cZXGWMzXNOHWuEWqVgCnJyktUlWB8AutYXIkJi8I0m63YnW/PzpmwcIRFiaSmYROTQhRVv9Le/ZclAQA2ZpWgvL5F5mg8w7a26aDRgwIR6Ovd7fi7I3W8zSyoZg2eCyjzbuPFdp6qgNliQ2yQL5LDA+QORxaOlUJc2qxI0nTQVAV/yh4bH4yM+CCYrTas23Va7nA8wlZpOTNXB3VL2gixrK4FZ2q4EaKzMWFRGKl+ZeawcAiC57fj70qStFKICYviWKw2xwjgVIUV3F5IGmX5YFchWlqtMkfj3tq342fBbfe4EaJrMWFREFEU2yUs3rWcub2kdr1Y2EtDWQ6dqUWDyYJAX62i+q90ZU5aFGICfVDZaMbGrBK5w3FrR0pqUdPUCoNeg9FxQXKHo2iOaSEmLE7HhEVB8isacbqqyb6cWcHD7a42KNgXGpUAk8WGsjrWHyiJNB00ZXAoVArfkFOjVmHB1EQA9uJb1hT0ndSOf3JyKLRsx39Ro+Ps+ysdPlMrcySeh795CiKNrkxICoa/3ruWM7enUasQH8IW/UokTQsocTlzV+6YEA8/nRrHy+odvWOo96T+KzM4HXRJaTH2hOVYaT2sHCF2KiYsCrL5RNt0kJd1t+1KYhjrWJSmpdXqWK45ReH1K5JAPy1uGTcIABvJ9VWT2eKY3riMBbeXlBTmD1+tGs2tVrZmcDImLArRbLZiV1u7c29dztweVwopz/7CapgtNkQa9UhuK4x2B/dOS4IgAD8dL8epc/wD0lu7T1Wh1SpiULAvEr1oI9a+UqsEjGgrvD3CxoVOxYRFIXadqnQsZ06J8M7lzO1xpZDy7HAsZw5zqxVsSWH+mJVqH7Vcu71A3mDc0M8nz68Ocqf/7nKSVgqxjsW5mLAohNTd9nIvXs7cnrRSKJ+7NivG9jz7Hy537HJ6X9sS508zi1HTZJY5Gvdhs4nY1HZvYv+VnkvjCItLMGFRiPP1K7wpAEBimH3ouaiqiTufKkB9SysOFds/LbrjCrYpg0ORGmVAc6sV6/cWyR2O2/jswBnkVzTCoNfgMhbc9phUeHv4TC1XpzkRExYFyK9oRGFlE7RqAVNTeFMAgJhAX+g0KrRaRXaMVIA9+VWw2kQkhPphULD71TEIguBoJPfejgK0Mgm+pJZWK/72XQ4A4NFfpMDow3b8PTU00gCtWkBdiwXF1bx/OQsTFgWQpoMmJIYgwIuXM7enUgmOAj/Wscivff2Ku7puTAzCAnQorW3B14fL5A5H8dZuL0BJbQtiAn1w77REucNxKzqNCkMjDQA4LeRMTFgUoH07fjqPK4WUY4cb7B90KXqNGndPTgAArOUuzhdV1WjGG5tyAQC/vXIYfLRqmSNyP+frWFh46yxMWGTW0tp+OTP7r7THlULKUNlgwrFS+6dEdyy4bW/+pARo1QIOnK7hCo6LePWnk6g3WTA82ogbMmLlDsctSXUsHGFxHiYsMtt5qhImiw0xgT4YwuXMHZxfKdQkcyTebdcp+2aHqVEGhAXoZY6mf8INesxNjwYAvL+zQN5gFKqwshEf7CoEADx1dSrUCt+CQanSY7m02dmYsMhsS9t00OXDIric+QJSt1tOCcnLnZczd+XXU+zTQv/JKuES5y48/20OWq0iZgwN51LmfkiNMkIQgPJ6E8rruSeaMzBhkZlUcMv6lc4GtyUsxdVNMFu4qkMu0oaH09y44La9cQnBGB5thMliw4Z9xXKHoygHTlfjq0OlEARg6dxUucNxa/56jeMexmkh52DCIqOCikYUtC1nnsblzJ2EG/Tw16lhE4HTVZwWkkNJTTPyKxqhEoCJg0PkDscpBEFwjLJ8sLsQNm5QBwAQRREr/nscAHDz2EEYHm2UOSL3J9WxHGXC4hRMWGQkja6MT+By5q4IgoAErhSSlbQ6aNSgII/qw3H9mBgYfDQorGzClradiL3dD8fKsaegCnqNCr+9cqjc4XgEqY6FK4WcgwmLjBzdbTkd1C2uFJLXjrb6FXdeztwVP50Gt46LAwD8c2ehzNHIz2K1YeXXxwAA91+WhOhAX5kj8gznO95yhMUZmLDIpKSm2fHplcuZu8c9heQjiiJ25Lp/w7ju/KptWmhTTjmKvHzK8eN9Rcg714gQfx0enpksdzgeQ+rFcrqqCbXNrTJH4/6YsMjkpR9OwGyxYWJSCIZGcjlzd7hSSD75FY0oq2uBTq3C+MRgucNxuqQwf0wfEgZRhGMZrzdqMFnw9+9PAgB+cwVb8DtTkJ8OsUH20SrWsfQfExYZnDhbj08z7asTlsxN5XLmi0gK45SQXLa3jQCOTQjy2E6nv56SCMA+wtDSapU3GJm89P0JVDSYkBjqh7smJcgdjsdhx1vnYcIig+e/yYFNBOamR2FsvOd9cnUmKWEprW1Bs9k7/6DIZWdb/YqnLGfuyhWpEYgN8kVNUyu+OFgidzgD7rsjZXh7m32bgv+bNwI6Df8kOFt6LFcKOQt/OwfY3oIq/HDsLNQqAb+7apjc4ShesJ8WRh/7CqrCKo6yDBSzxYafT7YV3Hrwknu1SsD8yfEAgH962bTQ6com/HbDQQD2QtvZIyJljsgzSSMshznC0m9MWAaQvc+BvRL/9glxSA5n7cqlCIKApLbrlH+OCctA2Z1fifoWC8ICdBgTFyR3OC51+/g46NQqHCquRVZRjdzhDIiWVise/TAT9S0WjI0PwhI2iXMZaaVQbnkDR4n7iQnLAPru6FnsP10DX60ai2cNkTsct5EU6geAK4UG0rdHygAAvxwR6fF7yYQG6HHNKPv+Qt6yxPmZL4/i8Jk6BPtp8dpdY6FV80+Bq0Qa9QgL0MEmAsfLOC3UH/wtHSAWqw3Pf2PvInn/ZUmIMPrIHJH74EqhgWWzifj+6FkAwJUjomSOZmBIS5y/OFSCqkbP3l/oP1lnsG73aQgC8NIdGYgJYs8VVxIEASO4c7NTMGEZIJ9mFiPvXCOC/bR48PLBcofjVrhSaGAdOlOLs3Um+OvUmJriWQ3jujMmLggjYwNhttjwyb4iucNxmZNn67H039kAgP/viiG4fCibVg6EdK4UcgomLAOg2WzF3384AQB47Ioh7HPQS+cTFu9u7jVQpOmgmakR0Gs8cznzhQRBcIyyfLCrEFYP3F+o0WTBI+v2o8lsxWUpYXic09IDJo0jLE7Rr4Rl5cqVEAQBixcvdjy3Zs0azJw5E0ajEYIgoKam5pLnWbZsGQRB6PBITfWcIrC1O/Jxts6E2CBf3N22IoF6TpoSqmgwob6F3SJd7bu2hOWqNO+YDpJcNzoGQX5aFFc3Y9PxcrnDcSpRFPG/n2Ujt7wBkUY9XrpjjMfXJimJtKfQ8bJ6tFq583xf9Tlh2bt3L1avXo1Ro0Z1eL6pqQlz5szBU0891avzpaWlobS01PHYtm1bX0NTlOpGM97cnAcA+N1VQ73mE6szGX20CAvQAQAKOMriUrnlDcg71witWvC6Pa58tGrcPt6+v9D7HrbE+cM9p/F5VgnUKgGv3TUWYQF6uUPyKnHBfjDoNTBbbMgtb5A7HLfVp4SloaEB8+fPx1tvvYXg4I6NzxYvXowlS5Zg8uTJvTqnRqNBVFSU4xEW5hm9H97YnIv6FguGRxtx/ehYucNxW4ncU2hAfHfUProyNTnMK6cu756cAEEAtp44h1PnPOMPy4HT1Vj+xVEAwB/mDMOExBCZI/I+KpWA4Y46Fk4L9VWfEpZFixZh3rx5mD17ttMCOXnyJGJiYjB48GDMnz8fp0+fvujxJpMJdXV1HR5KU1zdhPd22D+p/WHOMKg4BNtnXCk0ML490rY6KM07m4jFhfhhVqp9M1JPaCRXUtOMhe9nwmyx4coRkVg4nQX/ckl37NzMwtu+6nXCsn79euzfvx8rVqxwWhCTJk3Cu+++i2+++QZvvvkm8vPzMX36dNTX13f7mhUrViAwMNDxiIuLc1o8ztDSasWTGw7BbLVhanIoq/H7iSuFXK+stgUHi2ogCPb+K95K2l/o033FaDRZ5A2mHxpNFtz/3j5UNJgwPNqIv98+hvuWyUjqeMsW/X3Xq4SlqKgIjz/+ONatWwcfH+f1EZk7dy5uvfVWjBo1CldddRX++9//oqamBp988km3r1m6dClqa2sdj6Ii5SxFtNpELF6fhZ2nKuGvU+NP147gjaKfmLC43vdt00EZcUGIMHhvn6DLUsIwOMwf9SYL/n3gjNzh9InNJuJ/Ps7CsdI6hAXo8PaC8fDXa+QOy6s59hQqrYPNA1ehDYReJSyZmZkoLy/H2LFjodFooNFosGXLFrzyyivQaDSwWp3TdjgoKAhDhw5Fbm5ut8fo9XoYjcYODyUQRRH/93k2vjlSBp1ahbd+PR6pUcqIzZ1JNSwFrGFxme+kZnFetjroQirV+SXO7+8ogCi63x+Xv36Xg++OnoVOo8LqX41HLJvDyS453B96jQoNJgsKq7h4oC96lbDMmjUL2dnZyMrKcjzGjx+P+fPnIysrC2q1c1bANDQ0IC8vD9HR0U4530D663c5+GhPEVQC8PIdYzx647iBlBhmb89f09SKag/vRCqH2qZW7MyrBOB9y5m7cvO4QfDTqXGyvAE7T1XKHU6v/Ht/Md5oW5n4/M2jMC6BO8IrgUatQmqUAQAbyPVVrxIWg8GA9PT0Dg9/f3+EhoYiPT0dAFBWVoasrCzH6IiU4FRVVTnOM2vWLLz22muOr3/3u99hy5YtKCgowI4dO3DjjTdCrVbjzjvvdMbPOGDe2ZaP1zfZbxTP3jgSc0e6X8KlVH46DaLatjPgSiHn25RTDotNxJCIAMf0mzcz+mhx89hBAID3d7hP8W1mYRWW/MveyXbRL5JxQwZXJipJWqxUeMs6lr5weqfbVatWISMjAwsXLgQAzJgxAxkZGdi4caPjmLy8PFRUVDi+Li4uxp133olhw4bhtttuQ2hoKHbt2oXwcPcpVP33/mI886V96eCTVw3DnRPZIM7ZpFEWrhRyPmk5M0dXzvt127TQd0fLcKamWeZoLq24ugkPvp8Js9WGq9Ii8dtfDpM7JLpAGlv090u/q7A2b97c4etly5Zh2bJlF31NQUFBh6/Xr1/f3zBk9dPxs3jy00MAgPumJeHRmckyR+SZksICsOtUFQtvnayl1YrNOecAeO9y5q4MiTRganIoduRVYt2uQvx+jnK7bzeYLHjgvX2obDRjRNuKILZRUJ70di36RVHkYoxe4l5C/bSvoAqPrtsPq03ETRmx+L95w/lL6CJJbSMsTFica3tuBZrMVkQH+mBk25A12UlLnNfvLUJLq3MWFThbfkUj7vnHHhwvq0e4QY+3F4yHn44rgpRoWJQBapWAqkYzyupa5A7H7TBh6YdjpXW47929aGm14YrUCDx3yyh+qnGhpLAAAExYnE3a7PDKEZFMti8we3gEYgJ9UNVoxleHSuUOp4NWqw2vb8rFVS9txb7Cavjp1Fjzq3GI4YogxfLRqpESbr+PHWEdS68xYemj/IpG/OqdPahrsWB8QjBev2sstGpeTldq34vFHZeaKpHVJuKHY/aN/li/0plGrcL8yW1LnHcWyBtMO1lFNbj21W144dscmC02TB8Shm8Xz0BGPFcEKZ1Ux3KslAlLb/EvbB+U1DTj7rd3o6LBhBHRRrxzzwT46ripoavFh/hBJQBNZivO1ZvkDscj7CuoQlWjGYG+WkxI4h4zXbljQhx0ahUOFtciq6hG1lgaTRYs/+IIbnxjO46X1SPYT4u/3z4a7983EXEhfrLGRj0zQup4y4Sl15iw9FJlgwl3v7MbZ2qaMTjMH+/fPxGBvt63SZwcdBqV46Z8itNCTiE1i5s1PIIjhN0IDdDjmtH2FgXv7SiQLY5Nx8tx5d+3Yu32AogicFNGLH787UzcmDGIU3luZEQ0E5a+4h2qF+paWvHrf+zBqXONiAn0wT8fmMRt2gcYW/Q7jyiK7epXOB10MQvaim+/OlQ64KN7FqsNy784gnvf3YszNc2IC/HF+/dNxIu3j0GIv25AY6H+G96WsBRWNqG+pVXmaNwLE5YeajZbcf+7e3GkpA6h/jp88MAktruWgdSinwlL/x0rrUdxdTP0GhVmDGVH5osZHReEMXFBMFtt+HjvxXeSd6a6llY88P4+rN1eAAB44LIkfLt4BmZwM1W3FeyvQ0ygvQnm8bLuN/ilzpiw9IDZYsPDH2Rib0E1DD4avH//RAxuq/SmgTU43J6wnDrHhKW/vjxUAgCYPiScy2B7YMFUe/HtP3cVotns+iXOpyubcPMbO7A55xx8tCq8OX8s/u+aEfxv5QFGcOfmPmHCcgnWtl1Pt5w4B1+tGmvvmYC0GPaqkMv5KaEGmSNxbyaLFZ/ss+9wfss4tm/viatHRiMm0Adn60x46YcTLn2vPflVuOGN7ThZ3oBIox4bHprKrT48iDQtxISld5iwXIQoinjq39n4KrsUWrWA1b8ah/GJXEkhJylhOV3VBCu3aO+zbw6XoaLBjEijHrOHs7ttT+g1ajxzg33PtLe35ePwGde0V9+wrwjz396FqkYzRsYGYuNjl2HkIH5I8iQsvO0bJiwXcbbOhB+Pl0MlAK/ckcF5YwWICfSFTqNCq1XEmWrl7++iVOt22esw7pwYDw1XB/XYrOGRuGZUNKw2EX/41yFYrDanndtmE7Hi62N48tNDaLWKuHpkFD55aAoi2zb9JM8hTQnlnK1HqxN/hzwd71QXERXogw0PT8FLd2RwOFYhVCoBSW2Ft6c4LdQnx8vqsKegCmqVgDsmcJPO3nr62jQE+mpxpKQO72zLd8o561ta8dAHmVi95RQA4DdXpOC1O8eyv5OHigv2Q4BeA7PFxnq8XmDCcglJYf64bnSM3GFQO4ncU6hfpNGVK0dEIiqQn957K9ygx//OGw4AePH7E/3ePTzvXANueH07vj96FjqNCi/fMQZPXDmM23x4MJVKwPBoAwDgaCl3bu4pJizkdrinUN81mCz49/5iAMDdbS3nqfduHTcIU5NDYbLY8NRn2X3eKuK7I2W4/rXtyDvXiCijDz5+cDKuH8MiaG8wgoW3vcaEhdzOYDaP67PPD5xBo9mKweH+mJocKnc4bksQBKy4aST0GhV25FViQ2Zxr15vs4l48bscPPjPTDSYLJiYGIIv/r/LuBeQF2GL/t5jwkJuJymcCUtfiKKID3YVAgDmT0pgO/d+Sgj1xxO/HAoAeParYz3ugFvbbG8G98pPuQCAe6YmYt3CSQg3sGu2NxkRbV/5dbSkjpu59hATFnI70tLmMzXNaGl1fQMvT5FZWI3jZfXw0apwy9hBcofjEe6/LAlpMUbUNrdi+RdHLnl8Tlk9rn9tG346Xg69RoW/3z4ay65L4z5OXmhIZADUKgHVTa0oq2uROxy3wH8l5HZC/XUw+GggivZ+LNQz/2wbXbludAwC/bhhpzNo1Co8d/MoqFUCvjxUih/aNpOUiKKIstoWfHukDM99cxw3vrEdBZVNiA3yxb8emYobM5g4eisfrRopbR3TWcfSM+zxTG5HEAQkhfnjUHEtTp1rxNBIg9whKV5FgwlfZ9s3OvzV5ER5g/Ew6bGBeGB6ElZvOYU//ucwRABHSmqRXVyLQ2dqO00VTUsJxat3juXGhYQRMUbknK3H0ZI6zGIDx0tiwkJuSUpYWMfSM5/sK4LZasPoQYHsmuoCi2cNxTeHy1BY2YSF7+/r8D21SsCQiACMGhSICYkhuDEjls36CIB9pdBnB86w8LaHmLCQW+KeQj1ntYn4cLe99wqXMruGr06NF24ZjYc/yESIvw6jYu2J4ahBQRgRbWQDOOoSVwr1DhMWcktSwlJQwRqWS9lyohzF1c0I9NXiWjZBdJmJSSHY/8dfyh0GuRFpE8TCyibUt7TC4MPasovhuCS5pcFtzeNOcUrokj5o62x767hB8NHykz6RUoT46xDd1m36eFm9zNEoHxMWcktSe/6KBhPqWlpljka5iqqasCmnHAAwn9NBRIrDjrc9x4SF3JLBR4uwAHujrf7u5eLJPtxzGqIITB8S5phGIyLlcNSxMGG5JCYs5LbYov/iTBYrPtlbBIDFtkRKJY2wHCtjwnIpTFjIbUkjBtyevWvfHC5DZaMZUUYfzEqNkDscIuqCNMJyvKweFqtN5miUjQkLuS1pT6GCSiYsXZGWMt8+IY59P4gUKi7YDwF6DcwWGxcRXALvYuS2kjgl1K3c8gbszq+CSgDumBgndzhE1A2VSsDwaHu3btaxXBwTFnJbjhqWc43c7fQC6/fYR1euSI1AdKCvzNEQ0cU4VgqxgdxFMWEhtxUX4gdBAOpNFlQ0mOUORzFaWq34dH8xAOCuSfEyR0NEl8KVQj3DhIXclo9Wjdgg++gBp4XO++ZwGWqaWhEb5IvLh7LYlkjpRkTb9/c6WlrH0eKLYMJCbo17CnXWvthWrRJkjoaILmVIZADUKgFVjWacrTNd+gVeigkLubXzvVi4pxAAnDxbjz0FVVCrBNw+gcW2RO7AR6tGSrh9u5GjpbUyR6NcTFjIrXGEpaMP24ptZ6VGINLoI3M0RNRTrGO5NCYs5NaS2j6VsIbFXmz7r0x7se2dLLYlciuOpc1cKdQtJizk1pJCpeZxTbDavLtY7atDpahrsSA2yBczhoTLHQ4R9YKj8JYjLN1iwkJuLTbYF1q1ALPFhpKaZrnDkZU0HXTnRBbbErkbaYSloLIJDSaLzNEoExMWcmtqlYCEUHa8zSmrR2ZhNTQqAbeNZ7EtkbsJDdAjqq3u7DinhbrEhIXcnlR46817Cn24uxAAMHt4JCJYbEvklhyFt0xYusSEhdzeYC/ftbnZbMW/D5wBwM62RO7M0aKfdSxdYsJCbs/bN0H88lAJ6lssiA/xw2UpYXKHQ0R9JI2wHGHC0iUmLOT2Er08YZGKbe+YGAcVi22J3NaoQedb9Ne1tMocjfIwYSG3J00JFVc3wWSxyhzNwDpWWocDp2ugUQm4dRyLbYnc2aBgPwwO84fVJmJHbqXc4SgOExZye+EGPfx1athEoKjKu1r0S/sGXZUWhXCDXuZoiKi/Zgy191D6+eQ5mSNRHiYs5PYEQUBSuPftKWSyWLHxYAkA+3QQEbm/6UPsdWhbT57jzs0XYMJCHiEpTGrR7z17Cm06fg61za2IMvpgajKLbYk8weTBodCqBRRVNaOg0ns+gPUEExbyCN64UujztqXM14+JYWdbIg/hr9dgfEIIAE4LXYgJC3mEpDA/AN7Ti6W2qRU/HS8HANw4NlbmaIjImaYPbZsWOsGEpT0mLOQRzk8JeUfC8lV2KcxWG1KjDEiNMsodDhE5kbR56c68SpgtNpmjUY5+JSwrV66EIAhYvHix47k1a9Zg5syZMBqNEAQBNTU1/T4n0aVIuzaX15u8YuOwzw4UAwBuzODoCpGnGRFtRKi/Do1mK/afrpY7HMXoc8Kyd+9erF69GqNGjerwfFNTE+bMmYOnnnrKaeckupRAPy1C/XUAgLxyzy68Lapqwt6CaggCcP0YJixEnkalEs6vFuK0kEOfEpaGhgbMnz8fb731FoKDgzt8b/HixViyZAkmT57stHMS9YTU1vpwSa3MkbiWVGw7NTkUUYHc6JDIE53vx1IhcyTK0aeEZdGiRZg3bx5mz57ttEB6e06TyYS6uroOD/Ju6bH2ttaHz3ju74Ioivgsy56w3JgxSOZoiMhVLmsbYTlcUovKBpPM0SiDprcvWL9+Pfbv34+9e/c6LYi+nHPFihVYvny502Ig95ceY09YjnjwCEv2mVqcOtcIH60KV6VFyh0OEblIhMEHw6ONOFZah225FZz+RS9HWIqKivD4449j3bp18PFxzlB0X8+5dOlS1NbWOh5FRUVOiYfc18i2EZbjpfUeW1n/7/320ZVfjoiCwUcrczRE5EozHHUsnBYCepmwZGZmory8HGPHjoVGo4FGo8GWLVvwyiuvQKPRwGrt/cZzfT2nXq+H0Wjs8CDvFhfiC4OPBmarDSfL6+UOx+larTZ80daK/yauDiLyeO33FWKb/l5OCc2aNQvZ2dkdnrv33nuRmpqKP/zhD1Cr1b0OwBXnJO8kCALSYwKx81QlDp+pRVrbFJGn2HayApWNZoT66xzz20TkucYnBsNHq0J5vQk5Z+u9vudSrxIWg8GA9PT0Ds/5+/sjNDTU8XxZWRnKysqQm5sLAMjOzobBYEB8fDxCQuzthmfNmoUbb7wRjz32WI/OSdRTIwdJCUsdbp8gdzTO9Vnb6qBrR8dAq2bPRyJPp9eoMXlwKDbnnMPWE+e8PmFx+l1v1apVyMjIwMKFCwEAM2bMQEZGBjZu3Og4Ji8vDxUVnJMj50trW9qcfcazCm8bTBZ8d7QMAJvFEXkTqest61j6sEroQps3b+7w9bJly7Bs2bKLvqagoKBX5yTqKanw9lhpHSxWGzQeMhLxzeEytLTaMDjcH6MGedZUFxF1b0bbvkJ7CqrQbLbCV+e9ZRKecTcnapMY6g9/nRomiw15HrQRoqMV/5hYCAJ3ZibyFsnhAYgJ9IHZYsPu/Eq5w5EVExbyKCqV4Ci29ZRpobLaFuzIs9+obuB0EJFXEQSBXW/bMGEhj3O+461nJCz/yToDUQQmJAYjLsRP7nCIaIBNd9SxePe+QkxYyOOkx7btKeQhCYu0OoijK0TeaVpKKFQCcLK8ASU1zXKHIxsmLORxpMLbo6V1sNrcu9nSsdI6HC+rh06twjUjY+QOh4hkEOSnw6hBQQDs/Zi8FRMW8jiDwwPgq1WjyWxFfoV7F95+c9i+lPnyYeEI9GMrfiJvJdWxbDnpvdNCTFjI46hVAkbEeMa00Na2m9Os1AiZIyEiOV3etrx5e26F248c9xUTFvJI6R6QsNQ2teJgUQ2A85+uiMg7jR4UBIOPBjVNrR6zArK3mLCQR0qLdf+lzdtyK2ATgZSIAMQE+codDhHJSKNWYVqyfZTlp+PlMkcjDyYs5JEchbcldbC56fDpz23TQVJrbiLyblelRwIAPj9wxit3b2bCQh4pJSIAOo0K9SYLCqua5A6n10RRdPRckFpzE5F3uyotCv46NU5XNWFvQbXc4Qw4JizkkbRqFYZHu28dS965BpTUtkCnUWFSUqjc4RCRAvjpNJg3KhoA8K/MYpmjGXhMWMhjOQpvS9wvYdnStjPrpKQQr97sjIg6unnsIADAV9mlaDZbZY5mYDFhIY/lzi36HdNBrF8honYmJIYgLsQXDSYLvj1SNmDvW1jZiFarbcDerytMWMhjjXQkLHVuVaDW0mp17MrK5cxE1J5KJThGWT4doGkhm03Eve/uxcwXNsv6AZAJC3msIZEB0KoF1Da3orjaffbf2FtQhZZWGyKNegyNDJA7HCJSGClh2Z5XMSB7C31/7CxOnWtEXUsrEkLl24CVCQt5LL1GjWFRBgDuNS0kTQdNHxIOQRBkjoaIlCYuxA+TkkIgiuc3R3UVURSxakseAOBXkxNg8JFvixAmLOTR0mPcr4Hc1raCW04HEVF3bh5nH2X5V2axS6e89xZU48DpGug0Ktw7Lcll79MTTFjIo0kdbw+X1MkcSc+U1bYg52w9BAGYnsL+K0TUtatHRsNXq8apikbsP13jsveRRlduGTcI4Qa9y96nJ5iwkEeTCm+PnKl1i8JbabPDUbGBCPbXyRwNESlVgF6DuelRAIB/7XdN8W1OWT1+Ol4OQQAenD7YJe/RG0xYyKOlRhmgVgmobDSjtLZF7nAu6Xx3W04HEdHF3dI2LfTFwRK0tDq/J8vqttGVuelRSAzzd/r5e4sJC3k0H60aQyLsK22UXnhrtYnYlsv6FSLqmcmDQxEb5Iv6Fgu+P3rWqec+U9OMjQdLAAAPX57s1HP3FRMW8nju0kAu+0wtappaYdBrMCYuSO5wiEjhVCoBN2bEAnD+tNDbP5+CxSZianIoRg0Kcuq5+4oJC3m88y36lV14K00HTU0JhVbNf5pEdGnSaqGtJ87hbJ1zpr2rG81Yv6cIgHJGVwAmLOQFRg5yjxEW1q8QUW8lhfljXEIwbCLwuZN6sry/sxDNrVakxRgxfYhyVisyYSGPNzzaCJUAlNebUO6kTyDOVtfSigNFNQC4fxAR9Y5UfPupE3qyNJuteG9nAQDgocuTFdW8kgkLeTw/nQbJ4W2FtwrduXlHbgWsNhGDw/wRFyJf62sicj/zRkVDr1HhZHlDv5tkbsgsQlWjGXEhvri6bdm0UjBhIa8gFd5mFyuzjmULu9sSUR8ZfbS4Ks2eXPRnQ0SL1YY1W08BABZOHwyNwmrplBUNkYs4VgopcIRFFMV29SvKmS8mIvchFd9uPFiC+pbWPp3jq+xSFFc3I8Rfh1vHxTkzPKdgwkJeYVRb4W1mYTVsNmV1vD1V0YgzNc3QqVWYPDhU7nCIyA1dlhKG2CBf1DS1YsE/9vQ6abFvcmgfXblnaiJ8dWpXhNkvTFjIK4weFAR/nRpVjWYcLVXWtJA0ujI+MRh+Oo3M0RCRO1KrBKz+1TgE+mqx/3QN7lm7Fw0mS49fvymnHMdK6+CnU+PXUxJcGGnfMWEhr6DTqDAl2T56saUtQVAKLmcmImdIjw3EugcmweijQWZhNRb8Y88lkxZRFPHejgI88sF+AMAdE+IR5KfMfcyYsJDXkBKCrQpKWEwWK3adqgLA5cxE1H/2pGWyI2m55yJJS0WDCfe9uxdPbzwCk8WGy4eGY/EvhwxwxD3HhIW8hpQQ7D9d3auhUlfKLKhGc6sV4QY9hkcb5A6HiDzAyEGB+OCBSTD4aLCvsBr3rt2DxgvueZtyyjHnpa3YlHMOOo0KT187Au/eOwFGH61MUV8aExbyGgmhfogL8UWrVcSuvEq5wwEAbDlpH+2ZPiRMUQ2aiMi9jRoUhA/utyctewuqce/avWg0WdDSasWyjUdw79q9qGgwY1ikARsfm4Z7pyUp/h7EhIW8hiAIjlGWn08qY1poq9R/hdNBRORko+OC8M/7J8Gg12BPQRUW/GMPrn9tO97dUQDAvhroP49NQ2qUUd5Ae4gJC3mV6W2JwdaTFTJHApTXt+BY24qlyxS0XwcReY4xcUF4//6JMOjt00M5Z+sRFqDH2nsnYNl1afDRKm/5cneYsJBXmZoSCrVKQH5FI4qqmmSNZVtb0pQea0RYgF7WWIjIc2XEB+O9+yciLsQXc9Ki8M3i6fjFsAi5w+o1Nn0gr2L00WJsfBD2FlRj68lzmD9Jvn4DjuXMnA4iIhcbGx+Mn39/hdxh9AtHWMjrOKaFZFzebLOJ+LlthGU6ExYioktiwkJeR+rHsiO3EharTZYYjpbWobLRDH+dGuMSgmWJgYjInTBhIa8zMjYQQX5a1JssyCqqkSWGrW2rlKYkh0Kn4T9DIqJL4Z2SvI5aJWBain1VjlyrhaTpKE4HERH1DBMW8kqXy1jH0miyILOwGgD3DyIi6ikmLOSVpg+1j7AcKq5BTZN5QN9716lKtFpFxIX4IjHUb0Dfm4jIXTFhIa8UHeiLIREBsInA9tyBbdPffjmz0lthExEpBRMW8lpy7d7M5cxERL3HhIW81vS2dvg/nzwHURQH5D2LqppwqqIRapWAqSmhA/KeRESegAkLea1JSfYlxSW1Lcg71zAg7yktZx4bH6TobdyJiJSGCQt5LV+dGhMTQwCc3zXZ1bicmYiob5iwkFebMVTqx+L6OhaL1YYdbQW+XM5MRNQ7/UpYVq5cCUEQsHjxYsdza9aswcyZM2E0GiEIAmpqai55njfffBOjRo2C0WiE0WjElClT8PXXX/cnNKIekRKHXacq0dJqdel7ZRXVoN5kQZCfFiNjA136XkREnqbPCcvevXuxevVqjBo1qsPzTU1NmDNnDp566qken2vQoEFYuXIlMjMzsW/fPlxxxRW4/vrrceTIkb6GR9QjwyINiDDo0dJqczRzcxVpOmhaShjUKi5nJiLqjT4lLA0NDZg/fz7eeustBAd33Lht8eLFWLJkCSZPntzj81177bW4+uqrMWTIEAwdOhTPPvssAgICsGvXrm5fYzKZUFdX1+FB1FuCIAzY7s3SNgCXs36FiKjX+pSwLFq0CPPmzcPs2bOdHQ+sVivWr1+PxsZGTJkypdvjVqxYgcDAQMcjLi7O6bGQdzhfx+K6wtuaJjMOFdcAON9ll4iIek7T2xesX78e+/fvx969e50aSHZ2NqZMmYKWlhYEBATgs88+w4gRI7o9funSpXjiiSccX9fV1TFpoT65LCUMggAcK61DeX0LIgw+Tn+PbbkVsInA0MgARAf6Ov38RESerlcjLEVFRXj88cexbt06+Pg496Y+bNgwZGVlYffu3XjkkUewYMECHD16tNvj9Xq9o0hXehD1RWiAHukx9iLYLTmumRbicmYiov7pVcKSmZmJ8vJyjB07FhqNBhqNBlu2bMErr7wCjUYDq7Xvqyx0Oh1SUlIwbtw4rFixAqNHj8bLL7/c5/MR9cbs4ZEAgE8zi51+blEUHe34uZyZiKhvepWwzJo1C9nZ2cjKynI8xo8fj/nz5yMrKwtqtdppgdlsNphMJqedj+hibpswCCoB2J1fhdxy53a9zS1vQGltC3QaFSYlhTj13ERE3qJXNSwGgwHp6ekdnvP390doaKjj+bKyMpSVlSE3NxeAvTbFYDAgPj4eISH2m/WsWbNw44034rHHHgNgr0eZO3cu4uPjUV9fjw8//BCbN2/Gt99+2+8fkKgnogN9MWt4JL4/ehYf7j6NP13bff1Ub21pmw6alBQCH63zknoiIm/i9E63q1atQkZGBhYuXAgAmDFjBjIyMrBx40bHMXl5eaioOL8io7y8HL/+9a8xbNgwzJo1C3v37sW3336LX/7yl84Oj6hbd02KBwB8mlnktCZyoijiy0OlAIAZrF8hIuozQRyobWpdrK6uDoGBgaitrWUBLvWJ1Sbi8hc2obi6GX+9dTRuGTeo3+fckVuBu97eDb1GhW1/uALhBr0TIiUi8hw9/fvNvYSI2qhVAu6caB9l+XB3oVPO+dom+9ToHRPimKwQEfUDExaidm4bHweNSsD+0zU4WtK/7sn7T1djR14lNCoBD16e7KQIiYi8ExMWonbCDXpclRYFAPhwT/9GWV7/yT66ctPYWMQGsVkcEVF/MGEhusD8tuLbzw+UoNFk6dM5jpbU4cfj5VAJwCMzU5wZHhGRV2LCQnSBKcmhGBzmjwaTBRsPlvTpHK9vto+uXD0yGklh/s4Mj4jIKzFhIbqAIAiOJc4f7CpEbxfS5Z1rwH+z7UuZF/2CoytERM7AhIWoCzePHQSdRoUjJXU4VFzbq9e+uTkPogjMHh6B4dFcYk9E5AxMWIi6EOyvw7yR0QCAD3ef7vHriqub8PmBMwA4ukJE5ExMWIi6IRXfbjxYgtrm1h69ZvWWU7DYRExLCUVGfLArwyMi8ipMWIi6MS4hGMMiDWhutTpGTS6mvK4FH+8rAsDRFSIiZ2PCQtSN9sW363Zfuvj27W35MFtsGBsfhCmDQwciRCIir8GEhegibhwbC1+tGifONiCzsLrb46obzfhgl73R3GNXpEAQhIEKkYjIKzBhIboIo48W142OAQCs3VGA6kYzbLbOIy3v7ihAk9mK4dFG/GJYxECHSUTk8TRyB0CkdPMnx+PjfUX46lApvjpUCrVKQLCfFsF+OoT46xAaoMPPJysAAIt+kczRFSIiF2DCQnQJowYF4ZZxg/Dt4TLUmyyw2kRUNJhR0WDucNzgMH/MTY+WKUoiIs8miL1t46lQdXV1CAwMRG1tLYxGNusi1zBbbKhuMqOywWz/30YzqhpMqG+xYO7IKKREGOQOkYjIrfT07zdHWIh6QadRIdLog0ijj9yhEBF5FRbdEhERkeIxYSEiIiLFY8JCREREiseEhYiIiBSPCQsREREpHhMWIiIiUjwmLERERKR4TFiIiIhI8ZiwEBERkeIxYSEiIiLFY8JCREREiseEhYiIiBSPCQsREREpnsfs1iyKIgD7NtVERETkHqS/29Lf8e54TMJSX18PAIiLi5M5EiIiIuqt+vp6BAYGdvt9QbxUSuMmbDYbSkpKYDAYIAiC085bV1eHuLg4FBUVwWg0Ou28nozXrHd4vXqH16v3eM16h9er9/pzzURRRH19PWJiYqBSdV+p4jEjLCqVCoMGDXLZ+Y1GI39xe4nXrHd4vXqH16v3eM16h9er9/p6zS42siJh0S0REREpHhMWIiIiUjwmLJeg1+vx9NNPQ6/Xyx2K2+A16x1er97h9eo9XrPe4fXqvYG4Zh5TdEtERESeiyMsREREpHhMWIiIiEjxmLAQERGR4jFhISIiIsVjwkJERESKx4TlEl5//XUkJibCx8cHkyZNwp49e+QOSRG2bt2Ka6+9FjExMRAEAZ9//nmH74uiiD/96U+Ijo6Gr68vZs+ejZMnT8oTrAKsWLECEyZMgMFgQEREBG644Qbk5OR0OKalpQWLFi1CaGgoAgICcPPNN+Ps2bMyRSy/N998E6NGjXJ0zpwyZQq+/vprx/d5vS5u5cqVEAQBixcvdjzHa9bRsmXLIAhCh0dqaqrj+7xenZ05cwZ33303QkND4evri5EjR2Lfvn2O77vy3s+E5SI+/vhjPPHEE3j66aexf/9+jB49GldddRXKy8vlDk12jY2NGD16NF5//fUuv//888/jlVdewapVq7B79274+/vjqquuQktLywBHqgxbtmzBokWLsGvXLnz//fdobW3FlVdeicbGRscx//M//4MvvvgCGzZswJYtW1BSUoKbbrpJxqjlNWjQIKxcuRKZmZnYt28frrjiClx//fU4cuQIAF6vi9m7dy9Wr16NUaNGdXie16yztLQ0lJaWOh7btm1zfI/Xq6Pq6mpMmzYNWq0WX3/9NY4ePYq//e1vCA4Odhzj0nu/SN2aOHGiuGjRIsfXVqtVjImJEVesWCFjVMoDQPzss88cX9tsNjEqKkp84YUXHM/V1NSIer1e/Oijj2SIUHnKy8tFAOKWLVtEUbRfH61WK27YsMFxzLFjx0QA4s6dO+UKU3GCg4PFt99+m9frIurr68UhQ4aI33//vXj55ZeLjz/+uCiK/B3rytNPPy2OHj26y+/xenX2hz/8Qbzsssu6/b6r7/0cYemG2WxGZmYmZs+e7XhOpVJh9uzZ2Llzp4yRKV9+fj7Kyso6XLvAwEBMmjSJ165NbW0tACAkJAQAkJmZidbW1g7XLDU1FfHx8bxmAKxWK9avX4/GxkZMmTKF1+siFi1ahHnz5nW4NgB/x7pz8uRJxMTEYPDgwZg/fz5Onz4NgNerKxs3bsT48eNx6623IiIiAhkZGXjrrbcc33f1vZ8JSzcqKipgtVoRGRnZ4fnIyEiUlZXJFJV7kK4Pr13XbDYbFi9ejGnTpiE9PR2A/ZrpdDoEBQV1ONbbr1l2djYCAgKg1+vx8MMP47PPPsOIESN4vbqxfv167N+/HytWrOj0PV6zziZNmoR3330X33zzDd58803k5+dj+vTpqK+v5/XqwqlTp/Dmm29iyJAh+Pbbb/HII4/gN7/5Dd577z0Arr/3a/p9BiLqlUWLFuHw4cMd5sqpa8OGDUNWVhZqa2vx6aefYsGCBdiyZYvcYSlSUVERHn/8cXz//ffw8fGROxy3MHfuXMf/HzVqFCZNmoSEhAR88skn8PX1lTEyZbLZbBg/fjz+8pe/AAAyMjJw+PBhrFq1CgsWLHD5+3OEpRthYWFQq9WdKsLPnj2LqKgomaJyD9L14bXr7LHHHsOXX36JTZs2YdCgQY7no6KiYDabUVNT0+F4b79mOp0OKSkpGDduHFasWIHRo0fj5Zdf5vXqQmZmJsrLyzF27FhoNBpoNBps2bIFr7zyCjQaDSIjI3nNLiEoKAhDhw5Fbm4uf8e6EB0djREjRnR4bvjw4Y5pNFff+5mwdEOn02HcuHH48ccfHc/ZbDb8+OOPmDJlioyRKV9SUhKioqI6XLu6ujrs3r3ba6+dKIp47LHH8Nlnn+Gnn35CUlJSh++PGzcOWq22wzXLycnB6dOnvfaadcVms8FkMvF6dWHWrFnIzs5GVlaW4zF+/HjMnz/f8f95zS6uoaEBeXl5iI6O5u9YF6ZNm9apHcOJEyeQkJAAYADu/f0u2/Vg69evF/V6vfjuu++KR48eFR988EExKChILCsrkzs02dXX14sHDhwQDxw4IAIQX3zxRfHAgQNiYWGhKIqiuHLlSjEoKEj8z3/+Ix46dEi8/vrrxaSkJLG5uVnmyOXxyCOPiIGBgeLmzZvF0tJSx6OpqclxzMMPPyzGx8eLP/30k7hv3z5xypQp4pQpU2SMWl5LliwRt2zZIubn54uHDh0SlyxZIgqCIH733XeiKPJ69UT7VUKiyGt2od/+9rfi5s2bxfz8fHH79u3i7NmzxbCwMLG8vFwURV6vC+3Zs0fUaDTis88+K548eVJct26d6OfnJ37wwQeOY1x572fCcgmvvvqqGB8fL+p0OnHixInirl275A5JETZt2iQC6PRYsGCBKIr25W1//OMfxcjISFGv14uzZs0Sc3Jy5A1aRl1dKwDi2rVrHcc0NzeLjz76qBgcHCz6+fmJN954o1haWipf0DK77777xISEBFGn04nh4eHirFmzHMmKKPJ69cSFCQuvWUe33367GB0dLep0OjE2Nla8/fbbxdzcXMf3eb06++KLL8T09HRRr9eLqamp4po1azp835X3fkEURbH/4zRERERErsMaFiIiIlI8JixERESkeExYiIiISPGYsBAREZHiMWEhIiIixWPCQkRERIrHhIWIiIgUjwkLERERKR4TFiIiIlI8JixERESkeExYiIiISPH+fyYUJ7nYJS/KAAAAAElFTkSuQmCC" 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" }, "metadata": {}, "output_type": "display_data" @@ -256,16 +255,19 @@ ], "source": [ "from matplotlib import pyplot as plt\n", - "pd.DataFrame(input_data.features[1, 0, :]).plot(title='q-Voltage')\n", - "pd.DataFrame(input_data.features[1, 1, :]).plot(title='d-Voltage')\n", - "pd.DataFrame(input_data.features[1, 2, :]).plot(title='q-Current')\n", - "pd.DataFrame(input_data.features[1, 3, :]).plot(title='d-Current')\n", - "pd.DataFrame(input_data.features[1, 4, :]).plot(title='Ambient temperature')\n", - "pd.DataFrame(input_data.features[1, 5, :]).plot(title='Coolant tempperature')\n", + "pd.DataFrame(features[1, 0, :]).plot(title='q-Voltage')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='d-Voltage')\n", + "pd.DataFrame(features[1, 2, :]).plot(title='q-Current')\n", + "pd.DataFrame(features[1, 3, :]).plot(title='d-Current')\n", + "pd.DataFrame(features[1, 4, :]).plot(title='Ambient temperature')\n", + "pd.DataFrame(features[1, 5, :]).plot(title='Coolant tempperature')\n", "plt.show()" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -276,38 +278,19 @@ "At the `fit` stage FedotIndustrial will transform initial time series data into features dataframe and will train regression model." ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { - "cell_type": "code", - "execution_count": 8, + "cell_type": "markdown", "metadata": { - "ExecuteTime": { - "start_time": "2023-08-28T10:35:27.965798Z" + "pycharm": { + "name": "#%% md\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current_model - regression_with_statistical_features\n", - "Current_model - regression_pca_with_statistical_features\n", - "Current_model - regression_with_reccurence_features\n", - "Current_model - regression_pca_with_reccurence_features\n", - "Current_model - regression_with_topological_features\n", - "Current_model - regression_pca_with_topological_features\n" - ] - } - ], - "source": [ - "metric_dict = evaluate_industrial_model(train_data,test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ "At the end of the experiment we can obtain the desired metric values using `calculate_regression_metric` method. Now there are five available metrics for classification task:\n", "- `explained_variance_score`\n", @@ -326,6 +309,9 @@ "ExecuteTime": { "end_time": "2023-08-28T11:01:34.941934Z", "start_time": "2023-08-28T11:01:34.928460Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -354,337 +340,22 @@ "best_baseline = df_baseline.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" ], "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Could it be done better? Tuning approach" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "metric_dict = {}" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current_model - regression_with_statistical_features\n", - "2023-10-09 17:54:44,392 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", - "2023-10-09 17:54:44,393 - DataSourceSplitter - Hold out validation is applied.\n", - "2023-10-09 17:54:44,403 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", - "2023-10-09 17:58:41,281 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [ridge, quantile_extractor]}\n", - "ridge - {}\n", - "quantile_extractor - {'window_size': 5} \n", - "Initial metric: [10.669]\n", - " 0%| | 0/15 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_scoremodel_params
regression_with_statistical_features0.667481122.11099411.0503847.9825026.2901210.667519281.2859950.500698{ridge: {'alpha': 1.23024974982525}, quantile_...
regression_pca_with_topological_features0.251683274.80460716.57723213.16882411.0664980.25169879.4049360.176296{ridge: {'alpha': 6.152967852376552}, pca: {'s...
regression_with_topological_features0.197249294.79424817.16957313.73014111.8501480.19726573.0425150.141186{ridge: {}, pca: {'svd_solver': 'full', 'n_com...
regression_with_reccurence_features0.040916352.20462418.76711515.52141713.7599670.04092756.8771070.029142{ridge: {'alpha': 0.07955461886043966}, recurr...
regression_pca_with_reccurence_features0.034086354.71288118.83382315.58430813.9144530.03409356.6814680.025209{ridge: {'alpha': 9.982927425974628}, pca: {'s...
regression_pca_with_statistical_features-0.000023367.23854719.16346916.03218414.537957-0.00002354.768756-0.002806{ridge: {}, pca: {'svd_solver': 'full', 'n_com...
\n" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_tuned.columns = list(metric_dict.keys())\n", - "df_tuned.T.sort_values('root_mean_squared_error:')" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "\"{ridge: {'alpha': 1.23024974982525}, quantile_extractor: {'window_size': 30, 'stride': 5}}\"" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], - "source": [ - "df_tuned.T.sort_values('root_mean_squared_error:')['model_params'].iloc[0]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "best_tuned = df_tuned.T.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" - ], - "metadata": { - "collapsed": false } }, { "cell_type": "markdown", "source": [ - "## Even better? AutoML approach" + "## AutoML approach" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -726,7 +397,10 @@ " metric_dict.update({f'run_number - {run}':metric})" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -740,7 +414,10 @@ "df_automl.sort_values('root_mean_squared_error:')" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -749,7 +426,10 @@ "## Compare with State of Art (SOTA) models" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -760,7 +440,10 @@ "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -771,17 +454,30 @@ "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", "df.index = df['algorithm']\n", "df = df.drop(['algorithm','ds/type'], axis=1)\n", - "df = df.replace(',','.', regex=True).astype(float)\n", + "df = df.replace(',','.', regex=True).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ "df['Fedot_Industrial_baseline'] = best_baseline\n", "df['Fedot_Industrial_tuned'] = best_tuned\n", "df['Fedot_Industrial_AutoML'] = 0\n", - "df['Fedot_Industrial_AutoML'].iloc[0] = df_automl['root_mean_squared_error:'].min()\n", - "df['Fedot_Industrial_AutoML'].iloc[1] = df_automl['root_mean_squared_error:'].max()\n", - "df['Fedot_Industrial_AutoML'].iloc[2] = df_automl['root_mean_squared_error:'].mean()\n", "df = df.T" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -802,7 +498,10 @@ "df.sort_values('min')" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -823,7 +522,10 @@ "df.sort_values('max')" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } }, { @@ -844,7 +546,10 @@ "df.sort_values('average')" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } } } ], @@ -869,4 +574,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/examples/real_world_examples/industrial_examples/health_monitoring/heart_rate_monitoring.ipynb b/examples/real_world_examples/industrial_examples/health_monitoring/heart_rate_monitoring.ipynb index dabf40218..372c48989 100644 --- a/examples/real_world_examples/industrial_examples/health_monitoring/heart_rate_monitoring.ipynb +++ b/examples/real_world_examples/industrial_examples/health_monitoring/heart_rate_monitoring.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2023-08-28T10:34:48.354623Z", @@ -40,59 +40,24 @@ "outputs": [], "source": [ "import pandas as pd\n", - "from functools import partial\n", - "\n", "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", "from fedot_ind.tools.loader import DataLoader\n", - "from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels\n", - "from examples.example_utils import init_input_data, calculate_regression_metric\n", - "from golem.core.tuning.sequential import SequentialTuner\n", - "from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder\n", - "from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "The list of basic fedot industrial models for experiment are shown below. We are using simple linear machine learning pipelines with 3 different feature generators: Statistical, Recurrence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." + "from fedot_ind.api.main import FedotIndustrial" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "outputs": [], "source": [ - "model_dict = {\n", - " 'regression_with_statistical_features': PipelineBuilder().add_node('quantile_extractor',\n", - " params={'window_size': 5}).add_node('ridge'),\n", - " 'regression_pca_with_statistical_features': PipelineBuilder().add_node('quantile_extractor',\n", - " params={'window_size': 5}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge'),\n", - " 'regression_with_recurrence_features': PipelineBuilder().add_node('recurrence_extractor',\n", - " params={'window_size': 20}).add_node('ridge'),\n", - " 'regression_pca_with_recurrence_features': PipelineBuilder().add_node('recurrence_extractor',\n", - " params={'window_size': 20}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge'),\n", - " 'regression_with_topological_features': PipelineBuilder().add_node('topological_extractor',\n", - " params={'window_size': 20}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge'),\n", - " 'regression_pca_with_topological_features': PipelineBuilder().add_node('topological_extractor',\n", - " params={'window_size': 20}).\n", - " add_node('pca', params={'n_components': 0.9}).add_node('ridge')\n", - "}\n", - "\n", - "dataset_name = 'IEEEPPG'\n", - "data_path = PROJECT_PATH + '/examples/data'\n", - "tuning_params = {'task': 'regression',\n", - " 'metric': RegressionMetricsEnum.RMSE,\n", - " 'tuning_timeout': 10,\n", - " 'tuning_iterations': 30}" + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" ], "metadata": { "collapsed": false, @@ -101,34 +66,31 @@ } } }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The list of basic fedot industrial models for experiment are shown below. We are using simple linear machine learning pipelines with 3 different feature generators: Statistical, Recurrence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "outputs": [], "source": [ - "def evaluate_industrial_model(train_data, test_data, task: str = 'regression'):\n", - " metric_dict = {}\n", - " input_data = init_input_data(train_data[0], train_data[1], task=task)\n", - " val_data = init_input_data(test_data[0], test_data[1], task=task)\n", - " with IndustrialModels():\n", - " for model in model_dict.keys():\n", - " print(f'Current_model - {model}')\n", - " pipeline = model_dict[model].build()\n", - " pipeline.fit(input_data)\n", - " features = pipeline.predict(val_data).predict\n", - " metric = calculate_regression_metric(test_target=test_data[1], labels=features)\n", - " metric_dict.update({model: metric})\n", - " return metric_dict\n", - "\n", - "\n", - "def tuning_industrial_pipelines(pipeline, tuning_params, train_data):\n", - " input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - " pipeline_tuner = TunerBuilder(input_data.task).with_tuner(\n", - " partial(SequentialTuner, inverse_node_order=True)).with_metric(tuning_params['metric']).with_timeout(\n", - " tuning_params['tuning_timeout']).with_iterations(tuning_params['tuning_iterations']).build(input_data)\n", - "\n", - " pipeline = pipeline_tuner.tune(pipeline)\n", - " return pipeline" + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=15,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "dataset_name = 'IEEEPPG'\n", + "data_path = PROJECT_PATH + '/examples/data'" ], "metadata": { "collapsed": false, @@ -150,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-08-28T10:35:13.321212Z", @@ -165,7 +127,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-10-12 16:35:49,508 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/IEEEPPG\n" + "2024-04-04 13:50:15,094 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/IEEEPPG\n" ] } ], @@ -177,10 +139,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "outputs": [], "source": [ - "input_data = init_input_data(train_data[0], train_data[1], task='regression')" + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" ], "metadata": { "collapsed": false, @@ -203,19 +166,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "outputs": [ { "data": { "text/plain": "(1768, 5, 1000)" }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "input_data.features.shape" + "features.shape" ], "metadata": { "collapsed": false, @@ -238,44 +201,44 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "outputs": [ { "data": { - "text/plain": "
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" 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" 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" 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" 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" 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" 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" 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" 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99xwAZySen5+PP/3pT7j77rv99l+xYgWeeOIJ7Nu3DyqVqlOD1Ov1SE5ORlNTE6uYEhERERFRhwmCgOLiYlit1g41iu9pBEFAS0sLqqurkZKSgn79+vntE0o8FVIG0WKxYOvWrVi6dKm0TS6XY+7cudiwYUPA63z++eeYPn06br31Vnz22WfIzMzE5ZdfjrvuuitsaVAiIiIiIqJAbDYbWlpakJubi/j4+GgPp1vExcUBAKqrq5GVldWlOCukALG2thZ2ux3Z2dle27Ozs7Fv376A1zly5AjWrFmDK664Al9//TUOHTqEW265BVarFffdd1/A65jNZpjNZul3vV4fyjCJiIiIiIgAAHa7HQCgVqujPJLuJQa/Vqu1SwFit+dXHQ4HsrKy8NJLL2Hy5MlYuHAh7rnnHqxYsSLodZYvX47k5GTpX35+fncPk4iIiIiIerGurs2LdeF6fCEFiBkZGVAoFKiqqvLaXlVVhZycnIDX6devH4YPH+4VxY4aNQqVlZVBqwgtXboUTU1N0r+SkpJQhklERERERESdEFKAqFarMXnyZKxevVra5nA4sHr1akyfPj3gdWbOnIlDhw55VQw6cOAA+vXrFzTNq9FooNPpvP4RERERUd/25m/H8bdPCuFwhFRjkYhCEPIU0yVLluDll1/G66+/jr179+Lmm29Gc3MzFi1aBAC4+uqrvYrY3Hzzzaivr8ftt9+OAwcO4KuvvsKjjz6KW2+9NXyPgoiIiIh6vXs/LcI7G4vx08GaaA+FKGKef/55DBo0CFqtFtOmTcOmTZu69f5C7oO4cOFC1NTUYNmyZaisrMTEiROxatUqqXBNcXGxV+nY/Px8fPvtt/jLX/6C8ePHIy8vD7fffjvuuuuu8D0KIiIiIurVPLOGNXpzG3sS9R6h9qAPh5D7IEYD+yASERER9W1NLVZMePA7AMCD543B1dMHRXdA1GOYTCYcPXoUgwcPhlarjfZwQhJKD/q2Hme39UEkIiIiIoqGplar9POyz3YjOU6F8ybmRXFE1FMJgoBWqz0q9x2nUnS42mhnetCHAwNEIiIiIop5ngEiANz+3g4GiNQprVY7Ri/7Nir3vefBeYhXdywE60wP+nDo9j6IRERERERd5RsgElH3YAaRiIiIiGIeA0QKlziVAnsenBe1++6ozvSgDwcGiEREREQU8xggUrjIZLIOT/OMJs8e9Oeffz4Adw/6xYsXd9v9xv4zQ0RERER9XqAA0WZ3QKngiinqvZYsWYJrrrkGU6ZMwdSpU/H000979aDvDgwQiYiIiCjmiQHi5dMG4J2NxQAAvcmGtAR1NIdF1K3a60HfHRggEhEREVHMK21oAQBkJ2mRqFHCaLahqdXKAJF6vcWLF3frlFJfzMkTERERUUxbd7AGX+6qAAAkxymRHKcCwHWJRN2BASIRERERxbQtxxqkn2cOzYDOFSA2tliiNSSiXosBIhERERHFtBaLDQDwh1OGYFh2EpLjnKukmEEkCj8GiEREREQU04xmOwAgwdWaQJxiqmeASBR2DBCJiIiIKKY1m50ZxASNs8k41yASdR8GiEREREQU08QAMVHjzCCmJ2oAALVGrkGkjhMEIdpD6FYOhyMst8M2F0REREQU05pdaxDjXQFidpIzQKzSm6I2Juo5VCoVZDIZampqkJmZCZlMFu0hhZUgCLBYLKipqYFcLoda3bXWLwwQiYiIiCimNbvWICa6pphm6bQAgGqDOWpjop5DoVCgf//+KC0txbFjx6I9nG4THx+PAQMGQC7v2iRRBohEREREFNOkNYiuIjXZOmYQKTSJiYkYNmwYrNbeuW5VoVBAqVSGJTvKAJGIiIiIYppRKlLjPHTNSnJnEAVB6HVTBql7KBQKKBSKaA8j5rFIDRERERHFtBaLq82FK0DMdK1BtNgcrGRKFGYMEImIiIgoZgmCIBWpEdtcaFUKqdUF1yEShRcDRCIiIiKKWS0WO8TuBGKbCwDIcmURq/UMEInCiQEiEREREcUssUCNTAbEqdzrxxK1zmCxxZVdJKLwYIBIRERERDHL6FHB1LMYjRgstlrtURkXUW/FKqZEREREFHOqDSZ8tr0cI/slAXCvPxRJAaKFASJRODFAJCIiIqKYc/1rW1BY1oT+qXEA3BVMRVo1M4hE3YFTTImIiIgo5hSWNQEAShtaAXgXqAGAeE4xJeoWDBCJiIiIKKZYbA6/bfFqnymmrt9NnGJKFFYMEImIiIgophyoMvht880gimsQWwIEiOsO1uD297bj3U3F3TNAol6MaxCJiIiIKKYcrjH6bfNbg9jGFNP7Pt+NIzXN+GxHOX43IdfvukQUHDOIRERERBRTGlusftt8g7z4NorUVDWZ3D/rTX6XE1FwDBCJiIiIKKY0tfoHiH5TTMU1iD4BotFsQ7PHtNNqg7kbRkjUezFAJCIiIqKYEihA9C1Sow2yBrHaJ2N46Uu/BVzTSOTrgy0l+L8PdsJq9y+S1JcwQCQiIiKimNKhDKK4BtE3QAyQMbz85d/CODrqjQRBwB0f7sKHW0uxdn9NtIcTVQwQiYiIiCimBAoQg61B9J1iGmjNYa3REsbRUW8k9tsEnMFiX8YAkYiIiIhiSkcCxLggVUxrgqw5NJj8b5NIVFjWJP1s4RRT6qynvtuP6ctXY+X6o9EeChEREVGvoQ8UIPquQQxSxTRY1dLd5fqA2y02B65ZuQnPrj7YmaFSL+EZIDabbVEcSfQxQOwCk82BiiYTyhtb29+ZiIiIiDpEzCB6rjsMmkEMsgbxsqn5kMnc24vrWwLe1w97q/DTgRr88/sDXR439VxHPHpvNpv9W6f0JQwQuyArSQMAqGL5ZCIiIqKwEQPEGQXp0jbfIjVSH0RL4AziSUPSsXHp6ThzdDaA4Fkhz4qVvusZqe/wLG7EDCJ1WpZOC8C/nDIRERERdY7V7pBaV5w8LEPa3tYaRM+iIuKBflaSFlk6LVLiVQD822EEEmz9IvV+1Xr3a2+0MEAM2fPPP49BgwZBq9Vi2rRp2LRpU9B9X3vtNchkMq9/Wq220wOOJWIGkQ1YiYiIiLw5HAIu+c8G/OGNLSFdz7NAzfQh7gyiXx9E1+8OATDbHDBZ7Tj7mXU4UtMMAMjSOY/TxMDSGCQr5Hl/wdYvxprF72xDwd++xsh7v8F7m4qjNo7HvtmH055ci8aW2K0Su3L9UUxfvtprCqkvQRBQbXC/9i2cYhqa999/H0uWLMF9992Hbdu2YcKECZg3bx6qq6uDXken06GiokL6d/z48S4NOlZkM4NIREREFFBJQws2Ha3Hd3uqggZngRhNzn0T1AoUZCZidD8dCjITkJ6g9tovUa2UsoilDS3YVtyAPRXuQjTicZo4NTXYtMGmFneA2BNO+pttdny5qwJ2hwCT1YH/bSmJ2lhW/HQYR2qb8e6m6I2hPQ9+uQcVTSbc9/nuoPs0tFhhtbuz0JxiGqKnnnoKN954IxYtWoTRo0djxYoViI+Px8qVK4NeRyaTIScnR/qXnZ3dpUHHCjGD2Gyxh/TBR0RERNTbOTxayYVyMl2sShqnVkIul+HLP52MVX8+BUqF92GrXC7D6FwdAGcFSt/CImJgGK8WA8TAWaGelkH0nQa7p0IPWxTaMjg8XuCesHbzUHXwDKJn9hAInm3uK0IKEC0WC7Zu3Yq5c+e6b0Aux9y5c7Fhw4ag1zMajRg4cCDy8/Nx3nnnYffu4BF8T5KgUUofPswiEhEREbl5Fo+p0nc8M+cOEJ2HqXK5DCpF4EPWcXnJAIDCUn3Q4C5R48wyBssK1RjdYxOnp8Yy8bnMS4lDgloBk9WBvRUGAM6pkmZb+IM1u0PwC0IrPJ5vR4w2lvdcm1rRZPL63ZPv+7OZaxA7rra2Fna73S8DmJ2djcrKyoDXGTFiBFauXInPPvsMb731FhwOB2bMmIHS0tKg92M2m6HX673+xSpxfnsoH3xEREREvZ1nf0LfDE1bTK7AUpw+2paxrgCxqLwp6Ml6cQ1ioIP+5d/sxWc7yqXf3/ztOH49XNvhsUZDjeu5zEnWYkyu8/Gf+9x6NDRb8NCXezHhge/azJaFymZ34Kynf8aCf6+H3ZU13FHSiJmPrZH2qWuO/hpEg8mKkx9fg+te2yxta/Lpp3n5yxsDXtf3vcM2F91s+vTpuPrqqzFx4kSceuqp+Pjjj5GZmYn//Oc/Qa+zfPlyJCcnS//y8/O7e5idJs6Hb4jhxblEREREkeaZQawO4UR6SwgB4uCMeABARVOr1/rBB88bI/3cVpGa//x0xG/bb0fqOzzWaBCTEllJGvx+Sn9p+95KPVb+chQmqwPLv94btvvbX2XAwWoj9lcZUOvKtj67+qDXPqG8vt1ly7EGlDa0Ys2+amldqW8CZ8OROhhMVr/riu+d/qlxALgGMaQAMSMjAwqFAlVVVV7bq6qqkJOT06HbUKlUmDRpEg4dOhR0n6VLl6KpqUn6V1ISuwtfk+OcpZN9z1AQERER9WWdzSC6p5i2HyBKx2EtVmmK6WMXjsPV0wdJ+yS41iC2V5nyxEGpANwZulglPs5snRa/n5KPEwakAAD0Hsei5U3hewy7y90z+cRAMN6n5Ugor293KSxrkn4uKnf+LD5XI3OSkJvsLFrk+XhEYgZxcEYCAAaIIQWIarUakydPxurVq6VtDocDq1evxvTp0zt0G3a7HYWFhejXr1/QfTQaDXQ6nde/WKVjgEhERETkxzNA7NQaxA5kEMXjMIPZhkrXfYjVS0UJrjWInhnEphYrdpQ0eu03NCsp5LF2RlOrFTtLGrGvUh90TVxbxGxXpqtYohgke2ZQ91bo0WqxY0+5PmBvR7tDwO7yJq/L9Car3/GsIAj4aKt7WViwQLC80YSDVYZOPZ5w8QwQxZ89nytpOrLHfiLxNR/iChBDKVJzpMaI43Wxv3Y1FMr2d/G2ZMkSXHPNNZgyZQqmTp2Kp59+Gs3NzVi0aBEA4Oqrr0ZeXh6WL18OAHjwwQdx0kknYejQoWhsbMQTTzyB48eP44YbbgjvI4kSZhCJiIiI/Jk8pphWhlDMz9SJDKIgAIdd6+7EwEmU6LMG0WS14/Sn1qLW6L08SJyu2p3ZMIvNgTP/9ZMUkNx51gjcMntoSLfhmUEEgJR453Kng1Xe6w5HLVsFAFAr5Vh/1xxkJbkD50e+2ouVvxyFViXHz3fOQXqCBmf962dYHQLW3TkHWldw/sLaw9h41D3lVhy375q9WqMZZ/zrZ9x37mgsmjk4pMcTLkUBAkTP52pgWjy+21MVMINYZfDJIFrscDgEyOWyNu/zm8IK3Pz2NgDAq4tOxJwRWV1/IDEg5DWICxcuxJNPPolly5Zh4sSJ2LFjB1atWiUVrikuLkZFRYW0f0NDA2688UaMGjUKZ599NvR6PX799VeMHj06fI8iihggEhEREfnzzCDur+x4dklcg6jtQAZRo1RAq3IezlrsDshkwCDXQb5InA4pTjHdXd7kFxzOH5uD6UMyAHRvBvFAlcHr9n8+UBPybYhZvyyfDOLBakPA/S02Bw5UegePPx903q/J6sDmow2oM5pR3mRCjcGMfZXu2/Ednxg8e2Yrx+TqoNM6n+M9AYKvSKg1mlHhMa1WDBY9n6v8tOAnAMSps2PykqFVyWF3CDjagazgTx7PT2dey1jVqSI1ixcvxvHjx2E2m7Fx40ZMmzZNumzt2rV47bXXpN//9a9/SftWVlbiq6++wqRJk7o88FjBAJGIiIjIX4tHBrGp1YrShtYOXU8sbhPfgQwi4D4WA5xTBBN91sclutYgWuwOWGwOFJY2+V3/xSsnIzvZGXDVGs3d1ldQzGxlJDrvq6hM79VPsCN8M4jiNFsxgzh5YCqSfJ4DzxYgRrMNh2vcAWNhWZNX0FpY2gjA2edQzLbNH5vjuh2z1+39+H+z8dVts3DnWSMBAI1ROh4Wn1cxaD5e14KmVqvXcxXsmF0QBCmQ7Jesxah+zqVtgaaiBrtfAH7vq56s26uY9nbim03PAJGIiIhI0urTPL2wAwfcgMcU0w5kEAHvAFHsi+hJXIMIAMfqmrHLZxxiwJCeoIFCLoMgwG+fcNnlCiIumJQLjVIOo9mGY+1kqkxWO7Yer4fd4exx2OCq0OmbQRRbTWTrNFAqvKdGihm/kvoWvPXbcXgmc1f8dBhHar0DRgA4WtcMo9kGrUqOGQXpAICPtpZiT7leCv59x9DUasWecj1W761CSwR7CRa5ntfpBenIT3NWIt1d1iQFiFlJmqB1QxpbrLC4TghkJmk8emu63wOtFju2Hm/wCuaP1jZ7TVfdXa7HtuKGkNYvxioGiF3EDCIRERGRP5NPgNjR6YehFKkBvAPEsQECRKVCLt3WFa9s9MsMiYVJFHIZ0lztyy5+8dduKbgi3vfE/FQpUxVoTZynh77cg4te3ID/rj8iZbrUCjlS4p2P2/PxA0BWkhaTB6Z6bavSm2C1O3D+87/gsW/2AXBmy0S3v7dD+rmwzDke8fUa1U+HPFf7B4vdgbP/vQ4AkKRRSi1ExDHsKdfjnGfX4frXt+ChL8PXaqM9eyqcYx2Xl+wO8MqapMA4yzOD2OJ9zC6uP0yNV0GjVEjvIc/X5bFv9uKiF3/F10XOZXSCIGD+Mz8DcK5xjVMp0Gq148IXfsU1Kzd118OMGAaIXcQAkYiIiMifOFVU4Sr0IfbQ6+j1tJ2YYhoogwgAt84pAOBck3bANRXznRumYf7YHDx3+QnSfred5iwY4xC8p8iGg8XmwH7X+r5xeckYmO7u4diWtzcWAwAe/XqfR8CjgUzmfF59A8SCrEQ8dtF4nDcxFxdMygMgPm4D6potUCvlmDk0Hc9dPkkKjj0drDLAZLWjvNE5roFp8ZhRkIEEn9fjz2cMl34Wx2A02yAm2X47UtfOMxI+4lgHpMVLAd6usiZpbWFWkkYao8Fs88oEVvtUvx2Y5v+6/HrY+VgOuF4/o9kGk9WZdbxr/kgpawkAW483hPnRRR4DxC5igEhERETkTwywclwH3h09VmoJMYNotbsP9scECRAXnzYMQzLdwVBmkgYzhmbgxSsnY3Suu53alScNhMo1PTPcx3YHqgyw2B1IjlMhPy1OCkhCKYpT7TFlUuQbII7PS0ZGogbPXDoJZ4zOdt2HScpeTh6QirdvOAmTB6Z5Bccim0PAvkp3MZ1snRZalQKfLZ4p7TMyJwnXn+yuVuo7BsA5BVMfoCl9d/Acq3iS4JdDtdLU0SydxqvircFk87iu8zkVq996vi6CIKDF4l6zKd6P+N5QK+W46qSBfq1VerqQ21yQN88AsSPlcImIiIj6AnGKabZOg7LG1g4HXKYQi9SUNLRIP/sWqPE0Li8ZR2qapZ8DkclkSI5TodZoQWOLFbkp7szQsdpmVOlNmDYkPeB1HQ4BXxVWoNViR0FWAtQKBcb1T8bBKgN+OVSLY3XOcY7N00Emk0lBXnWAPoWAM5Nn8FnP9sUu5xRHz4DEMzhTymUYkZMk/S7ex5HaZmnK57j+7seepfNuCSJyTs90BaOu+xqckShd7vvaBAoQAWB3mR4WuwMjspOQk9w9QZTDIaDG6A4QB7gygI2uqaQprqmjAKSpoE2tViTHe/ePFJ9T8TlptdphNNuwv9IgZUXF6ajibYuP27e1Sk/HALGLdD5nI8Q3GxEREVFfJq4lFAODjgaI4vU60uYCAMbkOgM/38qdvsb3T8FnO8oBBA8QAeexXa3R4jfeG97YgsM1Rnz351MwLDvJ73prD1TjT+9u99q2c9mZuOq/m7z6QI7Jdd53lpSp8m+74HAIOONfP/tt/8oVIHoGJOmJaunnETlJXs+bGPTUN7vbeng+9rR4NZRyGWyuCGhkThL2VRpQVOo9PRNwTxUG4Bck63wCxCGZCThS04x/fX8Am47VIz8tDuvuPM3v8YRDXbMFdocAmQzISFRDqZBjQFo8iuudAXm/ZHeQnxynkgJEkW9WNl6tRJJGCYPZhiq92au4kviciMUpxQDRN4PY05NGDBC7SKtSIEmrhMFkQ43RxACRiIiICO61hDk65wF6Y0toAWJHp5jeu2AUMhM1uOKkAW3ud/Hk/jhe1wybQ8CVJw0Mul+g5UO1RjMOVTunGW4+1hAwQBSzk562lzR4BYcApH58YkBSEyCDeKS27cqmqfHuoDAjUYNl54zGrtJGXDbV+znIS4lDglqBZtdr0S9Zi3ljcqTL5XJnxlSsgHrx5P54+Ku9KCxrkqqQegY/X/7pZHxTVIHbTh/mdT8Kn2Do/Il5eMoVHAJASX0rbHYHlIrwr24TM53pCRrp9h8+fyw+2lYKALhkSr60b3KcCpV6k3eA6JNBBJxZREONDdUGk3eA6LqvJp8AMcsng2i02KDT9tyYgAFiGGQlaWAw2VCtN2Nolv8HBhEREVFf0yJlEJ0Hzx1tCSYGlnEdnGKapdNi2bmj290vOU6FB88b26H9AO/xevW7C9ICI1Am8Md91X7bsv3Wuvlfz7fSam6yFg0tVil49p3SeZ3HekBPcrkMo/rpsMVVOOX166ZCrfQO0hpa3NnFs8bm4OGv9uJAlQHiyk7P4GdsXnLASrGelHIZJuSn+G0/VGPEyByd/xW6yDfTCQCnDM/EKcMz/fYNFPy7eyW6r5+VpMXhmmZU681e7S7qmi2w2h1+AaJvBrGpxdqjA0QWqQkD6Q/c4P8H3hlFZU34ZHtpWG6LiIgoUg5WGfD4qn34bEdZtIdCMUBcSygeJxnMNtg70BReDII6ugYx3AIFEUUeQUKwBuqB1hK+vuG43zZxaqkY0LRY7H6983yD0EyPNg2eY+yIBI+ptwWZiX6Xe74keSlxSI1XweYQpNcq2DrFYAakxQecwhvuRvL7Kw34YEtJwAAvmEC9EMXXLTPJHeSJt3WsrlkqUAM4l5TVGs1+AaLv69HTi1cygxgG0iLjEKpQteWcZ9cDAFLi1ZgzIisst0lERNTdln22Gxtcpe1PGpLe6yr7UWhard4BIuDMyqUmqINdBYC7+qlYWCTSUgIEEZ4B275KPcw2u9/4AmUCAxGDjwSNEokaJYxmG6r0JiR6BG++AWJ+ahxMFrs0XTWUAPH0UVn46UANEtQKv6mgADBrWAbWHazF3FHZkMlkmDQgFWtcmc+MRDXi1R0LF1LjVWhoseL8SXlIS1AjPy0OJfXuVhFFZU34vcd0z66a97RzjabYqqMjRXDSEpzPmzit12p3SK+b5/WzXT9/v6cKDsF5rK+Uy1DeZEJpQ6tfgOhZzAjoeLY8VjFADIPOlCkOpsFjEfHWYw0MEImIqEewOwTsLG2Ufi9taGWA2MeJVUwTNUrEqxVosTiLg7QbILqyaW1VJO1OATOIHgGb1S7gQKXRqxoo4M5EPXjeGGhVCnxTWIEf99f43X5GosdURp0GxhrnMiUxu+dwCFKT+utmDkacWo5LpuTjjg92uccYQs2LK6cNhEohx0lBqq/+85IJ+Gx7OX4/pT8A4N5zRmNkThJsDiGk49APbpqOnw7U4urpzvWd/750ElYVVaJKb8KnO8qDTs3tDHF9JOBer9mR6avDXWtHd5c7x3KgygCrXYBOq0SuR4A4ynVbu12vw7i8ZAgAyptM2F3W5BcgDs5IwDOXTsTt7+0AwAwiwV1JqjoMU0yLyt1/PMfq2l6gTEREFCuO1Bi9GovXhGnZBfVcZpuzB51WpUBynEoKENvicAhSQZWEKAWIvtMQ64xmlDc5388T81Owo6QRhWVN/gGiK1Ewc2gGCjITMXtEJqY+strv9lUehVqykjQ4UtPsdQx5tK4ZRrMNWpUcfzt7pFR4RdfJKaZyucyveI2nrCQtbjxliPT74IwE3HnWyA7fvmhoVpJXLY5JA1IxaUAqDtcY8emOcuyp0IetUI0YQHsam9d+gChOfRUDfvH/sXnJkMnc2VXfdZZjXQHimn3VKCxzZpAB79fhvIl5+HxHOVbvq2aASO4MYjimmHqeXflyVwWuPKku6BkfIiKiWPDaL0fxdWGl1zZxVs27m4qRoFHidxNyozE0iiIxg6hRypEcp0JFk6ndA2exsA0Q/Qzi5zvLcce8Ebj9PWfriiEZCThpSLoUIHpqNtukdYRSP72k9jPo4r6/HqrDuoO1MNvcUx5H9dN5BVO6OPfzEUqAGG2D0xOkqbRioZrv91Rhe3EDFHIZThuZhUkDUkO6Td/nXy4DRvdru3gOAIzOdQaR5U0m3PbuduypcGcIvcackSBlvcXLxaWa3xRVSNt9X4dA2eeeiAFiGGSFM4Po84Zf/vVefLb45C7fLhERUXeoaGrF/V/s8dtepTehpL4FSz8ulA4Co3XAT5EnCIJXBjFQcZBAxOmlchmgVUWnlmL/1Hjp5zs+3IltxY0AgMkDU6UA42CVwes6pQ3OtXZJrnWFooLMBByuacaYXB12l+sxKD3e63riMeT7W0r8xjFloHfQpJK7n4+eFCDK5TKMztVh09F6FJY2YWSODje+sUW6/Nk1h3DssQUh3aZvgDgmN7lDVW+TtCqM6qfD3go9Pt9ZLm0XX1eRQi7D5IGpWHewFgAwIT9FKtrjOVOif6r32sOOvs9jHT+pw8BzDaIgCF4p6lCJb/gzR2fjuz1V0pQGIiKiWLTLVZmwf2ocFs8ZiuP1LXhx7WFUG8zSZXbXmqqpg9OiOVSKIDE4BACNSt7hzIqYhUtQK7t0PNUVJw1Jw/j+ydhV2oSiMvdUxqVnj8LRWmdFS9/K9eLx26h+3oHGe3+Yjs3H6nH6qCys2VuNyYO8gz7fdbqLZg7CgLR4aFUKLBjfL+gYtR3sERkrxuUlY9PRehSVNeHCE/r7XR6o6E9bxITKn04bioxEDWaP8G9pEcyLV5yAD7aW4PkfD0vbfIvMAMDjF43HD3urMDw7CZlJGgiCgIxEDWqNztkR54zv5/eZNm9MDgamx2NigDYfPQkDxDAQy/+2Wp1lipM62fekodkiVXu686yR+G5PFeqM5m5rLEpERNRV4oHajIJ0XDp1AD5wZUKq9Ca/3nEMEPsOzwBRq1R0OEBsNkd3/SEAyGQyLDwxH7tKm6SA9exxOUhLUKPZ7F5W5JkU8FzL5ikzSYOzxzkDvfnj/AO+TJ8G67edNqzdIj49kTiFs7CsCfUeBRlF+ysNGN8/pUO31WKx4VC1M1C/6qSBUtuQjhqUkYBrZwz2ChB9G90DzqDx6umDpN9lMhlG5iRh/SFngHjXWSP9TmJML0jH9IKevzSMAWIYxKuVSNIoYTDbUKU3Bw0QX/vlKL7cVQEASNQq8cDvxmBgegLW7q/Gi2sPQ29yfggNTI/H4IwEyGXO3jR1zZZOVYKz2h24+6NCzChIx0WT/c/WEBERdZUYBIoHgOL31bqDtdhwuE7a7/kfD2FfhR43zS7Ao1/tlQKFAWnxaLXasfi0oXj1l2M4aUg6LuZ3Vo9ndq0llMkAlUIWsPl8IFIGURPdDJnvFE5xPaEY0JltDuhbbTCYrXj0673SGtxx/UNrBO95fJeXEtdmcBilhGpYiIHztuJGLPzPBr/Lf/fcLyh6YF7Aaegv/XwYJqsDt50+DA6HgMtf3ii1ngg1OBSl+zzPHVkvCgBqpTth4zu9tDdhgBgmmToNDDU2VBtMGJrl34RUEAT849v9XvOW395YjL+dPQov/XwEG4/WS9unD0mHQi5DZpIGVXozqvSmTgWIn+0ox0fbSvHRtlIGiEREFHaCIPhlTjy/A20eHbjrmy34YGspPtha6nUbW443AAC+KXIeYH+4tZQBYi8grT9UKiCTyULIIEa3xYXIL0B0zRYTK7I2tVpRZTDhqe8OYNVud4GmQA3i2zIkMwFKuQw2h9Bu5uncCbl4b3MJhmQmhHQfsWBIRgIS1Ao0W+xSWwpfXxdW4BKfPol1RjMe/XofAOB3E3JhMNmwo6QRALqUqZP79IPsyPpFALjh5MFYs68as0dkRm0KdCRw3mKYZCe1XclUb7JJweE1rv4wha61GQ0tzg/L204fhv9eMwX3njMagPtsRmero3o2bBUEoY09iYiIQlfRZEKt0QKFXCatvcpNicMTF4+X9gnQlxsAcPm0AVAGu5B6PLENgMZVaKbDAaKrv11Hm7N3F98AMdsjwyQ2uq/Wm1Fc3yJtj1crMDjDP0nQlqwkLb6+fRZeuXoKHvjdmDb3nTk0A18sPhmf3jozpPuIBXK5DGNyvYPnYVmJWHfnHIzMcbbG8C3UCHgXoykqb8KuskYAzmzqw+eP7b4BBzFjaAZW/XkWnr1sUsTvO5IYIIaJeGYpWCVTsR+UTqvEJSc6z44UlTdBEARpusVpI7Nw+qhsad69+AHkuxC6ozyDQjEIJSIiChfx4G1YVqJX0YzzJuZJPwdbdnHhpDyvhuGe7A6e1OzpTFZ3BhEIIUCMgTWIQPAMIuA+gV+lNyE9Ue2xXQNFJ056DM9OwtzR2R16zOP6J0PXyVoX0ea7PjMnWYv8tHjcPLsAgH9lUsA7aCwsa5J+v+nUgk7X/OiqkTm6qN13pHCKaZh4VjL1tbu8CQv+vV7ab3h2EtRKOQwmG47XtUgflv4fRs7bvOeTIswdld3uNFOHQ8Did7chW6fFfeeOQX2z+0N4zpNr8c3tswJWaSIiIgpm/cFaPPHdfjx6wVgpA2CxOfCX93fg6yLnunrfaXWe63SCHTCPztUhW6dBpd7/JKi+1dorC3X0JZ3OIEpTTGNrDaLnMViWxwl8g6t+BBD8vU5OuSnex7G1RmexGjFw3Fuhx1e7KrDip8Ow2h1QK+VSJWTAGSyK759Qp/JSaJhBDJMM1xmkOqN/gHjfZ7uln7N0GqgUcoxypdN3lDRKC7J9P4wmelRz+qawot0xHK9vwdeFlXj1l2NoarF6ZTObWq1Y9llRxx8QERERgBve2IydJY248pWN0rY1+6rxVWEFxIkq4/r7H6z935nDAQD//P0E/MNjyikATBuchni10q+Co6in9xAj/wxiR/vDuYvURDeH4Zsh6pfsDm76u062H61pRo3Bfdz3UBSmPPYk507IRZLH6/rXM5yfEYPTE6BWymGyOnDrO9tQWNaEfZUGr+AQALYdb8S+Cmf/yXAEiOLrdddZI7t8W70NM4hh0taZMc9Sz+Ic9rF5ydhZ2oRfD9dKl+m03i/H76f0x88Ha/DlrgoUevThCcZkdRfAKSxr8lu7eKQm8KJgIiKiYMQDfc+lCsfqvL9PfKeOAcDNs4fikhPzkZWkhSAIOHV4JhI0ShypMWJ4tvMkqcqjhdMnt8zAFa9sRIvFzgCxF+hsBrHFEhtFanyzgZ4B4xiPlg3iyfjPF8/scJuGvipbp8W6u+ZArZSjscUqzWqTy2XIStKgtKE14PVunl2A/647ilbXcW5ynCosFUSvnDYAp4/M8gr+yYkZxDBp64NvYHq89LNYNUk88/HLIWcJ8ESN0q/XoUwmk9ZxBFq460v8UAWcH1q+axfFs3JEREQdletx8CSuDfRdKzS6n39pf4VcJq3VkslkyNZpkahRYnz/lIBNvicNSMWANOf3JQPEni/YGkSDydbmGlOjaw1itIvUePINRsRjuH2VBljtzscyMie09hZ9VUq8GvFqpd+Sp7aWUZ04KBUjXDPvAOfzH44KojKZDLkpcb26GmlnMUAMk45OnRCDOPFsa1mj82yJ7/RSkfghtL/KgJH3foN3NxUHvW3xQxUAHl+1D8frWrwurzaYvaZCEBFRbLE7BFz60gZc++qmmKk+7dlnbMG/13m1thAFCvg6wrfUfEezTACwqqgSJz26GhuP1LW7L3Uf8T27yOc9GyyDCAAGU/DXtzlG+iB68jzRDzinm3r20UtLUHutu6XQBWpU775M6zVLIdCMBQovvpvDxP2l5p+la/bI3N186lAAwIicJCR49FzRBQkQs3UaTB6YCsB5Nu6DLSVBx9DcgQzh8TpOMyUiilV7K/T47Ug91u6viZksWqtH/959lQbUGM0o8Sjt35X1O3+ZOwxalRx/OGUIgNACxJve2opKvQk3v72t0/dPXSe+Z3/cXwO9xzGQmEHUuDKIaqVcCqqOBumDBwANLc7CJcFOnEfSDScPhkYp92s/IZPJcPa4ftLvs4ZlRHpovU5bGcRsnRbzxmRDIZdBpZDhjNFZERxZ3xQ7+fseTvwg07daIQiCV7q62fXl+q+FE6SF/CqFHJvumYsx930LAGi1BA7uZDIZPvjjdGwvacBFL27Ango9bHaH33RUIPAU0p33nQmtSo4rXt6ILccbUM0MIhFRzNpb4V5vXm0wIyU++pU8m32+n/ZWGOAQnP0NDz5ydpcqNw7NSsLO+86UgohQAkRRfbOl0/dPXef9njUhOd75Gpqt3hlEwLl27+cDNSgqa8KkAakBb0+sn9Be5fZI+Ps5o3HHWSOk96enB88bg9vnDoNDEJAZpF0LdZxnwaocnRZj83T4YW81ACA9QY3ZI7KwY9kZkMlkUV+f2hcwgxgm4peaxe6QzpqJxMye7xe9Z4Wu4/Xe00E9yeUyTMpPRaJGCZPVgcNBis0EyiAmx6mgUSo82nB0rqci9Uxmmz1mpqkR9TTiFLlIMZpt2Hq8Qfo9Vj6vxe8Wsam9OL00s5M933x5HnyL36Xi8ouOstod7e9E3cJzurFnqy+TzXsNIgCMy3Ou0wvU704kFn2JhQARQMDgEHCewM9I1CArScs1bGHg+Xpn67wDbnEqepJWxeAwQhgghkmiRil9Ufqe+XT39An+ppa38+Eil8swOtf5wbqrtDHgPi0W74OZOI81IeKZmQe+2IOnvtvf5n1R71BjMGPqI6vx5/d3RHsoRD3O4RojJjzwHR76ck9E7m9bcQMmPfgd3tvsXkZw90eFGL1sFQ5WGSIyhmDExuVDMhMAAE986/wOEQvQhJMYIL6zsRiD7v4KV7zyW4eut+Df69osfELdxzPY82yvZRanmHpkEMflpbiuE7gyu9lml6rltrUmjXofz9c7M0nLQDDKGCCGiUwmk9pU+AaIYvGYhAAVud65cRqydRq8cMUJ7d7H0KxEAEBJkDLA4hTT4dmJyEuJw6uLTpQu8zwz8+81h9q9L+r5PtpWiqZWKz7bUR7toRD1OM+tOQST1YH/rj8akfv7fk+VVA1RVNbYihaLHf/4Nnon9Sw2Byyu7NzgjASvy3zP8ofDKcMzvX7/5VCdV9AhavGZ9nqgyhgzazb7muJ69zGJdwbReezjmUEUj2NKg8yaEgvpqRVypMRHfw0iRc7EASkYkpmAeLUCC8bn4K9njkBeShzuOXtUtIfWJzE8D6PkOBUaWqx+X1Jt9fSZUZCBjX+b26HbF8+u1AT4sgTcmcp5Y5x/WIGuK2psscTE2hbqPg6PqaUWm4MV1ohCYItwNkqcpvfIBWNR2tCKF9celi4zmqLXosgzEBuckQigSvo9sxsyiBPyU/D4ReNw10eF0raisiacNtL7vnz7/ALO78C0BH6vRZLN7kBds/u1aC+DmOU6qWAw29Bisfm1shADzMwkDadt9jE6rQpr/jrba9svd58WncEQM4jhFGhxvcMhSFM/u1qyWcwC+n4xVhtM2FOuh8Eklob2D0R95/LvLg88vSNaBEHAgSoDyhpbUaU3YWdJI3aWNPaJthx2h4DKpvCuNTLb7KjyuM3Cssaw3j5ROAmCgPLG1rCtl60zmr0qbwZittlRbTChrLEVB6sMbd63uBax2mBCs9mGXaWNfhmsYKr1JlhsgdfHNTRbYLLa0WKxYd3BWgDO1kbZPif0jtY2Y0+5Ho4IBa2CIOBwjRE7SxpR7Mr0aJRypPpkdLprPL4l7D/dXg5BEKTnq9Vix8Fqo9/1fIvpUPera7bA80+nOkAGUeNxcjJJo5SWvwQK8muk9YecXkoUTcwghlGgXoieX1iBArdQiB+YVR5n6CqbTDjlHz9KU4CC3Y/vVI2isibMHBo7ZZlX/nIs4FofrUqOn++c0y1rXWLFHR/sxMfby/DODdMwI0yvyT2fFOHDraXS7xe9uAFvXDfVb/oWUSx4Zd1RPPL1XjzwuzG4ZsagLt1WrdGMU/7xI4ZlJ+GzW2cG3e/GN7bi5wM10u++9y1WYAScB7KHaoxY9OpmaduwrER8v+TUNseyv9KAeU//jLmjsvDKNSd6XXakxoiznlmHM0dnY1epM3uoUsgwIicJZT7LCCr1Jpz973X446lDsHR+90+3+mxHud/a5QSN0q96tlbVPeeYh2cnef3++c5yDEiLx3/XH8WsYRlobLFi07F6v+t1pNUThZdvISXPDKLJ9Tfk2SNTJpMhS6fB8boWVBvMGOQzbVnMIPbm73yinoAZxDASp2x6Bohi9lAhl3mdResM8QPT86zb3kq9V3AIwKu/omhUPx3OHJ0t/R5qhbju9pPHgRrgLLCjUcphsjqw5VhDkGv1Dh9vLwMAPLP6YFhuTxAEr+BQ9Oya8Nw+Ubg98vVeAMB9n+/u8m1tOdaAFosdRWVNbRYt+dnnM2enT/Evz5ZA1QYz7vhgl9flB6uN7WYRX/r5CABIpdo9vf7rMVhsDny5q0LK0l07YxA0SgVmDM3AlIGpGJqViDG5Oql33K6S4JUfw8n38xhwzoC5eHJ/TB2chgn9kzF5YCpuml3QLfevUshxx7wRUg9gAHjux0Notdrx3Z6qgMEh4F7vT5HjmwX0XINoDDKrKTspeFX1amYQiWICM4hhlBznX6RGLByToFZ0eT69OHe/1miG3SFAIZehOsAHbKAMokIuw0tXT8GbG47h3s92x0z5dMAZ0BT5lLy+esZA6FtteHdTMQrLmrwa0vZW5iDT0EJVHGTxf3oCv3Cp9xM/S+wOAXXN5oCZCJPVP5Dwm7rv8RlZrTfB5vD/+6zWmzEoI/jX6JFa/2mQIr3PusLh2Ym4Z8FoAM7lCh/ePEO6bMPhOlz28m9es0e6k1iVMiNRg1qj83lJUCuRHKfC//44PSJjuHXOUNw6Zyh+OVSLK17ZGHS/h88fi0+3l2HL8Qa0MIMYceJ7cmROEvZVGlClN0m9oMVjoRSfhveZrmOZQH2ZpQxijLS4IOqrmEEMI3ENot5zimkHWlx0VHqCBnIZ4BCcZ7v1Jit+dq1b8dTWfWVKZ+5iZ23f+kO1fo2Ox+UlY5xrHcr24gbsLGmM2PqbaAl00NoRh2uMqHMdxB2qNnqVyfd0sNqA7/dUoayxFQeqDCgqa0JDBxtMm6x2bD1ezzLy1C6j2YYdJY3d3n9TEARsK25AeWOr1wkmz5L7ByqNKCprwoEqA5pcpfP3Vujx3qZiv9vbUdKIVUWVKKlvwZp9Vagxuj8jfz5YE3D64qFqI/ZXGmB3CNh6vB6lDS3YX2mA1e7AzpJGHPZYJ/fDnirsq9Rj45E6rCqqwLZi75kRvuvuPInZFDGI3VXaKD2ecGpqsWJHSSMO1zjHffnUfOmyaJWcH5sb/HkBnN8V4klRIwPEiBPfk2Ncr5PZ5pBOfogBYrJPgJgtzYYKlEEUp5jyhCZRNDGDGEbih2Bji/ugW98avHBMqBRyGbKStKjUm3DhC7/i5KEZWH/IP0BM0ga/L/FAI1aKvzS1WnHVfzcBAPola1HhKqwyup9O6r3125F6nPf8L/j7glG4YdaQqI21Owg+lUZDdbjGiNP/+RMKMhPw4U0zcM6z62CyBr6dwzXNuPGNLV7bhmcn4ru/tL2OCnCuafxoWynuP3c0rp05OORxUt9x5SsbsaOkEf+5ajLmjcnptvv5fGc5bn9vh/T7BzdNx5SBqV7B4pX/dWeepg5Kwz8vmYBznl0f8ESH0WzDTW9tDXhf724KfNLlBtff07wx2fh2t7u659njcvB1YWXAfYMZ10aAKGZTjGYbfj1Ui8tf2YjTR2bhv9eeGPQ6nXHd65ux9bgzcM3RaTF7ZJbUFqmt75XulByvwsD0eByvCzwzYkROkhS8cg1i5IkBXX5aHJLjVGhqtaJab0JynEo6We4bIGa1kUEUg0ZmEImiiwFiGAWqYrrf1eB4YHpCwOuEaskZw3HnR861MJ7B4Tnj+6HOaMGAtHjpTF4g4odutcE9DSSaij2+9B86byyO1jajxWLHkMxEmG12qBQyqTfYw1/t7XUBomcw15kM4neug9LDNc1Yf6gWJqsDCWoFJg9KQ1q8CjaHgG3HG1AepErqgargU+A8fbTNuabx+bWHGSBSm3aUNAIA3t1U3KEA0fd9L06fb4/vmt13NxYjLyUOdUGy4puO1eO3I3V+weGfThuKZwP0hh2Zk4TR/XSobbagxWyDTAYUZCbCZLWj1mjx+vz1DA4B+AWHvnKTtchNiUO/lDjoW63QKOU4f2Je0P0TNUrEqxVosdjx00Hn+sCNR+vhcAiQd+C56gir3SEFh4AzozmxfwounzYAR2qMuP7k6H323jFvBN749TgydRq0mG1QyGVIiVdjYn4KtCoF4l3r7pvbqVxL4dfq0cYrK0njDBANZgzLTgqaQRR/N5j8s+Di8heuQSSKLgaIYRQoQBTPZrd1djgUl5yYjw+3lvot0v/dhFyc2YGDscxE54eu1S6gocUa9Z5R4pfBuLxkzPUoogMAGqUCw7KSsKcitlpyhFOg9aqhKGt0B9jvu6aWzh2djWcuneSxTytmPrYm6G2YbXZolMFbsFg9iiDxS5s6yhwkk+3LdzZDndHcoexBjW9xDIPJa3ppIJ/uKPP6/eShGVhyxvCAAeLr1031aw8keujLPQFnb3TUX84Yjt9PyW9/Rw/ZOi2O1jZL3ylGsw1H65pRkJnY6XF4OuA6mSkal5cMuVyGRy8YF5bb74pzxufinPG5QS9PYAYxasQTuGqlHNk6LQ5WG1GlN8Fqd0gBu2+AGGxKsNlmR4Nr6jSrmBJFV6fWID7//PMYNGgQtFotpk2bhk2bNnXoeu+99x5kMhnOP//8ztxtzAvU5kI8YBnfPzwBIhB4rUpHp2OolXIpKPymqELabncI+GpXRcA1Ab5aLXZ8sbM84Nm/UNQZzVj5y1EAwQMP33Uv9c0W1BnN+GFPFbYer8eu0kasKqqAzR6eAi+R5vle0ZtsqDWasaqoEja7A6uKKvDepuI2n+fCMnfwLB6w+p6MEE8KBLPxSD22+JxwMFmdr/Gmo/V41iNTo1Uq8E2hc1z6Lr7+FNjxumas2VfV/o5tKGtsxaqiioDrAEsbWvDd7kq/y/QmK77YWd5u/8COEnugteVYbTO+2FXute21X4/hWG0zAOcU7DX7qlBjMGNHSSNe++UojtU2Y1+lHgafg8tD1Ub8L8j6W9Evh+q8fq8xmIPOoggWHAJdXx81rhPfB5mu+/R8DB9vK5UytqK1+6uxvbgBq4qcr3Gd0YyvCyukEz0l9S14/ddj+PVQLb7YWY7GFgvsDgHLv97nM0ZdyGOMFvF74vVfj+GzHWVcKx1BYnE1tUIu/V1UG8xetRh0PgFioqsndIvPZ414skilkPn13CSiyAo5g/j+++9jyZIlWLFiBaZNm4ann34a8+bNw/79+5GVlRX0eseOHcP//d//YdasWV0acCxzZxCdBy7NZpu02L+tAgShmpAfIEAM4YClX7IW9c0W3PNJES6YlId4tRKv/3oMD365B+PykvHFn05u8/p//9S5Hu2iE/rjn5dMCHn8oj+9ux2/HnYe7GQGOVs4bUiaV7a0sKwJT/9wANuLG732u3VOAe6YN7LTY4kWzwARAK54eSP2Vxkwa1iG1Dj7aF1zwN5nVrsDewNkV30DRHU77VWuXuk8wfPDklMwNMvZf+zZNQfx/I+H/fbdcrwBW1zT0IrrW3DnWT3vOY91pz6xFgDw/h9OwrQh6Z26jbs/2oV1B2sD9r6c+9RPMFkdeOmqyV6zDm59exvWHazFzbMLcFcnX1fPQlIdySDO+eda+MawL6w9jHUHa/HFn07GpzvK8Jf3d2JwRgLKGlthsTkwektpwCrMVXozqvTOdhKj++k6NPMgWKDW3nq73JS4dm+7LUM7kfXLC3Cfz/94GM//eBi/LT0dOclafLWrAre+s026/KWrJuPZNYdQWNaEO88agVtmD8Vf/7fT6zP1zNHZOH9Snl9GtL3iMLFEyiBa7Lj9vR2QyWT43YTgGUcKH7HNlkohl05Ulze2St9tSRql35TxeHXgDKIYIGYmaqK+/IWorws5g/jUU0/hxhtvxKJFizB69GisWLEC8fHxWLlyZdDr2O12XHHFFXjggQcwZEjvWkPmybOKqSAI2F2uhyA4F/tnhrEi17wxObhu5mAsnJKPv8wdjr8vGBXSAcs9C9zBhlgU5o0NxwCg3SlagHs9mvh/Z4nBIRA8g7j4tKFYPGcohria6RaVNfkFhwACBjM9gW+AKK5ZXedRnXZrkD6QB6oMUmGby6cNwLkTcnHbaUNx4qA0v31XXDkZf5k7HL/efRpunDUYy84Z7bePZ7/J9zyKcqgUMpwwIMVv/6OuLA91D9/MUCjE18Y3Mwy417369roT33Mfd+Hv2uDRuqG9DGKt0ewVHA7NSsS8Mc5p5kdcJ9b+t9k5lqO1zdJ7fU+FPuA6w3Mn5OLcCbm4ZvpA3DXfO8CdPcIdJMepFPjm9lm4buZg3O3a78ObpuPGWYOx7d4zcNOpBfjklhloyxmjs3HdzMG49MR83H76MJwz3rsNzwWT8rB0/kgsOWM4BqXHS9v/PHcY/nPVZL+G8x1xSxs9B3e5ejh+st37tfvtSL30mf7xtjJYbA6/99V3e6qw2eN9ctdZI/Hk7yf0qCIhCRrvKfJbg/RJpPCzuP7O1Uo5RuQ4T3zsKddL322+2UMAQYsKNbqml6ZGeekLEYWYQbRYLNi6dSuWLl0qbZPL5Zg7dy42bNgQ9HoPPvggsrKycP3112PdunXt3o/ZbIbZ7F5fotf3jDVoYoBosTtgsjqkL+ZwZg8BQKtSYNm5/gf4HTWjIANDMhNwpKYZVXoTCjITO9z2wnNaWjjXLwabzqVRKvB/80YgOU6FR77ei9V7uzb1Ltb4BoiB7C7XByzcIa5Fmj4kvd11QmeNzcFZY53ZIrHX2qaj9Vi1211MY7/HGqQsnVY6CC/ITMRbN0zD6GXfet1moAp01DWe0zs17WR+gxEEQSo973vCJ1irGM91pgPS4gPu0xGe7+fGdtow+I7t+pMH49wJufj2vm/RbLHDaLbB3EaQ6ZkllMmAZy9zr7v1bGA/eWAq7j1nNNbu/8l5vVwdRvXTeX2GThmUhimuEyt3z28/exroM/jLXV8BAEb10+FfCydK2z1PpP3ptGEdKsATyLDsJNx2+jD826c4DwAUletx5pgctPoU/PENnA5UGaSMj6fdrqnq//z9BFw0uX+nxhdNCWrvQ5mOnOik8BBP3KiVcozKdM5A2V2uR4Ormrvv+kPAc82o9/s1WFEbIoq8kALE2tpa2O12ZGd7FxPJzs7Gvn37Al5n/fr1+O9//4sdO3Z0+H6WL1+OBx54IJShxYRE11QKu0PA46v2Yburz1W4CtSEU3aSFkdqmnHvp0U4fVS214GFze6AUiHHkRojftxfgyumDYBW5TzQ8Awk+yV3/gyz7/qn9r4QxCB7W4Dsoej9zcVYeOKATo8pkgpLm7C9pAEvrzvS7r6tVjse/XqvdGDZPzUOFpsDX+xyriHtzHomwF1qXPTD3iqkxKlRkJXgNXXVWSVQiSSN0mvd19bjDVj+9V6cPiobUwen4cd91dAo5ZgxNCOkcXy+sxyp8SrMGpbZ/s6d4HAIePO345gyKLXNCr+i0oYWrCqqxGVTB4SlPY0nk9WOtzcWY86ITAxxTTM8XGPER1tLMTQrEZMHprrHLQCHqg34+UAtNCo5RmQnYcqgNNgdAl795SgMJhuumj4QcSqFVDF0e0kjNhyulYKArccb8NovR5GbEgeL3YFpg9O9bv9YbTP+t6XEqw9pcpwKH20thS5OhSq9CSnxKshlMuyt0MNsc2DqoDTMHZ2NXw7V4ucDNThtZJY0Fbax1X079c0W/HqoVno/1BnNePO340hQK6FVyf0qfY7LS0aiRolEjRJGsw13frgT+yq9C6f47h9sGmm8R8BQbTBhcHqCdLuR/jy22d2fdZ0NDkVjcgOvC/xqVzlMVjt2lXgHRr4FgIqCBE7ilNPOfpZEm2+2eltxI349XIsZBaF9FlHoxM8atVKOwRmJUrXdbccbAQQJEKWqszavauoMEIliR7dWMTUYDLjqqqvw8ssvIyOj4x/US5cuxZIlS6Tf9Xo98vNDq/gWDTKZDNlJGpQ3mfDar8ek7bG42F8MDg7XNONwjXeQUmu0ICdZizP/9TNsDgGCIEjtJfZWug/IzJ3o2yfSm7ynlrQ3RXZMns6r5UUgd31UiLPG9usRXy7nPrfe63fPNYeB/Hf90aCXTcxP6dQYfDNFJfWt+NcPB/z2EzOP/dPi/dY8/ufnI3h/Swm+/fMpWPTaZgDAngfneR2gt6W8sRW3vbsdAHDg4fntrpfsjHc3F+O+z3cDAI49tqDd/R9ftR9f7CyHRqXAVScNDOtY/vPTEfzrhwP453cK7HnwLADAY9/sw/d7nJnxf1w8Xtq3qdWKuU/97HX9Y48twA97q/DwV3sBOA/OFDIZnvvxkLTNk95kw/1f7JF+f+i8MdLPdUYzHv5qL37wycr/sLcaP+ytDvoYXvvlGLYvOwOLXtsMi82B9zaXYMeyMyCTyfwy4re8sw3b73Ve9vqG4wGzX6Lh2c7sQ1aSBkazLWCriBMHpWKzayr0pAEp2Fupx67SpoAVLgsyE3C4phlzRmRBLpdhYn4K1h+qxaQA06XDIS1Bjfpmi9d0VgCwOcJXQMszQPT8PAz0OQ44C/d4EgPuoVmJfpeplfKwVUSNNM+TlUq5DDaHgNve3Y4tfz8jiqPqG6w253tQo5BDIZdhTK4Om481SGta28ogCoLzBKj4fcEAkSh2hBQgZmRkQKFQoKrK+4CiqqoKOTn+LRYOHz6MY8eO4dxzz5W2OVxflkqlEvv370dBgf+6Co1GA42mZ5bTf/rSSfhke6lXY+VwTzENB88pnacMz8TInCS89LPzAKNKb0K2TgObazrado81K1Ue/fQ6Mj0ymBqD+3Yev2hcu0GOTqvCi1dMxqZj9dAo5RjVTwdBABpaLEhLUOOWt52FGXaXNYWcwYoF//z9BKw9UINjtc346UANdpc7A7H+qXEobWgFAAzJSIBCLsNB14Hd+P7JOHd8Ls70aQ/SUZecmA+T1Q65XIbUeDW+212JH/d7r0u7dU4BbnSdHPjHRePxZWE5JvRPkZ5vwDmVcFWR+2B+b4Uekwf6r4MMpMLj/XSgytAtfyueY+sIMfNfWh+4MXdXrHP1sPOs3udZcOU7j356gf6+7A7Baw1uSX2LX5aoLd/tcd9+lcGMsgbnY/z95P7YX2XArtL2p+ZZ7A5sOFwnTS1rarWitKEV+WnxfmNubLGiUm9Cv+Q4r88OwBmkXDApD/lp8chK0kgnB7J0GhxxraG8eHJ/zByajqO1LUiOU+HiE/rjjQ3HoFDIcP6kPMwZmYUvdpbj95P9TyC+fcNJ+HJXOS450XnZw+ePxS+Ha3FuG+0SuuKTW2bg+z1VuGKa90mFcFbU7J8ajxevOAFalQID0+OdvRAFwauJ/Pj+yXh/cwnWHaz1mmrZ2GKV3mtXnTQQSVollvxvp3T5S1dN7nKGM1pmD8/CQ+ePxaT8FByuMeL293agvtkS1j6RFJhUpMb19zs2LxmbjzVIa10DBXvxagVkMmeAaDTbGCASxaCQAkS1Wo3Jkydj9erVUqsKh8OB1atXY/HixX77jxw5EoWFhV7b/v73v8NgMOCZZ57pEVnBUE0dnIZJA1K8AsRY7OfjWfX0T67CJhuP1GFnaROqDWYUexwce35Ye64760qAKE5VHZqV2OFpoXNHZ/v1ShTNH5uDb4oqUdgDAkTfxuD3nTsaWTotLnH1RUvUKqUAce6obCkbffa4flAr5Xjqe2eW77KpA3DZ1M5PqdVpVVh82jDp95E5SX4B4u2nD5cOGsf1Tw46Be2djcXSz4WlTR0OED2Dm8Kypm4JEItDCPQami1SQB6pNZaef0erPdpb6AP8fdUZzV7TBKsNZuSnOgOFjvDMUu90HcAp5DI8dP5YHKgy4HfP/dLm9bUqOUxWh9c4Aedr5xkgzh2VjdKGFuyrdAad/ZLj/D4vzhnfD7fOGdrm/T103ljEqb3X0f3pdPd7VqtSSLMbfOUka70uG5SRgEGuYlfdYWB6QsCx2MLccmH+OHdBnCFBMn6D0hOw7uB6r1kedc1mlLuC9KwkDeaP6+cVIM4eEbwKeayTy2VStn9olvM5cQiA0WKDTstgoztZPNpcAP5LapIDtKuQyWRIUDunfDeb7YBz8kCbhW2IKLJCnmK6ZMkSXHPNNZgyZQqmTp2Kp59+Gs3NzVi0aBEA4Oqrr0ZeXh6WL18OrVaLsWPHel0/JSUFAPy29yaqTlSoizTPEtKj+zmnLTmr1jXh/z7YiQc9pqK9s7EYA9LicajaiA+3ugsuWGwOmKx2aX1iKKpdGcRwNV4fm5csBYixrtqnIJDvF6rn76eNzJICxDG5OmhU8qDX66pR/fynQnd0yqdngZvCMj0+3laKtftrcO6EXJwxOhtWuwPPrjmE2SMycbjaiIomE5otNq9WCEs/LsQpwzOlcv6riipwoMqIiyb3x2u/HMWNpwzByvXHUGc0Y1BGAvZXGnDG6Gyc20Y5e8Enu/LgF3twx7wRXkFHWWMrXll3BNfNHIxjde7KrGUNrXjq+wOYMyITkwakIhi7Q8C/vj+AyYNSMSeEg2yr3QGVQu4VOHkuzS1vavW7zjubir3aEWw6Wo9NHQwOgxmWlQitStGhs/bjXNkB3ymo/159EN8UVeKwK7udHKdCanwy9lUa8MwPB/HlrgqvgkhA8DYKnn8fvsFhT2RvY1p8d/FdXww431viSQHfCqXhrLIdbVqVAhqlHGabA00tVgaI3czsUaQGCBAgBvlciVcrXAGie7kJM4hEsSPkAHHhwoWoqanBsmXLUFlZiYkTJ2LVqlVS4Zri4mLI5bEfIHW38yfm4tMd5bjp1OClyaNpvEcmSFwPMCTTeXa9qdWK//zkvZ7lsW8CFyFqarV2KkAsqXce/OboutZPTDTatTbnQFXwohaxosrgPdVutE/hifF5KVAr5dAo5Jg2xJ2Jm5Cf4nXyQVyzFS5alQIZiWrUGp2FRlLaaFQ8qp8Oeyv0mJif4lc2f8vxeqly46+H63DG6Gx8XViBf68+iO92V7ZZeOSVdUdw37nOkxM3veWcxipmTF9e578O8/Od5Zg/Nido24BKn355K385CqVChr+d7W71svTjQvx8oAbf76nC5dPcGdlNx+qx6Vg9/r36YJtrF385VIvnfjyEwRkJ7QaIntVCa41mZCdpA2YKAf/1YwDw9A/B1/CJxPVlE/onY2cHpoxOGeQMfts7KJs0IEWaDSFmfsW1s/sqDV6va//UOOSlxOGDraXYU6EPWExmfJBs9EWT++OJb/djaoB2LT3R76fkY+UvRzG9kz0tOyM9QQOdVum31lskziAR13ReN3NwxMYWCclxKlQbzGhqtaL3zVOKLZ5tLgBnVlsM0IHg2cBEjRLVBjMDRKIY1akiNYsXLw44pRQA1q5d2+Z1X3vttc7cZY/zyAXjcNbYHJw+qnPrw7rbiYPS8N9rpkjTcQDg1jlDsXL9UVjtQoeaTAPOD/RgLSraImb6RvULT5AjZp062q4jmsQMSbxagQ9umu5X0CU5XoV3b5wGpVwOjVKB7/5yCprNNqmQz2e3zoRGJe+Wgi6fLT4ZR2ua0Wq1Y0QbAehri07E1uMNmDsqG18VlqOxxQq1Uo57PinyytjVNZths7t7r7UVHALO6akA2mxv4OtgtTFg9tPz9jz9uK/aK0DcIa45bGgNWuXRbLNDowx8IkTsQVfe2OpVkS8Qzym1VXozEjRKiDMQ5TLAczaiGKgH8vp1U3HNyk1e22YNy8B1MwfjRFdF2dNGZmFXaRMSNApUNpmQqFFif5UBCRolBqbFY0+FHhqlAmePc64fT/LItCSoFfjnJROQmxIHq92BhmYrJg1IwXM/HvK6z78vGI29Fe6S9oDzfb1gfC40SjmUCmfhmke+2itNtXz8onEYkaML2mfvhlmDMTA9HifH+FTxjrrzrBGYPDAVJw+L3ONRyGV458aTsPlYPTRKBUbkJOKiF92tqMQM44tXTsZvR+pw1hj/GgI9mWeASN1LLJQkTjFVyGXISdZK3wPBgj2p1YVHSxo9A0SimNGtVUz7sgSNEmeN7df+jlHkG7zqtCr8fcFoqeJjR3T2C1g8EA/XNEnxjHhTq7XT014jRSwUMWdkVtC2C55r+HwzhRM6WbW0I/JS4qRguy3ZOi3Odq2FumCSu2/a0z8c9AqCBAGoa7YEDbxEfzx1CP7z0xGp52MohVcKy5qCBoiB7rfKJ6s4MD1BOmGx9XhDwNvZX2nA+P4pQe8fcE610ptsQQ9uHA4BNUb346rWm5Du6iWqUcqR75rG3Z6ROUk4dbh/S5CbTy2Q1t+K026nFzizVuNdL5Hn+txpPhktzwIlgzMTAn5+ea6nTlArMCwrESNygp9IuPAE5x2/9PMRqSDRiYPSgq6dA5y9TwNVJe2ptCoFFoyP/HfB2LxkrzW9/ZK10msgnuzISNT0qudaJP4NMkDsfp5tLkRZSZoOBIjO96DRoxciM4hEsYNzQclLR4uEiGsHr/7vJtz81lb8ejh4iwZftUYzKppMkMmAMWEKEJPjVNIX1MKXfvPq6xZrxOInWb1o3Y8oUMBf3tgqFd0J5qTB6YhXK9BqtePCF37B3Kd+6vB93vnhLhytda8dtDsE3PNJIf76v53495pDfvvrTTZc/OKvWPTqJpTUtyBJ6z5PJmagfQsf/uX9HfjX9we8+nceqjZi8Tvb8K1H5dErX9mIY7XN+MMbW3Dtq5twpMaI43XNWPTqJly04levNi13frQLZz3tbGMRygFRsL/RcBZ28G2BIvJcMzwmN7nDFSLTE9XSzzz4i47e+HkTTF8NEA0mK/707nZ8tzu0ys2dZXcIUpVetcc0f8/ZAUEDRNfMGXGK6be7K6UTGG0tbyCiyGCASF5G99P5HRyLTh/pXl811dVwu9VqxzdFlbj85Y0dvg8xSzIgLR6JYWpELpPJpIPXnSWNUsuOWFStFwv0xF51264KtM5q49F6r7YOvuQyYHhOEqYNdmZNd5Y2wWQNrXfcW78dl37+YW8V3t5YLK2DDGTL8Qb8uL8G720u9juIHJOrwzifbOHhmmY8s/qg1GIEAK7670Z8uavCa7/CsibMfnItvttThbX7a/Dh1lJ8tK0MP+6v8WpPATjbDjS7npfkOBX+esZwAM6KvJ5Bqy/xOZ451Pu5DkfgJQb4V500KODlntnCkwo6vqYuziOjzwqF0XHzbGfF2FMCZJ97m74aIH5dWIEvdpbj+bWHI3J/Fo8quZ4ZxOyk9gPERNdnnNG1TvbZNe711bFY+Z2or+EUU/ISp1ZgWFaSVJXyq9tOhlohh8XuwOCMBNQYzJDLZEhNUOOLneWdug9xil9OmAOktHi1VPzGaI7dAwOxSE1vPKN/7cxBGJGThHi1Ait+Oowf9lZjtasRu0Iuk842Tx+SjicvmYB9FXr0S3ZOa3164SRsPFqHI7XNUlGkgswE3D1/FEb1S8Lucj2GZydhzpNrAQAT81MwPDsR/9tS6lW99oDPOsfrTx6M/20ugcF1pnrFlSfg18N1eGPDcRSW6aWDyFtmF2B8/xRMHpgKuQzYfKwBpQ0tXg3o97jGAHj3cAymUm+CylW067yJuZg/NgeHa5rxxLf7vfazCwLmj+uHL/90MoZkOv/O9lboEadWYkhGAsw2O2qNzr5u4tTQFVdOxi1vb5NaVwQqJx+qt66fhuL6lqDtTMbkJuOzW2eivsWCGSEEiJ7rN3tClefeaN6YbHxyywwMC3Nxq1ik66MBotjH1LfnaHcJFiB2ZMaAbxBf2eScvfHc5ZN6RfViop6OASL5Gdc/GfurDFDKZRiVo/OaRjYw3f2W+eMpQ/CfTmTqxPVl4c6geR4MxPI0NrFITW/MIKoUcilDkbW9DIAz0AKcLTu+dzVqn16Q7rfeMTlehTPH5KDZbJMCxBE5STjD1fuyf6r3tEeZDLju5MH435ZS7CnXS02xPaebAsDpo7Lw5gZ3hvGssf2QkxyHNzYcR1FZk1RZ9KLJ/VHgsTburLE5KKn3DhALy5pw/qQ8tFgCV4f0VWMwQ+n6+5k+JB1nje2HvRV6vwCx1HViQ5w+OjBdiYHp3j37hvoUSE3SqjBtcJoUICaqu/5xnhyvwrj4tqd9d2YNrFbFoDDaZDJZm+1aepO+mkEU11zXGM3S52F3MtudMyBkMkifc4BzTbVIF2Q2hOdrZLM7UNfs/F6cOrh3VC8m6ukYIJKfcXnJ+HBrKTKTNG1+wQxIdx+wx4dwxk/MIIY7g1bnse5Q39qxA/hIstodWPK/ndI0xd6YQfSU7TNNaN6YHGw5Vo+GFmub/S8TPKYdt1XJUwZgaGYitCo5jGYbZj6+BnKZzKsQDOAMxC127ymrI3OSoJTLvNaqBjqp0D/Vu2DPf9cfxZp91X6FboLxbEwvnhAYluVfoMV3fB3lWYypuw8GuyJYBVii7tAXA0Sr3YG9rtkTdoeAumZLt/e39Kxg6lm92fNzKVgLIvE1evO346hvtkAQnLNM0hN69/ciUU/B07rkZ+bQDCjksnYrjJ452l0a3eHZ4bsd1d2UQXzwvDHSz7F4YLD+YK3XtNxgZf57iwn57vePWiHHSUPSpOqb7RVDuuqkgQCAP5021O+yy6Y6O5v9ee5wKBVyqR1CRZMJZY2tXtOeAGcg/sgFYwEAS1zr/LQqhd+Z6kABokwmwyVT+nttO1rb7LWmUq2Q4y9zh/tcz/8xia0FlAo5zh6XA61KjhtnOfvP/X3BKP8rdMB8VyXZcFUD7i43njIEgHOaI1F3S3NVB65v4wRTb3OgyuD12Vdt6P5ppuL9qX2CQHHWx8g2Khx7rkX+qtC5ljszUeNVTZmIoocZRPIzNCsRG+4+DSnx6jb3y0zSYN2dczDrHz/CZHW02SvOk5RBbCOL1BkXTOqP4rpW/OuHAzEZIB6u8W5hEGzqTW8xe0QWfrpjNhpbrOiXrEWWTosnfj8BS88e1e7Jgft/Nwa3zClAv2T/lhuPnD8Ot58+HDnJztt44YrJ2F9p8DpJccvb21DW6Jy2mahR4oppAzFnRBb6Jbvvd+W1J2Lkvauk34OtjXv4/HH4v3kjcPLjP/oFn7fMLsANs4YgLUGN30/pj5R4FQ5VG5GRqMGMx9Z47etZeOGpSyai1WJHSrwK184cjNzkzp0syEuJw5a/z22zqE0smJifgk1/O106cCfqTuLsjEgESbHCt6VPtd6MMd3cwUQKEH168mbrtNj697les0F8BTohF+5jAiLqvNg+qqCo6Wh2Ky8lDjKZs99dU6sVWUntB4jiGrzuqFQ2sp/zjGUsBoiFPl/gbTVU7y0GpidgoEctE5VC3qHMsUIuCxgcAs6plDkeAZVaKfcrqpKeqJYCRPF5zvXp76hVKVCQmYDDNd5rFn2plXJkJWkRp1L4BYgnDEiVgh7x9sf3T/FqhyGNySM40qoU0jSsjvSdbEtGYs84qOrtGXOKHeJ7rTqEfqo9ne/3S0QziEr/k2vp7XwuBQwQWb2UKGZwiil1iVwug07r/KCf//Q63PjGFr+D42WfFWHo377G8Hu+wch7v8ERVxGR7jhbKH7p7ChpxF0f7gr77XfG5mP1mPjgd/hsR+eqvlLolB2cpjTIpxBMW3Rx/ufTglX7DBT8x/IaQaLeRPxuMZhsaG2jxU5vUljm7DUrzkwRe7p21eEaI2Y+tgZvbDjmd5nFVaQmUIDYHmYQiWIbA0TqMvGDvq7Zgu/3VMFodheIsdodeG9zCWwOARa7Q+pvJ5cB+amBm3GHYywA8P6WkoCZnEj7YW8VGlu8M5q3nz4sSqPpG+47dwzUSrnUWzCYv58zGnEqBa6dMajd23zqkolQKWT429kjMSQjAdOHpLeZDb1s6gDp54sn9w+6HxGFV5JGKfXe7CvTTMXWFmKlWs8CXF3x+Df7UNbYimWf7fa7zGJzF6kJVaAAsTuOCYioczjFlLrM94O+2mBGkiureLDKCIvN4dUDDwC2/v2MTp11DHUsepMt6i0valxncv902lAsdhVdYVXH7jUhPwWF95/Z7vM8OCMBO+47o0MHOCcOSsPBR84GAPzhlIJ293/0grG4/3ejAXTuAIqIOkcmkyFLp8HxuhZU6c1+LWN6I7PNmc0TK0SHa5lFWxWWxcs609s00PdyrBfbIupLeNRCXeb7QX+wyohqvQlWu0NaOD91kHfFyNRuKlbhF6zqTbA7BNg62UYgHKpcZ7AHZyRAo1QwOIyQjj7PGqWiW9aDymQy6fXuC+tNiWKJ2Ganr2QQxdk54qyGcAWInj0NfWfktLUGsT2BWmONzdOFfDtE1D2YQaQu8w3KbnprKwBnD7mZBc4WBOP6J+O3o3UQhI6vD+sM3y+dM/71MwBgTK4OX/7p5KgcqItFecLd1oOIiALLdGXSwrUWL5YJgiBlEMUKruELEN3fqUazTZodBHQtQAz0Xdxe5XQiihxmEKnLgpWuL21oxTdFzv5GY/OS8db105CXEoc3rp/abWORyWS4cFKe3/bd5XoYPNZGRpLU1qObmxYTEZHToHTneraDVYYoj6T7We0CxBUcWWHOIHpWbvYNtsUiNZpOLhe5dsYgxKsVSI1X4c6zRnR+kEQUdswgUpdlt1F5TG9yBmXj8pIxOCMBv9x9WreP56mFEyEA+GR7mdf2pharVHE1UkxWu/QcsMw/EVFkiOvZdpU2tbNnzydmD4HwZxA9b6faYMLQrETpdymD2Mk11vf/bgzu/92Yrg2QiLoFM4jUZYF6F3muJUjSKDEwLbLVyVos/tnCSPdGtNgc+OVQLQBAq5JL5ceJiKh7jXUFiAeqDDBZe3erC3H9oUwGZHZngOiXQXSmLTtTpIaIYhv/qqnLAvUuGpHtDhDH5Oki3gMuQeMfjOkjHCAufmcbrn99CwBnEM1CJUREkZGXEoe0BDVsDgEHevk0UzGDqFHKpZoAFpsjLIGxbwbRU1fWIBJRbONfNXWZbwbx4fPHegWN0Shd/Ze5w3HCgBRMGZgqbYtkBtHuELDuoDN7mJcSh0UzB0XsvomI+jqZTIbcFOd3U12YegLGKjGDqFEqkKhRQuE6IRuO7zzPE6t+axAZIBL1Wvyrpi7zXIP46AXjcOVJA5HtUZBlbBQCxPy0eHx8y0x8ePMMnD4yC0BkA8QjNUa0Wu2IVyvw851zsGjm4IjdNxERAQlq50yS5igVKIsUMYOoVckhk8mk5QyNLV37zrPZHV7F3cSCayIGiES9F/+qqctSPUpTW8RS2x4FWaLd/FaccrPuYG1E+iEKgoCVvxwF4GyvoYjw9FoiInIvNejtAaJnBhFwf+d19aSoWGBNVG0IXMW0s0VqiCh28a+ausxzfWFyvPOLSez5l6RRYlB6QlTGJdK5viy/KqzAM6sPdvv9rT1Qg3c3lQAAxuRGNzgmIuqr3AFi7y5S45lBBNwBYmNL16bW+gaY1cwgEvUZ/KumsPjn7yfg4sn9cc74XADAhP7JWDglH/csGBXxAjW+xC9LAHh2zaFuv7/fDtdJP189fWC33x8REflL1Dgzar09g2j2ySBmJDqXeNQauxYg1vhkDKsNZgiCIP1udVUxZQaRqPdh3X0Ki4sm98dFk/tLvysVcjx+8fgojsjNM0AEAIdD6NagtbDM2Xfr8YvGYUhmYjt7ExFRdxDXIBoDtD3qTXwziGKRON+qo6ESrz82T4eiMj1aLHYYzTYkufoJm5lBJOq1+FdNvZ7VZ93hu5uLO3S9jUfqUOQK9naVNuK1X47iSI0x4L4mqx1f7irHpqP1+NWVQYxGcR4iInKK76NrEMXK4p5VR387Uoc95fqQble8/sD0BCS5nkvPdYicYkrUezGDSL1eWoLa6/d7PinC6SOzkZOsDXINoLyxFQtf+g0AsP/hs7DwP7+h1WrH0KxE/LDkVL/9X/r5CJ76/oD0u1Iuw/DspDA9AiIiCpV7imnfWoMo1gCocWUA64xmXPnKRqTEq7D5nrkd7skrZhCzk7TI1GlgqLGhWm9GgWtmjMV18pVTTIl6H/5VU6933sQ83HRqAe6YN0LatqOkoc3r7CxplH7eeKQera6Gw4eqjWgKUDr8i53lXr8/euE4qPilSUQUNX21immWq82UmAEsaWiFzSGg1mjxq0zalmrX9bN0GmlaqdHjuRSrljODSNT78K+aej21Uo6754/ErXOGYuGUfADudYLB7Ks0SD+v2Vftddnucv/rDkiLl34enJGAS1z3Q0RE0ZEoBoh9ZA2ixieDKPYt9Oxf6FuJtC1SBlGnCVjwh0VqiHovTjGlPmVs/2S8v6UEhWWB12K8t6kYw7ITpbWHAPDD3iqvfV5YexhrD9RAIZfhwkl5KG1oxU8HaqTLlex7SEQUdfFikZpePsXUL4OoE6uYmmF3CF7rBqsNZgzzWf5gttnx9m/FOHVEpjR9FHBnELOTtO6CP14ZRK5BJOqtGCBSnzLOVTimqKwJgiB4rcXYcLgOd39cCADonxonbS9taAUATMxPwY6SRqw/VIv1h2oBAL8cqsWuUu+M4oLx/br1MRARUfsS+kqbC581iOkJaijkMtgdAiqaWr2yhlUBMoiv/XIMy7/ZB3wJHHtsgbS9xugMEDOTNFI2tsXCAJGoL2CASH3KyJwkKOUy1DdbUN5kQl6KOxA8UOWeVlrf7N8/avmF47B6bxX0JhsMJhve3VTsFxzOGZGJxXOGdt8DICKiDknso2sQlQo5hmUlYl+lAbvL9VImEPCuQirafMx/Tb7DIaCp1bnePiVeLa3n9MzGmlmkhqjXYoBIfYpWpcCw7CTsrdCjsLTJK0B0eDQAbrF4T0lKjlNhZE4SRvXTAQDsDgGfbi+TiteIbp0zFEp+WRIRRZ04xbS3B4i+GUTAOVtmX6UBRWVNqDK0nUHUqPy/swwmG8SvxOQ4FeIDZGOZQSTqvfhXTX3OeI9ppp4CZQ1FY/N0XtNRFXIZRufq/PZLjlOFaZRERNQVYgZRb7LhF9eygN7IN4MIAOP6O7/nCsua/DKI/9tSgjc2HJO2aT2uZ3Kd9BSzh3EqBdRKORIDBNtiFVNW7CbqffhXTX3OWI8vTk+eX6K+Jg9M89s2ZVCq3zYGiEREsSEl3v15fO+nRVEcSfcSZ7LEeWQCR7tmuxysMkrVSAGgpL4Fd364C8s+24061xpDAe7ZMzWuKahigCh+p7mnmAaoYsoMIlGvw79q6nN8C9WIPKfhAEBeShyev/wEPH7ROPzxlCF+t7N4zlA8duE4ZCSqpW06BohERDFBq1LglaunAACO1DbDYPLvYdsb6F3BnOf3T65r+UR5Uytqje7ZMZ4zZ0yuKaLi9QH3FFTfANFdpMa9rEKcYqphgEjU6/CvmvocsVBNXbMFFU2e/aG8M4jJcSosGN8PC08cIJ099ZSkVeHSqQNw/sQ8aZtWpfDbj4iIomPu6GzkJjv7Au4uD9zeqKfzDeYAICPR2erC4xwoAMDh8XurxXs6KeAuYtORDKLFzjWIRL0V/6qpzxEL1QDAolc347k1B/HDnirsqfA+eOjodNFAC/yJiCg2jA2y7ry3CBQgqpVypCe4Z7d4TrcV+a43BPwziGJWss0iNVyDSNTr8K+a+qTpQ9IBAPurDHjyuwN49Ou9fvt0NEA8a4yz72FWkiZ8AyQiorAY5bEerzcKFCACzv6FooHpCUjSes+EaQ0YIAbOIAZqGSIGiCpmEIl6Hf5VU59051kjsPLaKejnmnp0pLYZAHDtjEHSPh0NEMf1T8ZXt52Mb/98StjHSUREXSN+zlcb/Fs89HQOhyCtIfT9zsrWaaWfs5I0ficxA08xDbwGMUGsYuq6jiAI7immzCAS9Tqd+qt+/vnnMWjQIGi1WkybNg2bNm0Kuu/HH3+MKVOmICUlBQkJCZg4cSLefPPNTg+YKBy0KgVOG5mNEwZ4VyL1DBCbLR3vnTUmNxmpHtN5iIgoNoiBUqAm8T2d0WKT1hX6FknzDAizkjReASPgLDhjttmlNhmAey1+exlEsYIpwDWIRL1RyH/V77//PpYsWYL77rsP27Ztw4QJEzBv3jxUV1cH3D8tLQ333HMPNmzYgF27dmHRokVYtGgRvv322y4PnqirxLUporzUOOnn0obWSA+HiIjCTJxqWdVGK6OeqqnFGchplHK/ImmeAWG2TusXIJqsdq/sIeDOILqzks7AUFyD2GKxw+FwZw/F+yai3iXkv+qnnnoKN954IxYtWoTRo0djxYoViI+Px8qVKwPuP3v2bFxwwQUYNWoUCgoKcPvtt2P8+PFYv359lwdP1FXjPALEjEQ1VAq5lEW87fShURoVERGFixgY1TWbYfMIbHqDYOsPAWBCfor08/j+yZjQ3/uEaKvVjpJ67xOhYhBd0tACAMhMcj53iR6VvJstNmn9IQCoOMWUqNfxr93fBovFgq1bt2Lp0qXSNrlcjrlz52LDhg3tXl8QBKxZswb79+/H448/HnQ/s9kMs9l9pk+v752lqSn6xubppJ/FL8Jl54zGH04ZIvWRIiKinis9QQ2FXAa7Q0Ct0YKcZG37V+ohgq0/BIAzRmdj7f/NhkzmLFIjDBcwc2gG/vZJITYfa0CrxS5Vdj1hQAq2FTeiqdUKo9mGfRUGAO7vSI1SLj2HLRa71D5DIZdBIZdF4JESUSSFdNqntrYWdrsd2dnZXtuzs7NRWVkZ9HpNTU1ITEyEWq3GggUL8Oyzz+KMM84Iuv/y5cuRnJws/cvPzw9lmEQdlhKvRn6aMxDM1jmnIcnlMgaHRES9hFwuQ2aiOM20dxWqaSuDCACDMhIwMD0BACCTyTAsOwmDM5y/t1rtKHQFiCcPzZDWEq4/WAuL3QGdVokBafHSdRPUzmmmRrONLS6IermI/GUnJSVhx44d2Lx5Mx555BEsWbIEa9euDbr/0qVL0dTUJP0rKSmJxDCpjxKnmbJNBRFR75TlOgF43vO/SP3/eoP2AsRA4l0VST0ziGPzkqXvwJve2iptk8nc2UHPQjVSBVOuPyTqlUL6y87IyIBCoUBVVZXX9qqqKuTk5AS/E7kcQ4cOxcSJE/HXv/4VF198MZYvXx50f41GA51O5/WPqLvMH9sPSrkMMwoyoj0UIiLqBrOGuT/fd5Y0Rm8gYWYwOauK+vY4bItYzKbVakd5o3MN4pDMBMwalum13/DsJK/f410BotFsg9nmDLIZIBL1TiH9ZavVakyePBmrV6+WtjkcDqxevRrTp0/v8O04HA6vNYZE0XTuhFzsfnAezp+UF+2hEBFRN7hj3khMHuhsayROq+wNjK62EwmajgeIcSp3RVKxr2GiRoVHLxgLlcKdMRSzriLxPlrMdphdU0y1KgaIRL1RSEVqAGDJkiW45pprMGXKFEydOhVPP/00mpubsWjRIgDA1Vdfjby8PClDuHz5ckyZMgUFBQUwm834+uuv8eabb+LFF18M7yMh6gKNUtH+TkRE1GOdOjwTW483SNMqewOxL2FiKAGi2hnUNbVaYHc1UUzQKCCTyTCqnw67Sp3PT3aSdzGfRFeri2aLDfFW589afncS9UohB4gLFy5ETU0Nli1bhsrKSkycOBGrVq2SCtcUFxdDLnefUWpubsYtt9yC0tJSxMXFYeTIkXjrrbewcOHC8D0KIiIiojaI6817Uwax2eIMEMV1hR0R59q31mCRtonXz0rSAnAFiD59ExPU7immOteaRw0ziES9UsgBIgAsXrwYixcvDniZb/GZhx9+GA8//HBn7oaIiIgoLMa6AsQjtc0wmm0hZd1iVbPZOUU0QdPxTJ44xbTWaJZ+F1tVeE4rDTbFtNlsg9lV6IcZRKLeiad+iIiIqNfLTNIgR6eFIAB7yntHf+VOTTF1BYg1rgDRc/1iikc1VN8ppmIQavRYg8gMIlHvxL9sIiIi6hPG9rJppp0qUuNagyhWQE30yD7KPdpa6OK8b9NdpMYmtQphBpGod2KASERERH2CuA6xtxSqEdcghpZBDBz4AUBKvDuD6NkDEQASXWsQmy02ZhCJermePwGfiIiIqAPG9Xf2Ve4tGcQW1xrEeHXHM3n9U+O8fk/wKHBz2dQBWL23GmeMzva7XoLUB9HODCJRL8cAkYiIiPoEcYrp4Rojms22kKZmxqLOTDHtnxqHlHgVGlusruu6g7wEjRLv/uGkgNcT93MWqWEGkag34182ERER9QlZSVpk6zQQBGB3LyhU05kiNTKZTJpqC3Q8uPSsYmqyOTOI7CFM1DsxQCQiIqI+o7f0Q3Q4BDRbxDYXoWVCx3oEiB0NLqUA0cIMIlFvx79sIiIi6jPG9pJCNa2udYBAaBlEAJ3KICZKGUS7lEHkGkSi3okBIhEREfUZvSWDKE4vlcsAbYiZPM8AUaXo2HXFQjhGrkEk6vX4l01ERER9xjifQjU9lVSgRq30a0nRHs9KpmWNrR26TqJnH0RXmwtmEIl6JwaIRERE1Gdk6bTISnIWqtlfZYj2cDqt2dy59YeAd49D37YXwbjXINrR6lr7yAwiUe/Ev2wiIiLqU/LT4gEAlU2mKI+k85otYouLzmXxvvzTybhx1mDcdGpBh/b3XOfY2GIBwAwiUW/VsxsAEREREYUoW6cBAFTre3CA2IkWF57G5iV7VTNtj0Yph0Iug90hoK7ZFSCqGCAS9UbMIBIREVGfkpWkBQBUGcxRHknniWsQ49WROdcvk8mkQjV1RufzplHyMJKoN+JfNhEREfUpWVIGsecGiF1Zg9hZYrZSb3IGp8wgEvVODBCJiIioT8l2ZRCrDT13immLRZxiGrkgzTcYZZEaot6Jf9lERETUp/SGDKLU5iKCGUTf+2KRGqLeiQEiERER9SnZOmcG8VhdM+78cCeO1zVHeUSh62qRms5IUHsHhMwgEvVO/MsmIiKiPiU3JQ4apRxmmwP/21KKFT8djvaQQmZ0rUGMVJEawD+DGMc1iES9EgNEIiIi6lMSNUq8c+M0XDFtAABgV2lTlEcUupYu9kHsDN9sZWaSJmL3TUSRwwCRiIiI+pzJA9OkJvEHqgww2+xRHlFoojLF1CMY1WmVrGJK1EsxQCQiIqI+qX9qHFLiVbDaBeyvNER7OCGJdpEacR0nEfU+DBCJiIioT5LJZBiXlwwAKCzrWdNMxT6IkcwgpsarpZ/FSrBE1PswQCQiIqI+a6wrQCzqaQGiaw1ivDpy0zxH9dNJP4u9JImo92GASERERH1Wz80gRn6KqfhcAUBcBANTIoosBohERETUZ4lBz/7KnlWoJhpTTNMS3FNM64yWiN0vEUUWA0QiIiLqs/qnxiE5rmcVqmlqsUpFatIS1e3sHV4XndAfAHDDrMERvV8iihwGiERERNRn9cRCNeI4B6TFQ6dVRfS+H79oHH5bejqmDEqL6P0SUeQwQCQiIqI+racVqhEDRM81gZGiVMiRk8wCNUS9WeQmrhMRERHFIDHQendTCf63pRQAoJDJ8OczhuGW2UOjObSAxEB2bBQCRCLq/ZhBJCIioj5tekG6VIDF7hBgdwiw2B34fEd5lEcWWDQziETU+zGDSERERH1aWoIav959GvStVgDAoRojLn95I2oM5iiPzF9TixXF9S0AgLF5unb2JiIKHQNEIiIi6vO0KgW0KmdvP6XCOcGqrtkCi80BtTJ2JlwVlTuzh/lpcUiJj2wFUyLqG2LnE4+IiIgoBqTGq6BSyAAAtcbYyiKK00vH56VEdyBE1GsxQCQiIiLyIJPJkJXkrNRZpTdFeTTeClmghoi6GQNEIiIiIh9ZOg0AoEofWxnEIhaoIaJuxgCRiIiIyEdWkjNArDHETgaxqdWK43UsUENE3YsBIhEREZGPbJ04xTR2Moi7WaCGiCKAASIRERGRDzGDWB1DGcSyhlYAwOCMxCiPhIh6s04FiM8//zwGDRoErVaLadOmYdOmTUH3ffnllzFr1iykpqYiNTUVc+fObXN/IiIiomjLisEMYrWrL2O2K3glIuoOIQeI77//PpYsWYL77rsP27Ztw4QJEzBv3jxUV1cH3H/t2rW47LLL8OOPP2LDhg3Iz8/HmWeeibKysi4PnoiIiKg7iBnEWKpiWu0ai1hAh4ioO4QcID711FO48cYbsWjRIowePRorVqxAfHw8Vq5cGXD/t99+G7fccgsmTpyIkSNH4pVXXoHD4cDq1au7PHgiIiKi7iCuQawxxE4GUcxmimMjIuoOIQWIFosFW7duxdy5c903IJdj7ty52LBhQ4duo6WlBVarFWlpaUH3MZvN0Ov1Xv+IiIiIIkXMINY1W2CxOaI8Gqcq13pIsUcjEVF3CClArK2thd1uR3Z2ttf27OxsVFZWdug27rrrLuTm5noFmb6WL1+O5ORk6V9+fn4owyQiIiLqktR4NVQKGQCg1hgbWcRqVwaRU0yJqDtFtIrpY489hvfeew+ffPIJtNrgZ7+WLl2KpqYm6V9JSUkER0lERER9nVwuQ2aiMxD7Yme5tH3jkTqp3UQkCYIgTXflFFMi6k7KUHbOyMiAQqFAVVWV1/aqqirk5OS0ed0nn3wSjz32GH744QeMHz++zX01Gg00Gp4dIyIioujJTYlDeZMJy7/Zh4sm94fV7sDCl34DABx7bEFEx9LUaoXF7pzqKgauRETdIaQMolqtxuTJk70KzIgFZ6ZPnx70ev/4xz/w0EMPYdWqVZgyZUrnR0tEREQUIXfNHyn9vKu0EUdrmqXfTVZ7RMfS0GIFACRqlFAr2caaiLpPyJ8wS5Yswcsvv4zXX38de/fuxc0334zm5mYsWrQIAHD11Vdj6dKl0v6PP/447r33XqxcuRKDBg1CZWUlKisrYTQaw/coiIiIiMLsxEFpuGBSHgCgsFQPo9kmXdbUao3oWMT7S45TRfR+iajvCTlAXLhwIZ588kksW7YMEydOxI4dO7Bq1SqpcE1xcTEqKiqk/V988UVYLBZcfPHF6Nevn/TvySefDN+jICIiIuoG4/KSAQD/+uEAtpc0StujFSDqGCASUTcLaQ2iaPHixVi8eHHAy9auXev1+7FjxzpzF0RERERRN2lAivTzi2sPSz9HL4PYqUM3IqIO4yR2IiIioiAm5qdgwfh+ftubWjjFlIh6JwaIREREREHIZDI8ev44v+2RziDqGSASUYQwQCQiIiJqQ3K8CgPS4r22/fWDnShrbI3I/R+tbcYT3+53joUBIhF1MwaIRERERO2YMijVb9vdH+2KyH0v/M8G6WcGiETU3RggEhEREbXjnrNH4eHzx2JMrk7atvlYfUTuu9pgln5mgEhE3Y2lsIiIiIjakZ6owZUnDURDswW7y/UAgKwkbbffb32zxet3trkgou7GDCIRERFRB1XqTdLPSoWsW+/L4RBw8YpfvbaZbY5uvU8iIgaIRERERB00f6y75UWN3tzGnl23p0KPIzXNXttOGOC/FpKIKJwYIBIRERF10Myh6Xj7hmkAAIPZhhaLrdvuq6HFPb103Z1z8PVtszA0K7Hb7o+ICOAaRCIiIqIOk8lkmFGQjjiVAq1WO6r1ZgzK6J7DKbHX4tTBacj3abNBRNRdmEEkIiIiCoFMJkO2TgMAWPTaZgiC0C33IwaIrFxKRJHEAJGIiIgoRBPyUwA4m9h7tqEIJwaIRBQNDBCJiIiIQvTP30+QfhYDuXBjgEhE0cAAkYiIiChESoUcg9Kd6wK7K0DUM0AkoihggEhERETUCWLg1tTCDCIR9R4MEImIiIg6QScGiN08xVQXx6LzRBQ5DBCJiIiIOiE5QgEiM4hEFEkMEImIiIg6gQEiEfVGDBCJiIiIOkEM3J5ZfRBv/XY87Lcvrm1kgEhEkcQAkYiIiKgTPAO3v39aFNbbFgQBRrMNAJCkZYBIRJHDAJGIiIioE3wze3aHELbbNlkdEG8uQcMiNUQUOQwQiYiIiDoh3idw23ysHiX1LThcY+zybYvZQwCIVym6fHtERB3FU1JEREREnSAI3hnDS1/6Tfr5yd9PwMWT+3f6tptdAWKCWgG5XNbp2yEiChUziERERESdcOboHMwcmo4J/ZP9LttV2til2xYziL5ZSiKi7sYAkYiIiKgT4tQKvH3DSfhs8cm4fNoAr8uq9KYu3XaLxQ4ASGSASEQRxgCRiIiIqIvG5XlnEasN5i7dnjTFVMP1h0QUWQwQiYiIiLpoRE6S1+/V+q4FiEZpDSIziEQUWQwQiYiIiLpoUn4KbpldgJtOLQAAVBtMfkVsQtFiETOIDBCJKLIYIBIRERF1kUwmw51njcSSM4YDAKx2AQ0t1k7fntHsXIPIAJGIIo0BIhEREVGYqJVypCWoAXStUI24BjGRaxCJKMIYIBIRERGFUVaSBkDXCtU0cw0iEUUJA0QiIiKiMMrSaQF0MYNoYR9EIooOBohEREREYSRmEGu6lEEU+yByiikRRRYDRCIiIqIwytY5A8SuZBClNhfMIBJRhDFAJCIiIgqjrCTnFNOu9EKU2lxwDSIRRRgDRCIiIqIwkjKIhs5nEFstzimmcWpOMSWiyGKASERERBRGmWHJILoCRBUDRCKKLAaIRERERGEkZhCrDSYIgtCp2zBZnQFiPDOIRBRhDBCJiIiIwigrSQu5DLDaBVR2slBNqytA1DKDSEQR1qkA8fnnn8egQYOg1Woxbdo0bNq0Kei+u3fvxkUXXYRBgwZBJpPh6aef7uxYiYiIiGKeWinH0KxEAEBRmb5Tt8E1iEQULSEHiO+//z6WLFmC++67D9u2bcOECRMwb948VFdXB9y/paUFQ4YMwWOPPYacnJwuD5iIiIgo1o3NSwYAFJY1der6JqsDANcgElHkhRwgPvXUU7jxxhuxaNEijB49GitWrEB8fDxWrlwZcP8TTzwRTzzxBC699FJoNJouD5iIiIgo1o1zBYhFnQgQbXYHLHZngMg1iEQUaSEFiBaLBVu3bsXcuXPdNyCXY+7cudiwYUPYBmU2m6HX673+EREREfUUo/rpAACHqo0hX1dcfwhwDSIRRV5IAWJtbS3sdjuys7O9tmdnZ6OysjJsg1q+fDmSk5Olf/n5+WG7bSIiIqLulpscB6BzlUzFAFEmAzRK1hMkosiKyU+dpUuXoqmpSfpXUlIS7SERERERdViWq9WFyeqAwWwL6bomi3v9oUwmC/vYiIjaogxl54yMDCgUClRVVXltr6qqCmsBGo1Gw/WKRERE1GNpVQrotEroTTZU603QaVUdvm6L1RlQcv0hEUVDSBlEtVqNyZMnY/Xq1dI2h8OB1atXY/r06WEfHBEREVFPlaXTAgCq9eaQrie2uOD6QyKKhpAyiACwZMkSXHPNNZgyZQqmTp2Kp59+Gs3NzVi0aBEA4Oqrr0ZeXh6WL18OwFnYZs+ePdLPZWVl2LFjBxITEzF06NAwPhQiIiKi2JGt0+BQtRFVBlNI1xPXILLFBRFFQ8gB4sKFC1FTU4Nly5ahsrISEydOxKpVq6TCNcXFxZDL3YnJ8vJyTJo0Sfr9ySefxJNPPolTTz0Va9eu7fojICIiIopBWUnODGJViBlEkxggcoopEUVByAEiACxevBiLFy8OeJlv0Ddo0KCQq3cRERER9XRioZr2ppgerDLg8lc2wmS1w2S1w2p3HjdxiikRRUOnAkQiIiIiapuUQWxniulPB2pQY/APIlmkhoiiISbbXBARERH1dNmuDGJNOxnEQMEhwDWIRBQdDBCJiIiIuoGYQazUm1BtMMHhCLzkpkofOMPIAJGIooEBIhEREVE3EDOIxfUtmPrIalzz6qaA+1UHySBqOcWUiKKAASIRERFRNxAziKJ1B2sDFu4TM4gnDEjx2t5Pp/Xbl4iouzFAJCIiIuoGgdpUtFjsftvEDOI/Lp6AW2YXSNvH9k/uvsEREQXBAJGIiIgoQppardLPdoeAIzVGGEw2AM4pqQkad4H5cXkMEIko8tjmgoiIiChCmlqtyE2JAwA8+MVuvL7hOABnQZpEjRIqhUzaNyNRE5UxElHfxgwiERERUTd57vJJmDQgBWql85DLM4MoBocAMDgjATKZDAunDMDUwWl44HdjIj5WIiKAGUQiIiKibnPO+FycMz4X5z//C3aUNHoFiJ7E6aTJ8Sr874/TIzlEIiIvzCASERERdbPkOBUABA0QWZCGiGIFA0QiIiKibiYGiHqPAFHmXm6IYVmJkR4SEVFADBCJiIiIuplvgGiy2iG2RLx82gBMG5wWraEREXlhgEhERETUzXynmIqBokwGPHzeWMg804lERFHEAJGIiIiom/kGiOL/yXEqyOUMDokodjBAJCIiIupmKfHOALGu2QLAO0AkIoolDBCJiIiIutlQVxGavRV6CILAAJGIYhYDRCIiIqJuNqqfDgq5DLVGCyr1JgaIRBSzGCASERERdTOtSiG1sigsbZICRB0DRCKKMQwQiYiIiCJgbF4yAGBfpQE1BjMAIDWeASIRxRZltAdARERE1BfkpcQBACr1JpQ1tAIARmQnRXNIRER+mEEkIiIiioAsnQYAUK03oaisCYA7q0hEFCsYIBIRERFFQHaSFgCwo6QJdc0WKOQyjOqni/KoiIi8MUAkIiIiigAxg1hrdK4/HJaVCK1KEc0hERH5YYBIREREFAHZOq3X7+P7c3opEcUeBohEREREEZCeoIZM5v59HNcfElEMYoBIREREFAFKhRzpCRrpdxaoIaJYxACRiIiIKEJG9XO2tUjUKFmghohiEvsgEhEREUXIs5dNwsaj9RiZk8QCNUQUkxggEhEREUVISrwa88bkRHsYRERBcYopERERERERAWCASERERERERC4MEImIiIiIiAgAA0QiIiIiIiJyYYBIREREREREABggEhERERERkQsDRCIiIiIiIgLAAJGIiIiIiIhcGCASERERERERAAaIRERERERE5KKM9gA6QhAEAIBer4/ySIiIiIiIiHoWMY4S46q29IgA0WAwAADy8/OjPBIiIiIiIqKeyWAwIDk5uc19ZEJHwsgoczgcKC8vR1JSEmQyWbSHI9Hr9cjPz0dJSQl0Ol20h0O9HN9vFCl8r1Gk8L1GkcL3GkVKrL7XBEGAwWBAbm4u5PK2Vxn2iAyiXC5H//79oz2MoHQ6XUy9Aah34/uNIoXvNYoUvtcoUvheo0iJxfdae5lDEYvUEBEREREREQAGiEREREREROTCALELNBoN7rvvPmg0mmgPhfoAvt8oUvheo0jhe40ihe81ipTe8F7rEUVqiIiIiIiIqPsxg0hEREREREQAGCASERERERGRCwNEIiIiIiIiAsAAkYiIiIiIiFwYIHbB888/j0GDBkGr1WLatGnYtGlTtIdEPczy5ctx4oknIikpCVlZWTj//POxf/9+r31MJhNuvfVWpKenIzExERdddBGqqqq89ikuLsaCBQsQHx+PrKws3HHHHbDZbJF8KNSDPPbYY5DJZPjzn/8sbeP7jMKprKwMV155JdLT0xEXF4dx48Zhy5Yt0uWCIGDZsmXo168f4uLiMHfuXBw8eNDrNurr63HFFVdAp9MhJSUF119/PYxGY6QfCsUwu92Oe++9F4MHD0ZcXBwKCgrw0EMPwbP+It9r1Bk///wzzj33XOTm5kImk+HTTz/1ujxc76tdu3Zh1qxZ0Gq1yM/Pxz/+8Y/ufmgdI1CnvPfee4JarRZWrlwp7N69W7jxxhuFlJQUoaqqKtpDox5k3rx5wquvvioUFRUJO3bsEM4++2xhwIABgtFolPa56aabhPz8fGH16tXCli1bhJNOOkmYMWOGdLnNZhPGjh0rzJ07V9i+fbvw9ddfCxkZGcLSpUuj8ZAoxm3atEkYNGiQMH78eOH222+XtvN9RuFSX18vDBw4ULj22muFjRs3CkeOHBG+/fZb4dChQ9I+jz32mJCcnCx8+umnws6dO4Xf/e53wuDBg4XW1lZpn7POOkuYMGGC8Ntvvwnr1q0Thg4dKlx22WXReEgUox555BEhPT1d+PLLL4WjR48KH3zwgZCYmCg888wz0j58r1FnfP3118I999wjfPzxxwIA4ZNPPvG6PBzvq6amJiE7O1u44oorhKKiIuHdd98V4uLihP/85z+RephBMUDspKlTpwq33nqr9Lvdbhdyc3OF5cuXR3FU1NNVV1cLAISffvpJEARBaGxsFFQqlfDBBx9I++zdu1cAIGzYsEEQBOeHmFwuFyorK6V9XnzxRUGn0wlmszmyD4BimsFgEIYNGyZ8//33wqmnnioFiHyfUTjdddddwsknnxz0cofDIeTk5AhPPPGEtK2xsVHQaDTCu+++KwiCIOzZs0cAIGzevFna55tvvhFkMplQVlbWfYOnHmXBggXCdddd57XtwgsvFK644gpBEPheo/DwDRDD9b564YUXhNTUVK/v0LvuuksYMWJENz+i9nGKaSdYLBZs3boVc+fOlbbJ5XLMnTsXGzZsiOLIqKdramoCAKSlpQEAtm7dCqvV6vVeGzlyJAYMGCC91zZs2IBx48YhOztb2mfevHnQ6/XYvXt3BEdPse7WW2/FggULvN5PAN9nFF6ff/45pkyZgt///vfIysrCpEmT8PLLL0uXHz16FJWVlV7vt+TkZEybNs3r/ZaSkoIpU6ZI+8ydOxdyuRwbN26M3IOhmDZjxgysXr0aBw4cAADs3LkT69evx/z58wHwvUbdI1zvqw0bNuCUU06BWq2W9pk3bx7279+PhoaGCD2awJRRvfceqra2Fna73etACQCys7Oxb9++KI2KejqHw4E///nPmDlzJsaOHQsAqKyshFqtRkpKite+2dnZqKyslPYJ9F4ULyMCgPfeew/btm3D5s2b/S7j+4zC6ciRI3jxxRexZMkS/O1vf8PmzZtx2223Qa1W45prrpHeL4HeT57vt6ysLK/LlUol0tLS+H4jyd133w29Xo+RI0dCoVDAbrfjkUcewRVXXAEAfK9RtwjX+6qyshKDBw/2uw3xstTU1G4Zf0cwQCSKEbfeeiuKioqwfv36aA+FepmSkhLcfvvt+P7776HVaqM9HOrlHA4HpkyZgkcffRQAMGnSJBQVFWHFihW45pprojw66k3+97//4e2338Y777yDMWPGYMeOHfjzn/+M3NxcvteIuoBTTDshIyMDCoXCr8JfVVUVcnJyojQq6skWL16ML7/8Ej/++CP69+8vbc/JyYHFYkFjY6PX/p7vtZycnIDvRfEyoq1bt6K6uhonnHAClEollEolfvrpJ/z73/+GUqlEdnY232cUNv369cPo0aO9to0aNQrFxcUA3O+Xtr5Dc3JyUF1d7XW5zWZDfX09328kueOOO3D33Xfj0ksvxbhx43DVVVfhL3/5C5YvXw6A7zXqHuF6X8Xy9yoDxE5Qq9WYPHkyVq9eLW1zOBxYvXo1pk+fHsWRUU8jCAIWL16MTz75BGvWrPGbajB58mSoVCqv99r+/ftRXFwsvdemT5+OwsJCrw+i77//Hjqdzu8gjfqm008/HYWFhdixY4f0b8qUKbjiiiukn/k+o3CZOXOmX7ueAwcOYODAgQCAwYMHIycnx+v9ptfrsXHjRq/3W2NjI7Zu3Srts2bNGjgcDkybNi0Cj4J6gpaWFsjl3oeyCoUCDocDAN9r1D3C9b6aPn06fv75Z1itVmmf77//HiNGjIjq9FIAbHPRWe+9956g0WiE1157TdizZ4/whz/8QUhJSfGq8EfUnptvvllITk4W1q5dK1RUVEj/WlpapH1uuukmYcCAAcKaNWuELVu2CNOnTxemT58uXS62HzjzzDOFHTt2CKtWrRIyMzPZfoDa5FnFVBD4PqPw2bRpk6BUKoVHHnlEOHjwoPD2228L8fHxwltvvSXt89hjjwkpKSnCZ599JuzatUs477zzApaInzRpkrBx40Zh/fr1wrBhw9h6gLxcc801Ql5entTm4uOPPxYyMjKEO++8U9qH7zXqDIPBIGzfvl3Yvn27AEB46qmnhO3btwvHjx8XBCE876vGxkYhOztbuOqqq4SioiLhvffeE+Lj49nmoqd79tlnhQEDBghqtVqYOnWq8Ntvv0V7SNTDAAj479VXX5X2aW1tFW655RYhNTVViI+PFy644AKhoqLC63aOHTsmzJ8/X4iLixMyMjKEv/71r4LVao3wo6GexDdA5PuMwumLL74Qxo4dK2g0GmHkyJHCSy+95HW5w+EQ7r33XiE7O1vQaDTC6aefLuzfv99rn7q6OuGyyy4TEhMTBZ1OJyxatEgwGAyRfBgU4/R6vXD77bcLAwYMELRarTBkyP+3d8c2CMNAGEZ/qgzhjjm8SwbJAtnKM3iio3GDFFEgBBTvbWDpivtkWb7XcRxP3waYNd4xxrjcz/Z9r6rPzdWcs3rvtW1btdbqPM9vHfGlW1XVb+4uAQAA+CfeIAIAAJBEIAIAALAIRAAAAJIIRAAAABaBCAAAQBKBCAAAwCIQAQAASCIQAQAAWAQiAAAASQQiAAAAi0AEAAAgiUAEAABgeQDxzZD4m2q55AAAAABJRU5ErkJggg==" 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" }, "metadata": {}, "output_type": "display_data" @@ -283,11 +246,11 @@ ], "source": [ "from matplotlib import pyplot as plt\n", - "pd.DataFrame(input_data.features[1, 0, :]).plot(title='1 channel PPG signal')\n", - "pd.DataFrame(input_data.features[1, 1, :]).plot(title='2 channel PPG signal')\n", - "pd.DataFrame(input_data.features[1, 2, :]).plot(title='x-axis acceleration')\n", - "pd.DataFrame(input_data.features[1, 3, :]).plot(title='y-axis acceleration')\n", - "pd.DataFrame(input_data.features[1, 4, :]).plot(title='z-axis acceleration')\n", + "pd.DataFrame(features[1, 0, :]).plot(title='1 channel PPG signal')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='2 channel PPG signal')\n", + "pd.DataFrame(features[1, 2, :]).plot(title='x-axis acceleration')\n", + "pd.DataFrame(features[1, 3, :]).plot(title='y-axis acceleration')\n", + "pd.DataFrame(features[1, 4, :]).plot(title='z-axis acceleration')\n", "plt.show()" ], "metadata": { @@ -313,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": { "ExecuteTime": { "start_time": "2023-08-28T10:35:27.965798Z" @@ -327,29 +290,143 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n", - "Current_model - regression_pca_with_statistical_features\n", - "Current_model - regression_with_reccurence_features\n", - "Current_model - regression_pca_with_reccurence_features\n", - "Current_model - regression_with_topological_features\n", - "Current_model - regression_pca_with_topological_features\n" + "2024-04-04 13:50:55,492 - Initialising experiment setup\n", + "2024-04-04 13:50:55,692 - Initialising Industrial Repository\n", + "2024-04-04 13:50:56,560 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-04 13:50:57,292 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-04-04 13:50:57,327 - State start\n", + "2024-04-04 13:50:57,584 - Scheduler at: inproc://10.64.4.217/16776/1\n", + "2024-04-04 13:50:57,585 - dashboard at: http://10.64.4.217:54489/status\n", + "2024-04-04 13:50:57,585 - Registering Worker plugin shuffle\n", + "2024-04-04 13:50:57,847 - Start worker at: inproc://10.64.4.217/16776/4\n", + "2024-04-04 13:50:57,848 - Listening to: inproc10.64.4.217\n", + "2024-04-04 13:50:57,849 - Worker name: 0\n", + "2024-04-04 13:50:57,849 - dashboard at: 10.64.4.217:54490\n", + "2024-04-04 13:50:57,850 - Waiting to connect to: inproc://10.64.4.217/16776/1\n", + "2024-04-04 13:50:57,850 - -------------------------------------------------\n", + "2024-04-04 13:50:57,851 - Threads: 8\n", + "2024-04-04 13:50:57,851 - Memory: 31.95 GiB\n", + "2024-04-04 13:50:57,852 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-f6_ivs28\n", + "2024-04-04 13:50:57,852 - -------------------------------------------------\n", + "2024-04-04 13:50:57,863 - Register worker \n", + "2024-04-04 13:50:57,865 - Starting worker compute stream, inproc://10.64.4.217/16776/4\n", + "2024-04-04 13:50:57,865 - Starting established connection to inproc://10.64.4.217/16776/5\n", + "2024-04-04 13:50:57,866 - Starting Worker plugin shuffle\n", + "2024-04-04 13:50:57,867 - Registered to: inproc://10.64.4.217/16776/1\n", + "2024-04-04 13:50:57,868 - -------------------------------------------------\n", + "2024-04-04 13:50:57,869 - Starting established connection to inproc://10.64.4.217/16776/1\n", + "2024-04-04 13:50:57,873 - Receive client connection: Client-37ef08a3-f271-11ee-8188-b42e99a00ea1\n", + "2024-04-04 13:50:57,875 - Starting established connection to inproc://10.64.4.217/16776/6\n", + "2024-04-04 13:50:57,876 - LinK Dask Server - http://10.64.4.217:54489/status\n", + "2024-04-04 13:50:57,877 - Initialising solver\n", + "2024-04-04 13:50:57,954 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 13:50:57,956 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 13:50:58,078 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-04 13:52:13,408 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [12.963]\n", + " 0%| | 0/100 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
regression_with_statistical_features-7.4682668182.08546090.45488177.12018570.302458-1.601039495.729209-1.868315
regression_pca_with_statistical_features-0.2429501200.94485334.65465127.73720924.3733130.02508769.736723-0.031624
regression_with_reccurence_features-3.7091234549.98097167.45354749.10446036.759664-1.752046471.482557-0.826332
regression_pca_with_reccurence_features-0.1654771126.09063633.55727426.91919523.6182490.26512681.004285-0.001200
regression_with_topological_features-0.4121421364.41922136.93804629.38025524.066633-0.04829976.914691-0.092734
regression_pca_with_topological_features-0.4121421364.41922136.93804629.38025524.066633-0.04829976.914691-0.092734
\n" + "text/plain": " r2 rmse mae\n0 0.089 29.674 24.49", + "text/html": "
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r2rmsemae
00.08929.67424.49
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" }, - "execution_count": 25, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_baseline = pd.concat([x for x in metric_dict.values()],axis=1)\n", - "df_baseline.columns = list(metric_dict.keys())\n", - "df_baseline = df_baseline.T\n", - "df_baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [], - "source": [ - "best_baseline = df_baseline.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" + "metrics" ], "metadata": { "collapsed": false, @@ -415,7 +472,7 @@ { "cell_type": "markdown", "source": [ - "## Could it be done better? Tuning approach" + "## AutoML approach" ], "metadata": { "collapsed": false, @@ -426,280 +483,92 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n", - "2023-10-12 17:08:06,755 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", - "2023-10-12 17:08:06,757 - DataSourceSplitter - Hold out validation is applied.\n", - "2023-10-12 17:08:06,771 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", - "2023-10-12 17:13:01,150 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [ridge, quantile_extractor]}\n", - "ridge - {}\n", - "quantile_extractor - {'window_size': 5} \n", - "Initial metric: [76.599]\n", - " 0%| | 0/15 [00:00\n", + "2024-04-04 15:10:53,050 - Starting worker compute stream, inproc://10.64.4.217/16776/12\n", + "2024-04-04 15:10:53,051 - Starting established connection to inproc://10.64.4.217/16776/13\n", + "2024-04-04 15:10:53,052 - Starting Worker plugin shuffle\n", + "2024-04-04 15:10:53,052 - Registered to: inproc://10.64.4.217/16776/9\n", + "2024-04-04 15:10:53,053 - -------------------------------------------------\n", + "2024-04-04 15:10:53,054 - Starting established connection to inproc://10.64.4.217/16776/9\n", + "2024-04-04 15:10:53,058 - Receive client connection: Client-62163863-f27c-11ee-8188-b42e99a00ea1\n", + "2024-04-04 15:10:53,059 - Starting established connection to inproc://10.64.4.217/16776/14\n", + "2024-04-04 15:10:53,061 - LinK Dask Server - http://10.64.4.217:56162/status\n", + "2024-04-04 15:10:53,061 - Initialising solver\n", + "2024-04-04 15:10:53,241 - AssumptionsHandler - Initial pipeline fitting started\n", + "2024-04-04 15:12:11,701 - AssumptionsHandler - Initial pipeline was fitted successfully\n", + "2024-04-04 15:12:11,702 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 199.8 MiB, max: 208.0 MiB\n", + "2024-04-04 15:12:11,707 - ApiComposer - Initial pipeline was fitted in 78.6 sec.\n", + "2024-04-04 15:12:11,709 - AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", + "2024-04-04 15:12:11,718 - ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 15 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'dtreg', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "2024-04-04 15:12:11,744 - ApiComposer - Timeout is too small for composing and is skipped because fit_time is 78.602621 sec.\n", + "2024-04-04 15:12:11,749 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 15:12:11,751 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 15:12:11,879 - ApiComposer - Hyperparameters tuning started with 14 min. timeout\n", + "2024-04-04 15:12:11,882 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-04 15:13:32,973 - SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [13.07]\n", + " 0%| | 0/100000 [00:00 3\u001B[0m auto_probs \u001B[38;5;241m=\u001B[39m \u001B[43mindustrial_auto_model\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_proba\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtest_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m auto_metrics \u001B[38;5;241m=\u001B[39m industrial_auto_model\u001B[38;5;241m.\u001B[39mget_metrics(target\u001B[38;5;241m=\u001B[39mtest_data[\u001B[38;5;241m1\u001B[39m],\n\u001B[0;32m 5\u001B[0m rounding_order\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3\u001B[39m,\n\u001B[0;32m 6\u001B[0m metric_names\u001B[38;5;241m=\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mr2\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mrmse\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmae\u001B[39m\u001B[38;5;124m'\u001B[39m))\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\api\\main.py:261\u001B[0m, in \u001B[0;36mFedotIndustrial.predict_proba\u001B[1;34m(self, predict_data, predict_mode, **kwargs)\u001B[0m\n\u001B[0;32m 259\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 260\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcondition_check\u001B[38;5;241m.\u001B[39msolver_is_fedot_class(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver):\n\u001B[1;32m--> 261\u001B[0m predict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolver\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_proba\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 262\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 263\u001B[0m predict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver\u001B[38;5;241m.\u001B[39mpredict(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredict_data, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mprobs\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39mpredict\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\main.py:325\u001B[0m, in \u001B[0;36mFedot.predict_proba\u001B[1;34m(self, features, save_predictions, probs_for_all_classes)\u001B[0m\n\u001B[0;32m 323\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_predict(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprediction)\n\u001B[0;32m 324\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 325\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mProbabilities of predictions are available only for classification\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 327\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprediction\u001B[38;5;241m.\u001B[39mpredict\n", + "\u001B[1;31mValueError\u001B[0m: Probabilities of predictions are available only for classification" ] } ], "source": [ - "metric_dict = {}\n", - "with IndustrialModels():\n", - " for model in model_dict.keys():\n", - " print(f'Current_model - {model}')\n", - " pipeline = model_dict[model].build()\n", - " tuned_pipeline = tuning_industrial_pipelines(pipeline, tuning_params, train_data)\n", - " tuned_pipeline.fit(input_data)\n", - " features = tuned_pipeline.predict(val_data).predict\n", - " metric = calculate_regression_metric(test_target=test_data[1], labels=features)\n", - " metric = metric.T\n", - " metric.columns = metric.columns.values\n", - " metric['model_params'] = metric['model_params'] = str(\n", - " {node: node.parameters for node in tuned_pipeline.graph_description['nodes']})\n", - " metric_dict.update({model: metric})" + "industrial_auto_model = evaluate_loop(api_params=params, finetune=False)" ], "metadata": { "collapsed": false, @@ -710,22 +579,13 @@ }, { "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": " r2_score: mean_squared_error: \\\nregression_pca_with_statistical_features -0.267307 1224.479283 \nregression_pca_with_topological_features -0.358628 1312.713985 \nregression_with_topological_features -0.445373 1396.527196 \nregression_pca_with_reccurence_features -0.824586 1762.925179 \nregression_with_statistical_features -4.418449 5235.335498 \nregression_with_reccurence_features -5.360063 6145.128132 \n\n root_mean_squared_error: \\\nregression_pca_with_statistical_features 34.99256 \nregression_pca_with_topological_features 36.231395 \nregression_with_topological_features 37.370138 \nregression_pca_with_reccurence_features 41.987203 \nregression_with_statistical_features 72.355618 \nregression_with_reccurence_features 78.390868 \n\n mean_absolute_error \\\nregression_pca_with_statistical_features 28.002211 \nregression_pca_with_topological_features 29.108821 \nregression_with_topological_features 29.633046 \nregression_pca_with_reccurence_features 33.59318 \nregression_with_statistical_features 48.349462 \nregression_with_reccurence_features 44.090572 \n\n median_absolute_error \\\nregression_pca_with_statistical_features 24.469579 \nregression_pca_with_topological_features 24.356167 \nregression_with_topological_features 23.844611 \nregression_pca_with_reccurence_features 28.397834 \nregression_with_statistical_features 36.018301 \nregression_with_reccurence_features 26.029369 \n\n explained_variance_score max_error \\\nregression_pca_with_statistical_features 0.01928 70.151608 \nregression_pca_with_topological_features -0.093163 76.709922 \nregression_with_topological_features -0.068693 78.899169 \nregression_pca_with_reccurence_features 0.050515 112.092017 \nregression_with_statistical_features -4.253361 632.934648 \nregression_with_reccurence_features -5.186325 628.270569 \n\n d2_absolute_error_score \\\nregression_pca_with_statistical_features -0.041481 \nregression_pca_with_topological_features -0.082638 \nregression_with_topological_features -0.102136 \nregression_pca_with_reccurence_features -0.249424 \nregression_with_statistical_features -0.798252 \nregression_with_reccurence_features -0.639852 \n\n model_params \nregression_pca_with_statistical_features {ridge: {'alpha': 3.6182614123314063}, pca: {'... \nregression_pca_with_topological_features {ridge: {'alpha': 8.456083606865826}, pca: {'s... \nregression_with_topological_features {ridge: {'alpha': 2.0374680133907237}, pca: {'... \nregression_pca_with_reccurence_features {ridge: {'alpha': 1.6319331955058072}, pca: {'... \nregression_with_statistical_features {ridge: {'alpha': 4.447241082992835}, quantile... \nregression_with_reccurence_features {ridge: {'alpha': 0.5841302173429871}, recurre... 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r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_scoremodel_params
regression_pca_with_statistical_features-0.2673071224.47928334.9925628.00221124.4695790.0192870.151608-0.041481{ridge: {'alpha': 3.6182614123314063}, pca: {'...
regression_pca_with_topological_features-0.3586281312.71398536.23139529.10882124.356167-0.09316376.709922-0.082638{ridge: {'alpha': 8.456083606865826}, pca: {'s...
regression_with_topological_features-0.4453731396.52719637.37013829.63304623.844611-0.06869378.899169-0.102136{ridge: {'alpha': 2.0374680133907237}, pca: {'...
regression_pca_with_reccurence_features-0.8245861762.92517941.98720333.5931828.3978340.050515112.092017-0.249424{ridge: {'alpha': 1.6319331955058072}, pca: {'...
regression_with_statistical_features-4.4184495235.33549872.35561848.34946236.018301-4.253361632.934648-0.798252{ridge: {'alpha': 4.447241082992835}, quantile...
regression_with_reccurence_features-5.3600636145.12813278.39086844.09057226.029369-5.186325628.270569-0.639852{ridge: {'alpha': 0.5841302173429871}, recurre...
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" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 12, + "outputs": [], "source": [ - "df_tuned = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_tuned.columns = list(metric_dict.keys())\n", - "df_tuned.T.sort_values('root_mean_squared_error:')" + "auto_labels = industrial_auto_model.predict(test_data)\n", + "auto_metrics = industrial_auto_model.get_metrics(target=test_data[1],\n", + " rounding_order=3,\n", + " metric_names=('r2', 'rmse', 'mae'))" ], "metadata": { "collapsed": false, @@ -736,19 +596,20 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "outputs": [ { "data": { - "text/plain": "\"{ridge: {'alpha': 3.6182614123314063}, pca: {'svd_solver': 'full', 'n_components': 0.47009543669720244}, quantile_extractor: {'window_size': 24, 'stride': 8}}\"" + "text/plain": " r2 rmse mae\n0 0.077 29.869 24.641", + "text/html": "
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r2rmsemae
00.07729.86924.641
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" }, - "execution_count": 29, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_tuned.T.sort_values('root_mean_squared_error:')['model_params'].iloc[0]" + "auto_metrics" ], "metadata": { "collapsed": false, @@ -759,111 +620,37 @@ }, { "cell_type": "code", - "execution_count": 30, - "outputs": [], - "source": [ - "best_tuned = df_tuned.T.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" } - } - }, - { - "cell_type": "markdown", + ], "source": [ - "## Even better? AutoML approach" + "predictions = np.vstack([test_data[1].flatten(), labels.flatten(), auto_labels.flatten()]).T\n", + "all_prediction = pd.DataFrame(predictions, columns=['target', 'baseline', 'automl'])\n", + "all_prediction.plot()\n", + "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } } }, { "cell_type": "code", - "execution_count": 31, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-10-13 01:25:55,375 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 428.0 MiB, max: 1009.2 MiB\n", - "2023-10-13 01:25:55,389 - ApiComposer - Initial pipeline was fitted in 1444.9 sec.\n", - "2023-10-13 01:25:55,391 - AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", - "2023-10-13 01:25:55,397 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 10 min. Set of candidate models: ['rfr', 'ridge', 'scaling', 'normalization', 'pca', 'catboostreg', 'xgbreg', 'svr', 'dtreg', 'treg', 'knnreg', 'fast_ica', 'kernel_pca', 'isolation_forest_reg', 'rfe_lin_reg', 'rfe_non_lin_reg'].\n", - "2023-10-13 01:25:55,400 - ApiComposer - Timeout is too small for composing and is skipped because fit_time is 1444.859073 sec.\n", - "2023-10-13 01:25:55,713 - ApiComposer - Model generation finished\n", - "2023-10-13 01:25:55,803 - FEDOT logger - Already fitted initial pipeline is used\n", - "2023-10-13 01:25:55,804 - FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [rfr, scaling]}\n", - "rfr - {'n_jobs': 8}\n", - "scaling - {}\n", - "2023-10-13 01:25:55,805 - MemoryAnalytics - Memory consumption for finish in main session: current 428.4 MiB, max: 1009.2 MiB\n", - "2023-10-13 01:58:37,839 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 427.9 MiB, max: 1009.1 MiB\n", - "2023-10-13 01:58:37,853 - ApiComposer - Initial pipeline was fitted in 1444.7 sec.\n", - "2023-10-13 01:58:37,854 - AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", - "2023-10-13 01:58:37,859 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 10 min. Set of candidate models: ['rfr', 'ridge', 'scaling', 'normalization', 'pca', 'catboostreg', 'xgbreg', 'svr', 'dtreg', 'treg', 'knnreg', 'fast_ica', 'kernel_pca', 'isolation_forest_reg', 'rfe_lin_reg', 'rfe_non_lin_reg'].\n", - "2023-10-13 01:58:37,861 - ApiComposer - Timeout is too small for composing and is skipped because fit_time is 1444.740729 sec.\n", - "2023-10-13 01:58:38,190 - ApiComposer - Model generation finished\n", - "2023-10-13 01:58:38,284 - FEDOT logger - Already fitted initial pipeline is used\n", - "2023-10-13 01:58:38,285 - FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [rfr, scaling]}\n", - "rfr - {'n_jobs': 8}\n", - "scaling - {}\n", - "2023-10-13 01:58:38,286 - MemoryAnalytics - Memory consumption for finish in main session: current 428.2 MiB, max: 1009.1 MiB\n", - "2023-10-13 02:31:15,221 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 427.9 MiB, max: 1009.1 MiB\n", - "2023-10-13 02:31:15,236 - ApiComposer - Initial pipeline was fitted in 1443.5 sec.\n", - "2023-10-13 02:31:15,237 - AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", - "2023-10-13 02:31:15,242 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 10 min. Set of candidate models: ['rfr', 'ridge', 'scaling', 'normalization', 'pca', 'catboostreg', 'xgbreg', 'svr', 'dtreg', 'treg', 'knnreg', 'fast_ica', 'kernel_pca', 'isolation_forest_reg', 'rfe_lin_reg', 'rfe_non_lin_reg'].\n", - "2023-10-13 02:31:15,245 - ApiComposer - Timeout is too small for composing and is skipped because fit_time is 1443.453459 sec.\n", - "2023-10-13 02:31:15,575 - ApiComposer - Model generation finished\n", - "2023-10-13 02:31:15,670 - FEDOT logger - Already fitted initial pipeline is used\n", - "2023-10-13 02:31:15,672 - FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [rfr, scaling]}\n", - "rfr - {'n_jobs': 8}\n", - "scaling - {}\n", - "2023-10-13 02:31:15,674 - MemoryAnalytics - Memory consumption for finish in main session: current 428.2 MiB, max: 1009.1 MiB\n", - "2023-10-13 03:03:54,741 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 427.9 MiB, max: 1009.1 MiB\n", - "2023-10-13 03:03:54,755 - ApiComposer - Initial pipeline was fitted in 1443.3 sec.\n", - "2023-10-13 03:03:54,757 - AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", - "2023-10-13 03:03:54,763 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 10 min. Set of candidate models: ['rfr', 'ridge', 'scaling', 'normalization', 'pca', 'catboostreg', 'xgbreg', 'svr', 'dtreg', 'treg', 'knnreg', 'fast_ica', 'kernel_pca', 'isolation_forest_reg', 'rfe_lin_reg', 'rfe_non_lin_reg'].\n", - "2023-10-13 03:03:54,765 - ApiComposer - Timeout is too small for composing and is skipped because fit_time is 1443.284791 sec.\n", - "2023-10-13 03:03:55,095 - ApiComposer - Model generation finished\n", - "2023-10-13 03:03:55,193 - FEDOT logger - Already fitted initial pipeline is used\n", - "2023-10-13 03:03:55,194 - FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [rfr, scaling]}\n", - "rfr - {'n_jobs': 8}\n", - "scaling - {}\n", - "2023-10-13 03:03:55,195 - MemoryAnalytics - Memory consumption for finish in main session: current 428.2 MiB, max: 1009.1 MiB\n", - "2023-10-13 03:36:32,894 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 427.9 MiB, max: 1009.1 MiB\n", - "2023-10-13 03:36:32,909 - ApiComposer - Initial pipeline was fitted in 1445.2 sec.\n", - "2023-10-13 03:36:32,910 - AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", - "2023-10-13 03:36:32,915 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 10 min. Set of candidate models: ['rfr', 'ridge', 'scaling', 'normalization', 'pca', 'catboostreg', 'xgbreg', 'svr', 'dtreg', 'treg', 'knnreg', 'fast_ica', 'kernel_pca', 'isolation_forest_reg', 'rfe_lin_reg', 'rfe_non_lin_reg'].\n", - "2023-10-13 03:36:32,918 - ApiComposer - Timeout is too small for composing and is skipped because fit_time is 1445.222377 sec.\n", - "2023-10-13 03:36:33,251 - ApiComposer - Model generation finished\n", - "2023-10-13 03:36:33,344 - FEDOT logger - Already fitted initial pipeline is used\n", - "2023-10-13 03:36:33,346 - FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [rfr, scaling]}\n", - "rfr - {'n_jobs': 8}\n", - "scaling - {}\n", - "2023-10-13 03:36:33,347 - MemoryAnalytics - Memory consumption for finish in main session: current 428.2 MiB, max: 1009.1 MiB\n" - ] - } - ], + "execution_count": 22, + "outputs": [], "source": [ - "metric_dict = {}\n", - "runs = 5\n", - "for run in range(runs):\n", - " with IndustrialModels():\n", - " auto_ml = PipelineBuilder().add_node('quantile_extractor',\n", - " params={'window_size': 5}).add_node('fedot_regr',\n", - " params={'timeout': 10,'logging_level':30}).build()\n", - " auto_ml.fit(input_data)\n", - " features = auto_ml.predict(val_data).predict\n", - " metric = calculate_regression_metric(test_target=test_data[1], labels=features)\n", - " metric = metric.T\n", - " metric.columns = metric.columns.values\n", - " metric['model_params'] = metric['model_params'] = str({node:node.parameters for node in auto_ml.graph_description['nodes']})\n", - " metric_dict.update({f'run_number - {run}':metric})" + "import numpy as np\n", + "features = np.array(test_data[0].values.tolist()).astype(float)" ], "metadata": { "collapsed": false, @@ -874,23 +661,56 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 23, "outputs": [ { "data": { - "text/plain": " r2_score: mean_squared_error: root_mean_squared_error: \\\nrun_number - 0 -0.214571 1173.525382 34.256757 \nrun_number - 1 -0.214571 1173.525382 34.256757 \nrun_number - 2 -0.214571 1173.525382 34.256757 \nrun_number - 3 -0.214571 1173.525382 34.256757 \nrun_number - 4 -0.214571 1173.525382 34.256757 \n\n mean_absolute_error median_absolute_error \\\nrun_number - 0 28.322523 26.695466 \nrun_number - 1 28.322523 26.695466 \nrun_number - 2 28.322523 26.695466 \nrun_number - 3 28.322523 26.695466 \nrun_number - 4 28.322523 26.695466 \n\n explained_variance_score max_error d2_absolute_error_score \\\nrun_number - 0 0.140885 82.363357 -0.053394 \nrun_number - 1 0.140885 82.363357 -0.053394 \nrun_number - 2 0.140885 82.363357 -0.053394 \nrun_number - 3 0.140885 82.363357 -0.053394 \nrun_number - 4 0.140885 82.363357 -0.053394 \n\n model_params \nrun_number - 0 {fedot_regr: {'timeout': 10, 'logging_level': ... \nrun_number - 1 {fedot_regr: {'timeout': 10, 'logging_level': ... \nrun_number - 2 {fedot_regr: {'timeout': 10, 'logging_level': ... \nrun_number - 3 {fedot_regr: {'timeout': 10, 'logging_level': ... \nrun_number - 4 {fedot_regr: {'timeout': 10, 'logging_level': ... 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r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_scoremodel_params
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" }, - "execution_count": 32, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" 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" 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hCIIgiDQnGo1i7dq1CIfDaNOmDfLy8rIuCK2qqqirq8Mvv/yCtWvXonv37paB+qwg4YYgCIIg0py6ujpEo1G0b98eRUVFqW5OYBQWFiI3Nxc//fQT6urqUFBQ4KoecigmCIIgiAzBrSYjk/DjGrP/LhEEQRAEcUBBwg1BEARBEFkFCTcEQRAEQWQVJNwQBEEQBBE4U6dORadOnVBQUIBBgwZh/vz5gZ2LhBsiLfh4xVb8b/HGVDeDIAiCCIB///vfuOGGG3D77bfjm2++Qd++fTFy5Ehs3bo1kPPRUnAiLZjw/NcAgAGdytG2rDDFrSEIgkh/VFXFvvrURCouzA07irPzyCOP4NJLL8WECRMAAE8//TTeffddPPfcc7j55pt9bx8JN0RasX1PHQk3BEEQEuyrj6DnbR+k5Nzf3zUSRXlyIkRdXR0WLlyIyZMnx7eFQiEMHz4cc+fODaR9ZJYi0goVaqqbQBAEQfjItm3bEIlE0KpVK932Vq1aoaqqKpBzkuaGSDmqmhBooiTbEARBSFGYG8b3d41M2bnTGRJuiJTDyDY6QYcgCIIQoyiKtGkolbRo0QLhcBhbtmzRbd+yZQsqKysDOSeZpYi0gjQ3BEEQ2UVeXh769++Pjz76KL4tGo3io48+wuDBgwM5Z/qLfETWw8ozpLkhCILIPm644QZccMEFGDBgAAYOHIjHHnsMe/fuja+e8hsSboiUQz43BEEQ2c2ZZ56JX375Bbfddhuqqqpw2GGHYcaMGSYnY78g4YZIOaS5IQiCyH4mTZqESZMmJeVcgfncbN++HePHj0dJSQnKyspw8cUXY8+ePZblr776ahx88MEoLCxEhw4dcM0112DXrl26coqimP5/5ZVXgroMIgmw8gxpbgiCIAivBKa5GT9+PDZv3oxZs2ahvr4eEyZMwGWXXYaXXnqJW37Tpk3YtGkTHnroIfTs2RM//fQTrrjiCmzatAmvvfaaruy0adMwatSo+O+ysrKgLoNIAmxsG4pzQxAEQXglEOFm+fLlmDFjBr7++msMGDAAAPDEE09gzJgxeOihh9CmTRvTMb1798Z///vf+O+uXbvi3nvvxbnnnouGhgbk5CSaWlZWFtjyMSL56JeCp64dmcBXa37Fjf/5Fvec2hvH96hIdXMIgiDSkkDMUnPnzkVZWVlcsAGA4cOHIxQKYd68edL17Nq1CyUlJTrBBgAmTpyIFi1aYODAgXjuueds/TRqa2tRXV2t+59IT0i4sebsv32FjTv3xXNxEQRBEGYC0dxUVVWhokL/VZmTk4Py8nLpUMvbtm3D3Xffjcsuu0y3/a677sIJJ5yAoqIizJw5E1dddRX27NmDa665RljXlClTcOeddzq/ECIp6H1uSLqxgm4PQRzYHAiLLvy4Rkeam5tvvpnr0Mv+v2LFCs+Nqq6uxtixY9GzZ0/ccccdun233norjj76aPTr1w9//OMf8Yc//AEPPvigZX2TJ0/Grl274v9v2LDBcxsJ/9D73BAEQRBGcnNzAQA1NTUpbknwaNeoXbMbHGlubrzxRlx44YWWZbp06YLKykps3bpVt72hoQHbt2+39ZXZvXs3Ro0ahaZNm+KNN96wvbhBgwbh7rvvRm1tLfLz87ll8vPzhfuI1EOaG4IgCGvC4TDKysric2tRUREURUlxq/xFVVXU1NRg69atKCsrQzjsPn+VI+GmZcuWaNmypW25wYMHY+fOnVi4cCH69+8PAJg9ezai0SgGDRokPK66uhojR45Efn4+3nrrLRQUFNiea/HixWjWrBkJL9kCyTYEQRBcNOWAUXmQbfixaCgQn5tDDjkEo0aNwqWXXoqnn34a9fX1mDRpEs4666z4SqmNGzdi2LBheOGFFzBw4EBUV1djxIgRqKmpwYsvvqhz/G3ZsiXC4TDefvttbNmyBUceeSQKCgowa9Ys3Hffffj9738fxGUQSYKVZ0hzQxAEwUdRFLRu3RoVFRWor69PdXMCITc315PGRiOwODfTp0/HpEmTMGzYMIRCIYwbNw6PP/54fH99fT1WrlwZt61988038ZVU3bp109W1du1adOrUCbm5uZg6dSquv/56qKqKbt264ZFHHsGll14a1GUQSYDSLxAEQcgTDod9EQCymcCEm/LycmHAPgDo1KmTblI77rjjbD2kR40apQveR2QHlH6BIAiC8JPA0i8QhCyUfoEgCILwExJuiNSjCn8QBEEQhGNIuDnAiURVfLJyK3bW1KWsDWycG5HmZuPOfZi35tcktYggCILIZEi4OcD555frcOG0r3HaU1+mrA0yuaWOvn82znz2KyzesDMpbSIIgiAyFxJuDnDe/m4TAGDNL3tT1gYnS8EXr98RbGMIgiCIjIeEGyLlsCuk7DxuQqHsishJEARB+A8JN0RaYbcUPNvCjRMEQRD+Q8INkXKcmKVIcUMQBEHYQcINkXJ0cW6i1mVDpLkhCIIgbCDhhkg57FLwCGluCIIgCI+QcEOkHt1ScPK5IQiCILxBwg2RclhxJkJmKYIgCMIjJNwQKUefW4rMUgRBEIQ3SLghUo4+/YKdcEPSDUEQBGENCTcHMDOWVmHR+p2pboZOc/PwzB9M+6NMwqlUyTbb9tTikn8uwEfLt6SmAQRBEIQ0JNwcwFzx4sJUNwGA3udm1756k1Mxu4IqVZqb+95bjg+Xb8HF/1yQkvMTBEEQ8pBwQ6QdRstUJJp64eaX3bUpOS9BEAThHBJuiJRj1NQY/W5Y4SZMPZYgCIKwgaYKIuUYNTVRo+ZGV4AcigmCIAhrSLgh0g6j5iaqM0sluzUxKHggQRBE5kDCDZFyjJob4++GNPC5IdGGIAgicyDhhkg5Kqx9bljNjXUUnOAgxQ1BEETmQMINkXLMPjfipeB2uaeCgmQbgiCIzIGEGyLlGMUVo0NxQ0QV7iMIgiAIIyTcECnHqI2xXhqeIs0N2aUIgiAyBhJuAmb3/nrsqqlPdTOkaYhEsaV6f1LPaRRXauoiut9snBunVqmqXfvREIliV009Vv+yx7b89r11qKlrMG03ija/7K7F/vqIqZyfbN61T3ftsmzauU/np0QQBHGgQcJNgESjKvrcMRN975oZ+EToF2c9+xUG3fcRFv60I2VtOOr+2brfrObGyZQ9f+12HDnlI5z17Fc4/uFPMOzhOXhvyWZh+Z01dTj87lnod9cs0z5WcbNhew2OuPdDDHt4joPWOGPW91sweMpsTHrpG0fHfbCsCkfdPxtXv7wooJYRBEGkPyTcBEh9NBr/e9POfSlsiTwLGoWa/3y9IWnntNPGsEvB7bKGs0yf9xOA2DVt31sHAFi9Vay9WbJxFwCgtiHK2ZuQbmav2AoA2BjgM316zmoAwPtLqxwd99QnsePetRDiCIIgsh0SbgKEnYczzUjgRIjwjvW53JqleGWtrDVhiwiBrOYmGSu2GlyalVK1mowgCCKdIOEmQHTCTYbNOUkVbWxOxijAPLcrYnGynFDidbASEpJxb9wKKeRqQxAEQcJNoFhNpOlOMjU3dmdqYKQbr5oJK0dbVnNj1Jwke62UG0diINkaN4IgiPSEhJsASYclzK5JYnNtNTeqO7MUD2vNDSPcRAzCjc4s5a0NMrgXbnxuCEEQRAZCwk2ARD0sYU41yfW4sfO5kS9rh5XQwGpuWGdwAFAY3U0y7o1bDQz53BAEQZBwEyjsPJppU04yJ0n71VKsWcpBvZxtVsJNTlhWcxP8vSGzFEEQhHtIuAmQiMslzOlAMs0bjhyKvZqlLC6M1c40RAyamyQ73bi9TjJLEQRBkHATKOwXvtsv8SDJMSx9ZtubXIdiG7OUj+2yeg5sO+qtyiXD58b1aqn062cEQRDJhoSbANFNyry4cCnGGNdFF08mie2wm48jrFnK47mshAZ2l0lzk+T1Um6FYZJtCIIgSLgJFHZ+Ssdl4SbhRhB1sKauAQ9+sAJLN+6CqqqY+vGP+HjlVtv699YmjmN5f8lmPPf5WstjZy6rwt8+XYPnv1iLt79lou1K3sbnPl+Lt7/dZNq+Z3+sTUt+3mXax15+vcHnhpVt1myzz1HllI9XbMVTn6yOa8/c5oZKheZG5nkSBJEdqKqKJz/5ER+v4M8B7363Gf/8cl1yG8UhJ9UNyGbYCSqShqobo3Cjl20SP/7y4So88+kaTP14NaZdeAQe/GAlAGDd/WMt63901g/4++drMfXj1bqyV06P5Usa3LU5Dmldwj32sn8t5G6XWS21oqoad73zPXffW40Cj7FNxrobTKulErw83//UFBOe/xoAcGi7UhzdrUVGmaW053l0txY4uLJp0s9PEETy+GzVNvx5hngOmNiYD+/Y7i3QpWVxUtvGQpqbAInqfG5S2BABVmYpdm7/fnN1/O+fd9RI188ex2PbnloAzkwpMgqNbbvr5Ctk0JuljKul+GYpv1dObd4Vy8ju1kUrlTK0lr+LIIjsZfMuuZx6u/bVB9wSa0i4CZCITnOTfmYpo0NxVMJx18ll2K0w0k7hJHaNjCzhNRYOANRLSqNBPVa3ZqlUxrlJ9ooygiDSC3b8scrVlwxIuAkQnc9NGgo3IcNsJMrhxGotnEyedk64Wk3OYtfYF3Y7v+s0N5LpF4IyA7k1S6Wyl5FsQxAHNuywaZxfkg0JNwGiM0uloUOxse/JpDnw8yo0QclJnTIyots26paCS8a5CUpodVtvKoVokemOIIgDA9ZXkYSbLEZn5klDzY2xSXoBjN9eJzKatFnKkeomuPto5XMjc0xQbXFCKrsZyTYEkf1YaeRZ7T+ZpbIY9ivaaOZIB4wTqN7nhn+MVzMMK8homhInNcqUdet3wh5lXC0l+goJSiPnXgOT3H7G3muSbQjiwIYdD1Ms25BwEyTsvJeOPjdGIUCf5iCYSUuXb0vV/yuDnEOxO9hrNsa5yRSfm2R3M/Z8ZJYiiAObCDNupno8IOEmQNI9t5SxRX5rbnidm38O+Tqlzu/WoZj5W9YsFZS5MVOC+LHnI9mGIA4ALN5z9qOMzFJZDDvwp6NZyjgRyqRfcORzY3NON+ajIJeC61dLGZaCC97ToB6ra81NkvtZlMxSBEE0kk4WChJuAiSZDsVu6jfOn7oIxYLJ1auPCS/Dg7Ol4AlE1+x+KbjYLCWSl4wvczSq6trlPl6Nvk4r2P2iaw+q/7HnM2rq0tGJniCI4CDh5gDh6pcWxf8O8qFP/fhH9Lt7Flb/4iznkVGAiaj8SZKds7Sw2zLwzBQ8zY0jh+LGY1b/sgf97p6FqR//6OBo63p/9/Tc+G82ceYzc1bj9UUbLdsDAA/MWIEuf3oP3W55D3tqG7B1934MvO9DTHlvuae29b1zJj794Rfuvhv+vRhDHvwYNXUNAPhmqW837MRhd83Ei1/95KkdPESamw+/34K+d87EzGVVvp+TIIj0JOJRM+8nJNwEyKbGUPpAsMLNgx+sxK599bj//RWOjjP2Pb1Zynt7+WYp8/ndOBTf+fb32LWvPp7nilfGCcbUAawZcYrFfWVf5qc+WQ0gdo0zllbhmTlrsG1PHZ75dI10O3gDwu7aBpz/3Hxu+dcXbcTPO/bhg0YhgtfNrv/3YlTvb8D/vblUuh2yiLr1JS8swO7aBmGOMIIgMhMr83NUwrUhWZBwkySSEcTPqaRs6VAcUI4ineZG+9dBuzWha399xKKMc6zuhRWiyT0/JzWvFjkUEwSRKhokTOTJgoSbJJFOtkgNa4diHzQ3nNlOZYSmqCuzVOzf2gax9OWHOlT2eYn8Sgpyw57b4ARLLViAQofOfEkuxQRxQKMfN8ksdUCQjsKNlVlKF7/EZf2841ihyZVZqvHfWgvNjR9ICzeCxhfkunu1vMaG4AmlQYocKmluCOKAwmqMkknhkyxIuEkSaSncQKy5CUro5p3CiZZIe3ksNTcu2mUl6Fm3h789P8eb5sZpiAit/cnuZ2nYrQmCSBGy8cGSAQk3SSIdg/hpE5MWbEnn6e6LWYp3To43vQuHYkufGx9utayPlEiY8BrAyq0Gh9ecICOFpmO/JggiOZii3HN8KlMFCTdJIqgvak/+JY2H5jROxDLxUpxhHaFYk/LdnMpKuHFTo0mLJZ04U1TO2w10Ko5YnS1IaxEJNwRxYMGOJ5YrbsksdWAQVITiuoj7ZU3axJQbjnWDBp3PDetL4d/0qMu35SpCsYRZKpmaG0E5r20QJep0Q5C+MKkewAiCSB3Gj5sGnxeleIGEmyQRVLTW/XWJSd7pRKMVD3M0N340184spZ3PSbu1dvm9FNx4kOzzEi2Zj6reJv5Mcc5NJwdCgiCSi1UIjVSPByTcJImg4tzsYyb5eocSSUJzw/O58Y5dED9Nynci4WtNtLpUN6YS4xGymjbRudyaC7XjnAo3VucLcom23kGcpBuCOJCwDCeS4uEgJ7WnP3AIyueGFW721zlbHq11vpyQ2SzltWfOXrEFK7fsjv/+as2vOLJLc51G5JOVW1G1az+2VO/nVcFvs8QE6uZem15SVcW3G3biu593Sh231XANKvRt/XjlVuSEFJQW5qIoLwerf9mDkb0qTfXNXf0rTh/QniuQ/LjVWXoNIBYpec0283Erqqrx3nebMbhrC3RoXoSFP+3A2D6tpRyhd+2rx1uLN6Jv+zI0K8qLb0/1YEYQhHPqI1G8t2QzjuzSHK1KChwd63aVaTIg4SZJBPXQWfPMPgexX9gvfZ5Zim2t0+/+7zdV46LnF+i2nfXsV/j2thG6l+HD5Vvx4fKtjuqWygru4lYbH080quLUJ7+wrUt7rmMe/9yyDROmfW069uVLj8Tgrs11215ftBGTxxzC1dwMf2SOsB28Zn754zZc8SI//cGoxz4DADw+O5Gba8/+BpwzqIPwHBpPfbIaT89ZDUUB5vz+eMs2EASR3jz76Ro8+MFKNCvKxaLbRjg6NugUPl4gs1SSCMosxXYm6xVEetjmxM1SAodip4gSeO7cV+d5dY2MuceNIGmstyGqSglJ2qm27am1rI+HphUylv1ld60vDsXfbdzlqPyXq7dJlft5Rw2AWB9qiLI+XyTeEESm8eHyLQCAHTX1UuXZoYmn8dZI9XAQmHCzfft2jB8/HiUlJSgrK8PFF1+MPXus1eqXX345unbtisLCQrRs2RKnnHIKVqzQJy1cv349xo4di6KiIlRUVOCmm25CQ0NDUJfhG7JLi53CdiBHmhvm75zG1VI64cZDbimRaUOB4l24kSjjyufGrUOxyOdG4tgGgUO1orhwKOac0GmoHdlVcXlM3qwaxhTKNsFjmB+CIJKEF6uCSbg5EIL4jR8/HsuWLcOsWbPwzjvv4NNPP8Vll11meUz//v0xbdo0LF++HB988AFUVcWIESMQicQG0EgkgrFjx6Kurg5ffvkl/vnPf+L555/HbbfdFtRl+EZQmhu2cznR3LDH5XCD+LlHKNwo3ldhyWlTzIXybBJZGo+Rdc4WCUEy7YzEHarN+CEbONX+yAok+SLhhrkQP5eyEwQRHF6iChuPzHrNzfLlyzFjxgz8/e9/x6BBg3DMMcfgiSeewCuvvIJNmzYJj7vsssswZMgQdOrUCYcffjjuuecebNiwAevWrQMAzJw5E99//z1efPFFHHbYYRg9ejTuvvtuTJ06FXV1dUFcim8EtRScnZT3OXAoZjteDscs5cXEkGMxS3o1XcjYcXm32i5Lt7FZ9RZxdFiEcW4k2pnQ3JjL+hFbyGkdsqXZ5u6tY7WmiR0k2xBEZuBUc8O+26phmIxmu8/N3LlzUVZWhgEDBsS3DR8+HKFQCPPmzZOqY+/evZg2bRo6d+6M9u3bx+vt06cPWrVqFS83cuRIVFdXY9myZcK6amtrUV1drfs/2QQVxI+tdl99RFp4YDuetlpK1MlFE5XoXEFqbmSO52lu7IQbq2BUVohut8zhkUbbn7GoorjILcUZSIIyS7H3qqaWr7kJMuUDQRD+4cWq4HbcTAaBCDdVVVWoqKjQbcvJyUF5eTmqqqosj33yySdRXFyM4uJivP/++5g1axby8vLi9bKCDYD4b6t6p0yZgtLS0vj/mrCUTIIKU88KGFFVPmKxHw7FoiKasMQr792h2L4MT0vGS2RpFbRQ9j6KBEJVtXdIFvncAP6YdZzWIHtK9tboNTcJyOeGIDIDL1YF45EZG8Tv5ptvhqIolv8bHYCdMn78eCxatAhz5szBQQcdhDPOOAP798vHQeExefJk7Nq1K/7/hg0bPNXnhqCWghurZSMWW6EzSzUKI047pqiIQLZBVFV9cCh2Z5bi+dzo22LwuZE0S3lxKI5HaOaUdh7Ez7wt5FDCkA32p+o0Nwnhhm1CkIEDCYLwDy/aFssgfq5r9QdHcW5uvPFGXHjhhZZlunTpgsrKSmzdqo9f0tDQgO3bt6Oy0hy4jEXTrnTv3h1HHnkkmjVrhjfeeANnn302KisrMX/+fF35LVtiy9is6s3Pz0d+fr7lef2Gt7Q4CIyda199BKXIdXSc5nMjyi0lInaN5klMpLnxmpIgdlL7IjxBkmeWiqhq/AUwHlIvqbkR3ieJdlppbvxwKXbscyOruWEavFfoUOzo1ARBpAinH97sSlrL3FIpVt04Em5atmyJli1b2pYbPHgwdu7ciYULF6J///4AgNmzZyMajWLQoEHS54up9lXU1tbG67333nuxdevWuNlr1qxZKCkpQc+ePZ1cSuC4XVrsFJ5wIwN7FDcruK40f6YSXVFYoLmJRL1rbmSO55XJCStQFP1zYf82rZaSXEEgzi1lf7zQxwmKc80Ntx5nyAokbLNrGLMUO5jRaimCyAwaHMb9UIU/rOaQ5BOIz80hhxyCUaNG4dJLL8X8+fPxxRdfYNKkSTjrrLPQpk0bAMDGjRvRo0ePuCZmzZo1mDJlChYuXIj169fjyy+/xOmnn47CwkKMGTMGADBixAj07NkT5513Hr799lt88MEH+L//+z9MnDgx6ZoZO4wPNqjl/8Y5VHbFlF5zw3EoljFLObwmVVVTthQ8pCjINWiUrPKgSPvcCHNL2R9rpblJxVJwWVMSO4DtreXHuSGrFEFkBo41NwY/T11dmepz44Tp06ejR48eGDZsGMaMGYNjjjkGzz77bHx/fX09Vq5ciZqaWLTTgoICfPbZZxgzZgy6deuGM888E02bNsWXX34Z19KEw2G88847CIfDGDx4MM4991ycf/75uOuuu4K6DNcYVXIRL1HxLHCtueE4FDs1S4mTRvLLv7rwZ5zx9Fyp9omQeV/ue8/s9xVSlLj5TUP/kuprnr92u1x7PPjcRCIq3luyGeOe+tLV8XbMW/uraduCdeLrkpWF2Hv1/Jfr4n9TnBt75q/djtOe/AJLHUaPJgge97zzPa5+eZFuHFqwLtbHlvxs3cfqI1FMmDYf2/Y4DKNiofE+IHJLlZeX46WXXhLu79Spk+6BtGnTBu+9955tvR07dpQql2pMmpskORTXSTrCsg0M8xyKfWyTxrOfrvFQaww7mWtvrXj1jjH+Ditvuv3KECl4ZOzNDVEVV03/xrTdaD6TgVf+f4vNMaV+9/RcrLt/LLcOWR8d4Qoxptewt1pVVVoa3sgZz8SE+/P+Mc9xHh+CMPL3z9cCAK4Y2gW92pQCiL3jAHDO377CkjtHCo/9aPkWfLzyF8fntJon9GNDagUdyi0VEMbJRtLK4Rij5CwbOIk9LleLUCyY7MVxbkTbg+vUbn12wiEFuQZnID+WLYrut5M4N6Kak4285kawQ9dnEpXJ+i8dSMjm8SEIGWo5H7W7BR96Gm7nJPZtNvqS6jwbstUsdaBjnPSCMksZBQnZDqVzKI7HuUm0UWq1lGACTmWfFp1b4ZilIhZmKVnEQoyc5oaHfKRg9gvK+12XPa/M8ndWc+PUYZEgCGe4WbBiF9hUhGhRhrEdqf6kIeEmIEyam4CetHHekJ2j2QkqHI9Q7KweseZGrg1usNMKifbHzFJizY1b4Uboc6Pat9XKVOnl/rvFjc8NiyhCMWluCMJ/2PHFTaiRglxzYFMZohYfVRkbxI9wT7KWgst+weuD+HESZ0ppbkR1B9er7WoW3eaQYl5erfO5cdsekYAncazVgCRzvM667eACxAKgfz43bE0NQdlkCeIAhn0NXWlucl1qbgRtAJzPIUFCwk1AGJ9rUKp5Y+eSNks1FgwpiVxQrFmKrUY05XlZKeQWOw2LqE2xfE3i1VJuX0RRe6RWmwmFBEnh0rW2ib/dTeJMEVGPX5UEQVjj9R1zu6JRn/LHnVtEMiDhJiBM6rqAPl5NPjeyxzX+qyhKvJM7NksJ2yTZCBfY1S3aH1IUU5A6qzg3XtvjJM6Nm2MBD9omwXbPq6VUfhnZaM8EQcije99cDWDeP47I5+YAxEpdF+R5pLOCNxaLaW60upz5oIhNMqk0S4lLGCdvtqhb5YKVc61dlVbmHSmzFDvISJTXELXZs88N8zd7bQ3kc2OCVsYTXtGN1y4GMLdjnm4hgzHODfncZD+pyi0lexbtOAVK3KGYnYSkmisoE6QVwlZzI9iucHxu/Fgt5WU5vMhUGXNGlji3D19eLNIRioUOxYntOuGGVkuZINmG8Ar7GrqZX9wKHzpfH6PmRvfBRT43WYnxsSbLoVi2PyXMUnzNDVuR6CtTZpLzG7u6hVoJ2PncuGyPaDm8RH1W5h05nxv9D2mtnaDNXuPc6DQ3TFtotRRB+A/7HrvT3Lj8OBK0wdQO0txkJ8Z+s3TTLgcmIxXLNu2SWmVilm3kzqF1QkUBwnGfG2eT/epf9vDbJNUCd2zYUWPa9u2GnVi6cReq99fjx638NoUU89cyG83ZvUMxf7vMc1i7zXwtsscambd2u3TKCFFY9nXb9gqPWbdtLxb+tB17ahvEqQPYLzqm65JZygxFbCY0lm3apRtL99Y2CMcxFnbsEbk9/LBlt8Xx5mO++3mnTkDZvGsftlbv15XRORQbpii/otz7AQk3QWHyhQFemr9e6tCn5qzG2Mc/x42vfmtb1thBnVoAdGYph85gv3t6LlZUVZt3BNirv/jxV2zfq8+FcsrUL3DSE5/j0Dtm4py/zRMea9Tc/P2ztfG/ffe5kahv255a4bFOfW7e+W4zznz2K4mjEuHZjXy0Yiu+32R+nhu21+C4hz7BuKfmovftH3CjoQJ6oYw1RdWTWcoEiTYEACxavwNjH/8cwx6eg8279gEAhj08B8MfmYNv1u+wPJYde0Ra4BGPfoqPV27lV8A55Dd//QKPfvgDAGB/fQSDp8zGwPs+0n1o630VxT43qYaEm4DgfX3/k0kyaMVTH68GwM8NZMTkUCx1BoFDscPEmQDw2Q/bOG3yr4OPPbS1adtaCw2DCLvEmX77r8j6zYgrlini/0Ay5wdzrhn5JKLav/rs76S5IQg+67cntLc/74gJN1WNmpIPllVZHitanWjkzUUbudtFhzwx+0cAwC+7Ex9fdRG56PVWK6mSDQk3AcF7sPk5khEhHXzWmeMMSJqlNIdiRUGIE8SPFXSsHE1zw+Z9fnbqfu3LTNvchA0PhRTk5VhFKHZcJQDx/fYi4LnR3PgFz1oi+zWmFTPeSwriZ4asUgSgF/z31UUcHSty4Dci6mp++NxYZQUnh+IshfdY3ebysDyP5zg3TIRiFzEK8jgCW9Bd2iikyBBSYEqcGXGhqTIi41zrvE455+Ag7jNvIJTNaK+VMq6OqqcgfiZkV6YR2Q37ruyrdybc6Hxu3KyWEmwPGwOCGc5lFUKD0i8cAPAmJ7fhrq1wG6E4sRQcTBA/557uPEEj6LDbnHdP4hjFJFwKFoc5QnitHm6BvGnR//vM1dzICjeN7TG62JDmhiD4sCsJ9zsUbnSaGxdjgeiDTltgIvKtsYrsTkH8DgB484GsWcrJ3G3uoM5MCIqiJNIvuPB055mlgv5QdzNXhhQFeUnV3HgxS0kG8XN9BjE8jYLTe2McaGkpOAdS3BDQC/5OzVKyfm2ilXmijyNtPpBZam4Z5ybFqhsSbgKCNz0FYZbyI7eUZpbSSd1MRVb+AfxrCrZTuxFEFI5Zyg+fG3FuKQ9OypAM4peksUPWIVgrZdT0UBA/MyTbEIB+hapTsxQ7RrtJcSIaP+LCjcD8JJongPRaLZWT6gZkLVzNTTr63CQcihui3if7WJvcHyuDG/tySFGQl+N/4kwRXqpTZQWjJDkUywqTWjGTcEOaG4Lgwmo1HQs3zN9uIhSLDtHM/qKl5lbWfNWF9j8oSLgJCL5DseRqKQcY1YXympvYvyEmiJ+bpeC8lyroTu1GcAgpQI5Jc8P/2wniPEte7oKspiQ5w4e8MKlyy1PiTDO0WooA9Gap/Y7NUon3zI1fm2js0sbJiEBDo9PoGN51N36bQUFmqYDgLgWXdCh2Er3Uylvd+ri47saTzw1v4gtac+PGLBXT3Ih9bnyPUOxVc5MisxSv7zlfCm40S6X6Gy79oNVSBODNLMW+VlZ+baKeJnqt4wtMBA7LVuZ8yi11AODF58bJV53bxJn6IH7uV0vxBA0/g/jxcGPXVTgOxX6EChcH8VM95KuSjHPjrnrHyOat0UoZhRlaLUUQfLwsBWc/yCz92gTzieiDjhcaRDQ1WOWWSrX7DQk3AcF7sG7iszg9zzUvL8JpT36BDdv5eYs0EkH8+MJNVFWxfHM1Jr/+HbYYcouw8Pwpgu7Tz32+1vEx4ZD5/mv34INlVbjjrWWu2vLAjBWY+NI3pu1e7sHk15fo8l6J0MK1+4lxHFz/aw0emvmD1LFxzY3JLOVvj3hl/nr85cNVro5VVRUPzFiB/y3mR21NFsYPmIU/bcctbyzBrn31nup94qNVeGkeP83Lu99txn3vLQ8sie+BymMf/oD/fL3B1bH6IH5yHwHfrN+B615ZhEkvLeLWIz5XFHe+vQwvzVuPya8vwRJBjjhtPniZSRfE9pm/zk68e+f8bR4emLEiEQYijeLckM9NQPCeaxCqaJ6W5Jv1O3HpCwsw47ohwuPiS8EZsxT7gqgARv/lM1fnD3oJ4DvfbXZ8jAIFxx3UEs9+uia+TfvYufxfCz21511Oe7zcApmkeQBw9cuL7As5xDjpvjB3nfSxapJ8bm5+fQkAYEyfSnRv1dTRsV/8+Cue+iSW3uSUw9r62i4nGEeCcU/F8n1FVWDKaX1c1fnj1j14eFZMED1nUAfTfk0IH9CxGUb0qnR1DkLP0o278FijoH3GEe0dH+8mzs1pT35pWY+INxZtxLQv1tmWa0w1iBe/YoSbxgFt/a81JlPUU5+sxrAeFRjQqRzptHaANDcBoU3w+TkhDOxUDkDeXOMszg1/+4oqcTZYIGHaCYeUuEMxq9qUFVAyxeIQCgFHdWuBVy47Ered1BOAvHmrf8dm0udpkhdzGk9GjIc1vzjPsWWHse85UZVrl2w0S+2vD6aT7K5tcHzMr3v5yUrTBavM7HbIan1+NSSeJdyzs8abps2LWYrFyr9F+6j+RZCo10hOyCwWaB8sNfX8d07LQ0VZwQ8AEkHygEPblQIIJridW/8WrbOGQ2xuKbZe2XrME1dQPjclBe4VjZqT3JFdmqN1aQEAeQHkuINamrblCMIkty4rBBB8IENZhh9S4ai80aGYF4pdhHbJxufvZdC2IpNdcp0sGpDH/48nwhqvTrP1HnJL6doh0Qyjz6EI3iuvjWdhQb/VEmtaxcBJNiTcBARr9gnFgyJJDj4OBj73q3xix+WElPhELeP4aRXlN9EmV02yxclEayTE3NMQz4HaAt7jELUlHrrcYfuCwuk9M16rk5VOWl803lenYeVlzgEEJSAkB1HLk3FJGXzb0g6vHzG6CMUe3hOrD0rtecsuaOHnlorVHxKMJ5qPIGluDgA0iV5REp0rCI2G25dL868JsZobicqMLwjPxhqUcOOlWvad1ASdqCp3zbxJVKS50Yqm+qtFwxjbxyluVjoZ76mXL1IjbNVu5uh0F4i8NE+2y9EydP/wOqazHw9ePgJk2mGM0C4iHAqZc0YxH8M8NM0NO1ykeggk4SYgEpob/WTqN+5zIjWapRTW50ZCuMnVByLkmaXSY1rXw05q2jseVVWpAYU34YiEBvarJx3ug2gwEmEsbbUKg5dXDOAINz5qbti601xOsUbQdi+Ch26JbqpnlgMEr/e53jfNjX0Z2dW64ZD5HdZ+i/qnprnR3w8yS2Ul2mNVFIUbztoKPxyK7WB9bpyYpQpyjWYpXpuC6dReqmXNUkpc2FSlBhTeCy0SGjThJuhYP7LwnAMtMUgM9RYdzPglGE+/EKDPDXtfM1kDEYjHjcr/OyknP0DxmjatwTefGyuH4hjSmhtFEeYsFPkYacJNOuWWIuEmILTOpiiJiTWI5+72y4HnUCyjuSkwaG64k3hQZikPN5CVRTRNVSQqN6A48blRmGedDu+5U82N8R5bCbzGukVLwf30udEJN1k4SXszS6WPv8OBgnezlE+aGwkhS7al4ZBiui7tnRZNEZoGKiorYCcBEm4CIq65gV5T4DdeV0uFQok4NzLBvYw+N/wgfgFpbjwcyzrCJYRNSbMUZ5tQc6P53DhuYTCEBaYjEU4C8BmdC0WJM4PyuclkgvD9kTVLZaFMmDL0k7nzzukmzg2/Hfbnlg3eyBNutN+ia4w7FAsSbKYCEm4CIrEU3IVZylH6BYcNa0RTH+Ywwo2VCULDqLnhqSEDk9g91MveU81SE4nKmaVCnAciEhqC1NK5Ideh5sYoy1iFdTcKMaIIxYGZpTJ4lha13YvQozNLWZ47g29cmiFKKCkL+37VR1TXAS+thm7tcUuvDoViKqu9d6IqaiOc1VKkuclWzGYpeUFEPPgYpXu3mhutHtahWAvEZIXR54b3NRDU17UnzY1i1txEVdW1WUrkyxJifG5qA4rv4oSwQ58bVVXx655aRKMxrZaVQ7Fxn/ZrjyG4np1w4+SLle1vmexzEwSsxjSVE0ttQyRjUzyw2lyZfukmCXY0qqK2IVa3UTMqOqedlnlffYNwv/aeyAo3vHExqsbatmsfPwBkfYMm/CTOYZnvKgmQcBMQ+tVS2jbnLzybA+e5z9eix60zMGNpIty/VZVzV//K3V7bEMG1rywGENNiOImFkp+j19zw/HT8NEvJOsHZwQafSjj9utcqiM1Sse1Pz1mN1xelNn8RIF7RJOKl+evR/54P0eVP76HHrTPwwxZxpGtT0lZVxdKNu3CZIZ2FlQD5j3ifrpJqn24peAbLNsI4N14q1U20qTFL7d5fjz53zMRpT5lTBGQCt/5vKXrcOgMPfbASPW6dgX/ZpB9h77Psh+Zvn/wCh94xE3trG0w+baLx6NIXFqDHrTOEef7eW1KFHrfOwJKfdwnPK+vsu+CnHRh430e6bd9u2Iket86IpwkxUheJtZuVZ659ZXFKV+2RcBMQ7GqpuM+NC0FWE0IA4K53vjdts/pC0sob+aEqkbsozJilZLDKrK3B688y53j87H663/ef1ge/698OPSqb4oqhXX1zKGbNhHZfZ3856zCuGl90PZqiZPd+56kBgsBpED9jSoetFtq89uVFut8qEM+zAySWnlo5qt/d2Edv+M9iqfbJfn2mOyLTkF8Cm9WrEqRQ+MWP21DXEMXiDTuDO0mAaPmU/vrxjwCAW/9nnVDXjQPttz/vQm1DFPPXbTeZgUU+bh8u3woglh/KigdnrhTu8/LuPPiBuF4gkWLFOB/USiQADgoSbgIinnUbejOIDHaDD1uNpa1V6mzOJkCjrwk3QrHh9y1jDsHq+8bgnlN7W9b9m75tdL/PGtgBTfJzMOO6Ibh5dA9H+qAelU2x7v6x8d8Kxyxl53Nz0dGdccphbbnhyHNsfG7SBa9B/EQ8+LtDzVo1VR/3SNNuyXR72XeDFXDdmGTT6+mY8dK+7BD7Mguv/dH4wRaxyTwpSn9ghR9BZO1W0mrjqFE75CTCud+QcBMQbG6phKbAn7ojki+UjLtFNOpsQjaaY/jpF/ht8pI+IVavfFnjNfF8blQV2Fdn/2XBa7XIl8XrNfqN06XgsoRDisnkpULVDWaJeyGzkkPuvGzfT7XDoheCjnMjE46f8I4XgUHhHG/npyJKf8DWKcLKf84OO63P/kbTs7GYnbAWJCTcBESizyq6pccy2I09eo90C+FGYhSLqqozzY0iI9wYfmvO1dJn8Y7x0nVxbph0E1aam0QKDQdB/NJs5hBpmLwSUhTuPWD7ZhCamyyxSgWzWioNHIozWeB0Q9RjugGjLGOn6ZB5nY3jeZDpfzS0cdQUSiKFTsUk3ASENtCEFP87l59mKRXOvu6NLw5/KTi/UTJLza1w4qhs0tww7Wafh9v0C3ZB/NKFoDQ3oZBiMnmpqv7r0MlKLScrTeJ/Z+FM6sksJblyh1aZ+UdUUovOQ1HM8WTstCsyH6KiIkH6q8WFG4fXEyQk3ASE3izldCm4PNbqZ34vjxg0P3aqThajJoCndhS1yOvSaGdmKf1v9lawKRLkloKb749oFVJALi6ucZx+QZKQYr4HKgSaG4n6nCxTjZ8vo2Ub/x2K2dthGcSPZBvfkBUoZY4HvJulAPG4H6T/izaOGt9jt3F7/CDNhuLsQ4G3IH52A5FVfxW9B+zyw6jqzJQio7kR5SWp89jRHb2aEj43dkvB2eX8RjLG5yYgs1RYUUyCk6qC63Pj53LQSJZoboIQMNIh/ULmPhF3ONXcsM+I73Pjv0OxNoIFGXtI04CbhTXS3GQdOs2NA98DI3Z+M9ZfaPxj2eWGqkefG94LI2pTbb1HKd6D5ka/FFzO50aDH8QvU8xSwbziisJ3KGb7gyZY+Tm8uQmalll48blh/s7Om5N2OF0KLor8q8FNZ8OUkfKjFAgUQSa1FK6WIs1N9sE60XrJLeXEuVj2WFb1GVUdLgU3TJYykrlWwrvmxoPPjU5zE/s3qqpxL38reD4KokEm7RyKA1wtJau58RNZZ/p0R3RnPHUfVfC36Rzp1UczGb0Tt31/NAahtDRLqdo2RriReKdEc0KgPjd1fJ8bq9x0QUPCTUAkNDcuzFIOvt6szVIC26tRc+NkKbjR50YyiB/A19wENfkaa+XFuYlK55YybxP51qSZVSowM1lIMfcFFfoB1MlqKVn89LlJpXAUtHxh/BAwmkMCO2/mypuu0CWKlLh2UUJKDZ7mRu+kb11/QzRqmhOc5pZyQzyIn+EcqUzBQMJNQEQ5qkQ3fctKDfn5qm14beHPwv2iQ1knLxVy8XA0jJOlsTPv2FuHaV+s5R6rhei2qs8Kb3FuzOfcWxfBpl38cOYsTlZLOXHOTgaBLQUPKZwgfqpuANW0fEYhoiESxb+++gk/bhWndhCxaP3OxOkcHLd4w068tvBn3bO06k8bttfgn1+ui/sSfLCsCp+t+sVZY2347ued+M+CDabtn636BR8ss09HMev7LZjzQ6JNVkvBUyF0TJ/3E6JRFZt27sPzX6zF3tr0iNrtJ07NpN8x6RF+3rEP636t0e3nCSDscupPf9hmWf8XP/JT7gDAmwGmg6mLRPHlj9tMc1wqNTc5KTtzlqM9UjaIn3ScG3Y+spibzv3HPPl6GIxOmU7yNxm1PEaz1KSXvzEJDNplH9K6xFTfwM7l+GyV9Qsbr0e6leZr79qymLvvW4kw8TxNmki1n35mqaBWSyno16FMFw5eqLkxHPvy/PW2Ye1FTH59SfxvJw6Sp079AgBw7pEd4tusjh728BzURaKoqt6Pi47ujMsb82WtnTLGF7OOAgW/+WusTW1KC5ntwHn/mA8A+PqW4WjZNJ97/I69dbj0hQUAgB/vHY2ccMhy5U4qsqnf8sZSFOWFcd97K/DL7lqs3LIbU047NDknTxJOHYrPeCaRm4ntyxrGD09Ar7mxS7/AQwHww5bdUh9yXjjn7/PQtqxQt418brIQ1qFY8aS5EdWvr2xkr1acYwUOxQZVakFuGM+e11+qPUaNhfF9tvpyOHNAe9x+ck88fW7iXJcP6YrbTuqJWdcPsT23EzOCdun/vfIo3PfbPji2e4v4PsemGl7xNFa/P3x63/jffpilrhja1bQtrCg4Z2AH3HZST3Rt2QRArC+wZkqRFmuRT3mH3DwCNneW1WSk+YfNXf0rdtYkMiH7pdlnX82VguSk2/fyMzADwK599fG/tQ8MqwjFqequSzdW45fG/GR2WodMJOLQLOWsvtjffggImwMWbDSMZihaLZWVxB5qSFHgNLcUi8j/xlhVcX6uqYzY54b5OmisZ0SvShzZpdy2PUYfGRmBQ1OX54RDmHB0Z/Rqk9DgFOaFcdExndG9VVPbepygXXv/js1wzqAOXJ8bp3XJkGqZp12zQvRqm7i/TrOC8/htv7ambTGfmxAuOqYzejRq5FRV1fsHaKdWjcf6ozrwuhRc5nBjkSD8FkRLuC1TqzD3UCum8yc2HKrT3CQxiF+aWWl9x28H93qdVt28zS3GhMcsRm2LF4w+QxTnJgthY6Q4zS3FjgeiwcE48PHmC9EcwnZAp4OeMXGmm9eObZeTydfJuawmUKeTK7e05HNJBez1+aG54Y2LuojPzHZ28o+nHTEc69t85+JW6003zs3Efj1f9h6IqpTND6Vpy3QTrcmhmH9s0KSbD5rfsJoJP3pGRLeS1R/NjaIAeTnJeQ5GYYYiFGchCZ8b57mlWET2fZNHvINjWQc1tkky7hmmODdSgav0v9nJ14lPiJPbZzWAO7ZKOTBLBRkoS5aQTnj0/orzhEHeNhUGs5Sg3/s1uXq91VL9yWZ1i1vYd1MUf0Q6mWjcLCV2ukmWzG0UqrI91YPf6UD0McjM29wSlO+dEWNbabVUFsJqbrzklhJNBDKaG3GEYv4XnpTmxsbnRgZ2YvTDbMLDyunTqTaDV5Xoqz+oDxVnTfZXc8MbGFmZSYkLMQbNTWMZ4y3xyyzlJO6RNxLtDTqFipXfDAurEdHuudXKHf17njyyXHEDVlHhRbbR7hMvArcfAoJVX/IzJEJtg35FLMW5yUJURrpJxFWRO5admEVjg7E/clf0CI6tN6RfSJzXvm0mnxv7Q0ywVRiTL/qF1aAqu9pFe4ZOJuOgzFIFuWHpsnrNjffZhffRx+ujKvhmKfOxnpsUO18SNDfJ8bnhn1H2TAnhRuzc6vQ99wu/BNl0hWdGcoP2AaFPjaOZpbz1OQWKZb/1s0cbT0OamywkbpYCG+fGT7OUjOZG4FAsUKXKDERhUyZob9eUrCB+LEHmfwoqMJwz4YbV3Hh/xXn3izVPan+qhjg3IUajoydNHIpd+Nz49Xx1fjyicPkWExLPHKLX3Bh9btjfQfZ//e+s19z4FFRSi0fVwHMo9sHnJsgAflaQ5iYL4UUodtP5vZilhNlhdbrUxJ8yA5HR58adWSrxd2BB5iwdip3VxbuPQifQgD5UCnLkX1W2uX4IjzzhJiTQLurNUppDsf5m+TXhuRk2ZYP4icoEMUmIqrQSpNhdDRyfG1MQP9et80a2p3rwazGQ9o4Zo8cD/iyntsorFaQ/FjkUZyFax4z53LjX3IgmaXOIbY5ZSjCu1ItWS0kMRMbJ0mkmXON5ggp6Z3UtsqpyVvsmS1DJ6Qry3Glu/Li9vGcUkvC50eRWkwnVN7OUV82Nc4KIcyN6h6zOpQsexzNLGcqrKbIO+N0X0w2/zFKa479Oc9NYtazmxuqjwdosFZwAQmapLCQ+MSr6RI0ysIOA2OfGIDBwyggdiqN8nxspzY0PDsW8r3u/sV4t5eyc3JVBLr62vVCQIy/csM31Zym4nOZGharrW0KfG5/MIl5vtZRgDlX3TP1bCp64ByKNi6VZiikY97lh5hGjqUvnUBygkGFssVNNWabhl0NxQnPj3udGtCJKAZmlCJ/YVVOP8X+PpUbYvb/BU24p6aXgkj439723HFM/Xh3/rZfaJTQ3BjPS3DW/4oSHP8Gi9TuwsoofadUqoFhQmhurOd2XpeACghpDCnKdmKUSDfbDoZMngOoEHkZDo3dc1cxSxvZZn++9JZsx/JE5WFFVbVnumpcXWVdkg8xktHRjNYY/8mn8dxAO4zO/38LdbiUos/e5gaO5sSovuv3//HIdRj76KbZW+xfN1u5dW7VlN4Y/MgdvfbtJt/36fy/GRc9/nZTkpne9/T1Of/pLV74tTtMviMhtvFEvzV/P1Bf7V1b7YWXiT1X8LUq/kGV8t3Fn/O/Nu/YnlsS60dwwf7MDhcnnhpv/yFz3s5+u0f2+8ze9uPWLGNK9pWnbml/24tIXFuLTH+QSCzZvkofSwlw0K8pFSaE5srIINq2AFxwvBTf8LswNo20zflRPqy+k0/u3c3RelonHd5Mqd8fJvXTP0Q/FmNEUWVaUizZlbD4kvhAT9z03OZlaN+qq6d/gx617MHH6N5bl9tZF4oktXeFR6+gF9hasEHwUWJ2Kp03Shbkx+txImJ9vf2sZVm7ZjUc//EF8YocoNmap6/+zGD9u3aMTVGsbInhj0UbMXrEVG7bv860tIp77Yi2+XrcDc1Y6T4zKCh52PcNq/NeCo7JpErTydQ2Swo3gZVcUxVL741XusXqdKf1ClmEcAP3KLcVOyl4cijUuGNwRJ/RI5KSym3SmnNYHzZrkcffVNkSwT3KiyQmHMP+WYZj3p+GOBI1x/dth6Z0jcd6RHePb7v1tb25ZqxdWURTcNPJg6fOyt+WxMw/DottOFLZb9IXUtWUTXH1Cd+lzGunZpgQL/284d1+L4nysu38slt45EsN7ttIJur5obpg6urRogi9vPgHF+Ymcu4nVUvzj3Nr0a+r0/Yk3OXi5PDft8usDWKbZVv5b7FjCXQpuuDbdSiqbi/BiSjDWbdf/amrNY8b++sRknpukyLqAO+2G3ixlfbzV+J/LMSlp7ZEdV60CdlrGuZGq3cyyO0di6Z0jsXbKWGGZc5mxOtlQVvAAMHYkv3JLxYQV81darJwZu4GluED/+O1WDRdZOLXm54SFLyHvqvMd+JCwFOfn6AQ+UZvsBMl8idVH7Io3jdxwCAW5YeHyXdEXUnFBrqeJOKQopucVrzs/3PhvTmNZ/XFeYQW59uVFKMrTt4P1uWER+VPJNskqVouojBPcHBu0ep+t3drMxPG5sdLcOIif42WFnR9LwVltXFBmax5u3hV2HLDrGlbmJd7Hklb1vjrvZikr65DbLt2E+cBpkhfG3jrz+F/qQDPvN6S5CQBjH3aeW4qvymVfdLPmhmOWsjmP8WX24uiZnxPCPk7nDgJ9ADl+m+2+opwsUWVLxrUUgrKir20F3rQMCqyC4inC3744FDP18a5OdF2iODfyq9WM2gd3o7CoL7ipzT+zlH2/tfa54Qk3FgKMg2Z76TOiDzsnsONIMo0abkJCOcktZeU6wwtm6lRzY5ViIahVnBrpmEOMhJsAMHYkT7mldPUk/pYZY+36m3lSdFaeJT83JPR/8Pu9klleandKJ++iszg3/B0hxaMWRRELq8btIp8tt8gOXKKvdpNDseR5ZWLMOI1V4zUgn29LwWXOJfm1rY03VoKRlVbHiBfNjdVCB9m+yE7myXSEdROTx4lDsZWAwbvnWnFZvzKraOQRS6dk7/c4yMCobiHhJgBEiQK9RihmJ0fjJOokQnH8GItzyZRnKbAwS7nB62onu3vtRNBQOH+L6hc50LEJVN2gwOJ4w2a98OfvoMOrTaQ9CwuEepGTvBHjneTdchm/GVFaAjdDejInW09mKUN5/X2yvgYvUa1lNMp27NcJN66b4hg372fEgVkqYuHLxDMpafdSVrgRpbKJqmogZinduQ8k4Wb79u0YP348SkpKUFZWhosvvhh79uyxPObyyy9H165dUVhYiJYtW+KUU07BihUrdGUURTH9/8orrwR1Ga4wdiSnS8GFq6WYDiSl6rfpb8Zj7DU94n35uf6apSwjDIfsJ2+7ey3zLmoTgj5gXeM+wYggMluEFG9aFKvjjZuDHGeszFImZ1JBQ0ICgd10LqPmhnPPZQZmkdYipT43EhpH6yB+ib+d5pay1dx4iBpudih2XodOcxOwdMPWz2ur3TvLS3QpLOtQcxM3S0mOqyIBQ1WDv4/pmEMsMOFm/PjxWLZsGWbNmoV33nkHn376KS677DLLY/r3749p06Zh+fLl+OCDD6CqKkaMGIFIRP9wp02bhs2bN8f/P/XUU4O6DFd4dSjWm6LYiUB8Dl7XsutwxnfBXtNjYZbKCVk4FDt/saxMIW6CHJrrcKK5MetuhD43Is0NFG8+N42CvGif8VyJv+XI85DAVLxaqnG76YDEn9ZPyd7nRqZniVYRuemXXnxudMuxJcrLam6iHM2N8c44McF587nxXherqQhaUcZqWp0kH9ZwpLmx6Ds8f5m4Q7HH1VKx4JrB3sh0NEsFslpq+fLlmDFjBr7++msMGDAAAPDEE09gzJgxeOihh9CmTRvucazw06lTJ9xzzz3o27cv1q1bh65du8b3lZWVobKyMoim+4JZuIn96+ZF1avwGbOUzFJwB3WL6pDdn58TRk1dg80Z5bFaJSFyuGaxu9eOvjQ4mjTRxCMUbjz63FgdablP8pQFuSHUSQTcEohXAMyChsihWBSd14hJ+8D1ubF/qUTaGjfvYzJXZ1lNSOx183JLGQ91Yo7z5nNj/9FlB7s6KGgzoD79jHm/3TvrJHGmpXDD0ZZpz1M+xIZYc2OZW0qqdmvSUbgJRHMzd+5clJWVxQUbABg+fDhCoRDmzZsnVcfevXsxbdo0dO7cGe3bt9ftmzhxIlq0aIGBAwfiueeesx3gamtrUV1drfs/SER2Z6+5pVhthkxuKbuzWX3xc8tb7LPU3Lh4e2R9bkRttve5kW8L3+eGX1a03FNR3A307PGu9kmetVAyd5W1WUq/XWyWsq5PtI+7FNzi+MRxrLaGOdZFv/SiuXGax83KlKAzS6n2ZiknQp2fmhuZlY1GkulQbKd5cWKWstMEOjdLxf6VdigW+EpFk2CWOmCEm6qqKlRUVOi25eTkoLy8HFVVVZbHPvnkkyguLkZxcTHef/99zJo1C3l5icBxd911F/7zn/9g1qxZGDduHK666io88cQTlnVOmTIFpaWl8f+NwpLfmH1uYv/K55bimxV0k4LEF5JTp1pvPjdhf31uLBojtVrKR82Nk9xSYp8bbw7FVtdjnDTc5BFyG3codn7zeQF9f9q4cx8W/rQDX/64zXJCqW1I9KHte+vwzfod2F8fwZert+n2iY7XqI9E8eXqbdhfH9G9B6u2JKIBq1Cxvz6CmcuqsP7XGpurjLFppzhi7o9b92DNL2K/QjY43o9b+eU+YaLkbt9bh/lrt0NVVaz+ZY/uGHay4pmljM+CZ47btqcW7y/ZjOr99bqyVlrTXfvqMW/Nr+Ll9RIaZRFfrt6GhkjUINzIH2/HN+t34JfdtbptduYabSyu2rUf3/2807TfiVnKSsDgOXE79rkRaG7mrfkVu/bVc/cB7lYNGklmPCJZHJmlbr75ZjzwwAOWZZYvX+6pQePHj8eJJ56IzZs346GHHsIZZ5yBL774AgUFBQCAW2+9NV62X79+2Lt3Lx588EFcc801wjonT56MG264If67uro6UAFH6HPjJs2G0CyV2N66tIAv3dj0WadmKSvdQ144pIss6qAZXKwEgcrSAtsWlRVZB4+SeRe1x6h38I79aMO0gaVLy2Ks40yUXoUbK4zVsl9Rsr403SuKsX67/QTPuwLt/EbBjh3wjr5/tlQ7bnljqe73aU9+icqSAlRV78fYPq1N5UUD859nrMDfPluLkb1a4c+/6xvfvqMmMcirKvDMnDV49MMfUFqYi29vH2Hbviunf4N195sjsu6ri2D4I3MAAD/cMxp5nCCRry/62bZ+lrve+R4A8ODvDsVNr30HAFhx96hYEEnmsnm5pawcijUu/9dCLPxpB8b0qcST4/vHt4ctHIpPfuJzrN9eg0fO6IvTDjenEzFO4E6Ek3P+Ng+Tju+mC/zmV26phT9tx7in5gKA7vnZaTS0O3HklI8AAB/eMBTdKooTxztYCm4lSPGWcTv1uRGtllqzbS8emSVOqdGtohhfr9shdQ4Ra7bt9XR8EDjS3Nx4441Yvny55f9dunRBZWUltm7dqju2oaEB27dvt/WVKS0tRffu3TFkyBC89tprWLFiBd544w1h+UGDBuHnn39GbW2tsEx+fj5KSkp0/weJ8YXJb0x6yPvytEOvueH73Lx4ySCuytf4stmtZGDrb1FsTrOg7f7XxQPRzpBbKSekeMvzY8BKzXnmEe1x7pEd8PS5h5sm9j+POxQjerbCNcOsUx2IBA3eeVmthrb3kmO74OyB7XH3qb0x7vB2OKFHBe4/rQ/uP60Pt15FgZVsiJ6t+X3yljGH4KaRB8fTXtxzam8oCnDZkC7CupoW5GLy6B7446geaNYkD3856zBceFQnvD3pGPxOkN9qyrg+mHJaH1uhj2uWarwwo8+Om2XAry00CwBVjYkc312y2dwewXwx7Yt1AIAPlm2xDOK3ckvMRG31ZSvDjpq6+N/7Be+5UWsgy6sLEvdkT23Mr03nXxP3uUkcYzZLmQWfhT/FJrSZhntk5XOjCcDvfGd+FgBHmHEonLwwd53OfOOX5ubTH7Zxt+t8Zji92zhOLNu0S/ebjUhu11QrkyZv3NGeiexHca4L01BpYS4eOeMw7r5WJfl4/aqjHNeZLjjS3LRs2RItW5oTJxoZPHgwdu7ciYULF6J//9gXwezZsxGNRjFo0CDp86mqClVVLQWXxYsXo1mzZsjPz5euN2iMfbgwNzY5ujHbiJc6x07SqiQfXVsWcyclK6dCwGzOYM/10Ol9ceG0rw3lYxzbvSX+PO5QnPP3ecyx8l8YMlhpOXLDIdxzakyImLFUb+Y844j2OOMIe62cKJTHI2f0xbWvLNZt054fkBDwCvPCmHLaoQCgy3UlIhbnhr/vj6N64MrjuuL6fy/GG4s26vZdahBizj2yI849siO27amNJ0Hl9ZHLhyYc8E85rC1OOawtgNhz5QkQFU0LcPbADjh7YAc8MGMFnvpktamM+Npi/9Y38DWWQSKaLthTi+YUVVU9ReV2ilXyQivYSZcXViLuc2Ph/2EVAyeqqjqTmUycG5HAaPyg0meJt60WiqL4lmmbRSRYsNt5ZYxtNppv9Q7F1m21uhbeSietvNVxo3pVYsay2BjoZgn/w6f3RasSsxa6e0UxZt0wVLetR2VTYZLXdCSQ1VKHHHIIRo0ahUsvvRRPP/006uvrMWnSJJx11lnxlVIbN27EsGHD8MILL2DgwIFYs2YN/v3vf2PEiBFo2bIlfv75Z9x///0oLCzEmDFjAABvv/02tmzZgiOPPBIFBQWYNWsW7rvvPvz+978P4jJcY3Qciws39ZHYgOohWJ6GdgptsONNnLxBTHcek+Ym8TfvS0LXbo5JSyjcuHKklivndv4UTby87YV5iYHH/fmsztlYt4P6dCkRfHa6tGoH1yzV+G+9QXPjYXW5NKJrjwkt1pODquqfp8y7CQANkajJBCDzBOpd2aUNAkJ8m3lSto5lI56Eo6reEd7P3FI8jZEViqIv55dwIzIJ2cWpCSmKroymhY8f4yS3lIVwy80tFRW3K94+pjkis5QVoRB/TOPV5XdA0KAJLHHm9OnTMWnSJAwbNgyhUAjjxo3D448/Ht9fX1+PlStXoqYmpuYsKCjAZ599hsceeww7duxAq1atMGTIEHz55Zdx5+Tc3FxMnToV119/PVRVRbdu3fDII4/g0ksvDeoyXGEcPAoaV6JE1Zjq3tZ5U6IPaR1emzBlzFLG99uUW0rg38NrlvF8tfVRX2NSyH71u33dRC8q77wFrObG5RkViAWj+DN0MHiwfhF+ryhxO4YZV4olI9+M8Mp1mhuLycHgxybz8bu/IYpii4lEVIVbzQ2v/Xa5pczlrc+h19zY3wRRdSZTuG1NehQ4F4hkkAndwHs+CvSrlYwJdxt0wpG7NgAinxuz0GqFG7OUyBeQ1540XBBlSWDCTXl5OV566SXh/k6dOuk6cZs2bfDee+9Z1jlq1CiMGjXKtzYGhVG9yZo19tdJCDcSaKfQ+iVvQjJ/odlpbhTu37zyxo5eY2FyczM+yS4tdPs1IaqeN2exz8+tIGHlUGz1DEWEDZOynzgV4LRnUJcKs5Tg2hWJMqqq7weRqCrV7/bVReIZ2J3QIBFHiAcvRg27TRtvrIQCe61Com1Swo2gPmNfdGeWYo/3SXMjECxtIwwbNNJGB329E7d1W618bqxWZMrGgpLR3IRDek1USOG/7TztXYYpbii3VBAY+3BuOBTvLE79Uuxs2wnNDe9Y69/mrODsPnN9vFVDGnt9DOBnPJdlOZf1y2TY1m4XGwOmtsHdBKUoEpobB/WxE1DQMSxkMZqlkvGlJ4otove5EZiloDdDyU6kbh3n610+J94Eap9byqhBsdaG6CP12iOtuXEonMQ0N2x9jg4XIkocyboQ8ExXCqx9JfVxbqyxWi3FNUtJaG7Ye2WVOFNUJhziR07nCUrpmGLBChJuAoA32bB+N3bI+dxowo12kIxZyvhlbTgvUwfX54ZpmRPNjRvkNTfu6hdqbnhmqRxWuHF3nYqiCDUicZ8bJ5qbkPNJOSjiDsVGn5tkDIYSly6aHKKqnBBkhPcO67QmguPcam4iHNOHXRA/kwaFOTVPIGSfnZfeZBRm3HTNIByKRYIFO1bzNCshwypQYxEnuaVkzaPG8lbHsc+Sl8LBiNFxOfbRJWeWyjSfGxJuAoDXGTW/G78C3WnvlNVXv1lFbNhg6Ky22aR1mhv9rr21Ys2Nm/FJdmJ0+74JfW44bwTrOyKK5WN7PogFKq1+J+agIM1STtHabRRukjEYii6dvZcizZZxtZTsfbR7h0X93a3PjT5QHEeQ0cxSujY41NxErPcbEWuUDeUcikoxh2J5U48sIgGhwUa4UWAdMTmiV5dZ4tQsxRNkjejNUuKPTw2jWU00zvIEpcwSbUi4CQRemG1HmhuOacSINqBZ+9wYjjGZpcS/+ZobfhsB/zU38mYptz43Ii0Kc+85N9+tScLa56ZRuHFwKazA5SUlgB9o7a4zTN7JCMku9LlR7MuoMPvcyGD7Dguq8cUspf3LMaeI0kzEylufg3UGd5NQVMNyKbhUDYp0ZnQnyPjccDU3iqITZk3CjYW2zOpc5vOYt2nP2NLnhvmb1cqIkmgat4uc/rPBoZiEmwDg9UVNuJGZHGW+VsyaG55DmrX923q1lPmcorQQgJ1DsfMRStq+67NZyu68bjU3oiWXbFvcKjr8XgruFK3Z9Q3efG7cXIfoi1yRKMNbCi6DnXAj6u/+mKXMfhgJh2KmDUYNisU+QL9aSk5zw99uF1vLDkXha6W8IhIs7ExgxhAXpg9GJ7mlHPvciNvFaw/rBMyLkA2YY+GIXlGe5oZ8bghuJ9bMUhNf+sakvgdiL8nk15dg+ryfHKmFQxZf/R+t2BqPaAqYBx7jIbarpdi/TZqb2HlknNpkkPa5cVm/kwjFLG4DFSqKIrH83N3V+L5aymEztPJz1/xq2O6somsMwRNlUAH8vKMGFz3/Ne5/fwVmr9iCq19ehL2MsH3ms3O5x77z3SZdP7j65UWmoJA8Jkz7Gp+t+kW3jX1n//nlT/G/Y+/1d3hp3nqdAOEEvs9NYtuve+sw6aVv8MlKNip8Yv+XP27DVS8tjP9+YMYKTP34R905nETa5bGlej9G/+UzPP7RKt32lVXOkhQr0PfnqBqL7DzxpW/w5Y/8KMMyGH1uvlm/A7976kv85q9fCMsAsT7MfpB+vW47rpq+EBt37sNPv+7VpVrRDldVFf/35hL8a+46XV1WiTN574rcUvDETtYJ2LhkXcNolhK9oryAgBkm25BwEwTswNO3XSkAoLAx+NPOmnr8b/Em0zGf/LAVL89fj1veWKpTHbZowo+8bFwKLpqTn2YizYpyXsVRLPYBaFNWwOzX79Mm/QLOMvcTerTiN84C2YnRrV+H6DBFSeSNGt07kSpEy1U19CD7CN3cei32edXcWA2aMlx0dGfhvrZlsTQbWnqI0w5vayqzp5Yv8Dk1S739rfm9sENVVcxYWoXZK7bi6TmrcdHzC0z1bKnmRzh/7EP9RPzZqm244sWF3LJGzvvHfOG+Rz9M5PGZ+f0WvDx/A/70xhJhxng7eFFw2QnvqU9W453vNuOHLXuYcon95/x9HjZsTyT83Lq7Fg9+sFJ3Dl3qDIn+ZNRSTH59CZZvNgsyb3LGOiuMQfxUVcXtby3Fu99t1kVEd4rxHXlk5g9Y0Jh+QoOnJVKgXyH52Ier8N6SKlz/78WY9NIiXVnt2Xzx46948av1uPV/y/RtsBBu25aZowTLaG4qmOjCbJybId3549Rh7csMW0RmqcQcdHiH2DFnHdFB2I5ju7cQ7ksVgcW5OZDRXpLDO5Rh+iVHAtDHSuHlsanel9CwsJOcMSJm/BwmzU3ioOMObhnPLrxtT63pGN552LoAvWPtCxcNRHmTPHSraJo41piJurFqVuL/w6iDcfzBFThEkDfJCtlgm2wrXrnsSOn6hZobRcEH1w/B+u016NWmNL59zk3HY9POfa6uxep8QOLZubVpuzVLndizFSYe3w2924iv6e2rjwEAvHblYKzeuhe925rL7hOEAUjKUnDVnNPK6fFBspPJOeXaoZhjMrJ75k7PxGonZI41nn6DRNJVWXR5s1Rg4w5xJnZZjILFr3vrTGV4mpscQ1wYjZ9+3Wsax7VSxizrifrF/fScQR3xj8/X6jRBCZ8bfdkzB7THvxdsAAAU5+fg05uOR26Ogte/SaRuOaR1Cc7Lz8G/vvpJd+z94w7Fqf3a4vznYsK5XXgKAJh+yZH4cese9G5bghtf/ZZb/m/nD8A3P+1ASWEuTnvyS0/vpF+Q5iYAtHfh0HZl8RgpbKwU/jK7xN8yA25cuOE8wY7lRfG/2a9n82IpfTtEk1GftqXo3bZUt030UrDna9W0wLUw4Ga1VPMm5mSfIoQOxSEFTQtydYINEEsw5/ZaAGutjJXflAxuzVJ5OSEc1r7MMvhXk/xYvy3Ky0GfdqVcTZlbPyS/8CKgeHGetUIzPbMaA545Wga906qq+1eE03vCOs168bmxQkbLqoCTW8oHe4hRcOEJhzwhJhxWuO8X7/rtBE4rs2Q4pGCIQSssetb9GjUp2jk7NC9C69JCw8epgoNaFcNIbljB4R2bxX+L7iwriBXmhYXvvkZBbhhHdWthmidSCQk3ARAxaFUAfQh/u3gEdss22e08nxvWA551MrOLc6NbCs50e+6qcAnhhhXonCJtlrJppwixQ7F8HU6w0tx4Nku5lG5kBEgZgUvkh5SMpJSqmvrVYjw0TQjbNKsgblbwLEZ2Fi6n2jy906xzs5RfGM1SUVX1pReJgvjpy/A0NyGuucpK4BGGArBpg3FeEJml7HLUxepSuANKLN6WPW61jEBwfcMpJNwEgNYZ2Q/iXKbj2mVv1dmcBR1FO4fC+epnBRo2w69tbin2b4X9m+dczL8G9gVlTXFOcZc4U34YFAlPQcVmsfa5UWzLWOE20JkoQSK7VeY5iFYAJieGn+op0JubL3AexjZowoJuybZLzQ1vRY99wDhn59jn0CwVFEaHYp8UN5CZq3m+a+GQIrjXcgIPi51DuVGjH3/Whm4j0vKzH5ahkCK1IlQ03rnVMgKpj7ulQT43AaBJ+iIJ225FEds37JZc8r76QzrhxtwuDZM/scQXQeIc/HaxL1iBB+HGzWopPzQ3QflgWAlNiVhF7kZxt22WSWwp06b9gpQUSQnip3pbLsydtlxUZzxmf12jWYppm9vVUqxQlFiRY9MehyLKfovlztz6A3xPjMJcsjQ3vH6UE1K4Qo+VUCwOBWB904wfvSL/KpnUMVZaWd2Hq6CMWy1jOkGamwCICx6CyYOflJJvPhINIhGDAMXWyHZsVnNj73PDmqXE5WL7RZqb5JqlIPGi8hA9m6Bixsj43LjFb80Ni4yMWSs0SwWPCm9firxb5+Z+Go/YxzVLudXcmM9k20YvPjcS5ZM19cVSZPjgcyMhWPIm9JDC97nhZ2q3q9/6+YdNZintWRvaxBRjd7GyUY4gZxRg1Mrzy3jR3KQ67pYGCTcBkBA8+Pttv+AMNmceidVSsd+KoWPz/rb3uUn8bRfQT5xVmxFuvGhuXPncyA+CovYH9cFiJSRYxSqSwa3PiVBzI6G2ZnEbtdkPVFX1tBSe95Xt5nYaB3S+Wcqtzw2rydD/K2yPw3M49bkJUroxam788IOTEVhFGkDedn7JRs2N4FR2475xzBOZIEVR1EMGs5Totol8K1m8+dykByTcBIAxwJ4Rnu2dLakK/mbRBjyez43eLCUWbowdW+S/wtc08dvlm3DjInGmkzFQNGkH5Zwq41Dsdo52e5xfiS1Fq6WS4XMTVd1rrgBwXzBfNDd1muaGMUu5jXPDjVBs3UbHq6VSKKCyKIpRc60KJ2AnyLzXPM1NTDMoa5ayqd9GG2Ic8rTuYgq+ahsMNOaOIJNiRvSOutUyphMk3AQAb7UUi12OGd2Xk9DnRqy50ZulWOFGX4dVVnC7iUn0gul8bvLcdy8XVimHPjf8aw3OLGVlA0+NWcqv3E+iTOnJCdeuponPjf4gTZvFzmeuNTccM7V9nBtn56plBFQ5xY3za5HpDebVUpIH2iAj3PB9a1TusVFVFebuE53Jzo/FqEkV5ZZii7HPQS/chIT3TeZ2uvUPA4KPHSULCTcBYMz7BOgnUK7mhtmvG8waO+/++gg27kwEs/pld63uHDpnsjBfuDG+JMZJVaQF4S4FN29qPEfib2+rpWQ1N/YqVn79/HN5jfYrwtrnxlvdbtvsl3CTSs1NJAr8uHWPfUEBW6r3m7bt3GcO8GaH8REkfG4SO+oEjtd22OWWkmmPHazPzc59daipa4inVOFR1xDlBiO1Ys22vbZCRizOTeK30aG4en89ahsiqI9Esasmdn5VVbF1937sMATm2763Li74yjjI/sKJZL17fwO3j0A13+Pte83Hb9tTi2hUxe799bpUODyMY15UbTxe0qGYXTwStljyLeNzkw2aG1otFQDaC8V2tuL8xK22+4KLcuJa/O7pL3Vl7nl3OQB7h+IcC82NsWM3yUu00S4Wj0jbwL6I+ZxUDLKUFOZKlZN5UXno82gB2tDux4SfE1JMg6mMz41bmhXJBy9kYfukF5oW5KTMrHHtK4uwomq36+O/XP2radtR9892XI/xjdYSybIfFLyouI7PE/e5sdPcOGMvI8hM/Xg1pn4cS9uy+r4x3Hfi2593oe+dM7Ho1hPRzEHwzBv/sxiPndVPuD+mudH7GLGvx6F3zESL4jy0bFqA5Zur8eXNJ+C+95bjne82AwBmXT8E3Vs1xdKNu3DSE5/j2O4t8K+LB5mEKt7te33RRtO2jTv34W+frTVt55mrbv3fMvRtX6Zr/4B7PsSwHhX4aMVW2GGMpVlVvR8D7vnQVE6nuWGaoPvADcmFuxCN803zrcffpChlPUKamwAwxqABgInHd4v/zbO9s3I2KzVrfXfpRn4CusQy4sQ2Ni+Ilc+NcVI984j2GNS5HH8c1QPtywvxm75tcPbADlwhRTRZdygvwthDW+OcQR2EmWmtePj0vjisfRluGXOIVHm375gxjs/lQ7rghB4VGNS5ucsaE/z78sHo07YUr14xOHEOi5Zq44vTpKN3nNwT7csL8X9j5e6Vxu0n98SAjs1wybGdHR0n4u8XDOBuT8ZScC+CjQg/loJrS4/9jkKfEG7syjm7CJFT+F4L7Q0QSyTpBLtcUwoMcb44Pjfb9tTF81jNXFYVF2yAhIDyYmPagc9WxZJtevLL4hBVVe5q0H/N/cm0zU6w+eOoHgDcaatZ2GSZ4VDI8oPq/MEdMaZPZTyK8VXHdcXQg1rir+f0w6HtSnHfaX24x712RWxse40Z29IV0twEgDbwsIJFsyZ5OHtgB7w8f72t5ob9yrAbpHgvRF6OrHCjP64gN4x/X57otI+fbfGFJZisQ4qCqeccbtlmK8b1b4dx/dtJl/dLczNZUpiSoX/HZvGcTOw5RGiDlVNN14VHd8aFFkkvRUw4ujMmWBznVCQ5tF0ZHj+7H655eZGnejIZ47sVifK3+3Uev3NL1YpiFdkcF4QAa4pzY3EKY7u1omb/Fb9ax9TXWGe7ZoX4uTH/lcztCCmJOeKNq45Cvw7NGrfL3UtROdYNIKa5Eddx1ym9db//0ChgAcBJh7YRHjegU7lpbEtXSHMTAIkgfvrt2pe5nc8Na9Kweye1c7D2fJFwY45zY1O5BaJjbaxZAcD6NTnxuVG4fweFVdu08xcIkqRmArkc6S0TVNd+YXYulRNCnCK/WsofzU2yUQxxZaJR634kEspEy6r9go2MrYsMLNHpWSHE+JElg94slbguVpMUUpSkpD9JZzJ3NE1jjBm7NTT7pt1qKb3mxvpc2jlYp848SbOUl68uYYCoJL9QbpeCixyKg0LGodhLROdUw/PLOJAGV+PqIe1d89tBXavNb4dikZBgh99PWIH+XkZtloKLhDJjf/R7mUAsBEHjuQxxoezuvVEIif8tKd3IaG7sfCYPBOgOBIBmdTJ2QkvNDXu8A7OUJqDsFyzHZV88k0OxZc3WCAWjJM9n7OmcyChOlr37gaXPTdwslR6vo5v7kWuRWfxAwKS5icqtanJ+HlnNjbN6awUr3uyq8f3dUYw+N840N1pZk7CtGn96fDBqog5WKJG5H+xHjNH3TwZd6ApBvaGQOETDgcKBPSIFBE9dCSRyh9jFEHBjlmK/YCKC42WXFMogk5QtGfi9FDworH1uYv/mZ7Dmxi4ZbLZjciiWXNXkFPncUs4QfRypSV4RrICfKFSEaAIPWnMTM0vF/s7RmaXsBSeRWUo2qKZQc8NohMKK4lobly2QcBMAIp8bTVVoF0PAiVlK6+dsR+bFxYjV5Z9wIxIkkj3FudXcuLF1e8FK5Zxumhs38NTginLg+N0YJ7S4hsVn1U18tZSTQKASiDQ3dsKF38/XaNaxyy0lirFkFG6CEDJ5keilfG7y9BqWxLFy5xalX9CZpcJK2vhRpYrMHU3TmLjPjaG3JsxS5heN3aLXvFi/lJoWSKe54UQ0jbVLf6yXgUmsuXFfpxvc+9wk26HYvi2Z7HMj0twcILINZyl4MGYp6SB+DusVaUD81njI4CS3lHm1VKywUQvi/2qpRIRiVpCS6e8FIodiaZ8b/nZj0FSR4HegQMJNAGguNSaH4ka/BJ5ZSvRlYfdSaoMS++UlNEsZRkRPc7rI5SbZZimXacHd2Lq9YO1zE/s3XTQ3bhyBg8rJlSkYrz6R4DJVq6Wc1SsyYdhqbryKr4r5pznOjRhRRnrTUnCfxTQV/I9YKYdinXAD5m9ZnxtGc8NcF6sRqm2IkuYm1Q3IRjR1pfHrQbPN8sxSorlBVa1VzJpQw3bkqH50EJ7Dm8+N4Es9pZobBz43Bjt50MjEuclkzY0odlOyhd1UYRQCEkvBfT5R3OfGrmJ/loL7LZyZ4GiT9aulbMxSAqEsxyjcGAQmr5el6lZLJbaHHAo3xqjCMogiFLMfR/URlXxuUt2AbEQzCxnfSW1Fyf8Wb8KG7TV49tPVmL82FuFTNFipsB4gtQ7MOgTqfW6YuoxLwS2vwhrRsalc/uvM54b9O9Vmqdi/GS3ccKNuZ7dZ6uX56zHr+y0AxKul/NZoJTRC1uWe/GQ1vt2w03G9Rp6Zs8YyCvGabXvxlw9XYa9N3iRZftiyB+8tqWLa5Uxz89ePf8T6X2vMDsX87z1fYM/1695aPDLrB8vywqXgsg7Fgi8lVlCqI80NCTdBwItQDOiXyx77549x33srcMYzcxuPEZmlrBWq3Sti4bOP7tYCANAkL4welSW6443t0pC18fIQ5y1xXaVn3PrcJKPNfduV2balTVlB8A0JiHbNirjbs0lxM+Sglrrfk19fgktfWND4S/9yaR84qTJLfffzLpwy9QvP5/vH52tx+tNzhfvvfud7PPrhD9i0i5NcUgab/mHMLWWEp534zdTPde93NKrqxkEV/voSseP8/xZv0iU45sEG62SH4G6NY7kd7DH9OzbjlulQXhSPfJxsTuzZCgAwqHN5Ss6vQekXAiCxWkr/VhbmiWVJqwVUVipoLW3AJcd0QUXTAhzVtTnalBXGQ3xbLwUXn9OOdHEoZnFiAmEHJL+yY/P48IahWLZpV/yF56H1k4qmBfjnRQNxwXPzA2tPUHSrKMbfzh+Aiqb5vkyqVozuXYn3l1bZF/SRP487FCN7VWLm91W46bXvTPvNEYr1//qFVl3WuDjZOUarKqwkIN7hO2vqdWap+mhUVy5mlhKfuHfbEhzeoRle4OSJ4pHnMG0Km2aFnSN6tSnFM+f1R35OCBdO+1p4vKIo+OT3x2HRhh04pW9b3b63Jx2DTbv24eDKpuhWUYxwCLj+3986ap9XHjmjL95fUoURvcRjXjIgzU0AiFZLGb3ZecdoNGlUXUYtNDdnHdEeLZvmA4ilXPhd/3ZoU1YIIJEfRL9aylhTEEvBU2iWclA2l1mDGaR2oVtFMU45rK2l4MXuGnpQS/zOQW6tIHB7P07s2Qp925fp6wqgPxTn56Bjc76mKCjOOKI9Soty0b1VU9O+aFQ1CRvxIH6+m6WC0Qj5zcBO/ny12+WWEtmY2LG3IaIaxkFr4bAwN4zT+7eXbmOewxhPrG+M8dpG9qrEoRZaXiAmEHVq0QS/7dfONMf0aVeKkb0qAcQ+2n7bL/ljSdOCXJxxRHuUFclniw8CEm4CIBLX3Oi3WwVpM76jWqeNORTzj7EyK2kvjT7OjeF4D/OOIug5qYz67WRSTqegc8kOfJhUArq0VN0x3jtTH42aNAFBCSFBxc/xG+lxQMYs5eL87OvdEFH14yBUy+eiKIqjsSTP4SpHtjzv3bcbl1OpHc8kSLgJgHj8A6NZyoHmRlOrxuzDgq8TqxU4EufwFsTP+Z6gcaIlSCvhxvAWpk/LvKEowV1LqlZh8fpYQ8T8hiZ8bvw9v/YK+52zym+kTb02l2GnuREdHrY0S1kLnU57ltPUI/k2wo1d387qjyEfIeEmABKrpZwIN/rf4bjmRrxs0Spct3bu4IL48Q9O6VeFg3PnplFiOeO9TPXY5efpg7qWVN0i3vUYzR4AE0k4RekXUo1fE7CqWn+0yNwH3vOxCRLvqN86FW70mhvzfrsxNN2ffbqQPiN8FiHKLcUuARQdoxFmNDciZPw4jBl2WTxpbgSHpnS1lJOl4Gmk202flmQQKbppvHeGZ5ZKRCgOxucm3YMmSmtu7MxSUTvNDf8+sLenPhIFDOOglUNxzIVZvoM5NkuFWZ8bnlnK+tzprrVLF0i4CQBRbikrzY3xZQsnpBPhAGk1gGgvp2WcGy8+NxnuUJxOGAe4VN5DP1Ea/wum7tQg1NwYtsmmSXBKUBohv5FNAmlvlrIep0S3gb0/DQaHb1W1FxAc+dw41tzwIxQnttkIN2ku2KYLJNwEgNb3jNoBqyBtJrNUmPG5ETkUW/ncaLKRlVnKy2qpDF8Knk4Y71mGXgaXoK4lVX4HXM1NJCoM4ud/4szMWC3ll2Y05nPj3CzFbm+I6DVrKmyETtVZ/3Kqucll/P34PjfWx6f7s08XSLgJgMRqKfdmKS3LslUQP8vVUo3/skebV2QJD7dFbJYizY1T0s3nxk+CuJRUZhvnvXINUdVkHgkut1Tjv2keWV9ac2PrX2IdoVh0d9n7Xm/QrNnFuQGC1dzkhq3DUJDmxh9IuPGRaFRFJJpYZmh8wQsEEr52HAvrcyN6Ea1eAm0fm5vKT5+bdMktlS7n9gKtfnCGnZNpkPDNUlGTJiDoCMXp7nch43OjqqqkWcpKc2N/Hxqies2aneZGhbVAZcSpQzFbntd83q1j7ydpbuSgCMU+8tDMlXjyk9Xx38ZOmiN4CfreORO7DblZNMHIKoif9Wqp2L+fr9qGaV+sxX2/7WN6KbxMD6JjU+tzk5lCQrrJNr61RwlOk5eqe8a7nlv/txRfrdHnX3pp3npcN7y77z43V7+8CP/66qd4TrpMpXp/PfrdNctWC2GXW0p0PDvW/eav+ojZalQi27kTzY1DsxQbhoInpPI+dnT5AtNca5cukObGR2rq9InKeGajo7o2N20zCjYAuxTcm8/N3DW/YtueOlz2r4WmerxMPKJjU+tzk7pzy3LVcV1RlBfG8EMSocnN/SS1F3L2wA5oWpDjOlLy2D6tUd4kD6N6VwZmlnID792z4srjuqIgN4RJx3eLb+NNPEbBRuOZOWsaV+okaNesUBhduWm+3Lem34JNn7alvtYHAPm51lPLf77eIGVeidislmoQCjfiY1SYNeW6/Srg5B086wj5aMYA0KI4D33alqJXmxKUc6L4iq536EEt0a5ZIQZ0aubofNMuPAJ54RAePbOvo+MyHRJufOTGEQfpfvMGwumXDJKqixVuRKoba6c9875k5JYirPnDqB747vYR6NwiMcGlm0Nx8+J8LLr1RDx0urvB8K/n9MP8Pw1DSUEud45446qjcFAr+ySBLYrzudtVG1OFCKt3777f9sG6+8figXF94tsOalWMpXeMxO9HHhzf5uSsqgpTZubC3DBm33gcBhqSCj5xdj8suu1EYV0FAmGheRPvIe7/N/ForLp3NC4f2sVzXQDw4O8OlQrOJ0Mkaq254WWjB6xjwVh9MGpIuwwpQLMmefjDqIPtCzcSDoXwv4lH4+1Jx3DHcFHffn7CEZhz0/GWC1N4HN+jAt/fNTIlqRhSCQk3PtK0IFfnCc+zQimKgiILx+LEsYl6xBGKrXxuzNuSoblJpTU41UKBLDnhkO7+mRyKk90gDiITqgyKolgeL+ujYPU83dwjq/7eJD/2TrJtC3Guw4l/VEgB9tXrJ99wSEE4pJhN1iHre5YvSM7oR9LXUEhBbjjkm+9XSFHszT6STzBis1oqErE3SxlRJfbL3gntnjlxKg4psXvudEWZoiiun7eX9zlTOfCuOGBYqVr0UlrFu9GQilAskVuKxU/NTTqSST43bEszRShzA+/SvF5vkKul2HfKzTJdY9lag+ZGq9NYt91ElytIF5KOfScUsjEL2SXDZIjaam74J7JyNI6q1rmlAPkPP62UE6GDFhAkBxJufIYVbkQOvzJqRd1qKUEZ6y9b805jPdn2kmXU5TBtzeql4DwBwaMQqtoEdnOD1k4rjVpsv3ydIUUxmaU0Z1JjPXbvYo4gboOfAr1fi3AUKJYaXCfnabDJnCnynbEzS1k55dotP2fRnlsOCTdpBwk3PsNqZUSd2CrejUYO43MjjFAssVqKRWbZZCaTUUMG8yjMZqmMuhJLuHE8JEcdq7vg9wQR/wJn6uVp8p2cV1EU7DeYpbTjjc/YzmogSvSajvOkolibfZwsZY6tlhJfpFuHYrs2yD5nrZgTE5MfpkTCHhJufEYn3AjuriOzFMRmKZk4NyzmIH7Z9ZJlUoRi9lFk2WPQwbs0r4KJojgXZO1OGZ+kFHab+SBnwg2wv0GvudHea2M1dn3XaSwVN4iDTjhDURQbs5D8u2q3oqohwlfBWAovFh+MjbsdORQDDoIWIrvf93SC4tz4TEGehObGkc+NhUOxw7fEbJZydHjak0mXww7+ptxSmXQhLpC9PNF9UJ3MPo3YTT6adoB9p3jHODNLmVdLJYQbg+bG1iyVOZ0ipFibfexi17A0RK0D/Ql9bizqjKr+5fyK+1A5MUtl0LPMZEhz4zOFzJJNkfqxwIFZKmq1FNzK50bCoTgI80cqLV+ZJBSoOrNU6toRNFyfGx8elNMa7CaUhOaG8bnhrnZ0cE6OWUoTYozV2PrcCDQ3yew6smZtBdarpZz4TEWj1voktw7FVjhpH/ncpC+kufEZvc+NqIy9TJl4ASwiFEtkBWfJcpebDDZLGX1usge+WUr2WLGfidNHba+5iWFnlnLyQcCbREVmKTs/JPFqKR97i834IDt+xHxuxPudaG4iKhCyaJhMhGLZYzRiikFJn5vGf52tlpIuSniAhBufKfTJLKU5EG7bU4f3l2zmlrF6AXkv0AfLqnS/6xoojneq0GtujGap7B79vF6far2Ahovd5MNzDOUJRE4mpu17683Ha8KNqd5gzFJfrfnV1XFG/rvwZ/y6t1aqbEix15zI+9xEobjI8PvK/A3Cfe8KxlMWp6ZTZ/GPsvv9ThfILOUzBTn2wo3MUnD22Dve/p5bxulqqc9WbdP9dpoTRQa/nBJlKS3M9VzHoW3LvDfEIcX5TMgAw8TVSRCePxP5dW+daVtIAXq3cR/yv0vLYscCkr1s0Og7YbMU3MnE9PL89aZtYcFkaBdpWGSW6tTCuq+c9exXlvtZrN7cG1/9Fve9t0KyJgXdLCJQO/F3idiYpUTw+p3G/e/bXIeDODyasOrILEWqm6RAmhufYVc1iL4WRcs6dWUkXgBrnxvr4+84uScqSwtsz2HFE2f3w+INO/GPz9d6qscL7cuLcNtJPV0JOR9cNwSvLtiAiUzuoGRxzqCO+GVPHfq2KzUJmeOP7Igtu2txbPcWSW9XMggpCm4/uReaF+ehpi6C6fPMQgDAF9CP6tocFx3TCR8t36Lb3jQ/h5ujTUNWc8N+MHj1ubFqB1vPLWMOQfdWTS2PE5mlzjyiA/q2K9Ml7E01IQW4dlh3QAX21jXgxa/0z5cXxG9Ez1aY+b3+mQJARCLJZRCw5sdhPSrQsmk+XvnarA1yY5ZysrJK48YTD7IvROgg4cZnWMFF1IdlOreMdG9Vxurokw5tjQuP7mxbvx0n922DwV2bp1S4AYCLjnF3LQdXNsX/ndTT59bIUVlagCmn9eHuyw2H8MdRPZLcouRSWpSLW8bG7v0363di+eZqqeNuHt0D+Tlh3btV3iQPVw7tinvfWy48TnbyYYvxg/h5k24SxyfquXSIfU4nURC/vHAIfxjVAx8sq8LqX/Z6aptfcbBiKWZyMHnMIQCAQZ2b4+qXF8X3G+Pynd6/Hc48oj1XuImq1qulgsC4GO/4HhU498iOaFtWiIdn/aArq/URJ0v13ShuRvaudH7QAQ6ZpXxGRnMTlrAhy2lurMxSVv44/qlFyX5MOMUolOcLzKN8Z2RN86E3H9l1Q7t+GncoZn1uOO+gV4uCdrzT10akudHqkwkMmiyM98gom0Q5y5FE41VD1D7gXhCwrdF8JHmZzrV2y2jjNdyYpTIpFEC6QMKNz7CdUDSgygj5MgKQVT1Wg6ef8gi9coRTjOO0SLixgq0ipNgLL/ZmKZ7PjbicWxK5pZwdJ9LcaNcls0ghWRhvkVEjxFstJbof0ag4iGlQGLPOa4IjL3mpVkz0fHi4+SBMRhDHbIPumM/kGLIK85CR3D1rblwe5xRjXdm+3JzwjnE5db5gYubHyNH/C4CbZduIrOaGnUP4DsXW57FDlH7BDpFmQKtPZpGCHX7mlrI7j2yE5phDcQo0N0xz4pobjhCu9QejZs2qn7jxuXGiGSJikHDjM3rNjX0ZETI+AtZmKfFxfr4mCvUgwiFeNDc84SCkKLYfDMb3yVg8ITRZa179SB3B/iuL6Ms9lAGaG6NZiWdmEj2+SFT1LZqwLCr0miXNHMUzS8WD+DnQrLgZM51ohogYdMd8xs5mD8hJ7l6FG7f+OE4hnxvCMR6EG55wEArZ92nj+yRKeaFPnOl/305kH3d2nKgtfvrc+CVDGO+tMRUDL/2BaByJqMk3SwH6a9C0Ynlhjlmq8V/jB6uVQOZGcyPyuSLEkHDjM2zHFQ24MmYpmYHV0ufG4jg/5RHycyMcYxj4eb4MIkIc4SCkyJilrH8rnDg3vPfEs+bGcD5ZRBOitj2dNDe2DsVRVXplVsRBWb8wBonUYpfxhHBtjHe2WsqNWYqmaqfQHfMZ9sUWxrnxSbix/Fp16Y/jlCDyUxHZjfGrlqfuF8ETDsKK4tih2PTuNP5ktf9BrJbirASXQmSV0K4jnX1ujMKJqppTIAg1Ny6D+HmFbU6BlVmqcZOz1VLO20OrpZxDwo3PsFoZUX+U0tzImK7SwKGYrFKEU4zTlXApOKdv8cw6imIvdBj7vFlzYy4XSJwbw7/Oj9QTXy2VYUvBjQKu6LZGUrAUPKa5MZuluKulGsvl0mqptIPumM/YDY6AnOAi871i9T65dTZ2CvncEDKwWhCT5saBWUpkKnLqcyPK5xUOWb+/fn1AO31vhB9KjdsLHNxDEb7pSIzCjUE4mfTyN2iI6B1xRPcjmiKfm9pIJP53gcRqKSeaGzc+N0H4f2U7gQk327dvx/jx41FSUoKysjJcfPHF2LNnj9Sxqqpi9OjRUBQFb775pm7f+vXrMXbsWBQVFaGiogI33XQTGhrEYdeTjd1S0lgZ83bji9OsKM/Wjm7lQW9tsfLTodi3qogspojRLBjzKJ1yWBvuMVzNTeO/7Lu1o6Ze+K4N6lwOALh8aFcAwJkD2gMAbjCEs0/Um9gWRJwb7fizB3YAAPTrUCZ1nMgsoWmBu1XEcjmVFKQ+6LzRLHV0N30akaUbq00JPUWKiQbGLOVHHjkep/Vrq/utGs6l3dOuFeZ8WaIgfm3LCoXno+/B5BDYmzB+/Hhs3rwZs2bNQn19PSZMmIDLLrsML730ku2xjz32GHcQiUQiGDt2LCorK/Hll19i8+bNOP/885Gbm4v77rsviMtwDDvIiiMUm7c/dHpf9KhsihMf/RRATNiZ84fjMGHa11i2iR+a3koVnTyHYnpTCXtywyEsvu1ERFWzf8ghrUtwRKdm+HrdDtt6eA7Ffxx1sE4QGdGzFW4aeTBKC3PRvDgfG3fsQ4fGZKRTTuuDq47virZlhbjnXXO6Bpn3V+PQdqVoiKj4XjJ1BJB4Lwd3bY7P/nC8dH63AsG7rrV3TJ9KzLjuWLQszkf/ez6Ubk8QGG9bu2ZF+GryMBz30MfYXx/T2NQ26DU3xqClbUoLsGnXfp3z8W0n9cRhHcpw5jNfYdseuQzlnZoXYd2vNdx9hblhzLx+CNo1K8RlQ7tg1GOfxffl54Txza0nIqQknHmL83Ow4P+GY/H6nbjkhQUAEv2QNUv9aUwPvPvdZmzcuY97XhkBWVESPlDf3THCtjxhJhDNzfLlyzFjxgz8/e9/x6BBg3DMMcfgiSeewCuvvIJNmzZZHrt48WI8/PDDeO6550z7Zs6cie+//x4vvvgiDjvsMIwePRp33303pk6diro6cRbY2tpaVFdX6/4PCrsIpwB/0MwNK+jaMvFlEImqqGhagIMrxQn1rDQ7Vu+Pn9oWu2ikBAHEJvWyojyUC7Jfty4Vf+nq6lES9Wk0yc/RvXeFeWF0b9UUFSUFCIeUuGADxDQdHZs34ZilGvdLmJU1DmrVFG2bybU7caLEn+3Li6R9KUTvejgu7CnoUVmC5sX5ztrDEOSrW1laIExNoyhmzVTrRs0HuxQ8HIqNkYe1l88o361CPH7m5YTQvrwofu80tDGsvEkeyor0/bVFcT5alSQE0hBHc9O8Sb4pGa5T2L5XUhCMxirbCUS4mTt3LsrKyjBgwID4tuHDhyMUCmHevHnC42pqanDOOedg6tSpqKw0JwqbO3cu+vTpg1atWsW3jRw5EtXV1Vi2bJmw3ilTpqC0tDT+f/v27V1emT3sOyqS0HnCTTgU0jkaa34JVgOs1QoJq68DP1c4+WniIrIXN92E1095uaVyQvrcUjLaRKNTv3Yunc+NzVdAfk7I8Zvk9t0TCTeZ9Pqx2hr2+amq2WFWE9pYh2I312r1CN1+5BXmJdqqVcG2P6qqnoWbDHqsaUsgwk1VVRUqKip023JyclBeXo6qqirhcddffz2OOuoonHLKKcJ6WcEGQPy3Vb2TJ0/Grl274v9v2GBOXe8XUvFpOG+p8QNOe6GtnM8szVKWPjfW7SMI/7HudE6VBmxtueGQPj6Nw7oAQXBAm4ryc8JJM8v6sdSbJUhfOdEHTx0j3Bg1vEafFW0cjTC5pXgZ1e1wG+jUCvZZxDU3IWthzSlk7veOoydw8803Q2lcmSD6f8WKFa4a8tZbb2H27Nl47LHHXB1vRX5+PkpKSnT/B4WMJoP3RWjszJpwY7UiytIsZTEA0ItDJBtXmhuuQ695X05YH+fGjTZRO4L9OLFb1VKQG3Ics8Ttq+dVE2CkSV5qHY9r61ktjnkptU64aRR93QhkQSysYMddrW1sv1GhIs+jcENDtHcc9fAbb7wRF154oWWZLl26oLKyElu3btVtb2howPbt27nmJgCYPXs2Vq9ejbKyMt32cePG4dhjj8Unn3yCyspKzJ8/X7d/y5YtACCsN9nIaG54Kx+Mx2krJa0EESvhhlYxEemE/0uoWbNUyHaVky0cnxu7yS8/J+zYzGRXOhxSTAHuAPFqKbd+MkX5Yeyu1a8yTaa/XG2E1eKYNTfaByAbE8eNSc9q/BTJH3a3gdWYa6Y2tq9EVe/CKAk33nEk3LRs2RItW7a0LTd48GDs3LkTCxcuRP/+/QHEhJdoNIpBgwZxj7n55ptxySWX6Lb16dMHjz76KE4++eR4vffeey+2bt0aN3vNmjULJSUl6Nmzp5NLCQyZgZUnABkHtLjmxqKXW+XksXYoDu7NIXdigoebiYl3BE9zkxvWx7nx4t/DHmr3oZKfG3J8LrvyIuFGZOZwG5smprmRW3EUBLX1Ed1vo3CjCXMNUTU+qIQ4z96OIMZBNqYQq4HSiKreNTekXfdOILrJQw45BKNGjcKll16Kp59+GvX19Zg0aRLOOusstGkTi2mxceNGDBs2DC+88AIGDhyIyspKrvalQ4cO6Ny5MwBgxIgR6NmzJ8477zz8+c9/RlVVFf7v//4PEydORH6++1UCfiLlzMgpU2dYGhmNmtWdpnos9llNJvTeEMnGrz6nvTts188JGzU3LgQpngnM5piCnJDvk1BOSAFv3acoSJxbZUuqIxrXRazNUtp99e5Q7Nw8bycwsuNubUPEtF/1QXNDwo13AgviN336dPTo0QPDhg3DmDFjcMwxx+DZZ5+N76+vr8fKlStRU8OPQcAjHA7jnXfeQTgcxuDBg3Huuefi/PPPx1133RXEJbhCplPyVMz1EaPmJvav2z6erKXgBCGDXZfjmUR4ZqGEUYo1S/nnc+OE/Nywc82NzZlEfj5Cs5Sz08fh+dz4pXWVMW+xGg9VNX+oaYqPqC63lBuzlHifH/LDfo7mRlVVzw7FNER7JzCvsvLycsuAfZ06dbJ9CXj7O3bsiPfee89z+4JCxueGp3Gpi+i/AGRWS1lhuRScvgqIJONXn+PllsoN6x173TmeOj/I1VJwO7OUQENjFY3cDU3y00dzw0O73r11ESxvDJIYN0s5OI/VcxVqbhxIeTzNTdSH1VI0RHuHckv5TFOJ8Oc8gcWYX0erx21OEaujgnxxKIYfwcOuz/G6TUtOQDrRainPPjeKVldiSLSLc9OsKA8tHAbNs2ubKGy/aFxx69vRmnMe37KCSzwA1ueGF9iR3VZTF5Gu19wW8T7R2FpRIv9MOe5RaJKfg5ZNvblJtG1WZF+IsISEG5857uAKnNy3Df40poewDPt1lhcOYUyfSozoGYvX88C4Phh+SAXGD+oIwP0Xr+USSFJ6EknGTTe+85RepsmeZ5bKDel9X2T9Fcb2aW2qt7xJHiYc3QmXHNNZmMvo5tE9cMphbTD0oJa4elh39LCIIu6UqeccjmO7t8CLFw/C0INiizceOaMvBnYux2mHt8U1w7rHy4YU4JDW5nP/9Zx+tue5YkhXDD+kAn/+3aG+td2OKaf1if/Nam6uOr6bqezlQ7uYtrn5znOSQPiFiwZiyEEt8cA4d/fkrlN6YVSvSvymbxtceFQnDD+kAlc05jTTuPUkuYUvT46P9YNXLjvSVVuIAM1SByrhkIInzrYeXFjNzcXHdsYfRyUEoTOP6IAzj+jA1OeuHanS3BAEDzcCdWVJAb64+QTMX7sdZzwzFwAzWRk0N24cii8b0gXvLtls2n77yb0sj2MnrNLCXMy4bgg63fxufJv2wfLmYl6qGeu2dWrRBP+6OLai9Jju+oSTj5xxGKJRFY9/tAoAcP+4Q7kfPycd2gaTXlpk2n72wPZ4eX4sgGlJYQ7+fsERuv1OVl61bJqPX3Y7W2119sAO+GTlVnywbEt8CfW1w7qbhMhxh7dD8yZyWjs7rMz6xn4y5KCWGHKQ/WpgEecP7oTzB3eK/9bu79NzVgMAjuxSjouP6SxVV2emHxDuIM1NCtDlVbEp69Zr3kqlTg7FRLKx7XOcedUyiB+zzRjETxb2EL8F/hzBV4nX8+jea4dmJHaJuah9srg1YWnnVePLu803JKqq3DAXbrTY6TQOGpOFEsFCwk0K0OWvsXlh3Qo3lpobMksRScbdCibOaqnGetjJNTekjzcj+87o6/f3ncgVOAb7eRanMW5Y/xDR6isnZ3dDrmBVFEskqnKFEnctFrcz2cuteSuriOAg4SYF6IUb67KuX0BLday7KmVwG1SMyG5cdTkLzU1DNDFRGDU3sv07MM2N4v/qJj+IMtINbzVPMhYDGM/LE3ojgobEV8o56E1RC3nCi3Dj5lDeyioiONLvDTwACDkYVYPwuSGnGyLpeFjBxKsmYpio9XFuXJzL+SGWiILu+fnqORVGWKHB7SpMt+fWMJrDeO2I8pYgwV16CKuPLS/yp1V0eBG8aMZEcJBwkwJyHGhu3K6Wso7M6apKgnCNK5+Yxn/ZSY2NXKsRC+Ln/Fz6bwy/zVICnxsfxSinU32DQGhwW58bjOY63ljESz8BuPNZsbpkL5obY+gOGUhzk1xIuEkBTnxu3EJLwYl0wt6fWDJCcdwspddC6OPcOPe58V1zI/iCSKXmRqQRcXVul8cZzXUih2IeiSSV8ucT1RWrx/3DKMglzU26Q8JNCtCp0G3Kus3Um6ql4BTEj+DhxVTEdime5kZR9Job2XOx82yyVkv5iVP/NpFGJF5fUnxujJobjs+NoJ37611oPiyuSWA5lMKd5oaEm2RCwk0KYO3xdlFQRVHK7QZjyi1FpBN22kLexGrVh40mFlcOxTrNjc9mKZHmxsdzONbc+Ci9uP3oMvoi8XxuIoKq3Zmlglkt5cbnxi7lBOEvJNykACfOj6KX0y7nFOWWItIJd5ob80E8zQ273fi3bJuSF+cmde+enebGCf6ZpcxlROYzLWWDM7OUeJ8X4aYgN7W5uQh7SLhJAaxq1u6LUSTc2L2YqTJLHd2thX0h4oDDblK30tyw+7RtPVuXAACaN+Yg0gkqsm2SLCfDGQPa6X6L4tz4yaHtSh2VF2lEErgTWTo21+dBal/Oz48FmO9LcyY3V6vGnE7DD6kAYL6+Q9uVAYiluAGAojx7AWOoRcRhL+Pgb/q2AWC+dh6je1cCAE7r19b9CQnHUPqFFMBK/W41N/ZmqUSBg1oV44ctexL7AnQo1l5kgmDxq8dp9dw06mAc2q4Uh3dsBsCoDXWuufHKXaf0xn8W/Bz/XSgx8bplzk3H4ecd++KTPY/P/nA8jv3zx7ptTh2Kj+3eAp+t2qbbNuHoWIqBU6d+Ed8249oh+GzVL9iwYx/6titF61KxcGM0w2s59QDg3WuOxeL1O3F8j5jw8q+LBqHvXTMBAE3zczCwczkA4HeHt0OL4jz0bluKgfd+JDzXxOO74rf92uLGV7/l7veyHH7C0Z3QqUUTHN6hzLbsw2f0xan92mJId/epHQjnkHCTAljhxk5VLDLT2r2Y7N6jurbQCTdB+dwc1bU5mbwILm58xHjbNCGmpCAXpw9on9geMpeRaJV0++zQfbAAKBSYLfx4PTo2b4KOzZtYlmlfXoQmeWHsrUs44Tp1KB7Vu9Ik3BzVtQU6t9CfuzAvjBG95D5qWHN68yZ5OvNdi+J8DGeEndKiRM6pYw9KaIRDIQUn9EiUEzHskFY26RfcP4yccAgn9rRvAwAU5eVgpOT9IfyDzFIpgB34am1WAIgc92zNUszuJvlh4T4/oZVShAi7Psc1S3HTL/CPdxPET2/K8velEAo3KQzDIIr8K4Ln16fJCm4ditmPMlGgQ7+wq52+w7IbEm5SAGt3tlsBIPrasnsx6xmVT1GeXkEX1ABLqRcIEV4SW7L9SqQZ1Afxk6yfcy6/KBCYpZI5oRrfRqeaG5522Gv7dcJNwCkqgsrbR2QGJNykAHaAthNuROORnVmKTdJm/Iqkd5pINrbxnHhB/JzU78rnxj+zlBGx5iZ1OF0txRduYtvcfsawdQbtdG33TCkkRnZDwk2KsQtM5Xa1FFtvbo59sjo/ILMUIcSV5kb+GFdmKd3fyTFLpRKncW54wk0ooU5zBfucgg50aBwjjf3Ca34tIr0h4SbF2GtuRMKNfL3GovROE8nGTZeLHyMxkbrJLeU12aYVotVSSTVLGe6brVnKcKO5mhuPbcrRmaVSOxDR4ofshoSbFGOXTE00INkN4Gy9xqJBvdKkuCFE2M1jTiMUm+tXmL/ljnETG0cW8WqpFDoUOzVLcR2KvZmlQjqzVJI1N6b9gZ6eSDEk3KQYu2RqovHI3iyVqNdY1i7lA0H4jZtJ3Yl/h9eVT747FKehz42dWcq4mzdOeB06wjqzVHJ9box9kByKsxsSblKMrVlKqLmxrpf1uTEWDeyVJtUNISDoacTrUnC/WxhkED9ZjGYmp5obrtnI41JwVqDJDXi1lJ3GmoSb7IaEmxRj9zUl0rLYaV/Yccz0DgflUEzSDSHAS5eTOdZdhOJgfG4UBSgQJVZM4XxqN5kbfWz4mpvYNuMiBTdtcKK5CbsQhIzXm2doM2mwsxsSblLEPaf2RquSfNz5m16W5a4d1p273W6guvqEbmhbVoibRh7MUcc6aytBeMU2txTz9+EdyjCyVyL66xGdytG7bQlOOayN8PiWTfNxWPsylBXlYlBjmH7bNkmVckdOOIQRPVuZwvOnMojfo2cehlYl+Xjo9L7c/dcNP0i3PDusKKb2a+POPy4YgIqm+Zh6zuGO2qAP4mc//fxpTA+0KS3AH0YezN3/l7MOQ0XTfNxxck/TPu1MT40/HC2b5uP5CQN1+2kczG4o/UKKOPfIjhg/qIPtoF9ZWoCmBTnYvb9Bt93uxWxTVojP/3g8FEXBqws26PalcoAlDkyc9Lj/XnmU7r3IDYfw9qRjLN+VcEjBG1cdFTuXm6zgDtony7PnD4Cqqug8+T3uOYPGqBTu3bYUX00eJrw/laUFmDt5GAbc8yGA2D29bvhBOP+5+fEy2qH9O5Zj3p/EdYnQxbmRkC4uG9IVlx7bRXieUw5ri9/0bYOlG6tN+7RjRvdpjVG9K8nn5gCDhJsUIjsw8F5CGZWqVn+yNDcU54YQ4WQe4b0XMu+K04mWFfKDWsWUbsuN7drDOvwqivm5sWOHm2tz41Bsdx5FUcCzWik2bU2zR0P4DJmlMgCeMOLkqyNJLjfkcUMIsdMWpkIwDlpzwz1nks7jFt1Hk8oLhOftCpyapWThpXKw9TEi6SarIeEmA+ANKE60L8b3PrgIxSTeEHzScR5hm5Ss9qUyt5QMxhVSfq+0DDk0S8nCk5PsaiezVHZDZqkMgPcKOtPcWA9YBBE09v01+YKxbrVUkt6KdPd3062YUszt9SoQ5ASkueG1yza3FH3aZzX0eDMA3kvqSLgxxnugLxYiyaRjl9P7ZKSuHemEXT4mr8INe7yfiTPdmKVoHMxuSLjJALhmKQdPLmkOxcFUSxCBkIqpLd3nU6NZyk7YcYrO58ZH1YmbqmgpeHZDwk0GwHsHnTjDJc2hmKQbQkA6fiUHtkLKQmxKv7ugx7gK06z19Va/3qHYT58b5ytKyecmuyHhJgPg25Pdm6XopSaSTTp+JaekSRn27pkdiv1bLeVn4kw3Gcwz60kQTiHhJgPg+9zIH58sYYYUN4QIW3fiFC8Fz0p8uKd+a7d0cW78XC1lkcGcODAh4SYD4L2kh3doJn1827JC2/r8oF/7skDqJTKfQ9uVWe7v0640OQ1h8HvlUmVJAQDgqK7NhWW6VRT7es4gqWia77sAGFScG67mxqbt6WgqJfyDloJnGLOuH4KZ32/BhKM7SR/Tt30Zxh3eDv/95mcA/n+xfnjDUMxYuhkTju7sb8VExvP+tcdi9oqtuPgY675xxdCuyA2HMOyQiiS1DL7bJV67cjDe+GYjzj2yo7DMSX1a+3tSSaZNOEK+7IVHYEv1fnSraIrFG3b62g5WCCnM9S9zuqxZ6tnz+uOyfy0EAOS7TP5JZAYk3GQA7EqA7q2aonurpo7ruOTYzgnhxudRvVtFMSadwE/wSRzYHNK6BIe0LrEtV5AbxsTjuyWhRQn8FvLbNSvC1YJEtwAw/JCKpGaiVhm71PEHywuNx/dIlPW7tayypjA3aM2NedtR3VrE/ybhJruhp5sB+CGMFDBfSeno3EkQyYY1z9JKPz5+m7DDzJdaYZ5/mhvZIH7s2Jfvo+aISD9IuMkA/BhfWBUwmZoJglbLyOC7zw1TYYGPwgXPOZmbcJjZRpqb7Iaebgbgx/jCCjeRqA8VEkSGk3whP7knTEdtVEhnlkq+zw0LaW6yGxJuMgA/VMMFeYlHvb8+4rk+gsh00j3PUzrgv1mKcSj20SzFTy5sva2ANDdZDT3dTMCH8SWP8eSrbSDVDUGQedaeIJeC+6m54UI+Nwc0JNxkAH58PbFfNqS5IYjsxw+rlO+am4B8bnjwmq6Qz80BAz3dDMDvD8z9DSTcEARhT5Cam7yAhQu+WSrxd9DCFZFaSLjJAPz+eqqtJ7MUQbColDyEi99hI3TCjY8Rinnwmk6amwMHeroZgN9fT6S5IYjs97lRfVkuFdxNyk2B5oaFhJvshp5uBnDTyIMBAOMHdfBUz9HdYjlvTu/fznObCCLTyQuH4qaRytKCwM4z9tBYyoXLhnQJ7Bw8ppzWBwBw/fCDXNdhlA+6tGzipUkoystBYW4Y4ZCCiqb5nuoy0rF5ke63SLZp3iQPAHBwpfNI70TmoKj+iPcZRXV1NUpLS7Fr1y6UlNiHhk8HduytQ1lRrqdkb9Goiur99SgryvOxZQSRueyvjyCqqijKCy4Tjaqq2FlTj2ZNkv/ebd9bh3IP5127bS+Of+gTAMDdp/TCeYM7eW7TvrqY5tjPpeAAUB+JYvLrS/DawliamZX3jEJ+jvkc++sjiERVNMmn7EOZiOz8TU83Q/BjYAyFFBJsCIIhGU6liqKkRLAB4EmwAfRGKb+EAb+FGo3ccAgtihPaIFEcI3IkPjAgsxRBEATBhfVbyQQfpQImGWcmtJcIDhJuCIIgCC6sgJAJEZ31CYLTv71EcJBwQxAEQXDRCTcZICuwK6AyoLlEgJBwQxAEQXDxsoAhFbAOxBnWdMJnSLghCIIguLDyQSYIOjrNTQa0lwgOEm4IgiAILjqH4hS2QxZaCUVokHBDEARBcMlknxviwIZ6AkEQBMElk1dLEQc2JNwQBEEQXFiBJhM0N82a5Ka6CUSaQBGKCYIgCC4hneYm/enVphSXDenie94qIvMg4YYgCILgkokrjv405pBUN4FIA8gsRRAEQXBhRZsDLsMykdGQcEMQBEFwoRQGRKYSmHCzfft2jB8/HiUlJSgrK8PFF1+MPXv2SB2rqipGjx4NRVHw5ptv6vYpimL6/5VXXgngCgiCIA5wGNlGJdUNkUEE5nMzfvx4bN68GbNmzUJ9fT0mTJiAyy67DC+99JLtsY899pilrXfatGkYNWpU/HdZWZkfTSYIgiAYQqS4ITKUQISb5cuXY8aMGfj6668xYMAAAMATTzyBMWPG4KGHHkKbNm2Exy5evBgPP/wwFixYgNatW3PLlJWVobKyMoimEwRBEI2wH5kqed0QGUQgZqm5c+eirKwsLtgAwPDhwxEKhTBv3jzhcTU1NTjnnHMwdepUS+Fl4sSJaNGiBQYOHIjnnnsOqo2+tLa2FtXV1br/CYIgCGtIc0NkKoFobqqqqlBRUaE/UU4OysvLUVVVJTzu+uuvx1FHHYVTTjlFWOauu+7CCSecgKKiIsycORNXXXUV9uzZg2uuuUZ4zJQpU3DnnXc6vxCCIIgDmEyISkwQPBwJNzfffDMeeOAByzLLly931ZC33noLs2fPxqJFiyzL3XrrrfG/+/Xrh7179+LBBx+0FG4mT56MG264If67uroa7du3d9VOgiCIAwWFHIqJDMWRcHPjjTfiwgsvtCzTpUsXVFZWYuvWrbrtDQ0N2L59u9DcNHv2bKxevdrkHDxu3Dgce+yx+OSTT7jHDRo0CHfffTdqa2uRn8+PSpmfny/cRxAEQfChleBEpuJIuGnZsiVatmxpW27w4MHYuXMnFi5ciP79+wOICS/RaBSDBg3iHnPzzTfjkksu0W3r06cPHn30UZx88snCcy1evBjNmjUj4YUgCMJnWLMUKW6ITCIQn5tDDjkEo0aNwqWXXoqnn34a9fX1mDRpEs4666z4SqmNGzdi2LBheOGFFzBw4EBUVlZytTodOnRA586dAQBvv/02tmzZgiOPPBIFBQWYNWsW7rvvPvz+978P4jIIgiAOaMihmMhUAotzM336dEyaNAnDhg1DKBTCuHHj8Pjjj8f319fXY+XKlaipqZGuMzc3F1OnTsX1118PVVXRrVs3PPLII7j00kuDuASCIIgDmkzMLUUQQIDCTXl5uWXAvk6dOtku4TbuHzVqlC54H0EQBBEcIZ1DMRmmiMyBcksRBEEQXEhzQ2QqJNwQBEEQBJFVkHBDEARBEERWQcINQRAEYQu53BCZBAk3BEEQBEFkFSTcEARBEASRVZBwQxAEQdiiUoxiIoMg4YYgCIIgiKyChBuCIAjCFnIoJjIJEm4IgiAIgsgqSLghCIIgCCKrIOGGIAiCsIXMUkQmQcINQRAEQRBZBQk3BEEQhC2kuCEyCRJuCIIgCILIKki4IQiCIGxRyemGyCBIuCEIgiAIIqsg4YYgCIIgiKyChBuCIAjCFjJKEZkECTcEQRCELV1bNkl1EwhCmpxUN4AgCIJIX/438Wis3bYX/TuWp7opBCENCTcEQRCEkL7ty9C3fVmqm0EQjiCzFEEQBEEQWQUJNwRBEARBZBUk3BAEQRAEkVWQcEMQBEEQRFZBwg1BEARBEFkFCTcEQRAEQWQVJNwQBEEQBJFVkHBDEARBEERWQcINQRAEQRBZBQk3BEEQBEFkFSTcEARBEASRVZBwQxAEQRBEVkHCDUEQBEEQWcUBmRVcVVUAQHV1dYpbQhAEQRCELNq8rc3jIg5I4Wb37t0AgPbt26e4JQRBEARBOGX37t0oLS0V7ldUO/EnC4lGo9i0aROaNm0KRVF8rbu6uhrt27fHhg0bUFJS4mvdRAK6z8mB7nNyoPucPOheJ4eg7rOqqti9ezfatGmDUEjsWXNAam5CoRDatWsX6DlKSkroxUkCdJ+TA93n5ED3OXnQvU4OQdxnK42NBjkUEwRBEASRVZBwQxAEQRBEVkHCjc/k5+fj9ttvR35+fqqbktXQfU4OdJ+TA93n5EH3Ojmk+j4fkA7FBEEQBEFkL6S5IQiCIAgiqyDhhiAIgiCIrIKEG4IgCIIgsgoSbgiCIAiCyCpIuCEIgiAIIqsg4cZHpk6dik6dOqGgoACDBg3C/PnzU92kjGLKlCk44ogj0LRpU1RUVODUU0/FypUrdWX279+PiRMnonnz5iguLsa4ceOwZcsWXZn169dj7NixKCoqQkVFBW666SY0NDQk81Iyivvvvx+KouC6666Lb6P77A8bN27Eueeei+bNm6OwsBB9+vTBggUL4vtVVcVtt92G1q1bo7CwEMOHD8eqVat0dWzfvh3jx49HSUkJysrKcPHFF2PPnj3JvpS0JRKJ4NZbb0Xnzp1RWFiIrl274u6779YlVqT77I5PP/0UJ598Mtq0aQNFUfDmm2/q9vt1X7/77jsce+yxKCgoQPv27fHnP//Ze+NVwhdeeeUVNS8vT33uuefUZcuWqZdeeqlaVlambtmyJdVNyxhGjhypTps2TV26dKm6ePFidcyYMWqHDh3UPXv2xMtcccUVavv27dWPPvpIXbBggXrkkUeqRx11VHx/Q0OD2rt3b3X48OHqokWL1Pfee09t0aKFOnny5FRcUtozf/58tVOnTuqhhx6qXnvttfHtdJ+9s337drVjx47qhRdeqM6bN09ds2aN+sEHH6g//vhjvMz999+vlpaWqm+++ab67bffqr/5zW/Uzp07q/v27YuXGTVqlNq3b1/1q6++Uj/77DO1W7du6tlnn52KS0pL7r33XrV58+bqO++8o65du1Z99dVX1eLiYvUvf/lLvAzdZ3e899576i233KK+/vrrKgD1jTfe0O33477u2rVLbdWqlTp+/Hh16dKl6ssvv6wWFhaqzzzzjKe2k3DjEwMHDlQnTpwY/x2JRNQ2bdqoU6ZMSWGrMputW7eqANQ5c+aoqqqqO3fuVHNzc9VXX301Xmb58uUqAHXu3LmqqsZexlAopFZVVcXLPPXUU2pJSYlaW1ub3AtIc3bv3q12795dnTVrljp06NC4cEP32R/++Mc/qsccc4xwfzQaVSsrK9UHH3wwvm3nzp1qfn6++vLLL6uqqqrff/+9CkD9+uuv42Xef/99VVEUdePGjcE1PoMYO3asetFFF+m2nXbaaer48eNVVaX77BdG4cav+/rkk0+qzZo1040bf/zjH9WDDz7YU3vJLOUDdXV1WLhwIYYPHx7fFgqFMHz4cMydOzeFLctsdu3aBQAoLy8HACxcuBD19fW6+9yjRw906NAhfp/nzp2LPn36oFWrVvEyI0eORHV1NZYtW5bE1qc/EydOxNixY3X3E6D77BdvvfUWBgwYgNNPPx0VFRXo168f/va3v8X3r127FlVVVbr7XFpaikGDBunuc1lZGQYMGBAvM3z4cIRCIcybNy95F5PGHHXUUfjoo4/www8/AAC+/fZbfP755xg9ejQAus9B4dd9nTt3LoYMGYK8vLx4mZEjR2LlypXYsWOH6/YdkFnB/Wbbtm2IRCK6gR4AWrVqhRUrVqSoVZlNNBrFddddh6OPPhq9e/cGAFRVVSEvLw9lZWW6sq1atUJVVVW8DO85aPuIGK+88gq++eYbfP3116Z9dJ/9Yc2aNXjqqadwww034E9/+hO+/vprXHPNNcjLy8MFF1wQv0+8+8je54qKCt3+nJwclJeX031u5Oabb0Z1dTV69OiBcDiMSCSCe++9F+PHjwcAus8B4dd9raqqQufOnU11aPuaNWvmqn0k3BBpycSJE7F06VJ8/vnnqW5K1rFhwwZce+21mDVrFgoKClLdnKwlGo1iwIABuO+++wAA/fr1w9KlS/H000/jggsuSHHrsof//Oc/mD59Ol566SX06tULixcvxnXXXYc2bdrQfT6AIbOUD7Ro0QLhcNi0mmTLli2orKxMUasyl0mTJuGdd97Bxx9/jHbt2sW3V1ZWoq6uDjt37tSVZ+9zZWUl9zlo+4iY2Wnr1q04/PDDkZOTg5ycHMyZMwePP/44cnJy0KpVK7rPPtC6dWv07NlTt+2QQw7B+vXrASTuk9W4UVlZia1bt+r2NzQ0YPv27XSfG7nppptw880346yzzkKfPn1w3nnn4frrr8eUKVMA0H0OCr/ua1BjCQk3PpCXl4f+/fvjo48+im+LRqP46KOPMHjw4BS2LLNQVRWTJk3CG2+8gdmzZ5tUlf3790dubq7uPq9cuRLr16+P3+fBgwdjyZIluhdq1qxZKCkpMU00ByrDhg3DkiVLsHjx4vj/AwYMwPjx4+N/0332ztFHH20KZfDDDz+gY8eOAIDOnTujsrJSd5+rq6sxb9483X3euXMnFi5cGC8ze/ZsRKNRDBo0KAlXkf7U1NQgFNJPZeFwGNFoFADd56Dw674OHjwYn376Kerr6+NlZs2ahYMPPti1SQoALQX3i1deeUXNz89Xn3/+efX7779XL7vsMrWsrEy3moSw5sorr1RLS0vVTz75RN28eXP8/5qamniZK664Qu3QoYM6e/ZsdcGCBergwYPVwYMHx/drS5RHjBihLl68WJ0xY4basmVLWqJsA7taSlXpPvvB/Pnz1ZycHPXee+9VV61apU6fPl0tKipSX3zxxXiZ+++/Xy0rK1P/97//qd999516yimncJfS9uvXT503b576+eefq927dz/glyizXHDBBWrbtm3jS8Fff/11tUWLFuof/vCHeBm6z+7YvXu3umjRInXRokUqAPWRRx5RFy1apP7000+qqvpzX3fu3Km2atVKPe+889SlS5eqr7zyilpUVERLwdOJJ554Qu3QoYOal5enDhw4UP3qq69S3aSMAgD3/2nTpsXL7Nu3T73qqqvUZs2aqUVFRepvf/tbdfPmzbp61q1bp44ePVotLCxUW7Rood54441qfX19kq8mszAKN3Sf/eHtt99We/furebn56s9evRQn332Wd3+aDSq3nrrrWqrVq3U/Px8ddiwYerKlSt1ZX799Vf17LPPVouLi9WSkhJ1woQJ6u7du5N5GWlNdXW1eu2116odOnRQCwoK1C5duqi33HKLbmkx3Wd3fPzxx9wx+YILLlBV1b/7+u2336rHHHOMmp+fr7Zt21a9//77PbddUVUmjCNBEARBEESGQz43BEEQBEFkFSTcEARBEASRVZBwQxAEQRBEVkHCDUEQBEEQWQUJNwRBEARBZBUk3BAEQRAEkVWQcEMQBEEQRFZBwg1BEARBEFkFCTcEQRAEQWQVJNwQBEEQBJFVkHBDEARBEERW8f9ePG2UU1cdxQAAAABJRU5ErkJggg==" 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" + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "df_automl = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_automl.columns = list(metric_dict.keys())\n", - "df_automl = df_automl.T\n", - "df_automl.sort_values('root_mean_squared_error:')" + "pd.DataFrame(features[1, 0, :]).plot(title='1 channel PPG signal')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='2 channel PPG signal')\n", + "pd.DataFrame(features[1, 2, :]).plot(title='x-axis acceleration')\n", + "pd.DataFrame(features[1, 3, :]).plot(title='y-axis acceleration')\n", + "pd.DataFrame(features[1, 4, :]).plot(title='z-axis acceleration')\n", + "plt.show()" ], "metadata": { "collapsed": false, @@ -913,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "outputs": [], "source": [ "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')" @@ -927,19 +747,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "outputs": [], "source": [ "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", "df.index = df['algorithm']\n", "df = df.drop(['algorithm','ds/type'], axis=1)\n", - "df = df.replace(',','.', regex=True).astype(float)\n", - "df['Fedot_Industrial_baseline'] = best_baseline\n", - "df['Fedot_Industrial_tuned'] = best_tuned\n", - "df['Fedot_Industrial_AutoML'] = 0\n", - "df['Fedot_Industrial_AutoML'].iloc[0] = df_automl['root_mean_squared_error:'].min()\n", - "df['Fedot_Industrial_AutoML'].iloc[1] = df_automl['root_mean_squared_error:'].max()\n", - "df['Fedot_Industrial_AutoML'].iloc[2] = df_automl['root_mean_squared_error:'].mean()\n", + "df = df.replace(',','.', regex=True).astype(float)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df['Fedot_Industrial_tuned'] = metrics['rmse'][0]\n", + "df['Fedot_Industrial_AutoML'] = auto_metrics['rmse'][0]\n", "df = df.T" ], "metadata": { @@ -951,18 +780,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 18, "outputs": [ { - "ename": "NameError", - "evalue": "name 'df' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[33], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mdf\u001B[49m\u001B[38;5;241m.\u001B[39msort_values(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmin\u001B[39m\u001B[38;5;124m'\u001B[39m)\n", - "\u001B[1;31mNameError\u001B[0m: name 'df' is not defined" - ] + "data": { + "text/plain": "algorithm min max average\nInceptionT_RMSE 2.326973 23.144061 3.698176\nSingleInception_RMSE 2.862294 24.226917 4.119866\nResNet_RMSE 3.517166 33.038647 5.051279\nFCN_RMSE 4.934538 36.772476 7.190419\nROCKET_RMSE 6.016981 27.583495 7.159721\nRIST_RMSE 6.967740 27.277590 11.477876\nMultiROCKET_RMSE 7.536678 29.900533 8.771684\nRDST_RMSE 8.051669 25.700414 11.626389\nFreshPRINCE_RMSE 8.489912 33.689079 10.115907\nDrCIF_RMSE 11.201905 34.916949 12.474585\n5NN-DTW_RMSE 19.122837 30.829134 20.452884\nRotF_RMSE 19.284382 20.824608 20.212812\nTSF_RMSE 19.425659 35.842706 20.472003\nXGBoost_RMSE 19.786821 31.457220 21.033698\nCNN_RMSE 20.023381 34.643293 22.426885\nRandF_RMSE 20.502073 31.888585 21.655840\n5NN-ED_RMSE 22.438166 27.111213 23.655007\n1NN-DTW_RMSE 22.511152 34.496717 23.987240\nFPCR-Bs_RMSE 25.327220 33.756958 26.398372\nFPCR_RMSE 25.530119 31.378985 26.395514\n1NN-ED_RMSE 26.666757 33.208862 27.769715\nGrid-SVR_RMSE 28.615448 37.254146 29.615798\nFedot_Industrial_tuned 29.674000 29.674000 29.674000\nFedot_Industrial_AutoML 29.869000 29.869000 29.869000\nRidge_RMSE 42.902687 68.129020 47.502751", + "text/html": "
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algorithmminmaxaverage
InceptionT_RMSE2.32697323.1440613.698176
SingleInception_RMSE2.86229424.2269174.119866
ResNet_RMSE3.51716633.0386475.051279
FCN_RMSE4.93453836.7724767.190419
ROCKET_RMSE6.01698127.5834957.159721
RIST_RMSE6.96774027.27759011.477876
MultiROCKET_RMSE7.53667829.9005338.771684
RDST_RMSE8.05166925.70041411.626389
FreshPRINCE_RMSE8.48991233.68907910.115907
DrCIF_RMSE11.20190534.91694912.474585
5NN-DTW_RMSE19.12283730.82913420.452884
RotF_RMSE19.28438220.82460820.212812
TSF_RMSE19.42565935.84270620.472003
XGBoost_RMSE19.78682131.45722021.033698
CNN_RMSE20.02338134.64329322.426885
RandF_RMSE20.50207331.88858521.655840
5NN-ED_RMSE22.43816627.11121323.655007
1NN-DTW_RMSE22.51115234.49671723.987240
FPCR-Bs_RMSE25.32722033.75695826.398372
FPCR_RMSE25.53011931.37898526.395514
1NN-ED_RMSE26.66675733.20886227.769715
Grid-SVR_RMSE28.61544837.25414629.615798
Fedot_Industrial_tuned29.67400029.67400029.674000
Fedot_Industrial_AutoML29.86900029.86900029.869000
Ridge_RMSE42.90268768.12902047.502751
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" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -977,8 +804,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "algorithm min max average\nRotF_RMSE 19.284382 20.824608 20.212812\nInceptionT_RMSE 2.326973 23.144061 3.698176\nSingleInception_RMSE 2.862294 24.226917 4.119866\nRDST_RMSE 8.051669 25.700414 11.626389\n5NN-ED_RMSE 22.438166 27.111213 23.655007\nRIST_RMSE 6.967740 27.277590 11.477876\nROCKET_RMSE 6.016981 27.583495 7.159721\nFedot_Industrial_tuned 29.674000 29.674000 29.674000\nFedot_Industrial_AutoML 29.869000 29.869000 29.869000\nMultiROCKET_RMSE 7.536678 29.900533 8.771684\n5NN-DTW_RMSE 19.122837 30.829134 20.452884\nFPCR_RMSE 25.530119 31.378985 26.395514\nXGBoost_RMSE 19.786821 31.457220 21.033698\nRandF_RMSE 20.502073 31.888585 21.655840\nResNet_RMSE 3.517166 33.038647 5.051279\n1NN-ED_RMSE 26.666757 33.208862 27.769715\nFreshPRINCE_RMSE 8.489912 33.689079 10.115907\nFPCR-Bs_RMSE 25.327220 33.756958 26.398372\n1NN-DTW_RMSE 22.511152 34.496717 23.987240\nCNN_RMSE 20.023381 34.643293 22.426885\nDrCIF_RMSE 11.201905 34.916949 12.474585\nTSF_RMSE 19.425659 35.842706 20.472003\nFCN_RMSE 4.934538 36.772476 7.190419\nGrid-SVR_RMSE 28.615448 37.254146 29.615798\nRidge_RMSE 42.902687 68.129020 47.502751", + "text/html": "
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algorithmminmaxaverage
RotF_RMSE19.28438220.82460820.212812
InceptionT_RMSE2.32697323.1440613.698176
SingleInception_RMSE2.86229424.2269174.119866
RDST_RMSE8.05166925.70041411.626389
5NN-ED_RMSE22.43816627.11121323.655007
RIST_RMSE6.96774027.27759011.477876
ROCKET_RMSE6.01698127.5834957.159721
Fedot_Industrial_tuned29.67400029.67400029.674000
Fedot_Industrial_AutoML29.86900029.86900029.869000
MultiROCKET_RMSE7.53667829.9005338.771684
5NN-DTW_RMSE19.12283730.82913420.452884
FPCR_RMSE25.53011931.37898526.395514
XGBoost_RMSE19.78682131.45722021.033698
RandF_RMSE20.50207331.88858521.655840
ResNet_RMSE3.51716633.0386475.051279
1NN-ED_RMSE26.66675733.20886227.769715
FreshPRINCE_RMSE8.48991233.68907910.115907
FPCR-Bs_RMSE25.32722033.75695826.398372
1NN-DTW_RMSE22.51115234.49671723.987240
CNN_RMSE20.02338134.64329322.426885
DrCIF_RMSE11.20190534.91694912.474585
TSF_RMSE19.42565935.84270620.472003
FCN_RMSE4.93453836.7724767.190419
Grid-SVR_RMSE28.61544837.25414629.615798
Ridge_RMSE42.90268768.12902047.502751
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" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.sort_values('max')" ], @@ -991,8 +828,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": "algorithm min max average\nInceptionT_RMSE 2.326973 23.144061 3.698176\nSingleInception_RMSE 2.862294 24.226917 4.119866\nResNet_RMSE 3.517166 33.038647 5.051279\nROCKET_RMSE 6.016981 27.583495 7.159721\nFCN_RMSE 4.934538 36.772476 7.190419\nMultiROCKET_RMSE 7.536678 29.900533 8.771684\nFreshPRINCE_RMSE 8.489912 33.689079 10.115907\nRIST_RMSE 6.967740 27.277590 11.477876\nRDST_RMSE 8.051669 25.700414 11.626389\nDrCIF_RMSE 11.201905 34.916949 12.474585\nRotF_RMSE 19.284382 20.824608 20.212812\n5NN-DTW_RMSE 19.122837 30.829134 20.452884\nTSF_RMSE 19.425659 35.842706 20.472003\nXGBoost_RMSE 19.786821 31.457220 21.033698\nRandF_RMSE 20.502073 31.888585 21.655840\nCNN_RMSE 20.023381 34.643293 22.426885\n5NN-ED_RMSE 22.438166 27.111213 23.655007\n1NN-DTW_RMSE 22.511152 34.496717 23.987240\nFPCR_RMSE 25.530119 31.378985 26.395514\nFPCR-Bs_RMSE 25.327220 33.756958 26.398372\n1NN-ED_RMSE 26.666757 33.208862 27.769715\nGrid-SVR_RMSE 28.615448 37.254146 29.615798\nFedot_Industrial_tuned 29.674000 29.674000 29.674000\nFedot_Industrial_AutoML 29.869000 29.869000 29.869000\nRidge_RMSE 42.902687 68.129020 47.502751", + "text/html": "
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algorithmminmaxaverage
InceptionT_RMSE2.32697323.1440613.698176
SingleInception_RMSE2.86229424.2269174.119866
ResNet_RMSE3.51716633.0386475.051279
ROCKET_RMSE6.01698127.5834957.159721
FCN_RMSE4.93453836.7724767.190419
MultiROCKET_RMSE7.53667829.9005338.771684
FreshPRINCE_RMSE8.48991233.68907910.115907
RIST_RMSE6.96774027.27759011.477876
RDST_RMSE8.05166925.70041411.626389
DrCIF_RMSE11.20190534.91694912.474585
RotF_RMSE19.28438220.82460820.212812
5NN-DTW_RMSE19.12283730.82913420.452884
TSF_RMSE19.42565935.84270620.472003
XGBoost_RMSE19.78682131.45722021.033698
RandF_RMSE20.50207331.88858521.655840
CNN_RMSE20.02338134.64329322.426885
5NN-ED_RMSE22.43816627.11121323.655007
1NN-DTW_RMSE22.51115234.49671723.987240
FPCR_RMSE25.53011931.37898526.395514
FPCR-Bs_RMSE25.32722033.75695826.398372
1NN-ED_RMSE26.66675733.20886227.769715
Grid-SVR_RMSE28.61544837.25414629.615798
Fedot_Industrial_tuned29.67400029.67400029.674000
Fedot_Industrial_AutoML29.86900029.86900029.869000
Ridge_RMSE42.90268768.12902047.502751
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" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.sort_values('average')" ], @@ -1002,6 +849,18 @@ "name": "#%%\n" } } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } } ], "metadata": { diff --git a/examples/real_world_examples/industrial_examples/sentiment_analysis/bitcoin_analysis.ipynb b/examples/real_world_examples/industrial_examples/sentiment_analysis/bitcoin_analysis.ipynb index 05935c88e..704d6a653 100644 --- a/examples/real_world_examples/industrial_examples/sentiment_analysis/bitcoin_analysis.ipynb +++ b/examples/real_world_examples/industrial_examples/sentiment_analysis/bitcoin_analysis.ipynb @@ -38,42 +38,49 @@ "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\dask\\dataframe\\_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 13.0.0. Please consider upgrading.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", + "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", + "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", "from fedot_ind.tools.loader import DataLoader\n", - "from examples.example_utils import init_input_data" + "from fedot_ind.api.main import FedotIndustrial" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" + ], "metadata": { + "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - }, - "source": [ - "The list of basic fedot industrial models for experiment are shown below. We are using simple linear machine learning pipelines with 3 different feature generators: Statistical, Recurrence, Topological. And for each of them we add PCA transformation with 90 % of explained dispersion." - ] + } }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "outputs": [], "source": [ - "from cases.utils import ts_regression_setup\n", + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=15,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", "dataset_name = 'BitcoinSentiment'\n", - "OperationTypesRepository, tuning_params, data_path, experiment_setup, model_dict = ts_regression_setup()\n", - "experiment_setup['output_folder'] = f'./{dataset_name}/results_of_experiment'" + "data_path = PROJECT_PATH + '/examples/data'" ], "metadata": { "collapsed": false, @@ -95,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-08-28T10:35:13.321212Z", @@ -110,7 +117,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-01-18 12:35:12,535 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/BitcoinSentiment\n" + "2024-04-04 13:59:55,515 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/BitcoinSentiment\n" ] } ], @@ -122,11 +129,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "outputs": [], "source": [ - "input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - "val_data = init_input_data(test_data[0], test_data[1], task=tuning_params['task'])" + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" ], "metadata": { "collapsed": false, @@ -149,19 +156,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "outputs": [ { "data": { "text/plain": "(232, 2, 24)" }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "input_data.features.shape" + "features.shape" ], "metadata": { "collapsed": false, @@ -184,12 +191,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "outputs": [ { "data": { "text/plain": "
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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -197,7 +204,7 @@ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -205,8 +212,8 @@ ], "source": [ "from matplotlib import pyplot as plt\n", - "pd.DataFrame(input_data.features[1, 0, :]).plot(title='Bitcoin close prices')\n", - "pd.DataFrame(input_data.features[1, 1, :]).plot(title='Traded volume')\n", + "pd.DataFrame(features[1, 0, :]).plot(title='Bitcoin close prices')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='Traded volume')\n", "plt.show()" ], "metadata": { @@ -232,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": { "ExecuteTime": { "start_time": "2023-08-28T10:35:27.965798Z" @@ -246,25 +253,784 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n" + "2024-04-04 14:01:15,534 - Initialising experiment setup\n", + "2024-04-04 14:01:15,537 - Initialising Industrial Repository\n", + "2024-04-04 14:01:15,680 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-04 14:01:16,655 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-04-04 14:01:16,697 - State start\n", + "2024-04-04 14:01:17,088 - Scheduler at: inproc://10.64.4.217/10940/1\n", + "2024-04-04 14:01:17,089 - dashboard at: http://10.64.4.217:54878/status\n", + "2024-04-04 14:01:17,090 - Registering Worker plugin shuffle\n", + "2024-04-04 14:01:17,476 - Start worker at: inproc://10.64.4.217/10940/4\n", + "2024-04-04 14:01:17,477 - Listening to: inproc10.64.4.217\n", + "2024-04-04 14:01:17,478 - Worker name: 0\n", + "2024-04-04 14:01:17,479 - dashboard at: 10.64.4.217:54879\n", + "2024-04-04 14:01:17,480 - Waiting to connect to: inproc://10.64.4.217/10940/1\n", + "2024-04-04 14:01:17,481 - -------------------------------------------------\n", + "2024-04-04 14:01:17,482 - Threads: 8\n", + "2024-04-04 14:01:17,482 - Memory: 31.95 GiB\n", + "2024-04-04 14:01:17,483 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-qb4omw5q\n", + "2024-04-04 14:01:17,484 - -------------------------------------------------\n", + "2024-04-04 14:01:17,489 - Register worker \n", + "2024-04-04 14:01:17,491 - Starting worker compute stream, inproc://10.64.4.217/10940/4\n", + "2024-04-04 14:01:17,492 - Starting established connection to inproc://10.64.4.217/10940/5\n", + "2024-04-04 14:01:17,494 - Starting Worker plugin shuffle\n", + "2024-04-04 14:01:17,495 - Registered to: inproc://10.64.4.217/10940/1\n", + "2024-04-04 14:01:17,496 - -------------------------------------------------\n", + "2024-04-04 14:01:17,497 - Starting established connection to inproc://10.64.4.217/10940/1\n", + "2024-04-04 14:01:17,501 - Receive client connection: Client-a942dba8-f272-11ee-aabc-b42e99a00ea1\n", + "2024-04-04 14:01:17,503 - Starting established connection to inproc://10.64.4.217/10940/6\n", + "2024-04-04 14:01:17,505 - LinK Dask Server - http://10.64.4.217:54878/status\n", + "2024-04-04 14:01:17,506 - Initialising solver\n", + "2024-04-04 14:01:17,592 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 14:01:17,593 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 14:01:17,598 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-04 14:01:36,874 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [0.203]\n", + " 0%| | 0/100 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
regression_with_statistical_features0.2511860.0555320.2356520.159930.1168330.2974520.9963980.24421
\n" + "text/plain": " r2 rmse mae\n0 0.301 0.228 0.15", + "text/html": "
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r2rmsemae
00.3010.2280.15
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" }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_baseline = pd.concat([x for x in metric_dict.values()],axis=1)\n", - "df_baseline.columns = list(metric_dict.keys())\n", - "df_baseline = df_baseline.T\n", - "df_baseline.sort_values(by='root_mean_squared_error:')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "best_baseline = df_baseline.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" + "metrics" ], "metadata": { "collapsed": false, @@ -330,7 +1076,7 @@ { "cell_type": "markdown", "source": [ - "## Could it be done better? Tuning approach" + "## AutoML approach" ], "metadata": { "collapsed": false, @@ -341,8647 +1087,592 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 185/185 [00:01<00:00, 139.47it/s]\n", - "100%|██████████| 47/47 [00:00<00:00, 581.80it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:30:55,157 - SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [ridge, quantile_extractor]}\n", - "ridge - {}\n", - "quantile_extractor - {'window_size': 0} \n", - "Initial metric: [0.212]\n", - " 0%| | 0/3 [00:00\n", + "2024-04-04 15:04:39,656 - Starting worker compute stream, inproc://10.64.4.217/10940/12\n", + "2024-04-04 15:04:39,656 - Starting established connection to inproc://10.64.4.217/10940/13\n", + "2024-04-04 15:04:39,657 - Starting Worker plugin shuffle\n", + "2024-04-04 15:04:39,658 - Registered to: inproc://10.64.4.217/10940/9\n", + "2024-04-04 15:04:39,659 - -------------------------------------------------\n", + "2024-04-04 15:04:39,660 - Starting established connection to inproc://10.64.4.217/10940/9\n", + "2024-04-04 15:04:39,663 - Receive client connection: Client-8386ce32-f27b-11ee-aabc-b42e99a00ea1\n", + "2024-04-04 15:04:39,665 - Starting established connection to inproc://10.64.4.217/10940/14\n", + "2024-04-04 15:04:39,667 - LinK Dask Server - http://10.64.4.217:55991/status\n", + "2024-04-04 15:04:39,667 - Initialising solver\n", + "2024-04-04 15:04:39,708 - AssumptionsHandler - Initial pipeline fitting started\n", + "2024-04-04 15:04:40,528 - AssumptionsHandler - Initial pipeline was fitted successfully\n", + "2024-04-04 15:04:40,530 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.7 MiB, max: 1.4 MiB\n", + "2024-04-04 15:04:40,531 - ApiComposer - Initial pipeline was fitted in 0.8 sec.\n", + "2024-04-04 15:04:40,534 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", + "2024-04-04 15:04:40,541 - ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 15 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'dtreg', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "2024-04-04 15:04:40,564 - ApiComposer - Pipeline composition started.\n", + "2024-04-04 15:04:40,567 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 15:04:40,568 - DataSourceSplitter - Hold out validation is applied.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n", - " 0%| | 0/185 [00:00']\n", + "2024-04-04 15:04:49,643 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:08:09,271 - IndustrialDispatcher - 21 individuals out of 21 in previous population were evaluated successfully.\n", + "2024-04-04 15:08:09,340 - IndustrialEvoOptimizer - Generation num: 2 size: 21\n", + "2024-04-04 15:08:09,341 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-04 15:08:09,343 - IndustrialEvoOptimizer - Next population size: 21; max graph depth: 6\n", + "2024-04-04 15:08:13,820 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:09:43,045 - full garbage collections took 10% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:09:54,671 - full garbage collections took 15% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:10:05,520 - full garbage collections took 32% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:10:10,811 - full garbage collections took 34% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:10:21,713 - full garbage collections took 34% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:10:36,615 - full garbage collections took 36% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:10:47,203 - full garbage collections took 36% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:11:00,407 - full garbage collections took 37% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:11:41,945 - full garbage collections took 37% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:11:48,837 - full garbage collections took 38% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:12:01,590 - full garbage collections took 39% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:12:01,603 - IndustrialDispatcher - 18 individuals out of 18 in previous population were evaluated successfully.\n", + "2024-04-04 15:12:06,065 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:12:50,422 - full garbage collections took 31% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:13:04,146 - full garbage collections took 31% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:13:47,916 - full garbage collections took 32% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:13:47,935 - IndustrialDispatcher - 3 individuals out of 3 in previous population were evaluated successfully.\n", + "2024-04-04 15:13:47,937 - ReproductionController - Reproduction achieved pop size 21 using 2 attempt(s) with success rate 0.986\n", + "2024-04-04 15:13:47,940 - RandomAgent - len=24 nonzero=13 avg=-3071615529417068171896029184.000 std=10640388316535875460552196096.000 min=-39931001882421884585380937728.000 max=0.038 \n", + "2024-04-04 15:13:47,942 - RandomAgent - actions/rewards: [(>, -1.0), (>, -1.0), (>, -1.0), (, -3.9931001882421885e+28), (, -0.1221), (>, -0.007), (, -0.0015), (>, 0.0), (>, 0.0), (>, 0.0), (, 0.0), (>, 0.002), (>, -0.0089), (>, -0.31), (>, 0.0384), (, 0.0), (>, 0.0), (>, 0.0), (>, 0.0), (>, -5.087), (, 0.0), (>, 0.0), (, 0.0), (>, -0.0004)]\n", + "2024-04-04 15:13:47,945 - RandomAgent - exp=[0.2 0.2 0.2 0.2 0.2] probs=[0.2 0.2 0.2 0.2 0.2]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/185 [00:00']\n", + "2024-04-04 15:13:48,049 - GroupedCondition - Optimisation stopped: Time limit is reached\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/185 [00:00']\n", + "2024-04-04 15:13:48,073 - IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "2024-04-04 15:13:48,075 - IndustrialEvoOptimizer - spent time: 9.1 min\n", + "2024-04-04 15:13:48,083 - GPComposer - GP composition finished\n", + "2024-04-04 15:13:48,088 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 15:13:48,090 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 15:13:48,103 - ApiComposer - Hyperparameters tuning started with 6 min. timeout\n", + "2024-04-04 15:13:48,108 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/185 [00:00 3\u001B[0m auto_probs \u001B[38;5;241m=\u001B[39m \u001B[43mindustrial_auto_model\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_proba\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtest_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m auto_metrics \u001B[38;5;241m=\u001B[39m industrial_auto_model\u001B[38;5;241m.\u001B[39mget_metrics(target\u001B[38;5;241m=\u001B[39mtest_data[\u001B[38;5;241m1\u001B[39m],\n\u001B[0;32m 5\u001B[0m rounding_order\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3\u001B[39m,\n\u001B[0;32m 6\u001B[0m metric_names\u001B[38;5;241m=\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mr2\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mrmse\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmae\u001B[39m\u001B[38;5;124m'\u001B[39m))\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\api\\main.py:261\u001B[0m, in \u001B[0;36mFedotIndustrial.predict_proba\u001B[1;34m(self, predict_data, predict_mode, **kwargs)\u001B[0m\n\u001B[0;32m 259\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 260\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcondition_check\u001B[38;5;241m.\u001B[39msolver_is_fedot_class(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver):\n\u001B[1;32m--> 261\u001B[0m predict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolver\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_proba\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 262\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 263\u001B[0m predict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver\u001B[38;5;241m.\u001B[39mpredict(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredict_data, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mprobs\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39mpredict\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\fedot\\api\\main.py:325\u001B[0m, in \u001B[0;36mFedot.predict_proba\u001B[1;34m(self, features, save_predictions, probs_for_all_classes)\u001B[0m\n\u001B[0;32m 323\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_predict(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprediction)\n\u001B[0;32m 324\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 325\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mProbabilities of predictions are available only for classification\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 327\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprediction\u001B[38;5;241m.\u001B[39mpredict\n", + "\u001B[1;31mValueError\u001B[0m: Probabilities of predictions are available only for classification" ] - }, + } + ], + "source": [ + "industrial_auto_model = evaluate_loop(api_params=params, finetune=False)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "auto_labels = industrial_auto_model.predict(test_data)\n", + "auto_metrics = industrial_auto_model.get_metrics(target=test_data[1],\n", + " rounding_order=3,\n", + " metric_names=('r2', 'rmse', 'mae'))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " 17%|█▋ | 5/30 [00:01<00:17, 1.42trial/s, best loss: 0.21230903633242995]" - ] - }, + "data": { + "text/plain": " r2 rmse mae\n0 0.348 0.22 0.141", + "text/html": "
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" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auto_metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/185 [00:00", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - " 20%|██ | 6/30 [00:02<00:16, 1.43trial/s, best loss: 0.21230903633242995]" + "2024-04-04 15:20:22,532 - OperationsAnimatedBar - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-04 15:20:22,803 - MovieWriter ffmpeg unavailable; using Pillow instead.\n", + "2024-04-04 15:20:22,804 - Animation.save using \n", + "2024-04-04 15:20:36,222 - OperationsAnimatedBar - The animation was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\history_animated_bars.gif\".\n", + "2024-04-04 15:20:36,224 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-04 15:20:36,254 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-04 15:20:36,262 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-04 15:20:36,380 - default - The figure was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\fitness_by_gen.png\".\n" ] - }, + } + ], + "source": [ + "industrial_auto_model.solver.current_pipeline.show()\n", + "industrial_auto_model.plot_operation_distribution(mode='each')\n", + "industrial_auto_model.plot_fitness_by_generation()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/185 [00:00", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.vstack([test_data[1].flatten(),labels.flatten(),auto_labels.flatten()]).T\n", + "all_prediction = pd.DataFrame(predictions,columns=['target','baseline','automl'])\n", + "all_prediction.plot()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 23%|██▎ | 7/30 [00:02<00:13, 1.68trial/s, best loss: 0.21230903633242995]" + "2024-04-04 17:45:20,753 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-04 17:45:21,494 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-04 17:45:21,546 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/185 [00:00", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "industrial_auto_model.solver.history.show.fitness_box(best_fraction=0.5, dpi=100)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/185 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
dayshoursminutessecondsmilliseconds
Data Definition (fit)00001
Data Preprocessing0000236
Fitting (summary)001439717
Composing0098389
Train Inference0004156
Tuning (composing)00526624
Tuning (after)00000
Data Definition (predict)00000
Predicting0002474
\n" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "industrial_auto_model.solver.return_report()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 30%|███ | 9/30 [00:03<00:13, 1.56trial/s, best loss: 0.21230903633242995]" + "2024-04-04 17:46:54,197 - OperationsKDE - Visualizing optimization history... It may take some time, depending on the history size.\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/185 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_scoremodel_params
regression_with_statistical_features0.2511860.0555320.2356520.159930.1168330.2974520.9963980.24421{ridge: {}, quantile_extractor: {'window_size': 0}}
\n" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_tuned.columns = list(metric_dict.keys())\n", - "df_tuned.T.sort_values('root_mean_squared_error:')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "\"{ridge: {}, quantile_extractor: {'window_size': 0}}\"" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned.T.sort_values('root_mean_squared_error:')['model_params'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "best_tuned = df_tuned.T.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Even better? AutoML approach" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 12:40:49,897 - Initialising experiment setup\n", - "2024-01-18 12:40:49,898 - Initialising Industrial Repository\n", - "2024-01-18 12:40:49,902 - Initialising Dask Server\n", - "Creating Dask Server\n", - "2024-01-18 12:40:52,112 - State start\n", - "2024-01-18 12:40:52,169 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-_vbfxq5u', purging\n", - "2024-01-18 12:40:52,171 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-bwhny203', purging\n", - "2024-01-18 12:40:52,252 - Scheduler at: inproc://10.64.4.229/19444/1\n", - "2024-01-18 12:40:52,253 - dashboard at: http://10.64.4.229:60725/status\n", - "2024-01-18 12:40:52,254 - Registering Worker plugin shuffle\n", - "2024-01-18 12:40:52,409 - Start worker at: inproc://10.64.4.229/19444/4\n", - "2024-01-18 12:40:52,410 - Listening to: inproc10.64.4.229\n", - "2024-01-18 12:40:52,411 - Worker name: 0\n", - "2024-01-18 12:40:52,412 - dashboard at: 10.64.4.229:60738\n", - "2024-01-18 12:40:52,413 - Waiting to connect to: inproc://10.64.4.229/19444/1\n", - "2024-01-18 12:40:52,414 - -------------------------------------------------\n", - "2024-01-18 12:40:52,414 - Threads: 8\n", - "2024-01-18 12:40:52,414 - Memory: 31.95 GiB\n", - "2024-01-18 12:40:52,415 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-0da304hr\n", - "2024-01-18 12:40:52,416 - -------------------------------------------------\n", - "2024-01-18 12:40:52,429 - Register worker \n", - "2024-01-18 12:40:52,430 - Starting worker compute stream, inproc://10.64.4.229/19444/4\n", - "2024-01-18 12:40:52,431 - Starting established connection to inproc://10.64.4.229/19444/5\n", - "2024-01-18 12:40:52,432 - Starting Worker plugin shuffle\n", - "2024-01-18 12:40:52,433 - Registered to: inproc://10.64.4.229/19444/1\n", - "2024-01-18 12:40:52,434 - -------------------------------------------------\n", - "2024-01-18 12:40:52,435 - Starting established connection to inproc://10.64.4.229/19444/1\n", - "2024-01-18 12:40:52,438 - Receive client connection: Client-ab7e103c-b5e5-11ee-8bf4-b42e99a00ea1\n", - "2024-01-18 12:40:52,439 - Starting established connection to inproc://10.64.4.229/19444/6\n", - "2024-01-18 12:40:52,440 - Initialising solver\n", - "2024-01-18 12:40:52,570 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.5 MiB, max: 0.7 MiB\n", - "2024-01-18 12:40:52,571 - ApiComposer - Initial pipeline was fitted in 0.0 sec.\n", - "2024-01-18 12:40:52,572 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", - "2024-01-18 12:40:52,582 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 20 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'knnreg', 'dtreg', 'inception_model', 'omniscale_model', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'topological_extractor', 'quantile_extractor', 'signal_extractor', 'recurrence_extractor', 'minirocket_extractor', 'isolation_forest_reg', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_features'].\n", - "2024-01-18 12:40:52,600 - ApiComposer - Pipeline composition started.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 0%| | 0/5 [00:00.on_destroy at 0x0000028885740700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785373259120\n", - "Exception ignored in: .on_destroy at 0x0000028883DEAB80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785447430832\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028886EDC940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785376456848\n", - "Exception ignored in: .on_destroy at 0x0000028885BC3EE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785289559952\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028886EDC040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785312318928\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028889D01670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785450988432\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028889332940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785382482448\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028962416790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785430891856\n", - "Exception ignored in: .on_destroy at 0x0000028926289790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2788977986032\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028889753310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2788978637968\n", - "Exception ignored in: .on_destroy at 0x0000028988292B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789718588848\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 12:45:51,938 - IndustrialDispatcher - 8 individuals out of 9 in previous population were evaluated successfully.\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x00000289DB059820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785448902448\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 12:47:08,103 - IndustrialDispatcher - 10 individuals out of 11 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A1C1650D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785289032112\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A1E8499D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792241823792\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A1C1AF430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785437631856\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A123A2B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792238817808\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A20E9B790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792292855312\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A224DA8B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792259595408\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A21F8AEE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785436812752\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A28A98280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792282518704\n", - "Exception ignored in: .on_destroy at 0x0000028A28D6D040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792239920976\n", - "\n", - " 0%| | 0/47 [00:00.on_destroy at 0x0000028A28AE4D30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785290642128\n", - "\n", - " 0%| | 0/46 [00:00.on_destroy at 0x0000028A289ED430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792286631088\n", - "\n", - " 0%| | 0/46 [00:00.on_destroy at 0x0000028A356B2A60>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792283216176\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x000002898E897700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2791924897424\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x000002898E650790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789829088432\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x000002898ECACAF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789824554544\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A2E761E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785373533552\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A2FD88E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789827600720\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A2EE96E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792293078288\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A2F05FAF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792499117008\n", - "Exception ignored in: .on_destroy at 0x0000028A2EE968B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792522593552\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A2FF38430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792529979696\n", - "Exception ignored in: .on_destroy at 0x0000028A2F70BEE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792525412464\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000288881DE9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789829057776\n", - "Exception ignored in: .on_destroy at 0x00000288881C8670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792291950096\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A2FF7C790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785290918672\n", - "Exception ignored in: .on_destroy at 0x00000288881DE430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789825678000\n", - "Exception ignored in: .on_destroy at 0x00000288803FD3A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792280493392\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028881701040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785380994768\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x000002888524EB80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792539696464\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A302D94C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785311531824\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "SVD estimation: 0%| | 0/185 [00:00.on_destroy at 0x000002892B1F65E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789431041936\n", - "Exception ignored in: .on_destroy at 0x0000028911B594C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792566191760\n", - "Exception ignored in: .on_destroy at 0x000002895C08F9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792566190800\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000288FFE9B310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785374026224\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:00:22,034 - IndustrialDispatcher - 9 individuals out of 10 in previous population were evaluated successfully.\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x00000288850D7700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785467775760\n", - "Exception ignored in: .on_destroy at 0x000002893A160940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792563362960\n", - "Exception ignored in: .on_destroy at 0x000002891FDE11F0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792563950864\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028889A9F280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785380391216\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000289770D51F0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792538042416\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028885BC3790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785401096496\n", - "Exception ignored in: .on_destroy at 0x00000289854494C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785315465712\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028889D01AF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785449081232\n", - "Exception ignored in: .on_destroy at 0x0000028987A1CD30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792282342480\n", - "Exception ignored in: .on_destroy at 0x00000288FFE9BCA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792542699408\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000288859D31F0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785315944368\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028888F34E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789718600560\n", - "Exception ignored in: .on_destroy at 0x0000028888F349D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792565684336\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000289770EA700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2787730215920\n", - "Exception ignored in: .on_destroy at 0x0000028987C03AF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785401278896\n", - "Exception ignored in: .on_destroy at 0x000002898574D040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792278693424\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:02:04,613 - IndustrialDispatcher - 10 individuals out of 10 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A21917CA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792284733328\n", - "\n", - " 0%| | 0/47 [00:00.on_destroy at 0x0000028A1F70B670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792543866672\n", - "Exception ignored in: .on_destroy at 0x0000028A21D9A4C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785432693616\n", - "\n", - " 0%| | 0/47 [00:00.on_destroy at 0x0000028A219E5DC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785432478224\n", - "Exception ignored in: .on_destroy at 0x0000028A28B54430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792239997072\n", - "Exception ignored in: .on_destroy at 0x0000028A28A5D9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785444810800\n", - "\n", - " 0%| | 0/46 [00:00.on_destroy at 0x000002898ED4BEE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792259327824\n", - "\n", - " 0%| | 0/47 [00:00.on_destroy at 0x000002898ED5FA60>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785449519792\n", - "Exception ignored in: .on_destroy at 0x000002898EBEFF70>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792536044048\n", - "\n", - " 0%| | 0/47 [00:00.on_destroy at 0x0000028A2E748CA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785467830704\n", - "\n", - " 0%| | 0/46 [00:00.on_destroy at 0x000002898E725EE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792258121616\n", - "\n", - " 0%| | 0/46 [00:00.on_destroy at 0x0000028A2EFDF8B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792522716624\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x000002898E8379D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792032665392\n", - "Exception ignored in: .on_destroy at 0x0000028A356AF160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792260335728\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A2F71E4C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792501406288\n", - "Exception ignored in: .on_destroy at 0x0000028A2F2BADC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792522660816\n", - "Exception ignored in: .on_destroy at 0x0000028A2F6B7790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792220709680\n", - "Exception ignored in: .on_destroy at 0x0000028A2EFDFF70>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792257838992\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A2FA83820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792532358800\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:13:54,901 - IndustrialDispatcher - 17 individuals out of 18 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 60%|██████ | 3/5 [16:34<12:18, 369.04s/gen]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:13:55,038 - GroupedCondition - Optimisation stopped: Time limit is reached\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 60%|██████ | 3/5 [16:34<11:02, 331.38s/gen]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:13:55,454 - ApiComposer - Model generation finished\n", - "2024-01-18 13:13:56,140 - FEDOT logger - Final pipeline was fitted\n", - "2024-01-18 13:13:56,142 - FEDOT logger - Final pipeline: {'depth': 1, 'length': 1, 'nodes': [treg]}\n", - "treg - {}\n", - "2024-01-18 13:13:56,150 - MemoryAnalytics - Memory consumption for finish in main session: current 142.4 MiB, max: 204.0 MiB\n", - "Creating Dask Server\n", - "2024-01-18 13:13:57,011 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.3 MiB, max: 0.5 MiB\n", - "2024-01-18 13:13:57,013 - ApiComposer - Initial pipeline was fitted in 0.0 sec.\n", - "2024-01-18 13:13:57,014 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", - "2024-01-18 13:13:57,022 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 20 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'knnreg', 'dtreg', 'inception_model', 'omniscale_model', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'topological_extractor', 'quantile_extractor', 'signal_extractor', 'recurrence_extractor', 'minirocket_extractor', 'isolation_forest_reg', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_features'].\n", - "2024-01-18 13:13:57,036 - ApiComposer - Pipeline composition started.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 0%| | 0/5 [00:00.on_destroy at 0x0000028A2FEFF550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792566045648\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A317A2310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792282517648\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A2EFA7B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792539704080\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:15:31,230 - IndustrialDispatcher - 12 individuals out of 13 in previous population were evaluated successfully.\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A31E681F0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792259245424\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A302D9C10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789825751152\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000288801828B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792545249968\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028888156280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792564408464\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A2EE968B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792241880784\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028885C2AB80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792567881424\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A289D5700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792542590416\n", - "Exception ignored in: .on_destroy at 0x0000028A2BA050D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789824187344\n", - "Exception ignored in: .on_destroy at 0x0000028A2FC5C670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792549702416\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x000002898E35E160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789709718320\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A217D9C10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785447264400\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A20F625E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785289110992\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:19:49,949 - IndustrialDispatcher - 7 individuals out of 7 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x0000028A12248B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792530040272\n", - "Exception ignored in: .on_destroy at 0x0000028889377670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785447697168\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x00000289DB059C10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785352526224\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000288896CAB80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789717621136\n", - "Exception ignored in: .on_destroy at 0x0000028A0BACC0D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792259277712\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028A1E8800D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792554872528\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x000002898BA01C10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785382434640\n", - "\n", - " 0%| | 0/185 [00:00.on_destroy at 0x000002898B9ED160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792287460400\n", - "Exception ignored in: .on_destroy at 0x00000289883295E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2785376803600\n", - "Exception ignored in: .on_destroy at 0x0000028889396B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2789828039664\n", - "Exception ignored in: .on_destroy at 0x00000288889929D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792536360976\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x0000028987D5B3A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792281021392\n", - "\n", - " 0%| | 0/186 [00:00.on_destroy at 0x00000288896A3550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792277671984\n", - "Exception ignored in: .on_destroy at 0x00000288870BEB80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792543044880\n", - "Exception ignored in: .on_destroy at 0x000002892F8D58B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 2792542939824\n", - "\n", - " 0%| | 0/185 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
run_number - 10.3201440.0504180.2245390.1531910.1105110.3403881.1846260.276061
run_number - 00.3165300.0506860.2251350.1549550.1166580.3410301.1763260.267723
run_number - 20.2484050.0557380.2360890.1659100.1182690.2948401.0567690.215951
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" }, - "execution_count": 7, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df_automl = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_automl.columns = list(metric_dict.keys())\n", - "df_automl = df_automl.T\n", - "df_automl.sort_values('root_mean_squared_error:')" + "industrial_auto_model.solver.history.show.operations_kde(dpi=100)" ], "metadata": { "collapsed": false, @@ -9004,11 +1695,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "outputs": [], "source": [ - "from cases.utils import sota_compare\n", - "df = sota_compare(data_path,dataset_name, best_baseline,best_tuned,df_automl)" + "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')\n", + "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", + "df.index = df['algorithm']\n", + "df = df.drop(['algorithm','ds/type'], axis=1)\n", + "df = df.replace(',','.', regex=True).astype(float)" ], "metadata": { "collapsed": false, @@ -9019,19 +1713,12 @@ }, { "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": "RandF_RMSE 0.156592\nRotF_RMSE 0.160615\nDrCIF_RMSE 0.160874\nFreshPRINCE_RMSE 0.163742\nXGBoost_RMSE 0.164314\n5NN-ED_RMSE 0.165432\n5NN-DTW_RMSE 0.167050\nRIST_RMSE 0.168419\nMultiROCKET_RMSE 0.170421\nRDST_RMSE 0.171500\nInceptionT_RMSE 0.172068\nSingleInception_RMSE 0.172336\nFPCR_RMSE 0.177314\nFPCR-Bs_RMSE 0.178046\nTSF_RMSE 0.179517\nFCN_RMSE 0.186531\nRidge_RMSE 0.197403\nResNet_RMSE 0.199565\n1NN-ED_RMSE 0.207274\nCNN_RMSE 0.214186\nGrid-SVR_RMSE 0.214427\n1NN-DTW_RMSE 0.216525\nFedot_Industrial_AutoML 0.224539\nROCKET_RMSE 0.235097\nFedot_Industrial_tuned 0.235652\nFedot_Industrial_baseline 0.235652\nName: min, dtype: float64" - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 17, + "outputs": [], "source": [ - "df.sort_values('min')['min']" + "df['Fedot_Industrial_tuned'] = metrics['rmse'][0]\n", + "df['Fedot_Industrial_AutoML'] = auto_metrics['rmse'][0]\n", + "df = df.T" ], "metadata": { "collapsed": false, @@ -9042,13 +1729,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "outputs": [ { "data": { - "text/plain": "RIST_RMSE 0.227326\nRDST_RMSE 0.233406\nDrCIF_RMSE 0.235080\nFedot_Industrial_baseline 0.235652\nFedot_Industrial_tuned 0.235652\nRandF_RMSE 0.235702\nFedot_Industrial_AutoML 0.236089\n5NN-ED_RMSE 0.237231\n5NN-DTW_RMSE 0.238461\nFreshPRINCE_RMSE 0.242154\nRotF_RMSE 0.245572\nXGBoost_RMSE 0.247072\nTSF_RMSE 0.247288\nMultiROCKET_RMSE 0.252652\nFCN_RMSE 0.253077\nInceptionT_RMSE 0.256789\nFPCR_RMSE 0.261870\nResNet_RMSE 0.266606\nFPCR-Bs_RMSE 0.270278\nSingleInception_RMSE 0.273212\n1NN-DTW_RMSE 0.287127\nGrid-SVR_RMSE 0.289215\nCNN_RMSE 0.289748\nRidge_RMSE 0.303183\n1NN-ED_RMSE 0.308109\nROCKET_RMSE 0.322641\nName: max, dtype: float64" + "text/plain": "Fedot_Industrial_AutoML 0.220000\nRIST_RMSE 0.227326\nFedot_Industrial_tuned 0.228000\nRDST_RMSE 0.233406\nDrCIF_RMSE 0.235080\nRandF_RMSE 0.235702\n5NN-ED_RMSE 0.237231\n5NN-DTW_RMSE 0.238461\nFreshPRINCE_RMSE 0.242154\nRotF_RMSE 0.245572\nXGBoost_RMSE 0.247072\nTSF_RMSE 0.247288\nMultiROCKET_RMSE 0.252652\nFCN_RMSE 0.253077\nInceptionT_RMSE 0.256789\nFPCR_RMSE 0.261870\nResNet_RMSE 0.266606\nFPCR-Bs_RMSE 0.270278\nSingleInception_RMSE 0.273212\n1NN-DTW_RMSE 0.287127\nGrid-SVR_RMSE 0.289215\nCNN_RMSE 0.289748\nRidge_RMSE 0.303183\n1NN-ED_RMSE 0.308109\nROCKET_RMSE 0.322641\nName: max, dtype: float64" }, - "execution_count": 24, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -9065,13 +1752,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "outputs": [ { "data": { - "text/plain": "DrCIF_RMSE 0.197055\nRandF_RMSE 0.199306\nFreshPRINCE_RMSE 0.201173\nRotF_RMSE 0.201431\nRDST_RMSE 0.202498\nRIST_RMSE 0.202756\nMultiROCKET_RMSE 0.204024\n5NN-ED_RMSE 0.207243\n5NN-DTW_RMSE 0.207365\nXGBoost_RMSE 0.208650\nTSF_RMSE 0.209727\nFPCR_RMSE 0.211556\nFPCR-Bs_RMSE 0.215564\nInceptionT_RMSE 0.218467\nFCN_RMSE 0.220598\nSingleInception_RMSE 0.224385\nResNet_RMSE 0.226852\nFedot_Industrial_AutoML 0.228588\nRidge_RMSE 0.232403\nFedot_Industrial_tuned 0.235652\nFedot_Industrial_baseline 0.235652\nCNN_RMSE 0.246243\nGrid-SVR_RMSE 0.246649\n1NN-DTW_RMSE 0.260328\n1NN-ED_RMSE 0.264244\nROCKET_RMSE 0.268838\nName: average, dtype: float64" + "text/plain": "DrCIF_RMSE 0.197055\nRandF_RMSE 0.199306\nFreshPRINCE_RMSE 0.201173\nRotF_RMSE 0.201431\nRDST_RMSE 0.202498\nRIST_RMSE 0.202756\nMultiROCKET_RMSE 0.204024\n5NN-ED_RMSE 0.207243\n5NN-DTW_RMSE 0.207365\nXGBoost_RMSE 0.208650\nTSF_RMSE 0.209727\nFPCR_RMSE 0.211556\nFPCR-Bs_RMSE 0.215564\nInceptionT_RMSE 0.218467\nFedot_Industrial_AutoML 0.220000\nFCN_RMSE 0.220598\nSingleInception_RMSE 0.224385\nResNet_RMSE 0.226852\nFedot_Industrial_tuned 0.228000\nRidge_RMSE 0.232403\nCNN_RMSE 0.246243\nGrid-SVR_RMSE 0.246649\n1NN-DTW_RMSE 0.260328\n1NN-ED_RMSE 0.264244\nROCKET_RMSE 0.268838\nName: average, dtype: float64" }, - "execution_count": 25, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/real_world_examples/industrial_examples/sentiment_analysis/ethereum_analysis.ipynb b/examples/real_world_examples/industrial_examples/sentiment_analysis/ethereum_analysis.ipynb index d88295aa2..7bc1d67d2 100644 --- a/examples/real_world_examples/industrial_examples/sentiment_analysis/ethereum_analysis.ipynb +++ b/examples/real_world_examples/industrial_examples/sentiment_analysis/ethereum_analysis.ipynb @@ -38,22 +38,35 @@ "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\dask\\dataframe\\_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 13.0.0. Please consider upgrading.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", + "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", + "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", "from fedot_ind.tools.loader import DataLoader\n", - "from examples.example_utils import init_input_data" + "from fedot_ind.api.main import FedotIndustrial" ] }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "def evaluate_loop(api_params, finetune: bool = False):\n", + " industrial = FedotIndustrial(**api_params)\n", + " if finetune:\n", + " industrial.finetune(train_data)\n", + " else:\n", + " industrial.fit(train_data)\n", + " return industrial" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, { "cell_type": "markdown", "metadata": { @@ -67,13 +80,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "outputs": [], "source": [ - "from cases.utils import ts_regression_setup\n", + "initial_assumption = PipelineBuilder().add_node('quantile_extractor').add_node('treg')\n", + "params = dict(problem='regression',\n", + " metric='rmse',\n", + " timeout=15,\n", + " initial_assumption=initial_assumption,\n", + " n_jobs=2,\n", + " logging_level=20)\n", "dataset_name = 'EthereumSentiment'\n", - "OperationTypesRepository, tuning_params, data_path, experiment_setup, model_dict = ts_regression_setup()\n", - "experiment_setup['output_folder'] = f'./{dataset_name}/results_of_experiment'" + "data_path = PROJECT_PATH + '/examples/data'" ], "metadata": { "collapsed": false, @@ -95,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-08-28T10:35:13.321212Z", @@ -110,7 +128,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-01-18 13:37:08,808 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/EthereumSentiment\n" + "2024-04-04 15:25:28,846 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS/examples/data/EthereumSentiment\n" ] } ], @@ -122,11 +140,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "outputs": [], "source": [ - "input_data = init_input_data(train_data[0], train_data[1], task=tuning_params['task'])\n", - "val_data = init_input_data(test_data[0], test_data[1], task=tuning_params['task'])" + "import numpy as np\n", + "features = np.array(train_data[0].values.tolist()).astype(float)" ], "metadata": { "collapsed": false, @@ -149,19 +167,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "outputs": [ { "data": { "text/plain": "(249, 2, 24)" }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "input_data.features.shape" + "features.shape" ], "metadata": { "collapsed": false, @@ -184,12 +202,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "outputs": [ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -197,7 +215,7 @@ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -205,8 +223,8 @@ ], "source": [ "from matplotlib import pyplot as plt\n", - "pd.DataFrame(input_data.features[1, 0, :]).plot(title='Bitcoin close prices')\n", - "pd.DataFrame(input_data.features[1, 1, :]).plot(title='Traded volume')\n", + "pd.DataFrame(features[1, 0, :]).plot(title='Bitcoin close prices')\n", + "pd.DataFrame(features[1, 1, :]).plot(title='Traded volume')\n", "plt.show()" ], "metadata": { @@ -232,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "ExecuteTime": { "start_time": "2023-08-28T10:35:27.965798Z" @@ -246,23 +264,777 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n" + "2024-04-04 15:27:05,531 - Initialising experiment setup\n", + "2024-04-04 15:27:05,534 - Initialising Industrial Repository\n", + "2024-04-04 15:27:06,157 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-04-04 15:27:07,200 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-04-04 15:27:07,239 - State start\n", + "2024-04-04 15:27:07,993 - Scheduler at: inproc://10.64.4.217/19992/1\n", + "2024-04-04 15:27:07,994 - dashboard at: http://10.64.4.217:56751/status\n", + "2024-04-04 15:27:07,995 - Registering Worker plugin shuffle\n", + "2024-04-04 15:27:08,768 - Start worker at: inproc://10.64.4.217/19992/4\n", + "2024-04-04 15:27:08,769 - Listening to: inproc10.64.4.217\n", + "2024-04-04 15:27:08,770 - Worker name: 0\n", + "2024-04-04 15:27:08,771 - dashboard at: 10.64.4.217:56752\n", + "2024-04-04 15:27:08,772 - Waiting to connect to: inproc://10.64.4.217/19992/1\n", + "2024-04-04 15:27:08,773 - -------------------------------------------------\n", + "2024-04-04 15:27:08,774 - Threads: 8\n", + "2024-04-04 15:27:08,775 - Memory: 31.95 GiB\n", + "2024-04-04 15:27:08,776 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-bmb2rdea\n", + "2024-04-04 15:27:08,776 - -------------------------------------------------\n", + "2024-04-04 15:27:08,799 - Register worker \n", + "2024-04-04 15:27:08,803 - Starting worker compute stream, inproc://10.64.4.217/19992/4\n", + "2024-04-04 15:27:08,804 - Starting established connection to inproc://10.64.4.217/19992/5\n", + "2024-04-04 15:27:08,805 - Starting Worker plugin shuffle\n", + "2024-04-04 15:27:08,806 - Registered to: inproc://10.64.4.217/19992/1\n", + "2024-04-04 15:27:08,807 - -------------------------------------------------\n", + "2024-04-04 15:27:08,808 - Starting established connection to inproc://10.64.4.217/19992/1\n", + "2024-04-04 15:27:08,812 - Receive client connection: Client-a7ae74e4-f27e-11ee-8e18-b42e99a00ea1\n", + "2024-04-04 15:27:08,814 - Starting established connection to inproc://10.64.4.217/19992/6\n", + "2024-04-04 15:27:08,816 - LinK Dask Server - http://10.64.4.217:56751/status\n", + "2024-04-04 15:27:08,818 - Initialising solver\n", + "2024-04-04 15:27:08,914 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 15:27:08,915 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 15:27:08,919 - SequentialTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "2024-04-04 15:27:29,142 - SequentialTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [treg, quantile_extractor]}\n", + "treg - {}\n", + "quantile_extractor - {} \n", + "Initial metric: [0.248]\n", + " 0%| | 0/100 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
regression_with_statistical_features0.2804230.0517870.2275680.1731290.1306170.2876860.7433350.187089
\n" + "text/plain": " r2 rmse mae\n0 0.264 0.23 0.175", + "text/html": "
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r2rmsemae
00.2640.230.175
\n
" }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_baseline = pd.concat([x for x in metric_dict.values()],axis=1)\n", - "df_baseline.columns = list(metric_dict.keys())\n", - "df_baseline = df_baseline.T\n", - "df_baseline.sort_values(by='root_mean_squared_error:')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "best_baseline = df_baseline.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" + "metrics" ], "metadata": { "collapsed": false, @@ -330,7 +1099,7 @@ { "cell_type": "markdown", "source": [ - "## Could it be done better? Tuning approach" + "## AutoML approach" ], "metadata": { "collapsed": false, @@ -341,10104 +1110,779 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Current_model - regression_with_statistical_features\n", - "2024-01-18 13:37:22,458 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", - "2024-01-18 13:37:22,460 - DataSourceSplitter - Hold out validation is applied.\n", - "2024-01-18 13:37:22,462 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 199/199 [00:00<00:00, 838.37it/s]\n", - "100%|██████████| 50/50 [00:00<00:00, 1165.90it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:37:22,969 - SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [ridge, quantile_extractor]}\n", - "ridge - {}\n", - "quantile_extractor - {'window_size': 0} \n", - "Initial metric: [0.243]\n", - " 0%| | 0/3 [00:00\n", + "2024-04-04 15:38:04,184 - Starting worker compute stream, inproc://10.64.4.217/19992/12\n", + "2024-04-04 15:38:04,185 - Starting established connection to inproc://10.64.4.217/19992/13\n", + "2024-04-04 15:38:04,187 - Starting Worker plugin shuffle\n", + "2024-04-04 15:38:04,189 - Registered to: inproc://10.64.4.217/19992/9\n", + "2024-04-04 15:38:04,190 - -------------------------------------------------\n", + "2024-04-04 15:38:04,191 - Starting established connection to inproc://10.64.4.217/19992/9\n", + "2024-04-04 15:38:04,195 - Receive client connection: Client-2e520fdc-f280-11ee-8e18-b42e99a00ea1\n", + "2024-04-04 15:38:04,197 - Starting established connection to inproc://10.64.4.217/19992/14\n", + "2024-04-04 15:38:04,199 - LinK Dask Server - http://10.64.4.217:57064/status\n", + "2024-04-04 15:38:04,200 - Initialising solver\n", + "2024-04-04 15:38:04,244 - AssumptionsHandler - Initial pipeline fitting started\n", + "2024-04-04 15:38:05,696 - AssumptionsHandler - Initial pipeline was fitted successfully\n", + "2024-04-04 15:38:05,698 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.8 MiB, max: 1.5 MiB\n", + "2024-04-04 15:38:05,699 - ApiComposer - Initial pipeline was fitted in 1.5 sec.\n", + "2024-04-04 15:38:05,701 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", + "2024-04-04 15:38:05,711 - ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 15 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'dtreg', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "2024-04-04 15:38:05,745 - ApiComposer - Pipeline composition started.\n", + "2024-04-04 15:38:05,748 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 15:38:05,751 - DataSourceSplitter - Hold out validation is applied.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/199 [00:00']\n", + "2024-04-04 15:38:17,160 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:42:27,247 - IndustrialDispatcher - 21 individuals out of 21 in previous population were evaluated successfully.\n", + "2024-04-04 15:42:27,367 - IndustrialEvoOptimizer - Generation num: 2 size: 21\n", + "2024-04-04 15:42:27,369 - IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "2024-04-04 15:42:27,371 - IndustrialEvoOptimizer - Next population size: 21; max graph depth: 6\n", + "2024-04-04 15:42:45,478 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:44:27,685 - full garbage collections took 11% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:44:54,593 - full garbage collections took 18% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:45:07,049 - full garbage collections took 21% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:45:22,153 - full garbage collections took 22% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:45:40,534 - full garbage collections took 23% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:45:43,441 - full garbage collections took 24% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:46:18,501 - full garbage collections took 24% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:46:38,344 - full garbage collections took 24% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:46:56,516 - full garbage collections took 25% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:47:09,528 - full garbage collections took 25% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:47:09,551 - IndustrialDispatcher - 16 individuals out of 17 in previous population were evaluated successfully.\n", + "2024-04-04 15:47:09,858 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:47:09,865 - IndustrialDispatcher - 0 individuals out of 4 in previous population were evaluated successfully. 0.0% is a fairly small percentage of successful evaluation.\n", + "2024-04-04 15:47:27,180 - full garbage collections took 25% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:47:43,677 - full garbage collections took 26% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:00,063 - full garbage collections took 26% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:14,872 - full garbage collections took 27% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:15,002 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:48:15,005 - IndustrialDispatcher - 0 individuals out of 3 in previous population were evaluated successfully. 0.0% is a fairly small percentage of successful evaluation.\n", + "2024-04-04 15:48:24,421 - full garbage collections took 27% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:33,900 - full garbage collections took 27% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:42,849 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:43,008 - IndustrialDispatcher - Number of used CPU's: 2\n", + "2024-04-04 15:48:43,011 - IndustrialDispatcher - 0 individuals out of 3 in previous population were evaluated successfully. 0.0% is a fairly small percentage of successful evaluation.\n", + "2024-04-04 15:48:46,204 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:49,219 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:52,254 - full garbage collections took 28% CPU time recently (threshold: 10%)\n", + "2024-04-04 15:48:52,260 - ReproductionController - Reproduction achieved pop size 19 using 4 attempt(s) with success rate 0.976\n", + "2024-04-04 15:48:52,262 - RandomAgent - len=22 nonzero=12 avg=-0.275 std=0.507 min=-1.000 max=0.681 \n", + "2024-04-04 15:48:52,263 - RandomAgent - actions/rewards: [(>, -1.0), (>, -1.0), (>, -1.0), (>, 0.0), (, 0.0), (>, 0.0), (>, -0.3152), (, 0.0675), (>, 0.0), (>, 0.0), (, -0.0161), (>, 0.6814), (, 0.0), (>, 0.0), (>, 0.138), (, -0.2646), (, -0.5715), (, -0.0674), (>, 0.0501), (, 0.0), (>, 0.0), (>, 0.0)]\n", + "2024-04-04 15:48:52,265 - RandomAgent - exp=[0.2 0.2 0.2 0.2 0.2] probs=[0.2 0.2 0.2 0.2 0.2]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/199 [00:00']\n", + "2024-04-04 15:48:52,367 - IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "2024-04-04 15:48:52,368 - IndustrialEvoOptimizer - spent time: 10.8 min\n", + "2024-04-04 15:48:52,369 - GroupedCondition - Optimisation stopped: Time limit is reached\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/199 [00:00']\n", + "2024-04-04 15:48:52,388 - IndustrialEvoOptimizer - no improvements for 2 iterations\n", + "2024-04-04 15:48:52,390 - IndustrialEvoOptimizer - spent time: 10.8 min\n", + "2024-04-04 15:48:52,396 - GPComposer - GP composition finished\n", + "2024-04-04 15:48:52,402 - DataSourceSplitter - Stratificated splitting of data is disabled.\n", + "2024-04-04 15:48:52,411 - DataSourceSplitter - Hold out validation is applied.\n", + "2024-04-04 15:48:52,417 - ApiComposer - Hyperparameters tuning started with 4 min. timeout\n", + "2024-04-04 15:48:52,423 - SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/199 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2rmsemae
00.2880.2260.172
\n" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auto_metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/199 [00:00", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - " 23%|██▎ | 7/30 [00:02<00:16, 1.36trial/s, best loss: 0.24258683647228527]2024-01-18 13:37:27,487 - build_posterior_wrapper took 0.000998 seconds\n", - "2024-01-18 13:37:27,488 - TPE using 7/7 trials with best loss 0.242587\n" + "2024-04-04 15:53:47,103 - OperationsAnimatedBar - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-04 15:53:47,267 - MovieWriter ffmpeg unavailable; using Pillow instead.\n", + "2024-04-04 15:53:47,268 - Animation.save using \n", + "2024-04-04 15:53:57,302 - OperationsAnimatedBar - The animation was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\history_animated_bars.gif\".\n", + "2024-04-04 15:53:57,304 - FitnessBox - Visualizing optimization history... It may take some time, depending on the history size.\n", + "2024-04-04 15:53:57,336 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-04 15:53:57,342 - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "2024-04-04 15:53:57,437 - default - The figure was saved to \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\results_of_experiments\\fitness_by_gen.png\".\n" ] - }, + } + ], + "source": [ + "industrial_auto_model.solver.current_pipeline.show()\n", + "industrial_auto_model.plot_operation_distribution(mode='each')\n", + "industrial_auto_model.plot_fitness_by_generation()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/199 [00:00", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.vstack([test_data[1].flatten(),labels.flatten(),auto_labels.flatten()]).T\n", + "all_prediction = pd.DataFrame(predictions,columns=['target','baseline','automl'])\n", + "all_prediction.plot()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 27%|██▋ | 8/30 [00:03<00:15, 1.42trial/s, best loss: 0.24258683647228527]2024-01-18 13:37:28,128 - build_posterior_wrapper took 0.000997 seconds\n", - "2024-01-18 13:37:28,129 - TPE using 8/8 trials with best loss 0.242587\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/199 [00:00", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "industrial_auto_model.solver.history.show.fitness_box(best_fraction=0.5, dpi=100)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/199 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
dayshoursminutessecondsmilliseconds
Data Definition (fit)00000
Data Preprocessing0000340
Fitting (summary)001455282
Composing001048170
Train Inference000164
Tuning (composing)0045682
Tuning (after)00000
Data Definition (predict)00000
Predicting0000249
\n" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "industrial_auto_model.solver.return_report()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 33%|███▎ | 10/30 [00:04<00:12, 1.54trial/s, best loss: 0.24258683647228527]2024-01-18 13:37:29,299 - build_posterior_wrapper took 0.000998 seconds\n", - "2024-01-18 13:37:29,300 - TPE using 10/10 trials with best loss 0.242587\n" + "2024-04-04 17:48:37,193 - OperationsKDE - Visualizing optimization history... It may take some time, depending on the history size.\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/199 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_scoremodel_params
regression_with_statistical_features0.2804230.0517870.2275680.1731290.1306170.2876860.7433350.187089{ridge: {}, quantile_extractor: {'window_size': 0}}
\n" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_tuned.columns = list(metric_dict.keys())\n", - "df_tuned.T.sort_values('root_mean_squared_error:')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "\"{ridge: {}, quantile_extractor: {'window_size': 0}}\"" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tuned.T.sort_values('root_mean_squared_error:')['model_params'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "best_tuned = df_tuned.T.sort_values('root_mean_squared_error:')['root_mean_squared_error:'].iloc[0]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Even better? AutoML approach" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:38:01,193 - Initialising experiment setup\n", - "2024-01-18 13:38:01,194 - Initialising Industrial Repository\n", - "2024-01-18 13:38:01,197 - Initialising Dask Server\n", - "Creating Dask Server\n", - "2024-01-18 13:38:01,988 - State start\n", - "2024-01-18 13:38:02,503 - Scheduler at: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:02,505 - dashboard at: http://10.64.4.229:63312/status\n", - "2024-01-18 13:38:02,505 - Registering Worker plugin shuffle\n", - "2024-01-18 13:38:05,925 - Start worker at: inproc://10.64.4.229/18696/4\n", - "2024-01-18 13:38:05,926 - Listening to: inproc10.64.4.229\n", - "2024-01-18 13:38:05,927 - Worker name: 0\n", - "2024-01-18 13:38:05,928 - dashboard at: 10.64.4.229:63315\n", - "2024-01-18 13:38:05,929 - Waiting to connect to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:05,929 - -------------------------------------------------\n", - "2024-01-18 13:38:05,930 - Threads: 1\n", - "2024-01-18 13:38:05,930 - Memory: 22.35 GiB\n", - "2024-01-18 13:38:05,931 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-q7kahz35\n", - "2024-01-18 13:38:05,931 - -------------------------------------------------\n", - "2024-01-18 13:38:05,933 - Start worker at: inproc://10.64.4.229/18696/5\n", - "2024-01-18 13:38:05,934 - Listening to: inproc10.64.4.229\n", - "2024-01-18 13:38:05,934 - Worker name: 1\n", - "2024-01-18 13:38:05,935 - dashboard at: 10.64.4.229:63316\n", - "2024-01-18 13:38:05,936 - Waiting to connect to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:05,936 - -------------------------------------------------\n", - "2024-01-18 13:38:05,937 - Threads: 1\n", - "2024-01-18 13:38:05,937 - Memory: 22.35 GiB\n", - "2024-01-18 13:38:05,938 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-nz2belfp\n", - "2024-01-18 13:38:05,939 - -------------------------------------------------\n", - "2024-01-18 13:38:05,940 - Start worker at: inproc://10.64.4.229/18696/6\n", - "2024-01-18 13:38:05,941 - Listening to: inproc10.64.4.229\n", - "2024-01-18 13:38:05,942 - Worker name: 2\n", - "2024-01-18 13:38:05,942 - dashboard at: 10.64.4.229:63317\n", - "2024-01-18 13:38:05,943 - Waiting to connect to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:05,943 - -------------------------------------------------\n", - "2024-01-18 13:38:05,944 - Threads: 1\n", - "2024-01-18 13:38:05,944 - Memory: 22.35 GiB\n", - "2024-01-18 13:38:05,945 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-oodu9kvz\n", - "2024-01-18 13:38:05,945 - -------------------------------------------------\n", - "2024-01-18 13:38:05,947 - Start worker at: inproc://10.64.4.229/18696/7\n", - "2024-01-18 13:38:05,947 - Listening to: inproc10.64.4.229\n", - "2024-01-18 13:38:05,948 - Worker name: 3\n", - "2024-01-18 13:38:05,948 - dashboard at: 10.64.4.229:63318\n", - "2024-01-18 13:38:05,949 - Waiting to connect to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:05,949 - -------------------------------------------------\n", - "2024-01-18 13:38:05,950 - Threads: 1\n", - "2024-01-18 13:38:05,950 - Memory: 22.35 GiB\n", - "2024-01-18 13:38:05,950 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-xb1oe3hk\n", - "2024-01-18 13:38:05,951 - -------------------------------------------------\n", - "2024-01-18 13:38:05,952 - Start worker at: inproc://10.64.4.229/18696/8\n", - "2024-01-18 13:38:05,953 - Listening to: inproc10.64.4.229\n", - "2024-01-18 13:38:05,954 - Worker name: 4\n", - "2024-01-18 13:38:05,955 - dashboard at: 10.64.4.229:63319\n", - "2024-01-18 13:38:05,955 - Waiting to connect to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:05,956 - -------------------------------------------------\n", - "2024-01-18 13:38:05,957 - Threads: 1\n", - "2024-01-18 13:38:05,957 - Memory: 22.35 GiB\n", - "2024-01-18 13:38:05,958 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-f_gohqg5\n", - "2024-01-18 13:38:05,959 - -------------------------------------------------\n", - "2024-01-18 13:38:05,960 - Start worker at: inproc://10.64.4.229/18696/9\n", - "2024-01-18 13:38:05,961 - Listening to: inproc10.64.4.229\n", - "2024-01-18 13:38:05,961 - Worker name: 5\n", - "2024-01-18 13:38:05,962 - dashboard at: 10.64.4.229:63320\n", - "2024-01-18 13:38:05,962 - Waiting to connect to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:05,963 - -------------------------------------------------\n", - "2024-01-18 13:38:05,964 - Threads: 1\n", - "2024-01-18 13:38:05,965 - Memory: 22.35 GiB\n", - "2024-01-18 13:38:05,965 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-e80rw4cd\n", - "2024-01-18 13:38:05,966 - -------------------------------------------------\n", - "2024-01-18 13:38:05,967 - Event loop was unresponsive in Scheduler for 3.46s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability.\n", - "2024-01-18 13:38:05,978 - Register worker \n", - "2024-01-18 13:38:05,980 - Starting worker compute stream, inproc://10.64.4.229/18696/4\n", - "2024-01-18 13:38:05,981 - Starting established connection to inproc://10.64.4.229/18696/10\n", - "2024-01-18 13:38:05,983 - Starting Worker plugin shuffle\n", - "2024-01-18 13:38:05,985 - Register worker \n", - "2024-01-18 13:38:05,986 - Starting worker compute stream, inproc://10.64.4.229/18696/5\n", - "2024-01-18 13:38:05,987 - Starting established connection to inproc://10.64.4.229/18696/11\n", - "2024-01-18 13:38:05,988 - Register worker \n", - "2024-01-18 13:38:05,989 - Starting worker compute stream, inproc://10.64.4.229/18696/6\n", - "2024-01-18 13:38:05,990 - Starting established connection to inproc://10.64.4.229/18696/12\n", - "2024-01-18 13:38:05,991 - Register worker \n", - "2024-01-18 13:38:05,992 - Starting worker compute stream, inproc://10.64.4.229/18696/7\n", - "2024-01-18 13:38:05,994 - Starting established connection to inproc://10.64.4.229/18696/13\n", - "2024-01-18 13:38:05,995 - Register worker \n", - "2024-01-18 13:38:05,996 - Starting worker compute stream, inproc://10.64.4.229/18696/8\n", - "2024-01-18 13:38:05,997 - Starting established connection to inproc://10.64.4.229/18696/14\n", - "2024-01-18 13:38:05,998 - Registered to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:05,999 - -------------------------------------------------\n", - "2024-01-18 13:38:06,004 - Starting Worker plugin shuffle\n", - "2024-01-18 13:38:06,005 - Register worker \n", - "2024-01-18 13:38:06,006 - Starting worker compute stream, inproc://10.64.4.229/18696/9\n", - "2024-01-18 13:38:06,007 - Starting established connection to inproc://10.64.4.229/18696/15\n", - "2024-01-18 13:38:06,008 - Starting established connection to inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,009 - Starting Worker plugin shuffle\n", - "2024-01-18 13:38:06,010 - Starting Worker plugin shuffle\n", - "2024-01-18 13:38:06,011 - Starting Worker plugin shuffle\n", - "2024-01-18 13:38:06,012 - Registered to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,012 - -------------------------------------------------\n", - "2024-01-18 13:38:06,013 - Registered to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,014 - -------------------------------------------------\n", - "2024-01-18 13:38:06,015 - Registered to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,015 - -------------------------------------------------\n", - "2024-01-18 13:38:06,016 - Registered to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,017 - -------------------------------------------------\n", - "2024-01-18 13:38:06,018 - Starting Worker plugin shuffle\n", - "2024-01-18 13:38:06,019 - Starting established connection to inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,020 - Starting established connection to inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,020 - Starting established connection to inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,021 - Starting established connection to inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,022 - Registered to: inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,022 - -------------------------------------------------\n", - "2024-01-18 13:38:06,023 - Starting established connection to inproc://10.64.4.229/18696/1\n", - "2024-01-18 13:38:06,026 - Receive client connection: Client-aa1214e2-b5ed-11ee-8908-b42e99a00ea1\n", - "2024-01-18 13:38:06,027 - Starting established connection to inproc://10.64.4.229/18696/16\n", - "2024-01-18 13:38:06,029 - Initialising solver\n", - "2024-01-18 13:38:06,173 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.4 MiB, max: 0.6 MiB\n", - "2024-01-18 13:38:06,175 - ApiComposer - Initial pipeline was fitted in 0.0 sec.\n", - "2024-01-18 13:38:06,176 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", - "2024-01-18 13:38:06,184 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 20 min. Set of candidate models: ['xgbreg', 'sgdr', 'treg', 'ridge', 'lasso', 'knnreg', 'dtreg', 'inception_model', 'omniscale_model', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'topological_extractor', 'quantile_extractor', 'signal_extractor', 'recurrence_extractor', 'minirocket_extractor', 'isolation_forest_reg', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_features'].\n", - "2024-01-18 13:38:06,335 - ApiComposer - Pipeline composition started.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 0%| | 0/5 [00:00 strategy for isolation_forest_reg\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BDD762280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1424764274992\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BFA0553A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1424929368752\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BFA2573A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425829952272\n", - "Exception ignored in: .on_destroy at 0x0000014BF9FD65E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425623406320\n", - "\n", - " 0%| | 0/199 [00:00 strategy for isolation_forest_reg\n", - "2024-01-18 13:39:20,677 - IndustrialDispatcher - 7 individuals out of 8 in previous population were evaluated successfully.\n", - "2024-01-18 13:39:27,493 - IndustrialDispatcher - 2 individuals out of 3 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C0216D9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425962923088\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C0B7BD9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425960120208\n", - "Exception ignored in: .on_destroy at 0x0000014C0B75E790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426122657840\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014C0B6FF550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426142263024\n", - "Exception ignored in: .on_destroy at 0x0000014BF9BFA550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426141745296\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:39:40,031 - IndustrialDispatcher - 2 individuals out of 2 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 20%|██ | 1/5 [01:33<06:14, 93.70s/gen]\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C09391940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425261434224\n", - "Exception ignored in: .on_destroy at 0x0000014C017AFEE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426084842448\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CB89A4D30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426059456848\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014CC794B940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429278997456\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CBECC63A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429302590224\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C21597CA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429041183792\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:49:05,661 - IndustrialDispatcher - 9 individuals out of 9 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD3DB9790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426480798192\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014C6EB66820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429101721040\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 13:50:35,119 - IndustrialDispatcher - 3 individuals out of 4 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 60%|██████ | 3/5 [12:28<10:01, 300.86s/gen]\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C20F76E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426148555728\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C2183C790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429308608112\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C21A71DC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429479192496\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD633B700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426032922768\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CBA3AE9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429029174416\n", - "\n", - " 0%| | 0/199 [00:00 strategy for isolation_forest_reg\n", - "2024-01-18 13:55:41,608 - IndustrialDispatcher - 12 individuals out of 14 in previous population were evaluated successfully.\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.69\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.61\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.54\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.50\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.45\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.40\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.36\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.32\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.32\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.29\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.28\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.26\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.26\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.26\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.25\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.25\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.25\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.25\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.24\n", - "Updating learning rate to 5.310453736087151e-05\n", - "Epoch: 30, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.401937993663639e-05\n", - "Epoch: 31, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.496461498523549e-05\n", - "Epoch: 32, Training Loss: 0.24\n", - "Updating learning rate to 5.5940180855970265e-05\n", - "Epoch: 33, Training Loss: 0.24\n", - "Updating learning rate to 5.694601391988759e-05\n", - "Epoch: 34, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.7982048573928125e-05\n", - "Epoch: 35, Training Loss: 0.24\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.9048217245206655e-05\n", - "Epoch: 36, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 6.014445039541848e-05\n", - "Epoch: 37, Training Loss: 0.24\n", - "Updating learning rate to 6.127067652537466e-05\n", - "Epoch: 38, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 6.242682217966645e-05\n", - "Epoch: 39, Training Loss: 0.24\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 6.361281195145507e-05\n", - "Epoch: 40, Training Loss: 0.24\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 6.48285684873908e-05\n", - "Epoch: 41, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 6.607401249265779e-05\n", - "Epoch: 42, Training Loss: 0.24\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 6.734906273614567e-05\n", - "Epoch: 43, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 6.865363605574823e-05\n", - "Epoch: 44, Training Loss: 0.24\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 6.99876473637869e-05\n", - "Epoch: 45, Training Loss: 0.25\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 7.135100965256086e-05\n", - "Epoch: 46, Training Loss: 0.24\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 7.274363400002132e-05\n", - "Epoch: 47, Training Loss: 0.24\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 7.4165429575572e-05\n", - "Epoch: 48, Training Loss: 0.25\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 7.561630364599293e-05\n", - "Epoch: 49, Training Loss: 0.24\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 7.709616158148823e-05\n", - "Epoch: 50, Training Loss: 0.24\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 7.86049068618595e-05\n", - "Epoch: 51, Training Loss: 0.24\n", - "Updating learning rate to 8.014244108279958e-05\n", - "Epoch: 52, Training Loss: 0.24\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 8.170866396231178e-05\n", - "Epoch: 53, Training Loss: 0.24\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 8.330347334725013e-05\n", - "Epoch: 54, Training Loss: 0.24\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 8.492676521998203e-05\n", - "Epoch: 55, Training Loss: 0.24\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 8.65784337051726e-05\n", - "Epoch: 56, Training Loss: 0.24\n", - "Updating learning rate to 8.825837107669056e-05\n", - "Epoch: 57, Training Loss: 0.24\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 8.996646776463348e-05\n", - "Epoch: 58, Training Loss: 0.24\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 9.170261236247544e-05\n", - "Epoch: 59, Training Loss: 0.24\n", - "Updating learning rate to 9.346669163433211e-05\n", - "Epoch: 60, Training Loss: 0.24\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 9.525859052234682e-05\n", - "Epoch: 61, Training Loss: 0.24\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 9.707819215419523e-05\n", - "Epoch: 62, Training Loss: 0.24\n", - "Updating learning rate to 9.892537785070724e-05\n", - "Epoch: 63, Training Loss: 0.24\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 0.00010080002713360836\n", - "Epoch: 64, Training Loss: 0.24\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 0.00010270201773337703\n", - "Epoch: 65, Training Loss: 0.24\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 0.00010463122559722004\n", - "Epoch: 66, Training Loss: 0.24\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 0.00010658752489716274\n", - "Epoch: 67, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 0.00010857078803825618\n", - "Epoch: 68, Training Loss: 0.24\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 0.00011058088566689972\n", - "Epoch: 69, Training Loss: 0.24\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 0.00011261768667927721\n", - "Epoch: 70, Training Loss: 0.24\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 0.00011468105822990782\n", - "Epoch: 71, Training Loss: 0.24\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 0.00011677086574031113\n", - "Epoch: 72, Training Loss: 0.24\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 0.00011888697290778419\n", - "Epoch: 73, Training Loss: 0.24\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 0.00012102924171429206\n", - "Epoch: 74, Training Loss: 0.24\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 0.0001231975324354691\n", - "Epoch: 75, Training Loss: 0.24\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 0.0001253917036497322\n", - "Epoch: 76, Training Loss: 0.24\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 0.00012761161224750575\n", - "Epoch: 77, Training Loss: 0.24\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.58\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.45\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.42\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.36\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.34\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.31\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.29\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.28\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.26\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.27\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.26\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.26\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.25\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.26\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.25\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 5.310453736087151e-05\n", - "Epoch: 30, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 5.401937993663639e-05\n", - "Epoch: 31, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 5.496461498523549e-05\n", - "Epoch: 32, Training Loss: 0.25\n", - "Updating learning rate to 5.5940180855970265e-05\n", - "Epoch: 33, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.694601391988759e-05\n", - "Epoch: 34, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.7982048573928125e-05\n", - "Epoch: 35, Training Loss: 0.25\n", - "Updating learning rate to 5.9048217245206655e-05\n", - "Epoch: 36, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 6.014445039541848e-05\n", - "Epoch: 37, Training Loss: 0.25\n", - "Updating learning rate to 6.127067652537466e-05\n", - "Epoch: 38, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 6.242682217966645e-05\n", - "Epoch: 39, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 6.361281195145507e-05\n", - "Epoch: 40, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 6.48285684873908e-05\n", - "Epoch: 41, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 6.607401249265779e-05\n", - "Epoch: 42, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 6.734906273614567e-05\n", - "Epoch: 43, Training Loss: 0.25\n", - "Updating learning rate to 6.865363605574823e-05\n", - "Epoch: 44, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 6.99876473637869e-05\n", - "Epoch: 45, Training Loss: 0.25\n", - "Updating learning rate to 7.135100965256086e-05\n", - "Epoch: 46, Training Loss: 0.25\n", - "Updating learning rate to 7.274363400002132e-05\n", - "Epoch: 47, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 7.4165429575572e-05\n", - "Epoch: 48, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 7.561630364599293e-05\n", - "Epoch: 49, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 7.709616158148823e-05\n", - "Epoch: 50, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 7.86049068618595e-05\n", - "Epoch: 51, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 8.014244108279958e-05\n", - "Epoch: 52, Training Loss: 0.25\n", - "Updating learning rate to 8.170866396231178e-05\n", - "Epoch: 53, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 8.330347334725013e-05\n", - "Epoch: 54, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 8.492676521998203e-05\n", - "Epoch: 55, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 8.65784337051726e-05\n", - "Epoch: 56, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 8.825837107669056e-05\n", - "Epoch: 57, Training Loss: 0.25\n", - "Updating learning rate to 8.996646776463348e-05\n", - "Epoch: 58, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 9.170261236247544e-05\n", - "Epoch: 59, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 9.346669163433211e-05\n", - "Epoch: 60, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 9.525859052234682e-05\n", - "Epoch: 61, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 9.707819215419523e-05\n", - "Epoch: 62, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 9.892537785070724e-05\n", - "Epoch: 63, Training Loss: 0.25\n", - "Updating learning rate to 0.00010080002713360836\n", - "Epoch: 64, Training Loss: 0.24\n", - "Updating learning rate to 0.00010270201773337703\n", - "Epoch: 65, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 0.00010463122559722004\n", - "Epoch: 66, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 0.00010658752489716274\n", - "Epoch: 67, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 0.00010857078803825618\n", - "Epoch: 68, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 0.00011058088566689972\n", - "Epoch: 69, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 0.00011261768667927721\n", - "Epoch: 70, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 0.00011468105822990782\n", - "Epoch: 71, Training Loss: 0.25\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 0.00011677086574031113\n", - "Epoch: 72, Training Loss: 0.25\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 0.00011888697290778419\n", - "Epoch: 73, Training Loss: 0.25\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 0.00012102924171429206\n", - "Epoch: 74, Training Loss: 0.25\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 0.0001231975324354691\n", - "Epoch: 75, Training Loss: 0.25\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 0.0001253917036497322\n", - "Epoch: 76, Training Loss: 0.25\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 0.00012761161224750575\n", - "Epoch: 77, Training Loss: 0.25\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 0.00012985711344055397\n", - "Epoch: 78, Training Loss: 0.25\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 0.00013212806077142556\n", - "Epoch: 79, Training Loss: 0.25\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.34\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.28\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.28\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.27\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.26\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.26\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.26\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.26\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.25\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.25\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.25\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.25\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.25\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.25\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.25\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.25\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 5.310453736087151e-05\n", - "Epoch: 30, Training Loss: 0.25\n", - "Updating learning rate to 5.401937993663639e-05\n", - "Epoch: 31, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.496461498523549e-05\n", - "Epoch: 32, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.5940180855970265e-05\n", - "Epoch: 33, Training Loss: 0.25\n", - "Updating learning rate to 5.694601391988759e-05\n", - "Epoch: 34, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.7982048573928125e-05\n", - "Epoch: 35, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.9048217245206655e-05\n", - "Epoch: 36, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 6.014445039541848e-05\n", - "Epoch: 37, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 6.127067652537466e-05\n", - "Epoch: 38, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 6.242682217966645e-05\n", - "Epoch: 39, Training Loss: 0.25\n", - "Updating learning rate to 6.361281195145507e-05\n", - "Epoch: 40, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 6.48285684873908e-05\n", - "Epoch: 41, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 6.607401249265779e-05\n", - "Epoch: 42, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 6.734906273614567e-05\n", - "Epoch: 43, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 6.865363605574823e-05\n", - "Epoch: 44, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 6.99876473637869e-05\n", - "Epoch: 45, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 7.135100965256086e-05\n", - "Epoch: 46, Training Loss: 0.25\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 7.274363400002132e-05\n", - "Epoch: 47, Training Loss: 0.25\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 7.4165429575572e-05\n", - "Epoch: 48, Training Loss: 0.25\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 7.561630364599293e-05\n", - "Epoch: 49, Training Loss: 0.25\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 7.709616158148823e-05\n", - "Epoch: 50, Training Loss: 0.25\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 7.86049068618595e-05\n", - "Epoch: 51, Training Loss: 0.25\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 8.014244108279958e-05\n", - "Epoch: 52, Training Loss: 0.25\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 8.170866396231178e-05\n", - "Epoch: 53, Training Loss: 0.25\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 8.330347334725013e-05\n", - "Epoch: 54, Training Loss: 0.25\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.37\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.30\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.29\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.28\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.27\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.27\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.27\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.27\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.27\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.27\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.26\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.27\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.27\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.27\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.27\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.27\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.27\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.27\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.27\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.27\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.26\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.27\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.27\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.26\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.33\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.29\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.28\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.28\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.27\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.26\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.27\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.26\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.26\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.26\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.26\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.26\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.25\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.26\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.26\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.26\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.26\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.25\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.310453736087151e-05\n", - "Epoch: 30, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.401937993663639e-05\n", - "Epoch: 31, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 5.496461498523549e-05\n", - "Epoch: 32, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 5.5940180855970265e-05\n", - "Epoch: 33, Training Loss: 0.26\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 5.694601391988759e-05\n", - "Epoch: 34, Training Loss: 0.26\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 5.7982048573928125e-05\n", - "Epoch: 35, Training Loss: 0.25\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 5.9048217245206655e-05\n", - "Epoch: 36, Training Loss: 0.26\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 6.014445039541848e-05\n", - "Epoch: 37, Training Loss: 0.26\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 6.127067652537466e-05\n", - "Epoch: 38, Training Loss: 0.26\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 6.242682217966645e-05\n", - "Epoch: 39, Training Loss: 0.26\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 6.361281195145507e-05\n", - "Epoch: 40, Training Loss: 0.26\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 6.48285684873908e-05\n", - "Epoch: 41, Training Loss: 0.26\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 6.607401249265779e-05\n", - "Epoch: 42, Training Loss: 0.26\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 6.734906273614567e-05\n", - "Epoch: 43, Training Loss: 0.26\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C02171CA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426086476560\n", - "\n", - " 0%| | 0/50 [00:00.on_destroy at 0x0000014CB2965F70>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429321452496\n", - "Exception ignored in: .on_destroy at 0x0000014CB593A4C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429205216880\n", - "Exception ignored in: .on_destroy at 0x0000014CD3745790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429230038544\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD311C9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429472958480\n", - "Exception ignored in: .on_destroy at 0x0000014CCE5DB5E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429275372880\n", - "Exception ignored in: .on_destroy at 0x0000014CCE87D550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429386795504\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014CDA32DC10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429031264880\n", - "Exception ignored in: .on_destroy at 0x0000014C51578160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1427848973456\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BFA216670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429045890480\n", - "Exception ignored in: .on_destroy at 0x0000014CC66C45E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426174886800\n", - "Exception ignored in: .on_destroy at 0x0000014CC6CC2E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429928482256\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD4D53AF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429089625456\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CC1D50430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426119540432\n", - "Exception ignored in: .on_destroy at 0x0000014C219164C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429644995536\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CDA14CC10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429708143952\n", - "Exception ignored in: .on_destroy at 0x0000014CD8DC49D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426148046192\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CCB444DC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429479979120\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014CE1AF9820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429339761232\n", - "Exception ignored in: .on_destroy at 0x0000014CB2BB8790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425259680848\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD8D341F0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429481320144\n", - "Exception ignored in: .on_destroy at 0x0000014CE3208430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426181139120\n", - "Exception ignored in: .on_destroy at 0x0000014CC9D28B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429741727888\n", - "Exception ignored in: .on_destroy at 0x0000014CE330B3A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429743396496\n", - "Exception ignored in: .on_destroy at 0x0000014CE359EEE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429743851920\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD62CB550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429753785776\n", - "Exception ignored in: .on_destroy at 0x0000014CC174E5E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429747130256\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CE3CFA940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429129641680\n", - "Exception ignored in: .on_destroy at 0x0000014CE20333A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429739575696\n", - "Exception ignored in: .on_destroy at 0x0000014CE1E7FE50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429227934032\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C019915E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429750102224\n", - "Exception ignored in: .on_destroy at 0x0000014CBA2118B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426182887728\n", - "Exception ignored in: .on_destroy at 0x0000014CE251D8B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429485205232\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CE26CC0D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429640291504\n", - "Exception ignored in: .on_destroy at 0x0000014C01DB9D30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429731734800\n", - "Exception ignored in: .on_destroy at 0x0000014BE61B2310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425826503152\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CE2DAFE50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429736547312\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014CD4A37280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429783543120\n", - "Exception ignored in: .on_destroy at 0x0000014CE5D2BEE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429784818000\n", - "Exception ignored in: .on_destroy at 0x0000014CD60BA5E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429041275216\n", - "Exception ignored in: .on_destroy at 0x0000014BE6048040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429788074128\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 14:49:17,843 - IndustrialDispatcher - 12 individuals out of 13 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CE24FC9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429650407024\n", - "Exception ignored in: .on_destroy at 0x0000014CD4754430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429651673808\n", - "Exception ignored in: .on_destroy at 0x0000014C0668D0D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426530801584\n", - "Exception ignored in: .on_destroy at 0x0000014CD4466790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429647560112\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD4524280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429646582672\n", - "Exception ignored in: .on_destroy at 0x0000014CE6355D30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429725434288\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C2118FD30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429640779024\n", - "Exception ignored in: .on_destroy at 0x0000014CE1AB2310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429796956848\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CE66E0CA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429727898576\n", - "Exception ignored in: .on_destroy at 0x0000014CE66E0C10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429567927856\n", - "Exception ignored in: .on_destroy at 0x0000014CD305A790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429799968816\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014CE3068550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429838211472\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.38\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.33\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.26\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.26\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.25\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.25\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.24\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.24\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.24\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.24\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.24\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.24\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.24\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.25\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.24\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.24\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.25\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.24\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.24\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.24\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.24\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.24\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.42\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.34\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.29\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.27\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.26\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.25\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.25\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.25\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.25\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.25\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.24\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.25\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.25\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 5.310453736087151e-05\n", - "Epoch: 30, Training Loss: 0.25\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 5.401937993663639e-05\n", - "Epoch: 31, Training Loss: 0.25\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 5.496461498523549e-05\n", - "Epoch: 32, Training Loss: 0.25\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 5.5940180855970265e-05\n", - "Epoch: 33, Training Loss: 0.25\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 5.694601391988759e-05\n", - "Epoch: 34, Training Loss: 0.25\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 5.7982048573928125e-05\n", - "Epoch: 35, Training Loss: 0.25\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 5.9048217245206655e-05\n", - "Epoch: 36, Training Loss: 0.25\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 6.014445039541848e-05\n", - "Epoch: 37, Training Loss: 0.24\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 6.127067652537466e-05\n", - "Epoch: 38, Training Loss: 0.25\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.51\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.42\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.35\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.32\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.28\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.26\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.26\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.25\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.25\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.25\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.27\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.25\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.25\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.25\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.25\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.25\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.25\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.25\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.25\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.25\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.25\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.69\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.57\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.52\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.43\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.38\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.34\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.29\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.30\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.27\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.29\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.27\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.27\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.27\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.26\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.26\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.27\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.26\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.26\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.26\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.26\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.26\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.26\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.26\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.26\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 5.310453736087151e-05\n", - "Epoch: 30, Training Loss: 0.26\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 5.401937993663639e-05\n", - "Epoch: 31, Training Loss: 0.26\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 5.496461498523549e-05\n", - "Epoch: 32, Training Loss: 0.26\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 5.5940180855970265e-05\n", - "Epoch: 33, Training Loss: 0.26\n", - "Updating learning rate to 5.694601391988759e-05\n", - "Epoch: 34, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 5.7982048573928125e-05\n", - "Epoch: 35, Training Loss: 0.27\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 5.9048217245206655e-05\n", - "Epoch: 36, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 6.014445039541848e-05\n", - "Epoch: 37, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 6.127067652537466e-05\n", - "Epoch: 38, Training Loss: 0.26\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 6.242682217966645e-05\n", - "Epoch: 39, Training Loss: 0.27\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 6.361281195145507e-05\n", - "Epoch: 40, Training Loss: 0.26\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 6.48285684873908e-05\n", - "Epoch: 41, Training Loss: 0.27\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 6.607401249265779e-05\n", - "Epoch: 42, Training Loss: 0.26\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 6.734906273614567e-05\n", - "Epoch: 43, Training Loss: 0.26\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 6.865363605574823e-05\n", - "Epoch: 44, Training Loss: 0.26\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 6.99876473637869e-05\n", - "Epoch: 45, Training Loss: 0.26\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 7.135100965256086e-05\n", - "Epoch: 46, Training Loss: 0.27\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 7.274363400002132e-05\n", - "Epoch: 47, Training Loss: 0.26\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 7.4165429575572e-05\n", - "Epoch: 48, Training Loss: 0.26\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n", - "Epoch: None, Batch Size: 16, Activation_function: ReLU\n", - "Not enough class samples for valudation\n", - "Epoch: 1, Training Loss: 0.44\n", - "Updating learning rate to 4.0015653426700784e-05\n", - "Epoch: 2, Training Loss: 0.37\n", - "Updating learning rate to 4.006261268584585e-05\n", - "Epoch: 3, Training Loss: 0.31\n", - "Updating learning rate to 4.0140874714629645e-05\n", - "Epoch: 4, Training Loss: 0.28\n", - "Updating learning rate to 4.025043440859821e-05\n", - "Epoch: 5, Training Loss: 0.27\n", - "Updating learning rate to 4.039128462198242e-05\n", - "Epoch: 6, Training Loss: 0.27\n", - "Updating learning rate to 4.056341616816338e-05\n", - "Epoch: 7, Training Loss: 0.26\n", - "Updating learning rate to 4.0766817820272246e-05\n", - "Epoch: 8, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.1001476311922124e-05\n", - "Epoch: 9, Training Loss: 0.26\n", - "Updating learning rate to 4.126737633807355e-05\n", - "Epoch: 10, Training Loss: 0.25\n", - "Updating learning rate to 4.156450055603218e-05\n", - "Epoch: 11, Training Loss: 0.26\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.1892829586580933e-05\n", - "Epoch: 12, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.225234201524295e-05\n", - "Epoch: 13, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.2643014393678935e-05\n", - "Epoch: 14, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.306482124121608e-05\n", - "Epoch: 15, Training Loss: 0.26\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.351773504651061e-05\n", - "Epoch: 16, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.400172626934122e-05\n", - "Epoch: 17, Training Loss: 0.25\n", - "Updating learning rate to 4.451676334253673e-05\n", - "Epoch: 18, Training Loss: 0.25\n", - "Updating learning rate to 4.50628126740342e-05\n", - "Epoch: 19, Training Loss: 0.25\n", - "EarlyStopping counter: 1 out of 15\n", - "Updating learning rate to 4.563983864907021e-05\n", - "Epoch: 20, Training Loss: 0.26\n", - "EarlyStopping counter: 2 out of 15\n", - "Updating learning rate to 4.624780363250431e-05\n", - "Epoch: 21, Training Loss: 0.26\n", - "EarlyStopping counter: 3 out of 15\n", - "Updating learning rate to 4.688666797127237e-05\n", - "Epoch: 22, Training Loss: 0.26\n", - "EarlyStopping counter: 4 out of 15\n", - "Updating learning rate to 4.75563899969742e-05\n", - "Epoch: 23, Training Loss: 0.26\n", - "EarlyStopping counter: 5 out of 15\n", - "Updating learning rate to 4.825692602859028e-05\n", - "Epoch: 24, Training Loss: 0.25\n", - "EarlyStopping counter: 6 out of 15\n", - "Updating learning rate to 4.8988230375331576e-05\n", - "Epoch: 25, Training Loss: 0.26\n", - "EarlyStopping counter: 7 out of 15\n", - "Updating learning rate to 4.9750255339618796e-05\n", - "Epoch: 26, Training Loss: 0.26\n", - "EarlyStopping counter: 8 out of 15\n", - "Updating learning rate to 5.0542951220193966e-05\n", - "Epoch: 27, Training Loss: 0.26\n", - "EarlyStopping counter: 9 out of 15\n", - "Updating learning rate to 5.136626631536186e-05\n", - "Epoch: 28, Training Loss: 0.25\n", - "EarlyStopping counter: 10 out of 15\n", - "Updating learning rate to 5.222014692636209e-05\n", - "Epoch: 29, Training Loss: 0.25\n", - "EarlyStopping counter: 11 out of 15\n", - "Updating learning rate to 5.310453736087151e-05\n", - "Epoch: 30, Training Loss: 0.26\n", - "EarlyStopping counter: 12 out of 15\n", - "Updating learning rate to 5.401937993663639e-05\n", - "Epoch: 31, Training Loss: 0.26\n", - "EarlyStopping counter: 13 out of 15\n", - "Updating learning rate to 5.496461498523549e-05\n", - "Epoch: 32, Training Loss: 0.26\n", - "EarlyStopping counter: 14 out of 15\n", - "Updating learning rate to 5.5940180855970265e-05\n", - "Epoch: 33, Training Loss: 0.25\n", - "EarlyStopping counter: 15 out of 15\n", - "Early stopping\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CECD01AF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426084606576\n", - "Exception ignored in: .on_destroy at 0x0000014CECD22550>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429911454032\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C217D60D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1428798822800\n", - "Exception ignored in: .on_destroy at 0x0000014CEE003280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429566532368\n", - "Exception ignored in: .on_destroy at 0x0000014C217D6E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429276228752\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CEDFCDD30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429772521264\n", - "Exception ignored in: .on_destroy at 0x0000014C21B7A280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429766866480\n", - "Exception ignored in: .on_destroy at 0x0000014CB593A940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429765831056\n", - "Exception ignored in: .on_destroy at 0x0000014C161AFDC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429914042064\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CE3B1CDC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429912428432\n", - "Exception ignored in: .on_destroy at 0x0000014CEDF32820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426174458736\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014CEE08CA60>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429922263568\n", - "Exception ignored in: .on_destroy at 0x0000014CE2B170D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429901631088\n", - "Exception ignored in: .on_destroy at 0x0000014CE2F12700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426147106128\n", - "Exception ignored in: .on_destroy at 0x0000014C1B52EC10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429738865552\n", - "Exception ignored in: .on_destroy at 0x0000014CBEA88EE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429765831056\n", - "Exception ignored in: .on_destroy at 0x0000014C088999D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429739669904\n", - "Exception ignored in: .on_destroy at 0x0000014BE60DD8B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425922506640\n", - "Exception ignored in: .on_destroy at 0x0000014CED845EE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426182657488\n", - "Exception ignored in: .on_destroy at 0x0000014CBECEA700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429741664464\n", - "Exception ignored in: .on_destroy at 0x0000014CBCAFB790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429778612944\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CBCE64AF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429483147312\n", - "Exception ignored in: .on_destroy at 0x0000014BEE36A5E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429780405584\n", - "Exception ignored in: .on_destroy at 0x0000014CEDA8C670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429092568496\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BFF69F4C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1424084024080\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CC6E63F70>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429592845648\n", - "\n", - " 0%| | 0/200 [00:00 strategy for isolation_forest_reg\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C8AFE3430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429773202320\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BFF614160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429837401616\n", - "Exception ignored in: .on_destroy at 0x0000014C81C0AC10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429735395472\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C95F188B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426458403728\n", - "Exception ignored in: .on_destroy at 0x0000014C11F36160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429915068240\n", - "Exception ignored in: .on_destroy at 0x0000014CAB746040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429567212112\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C22300280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425348924496\n", - "Exception ignored in: .on_destroy at 0x0000014C22300280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426780572240\n", - "Exception ignored in: .on_destroy at 0x0000014C067DBDC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429126528816\n", - "Exception ignored in: .on_destroy at 0x0000014CB202D4C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429772084400\n", - "Exception ignored in: .on_destroy at 0x0000014CD3035E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429303572016\n", - "Exception ignored in: .on_destroy at 0x0000014CD2D67F70>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429901352272\n", - "Exception ignored in: .on_destroy at 0x0000014C1643C700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429780167344\n", - "Exception ignored in: .on_destroy at 0x0000014CD2BD7160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429779469104\n", - "Exception ignored in: .on_destroy at 0x0000014CE5534DC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429738254224\n", - "Exception ignored in: .on_destroy at 0x0000014CB515DC10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429730087728\n", - "Exception ignored in: .on_destroy at 0x0000014CBFF12AF0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426504759984\n", - "Exception ignored in: .on_destroy at 0x0000014CD2BB1820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429268594736\n", - "Exception ignored in: .on_destroy at 0x0000014C08E5CB80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1424929858736\n", - "Exception ignored in: .on_destroy at 0x0000014BFA43F3A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429907949488\n", - "Exception ignored in: .on_destroy at 0x0000014BEE488700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429037023952\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 14:57:29,060 - DataOperation - Can not find evaluation strategy because of Impossible to obtain strategy for isolation_forest_reg\n", - "2024-01-18 14:57:35,846 - IndustrialDispatcher - 8 individuals out of 11 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Exception ignored in: .on_destroy at 0x0000014CD4D48B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429927402544\n", - "Exception ignored in: .on_destroy at 0x0000014CB95244C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429753766800\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD4EF7280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429755819280\n", - "Exception ignored in: .on_destroy at 0x0000014C10CB59D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429100366704\n", - "Exception ignored in: .on_destroy at 0x0000014C0ECB6670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429737939472\n", - "Exception ignored in: .on_destroy at 0x0000014CC2E17940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429466754288\n", - "Exception ignored in: .on_destroy at 0x0000014CC17C1820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425954442672\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BDD756B80>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429910254480\n", - "Exception ignored in: .on_destroy at 0x0000014C08D4C040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429775798992\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD3CDBF70>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426180913168\n", - "Exception ignored in: .on_destroy at 0x0000014C087FA4C0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429775470096\n", - "\n", - " 0%| | 0/50 [00:00.on_destroy at 0x0000014CC00F4E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429035103600\n", - "Exception ignored in: .on_destroy at 0x0000014CC0112CA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429128405136\n", - "\n", - " 0%| | 0/50 [00:00.on_destroy at 0x0000014CC003BEE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429299881040\n", - "Exception ignored in: .on_destroy at 0x0000014CD2F3E790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425628717488\n", - "Exception ignored in: .on_destroy at 0x0000014CD2BD48B0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429089396272\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CBA25A940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429333310032\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014BEE574D30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429088015632\n", - "Exception ignored in: .on_destroy at 0x0000014CC8D8F040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1428919727376\n", - "\n", - " 0%| | 0/49 [00:00.on_destroy at 0x0000014C09397E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429473955728\n", - "Exception ignored in: .on_destroy at 0x0000014C0F228790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429092058960\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014BAEE0F430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429489531376\n", - "Exception ignored in: .on_destroy at 0x0000014C08F92820>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425633166608\n", - "\n", - " 0%| | 0/50 [00:00.on_destroy at 0x0000014C08D7C940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425965696944\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CCE3C5670>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426677850544\n", - "Exception ignored in: .on_destroy at 0x0000014C09102D30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429036115888\n", - "Exception ignored in: .on_destroy at 0x0000014CCB89D790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429035191536\n", - "\n", - " 0%| | 0/50 [00:00.on_destroy at 0x0000014CE4C9F700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1427206403920\n", - "Exception ignored in: .on_destroy at 0x0000014CDD3F6940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426461991632\n", - "Exception ignored in: .on_destroy at 0x0000014CCEA8AC10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429296829808\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014CCDC31790>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429794835120\n", - "Exception ignored in: .on_destroy at 0x0000014CD3D4C700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429751362352\n", - "Exception ignored in: .on_destroy at 0x0000014C0D470310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429928884240\n", - "Exception ignored in: .on_destroy at 0x0000014C104F9160>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429033437392\n", - "\n", - " 0%| | 0/49 [00:00.on_destroy at 0x0000014CC360F3A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429149688240\n", - "Exception ignored in: .on_destroy at 0x0000014CCEC48280>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429225941968\n", - "Exception ignored in: .on_destroy at 0x0000014CD29989D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429339659888\n", - "Exception ignored in: .on_destroy at 0x0000014CBE34D0D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429087942480\n", - "Exception ignored in: .on_destroy at 0x0000014CD384C9D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1424181796656\n", - "Exception ignored in: .on_destroy at 0x0000014B8F020310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429098634672\n", - "Exception ignored in: .on_destroy at 0x0000014BA88B60D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426456887632\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CCC7F21F0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429299647568\n", - "Exception ignored in: .on_destroy at 0x0000014CC920EDC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429237975056\n", - "Exception ignored in: .on_destroy at 0x0000014C083DAC10>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426063664112\n", - "Exception ignored in: .on_destroy at 0x0000014CE466BD30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426207889136\n", - "Exception ignored in: .on_destroy at 0x0000014B7F295A60>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425342515504\n", - "Exception ignored in: .on_destroy at 0x0000014C22442310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429042296752\n", - "Exception ignored in: .on_destroy at 0x0000014CDBD6E3A0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426677850832\n", - "Exception ignored in: .on_destroy at 0x0000014CDBE0B700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429715974544\n", - "Exception ignored in: .on_destroy at 0x0000014CCF6E49D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1428805351920\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014C01895310>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425955250608\n", - "Exception ignored in: .on_destroy at 0x0000014CB93D9EE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429397632560\n", - "Exception ignored in: .on_destroy at 0x0000014CC8EC5040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429745793040\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CDBE0BD30>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429834881136\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CDBE06040>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429030497232\n", - "Exception ignored in: .on_destroy at 0x0000014CD4EA1940>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429567336688\n", - "\n", - " 0%| | 0/199 [00:00.on_destroy at 0x0000014CD4AC6CA0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429031259376\n", - "Exception ignored in: .on_destroy at 0x0000014C086A15E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425921262960\n", - "Exception ignored in: .on_destroy at 0x0000014CD8DC4430>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1425959862320\n", - "\n", - " 0%| | 0/200 [00:00.on_destroy at 0x0000014C08928700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429463373648\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 15:03:31,468 - IndustrialDispatcher - 15 individuals out of 16 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Exception ignored in: .on_destroy at 0x0000014CDA225700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426208354640\n", - "Exception ignored in: .on_destroy at 0x0000014CE49270D0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1426481369424\n", - "Exception ignored in: .on_destroy at 0x0000014CB29655E0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429766336400\n", - "Exception ignored in: .on_destroy at 0x0000014C210E3F70>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429044401328\n", - "Exception ignored in: .on_destroy at 0x0000014CE1771EE0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429796201840\n", - "Exception ignored in: .on_destroy at 0x0000014CCE9A4DC0>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429278224080\n", - "Exception ignored in: .on_destroy at 0x0000014CB5152700>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429588752304\n", - "Exception ignored in: .on_destroy at 0x0000014CDD6F0E50>\n", - "Traceback (most recent call last):\n", - " File \"D:\\WORK\\Repo\\Industiral\\IndustrialTS\\venv_3.9_new\\lib\\site-packages\\joblib\\_dask.py\", line 82, in on_destroy\n", - " del self._data[key]\n", - "KeyError: 1429477001808\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 15:03:58,529 - IndustrialDispatcher - 4 individuals out of 6 in previous population were evaluated successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 60%|██████ | 3/5 [17:03<10:42, 321.17s/gen]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 15:03:58,706 - GroupedCondition - Optimisation stopped: Time limit is reached\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 60%|██████ | 3/5 [17:03<11:22, 341.18s/gen]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 15:03:59,846 - ApiComposer - Model generation finished\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 249/249 [00:01<00:00, 182.36it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-18 15:04:04,053 - FEDOT logger - Final pipeline was fitted\n", - "2024-01-18 15:04:04,054 - FEDOT logger - Final pipeline: {'depth': 3, 'length': 3, 'nodes': [treg, quantile_extractor, wavelet_basis]}\n", - "treg - {}\n", - "quantile_extractor - {}\n", - "wavelet_basis - {}\n", - "2024-01-18 15:04:04,055 - MemoryAnalytics - Memory consumption for finish in main session: current 386.5 MiB, max: 397.1 MiB\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 107/107 [00:00<00:00, 351.72it/s]\n" - ] + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from cases.utils import evaluate_automl\n", - "metric_dict, model_list = evaluate_automl(experiment_setup, train_data, test_data, runs=3)" + "industrial_auto_model.solver.history.show.operations_kde(dpi=100)" ], "metadata": { "collapsed": false, @@ -10448,51 +1892,43 @@ } }, { - "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": " r2_score: mean_squared_error: root_mean_squared_error: \\\nrun_number - 1 0.268736 0.052628 0.229408 \nrun_number - 2 0.224043 0.055845 0.236315 \nrun_number - 0 0.160466 0.060420 0.245805 \n\n mean_absolute_error median_absolute_error \\\nrun_number - 1 0.173684 0.130673 \nrun_number - 2 0.179636 0.141167 \nrun_number - 0 0.181926 0.143165 \n\n explained_variance_score max_error d2_absolute_error_score \nrun_number - 1 0.288242 0.719002 0.184483 \nrun_number - 2 0.239848 0.673758 0.156537 \nrun_number - 0 0.183769 0.827246 0.145786 ", - "text/html": "
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r2_score:mean_squared_error:root_mean_squared_error:mean_absolute_errormedian_absolute_errorexplained_variance_scoremax_errord2_absolute_error_score
run_number - 10.2687360.0526280.2294080.1736840.1306730.2882420.7190020.184483
run_number - 20.2240430.0558450.2363150.1796360.1411670.2398480.6737580.156537
run_number - 00.1604660.0604200.2458050.1819260.1431650.1837690.8272460.145786
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" - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", "source": [ - "df_automl = pd.concat([x for x in metric_dict.values()],axis=0).T\n", - "df_automl.columns = list(metric_dict.keys())\n", - "df_automl = df_automl.T\n", - "df_automl.sort_values('root_mean_squared_error:')" + "## Compare with State of Art (SOTA) models" ], "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } } }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 17, + "outputs": [], "source": [ - "## Compare with State of Art (SOTA) models" + "df = pd.read_csv(data_path+'/ts_regression_sota_results.csv',sep=';')\n", + "df = df[df['ds/type'] == dataset_name].iloc[:,:25]\n", + "df.index = df['algorithm']\n", + "df = df.drop(['algorithm','ds/type'], axis=1)\n", + "df = df.replace(',','.', regex=True).astype(float)" ], "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } } }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "outputs": [], "source": [ - "from cases.utils import sota_compare\n", - "df = sota_compare(data_path,dataset_name, best_baseline,best_tuned,df_automl)" + "df['Fedot_Industrial_tuned'] = metrics['rmse'][0]\n", + "df['Fedot_Industrial_AutoML'] = auto_metrics['rmse'][0]\n", + "df = df.T" ], "metadata": { "collapsed": false, @@ -10503,13 +1939,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "outputs": [ { "data": { - "text/plain": "FreshPRINCE_RMSE 0.180515\nDrCIF_RMSE 0.180643\nRotF_RMSE 0.187496\nRandF_RMSE 0.193914\nFPCR_RMSE 0.195632\nTSF_RMSE 0.200847\nFPCR-Bs_RMSE 0.201391\n5NN-DTW_RMSE 0.205914\nRDST_RMSE 0.208743\nRidge_RMSE 0.213185\n5NN-ED_RMSE 0.215282\nRIST_RMSE 0.222417\nFCN_RMSE 0.222711\nXGBoost_RMSE 0.224208\nResNet_RMSE 0.224795\nInceptionT_RMSE 0.224887\nMultiROCKET_RMSE 0.224921\nSingleInception_RMSE 0.226110\nGrid-SVR_RMSE 0.227387\nFedot_Industrial_baseline 0.227568\nFedot_Industrial_tuned 0.227568\nFedot_Industrial_AutoML 0.229408\nCNN_RMSE 0.234338\n1NN-DTW_RMSE 0.255744\nROCKET_RMSE 0.257576\n1NN-ED_RMSE 0.267144\nName: min, dtype: float64" + "text/plain": "FreshPRINCE_RMSE 0.180515\nDrCIF_RMSE 0.180643\nRotF_RMSE 0.187496\nRandF_RMSE 0.193914\nFPCR_RMSE 0.195632\nTSF_RMSE 0.200847\nFPCR-Bs_RMSE 0.201391\n5NN-DTW_RMSE 0.205914\nRDST_RMSE 0.208743\nRidge_RMSE 0.213185\n5NN-ED_RMSE 0.215282\nRIST_RMSE 0.222417\nFCN_RMSE 0.222711\nXGBoost_RMSE 0.224208\nResNet_RMSE 0.224795\nInceptionT_RMSE 0.224887\nMultiROCKET_RMSE 0.224921\nFedot_Industrial_AutoML 0.226000\nSingleInception_RMSE 0.226110\nGrid-SVR_RMSE 0.227387\nFedot_Industrial_tuned 0.230000\nCNN_RMSE 0.234338\n1NN-DTW_RMSE 0.255744\nROCKET_RMSE 0.257576\n1NN-ED_RMSE 0.267144\nName: min, dtype: float64" }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -10526,13 +1962,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "outputs": [ { "data": { - "text/plain": "Fedot_Industrial_baseline 0.227568\nFedot_Industrial_tuned 0.227568\nFedot_Industrial_AutoML 0.245805\nDrCIF_RMSE 0.256310\nRIST_RMSE 0.256706\nRandF_RMSE 0.257411\nRDST_RMSE 0.257594\nFreshPRINCE_RMSE 0.260502\nRotF_RMSE 0.263084\n5NN-ED_RMSE 0.264245\nFPCR_RMSE 0.268228\n5NN-DTW_RMSE 0.268344\nTSF_RMSE 0.269265\nFPCR-Bs_RMSE 0.276976\nXGBoost_RMSE 0.281631\nInceptionT_RMSE 0.288174\nGrid-SVR_RMSE 0.290551\nMultiROCKET_RMSE 0.291900\nFCN_RMSE 0.295570\nSingleInception_RMSE 0.296679\nResNet_RMSE 0.302419\nRidge_RMSE 0.314523\nROCKET_RMSE 0.319622\nCNN_RMSE 0.348164\n1NN-ED_RMSE 0.427555\n1NN-DTW_RMSE 0.435497\nName: max, dtype: float64" + "text/plain": "Fedot_Industrial_AutoML 0.226000\nFedot_Industrial_tuned 0.230000\nDrCIF_RMSE 0.256310\nRIST_RMSE 0.256706\nRandF_RMSE 0.257411\nRDST_RMSE 0.257594\nFreshPRINCE_RMSE 0.260502\nRotF_RMSE 0.263084\n5NN-ED_RMSE 0.264245\nFPCR_RMSE 0.268228\n5NN-DTW_RMSE 0.268344\nTSF_RMSE 0.269265\nFPCR-Bs_RMSE 0.276976\nXGBoost_RMSE 0.281631\nInceptionT_RMSE 0.288174\nGrid-SVR_RMSE 0.290551\nMultiROCKET_RMSE 0.291900\nFCN_RMSE 0.295570\nSingleInception_RMSE 0.296679\nResNet_RMSE 0.302419\nRidge_RMSE 0.314523\nROCKET_RMSE 0.319622\nCNN_RMSE 0.348164\n1NN-ED_RMSE 0.427555\n1NN-DTW_RMSE 0.435497\nName: max, dtype: float64" }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -10549,13 +1985,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "outputs": [ { "data": { - "text/plain": "DrCIF_RMSE 0.223448\nFreshPRINCE_RMSE 0.225876\nFedot_Industrial_baseline 0.227568\nFedot_Industrial_tuned 0.227568\nRotF_RMSE 0.229423\nFPCR_RMSE 0.231336\nRandF_RMSE 0.233020\nTSF_RMSE 0.234477\nFPCR-Bs_RMSE 0.235169\nFedot_Industrial_AutoML 0.237176\nRDST_RMSE 0.239879\nRIST_RMSE 0.241129\n5NN-DTW_RMSE 0.241217\n5NN-ED_RMSE 0.246046\nMultiROCKET_RMSE 0.249028\nInceptionT_RMSE 0.251960\nXGBoost_RMSE 0.252351\nFCN_RMSE 0.252702\nResNet_RMSE 0.255854\nGrid-SVR_RMSE 0.257539\nSingleInception_RMSE 0.257753\nRidge_RMSE 0.262168\nCNN_RMSE 0.271610\nROCKET_RMSE 0.287077\n1NN-DTW_RMSE 0.318791\n1NN-ED_RMSE 0.333878\nName: average, dtype: float64" + "text/plain": "DrCIF_RMSE 0.223448\nFreshPRINCE_RMSE 0.225876\nFedot_Industrial_AutoML 0.226000\nRotF_RMSE 0.229423\nFedot_Industrial_tuned 0.230000\nFPCR_RMSE 0.231336\nRandF_RMSE 0.233020\nTSF_RMSE 0.234477\nFPCR-Bs_RMSE 0.235169\nRDST_RMSE 0.239879\nRIST_RMSE 0.241129\n5NN-DTW_RMSE 0.241217\n5NN-ED_RMSE 0.246046\nMultiROCKET_RMSE 0.249028\nInceptionT_RMSE 0.251960\nXGBoost_RMSE 0.252351\nFCN_RMSE 0.252702\nResNet_RMSE 0.255854\nGrid-SVR_RMSE 0.257539\nSingleInception_RMSE 0.257753\nRidge_RMSE 0.262168\nCNN_RMSE 0.271610\nROCKET_RMSE 0.287077\n1NN-DTW_RMSE 0.318791\n1NN-ED_RMSE 0.333878\nName: average, dtype: float64" }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } diff --git a/fedot_ind/api/main.py b/fedot_ind/api/main.py index 351434d61..b1757f315 100644 --- a/fedot_ind/api/main.py +++ b/fedot_ind/api/main.py @@ -302,6 +302,7 @@ def finetune(self, tuned_metric = abs(pipeline_tuner.obtained_metric) self.solver = model_to_tune + def get_metrics(self, target: Union[list, np.array] = None, metric_names: tuple = ('f1', 'roc_auc', 'accuracy'), diff --git a/fedot_ind/core/repository/model_repository.py b/fedot_ind/core/repository/model_repository.py index 36c4b13e5..f2e0d8c37 100644 --- a/fedot_ind/core/repository/model_repository.py +++ b/fedot_ind/core/repository/model_repository.py @@ -86,6 +86,8 @@ # isolation_forest forest 'isolation_forest_class': IsolationForestClassImplementation, 'isolation_forest_reg': IsolationForestRegImplementation, + 'chronos_extractor': ChronosExtractor, + 'riemann_extractor': RiemannExtractor, }, 'SKLEARN_REG_MODELS': { 'gbr': GradientBoostingRegressor,