From 81c6ed2392c95a8db949f747caf5fcdf2a599046 Mon Sep 17 00:00:00 2001 From: Jiadai Zhao Date: Fri, 15 Feb 2019 23:04:42 -0800 Subject: [PATCH] Addressing Large Hadron Collider Challenges by Machine Learning Week2 --- .gitignore | 4 + .../Week2/Particle identification quiz.pdf | Bin 0 -> 682155 bytes .../Week2/Particle_identification.ipynb | 1621 +++++++++ .../Week2/README.md | 12 + .../Week2/pic/features.jpeg | Bin 0 -> 466716 bytes .../Week2/pic/pid.jpg | Bin 0 -> 55373 bytes .../Week2/utils.py | 228 ++ .../recap_ml-checkpoint.ipynb | 3051 ----------------- .../recap_tensorflow-checkpoint.ipynb | 1204 ------- 9 files changed, 1865 insertions(+), 4255 deletions(-) create mode 100644 Addressing Large Hadron Collider Challenges by Machine Learning/Week2/Particle identification quiz.pdf create mode 100644 Addressing Large Hadron Collider Challenges by Machine Learning/Week2/Particle_identification.ipynb create mode 100644 Addressing Large Hadron Collider Challenges by Machine Learning/Week2/README.md create mode 100644 Addressing Large Hadron Collider Challenges by Machine Learning/Week2/pic/features.jpeg create mode 100644 Addressing Large Hadron Collider Challenges by Machine Learning/Week2/pic/pid.jpg create mode 100644 Addressing Large Hadron Collider Challenges by Machine Learning/Week2/utils.py delete mode 100644 Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_ml-checkpoint.ipynb delete mode 100644 Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_tensorflow-checkpoint.ipynb diff --git a/.gitignore b/.gitignore index 9c58c52..0087e0a 100644 --- a/.gitignore +++ b/.gitignore @@ -36,3 +36,7 @@ Natural Language Processing/Week3/StarSpace_embeddings *.sqlite3 *.pkl Addressing Large Hadron Collider Challenges by Machine Learning/Week1/week1_slides.pdf +Addressing Large Hadron Collider Challenges by Machine Learning/Week2/Particle_identification_87_2.ipynb +Addressing Large Hadron Collider Challenges 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There are six particle types: electron, proton, muon, kaon, pion and ghost. Ghost is a particle with other type than the first five or a detector noise. \n", + "\n", + "Different particle types remain different responses in the detector systems or subdetectors. Thre are five systems: tracking system, ring imaging Cherenkov detector (RICH), electromagnetic and hadron calorimeters, and muon system.\n", + "\n", + "![pid](pic/pid.jpg)\n", + "\n", + "You task is to identify a particle type using the responses in the detector systems. \n", + "\n", + "# Attention\n", + "\n", + "Data files you should download from https://github.com/hse-aml/hadron-collider-machine-learning/releases/tag/Week_2" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas\n", + "import numpy\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import log_loss\n", + "import utils" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download data\n", + "\n", + "Download data used to train classifiers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read training file" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pandas.read_csv('training.csv.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TrackPTrackNDoFSubdetector2BremDLLbeElectronMuonLooseFlagFlagSpdSpdEEcalDLLbeElectronDLLmuonRICHpFlagElectronEcalDLLbeMuon...TrackNDoFRICHpFlagMuonRICH_DLLbeKaonRICH_DLLbeElectronHcalEMuonFlagFlagMuonPrsERICH_DLLbeMuonRICH_DLLbeProton
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" + ], + "text/plain": [ + " TrackP TrackNDoFSubdetector2 BremDLLbeElectron MuonLooseFlag \\\n", + "0 74791.156263 15.0 0.232275 1.0 \n", + "1 2738.489989 15.0 -0.357748 0.0 \n", + "2 2161.409908 17.0 -999.000000 0.0 \n", + "3 15277.730490 20.0 -0.638984 0.0 \n", + "4 7563.700195 19.0 -0.638962 0.0 \n", + "\n", + " FlagSpd SpdE EcalDLLbeElectron DLLmuon RICHpFlagElectron \\\n", + "0 1.0 3.2 -2.505719 6.604153 1.0 \n", + "1 1.0 3.2 1.864351 0.263651 1.0 \n", + "2 0.0 -999.0 -999.000000 -999.000000 0.0 \n", + "3 1.0 3.2 -2.533918 -8.724949 1.0 \n", + "4 1.0 3.2 -2.087146 -7.060422 1.0 \n", + "\n", + " EcalDLLbeMuon ... TrackNDoF RICHpFlagMuon RICH_DLLbeKaon \\\n", + "0 1.929960 ... 28.0 1.0 -7.213300 \n", + "1 -2.061959 ... 32.0 1.0 -0.324317 \n", + "2 -999.000000 ... 27.0 0.0 -999.000000 \n", + "3 -3.253981 ... 36.0 1.0 -35.202221 \n", + "4 -0.995816 ... 33.0 1.0 25.084287 \n", + "\n", + " RICH_DLLbeElectron HcalE MuonFlag FlagMuon PrsE \\\n", + "0 -0.280200 5586.589846 1.0 1.0 10.422315 \n", + "1 1.707283 -0.000007 0.0 1.0 43.334935 \n", + "2 -999.000000 -999.000000 0.0 0.0 -999.000000 \n", + "3 -14.742319 4482.803707 0.0 1.0 2.194175 \n", + "4 -10.272412 5107.554680 0.0 1.0 0.000015 \n", + "\n", + " RICH_DLLbeMuon RICH_DLLbeProton \n", + "0 -2.081143e-07 -24.824400 \n", + "1 2.771583e+00 -0.648017 \n", + "2 -9.990000e+02 -999.000000 \n", + "3 -3.070819e+00 -29.291519 \n", + "4 -5.373712e+00 23.653087 \n", + "\n", + "[5 rows x 50 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List of columns in the samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, **Spd** stands for Scintillating Pad Detector, **Prs** - Preshower, **Ecal** - electromagnetic calorimeter, **Hcal** - hadronic calorimeter, **Brem** denotes traces of the particles that were deflected by detector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- ID - id value for tracks (presents only in the test file for the submitting purposes)\n", + "- Label - string valued observable denoting particle types. Can take values \"Electron\", \"Muon\", \"Kaon\", \"Proton\", \"Pion\" and \"Ghost\". This column is absent in the test file.\n", + "- FlagSpd - flag (0 or 1), if reconstructed track passes through Spd\n", + "- FlagPrs - flag (0 or 1), if reconstructed track passes through Prs\n", + "- FlagBrem - flag (0 or 1), if reconstructed track passes through Brem\n", + "- FlagEcal - flag (0 or 1), if reconstructed track passes through Ecal\n", + "- FlagHcal - flag (0 or 1), if reconstructed track passes through Hcal\n", + "- FlagRICH1 - flag (0 or 1), if reconstructed track passes through the first RICH detector\n", + "- FlagRICH2 - flag (0 or 1), if reconstructed track passes through the second RICH detector\n", + "- FlagMuon - flag (0 or 1), if reconstructed track passes through muon stations (Muon)\n", + "- SpdE - energy deposit associated to the track in the Spd\n", + "- PrsE - energy deposit associated to the track in the Prs\n", + "- EcalE - energy deposit associated to the track in the Hcal\n", + "- HcalE - energy deposit associated to the track in the Hcal\n", + "- PrsDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Prs\n", + "- BremDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Brem\n", + "- TrackP - particle momentum\n", + "- TrackPt - particle transverse momentum\n", + "- TrackNDoFSubdetector1 - number of degrees of freedom for track fit using hits in the tracking sub-detector1\n", + "- TrackQualitySubdetector1 - chi2 quality of the track fit using hits in the tracking sub-detector1\n", + "- TrackNDoFSubdetector2 - number of degrees of freedom for track fit using hits in the tracking sub-detector2\n", + "- TrackQualitySubdetector2 - chi2 quality of the track fit using hits in the tracking sub-detector2\n", + "- TrackNDoF - number of degrees of freedom for track fit using hits in all tracking sub-detectors\n", + "- TrackQualityPerNDoF - chi2 quality of the track fit per degree of freedom\n", + "- TrackDistanceToZ - distance between track and z-axis (beam axis)\n", + "- Calo2dFitQuality - quality of the 2d fit of the clusters in the calorimeter \n", + "- Calo3dFitQuality - quality of the 3d fit in the calorimeter with assumption that particle was electron\n", + "- EcalDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Ecal\n", + "- EcalDLLbeMuon - delta log-likelihood for a particle candidate to be muon using information from Ecal\n", + "- EcalShowerLongitudinalParameter - longitudinal parameter of Ecal shower\n", + "- HcalDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Hcal\n", + "- HcalDLLbeMuon - delta log-likelihood for a particle candidate to be using information from Hcal\n", + "- RICHpFlagElectron - flag (0 or 1) if momentum is greater than threshold for electrons to produce Cherenkov light\n", + "- RICHpFlagProton - flag (0 or 1) if momentum is greater than threshold for protons to produce Cherenkov light\n", + "- RICHpFlagPion - flag (0 or 1) if momentum is greater than threshold for pions to produce Cherenkov light\n", + "- RICHpFlagKaon - flag (0 or 1) if momentum is greater than threshold for kaons to produce Cherenkov light\n", + "- RICHpFlagMuon - flag (0 or 1) if momentum is greater than threshold for muons to produce Cherenkov light\n", + "- RICH_DLLbeBCK - delta log-likelihood for a particle candidate to be background using information from RICH\n", + "- RICH_DLLbeKaon - delta log-likelihood for a particle candidate to be kaon using information from RICH\n", + "- RICH_DLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from RICH\n", + "- RICH_DLLbeMuon - delta log-likelihood for a particle candidate to be muon using information from RICH\n", + "- RICH_DLLbeProton - delta log-likelihood for a particle candidate to be proton using information from RICH\n", + "- MuonFlag - muon flag (is this track muon) which is determined from muon stations\n", + "- MuonLooseFlag muon flag (is this track muon) which is determined from muon stations using looser criteria\n", + "- MuonLLbeBCK - log-likelihood for a particle candidate to be not muon using information from muon stations\n", + "- MuonLLbeMuon - log-likelihood for a particle candidate to be muon using information from muon stations\n", + "- DLLelectron - delta log-likelihood for a particle candidate to be electron using information from all subdetectors\n", + "- DLLmuon - delta log-likelihood for a particle candidate to be muon using information from all subdetectors\n", + "- DLLkaon - delta log-likelihood for a particle candidate to be kaon using information from all subdetectors\n", + "- DLLproton - delta log-likelihood for a particle candidate to be proton using information from all subdetectors\n", + "- GhostProbability - probability for a particle candidate to be ghost track. This variable is an output of classification model used in the tracking algorithm." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delta log-likelihood in the features descriptions means the difference between log-likelihood for the mass hypothesis that a given track is left by some particle (for example, electron) and log-likelihood for the mass hypothesis that a given track is left by a pion (so, DLLpion = 0 and thus we don't have these columns). This is done since most tracks (~80%) are left by pions and in practice we actually need to discriminate other particles from pions. In other words, the null hypothesis is that particle is a pion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Look at the labels set\n", + "\n", + "The training data contains six classes. Each class corresponds to a particle type. Your task is to predict type of a particle." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Electron', 'Ghost', 'Kaon', 'Muon', 'Pion', 'Proton'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(data.Label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the particle types into class numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0, 1, 2, 3, 4, 5}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Class'] = utils.get_class_ids(data.Label.values)\n", + "set(data.Class)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define training features\n", + "\n", + "The following set of features describe particle responses in the detector systems:\n", + "\n", + "![features](pic/features.jpeg)\n", + "\n", + "Also there are several combined features. The full list is following." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['TrackNDoF',\n", + " 'HcalE',\n", + " 'MuonFlag',\n", + " 'DLLkaon',\n", + " 'FlagBrem',\n", + " 'RICH_DLLbeMuon',\n", + " 'TrackQualitySubdetector2',\n", + " 'FlagHcal',\n", + " 'RICH_DLLbeElectron',\n", + " 'SpdE',\n", + " 'TrackPt',\n", + " 'EcalDLLbeMuon',\n", + " 'RICHpFlagMuon',\n", + " 'Calo2dFitQuality',\n", + " 'HcalDLLbeElectron',\n", + " 'PrsE',\n", + " 'TrackNDoFSubdetector2',\n", + " 'MuonLooseFlag',\n", + " 'FlagSpd',\n", + " 'MuonLLbeMuon',\n", + " 'FlagRICH2',\n", + " 'RICH_DLLbeProton',\n", + " 'DLLmuon',\n", + " 'RICHpFlagElectron',\n", + " 'TrackDistanceToZ',\n", + " 'Calo3dFitQuality',\n", + " 'RICH_DLLbeBCK',\n", + " 'RICHpFlagKaon',\n", + " 'DLLproton',\n", + " 'RICHpFlagProton',\n", + " 'HcalDLLbeMuon',\n", + " 'MuonLLbeBCK',\n", + " 'FlagPrs',\n", + " 'RICHpFlagPion',\n", + " 'DLLelectron',\n", + " 'EcalDLLbeElectron',\n", + " 'RICH_DLLbeKaon',\n", + " 'EcalShowerLongitudinalParameter',\n", + " 'PrsDLLbeElectron',\n", + " 'GhostProbability',\n", + " 'TrackQualityPerNDoF',\n", + " 'TrackP',\n", + " 'FlagMuon',\n", + " 'TrackNDoFSubdetector1',\n", + " 'FlagEcal',\n", + " 'FlagRICH1',\n", + " 'BremDLLbeElectron',\n", + " 'TrackQualitySubdetector1',\n", + " 'EcalE']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features = list(set(data.columns) - {'Label', 'Class'})\n", + "features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Divide training data into 2 parts" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "training_data, validation_data = train_test_split(data, random_state=11, train_size=0.90, test_size=0.10)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1080000, 120000)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(training_data), len(validation_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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8 rows × 49 columns

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" + ], + "text/plain": [ + " TrackNDoF HcalE MuonFlag DLLkaon FlagBrem \\\n", + "count 1.080000e+06 1.080000e+06 1.080000e+06 1.080000e+06 1.080000e+06 \n", + "mean 2.942315e+01 2.898032e+03 1.662343e-01 -1.452442e+01 7.918694e-01 \n", + "std 6.026995e+00 7.656111e+03 3.722910e-01 1.074946e+02 4.059709e-01 \n", + "min 7.000000e+00 -9.990000e+02 0.000000e+00 -9.990000e+02 0.000000e+00 \n", + "25% 2.600000e+01 -1.446553e-05 0.000000e+00 -1.069791e+01 1.000000e+00 \n", + "50% 3.000000e+01 5.772029e+02 0.000000e+00 2.762477e-06 1.000000e+00 \n", + "75% 3.400000e+01 3.047605e+03 0.000000e+00 8.460730e+00 1.000000e+00 \n", + "max 5.200000e+01 7.985253e+05 1.000000e+00 1.681698e+02 1.000000e+00 \n", + "\n", + " RICH_DLLbeMuon TrackQualitySubdetector2 FlagHcal \\\n", + "count 1.080000e+06 1.080000e+06 1.080000e+06 \n", + "mean -4.926364e+01 1.609497e+01 7.646630e-01 \n", + "std 2.148977e+02 7.769702e+00 4.242095e-01 \n", + "min -9.990000e+02 3.229625e-04 0.000000e+00 \n", + "25% -4.503437e+00 1.071093e+01 1.000000e+00 \n", + "50% -4.692121e-01 1.524483e+01 1.000000e+00 \n", + "75% 1.300734e+00 2.041093e+01 1.000000e+00 \n", + "max 1.428335e+02 1.048634e+02 1.000000e+00 \n", + "\n", + " RICH_DLLbeElectron SpdE ... GhostProbability \\\n", + "count 1.080000e+06 1.080000e+06 ... 1.080000e+06 \n", + "mean -5.109546e+01 -1.445036e+02 ... 5.513315e-02 \n", + "std 2.152222e+02 3.546216e+02 ... 9.242857e-02 \n", + "min -9.990000e+02 -9.990000e+02 ... 1.456686e-03 \n", + "25% -1.309732e+01 0.000000e+00 ... 3.852832e-03 \n", + "50% -2.355985e+00 3.200000e+00 ... 9.668268e-03 \n", + "75% 1.672999e+00 3.200000e+00 ... 5.510186e-02 \n", + "max 1.861542e+02 3.200000e+00 ... 4.000019e-01 \n", + "\n", + " TrackQualityPerNDoF TrackP FlagMuon TrackNDoFSubdetector1 \\\n", + "count 1.080000e+06 1.080000e+06 1.080000e+06 1.080000e+06 \n", + "mean 1.282943e+00 1.613264e+04 8.207509e-01 6.198936e+00 \n", + "std 6.270840e-01 2.753921e+04 3.835609e-01 3.115005e+00 \n", + "min 4.504852e-02 1.115380e+03 0.000000e+00 1.000000e+00 \n", + "25% 8.856753e-01 4.135297e+03 1.000000e+00 4.000000e+00 \n", + "50% 1.106393e+00 8.064075e+03 1.000000e+00 6.000000e+00 \n", + "75% 1.448324e+00 1.749550e+04 1.000000e+00 8.000000e+00 \n", + "max 3.999967e+00 4.673862e+06 1.000000e+00 2.400000e+01 \n", + "\n", + " FlagEcal FlagRICH1 BremDLLbeElectron \\\n", + "count 1.080000e+06 1.080000e+06 1.080000e+06 \n", + "mean 8.175639e-01 8.325130e-01 -2.076723e+02 \n", + "std 3.862037e-01 3.734103e-01 4.054207e+02 \n", + "min 0.000000e+00 0.000000e+00 -9.990000e+02 \n", + "25% 1.000000e+00 1.000000e+00 -6.250978e-01 \n", + "50% 1.000000e+00 1.000000e+00 -5.329020e-01 \n", + "75% 1.000000e+00 1.000000e+00 -5.933648e-02 \n", + "max 1.000000e+00 1.000000e+00 4.791513e+00 \n", + "\n", + " TrackQualitySubdetector1 EcalE \n", + "count 1.080000e+06 1.080000e+06 \n", + "mean 6.851082e+00 2.346141e+03 \n", + "std 5.071566e+00 5.709488e+03 \n", + "min 9.406193e-06 -9.990000e+02 \n", + "25% 3.080789e+00 -3.348990e-06 \n", + "50% 5.845922e+00 6.599040e+02 \n", + "75% 9.508951e+00 3.001512e+03 \n", + "max 9.914788e+01 3.156805e+05 \n", + "\n", + "[8 rows x 49 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_data[features].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_data[features].isnull().any().any()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "training_scale = scaler.fit_transform(training_data[features].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "validation_scale = scaler.transform(validation_data[features].values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sklearn classifier\n", + "\n", + "On this step your task is to train **Sklearn** classifier to provide lower **log loss** value.\n", + "\n", + "\n", + "TASK: your task is to tune the classifier parameters to achieve the lowest **log loss** value on the validation sample you can." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 22min\n" + ] + } + ], + "source": [ + "%%time \n", + "gb = GradientBoostingClassifier(learning_rate=0.1, n_estimators=50, subsample=0.8, random_state=13,\n", + " min_samples_leaf=1, max_depth=3)\n", + "gb.fit(training_scale, training_data.Class.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Log loss on the cross validation sample" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# predict each track\n", + "proba_gb = gb.predict_proba(validation_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6670030859005405" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "log_loss(validation_data.Class.values, proba_gb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Keras neural network\n", + "\n", + "On this step your task is to train **Keras** NN classifier to provide lower **log loss** value.\n", + "\n", + "\n", + "TASK: your task is to tune the classifier parameters to achieve the lowest **log loss** value on the validation sample you can. Data preprocessing may help you to improve your score." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.layers.core import Dense, Activation\n", + "from keras.models import Sequential\n", + "from keras.optimizers import Adam\n", + "from keras.utils import np_utils\n", + "from keras.callbacks import EarlyStopping, ModelCheckpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def nn_model(input_dim):\n", + " model = Sequential()\n", + " model.add(Dense(100, input_dim=input_dim))\n", + " model.add(Activation('relu'))\n", + " \n", + " model.add(Dense(50))\n", + " model.add(Activation('relu'))\n", + "\n", + " model.add(Dense(6))\n", + " model.add(Activation('softmax'))\n", + "\n", + " model.compile(loss='categorical_crossentropy', optimizer=Adam())\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "callback = [EarlyStopping(monitor='val_loss', min_delta=0, patience=4, verbose=0, mode='auto'),\n", + " ModelCheckpoint('output/{val_loss:.4f}.hdf5', monitor='val_loss', verbose=1, save_best_only=True, mode='auto')]" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 1080000 samples, validate on 120000 samples\n", + "Epoch 1/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.7070 - val_loss: 0.6224\n", + "\n", + "Epoch 00001: val_loss improved from 0.63063 to 0.62237, saving model to output/0.6224.hdf5\n", + "Epoch 2/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.6170 - val_loss: 0.6075\n", + "\n", + "Epoch 00002: val_loss improved from 0.62237 to 0.60745, saving model to output/0.6075.hdf5\n", + "Epoch 3/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.6038 - val_loss: 0.5977\n", + "\n", + "Epoch 00003: val_loss improved from 0.60745 to 0.59774, saving model to output/0.5977.hdf5\n", + "Epoch 4/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5972 - val_loss: 0.5917\n", + "\n", + "Epoch 00004: val_loss improved from 0.59774 to 0.59168, saving model to output/0.5917.hdf5\n", + "Epoch 5/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5923 - val_loss: 0.5904\n", + "\n", + "Epoch 00005: val_loss improved from 0.59168 to 0.59038, saving model to output/0.5904.hdf5\n", + "Epoch 6/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5882 - val_loss: 0.5863\n", + "\n", + "Epoch 00006: val_loss improved from 0.59038 to 0.58633, saving model to output/0.5863.hdf5\n", + "Epoch 7/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5845 - val_loss: 0.5841\n", + "\n", + "Epoch 00007: val_loss improved from 0.58633 to 0.58408, saving model to output/0.5841.hdf5\n", + "Epoch 8/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5823 - val_loss: 0.5828\n", + "\n", + "Epoch 00008: val_loss improved from 0.58408 to 0.58282, saving model to output/0.5828.hdf5\n", + "Epoch 9/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5798 - val_loss: 0.5835\n", + "\n", + "Epoch 00009: val_loss did not improve from 0.58282\n", + "Epoch 10/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5786 - val_loss: 0.5775\n", + "\n", + "Epoch 00010: val_loss improved from 0.58282 to 0.57750, saving model to output/0.5775.hdf5\n", + "Epoch 11/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5765 - val_loss: 0.5796\n", + "\n", + "Epoch 00011: val_loss did not improve from 0.57750\n", + "Epoch 12/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5755 - val_loss: 0.5769\n", + "\n", + "Epoch 00012: val_loss improved from 0.57750 to 0.57690, saving model to output/0.5769.hdf5\n", + "Epoch 13/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5741 - val_loss: 0.5754\n", + "\n", + "Epoch 00013: val_loss improved from 0.57690 to 0.57543, saving model to output/0.5754.hdf5\n", + "Epoch 14/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5732 - val_loss: 0.5772\n", + "\n", + "Epoch 00014: val_loss did not improve from 0.57543\n", + "Epoch 15/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5723 - val_loss: 0.5740\n", + "\n", + "Epoch 00015: val_loss improved from 0.57543 to 0.57403, saving model to output/0.5740.hdf5\n", + "Epoch 16/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5716 - val_loss: 0.5724\n", + "\n", + "Epoch 00016: val_loss improved from 0.57403 to 0.57244, saving model to output/0.5724.hdf5\n", + "Epoch 17/50\n", + "1080000/1080000 [==============================] - 11s 11us/step - loss: 0.5706 - val_loss: 0.5718\n", + "\n", + "Epoch 00017: val_loss improved from 0.57244 to 0.57181, saving model to output/0.5718.hdf5\n", + "Epoch 18/50\n", + "1080000/1080000 [==============================] - 11s 11us/step - loss: 0.5698 - val_loss: 0.5743\n", + "\n", + "Epoch 00018: val_loss did not improve from 0.57181\n", + "Epoch 19/50\n", + "1080000/1080000 [==============================] - 11s 11us/step - loss: 0.5693 - val_loss: 0.5699\n", + "\n", + "Epoch 00019: val_loss improved from 0.57181 to 0.56993, saving model to output/0.5699.hdf5\n", + "Epoch 20/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5687 - val_loss: 0.5703\n", + "\n", + "Epoch 00020: val_loss did not improve from 0.56993\n", + "Epoch 21/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5678 - val_loss: 0.5701\n", + "\n", + "Epoch 00021: val_loss did not improve from 0.56993\n", + "Epoch 22/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5677 - val_loss: 0.5722\n", + "\n", + "Epoch 00022: val_loss did not improve from 0.56993\n", + "Epoch 23/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5672 - val_loss: 0.5680\n", + "\n", + "Epoch 00023: val_loss improved from 0.56993 to 0.56801, saving model to output/0.5680.hdf5\n", + "Epoch 24/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5664 - val_loss: 0.5718\n", + "\n", + "Epoch 00024: val_loss did not improve from 0.56801\n", + "Epoch 25/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5662 - val_loss: 0.5692\n", + "\n", + "Epoch 00025: val_loss did not improve from 0.56801\n", + "Epoch 26/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5658 - val_loss: 0.5696\n", + "\n", + "Epoch 00026: val_loss did not improve from 0.56801\n", + "Epoch 27/50\n", + "1080000/1080000 [==============================] - 12s 11us/step - loss: 0.5654 - val_loss: 0.5687\n", + "\n", + "Epoch 00027: val_loss did not improve from 0.56801\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nn = nn_model(len(features))\n", + "nn.fit(training_scale, np_utils.to_categorical(training_data.Class.values),\n", + " validation_data=(validation_scale, np_utils.to_categorical(validation_data.Class.values)),\n", + " epochs=50, verbose=1, batch_size=256, callbacks=callback)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "nn.load_weights('output/0.5680.hdf5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Log loss on the cross validation sample" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# predict each track\n", + "proba_nn = nn.predict_proba(validation_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5681154022580016" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "log_loss(validation_data.Class.values, proba_nn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quality metrics\n", + "\n", + "Plot ROC curves and signal efficiency dependece from particle mometum and transverse momentum values." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "proba = proba_nn" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Klz0bu81NBzPJtXfiEoWIu0dMGZboMqWBzdqENjUDDy3YiZv2xo7CjoGNKLmAgZ1En3PsB/m7SA/s9hg2m4HYDEvPKrAplA2U2DBSdowMA2UHbArDZlAfiJCf48LmMH83hiiUzTB/L/T+/pTC+q2Yn6mecIUyrDhUT3pDmb9/w4CathAJAyr8mRhKYRiKlNGbl6EUnZEEAG6HnZQVZhjm82Esv42MZJy43dF7nlKWpDd/m7Ye0WiFq+4yrN+l9WySHsE6VBzYVYqcWIiA22P9rq07uO+nlY+5b5h3ulJUBJtozvSjAJuy7nJlmPvKFLl50QDZiQhNWX5sysBuGOYdb+0XRdrxxYLUeIuwGykcysClDOa2VRF2ZdLl8eE0DOwYOIwUni5zbd2Uy40kEtjG5eI6ediyssDhQOx2PCefjLOiHFd5Oc6KKbhmzsBRUIAcpmJJRKomKq9DRQhtZfBh8vMxh9APi8uTzfuv/cSEV0ozflavXs3pp5++T7hSiqSRJG7EiSajJIwEiVSCuBEnlooRT/V+huMhuqIBuiIBotEw8USUWDxCNBIimogQTURIJhKkEgkSyRjJeIJUMkEqESeVSKISSewpwZkU3HEbxW0ZdHoT2FOCIyU4UjYcScHZvZ8SbGp/5JfCUEkMBUnikBou7SYi1p6IkJnlxuPJJCvLTVaGjSy3DW+ul6xMJxlOIctdRUayBY8bHNn5YCQHbClIxSGVgFQClUqgmraiUimMnKkEOgJkZXkwkgaGYWAkDVQySSplYKSEVEqRMoSUcpBUblLKgYGTqOElrrJwSpSkcpJSTlI4SSo3dfGFeO3N2DBI4cBQDvbGl1Lk3IGhbKSUi/bUFLJsbSjspJSDuPLsx/c7dlIpN6lh7QAEe4WFHajAO+jKWU57ErcricuZwilRMrwZZGbZcHucuD0u3N4MsnKzyPTnkOHPxZnlxp3lxOmyYbP3f1kN9fsYiW5BlTQMUoYikbJElDKFVN8taSiShkEyZe2nDBIpRTCWtP4BwooziCeNfmmShvm5pyVEttuB22HrCY8nDd7a08bich/xpEEsmSKWNMMTKYPalCKeTBFPGSSS/fPv/kxXbMoUSK5UAqeRxGkkcRgpcu2KAnuKAluCXJK0tQY4psiNT1JkqSRZRhx3PIorHiEzFceVSkI8jhGNEt2wAZvHg0qlUNHoyJXogxEO9+wHnnhi0DTO8nJcM2fiKCzEUVhI5tKjyVy6FIdfe5V0c6gIoaeB/yciJyulXgEQkeWYM3M+fVBrpplQRASn3YnT7sTjnNyXolKKhJEgmAjSEesgmowSTUYJJoKEE2G6El0E40G64l2EEiE640FCkS7CoQDBYCfBYAfJeBxiSRxJwZWwmaIqYcOVsJMRt+FO2MiI28mM2XElx/efmVKKcChKODS6h2Smt4MsXy4Z2V46G1uZs+IksvPyycjNxpPrJ6ewmJyCQlyZWdhEsAMbRvPiNVKQjEIiComQKbBSiV6BlYxCMmYeJ6PQ1QCuKWCY4ovOGpCXwJFhpYlBaqP12Z1PHJWIkUomScYM4jUbSWVPI6WcJAMtJO0+koadZMpGwsggkCpBYUNIkcJJQ3w+uY46DGUnhYOUcpFUbhLKTUJlEkgVk1CZ2Ehg4ByXPYYikXKQiDjM+XHxmHPh9n55mJP3dk+M3J8MR4gsZ4QMV5wMVxJDBXnhjRqy7J1kFRbgK87C5cnAneUio7AEt9+PLcsHjv4ra4gIdgG77dBt8TMFnCmaYklz6yfAkopANIFSUN8ZweWwEU8axFMGsYRBJJEimkgRjqeIJFI0dkbZ0RRkeoGHQCRBezhOZyRBOGaKsbFgiI243Ubc3v/e2WeBsuI+/6ELpoLOtDbMXvWCbDelvgzKLsyk0OumLDeTfDcUkqAs1ok/GcJRX4c47KS6giRbmkk2NZNqayPZ2EiioQEViw0seR8StbUkavddEcVRWkrWsqVkLjuGjAXzyVi0CFtm5pi+j8OFgzGzdBbmhIpgrheTQ+8CdP9USoWtNXReVEp9vM95/8JcHPHL9E6o2KSUGnFCxXnz5qlt20Za809zIBnvf7zpiFKKUCJEIB6gI9ZBR6yDYDxIIB6gJdLSs7UHW+kKthMJdREJBXEnbJZoslHUnkHMlcKVsOFK2nDHTQHljtvISEzOS82ZkUmWz4evqIRQIsms+QvIzssnp6CQbH8+nlw/Hn8eImnYCWkYkIpBImIJsG6BFoFEuHdr3QWubEhGIB6GeAiat6ASMeLuYuIxRTKuSMQVyYQikRQSCRvRuItIPAO7ipBI2oirLHMzskioDBIqk1jPcSYJlXHAuxudEibT3oXbHsfjDpGT3El2UR6ZXicZHifZPgeeXBfuHC+2rBzIyAWXBzJ85n5mLhzCgmkiSKYMQjFTMIXjSYKxJF3RJJ2RBEHrsyuWpCuaYFdVDd68QrqiSUKxpCWqEnSE45PWiuXPclKQ7aYg2025P5NgNMlp8wqp8Gcy1Z9FkRGBpkZSrS0km5uJ760hsXcvgX/+E1tWVr8Wo5EQp5PMpUvJOmEFmUcvJXPxIuy5uZNyXROBiKxRSi2fkLwOghCajjmN/GDMUEpVikglsFopdV2f83IxF4q7hP5LbOz779UAtBBKPw4nITQekkaS5nAzjeFGGsONNIebqQxUEoiZYqo12sr29u0A2FL0tCplxs2Wpsyonayog4y4KZwyY3bcCfNz/7rv+uNwu/GXlJFXPgVfUTG5xaW4s7LInzINT64fd5YnPYXSRJOMm61g8b5bsGdfRbuIR5LEOruItzcTd+QTC6eIhFLEYopoRIjG7ETiGQRiPloTU8mytZFUbuIqk8l0pBdSuCVElr2DTFsnXnsTPnsDOY4WMj12Mjw2MrMdZHhcODq2w5yzISvPbPHzzwBvKWTlQ6bf3BxjWRDg8GG47vxAJEl7OE4gmqCuI0IsadDcFaM9HKcjnKAjkiAUS9IeTlDbHqElOHJLzmgQgSKvmxkFHir8WZw4K5/jZ+RR5svEZhOMSIRETQ3hd94h2dxC8MUXUakUsV27IJEYMX/XzJlkHrOM7JNPJmvFirTqTjukhdDBQAuh9ONIF0KjxVAGgViA9lg7gXiA1kgrbdE2uuJdtEfbWde8jqZwE267m7pQHdF4hAxLEGWHHdhTQlbMFE2upA1PxI4n6sATseMw9v/l68zMJLeohNziUnKKivH4csnweimaNhNvQSGZ3pwjQyiNFcOAWACiHRiREJHOMJFAmEhnhGhXlG2bW3AZdpQ9E5VMEI0KiWCYmN1PNJFJzJicUbAOieC1N5PvqCbL1k62vZVcey25jnqy7c04JQ7uHPBVgKfAbFnKyoNwK8w4zRRNnkLILjI/3dmTUs+DwUQ+s+JJg8ZAlMZAlIZAlIbOKHUdUQLRBPWdEWrbI9R1RMfcddeNN8PBsql+FpR6Oarcx1HlPqb4s7DZzN+iSiSIrFtHZN06Ytu3E1m7jnjVCL7HNhueE1bgu/hiss88C3v2gfXnG4gWQmNEC6H0QwuhiUcp1a87ri3aRnO4mZZICw2hBpoiTbRGWmmNttIV6zJbkqJ2vGGHJZbsZMXseCIOMmN2PBE77uT+dZ3YHA48Pj9ujwdPrp/WmmpmH3cCvqIScgoK8RWVkFtSijvr4D5U042Rfh+GoYiHE0Tb24m2tdFV30ZXW4xQZ4xoKEkkZNAVtBOJOoglnEzUGEuXhPDY2vDam8myt+OxtZHrqMPvqMFnr8ctIfbRvf4ZpjDq2AtLLjfFUnYRZJdAThnkTj0kuugO9DMrZShagzGagzGaAjE21nYSTaZo6IxR2Rqitj1CY1eU0b7CPS47s4uyWTbVz1HlPuYUZ7OwNAeH5aifaGoi/OabhN9+h+imTUS3bYNkcsj8cj5wHv6PfpTMZcsOyj87WgiNES2E0g8thA4usVSMpnATezr30Bhu5O3Nb+Mr8dEabaU53ExzpJm2SBtGJIa/y0l2xEFRm5vsiAObErKiZledMzUxXTqZOT5yi0oomjGLohmzKJgyDV9RMVm+3COyRWkifx+plEE0mCDcGSfUGaOrNUpHY5hQR4RoR4hIMGF24UUUxn52q2ZIJwob+Y4qil3bybQFyHdUkmuvx2NvxS5DDNHrbknKKTfFkdtr+nZ58iFvltkC5avYxzn8QJKOz6xkyqC6LUx1W5i1ezt4bVcr2xu76AiP3O3VzXHT/Zw+r4gTZuZzVLkPl8P8TRvRKNGNGwm+8grBl14itmUrg6ku1/Tp+D/8IXwXXXRAfYq0EBojWgilH+n4UDmSGcweSimCiSDN4WYawg3UB+upC9VRF+zdAh0tZAftZEccFLe5caRMB/DuLrjxjpTrxu5ykVNQSE5BEQVTppFbXEpuaZnZFVdQiM2e/i0J4+Fg/D6UUsRCSdrqQwRaI0QCCbpaI7Q1hAk0Rwh1xHrmKRoPQgqPrZ0cewO5jnr8jhprvw6/vRYZaYZLsZliyTcF8mZA7jRTNHXvewrMbrtJEs6HyjNLKUVla5hNdZ1sqQ/wXnUHW+oDtI9CHLkcNhaU5nDa3EJWLSlldlF2zz8i8ZpaOh97jMC//kV81659zpWMDHLOPZe8a68hY8GCCb+ufcrTQmhsaCGUfhwqD5UjhfHaI2EkaAw1msKor0gK1dEYaqQl0IQtksCVMLvgnEkx/Zes7reckBNvxIHdGPvLS+x2VCpFbkkp045ahregkLyycnIKi8krLceZkTHmPNOFdPx9KEMRDSUIdcbpao0Q6owTbIvS3himvSFMe31ov/IvcW4lz1FNjr2RHHsjhc49+Ox1Y9M1To/Z1ZY/C4oWgq8c8mZaTt8lYB//lAnpaJPRopSiNRTnncp23tjdyjObGlAKGgLDT8lRnpvJuYtLuGhpGUsqelt7Ihs30f6//0vg6adR8fg+53nf/36Kv34rzpKSCb+WbrQQGiNaCKUfh/JD5XBksuyhlKIt2kZDqIGaYA0NoQb2du2lpsvcrwvVEUlE8ETt+IJOCjvc5HY58YXM7rj9aVFye73EurqYeezx5JVVkF8+BX9pOf6ycjKzvWk94+6h+PtQhqKzOUKgJUKoM8bezW0gQldrhEBrlHDnvi/MkXDZYxRmNVLo2I4vtQufvZYi5y7cttEPC+9HTgXkzwT/dFMgFc43W5UK5oJz+Dl0DkWbjER9Z4QXtjazpqqdtypb2dsWGTLt0RU+PnrCNM5fUkqWy5yCMNnWRuDpp+n42yPEtvZfF10yMsi/4QYKPvNpZBJabrUQGiNaCKUfh+ND5VDmYNmjRyiFG2gJt1AVqKK6q5rmcDN7u6rp7Gwl0RnEG3bg73LhDTvwhZzkBVzjakXqxma34y0oJLe4lLyyCnxFJfiKS/AVFZNTUIQ76+CuTXg4/j5SSYOutiidTRE6GsPUbGunrS5IPJoiGhy9TwuANzuJPztAnquBUtcWCtQGvNHtSGroF/mwiB1Kl0DJEihaAIXzoGCe2aJkcTjaZCBNgSgvbGvi2c1NvLKzmWhi31FrXreDC5eWcekxFRw7zRxOr5QismYNzT+5l/Dbb/dL7ygrZcZf/4qjcEKWBetBC6ExooVQ+nEkPFQOJdLZHsF4kK1tW2mJtFDdVc3Ojp3s7thNU2c9RmcEX8iBJ+IgJ+zAG3aSHbGTG9y/uW4cmRnkFBbhzc0nOy8fh8vNjKXH4C+twFdUjN0xuZPyp7M9JhqlFKGOGK21IToawwRaTaHUVNlFNDR6gZSZ48Jf6KQwL0phdgsFjt3kGjuwd+6B9koINTPEGt1DkzcLZp0JJUexrqqdo8+71pxL6QgglkzxwtZmHnuvhmc3NzKYe9j8Ei+fOX0WFx5d1uNLFHz5ZRpuv4NETU1POrvPR/m99+JZcfyE1U8LoTGihVD6cSQ96A8FDlV7hBNhqruqaQo30RRuoiXSQm2wlj2tu2horsboipIdMbvdcoNOfEHn/jtx221kFOSSXVBIfnE5BcUV+IvL8JeW4SsqmZDWpEPVHhOJUopge4zGPQE6GkO0N4Rpqw/RWhuyFo4eGRHwl3oomZFDyYxsKkqCeI1qaK+Clm3mzOMdVdC2e/QV85ZB4VwoPxamrIDiRea9oaSpAAAgAElEQVRot8N4dGNDZ5RH363hL29XD9p9Nr/Ey+fOmM35R5WaEznGYjTccQedj/5fv3SFN3+Jgk9MzLqfWgiNES2E0g/9oE8vDkd7KKXoiHX0dLdVBaqoDlifrXtwBlPkhBzkWP5I3rCD7Ii57U+3G9luXD4v3oICiqbNYOrUBeSXlOMvLScje3QTDB6O9pgoUgmDjqYwrXVBmiq7aKoK0LI3SCI20uq5Jt78DHKLMsnIdjF9ST7lc/xkOYNI3bvQvBUaN5uf9WthtKvNe0uh9GjTQXvqSph+MrgObvfqZGAYitd2tfKnt6p4ZlMjqUEE6U3vm8NnTp+F22En8My/qf/GNzCCwZ74gi98nsLPfna/66KF0BjRQij90A/69OJIs4ehDDpiHebUAKGGnqVOaoO1NATrCXa0EWnrwN2eNJczidnxhZzkBB14YuPvFlMuO+48H15/Pv7iMopLp5FXUoa/rIK8soqeLrcjzR77i2EoulqjtNYGaaw0hVFrTRehUTpoZ/vdTFmYR8lMH4VTvOSVebCnQlD1OtSvg3ceJJRy4InWm4sIj4TLa4qh4kVQchRMO9GcRPIwoiUY474XdvLHN6r3mQG7PDeTez+8lGOn5RHdto09l1xqzqZuUfTlm8m/4Yb9Kl8LoTGihVD6oR/06YW2x+B0xbv6TQnQEmmhtaOJQGMjXa3NGO1B7F0JvGEnOSEH2WEHtnHO4qzsgiM/h+ySImI2B8uPP4XS0ukUTplOpjdngq/syCAeSdK4J0D97k7qd3ZQv7OTVHLkVh6bTSiansPURXlMW5xP4RQvL770IqefchK07YHGjVD9OjRshIb15rpzI1G0yHTEnn0WzDnHnCzyMKC5K8Z//eU9XtvV2i/cJnDlcVP4r7PmUhDvoubGzxNZt64nvvS7d5F78cXjLlcLoTGihVD6oV+86YW2x/hJpBI9rUo1HdXUNuyhsbGarsZGEs0d2DrjeMMOckKOca/vlnIJku/BU15C/tRpzJq7lIVzlpOV7Z3gqzm8SSUMWmqDdDSE6GiKULO1jea9QVKDjI7qi91pw5ltMHNhKQVTvJTNySWvzFpwOJWAxk3QuhNq34Xt/4K2fScc3IfSo2H6KWZr0YxTzdm0D2GiiRQPvVrJvc/tIJLo7aZ0O2zceMZsrju2hIaLVpFsbOyJK73z2+R+8IPjKk8LoTGihVD6oV+86YW2x+QRSUbY27WXys5KdtdvY3vlOrbuXU9RzIu9K0FO2Im/y3TiHiuxDCAvk+ySYgrKpjB97lEsWXwS2R7fxF/IYUoqZVC3o4OGXZ00V3fRvLeLYNvIq8Nn5biYujCP6UcXMGV+Hq5My35KQct2qHsPGjZAzTuw943hMxM7FC+EY66FBReCt3gCruzgsLOpixv/9B5bG7r6hRfnuPnZKQXkfPnTGJ2dPeFlP/gBvgvOH3M5WgiNES2E0g/94k0vtD0ODrFUjJquGnOkW+N2ait30NXYSP3eXXhDDrwRB76gc0wtSQaKsA+M4mwySgsomjqDWbOOYk7FYkqzS7FJ+k4kmS6EOmPs3dJG5boW6nZ2EOkawS9IoGhaDtMW5VE210/pTB92Z5/vOdYFO/5tdqXteBaaNoMaxrl76kqzC23ueVCyeGIu6gBiGIonN9Rz73M72NnUv9vwq8fkcs6D3yZeWWkG2O1M/+MfyFy6dExlaCE0RrQQSj/0ize90PZIL7rtEU/F2dO5h21V66jZtZWaXVsxmgO4O1J4QrYxjW6LOwxCnhTxkkwcRbnkFpdQMXUu86cdzSz/bHxu3Yo0GEopwoE4Lzz9GmX+mTRVBajZ2k4sPPTK7HaHjdLZPqYsMH2M/KUebLY+top2wp6XzO29P0BimJmyC+ebcxktvgwqjp3AK5t84kmDB17ezd3P9H//3ry8gHPu+RKptjYAxO1m5lNP4aooHyybQdFCaIxoIZR+6BdveqHtkV6Mxh4d0Q527FnPxu1v0VxTSbChCXtDkOzA2Ft84g6DxikKT1kR5TPmUTF9PnPKFjDDN4MMx6G7ZttE0tcmylA0VgXYs66Fqg0ttNaFhp2rMTPHxcyjC5h1TBHlc3Ox2fvYSCmoeg2qXoXtz0DtO0NnlDsNFl1ithZNXblfa6cdSLY2BLjwZ68S7+Oo/om8IJc+eFvPsXvOHGY8/vdRL32jhdAY0UIo/dAv3vRC2yO92B97dARa2LD+Vfbu3kpbdRWhxhboiOCIj+1Z354dpzk3hi3PQ05pKUXTZjJ72mJm585mrn8uzkPkJTxRDGeTaChB9aZWard3UL25dVgfowyPkxlLC5h5dCHl8/04XQPW4QrUw85nzS60Hc9CcohlQ3KnwYmfh4UXQ/bELl8xGbSF4lx5/+vs6NNVdn3T21zx2l97jv0f+TAl3/zmqPLTQmiMaCGUfugXb3qh7ZFeTLQ9lFJEugLs2LKGndvW0l5fS1dVLUbrKIZ99yHuMAh4EnTmpJDCbLIrSpkzbxnLp57A4oLFOGyTu/TIwWQsNmlvCLFnXYs5dH/X0D5GdoeNWccWsvDEMsrm5vYsU9FDrAsqX4ENf4MtT0JqCIE1+2w4/da07zqLJVPc9Je1PL2xoSfsrq2PsHRrrzP57JdexFk08pxLWgiNES2E0g/94k0vtD3SiwNlD6UU4c4O6nZvY/u2d9n56qskEjHojCJjeDV0ZSZo9yWhKBujIAtfaRkr5p/G4uKjqPBW4LQd+q1H47WJkTKo39XJjnea2PVu05ALzHrzM5ixxOw+K53lQ2wDRFE8DDv/Y26bHoNYYN9MZpwKx38K5p6Ttt1mSim+9uh6Hn7HXIssMxHl/57675543wcvpezOO0fMRwuhMaKFUPqhX7zphbZHenGw7ZGIRqndvoWmmkoqd2+ipbqSSEMLxIZ2EB6IIYo2b5wWfxwp9FIwYwZTZi1kdt5s5vnnMcU7Zd8WkDRmImyiDEXNtnaqNrayZ10zgZbooOmyfC4q5vlZdGq5KYoGfk/hNlj/V1j3F3MpkIG4c+CYa2Dl5yCnbL/qPFlMv+Wpnv2Lqt/g0+8+0nM85/XXcPiHX9xWC6ExooVQ+nGwH/Sa/mh7pBfpaI/u1qOWvVXU7NlG1fYNNFfuIdHaiYxySa64w6DRH6XVF8fuyaR47hzmzT6GuYXzmembSamnNG3F0UTbxDAUTZUBNr9ax653m4lHBheZucVZHHvuNOYcX4zdPogjccNGWH0XbH1y3zhHBiy9Ck65GXyjH5F1INhY28kFP33FPFCKv71wF9kBcxRZ7hVXUHrH7cOer4XQGNFCKP1Ixwf9kYy2R3pxKNkjGY/TuHsne/dsYceOdXTW1hJr7YCukSclBEiJoiU3RltOnKjfQd6MaUybtYh5+fOZnTub2bmz08L3aDJtkkykqN7URtWmVna83Ugiuu8cQ5leJ/NOKGX+yhLyywZZvLd1F7zzIGx4BIIN/eMcGXD8J+HkL0JW3qRcw3j4+mMb+NOb1QCcsfddvrrmTz1xM/7xOBlz5w55rhZCY0QLofTjUHrQHwloe6QXh4M9osEgDTu3Ub19E1W7NtG6azepriFGQA0815WiwR+l2R8jnueibNY8FlQs4eTyk1lSsOSgtBodKJsYKYPK9a3sXtvMjrcbMQZZ4b10lo8TLp5J2ZxBuo+ScbN16Plv77vUR4YP3ncbLLs6bXyIrnnwLV7a3gxK8eenbyM3HgLAlpXFvHfXDHmeFkJjRAuh9ONweNAfTmh7pBeHoz2UYdDeUEftts1sefVFwkaEzvp6km1dI5+LoiU3Tk1hhHCBncJZs5lTsoCjCo9iRckKcjNyJ73+B8MmwfYYa/9Tzbrn9g4aXzrLx8pLZlE6e5DrVwo2P24KotYd/eMy/XDF/8KMUyah1mOjvjPCGT9YTTRh8MEdL3DDpl7foemPPELm4kWDnqeF0BjRQij9OBwf9Icy2h7pxZFkj2B7G/U7t9Gyt4qqHZto2LaVVGjklqNAVoK6giiNBTH882ezfOoKFucvZkH+Ako8JRNez4Npk1TSYPfaZra92UDVhtZ94ivm+1l29lSmLMzbt7XMSJmO1f+6FaId/eOWfwzO+AZ4Ciax9iPzxu5WPvSrN7Apg6ce/2pPuOfUU5j6q18Neo4WQmNEC6H040h60B8KaHukF0eyPZRh0FJTTe2WTTRW7qJm11Y6qqqHPSclCgHem9tBTWGE3IpyjipcwsL8hSwtWmpOALmfQ/jTxSYdjWFef2wXu9c27xOXV+ZhyRkVzDmuGFfGAL+qSAc88w1Y+4f+4Vn58OG/wJTjJ7HWI3P1b97k5R0tLG7Zxd2v/KInfM4rL+Mo2FeoaSE0RrQQSj/S5aGiMdH2SC+0PfoT6minetN69m5az94dm+moHryrqJuIK0VdQYQmf4yaoggJj52F+QtZXrKcFaUrWF68fMwO2Olmk9baIK8/touqjfu2EDlcNpaePZWjz5hCRvYAAdi8DZ64Capf6w0TG5z533DSTWAbMNP1AaI9FGfZt54F4BfP3c30rkYAci64gPIf3L1Pei2ExogWQulHuj1UjnS0PdILbY/hScRj1G7dTPXGdbz9+CMjpm/zxqksCVNdEqbDmyDXnUupp5TTp5zOqlmrmOKdMmIe6WqT9oYQG1+sZfOrdSTj/ecxcGU6OO786Rx1WgV254D1zdY8ZHaXJfvMZTT3XLj0AcjIOUC178+vX97Nt5/awn+/+VtOqt/YE75g65Z90mohNEa0EEo/0vWhcqSi7ZFeaHuMjc6mRqo2rGX3u2+zd9M64pGhfYzC7iRVJWFafHEa8qOEMlMsLVzKSeUncWzxsSwtXDroOmrpbpNoKMGWV+tZ+59qwoF4v7icwkzOvn4hJTN9/U9q2Ah/ugICtb1h3lK49FfmLNUHGKUUR932b4obq/jZ6h/1hM/4+2NkzJ/fL+1ECqGDPzmDRqPRaDT7ga+omCVnncOSs87BSKVo3LOT6o3rqd7wHns3b0QZvS0lWTEHC6p6Wzzas+M05Ffxt9It3Oe/D5vNxsK8hZxYfiIrS1eyuGAxGY6Mg3FZYyLD42TZ+6ey5MwK1j23l7XP7SViCaJAc4RHv7+GuSuKOfXKubizLKFXshg+vwae+bo5BxFAVz38bhWc+lVz/bJRrgY/EYgID11/HJf/Mkmb20tezBxR2Pidu5j2+99NWrlaCGk0Go3msMFmt1M6ex6ls+ex4uLLiYVD7HjrdXa+/Tq71rxldgv1wR904Q+6WFCVQzAjSX1BlNrC3fymaSO/Wv8rnDYnK0pXUBQuYm5wLmXZ6blkRTd2h41jzpnGkjMqeO/Zat55qrJnLqLtbzZSua6FJWdNYdnZU02HamcmXPAjmHICPPbJ3oxe+r654Ov5P4TihQes/sdNz2NBaQ6rK5Zx6a6XAIjXDO8Ttr/orjHNQSHdm5mPNLQ90gttj8khGgpSu3UzDbu207BzO3s3rSeVHHxpi6TNoCE/RkNelMa8KC2+OGK3saRgCSeUncCSgiUsK1pGtmuQWZ7TiPaGEKv/uI26Hf2Hzrsy7Jz+0fnMWV7cG9i2Bx6+BhrW94bZnHDuXXD8Jw5QjWFnUxc3ff2hfqPHZj//HM6yXhGqu8Y0Go1GoxkjGZ5sZh17PLOONYeKJ6JRqjasZdeaN9n+xiv9fIscho2K5kwqmjMBiDpTVBeHqSzdyq8a12LYwCY2Tiw7kfNmnMf7p70/LbvQ/CUeLrn5GLa+Uc/rj+0i3Gl2l8WjKf79601seqmWU66cS355NuTNgE88D89/C169F1BgJOCfX4Yd/4aLfg7ZhZNe59lFXjbmz+wXFnj6afI//vFJKU+3CGkOCvo/3vRC2yO90PY48KSSCfZu3sie995h95q36GisHzJt1JliZ0WQHVOCdHqSYM1heOaUM7lw1oWsLFtJljPrANV89CQTKba+Vs+rj+7sN8LM5hDmHl/CyZfN7vUfqlsLD18NHX3mcMougY8/A/7pk17X9TUdvHLdZzmj5r2esL6jx/SosTGihVD6oR/06YW2R3qh7XFwUUrRXl9L7bbNVG9YR/XGdYQ7OwZN2+lJsKs8yN6iCO3eBAg4xMHSoqWcP/N8Lpp90X5P5jjRhANxXv+/nWx9o//irO4sB5ffehy+QrMVjGgA/vF52Pz33kTeUvj0q+DJn/R6XvO5n3Hrc/f1HE+WENJdYxqNRqPR9EFEyCurIK+sgqPOeD/KMHjyz3/AE4+w7fWX+4kiX8jJMdv9HLPdTzAzSVVxmOriMGuMd3in8R0eWP8Al865lItmXzQpS3+Mh6wcF2ddt5BFp5bz4p+30bI3CEAsnOTPd7zJyZfPYfGp5eZ8Qlf8Dt79vSmIwBxVdv+pcP1Tk94ylH/CcfBc73GyvR2Hf5CFZveTAzcuTqPRaDSaQxCx2fCWT+XM6z/Fp37xOy646RZmH7cSh9vdL112xMGiyhzOe7OEy14o5/hNfqJ1zfxs7c84+5GzufjvF/OjNT9ie/v2g3Ql/SmZ6eOKW49jwUmlPWGphMGLf9rG6j9upafH6Jhr4Kxv9p4YqIHfXwytA1a3n2A+eMKsfsexrVsnpRzdIqTRaDQazSix2e3MW3ky81aeTCIaZcsrq9n93ttUrV9LMh7rSeeJOlhYlcPCqhyac2NsnxKkMrGbBzt38eDGBzm2+Fg+ueSTnFB6AjY5eG0SYhPOvHoBM48u5Kmf944W2/RyHXaHjVOunGsGnHIzxLrgFWuiw/Y9ZsvQJffDggsmpW4LSr1syvRRGOkEoO3Z5/CsXDnh5egWIY1Go9FoxoEzI4Ml7zuXi7/y//jcb/7MhV/+BkvOOhdXZn9H6cIONydtyOeyF8o5alcOzoSwpnENn3r2U7zvb+/j52t/zu6O3QfpKkymLyngI7etwONz9YStf6GGf/5ifc88RLzvNlh1Lz3e4fEg/PUq2PHspNQpP9vNa6VH9Rw3vvDypJSjhZBGo9FoNPuJw+ViznErOfuTN/LZX/+Ji7/6TeasOBFEetK4k3aO3ebniucrWLHJT2bUTnOkmV+s+wUXPX4RVz11FU/seoKEkTgo1+Av8fDRb6+kbE5uT9iedS0888BGEvGUGXDstXDN3/uf+PC1/UeXTSDBlaf17HvqJ6cMLYQ0Go1Go5lA7A4Hs449ngu/9HU+ed9DnPKR6/AW9M6/40zZWFCVwxXPl3PyunxKWzJAwfqW9Xz9la+z8k8r+c6b36Eh1DBMKZODw2nnghuPxpvfOyfS7vea+b+71xBoseZZmnk6fOql3pMSIXjgLEhNvIDzLD2633Gqs3PCy9BCSKPRaDSaScKbX8DxF13Gx350P6d99GPklvQ6JgvC7NpsznmrmA+uLmd+ZTaOpBBLxfjz1j9z9iNns+z3y3ho40M0hZsOWJ2dbjtX3XECM44u6Alr2Rvkz3e8SWezJYZKj4bL+6z/FWqC310IyTgTyfnLptCY2TtSLFZZOaH5wziFkIjYJ7oiGo1Go9EcrjhcLpavupTr7/klH7jxZopnzu4X7404OGFzPlc+P4UTNuaRFTFfs0mV5J4193D2I2dz8+qbebvh7QNSX7vdxnmfPorlH5jeE5aMG/zp9jcIdVpO4YsuhgWrek+qfs1cwHUCWVCaQ112ryDb9s7mCc0fRiGERMQvIp8RkUdFZK+IxIC4iHSKyNsi8mMROXnCa6bRaDQazWGGzW5nwSlncNV3fsQV3/wOi047C6e7txvKmRTmV3u5bHU5J67PI7/DdF42lMG/q/7Nx575GNc+fS0v7n2RyZ4QWURYceFMVlzUu9yFkVQ88dN1RINWN9jlv4clH+o96e0H4MW7J6wOTrsNh5HqOa6snPjuwiGFkIhMF5GHgDrg/2G6if8a+BLwKeBbwJvACcALIrJNRK4W6eMZptFoNBqNZh9EhCmLlnDuZ7/Ip+//PadfcwOZOb6eeJsS5tZ4WfVaKVe8MYM51dnYLD3wbtO73Pj8jVz2xGU8uv3RSXeuXn7edJadPbXnuLUmyD/uXUsykQKbDS7+BUzr0x7ywrehcdOEle/K6+0aM3bumLB8uxmuRWgDkATep5QqU0pdqpS6XSl1n1Lq10qpHyilblRKnQCUAPcAt1ibRqPRaDSaUeDKzOLY8y/m0/f/ngtu+hqFU6f3i89qMzhpYz4feXE6R+/0YU+Z7Q3b27dz2+u3seqxVfyr8l+T2kJ04gdnc/yqGT3HzdVd/PXbb6MMZYqhD/3BXKm+mwfPg1hwQspOllX07IfaD6yz9Dyl1CeUUq+OlIlSqlUpdb9SahHw0MRVT6PRaDSaIwObzc68ladw9fd/ypW3f48Zy5YjfSZbdEQVy7bn8tFnp7Fslx+x1k2tDdbylRe/wpVPXsmTu5+ctPodd/4MVl7aO9tzR2OYp36+nlTCgEw/XPW33sSxTnjyixNSrveEFT37uYGWCcmzL0MKIaVU3XgyVEod+PF+Go1Go9EcJogIFfMXcektt/GJ+x5k5WUfxuHqXc5DDMXR23K45sVZHLctD0fSbCHa0raFW1++lWufvpZtbZOz0Piys6fiyuxdlKJqYyuP/+Q9szVq1hnmchzdbHgYatbsd5mF83sdy+e0TfxcQvs1fF5EnCJyvYhMXGegRqPRaDQawBx+f+LlV/G53/yZ067+OJnenJ44iSRZtMvLVa/OYkG1D7F6xt5tepfLn7icW16+hXAiPKH1ERE+9v2TWXBi7zQA9Ts7+df9G82DC34MGb0TMvLrMyG+f3UomjONFL3ux6ngxHS5dTOsEBKRWSJyi4jcJyI3i0i+FZ4pIl8BKoHfALoVSKPRaDSaScLhcrH8gkv4+L2/5uQPXUNGtrcnTkIJVmzM5UPvzCG/0xxlplA8tfspVj22in/u/ueE+g/ZnTbOuHo+05f0DmvfvbaZx374LokkcO0/oO8sOy/cuV/lleZ5sdNb/+oXRvTYGRPDjRo7BVgP3Al8ELgLeEdElmM6Un8PeA84SSl11oTWSqPRaDQazT64s7JYcckVfOqXv+eUj1zXr8vM3Rxn1aulXLB5JplRU4g0RZr42stf46p/XkVVoGrC6iEinHPDIsTW21JTt6ODJ3+6zpxscf4HehO//jOIBsZdlt3WfzB6Z824PHeGZLgWoduBjUCFUqoEyANeBV4E3MCpSqkLlFKvT2iNNBqNRqPRDIvD6eT4iy7jE/c9yPJVl/Zzqi6oTHHl6qmcuLkAZ8IUERtaNnDBYxfwrde/RXO4eWLq4LJzww9PIdvfK8bqdnSw+ZU6c1X6vvz2/P0q662pS3v2k2+/tV95DWQ4IXQUcKdSqh5AKRUEvgZkAl9VSr0yngJFZKGIPCciYRGpE5E7RjNTtYgsF5F/i0iriLSJyH9EZMVI52k0Go1Gc7iSlePjtI9+jKu+cw9TFvau1I5hMLfSw4den9XTXQbw8PaHOffRc7n33XuJJCP7Xb4r08HVd57YL+yFP2yltVnBcTf0Bjash+D4lwkJzF/Ss6+aJna5keGEUD77+v50H28fT2Ei4gf+AyjgIuAO4GbM1qfhzptinecArgGutvb/LSLTxlMXjUaj0WgOF4pnzuaK/7mLVV+6FX+feXfswQSrXi/j3C3T8AfMeX7iRpwHNjzAeY+ex0s1Lw2V5aix2YRr7+ovhv75yw1ET/pm/4RvPTDuMvwlvYvWenZtGXc+gzHSqLEMEcnq3gCPFe7uG27FjYZPY7YoXaqUelYp9UtMEfQlEckZ5rzzAa913lNKqaeAS4Bs4APDnKfRaDQazRHD3BUncf0Pf85pV38csVmveENRsgcueqWMszaX446b4a3RVj7//Oe5++2793t0WbY/gzOvWdBzHGiO8MQvthM79qbeRG/+cty+QlmlxT37zYVTxl3PwRhJCL0AdPXZ2q3wlweEd42yvPOAZ5RSfb+Jv2CKo9OGOc+JOct13zFzQStML+mh0Wg0Go2F2Gwsv+ASrrrzHkrnzu8XN6XSwUdencXc6mzAXMPs95t/z8WPX8zuzt37Ve6CE0s59UNze46bqrp4pfVycFtLh8QC8NL3x5W3u6i3RaioaWLnEhpOCF0PfGyQbajw0TAf2No3QClVDYStuKF41ErzQxEpEpEi4EeYwuxvw5yn0Wg0Gs0RSfHM2Xz4jru58rbvUrFwcU+4isQ5cWM+l6ydiTdkTo5YH6rnor9fxFO7n9qvMo86vYLZy4t6jre+0cSagh/0JnjtpxCoH3O+eXm90wWkbPs1BeI+OIaKUEr9bkJLMvEDHYOEt1txQ9WlTkTOAJ4EvmAF1wPnKKUmxv1do9FoNJrDDBGhYsFirvjmXWx97SWef+h+ol1mp4yvLsUHm6bw5rxWtkwNgMAtL9/Ck7uf5LunfBef2zdC7oNz1rULqNnSTjRkLgb71rpCFs1aREanNffyqz+G8743pjyd3uye/VjfNc0mABlukiURKQGuAqZjCo8nlVLrx12YSAL4slLqJwPCa4HfKqW+McR5pZjdcZuAX1jBnwOWASdarUoDz/kk8EmAwsLCYx9++OHxVlszCQSDQbKzs0dOqDkgaHukF9oe6cfhYpNkNMLeV1+gbfvmfuF1hTFePLqJmMtcwEwQri+4nmWeZeMqJxFRbH+8V19MK97BBfLVnuPVpz8+pvzq2qMsu9Vcuyxuc7B0y8Y1Sqnl46rcAIYUQiKyDHgeyAGaMecRArheKfXHcRUm0gTcp5S6fUB4ELhdKXX3EOfdA1wKzFFKJawwF7ADeFwp9YXBzutm3rx5atu2yVl3RTM+Vq9ezemnn36wq6Gx+P/s3Xd4VFX6wPHvySSZdAgplBBAehUkIEVAehFBBRWxIS6yS3OxshYEGyp2xQosiEoUEES6oAQV/AHqgqgUhaCh10CA9Hl/f0xyyWRSJmECEd7P89wnc88999wzM8S8nsc4en8AACAASURBVKrfR/mi30f5c7F9J39u2cTyd17j1NGzm5hmBvnwReskUoKzrLQb69/Iw60fJtA3sMTP2LTqL9bO+8M6/0f0HQT45Az1vX8rhFXzuKxTqRkkXdHcOm+8fZvXAqGiOtqeA3YBNXMWVIwAFgCvnMPztpFvLFDO1Phg8o0dyqch8GtuEAQgIhk4W4jqFHqXUkoppdzUbNaCIZOn0LhTVyvN74yDgd/F0nRvJXJ3tJi3Yx53LruT05mnS/yM5t1cZ3f9cOqmsycb3i9RWSGB/sVnKqWiAqErgKdEZA+AiKQADwJROcFLaSwDehljQvOkDQJSca5YXZg/gaY5rUAAGGPsQFOc+50ppZRSqgQCQkLoM+p+2g685WxitoNWm0MZ8Ft9bNnOSdnbjm3jtiW3cehMyRYyNMbQ+trLrPPNZ/pzJjtn3NF3r55z/b2lqEAoCue4oLxyN/iIpHTeBdKB+caY7jnjeCYCr+SdUm+M+cMYMz3PfdOAasACY0xfY8y1wOdAVaBkYaVSSimlLFfdfDsDH32K0IizU9TD/kzn9m/qEHXcuX3GzhM7GbJsCHtP7S1R2U06uHZ/rTyRZ12hQ95dGLG0SrugYmBpFlQUkeNAN8AGLMK5mOKrwIR8WX1z8uTe9yPQG+eiih8Cs4AgoIeIbPbk2UoppZQqWK3mLbnr5beod+XZFaJNaiZ9N1Sj1j7nn/g9p/YwaPEgth0raiSLq+CKdpp3P9uJtCejBYlpOUN7vi3ZSJszAcHFZyqF872gIiLym4h0FZFAEakqIuNFJDtfnloicle+tK9EpJOIVMo5rhaRBE+fq5RSSqnC+QcG0e/+R2g7YNDZxGwHnTdHc+VvznFDJ9JPMGjxIJYlLvO43PYD6rqcbz7Tz/niwJYS1e/3ag1KlN9Tha4jhHPhRKWUUkpdIowxXDXoDuq2bse8Z8eTdioFRGi8OxR7huG75kdx4ODhbx5mwe8LeL9n8aNTfHwMNzzYkgUv/QTA3ozLOZDRgCqHt0LqcQgsdBlBF0FBAef03gqtX2EXROSDkhxlUjullFJKnXeVa9fl9udeo1KeDVzr7Auh25aqGOdSQ3y//3seXPMgRa1HmKta3YpUrXN2gcYVyQ8iYmDzJx7XKTjY7vkbKIFCAyFjTHtjTNl0yCmllFKqXKsQXZk7Jr/psl9Z9T3+XLuuKhVOOTuUVuxewb2r7yU1K7XY8jrcXM96fcoRye9pHeCnWeBBIAXgn5VRwnfgmaLGCH0LNMk9Mcb4GGO+McbUK+IepZRSSl0kfP38GDTheS5rEWelRZz057p1MVQ/5FxkMSEpgTuX3cmR1COFFQNAdM0wqjc82w228sT9yMHf4Nf5HtXFx8OAqaSKCoTy7+pugA44Z24ppZRS6hJg8/Xl+nFP0Lr/QCvNJwu6/a+y1TK07dg2uszpQlJKUpFldby5vst5YvqVsKbATSXcZFatXnymUvDuFq5KKaWUuuj4+NjodNtQrn94vJVmsoUB/6tDYJq12g195/fl8JnC90KvVC2YavUqWuffnrwHDm+FI78XW4f0zOxi85SGBkJKKaWU8kiduDb0f+BR61xS0rg5IZbAdGc4IQhjV48tcgD1lf3OrjZ9yhFJlvjDmuJ3ow+6rOY51LxwxQVCA40xI40xI4EROHcfuSk3Lc8xokxqp5RSSqlypd6V7el299k/+8Yh3PBzXfwznSHFz0d+5v6E+8l0ZBZ4f97ZYwBfHJsIW+YW+1xjL5vp80WtIwTwUAFp4wpIE+Cdc6+OUkoppcq7Fr36cnTvX2xasQQA/8PpDFxfk7ltd5PlK6z6axVDlw/lwz4fYozrkGMfmw8tuseyaZVzPNH+zEYcyaxF5OEdEFXf7VnWfaFlM0S5qHWEfEpw2AorRymllFIXn253j+DK68/uKG8/6WDwN7WszVo3H97MsC+HFXhvu3yrTf/v9HWwvuj2FJ/A87ygolJKKaVUUToOHkLr6260zm1pDgZ9V8vZTwRsOLCBWb/OcrvPx8fQrPPZWWA70jpx6lBykc/y9SuuE6t0NBBSSimlVKl1HDyE8Kpnd5n3P+3g8p1h1vmLP7zIxgMb3e8blHdZQh9+3BIJRSyaGBasLUJKKaWUKmeMMdz1imu3VsvfK1HlyNnAZeSqkSSdTHK7r9uQRtb51tSusGt1oc+x2/28VGNXGggppZRS6pz4+NgYPeNT/AKcq00jQp//xRCZ7A9AWnYa966+121a/WUtoqzX2dg5/fvmQp8RGODv/YqjgZBSSimlvMAeFMzgp1/EHuTcplQys7h+Sx1rk9Y/kv9gxZ8rXO8J9MXfz2Gdb1iTXmj5fjpGSCmllFLlWVSNWtzy1GTrPCvlDNftPtv99dqPr5GWleZyT90rzu4/9tvJqyDjdIFl+5eXrjFjjM0Yk22MaVkWFVJKKaXU31dkbE1a9RtgnVfcdobqR4IA2HtqL1P+N8Ulf732l7mcZ//8eYHl+oed53WEipF/Q1allFJKKQCuGnTH2fFCQI8tMdaeZB/89gF7UvZY12IahLvc+9eKZQUX6ldOWoRyFL6JiFJKKaUuab5+fvS//xHrXFIzaJUYaZ2/uPHsjvPGGELOzrYn6WhlcJwdN2TlK2eBkLYIKaWUUqpQtZq3pMtdw63zOokBVExxBjNfJ33N8bTj1rUrr2tovd6RejXy4wdu5fkEBrqleUOJAyERyc7ZVuOnsqiQUkoppS4OV/S6lrCoaOu869YYq0/ptZ9es9JrXX62tShdQtj33Vq3soy/Tp9XSiml1N+I8fGh98j7rPOwI0KTRGc/2MI/FlqLLAaG+uPnm2Xl2/FXFPkZHx8cZdAhpYGQUkoppcpMbONmNO7U1Tpv/kcFjEC2ZPPG/96w0uN6xlqvf0vtAVnuawo5fLwftmggpJRSSqkydfXtd1uv/bN8iNvmnCm2fPdyth/bDkCTrq470h/6ZbtbOdnG5vW6aSCklFJKqTIVVKEiDdp1tM6bJobhm+Xs5nph4wsABIT4YTcpVp4Nyw+6lZOtLUJKKaWU+jvq+c8xLud194QAsPHARnad2AVAmP2EdX3/XvcygjLT3BPP0TkHQsaYAGNMDW9URimllFIXJ//AIJpc3c06b7a7ojWD7I2fnGOFrqj5m3U9I9P73WAF8UaLUF8g0QvlKKWUUuoi1vHWu6zXwWd8aJLo3DZj/f71HDx9kJqd27rkP3Myw+X8cJj7bLJzpV1jSimllDovgiuG03bAIOu89bZKBKfaOJV5iiWJS/Cv2dwl/6/fuvaPOXy830pU6J72xpivPSzD++GZUkoppS5KV15/E7+sXsmp48cA6Lg5kuVtDzJ3+1zubjKUKn7LOJDpXGn6WNJx4OymrA7b+Z011gmoDBwt5kgprACllFJKqbz87AE06dzDOo8+bscv07Dn1B5W/rWKVhW+sK79sSkZcZzd3rTSySNer0+hLULAL8B2ERlURB6MMTcCn3q1VkoppZS6aLW7cTDrFzhDBx8x1N4XzPaap5j681TiKybhezSNLAkA4Oi+00RWd84wS7UHY890X2jxXBTVIrQeaFvE9VyCbsKqlFJKKQ/ZfH25vFtv67zdrxEAbD22ld9qtbCCIIB9v5/dnHVfRIzX61JUIDQZGFPE9VxLyduBp5RSSilVjAbtO7mc29OdIckMWxrRvr9b6Xu3J1uvqxz80+v1KDQQEpGdIvJFYdfz5EsVEe/XTCmllFIXrRpNL3c5r70vGICVJ3cQ4Xc2rMjOyrZeH6vhug2HN+j0eaWUUkpdEK2vu9F63TQxDJMzLrpi6FdW+p+/HLNeZwUEeb0OhQZCxpjpxph6nhZkjPEzxtxtjLnDO1VTSiml1MWszfU34evnD0Bwmi9VjzjHBi2NPOySLzXFubCiz3nea+wMsNkY860x5t/GmNbGmMC8GYwxNY0xA4wxU4F9wL2A+3axSimllFL52IOCqd/2Kuu8Xs7+Y18FB7jk2/+Hcw8yH9t5DIREZAzQAPgGeADnLLJTxpjTxphjxpgsYBfwCVAFuEdEWojIBq/XUimllFIXpaZdzq4pdNn+YGzZhiO+rgsnHtt/CgBzvnefF5EkEXlMRGoADYHbgceBF4FRQDegooj0E5HPvV47pZRSSl3UqjduRlCFitb5FTsqAPBnzCdW2uav9jhfGO8HQkUtqOhCRHYAO7xeA6WUUkpdsowxXNaiFb+uWQVAlaPObrG9QUepmZMn7XSm84WP95ct1FljSimllLqgruh9rfU68qSdyGR/EkPct9OQMmgR0kBIKaWUUhdU5dp1qRQTa5333FCZFPtxlzwZaVlwvscIKaWUUkqdD42uutp6ne0jOHfwOuvMyQww2jWmlFJKqYvQFX36W68DM2zWWKFcp4+na4uQUkoppS5O9qAgardsbZ233FGBPyv+ap3v2X6c4KMHvP5cj2eNARhj7EAMEJD/moj85q1KKaWUUurS06xbb3b9tBGAiJN2Eisfta6dOJxKZniU15/pUSBkjKkGvA/0Kegyzo48WwHXlFJKKaU8clmLltZrm8OQ7TjbAvT7xoNUL4O9xjxtEZoGtATuB34DMrxeE6WUUkpd0my+fkRfVodDiTsBsJ9OynPNp0wGS3saCF2FcwuNOV6vgVJKKaVUjojqNaxAqNY+A+HO9OwsB+Ln/c4nTwdLHwJSvf50pZRSSqk8Lu/Wy+U81efsTvRZeL9rzNNA6AlgnDEmzOs1UEoppZTKUb1RU5dzW9oh63VZBEKedo0NAGoAfxpjNgLJ+a6LiAzyas2UUkopdUkKiYjk1FHnFhuSfXaFaQd+Xn+Wpy1CkcBOYBPgB0TlO6I9faAxprEx5itjzBljzD5jzFPGGI86/YwxA4wxG40xqcaYo8aY5caYYE+frZRSSqny74peZ/cey8zabL3OErfVe86ZRy1CItLFGw8zxoQDq3DOPLsOqAO8jDMge7yYe4cBU4DJwEM4h091pYRrISmllFKqfKvVvCXfzp4JgG9WOiLZGGO7oLPGXBhj/EQksxS3/gsIBAaIyElgZc64o4nGmMk5aQU9LxJ4FRgjIlPzXFpQijoopZRSqhyLrlXb5VwcJzG2cDId3u8E8niLDWNMe2PMMmNMCpBmjEkxxiw1xrQrwfP6ACvyBTyf4AyOri74FgBuzvn5QQmepZRSSqm/qYjqNazXkrUfgGzx9/pzPAqEjDE9gASgOvAiMDLnZ3UgwRjT3cPnNQS25U0Qkb+AMznXCtMG2A78wxizxxiTaYxZb4xp7+FzlVJKKfU34me3W68dWXsByqRFyNOusWeBL4CbRETypD9ljPkMmIRz7E9xwnGfcQZwHGvJpAJVARrgHEf0MHA05+dyY0w9ETmY/wZjzHBgOEBUVBQJCQkeVE+dL6dOndLvpBzR76N80e+j/NHv5PyzRVWBnb8DkJ25Ez96ACB4d5yQp4FQM2B8viAo1/vA5yV4ZkFlmELSc/kAITgDseUAxph1wJ/AaGC820NE3s+pGw0aNJDOnTuXoIqqrCUkJKDfSfmh30f5ot9H+aPfyfm3xZHB3v/7FgDjc3b9oAz/UK8+x9MxQsk4Z3gVpC4Ft/IU5DhQsYD0CsWUcSznZ0JuQs44ox+Bxh4+WymllFJ/E7Wan92AVbKPIuIAIMs30KvP8TQQmgs8Z4y53RgTAGCMCTDG3I6z28zTPci2kW8skDEmFggm39ihfLbibDHK3x5mAIeHz1ZKKaXU30RoRCSBobmtP4I4jgKQbfPuWkKeBkLjgMU4Z22dNsacAE7nnC/Oue6JZUAvY0zedq1BOPcxW1PEfYtxBj3WekbGmApAHLC5sJuUUkop9fcVfVld67Ujax8AJ8JqefUZHgVCIpIqIrcBTYC7cLYC3QU0EZHbRSTNw+e9C6QD840x3XMGNE8EXsk7pd4Y84cxZnqe5/8ALASmG2OGGGP64hy8nQm85eGzlVJKKfU3UrVufeu1I2M7AKmBUV59RokWVBSRbRTdhVXc/ceNMd1wrhC9COe4oFdxBkP565V/243bcU7ZfwUIAtYCXUXkOEoppZS66NRqHsf/zf8UAEfWHkQcXh8sXWggZIxpDOwUkfSc10USkd88eWBOvq7F5KlVQNopYETOoZRSSqmLXLUGjVzOxXEMW3a6V59RVIvQL0BbYEPO68Kmt+dOffdo41SllFJKKU8YYwiu4MPpE855UZJ1iKOVmnr1GUUFQl1wbo4Kzhacotb5UUoppZTyOj98gQwAHNmHCfF0VLKHCg2ERGRNntcJ3n2sUkoppVTx6lQL4scTOYFQ1h6EaK+W7+leY9nGmCsLuRZnjMn2aq2UUkoppYCK0ZWt1+I4gRjvjsTxdB2hojb28AOyvFAXpZRSSikXMY2anT2RNMTHu4FQUbPGagC18iRdkbuqdB4BwBAg0au1UkoppZQCQiIi85z5cqJCYTt+lU5Rg6WHAhNwDpIW4J1C8qUCw7xaK6WUUkopIKBKvTxnWYjjtFfLLyoQehuYh7Nb7GfgtpyfeWUAf4mIdyf1K6WUUkoBJqwKvoHpZKXaAXBkJXm1/KJmjR0GDgMYYy4D9olIplefrpRSSilVFHsYvrZMsnAGQpJ1xKvFezpYuh0wtqALxpgHjTE3e69KSimllFI5jCHcnmqdOrIPe7V4TwOhR4DCljA6k3NdKaWUUsrrgszZyevi8O4Wo54GQnVxbrNRkK1AvUKuKaWUUkqdk7r+R63X3h6W7GkgdAaoXsi1WEAHSyullFKqTERWsOc5K2ppw5LzNBBaBYw3xrisa22MiQIeA770aq2UUkoppXJUqN387Imc8WrZRU2fz2sc8H/ATmPMcmA/UBXoBSQDD3u1VkoppZRSOeyZxwDvriidy6MWIRH5C2gOTMHZFdYn5+ebQEsR8e6kfqWUUkqpHD6VG5ZZ2Z62COWuK6Szw5RSSil1ftlDER87xuH9IcmejhECwBjT2BhzhzHmUWNMlZy0usaYUK/XTCmllFIKIKx6mQRB4GGLkDEmBPgvcCOQmXPfcuAAMAn4C3iwTGqolFJKqUubrz/iF4jJTC0+bwl52iL0CtAe6AaE4jp3bSnQ28v1UkoppZRySj+FzecCDpYGBgDjRGQ1kJ3v2p9ATa/WSimllFIqV3hNfHxKNJrHY56WGggcLeRaKO7BkVJKKaWUd9jsCAFlUrSngdBG4M5Crt0IrPNOdZRSSiml8vG142Pz7kKKVtEe5nscWGWMWQXMBQS4xhhzH85AqFOZ1E4ppZRSKigCXxxklkHRni6o+B3OgdJ2nIsqGuBJoDbQXUQ2lkHdlFJKKaXAHoLdP6NMii7JgoprgY7GmEAgHEgW8fKGH0oppZRS+dnDAEeZFO1xIJRLRFIB70/kV0oppZQqiM2fIJshuQyKLjQQMsZMBt4QkT05r4siwDHgBxH5ypsVVEoppdQlzseXUP8UIMjrRRfVInQT8DGwJ+d1cUKBcGPMSyIyzhuVU0oppZTCxxc/UzYr9RQaCInIZQW9Loox5l7gUUADIaWUUkp5h48Nf9sZnG0u3lXiMULFWAjU8nKZSimllLqUGUOglM1gaY/XqzbGXG6M+dQYs9MYk26MaZmT/qwxpg+AiPwpIveXSU2VUkopdckKsJXFKkIeBkI5gc6PQBVgFuCX53I6MMb7VVNKKaWUcvIPuLBbbDwHzBSRq4Fn813bBLTwaq2UUkoppfKw+3l7NI+Tp4FQQ+DTnNeS79pJoJLXaqSUUkoplY/dr/g8peFpIHQI53YaBWkC/OWd6iillFJKuQtK3Vcm5XoaCH0CPGWM6ZAnTYwx9XFOlf/Y6zVTSimllMrhL6k4tzr1Lk8DofHAD8Aazrb+LAR+AX4GJnm9ZkoppZRSOQKqx+E+OufceTTySETSgWuNMd1w7kIfiXNLja9EZKXXa6WUUkoplYePry9lsfFqsYGQMcYOPAgsztlHTPcSU0oppdR5ZZI2gLka5LRXyy22ayynNegxoKJXn6yUUkop5SHTsDcYm9fL9XRS/nogDucYoYvSyZMnOXToEJmZZbNypXJVoUIFtm7deqGroXKUt+/Dz8+P6OhowsLCLnRVlFLlhc2OIdPro4Q8DYQeBmYbYzKApcBB8o1YEpEzXq7beXPy5EkOHjxITEwMgYGBGOP9UenKVUpKCqGh3t88T5VOefo+RITU1FT27t0LoMGQUsrJ5lcGQ6U9nzW2HqgDvAH8jnMRxZR8x9/WoUOHiImJISgoSIMgpS4wYwxBQUHExMRw6NChC10dpVR54RcIjhNeL9bTFqG7KYs5a+VEZmYmgYGBF7oaSqk8AgMDtataKXWWPRTjG4Nk7fVqsZ5On5/p1aeWQ9oSpFT5or+TSikXNjsGm9dbZTztGlNKKaWUunBsfjgk1evFaiB0kZg4cSLGmAKPjz76CIDdu3djjGHx4sVeffaGDRuYOHGiV8ssK4mJiRhjqFGjBiLu/1/RuXNnbrzxxgLvbdWqFXfddZdLmsPhYNq0abRv356wsDACAgJo2rQpL774IqdOnSqLt+BCRJg0aRKxsbEEBgbSqVMnNm3a5NF9zz77LDVq1CAgIICOHTuyYsUKt3w//PADPXv2JCIigkqVKtG9e3fWr19fZFktW7Z0Kyv3317+45Zbbjm3D0ApdekwBkOW14vVQOgiUqFCBb7//nu3o3fv3mX63A0bNvDkk0+W6TO8JT4+HoCkpCTWrl17TmU5HA4GDRrE6NGjadeuHXPmzGHp0qUMHTqUt99+m/Hjx3ujykV6/vnnefrppxk3bhyLFi0iJCSE7t27c+DAgWLve+qppxg1ahQLFy6kYcOG9OvXj40bN1p5kpKS6N69O1lZWcyaNYsPP/yQrKwsevbsyZ9//lloWU2aNHErK9dLL73k8m/zmWee8d6HoZS6uKWnYHxCvF6sp4Ol1d+Ar68vbdu2vdDVKJSIkJ6eTkBAwAWrQ3x8PG3btmXLli3Ex8fToUOH4m8qxFtvvcVnn33Gl19+Sffu3a30rl27MmrUqHMOtIqTlpbG888/zyOPPMLo0aMBaNeuHbVq1WLKlCmFBhkZGRk899xzjBs3jnHjxgHQvn17fv/9d5588kmrxXDJkiWkpKQwf/58KlasaOWLjIxk6dKljBgxosCyevXqxW+//eZSVq4GDRqU63+jSqlyLLox8Gex2UpKW4QU06ZNo0mTJtjtdmrWrMnkyZPd8nzzzTd06dKFkJAQKlSoQOfOnfnf//7HzJkzGTNmDIDV3dG5c2fA2V0XGRnJd999R+vWrQkICGDu3LmAs6vk+uuvJywsjNDQUPr168cff/zh8kxjDK+//jqPPvooUVFRREdHM2rUKNLT00v1Pn/99Vd++eUXhg4dSv/+/Zk7dy5ZWaVvZn311Ve54YYbXIKgXAEBAXTr1q3UZXti3bp1nDx5kptvvtlKCw4Opl+/fixbtqzQ+3bu3ElKSopbvXv06MHKlSvJyMgAnLMpfX19CQk5+39gISEh+Pr6Wt2KnpallFLnLDsDYwv2erGFBkLGmBolObxeM1UqWVlZbkdRXnzxRUaMGMH111/P4sWLGTFiBOPHj2fKlClWnoSEBLp164afnx8ffPABn376KR07dmTv3r307duXBx54AMDq7nj77bete8+cOcOQIUMYNmwYy5cv58orryQ9PZ3+/fuzdetWpk6dysyZM0lMTOTqq6/m2LFjLvV7+eWX2bdvHx999BEPPfQQ7733Hq+//nqpPpvZs2fj6+vLwIEDGTx4MIcPH+arr0q3dV5SUhKJiYml7nYUkQK/q5J8d9u2bcNms1GvXj2X9EaNGrFt27ZC70tLSwPA39/fJd1ut5ORkcGuXbsAGDhwIEFBQTzwwAMcOnSIQ4cOcd999xEeHs5NN91UorJyDR06FJvNRtWqVbn//vtJTfX+wEel1EUqtGqZFFtU19huSrZ2kEcbgBhjGgNvAu2AZGAa8KSIZHt4vw+wEWgJ9BMR7478/Rs7evQofn5+bumJiYnUqlXLLf3kyZM8+eSTPP7440yYMAFw/p/8mTNneOaZZxgxYgQ2m41HHnmE5s2bs2LFCmtKc94AILfsgro8UlNTeeWVV7juuuustHfffZekpCR27NhB7dq1AWjTpg21a9fmvffe45FHHnEpe+bMmYCzy2Xt2rXMnz+fhx9+uGQfDvDJJ5/Qo0cPIiIi6NWrF+Hh4cTHx9OrV68Sl5W76nGNGqX7f4A1a9bQpUuXYvMV9t0BHD9+nJCQEGw211+98PBwzpw5Q0ZGhluAAlC7dm2MMWzcuNHlO9uwYQOAFYxWq1aN1atXc+211/LGG28AULVqVVasWEFUVFSJyrLb7YwaNYqePXsSFhZGQkICL7zwAjt37mThwoXFfg5KKYXN/e+bNxQVCPXL8zoMmAxsBeYDh4BoYCDQEHjIk4cZY8KBVcBvwHU4V6t+GWfL1OMe1nkYEONh3lKr9Z8lZf2IYu1+vm+J8leoUIFVq1a5pVerVq3A/N9//z2nT5/mpptucml96Nq1K08//TR79uwhMjKS9evX8/rrr5dqXRdjDH369HFJ27BhA82bN7eCIIDq1atz1VVX8d1337nk7dmzp8t548aN+eGHH0pcj/Xr17Nr1y4r4PP392fAgAHMnTuXd999t9Tjlkq71k1cXFyBg4nzK+y7K+r5ud1WhdWtQoUKDB48mGeffZamTZvSvHlzpk+fbv3byQ2s9u/fz4033khcXBzTpk0DnOOi+vbty7p166hRo0aBZX388cduZVWtWtWllbFz585UrlyZkSNHsmnTJlq0aFHsZ6GUusT5+FEWazsXGgiJiBUJGGNmAotFZES+bO8aY94F+gKfePC8fwGBwAAROQmsNMaEARONMZNz0gqVE0g9C/wHZ0uSysPX15dWrVp5nP/IkSMANGnSH2Qy7wAAIABJREFUpMDrSUlJ2Gw2RISqVUvXJBkeHu7WKrF//36io6Pd8lauXNllNhJgDdLN5e/vb3XHlER8fDx+fn507tyZ5ORkAPr27cv06dNZunQpAwYMAJyfYXZ2wY2T2dnZ+Po6f2ViYpyx+F9//VXiuoBzrI0nf/xzn1eQ8PBwUlJSyM7OdmkVSk5OJigoqMDWwVyvvfYagwYNomvXroAzEH388ceZOHEilStXBpzdpllZWcybN88qq2vXrtSrV4+XXnrJaiXKX1ZsbKxbWQW58cYbGTlyJD/99JMGQkqp4tl8L+heYwNwtgQV5DOgv4fl9AFW5At4PsEZHF3twf1PA2uB0g3sUC4qVaoEwOLFi9m4caPb0bx5c8LDw/Hx8WH//v2lekZBrRJVq1bl8OHDbukHDx606uRNDoeDOXPmkJmZSc2aNQkPDyc8PNwKfnKn1ANERUUVOvU8bwAXGxtL7dq1C1x7xxNr1qzBz8+v2GP37t2FltGwYUOys7PdBplv27aNhg0bFvn8qKgovv76a5KSkvjll1/4+eefCQ4OpkqVKlZX3LZt22jSpIlLQOXv70+TJk3YuXNnoWXt2rXLrayC5P7b0BWklVIeMTakDP5z4en0+VSgA7CygGsdAU//F70h8HXeBBH5yxhzJufaosJuNMZcDgwFmnv4rHNS0m6pv6N27doRGBjIvn376Nu38Pfbpk0bZs2axejRowv8o5Xb4pOWluZRF1NueYmJiVx22WWAc8zNunXrymRhxoSEBPbv388LL7zAlVde6XJtxowZzJkzx9p9vWPHjnz22Wfs3bvXavUBZ9fawYMH6dixo5U2duxYxo4dy+rVq93G+6SlpbFu3TqrlSQ/b3SN5S7iOHfuXB5/3NmzfObMGRYtWsTw4cOLLRucLUHVq1fn8OHD/Pe//+Xuu++2rtWsWZOlS5e6jDVKT0/nl19+oV+/foWWlZaW5lZWQebNmwc4PwullCpWQBg2Ea8vqehpIPQOMN4YEwF8wdkxQtcB/8TZXeWJcJwDpPM7nnOtKG8Cb4nIH8aYWsU9yBgzHBgOzv9jTUhIKDRvhQoVSElJKa7Ici09PZ2srKwCZ0FVr16datWqWSsdnzlzhpSUFGsg9L///W927NjBVVddhcPh4I8//uDbb79l9uzZADzxxBP079+fHj16MHToUIKCgtiwYQNXXHEFffr0sQYMT548mU6dOhEWFka9evVIT09HRNw+24EDB/Lcc8/Rq1cvHnvsMWw2G8899xwRERHceuutLvnT0tJczgsqMywsjP/85z88+uijBX42s2bNomLFitx9993Y7XaXazabjVmzZhEfH8/gwYO54YYbeOmll+jQoQMPP/wwsbGxbN++neeff542bdrQvn1769l33nknX3/9Nddccw333HMPXbp0wd/fny1btvD+++/Tp08fWrduXeh31qBBg0Kv5X2/RS0XcN999zFp0iQCAwOpX78+U6ZMITs7m6FDh1r1nD17NqNGjWLz5s3WdxUfH09WVha1atUiKSmJt956C2MMo0ePtu4bPHgw06ZNo1+/fgwbNgwRYerUqezfv5/bbrvNype/rLffftutrEmTJnHq1Cnatm1LaGgoa9eu5Y033qB///5cdtllhf7+paWlFfm7e7E6derUJfm+yzP9Ti48e9oRIMj7BYuIRwfwb2Av4ACyc37uA8aWoIxM4N8FpO8Fni3ivluAA0BYznktnCOmrvXkufXr15ei/Pbbb0Ve/zuYMGGC5HwmbsfTTz8tIiKJiYkCyKJFi1zu/fDDD6Vly5YSEBAgFStWlCuvvFJefvlllzwJCQnSsWNHCQwMlAoVKkjnzp3lf//7n4iIOBwOeeihh6Rq1apijJGrr77aqlNERESB9d28ebNcd911EhISIsHBwdK3b1/ZsWOHSx5A3nzzTbf3mbfM06dPCyBvvfVWgc/JyMiQ8PBwueeeewr97Bo3biy9e/e2zvfu3StDhgyR6Oho8fX1lZiYGBk9erScOHHC7d7s7GyZOnWqtGnTRoKDg8Vut0vTpk1l4sSJkpycXOgzvcXhcMgzzzwjMTExEhAQIB06dJCffvrJJc+MGTMEkMTERCtt5syZUr9+fbHb7RIdHS1Dhw6VI0eOuJW/atUq6dixo4SHh0t4eLh06tRJVq9e7ZInf1nDhw93Kys+Pl7i4uIkLCxM/Pz8pE6dOjJ+/HhJS0sr8v1dDL+bpZH/M1YXnn4n5UDKIXn9zvvlpZv7CvCDeBh7FHcYKWC/pcLkTF2PBarkBCZJIuIowf2HcLbqPJkv/RTOKfQvFnCPH7ALeAWYkZNcA9iMM0BaKiJFNuc0aNBAtm/fXuj1rVu30qhRI0/fhvKC3K6oc7V69Wquv/56kpKSCAsL80LNLk3e+j687VL93UxISLAWJlXlg34n5UB6Cm/+82EyUpN4cM6SH0XE89lBRSjRFhs5Qc+flH6N6204xwJZjDGxQHDOtYIEA9VxBkKv5Lv2CbATqFvK+qi/uXXr1jFs2DANgpRS6mLn7/19xqAEgZAxphpwLc6gJP+IWBGRcR4Uswx4yBgTmqcVZxDOwdhrCrnnFJB/5bkqQDzwKPkGX6tLy2OPPXahq6CUUup8KKMZph4FQsaYG3AGHjacA6XzbyAkgCeB0LvAvcB8Y8wLQG1gIvCK5JlSb4z5A1gjIv8QkSwgIV99auW83CIi6z15D0oppZT6uzuPCyrmMwn4ErhLRI4Vl7kwInLcGNMNmIJzqnwy8CrOYCh/vTzaskMppZRSqrQ8DYRigTHnEgTlEpHfgIIXVzmbp1Yx13cDugqbUkopdUnxfouQpytLrwOKX/REKaWUUqqseLY/e4l42iJ0P/BxzjT3lRSwKKKInPFmxZRSSiml8vLx8S8+Uwl5Ggj9nPNzBoW3S+mYHqWUUkqVmbIYE+NpIHQ3ZdExp5RSSinlsQs0a0xEZnr9yUoppZRSJZBZBmsJeTpYWv1NfP755/Ts2ZOIiAj8/f2JiYnhlltuYe3atS75jDFMmTKlzOuzY8cOJk6cSHJyQXvtei4xMRFjDDVq1KCgbWE6d+7MjTfeWOC9rVq14q677nJJczgcTJs2zdrBPSAggKZNm/Liiy9am9OWJRFh0qRJxMbGEhgYSKdOndi0aZNH9z377LPUqFGDgIAAWrZsyYoVKwrMO3/+fFq3bk1gYCARERH07t2b06dPF5h38eLFGGNo1cp1xfqNGzcydOhQ6tatS1BQEA0aNODJJ58kLS2t5G9aKaXOkf+FCoSMMYeNMYeKOrxeM1Vi9913HwMHDiQmJoZp06axatUqnn/+eVJSUujQoQM7d+4873XasWMHTz755DkHQvHx8QAkJSW5BXUl5XA4GDRoEKNHj6Zdu3bMmTOHpUuXMnToUN5++23Gjx9/TuV74vnnn+fpp59m3LhxLFq0iJCQELp3786BAweKve+pp55i1KhRLFy4kCZNmtCvXz82btzokm/atGnceuut9OnTh2XLljFt2jTq1atHVlaWW5lpaWk8+uijVK5c2e3ap59+ys6dOxk3bhxLly5l1KhRvPLKK9x2223n9gEopVSplMEoIU92ZsW54OGEfMfrwBac+4494a1dYMviuBR2n//8888FkBkzZhR4/YsvvpC9e/da5xSws3tZWLRokdvO5yIiJ0+eLFE5TZs2lbZt20pwcLCMHDnS7frVV18tAwcOLPDeuLg4GTJkiHX+xhtviDFGVq5c6ZY3NTVVVq1aVaK6lVRqaqqEhYXJk08+aaWdOnVKIiMj5bHHHiv0vvT0dAkNDZXx48e7pLds2VL69u1rnR8+fFhCQkLk/fff96g+Tz31lLRr106GDBkicXFxLtcOHTrklv+9994TQHbv3u1R+efiYvjdLA3d6bz80e+kfHjr7ge9vvu8Ry1CIjJRRJ7Md/wbuBzYALj/b6Y6r1577TVat27t1gWUq1+/flSrVs0lLTs7m0cffZSoqCiio6MZNWoU6enpLnk2bdpEt27dCAoKIjw8nNtuu42DBw+65HnuueeoW7cuAQEBVK5cmd69e3PgwAESEhLo168fAJdddhnGGGrVqlXi9/brr7/yyy+/MHToUPr378/cuXMLbNnw1KuvvsoNN9xA9+7d3a4FBATQrVu3UpftiXXr1nHy5EluvvlmKy04OJh+/fqxbNmyQu/buXMnKSkpbvXu0aMHK1euJCPDufPNnDlzABgyZEixdfnrr7+YPHkyL7zwQoHXo6Ki3NKuuOIKAA4d0oZgpdT55SP5d/jyQpnncrOICDANGO2d6qjSyMrK4vvvv6dnz54luu/ll19m3759fPTRRzz00EO89957vP7669b1w4cP07lzZ86cOcPs2bN58803WbNmDT169LD+6M6aNYtJkyZx//33s2LFCt555x3q1q3L6dOnadmyJS+99BLgHK/y/fffs2DBghK/v9mzZ+Pr68vAgQMZPHgwhw8f5quvvipxOeDsWktMTKR3796lul9EyMrKKvYoyrZt27DZbNSrV88lvVGjRmzbtq3Q+3LH5fj7u66jYbfbycjIYNeuXQCsX7+eBg0aMH36dKpXr46fnx9t2rRh3bp1bmU+8MAD3HzzzbRo0cKj9w/OQM7Hx4cGDXSNVaXU+SXG+0sWerz7fBFqA95f4ehCm1jhQtcAJp7wKNvRo0dJT08nNjbWJV1EyM4+uwqnzWbD5BloVqtWLWbOnAlAr169WLt2LfPnz+fhhx8GnIESwIoVKwgLCwOgfv36tGnThs8++4zBgwezYcMGevbsyciRI61yBwwYYL3O/WN5xRVXlKo1COCTTz6hR48eRERE0KtXL8LDw4mPj6dXr14lLmvv3r0A1KhRo1R1WbNmDV26dCk2X2JiYqHv9/jx44SEhGCzuS69FR4ezpkzZ8jIyHALdgBq166NMYaNGzfStm1bK33Dhg0AHDvm3AHnwIEDbN++nWeeeYbJkycTERHB5MmT6d27N7///rs1Fmj16tWsWLGCHTt2ePTec8t+9tlnueOOO6x/E0opdb742C7QgorGmJEFJPsDjYDbgLnerJQqGcmZRWXyjaZ/+eWXeeihh6zzN998k9Gjzzbe5W9Baty4MT/88IN1nhvk5P2Dd+WVV1KrVi2+++47Bg8eTIsWLZg+fToTJkygb9++xMXFuf2BPxfr169n165dTJgwAXC2hgwYMIC5c+fy7rvvEhAQUKpy839WnoqLi3MbmFyQ/N2Qnjy/sO8xV4UKFRg8eDDPPvssTZs2pXnz5nz88cesWrUKwPrcHQ4Hp06dYu7cuVbLV/v27alZsyZTpkzh6aefJisri3vvvZfHH3+cKlWqkJKSUux7ysjI4OabbyYkJIRXX3212PxKKeVtNof3u8Y8bREqaJ51OrAHeBt40ms1UiUWGRmJ3W5nz549Lul33HEHnTt3BqB169Zu91WsWNHl3N/f32Va9P79+2nSpInbfZUrV7ZaH+6++25SUlJ4//33eeqpp4iIiGDEiBFMnDjRKwFRfHw8fn5+dO7c2Zp51rdvX6ZPn87SpUut1idfX1+X1q+8srOz8fV1/lOPiYkBnGNjSiMkJMSjbqTc5xUkPDyclJQUsrOzXT6j5ORkgoKC8PPzK/Te1157jUGDBtG1q3Pf4tjYWB5//HEmTpxotfRUqlQJwPruAcLCwoiLi+O3334DYOrUqSQnJzNkyBCSk5NJSUkhIyOD7OxskpOTCQ4OdqmHiHDnnXfy66+/snbtWsLDw4v9DJRSytscF6pFSEQuvfWGPOyWKg98fX1p164dX375JU899ZSVXrly5QKnRHuqatWqBQ6IPXjwIHFxcQD4+Phw3333cd9995GUlMTHH3/MY489RkxMDP/6179K/WxwtmzMmTOHzMxMatas6XY9Pj7eCoSioqLYvXt3geXs37+f6OhowBk41K5dmxUrVjBs2LAS18kbXWMNGzYkOzubP/74w2WczbZt22jYsGGR5UZFRfH111+zZ88eTpw4QYMGDXjttdeoUqWK9bxGjRphjHFbb0lE8PFx/ipv376dPXv2UKVKFbdnhIeH8+GHH3L77bdbaffddx8LFy5k5cqVxdZRKaXKzoXbfV6Vc2PHjmX9+vV8+OGHXiuzTZs2rFixwqXbZOPGjezevZsOHTq45Y+NjeU///kPdevWtVoecse6lGYBvoSEBPbv388LL7zA6tWrXY4777yTxYsXW3Xr2LEjP/74ozUGKNf69es5ePAgHTt2tNLGjh3L/PnzWb16tdsz09LS+PrrrwutU27XWHFHUV1juYs4zp17tkf5zJkzLFq0iD59+nj02VSvXp0mTZqQlZXFf//7X+6++27r2rXXXouIuLy/EydO8OOPP9K8eXMARo8e7fJ5LlmyhF69elG/fn1Wr15Njx49rHufe+453nzzTT766KMCv3ellPpb83SePc5B0e/gXDtob87Pt4Ha3prLX1bHpbCOkIjI2LFjxcfHR4YOHSrz58+Xb775RhYsWCAjR450W2OIAtYRmjBhgkRERFjnhw4dkgoVKki7du3k888/l48++kiqV68uzZo1k/T0dBERGT58uPznP/+Rzz//XFavXi1PPPGEGGNkwYIFIiKye/duAWTs2LHyf//3f/Lzzz+LiHMdIUAmTJhQ6PsZNmyYVKxYUdLS0tyu/fjjjwLIrFmzREQkJSVF6tSpI7Vr15b//ve/8tVXX8mUKVMkKipK2rdvL9nZ2da92dnZcuONN0pAQIA88MADsnz5cvn666/l1VdflTp16sjYsWNL9sGXwqRJkyQwMFCmTJkiq1atkmuuuUYiIiLkwIEDVp4PPvhAbDaby3o9s2bNkunTp8vq1avlgw8+kBYtWkjTpk0lJSXFpfzrrrtOqlSpIjNnzpTFixdLp06dJDIyUo4dO1ZgfU6ePFngOkIff/yxAHLXXXfJ999/73IUtMaQt10sv5slpWvWlD/6nZQP7w0f6fV1hDwNguKAk8Ah4L/Aczk/DwIngJbeqlBZHJdKICQiMn/+fOnevbuEh4eLr6+vVK1aVQYMGCBLly51yedJICQi8tNPP0mXLl0kMDBQKlSoIIMHD3b5Yz1jxgxp3769hIeHS2BgoDRr1kymTZvmUsZLL70kNWrUEJvNJjVr1hQRkQMHDgggb731VoHvIyMjQ8LDw+Wee+4p9L02btxYevfubZ3v3btXhgwZItHR0eLr6ysxMTEyevRoOXHihNu92dnZMnXqVGnTpo0EBweL3W6Xpk2bysSJEyU5ObnQZ3qLw+GQZ555RmJiYiQgIEA6dOggP/30k0ueGTNmuC1GOXPmTKlfv77Y7XaJjo6W4cOHy5EjR9zKT0lJkX/9619SqVIlCQgIkG7dullBaEEKC4SGDBkiONui3Y7CFu/0povpd7Mk9I9u+aPfSfnw3vB/ez0QMiLF97cZY1bj7EbrIyJn8qQHAUsBh4h0LV2bVNlr0KCBbN++vdDrW7dupVGjRuexRmrJkiXceuutJCUl6TTsciAlJYXQ0NALXQ03l+rvZkJCgstgd3Xh6XdSPkwbMZwTx/bx4JwlP4pIq+LvKJ6nY4SuBCbnDYIAcs5fAtp4ozLq0rF+/XqGDRumQZBSSimPlcFOYx5Pn08FIgq5VgnQrahViTz00EPlsgVCKaVUeXaBdp8HlgDPG2NcpozknD8HLPJ2xZRSSiml8srE4fUyPW0Ruh9YCKwxxhzGOUg6OudYBzzg9ZoppZRSSuXhVwar/ni6oOJRoIMxpjfQGqgK7AfWi8iXXq+VUkoppVQ+pgy6xooNhIwxduBBYLGILAeWe70WSimllFLFugArS4tIOvAYULG4vEoppZRSZefCDZZej3NRRaWUUkqpi4ang6UfBmYbYzJwLqB4kHztU/nXGFJKKaWU8qoyWEioJC1CdYA3gN9xbreRku9QF9DEiROJjIx0SXM4HNx2220EBATw5Zd/jzHtEyZMwBjD+PHj3a7t3r0bYwyLFy92u/bLL79gjCEhIcEl/eDBg4wdO5Y6depgt9sJDw+nT58+rFixoqzegou9e/dyww03EBISQmRkJKNHj+bMmeL/nyEpKYmBAwcSFhZGhQoVuOWWWzh06JBLnrfffptWrVoRHh5OUFAQzZo14+233yb/avHp6ek88MADREdHExwcTN++fdm9e7dLnqlTp3pUllJKXWw8bRG6m7IYoaTKjIhwzz33MHfuXD777DN69ux5oavkkU8++QSA+Ph4nn766XMqa/v27XTp0oXg4GAefPBBGjduzMmTJ1m6dCn9+/dnw4YN1m7sZSErK4tevXrh7+/Pp59+SnJyMvfffz/Jycl89NFHRd7Xp08fHA4HM2bMwOFw8Mgjj9CnTx82bNiAzWYD4Pjx49xwww1cfvnlBAUF8dVXX1mB1oMPPmiVd++99zJv3jxeffVVoqKimDhxIj169GDLli0EBAQAkJyc7FFZSil10fHWpmXl+bgUNl3Nv2HqyJEjxWazybx58y5grQp38uRJt7QffvhBAOnWrZsAsmHDBpfriYmJAsiiRYvc7t2yZYsALhsjxsXFSZMmTQrcdHXz5s3y559/nvsbKcLs2bPFx8dHdu3aZaV9+umnYoyRHTt2FHtf3jybN28WQObOnVvkM2+99VZp1qyZdZ6UlCQ2m00++OADK23Pnj3i5+cnU6dOtdIK+j7yl3UhXAy/m6WhG3yWP/qdlA8z/jnU65uuen9lInXBPfDAA7z77rvMmjWLgQMHulybNWsWHTp0oFKlSoSHh9OlSxd++OEHtzLmzJlDs2bNsNvtxMbG8thjj5GVlWVdnzlzJsYYtmzZQo8ePQgODqZhw4bMnz+/1PWOj4/HbrczY8YMAgICiI+PL3VZ33zzDT/++CPPPfdcgfuZXX755dSoUaPU5Xti2bJltG7dmssuu8xKu/766/H392f58sJXodi0aRM1a9akXr16LvWtXLkyS5YsKfKZERERZGRkWOe5XaIDBgyw0mJiYujQoQPLli0rUVlKKXWhOXzsXi/To0DIGJNojNlVyPGHMeYnY8wMY4zOLLvAHnvsMV599VWmTZvGrbfe6nZ99+7d3HnnncydO5fZs2dTvXp1OnXqxK5du6w8X375JYMGDaJly5YsXLiQMWPG8NJLLzF69Gi38m699Vb69+/PggULqFevHrfccgt79uwpcb1FhE8//ZRrrrmG2NhYrrnmGj799FMcjtItp75mzRpsNhvdu3cv1f0Oh4OsrKwij+zs7CLL2LZtGw0bNnRJ8/f3p06dOmzbtq3Q+9LS0vD393dLt9vtbN261S09KyuLU6dOsWzZMmbNmsWoUaNc6lC9enVCQkJc7mnUqFGBdSiqLKWUuhh52iL0Gc7xRKE4B04vzvkZBvgBPwBtgf8zxvQqg3oqDxw9epRJkyYxduxYhg4dWmCeJ554guHDh9OtWzd69erFjBkzqFGjhsuYlSeeeILOnTvzwQcf0Lt3bx5++GGefvpppk6d6hbk3HfffYwZM4aePXsyc+ZMHA5HgYOZi/Ptt9+yZ88ebrnlFgBuueUW9u3bxzfffFPissA5SDkqKorAwMBS3X/33Xfj5+dX5NGtW7ciyzh+/DgVK7ovvxUeHs7x48cLva9u3bokJiZy9OhRK23fvn3s3buXY8eOueQ9cOAAfn5+hIaGcs011zBmzBjGjBlTqjoUV5ZSSl14F2Bl6RyHgB3AtSJi7TRvjAnEueHqX0BT4AvgSeD8TMkpQ80+aHahq8CWIVtKlD8sLIxGjRoxffp07rzzTlq0aOGWZ+vWrTz66KOsW7fOZRbSjh07AMjOzuann37itddec7lv0KBBjBs3ju+//56bbrrJSs87CDsiIoLo6OhStQjFx8cTHBzMtddeC8C1115LaGgo8fHxdO7cucTlARhT+l+YiRMnFtgClldoaGip6iAiRdbt1ltv5fHHH+cf//gHb7zxBg6Hg3/+858A1kDpXJGRkWzcuJFTp06RkJDA888/T0hICOPGjStxHTwpSymlLqgymD7vaSB0LzA8bxAEICKpxphXgWki8owxZirwsbcrqTzj5+fHkiVL6NChA3369GHt2rXUrl3bup6SkkLPnj2pXLkyr7zyCjVr1iQgIIBhw4aRlub8ao8cOUJmZiaVK1d2KTv3PH+LRP7WBn9/f6ssT2VlZTFv3jx69uxJRkaGNS6lV69ezJs3jylTpuDn54evr/Ofa0FdUrlpuXliYmI4fPgwaWlp1syokqhRowbVq1cvMk9xgVZ4eDjJyclu6cnJyQW20uSKiIhg9uzZ/OMf/6BmzZqAc2zRNddcw8mTJ13y+vr60qpVKwA6d+6Mj48PEydOZMyYMQQFBZWoDsWVpZRSFyNPu8YqApULuVYZyB2AcAIoeuCEKlMRERF8+eWX+Pr60qtXL5dWn++//549e/bw0Ucfcdttt9GhQwdatWrFiRMnrDyRkZH4+fm5rVlz8OBBACpVquT1Oq9cuZIjR46wYMECwsPDrWPevHkcO3bMGvAbERGBj48PBw4ccCtj//79AERHRwPOP+RZWVl89dVXpaqTN7rGGjZs6DYOJyMjg127drmNHcqvb9++7Nmzh19//ZWkpCQWLFjArl27aNu2bZH3tWzZkrS0NPbt22fVISkpidOnT7vkK2j8UnFlKaXUBXcBW4QWA5ONMSdwbr6aYYzxB/oDk3OuAzQDdnq/mudfSbulypPY2FiWL19Ox44d6dOnDwkJCYSGhpKamgo4B93mWrduHbt37yYuzjnO3WazERcXx9y5cxkxYoSVb86cOfj4+NCuXTuv1zc+Pp7w8PACZ5wNGjSI+Ph4+vbtS2BgIHFxcSxcuNDqKsq1cOFCqlSpQt26dQHo2LEjcXFxPProo3Tq1MmtG2vLli1UrFiR2NjYAuvkja5VeqHHAAAgAElEQVSxPn36MHv2bP7880+rZeeLL74gPT2d3r17F3kvOFtoGjduDDgHf2/bto158+YVec/atWux2+1Uq1YNONt1uWDBAm6//XbAOd7o22+/5e233y5RWUopdVHyZI49zhahhYADZ4tPcs5PR056xZx8NwJ9vDW331vHpbiOkIjI2rVrJTAwULp16ybp6ely4MABCQkJkW7dusmKFStk+vTpEhsbKzExMTJw4EDrvhUrVgggd911lyxfvlxefPFFsdvt8s9//tPKM2PGDAEkJSXF5Zk1a9aUBx54wC1fYmKiS77cdWtSU1MlNDRU/vWvfxX4vsaMGSMhISFy+vRpERFZsmSJGGNk0KBB8vnnn8uSJUtkxIgRYoyRd955x+Xebdu2SdWqVaV+/fryzjvvyJo1a2TRokUyevRosdvtsmnTJg8/3dLJyMiQJk2aSMuWLWXJkiUye/ZsqVy5stx2220u+bp27Spdu3Z1SXvwwQdlwYIFsnLlSnn22WclKChIJkyY4JKnVatW8uabb8qXX34pS5YskbFjx4qvr6+MGzfOJd/w4cMlIiJCZs2aJcuWLZM2bdpI3bp1JTU11cpzxRVXeFTW+XYx/G6Whq5ZU/7od1I+TB81wuvrCJUsMzQBhvx/e/cdHlWxP378PZBGEhJCCKEJoYYSvFIUEYFQBQHpCqIGLujlqigg6lfFSywoNriKDRApKiUUC9JBQAWuFMtPMRGk944EQgIkn98fZ7Nmk92UZVNIPq/nOU/YOTNz5uxk2cnMnBngadvPRp4qSH4eJbUhJCKyZMkS8fLykrvvvltSU1Nl+fLl0qhRI/Hz85PGjRvL0qVLpW3btg4NIRGRefPmSVRUlHh7e0vVqlXl2WeflStXrtjP57Yh9N5774mPj4+cOXPGIV56Q2jBggUCyMaNG53e1w8//CCAzJs3zx62bNkyadWqlfj7+4uvr680adJEZs+e7TT90aNH5bHHHpOaNWuKj4+PlCtXTjp37iyLFi1yGt/TDh48KD179pSAgAApX768PPzww/ZGXbq2bdtK27ZtHcL69+8vYWFh4uPjI1FRUTJ16tQseQ8bNkzq1q0rZcqUkdDQULn11lvlk08+kbS0NId4ycnJMmrUKKlQoYL4+/tL165dHRZ5FBGJiYnJVV4FrTh8Nt2hX7pFj9ZJ0ZAfDSEjkvPOGcaYUBE5nc35xiJSZMeSIiMj5Y8//nB5Pj4+ngYNGhRgiUqOmJgYSpUqxYwZMxzCExMTc/XUlSoYRbU+Supnc/369W4/Lanyh9ZJ0fDxo49w9uR+xsQt3S4izT2RZ24nS68xxgQ7O2GMaQGs90RhVPGzefNmRo8eXdjFUEopVRwYz297mtvJ0knASmNMRxG5YC+PMdFYawct8njJVLGQvj6RUkopde08/9hYbnuEugKlgWW2RRQxxnQDlgOzRcT5MsZKKaWUUkVYrhpCInIeuAMIBpYYY2KAz4H/ikj2zxgrpZRSSnlAPiwjlPvd50XkDNABqAx8DIwTkWfyoUxKKaWUUk4U4F5jxpg4F6dOA2eBJhniiIjc4+nCKaWUUkr9rWAnS4e5CE8Ffs3mvFJKKaWU513DZtquuGwIiUg7j19NKaWUUqoIyfUcIaWUUkqp4iZXDSFjzMfGmPkuzs01xnzk2WIppZRSSuW/3PYIdQJcbXu9COjsmeIod8XGxmKMoW7duk7P16lTB2MMsbGxBVuwa3Dx4kUCAgLw9/cnMTExy/nBgwfTvLnzFdb79evndDn8xYsX0759e8qVK4evry/16tVj7NixnDp1ytPFd2ratGnUrVsXPz8/mjVrxtq1a3Odrl69evj6+tKgQQM+/fTTLHEOHjxI3759CQoKIjg4mAEDBnDixAmHOO+//z7NmzcnJCQEf39/GjduzPvvv09uttpRSqnCVpiPz4cBZ1ycOwtU9Exx1LXw8/Nj7969bNu2zSF869at7N+/Hz8/v0IqmXu+/PJLkpKSuHTpEl9++eU15/fEE0/Qv39/atWqxSeffMKqVasYNWoUS5Ys4cEHH/RAibM3b948hg8fzgMPPMDy5ctp1KgR3bt357fffss23dy5c/nXv/5Fnz59WLJkCV26dOGBBx7g888/t8e5evUqXbt2JT4+nhkzZvDRRx+xbds2unbtSmpqqj3e2bNn6d27N7Nnz2bJkiX06NGDRx99lMmTJ+fbfSullKdIfjSFcrMzK/AH8IKLcy8Af+Z2l1egIbAWa9uOI8CLQOkc0twMzAD+tKX7AxgH+OXmmiVp9/lOnTo57P4uIjJ69Gjp3LmzhIaGyrhx4wqngJmk7z6fne7du0vNmjWlZs2acuedd2Y5HxMTI82aNXOatm/fvg47un/11VcCyPTp07PEvXr1qixbtiz3hXdTvXr1ZMiQIfbXqampEhUVJYMGDcox3f333+8Q1rt3b2nUqJH99Zw5c6RUqVKyc+dOe9gvv/wigCxYsCDb/O+9916HvIqS4vDZdIfudF70aJ0UDTNGPOLx3edz2yM0E3jaGPOIMSYQwBgTaIx5GHgKyNUcIWNMCLAGayGAnrZG0BO2xlR27gFqA68BdwLvAaOBz3JZ/hJjwIABxMXF2Yc6RIS4uDgGDBiQJW50dDT9+vVzCFu/fj3GGIdeilOnThETE0NoaCj+/v5ER0dn6XWKiIhgzJgxTJo0iWrVqhESEsKAAQM4d+6cW/dx9uxZVq5cyYABAxgwYACrVq3i9OnTbuUFMGnSJJo2bco///nPLOdKly5N165d3c47N/bs2cPOnTu5++677WGlSpWif//+LF++3GW6pKQkdu3aRceOHR3CO3fuzI4dO9i3bx8AP//8MzVq1HAYGr3xxhsJDw9n6dKl2ZYtNDSUy5cvu3FXSilVsIykeTzP3DaEXgM+BSYDfxljzgN/Ae8Cs2znc2M4UAboIyKrReRDrEbQaGNMUHbXF5E2IjJNRNaLyDvAk0AfY0yNXF67ROjTpw/Hjx/n+++/B+C7777j5MmT9O7d2+08e/XqxcqVK3nzzTeZP38+aWlptGvXjj///NMhXlxcHGvXrmXq1Km89tprfP311zz77LNuXXPhwoVcuXKFAQMGMHDgQK5evcrCha6mqWXvypUrbNq0iS5duriVHqyhp5yO9ManMwkJCQDUr1/fIbxBgwacOXOGkydPOk2XkpKCiODj4+MQ7uvr65BvcnJyljjp8eLj453ez4ULF1i+fDmzZ88ukKFBpZS6VmmlcrtXfO7lKkcRSQOGGWPeANoD5bFWmP5GRPKyvXhXYKVYe5elm4fVkGoLLHFxfWffEj/ZflYE9uehDLkSX7+Bp7PMswYJWb/AclKuXDm6dOnCvHnzaN26NfPmzaNLly6UK1fOrTKsWLGCjRs3sn79etq2bQtA+/btiYiI4I033mDKlCn2uN7e3nzxxRd4eVm/Vr///jvz5s3j/fffz/N1586dS4MGDbjxxhsBaNiwoX2uTF6dPn2alJQUqlevnue0APv27aNmzZo5xlu3bp3TCdpg9XABWeohJCTEfj4sLOsapSEhIZQvX56tW7c69Opt2bIFgDNnrKl7derU4cMPP+T06dOEhoYCcOTIEQ4fPmxvNKU7duwYlStXtr8eO3Ysw4cPz/H+lFKqsOXHZOk8Na1E5A+s+Tnuqg98kynPA8aYJNs5pw0hF24D0q6xPMXSgAEDGDlyJBMnTmThwoW88847bue1ZcsWwsLC7I0ggICAALp3727vdUrXrl07eyMIrMbLiRMnuHz5stPeCleOHj3Khg0bGDdunD1s4MCB/Oc//+Hw4cNUrVrVrXsxbq5IWqVKFbZu3ZpjvMjIyDyXIb0XKbuyDR8+nLfffptWrVrRrl07VqxYwSeffAJYw3oA9957L2PHjmXo0KG88847pKWl2RuN6XHSVahQga1bt3LhwgXWr1/PhAkT8Pb25j//+U+O5VdKqeImTw0hY0w1oB6Q5fEjEVmWiyxCAGeTRs7azuW2HJWA54BPMvUuKeCuu+5i2LBhPPfcc1y8eJEePXq4ndfRo0cJDw/PEh4eHm7vjUiXubfDx8cHEclzQyh9+K1Lly72OUZdu3bl+eefZ/78+YwePRoALy8vhyeiMkpNTbU3ykJDQ/H19eXAgQO5LkPm+7jppptyjJe5wZFRes/PuXPnCA4Otoen3192PXbPPfccu3btom/fvgCUL1+e2NhYnnzySXvdhIaGMmfOHIYOHUqNGtZoca9evbjzzjs5f97xI+Ll5WVfdiA6OppSpUrx6quvMmbMGPz9/XO8T6WUKiySD11CuWoIGWPKAnH8vV5QelEyTopw/S3gyNlECuMi3FlZfGxluQCMyibeQ8BDAGFhYaxfv95lnsHBwVnWqam2dUtuipOvnK2d40r6XJL0NF26dGHSpEn06tWLtLQ0EhMTERFSUlLscby8vEhKSnK4zuHDhwFrDZ/ExETKly/P8ePHs5Tl0KFDlCtXzh6e3uDJGC85Odl+H5nnz6Smprq8v/Q1clq0aOH0XPp8luDgYI4ePeo0n0OHDhEREWE/d+utt7Js2TKefvppp9fMzv79+2ncuHGO8ZYuXUrr1q2dnqtWrRoAP/74I+XLl7eH//LLL4SEhODn55dtfU+fPp1XX32VU6dOUatWLVasWIGPjw9169a1p2vTpg3x8fH8+eeflC1blqpVq9KiRQu6dOmSbd7169cnOTmZnTt3Urt27RzvsyAlJydn+9ktrtJ761TRoXVSNFxJ8fyDHbntEXoVqA60Br4HemP14tyHNWdoYC7zOQs4+9M3GOc9RQ6MNX4wG2gEtBKRs67iishUYCpAZGSkuJq7ARAfH0/ZsmVzunyR5uvrizHGfh8jRowgNTWVESNG2MOMMfj6+tpfR0RE8O233zrce/pwV0BAAGXLlqVNmza88sor/PTTT7Rp0wawnmRatWoVvXv3dsjbx8fHIa/0dYvKli1LYGCgQ3kTExOdvud79uxh+/btjBo1irvuusvh3PLly3n99dc5duwYdevWpUOHDkycOJH4+HhuueUWe7xDhw7x888/889//tN+jSeeeIK77rqLxYsXExMT45BvWloaq1atcjmZul69erkeGnP1e3TjjTdSr149li5dap+4npaWxldffcWdd96Zq9+/smXLUrt2bdLS0pg1axb9+vVzOkx48803A7BhwwZ27tzJ4sWLs83/p59+si8uWdR6hPz8/GjSpElhF6PArV+/3uV8M1U4tE6Khn1ffJ5zpDzKbUPoTmAs8IPt9RER2Qp8a4x5C+sJrrtdJc4gAWsukJ0x5gYgwHYuJ5OwHrvvJCK5iV9iRUdH5/ih7d27N9OnT2fUqFF069aNdevWsXLlSoc4d9xxB61ateKee+5hwoQJhIaG8uabb3Lp0iWefPLJPJcrIiKC6Oholwv4zZ07l1KlSjFmzBiqVKnicK5hw4ZMnDiRefPm8fzzz9OlSxduu+02unfvzrhx42jQoAH79+/n5ZdfpkaNGtx///32tD169GD06NEMHTqUjRs30rNnTwIDA0lISODDDz8kIiLCZUPIx8fH5QrWeREbG8t9991HREQErVq1YtasWezatYs5c+bY42zYsIEOHTqwdu1a+7ysr7/+mv3799OgQQNOnDjBtGnTSEhIYNasWQ75P/nkk7Rq1YrAwEC2bNnC+PHjGTt2rMOTajfffDMxMTFERkZy5coVVq9ezbvvvsuIESOKXCNIKaUyy5c18HOz2BBwEWht+3cicEeGcx2Ac7nM5xmsFarLZggbg7VIYlAu0qYCffO6WFJJWlAxO84WVHzllVekWrVqEhgYKIMGDZIvv/xSAPn111/tcU6cOCH333+/lCtXTvz8/KRNmzayZcsWh3xq1KiRZSHHGTNmCCCJiYn2sLCwMHnyySddLqjYqFEj6dSpk8t76Nq1qzRo0MD++ty5czJixAipVq2aeHl5ScWKFeWBBx6Qw4cPO02/cOFCiY6OlqCgIPH29pa6devKE088IUePHnV5TU+aOnWq1K5dW3x8fKRJkyayZs0ah/Pr1q0TwGHxtuXLl0vjxo2lTJkyEhISIgMGDJD9+/dnybt///4SFhYmPj4+EhUVJVOnTs0SZ9iwYVK3bl0pU6aMhIaGyq233iqffPKJ/PXXXx6/V08oDp9Nd+jifUWP1knRMOPxxzy+oKKRbNY+SWeMSQCeEZHPjTE/AhtFZITt3LPAYyJSKRf5hAC/A79hPTJfC5gI/FdExmaI9yewQUSG2l7fi7V44kxgSqZsd4vzx+vtIiMj5Y8/XD9cFh8fT4MGhf+4fHG3d+9e6tSpw65duwgLC7vuhyOLE1dDlYWtpH42dRim6NE6KRpmjnyc00d3MyZu6XYRufauenI/NLYa6Ah8jjU8NcsY0wxIAdoAb+UmExE5a4zpgLUQ4xKseUGTgFgn5co4+Tp9kvZg25HREKwGkiriNm3aRK9evahVq1aeJoIrpZRSQL4sJJTbhtDTgD+AiHxijLkA9MNaJfpRsvbSuCQiv2NNsM4uTkSm14PJ2gBS15lBgwYxaNCgwi6GUkqp61ShLagoIklY83jSX3+O1TuklFJKKXXdyu1eYw6MMeWMMc2MMRU9XSCllFJKqYKSbUPIGDPAGDPPGLPIGDPIFvY8cBTYAhy1nQsogLIqpZRSSnmUy4aQMeZBYA5QE2vBwxnGmEnAaKztLboB/4f1+Pxz+V9UpZRSSinPym6O0Aisx9pHAxhj7gNmAY+LyLu2OCuMMVeB4cCz+VpSpZRSSpVwnp8und3QWG0cd4P/0laC7ZnibQNqeLhcSimllFKO8uGxsewaQmWwVpROl/7UWEqmeJcBb08WSimllFIqiwJuCIHzbT3yZasPdW1iY2MxxtiPKlWq0LdvX3bv3m2PM3jwYI/smeUJ3377LcYYl7u1R0REMGbMGKfnKlSoQGxsrENYSkoKb775Jk2aNCEgIAB/f39uvvlmpk6dyuXLnt+tOLOUlBSeeOIJKlasSEBAAN26dWPfvn25Sjd69GgqVaqEv78/rVu3Ztu2bU7jTp06laioKPz8/AgPD+eee+7JEmfatGnUrVsXPz8/mjVrxtq1a7PEWb16Na1atSI4OJjw8HB69+7Nrl278nzPSilV8Ap2aAxgpTHmhDHmBNaTYgBr08Ns4cs9XirlluDgYDZv3szmzZt58803+fnnn+nQoQMXL1ode88//zwzZ84s3ELaLFy4EICNGzdy4MCBa8rr0qVLdOzYkZdffpmePXvy5Zdf8tVXX3HXXXcxduxYPvjgA08UOVuPPfYYM2fO5M0332ThwoWcOnWKTp06kZycnGO66dOnExsby6JFiwgMDKRjx47s37/fId7YsWN56qmnGDx4MCtXrmTy5MlUqFDBIc68efMYPnw4DzzwAMuXL6dRo0Z0796d3377zR5n+/btdOvWjapVq7JgwQLef/999uzZQ8+ePTl//rzn3hCllLpeuNqEDBiXl8NTm5/lx1FSN1397rvvBJC4uLhCKpVzly9flpCQEGnfvr0A8vrrr2eJ42wT13SZN48dPXq0+Pv7O2wUm+706dOyceNGj5XdmYMHD0rp0qVl1qxZ9rBDhw6Jt7e3TJs2Lcd006dPt4clJydLlSpV5JFHHrGH/fbbb1KqVClZtWpVtuWoV6+eDBkyxP46NTVVoqKiZNCgQfawp59+WsLDw+XKlSv2sF9++UUAWbZsWe5uuAAVh8+mO3SDz6JH66RomDn6cY9vuuqyR0hEXsjLkY9tNeWmZs2aAdiHaJwNjaX3Gvn7+xMSEsKgQYM4fvy4/fy+ffswxhAXF8e//vUvgoODqVatGuPGjSMtLc2tcq1cuZKzZ8/y9NNP07JlS+bOneveDQJJSUlMmTKF4cOHExUVleV8+fLlue2229zOPzdWrVoFQJ8+fexhVatW5fbbb2f5ctcdpr/++iupqal07NjRHubr60vr1q1ZunSpPWzWrFnUrl2bTp06ucxrz5497Ny5k7vvvtseVqpUKfr37+9QhitXruDv74+X198PjJYrVw4g/Q8gpZQquqR0znHyyK2VpdX1Ib0BVKlSJafnT548SXR0NElJScyZM4fJkyezYcMGOnXqlGVezVNPPUVgYCALFy7kvvvu48UXX7QPb+XV3LlzqVChAu3bt2fgwIH89NNPJCQkuJXX9u3buXjxIl26dHErvYhw9erVHI/sJCQkUK1aNQIDAx3CGzRokO19pQ+b+fj4OIT7+vqyf/9+kpKs5xN++OEHGjduTGxsLBUqVMDX15eOHTsSHx/vUAaA+vXrZynDmTNnOHnyJAD33XcfR44c4bXXXuPs2bMcPHiQ0aNHU69ePTp06JDtfSqlVKEz7v0Bnh1tCBUz6V/cO3fu5OGHH6Zs2bIOPQ4ZvfXWW4DVQ9OrVy/uu+8+Fi9ezK+//sqiRYsc4rZp04a33nqLTp06MWHCBP7xj3+wePHiPJcvKSmJr776ip49e+Ll5cXdd99N6dKlmTdvXt5vFjh8+DAA1atXdyv9rFmz8Pb2zvHIztmzZ+29KhmFhIRw9uxZl+nq1KkDwNatW+1hIsLWrVsREc6dOwfAsWPHWLVqFXFxcUybNo2FCxdy8uRJunTpYm9MpV8nczlCQkIczjdp0oSvv/6aCRMmUL58eapXr86OHTv4/PPP8fX1zfY+lVKqsJl8aAjldvf5Eue94d8UdhF45MP2eYp/+vRphy/t6tWrM3/+fCpXruw0/pYtW+jcuTNBQUH2sFtuuYWIiAi+//57Bg4caA/v3LmzQ9qGDRu6Ncl5yZIlXLhwgX79+gEQHh5OdHQ0c+fOzfIkWF4Y496TBD169HBoiHjy+iKSbbkaN25Mq1atGDNmDFWqVKF69epMnDiRnTt3AlC6tNUFnJaWxsWLF1m0aBENGjQAoFGjRtSrV4/PPvuMoUOHuixH+nBXeviOHTu499576dOnD/feey8XL15kwoQJ9OvXj//9738OvwtKKVXUmHx4akwbQsVIcHAwa9aswRhDpUqVqFKlSrZfxEePHqVRo0ZZwsPDwzlz5oxDWOaeBh8fnxyfiHJm7ty5hIeH07BhQ3uPR48ePRg5ciQ//vgjTZs2BcDLy4vU1FSneaSmptrnuFStWhWAAwcOUK9evTyXp3z58gQHB+c5XUYhISH2e8no3LlzTnuKMpo5cyb9+/e3z91q1KgRjz/+OJMnT6Z8+fL2Ml64cMHeCAKoVasWERER/P777/YypF8z4/2klyu9HM8//zx169Zl+vTp9jitW7emWrVqfPTRR4wePTrP96+UUtczHRorRry8vGjevDnNmjWjatWqOfaSVK5cmRMnTmQJP378uP1L2JPOnTvHihUrOH78ODVq1CAkJISQkBBGjhwJ4DBpOiwsjGPHjmXJIykpib/++ouKFSsC0Lx5cwICAli5cqVbZfLE0Fj9+vU5ePCgfZmCdAkJCVnm7GRWp04dfvrpJ3bv3k1CQgK//vorKSkpNG3a1H7djA2gjESEUqVK2cuQfs3MZShfvjxhYWH21zfddJNDnJCQEG644QaHNaeUUqqk0B4hF/I6LHU9atGiBR988AGJiYmULVsWsOar7Nu3j9tvv93j11u8eDEpKSnMmjWLChUq4O/vbz/36quvMm/ePF5//XX7QoszZszg4sWLBAQE2OMtWbIEEbGXr0yZMvzrX//igw8+YMiQITRs2NDhmufOnSM+Pp6WLVs6LZMnhsbShw0///xz7rvvPgCOHDnCd999x/vvv5+rPGrVqgXAqVOniIuLY/z48fZz3bt3Z9asWfz+++/2+9u9ezf79+/nH//4hz19vXr1WLBgAXfccQdgDaktWLCArl272vOqUaMGP/30k8O1T58+zYEDB4iIiHDj7pVSqgDlw8rShb7GT0EcJXUdocxiYmKkWbNm9tcnTpyQ4OBgadmypXzxxRfy6aefSrVq1aRx48aSkpIiIiJ79+4VQJYsWZJtXunxZsyY4fL6HTt2lPr164uIyPnz5x3OLVq0SAD59ttvRcRah6d8+fLStGlT+eyzz2T16tUyYcIECQgIkHvuucchbVJSktx+++1Srlw5eeGFF2TNmjWyZs0aGT9+vFSqVEn++9//Zvu+eMJDDz0koaGhMnv2bFm+fLm0aNFC6tSpI5cuXbLHeeGFF6R06dIO6d5++2359NNPZd26dTJ16lSpWbOmtG/fXlJTU+1xrl69Kk2bNpXIyEhZsGCBLF68WKKioqRBgwZy+fJle7w5c+ZIqVKl5KWXXpJvvvlGYmJixM/Pz2F9pS+++EIAiYmJkRUrVsiiRYvklltukeDgYDl8+HA+vkPuKQ6fTXfomjVFj9ZJ0TB7jOfXESr0RkpBHNoQsmRuvIiI/Pjjj9KuXTspU6aMBAcHy8CBA+XYsWP287ltCO3YsUMAWbp0qdNrHzt2TEqXLi3jx48XkawNoeTkZAkJCZHhw4fbwxISEqRPnz4SEhIiXl5eUqtWLRk7dqy9kZY5/RtvvCH/+Mc/pEyZMlKmTBlp3ry5TJw40aExkl+Sk5Nl1KhRUqFCBfH395euXbvKnj17HOKMGzdOrL89/jZhwgSJiIgQHx8fqVatmjz11FNy8eLFLPkfO3ZMBgwYIEFBQRIYGCi9evWS/fv3Z4k3depUqV27tvj4+EiTJk1kzZo1WeLMnz9fmjdvLmXLlpWwsDDp2rWrfP/999f4DuSP4vDZdId+6RY9WidFw+wxIz3eEDIixX8RtcjISPnjjz9cno+Pj3c5D0PlzowZM3j55ZfZtWuXfd5KdjIOx6nCV1Tro6R+NtevX090dHRhF0NloHVSNHzy5ChOHNjFmLil20XEI5tn6mRp5RGbNm1i5MiRuWoEKaWUUkWFTpZWHjFt2rTCLoJSSqliLj/mSuuf70oppceI7RQAAB4SSURBVJQqsbQhpJRSSqnrgri5i0B2tCGklFJKqeuCDo0ppZRSSnmQNoSUUkopVWJpQ0gppZRS14d8GBvThpBSSimlrgvmSqrH89SGkFJKKaWuC6m+nl/+UBtCxURsbCzGGPtRpUoV+vbty+7duz2S/4kTJ4iNjWXfvn0eyc8T9u7dizGG6tWr42yrmOjoaPr16+c0bfPmzRk8eLBDWFpaGh999BG33XYbQUFB+Pn5ERUVxRtvvMGFCxfy4xYciAivvPIKN9xwA2XKlKFNmzb8/PPPuUo3fvx4qlevjp+fH02bNmXlypUOcbZu3cqQIUOoU6cO/v7+REZG8sILL5CcnJwlr3fffZdGjRrh7+9PREQEI0aM4Ny5cw7xDh48SN++fQkKCiI4OJgBAwZw4sSJa38TlFIqG6VNaY/nqQ2hYiQ4OJjNmzezefNm3nzzTX7++Wc6dOjAxYsXrznvEydO8MILLxSphtDcuXMB60t548aN15RXWloa99xzD48++igtW7YkLi6OZcuWMWTIEN5//32ef/55TxQ5WxMmTOCll17i6aefZsmSJQQGBtKxY0eOHTuWY7oXX3yRRx55hC+//JJGjRrRo0cPtm7dao8zf/58du/ezdNPP82yZct45JFHmDhxIoMGDXLIa/LkyTz22GP069ePpUuX8swzzzBnzhxiYmLsca5evUrXrl2Jj49nxowZfPTRR2zbto2uXbuSmur5bmullEpnJB/+j/HU7q1F+Sipu89/9913AkhcXJzTNFevXnW6k7szv/76qwAe24E58+7z7oiKipJbb71VAgIC5OGHH85yvm3bttK3b1+naZs1ayYxMTH21++8844YY2T16tVZ4l66dMnpLu6edOnSJQkKCpIXXnjBHnbhwgWpUKGCPPfccy7TpaSkSNmyZeX55593CG/atKl069bN/vrEiRNZ0k6ZMkUA2bdvn70+WrRoIX369HGI9/bbb0upUqXkwoULIiIyZ84cKVWqlOzcudMe55dffhFAFixYkIe7zllx+Gy6Q3c6L3q0ToqGT599yuO7z2uPUDHWrFkzAHsvzuDBg2nevDlffPEFjRo1ws/Pjx9++AHA3nvk7+9PSEgIgwYN4vjx4/b0jRs3BqBdu3b24bd0e/fupVevXgQFBVG2bFl69OjBn3/+6VAWYwxvv/02zz77LGFhYdSqVYtHHnmElJQUt+5tx44d/PbbbwwZMoS77rqLBQsWcPXqVbfyApg0aRK9e/emY8eOWc75+fnRoUMHt/POjU2bNnH+/Hnuvvtue1hAQAA9evRg+fLlLtPt3r2bxMTELOXu1KkTq1ev5vLlywCEhYVlSdukSRMAhyGtK1euEBwc7BCvXLly9v8wwPpdqVGjBnXr1rXHufHGGwkPD2fp0qW5vWWllHKDriyt8iC9AVSpUiWHsKeeeopnnnmGZcuWUbNmTU6ePEl0dDRJSUnMmTOHyZMns2HDBjp16sTly5epXLkyn332GQDvvfeeffgNICUlhQ4dOhAfH8+0adOYOXMme/fupW3btpw5c8ahPG+99RZHjhzh008/5fHHH2fKlCm8/fbbbt3bnDlz8PLyom/fvgwcOJCTJ0+ydu1at/I6ePAge/fupUuXLm6lFxGuXr2a45GdhIQESpcu7dC4AGjQoAEJCQku06XP8fHx8XEI9/X15fLly+zZs8dl2k2bNlGqVCkiIyPtYcOGDbMPCyYmJvLTTz8xYcIEBg8eTGBgoP2ama+Xfs34+Phs71Mppa6N5xtCuvu8C2/d072wi8AT87/Oc5r0L9w9e/bw8MMPU7ZsWYfegtOnT7NmzRpuuukme9j//d//AbBy5UqCgoIAqFevHi1atGDRokUMHDiQG2+8EYCGDRty66232tPOmDGDAwcOsHPnTmrVqgVAixYtqFWrFlOmTOGZZ56xx42IiGDmzJkA3HbbbWzbto3Fixfz1FNP5fk+582bR6dOnQgNDeWOO+4gJCSEuXPncscdd+Q5r8OHDwNQvXr1PKcF2LBhA+3atcsx3t69e4mIiHB67uzZswQGBlK6tONEwJCQEJKSkrh8+bLTxketWrUwxrB161aHetmyZQtAlsZoumPHjjF+/Hjuv/9+goKCSExMBODf//43iYmJ9OjRg7S0NAB69erFlClT7Gnr1KnDhx9+yOnTpwkNDQXgyJEjHD58GF9f3xzfB6WUcpuuI6Syc/r0aby9vfH29iYyMpI9e/Ywf/58KleubI9TtWpVh0YQWF+anTt3tjeCAG655RYiIiL4/vvvs73mli1baNq0qb0RBFCtWjVatWqVJW3nzp0dXjds2JBDhw7l+T5/+OEH9uzZw4ABAwCrN6RPnz58/vnnWZ6Cygvj5mZ+zZo1Y+vWrTkeVapUyfP104ejXJUtODiYgQMHMn78eNatW8eZM2eYPHkya9asAcjSsAK4fPkyd999N4GBgUyaNMnh3Ny5c3nxxRd5+eWX2bBhAx9//DFbt25l6NCh9jj33nsvfn5+DB06lAMHDrBv3z6GDBni8npKKVWUaY9QMRIcHMyaNWswxlCpUiWqVKmS5Qs0PDw8S7qjR4/SqFGjLOHh4eEuexQypnWWZ3h4OPv373cIK1eunMNrHx8ftxouc+fOxdvbm+joaPtj3d26dWP69OksW7aMPn36AODl5eXyKabU1FS8vKxf/6pVqwJw4MCBPJcFIDAwMEvj0pn06zkTEhJCYmIiqampDo2Jc+fO4e/vj7e3t8u0//3vf7nnnnto3749ADfccANjx44lNjY2S92ICA888AA7duxg48aNhISE2M+lpaUxYsQIHn/8cXtPXps2bahSpQpdunRh5MiRNG3alNDQUObMmcPQoUOpUaMGYPUa3XnnnZw/fz7H90EppYoSbQi54M6wVGHz8vKiefPm2cZx1rNQuXJlp2vAHD9+3D7h2pXKlSuzY8cOp2nLly+fQ4nzLi0tjbi4OK5cuWL/Es5o7ty59oZQWFiYy8f9jx49SsWKFQGr4VCrVi1WrlzJsGHD8lwmTwyN1a9fn9TUVP7880+HOTsJCQnUr18/23zDwsL45ptvOHToEH/99ReRkZH897//pVKlSlmuN2rUKL788ktWr16dJd9Tp05x+vTpLI269EnVu3fvpmnTpoDV8Dx06BA7d+4kKCiIatWqERUVRffuhT+krJRSeaENIUWLFi344IMPSExMpGzZsoC1AN++ffu4/fbbgb8n42buwWnRogWzZ89m79691KxZE7Dm3GzatInY2FiPl3X9+vUcPXqU1157jVtuucXh3IwZM4iLi7PfR+vWrVm0aBGHDx+29/qANbR2/PhxWrdubQ8bOXIkI0eOZN26dVkaNcnJyWzatMne45JZ+tBYTrIbGktfxHHBggWMHTsWgKSkJJYsWcJDDz2UY95gDUlWq1aN5ORkPv74Y/75z386nH/11VeZPHkycXFx9nrNKCwsDH9/f3788Uf69+9vD9++fTtAlkaVl5cXDRs2BKzGYEJCAgsXLsxVWZVSyi1uTmHIlqeewy/KR0ldRyizmJgYadasWZbwEydOSHBwsLRs2VK++OIL+fTTT6VatWrSuHFj+zpDly5dkjJlysiAAQNk06ZNsnXrVhERSU5Olpo1a0pkZKTMnz9fFi5cKFFRUVKlShU5ffq0/RqATJ482f76/PnzTssMyLhx41zew7Bhw6RcuXKSnJyc5dz27dsFkNmzZ4uISGJiotSuXVtq1aolH3/8saxdu1beffddCQsLk9tuu01SU1PtaVNTU6Vfv37i5+cnTzzxhKxYsUK++eYbmTRpktSuXVtGjhyZ7XvrCa+88oqUKVNG3n33XVmzZo3ceeedEhoaKseOHbPHmTVrlpQuXVr27dtnD5s9e7ZMnz5d1q1bJ7NmzZKbbrpJoqKiJDEx0R7ns88+E0AGDx4smzdvdjhOnDhhX0do5MiR4uvrKy+++KKsXbtWpk6dKpUrV5YWLVo4vF9jxoyRzz//XFavXi3jx48Xf3//bOvNXcXhs+kOXbOm6NE6KRo+HfuMx9cRKvRGSkEc2hCyuGoIiYj8+OOP0q5dOylTpowEBwfLwIEDHb6ARUQ+/fRTqVu3rnh7e4vVhrbs3r1bevbsKYGBgRIQECDdunVzWGxPJHcNoYsXLwog7733ntMyXr58WUJCQuTBBx90eY8NGzaULl262F8fPnxYYmJipGLFiuLl5SVVq1aVRx99VP76668saVNTU2XatGnSokULCQgIEF9fX4mKipLY2Fg5d+6cy2t6Slpamrz88stStWpV8fPzk9tvv11+/PFHhzgzZswQQPbu3WsPmzlzptSrV098fX2lYsWK8tBDD8mpU6cc0sXExAjg9JgxY4a9IZScnCwvv/yyREZGSpkyZaR69ery4IMPyvHjxx3y69+/v4SFhYmPj49ERUXJ1KlT8+U9KQ6fTXfol27Ro3VSNHz2/LMebwgZkax7NBU3kZGR8scff7g8Hx8fT4MGDQqwRCrjMFy6devW0atXLw4ePOjwBJvKf87qoygoqZ/N9evXEx0dXdjFUBlonRQNc/7zHEf/+IUxcUu3i0j2k2JzSR+fV0XGpk2bGDZsmDaClFJKueD5zhudLK2KjOeee66wi6CUUqpI0y02lFJKKVVS6crSSimllFKeow0hm5IwaVyp64l+JpVSmbm7FVJ2tCEEeHt7c+nSpcIuhlIqg0uXLmW7tYhSSnmCNoSAihUrcvjwYZKSkvSvUKUKmYiQlJTE4cOH7dugKKUUkC9zhPSpMbA/rn3kyBGuXLlSyKUpGZKTk/Hz8yvsYiibolYf3t7ehIeH61IKSql8pw0hm6CgIP1PtwCtX7/evpmnKnxaH0qpkqrAh8aMMQ2NMWuNMUnGmCPGmBeNMaVzkS7YGDPDGHPWGPOXMeYzY0xoQZRZKaWUUsVTgfYIGWNCgDXA70BPoDbwFlaDbGwOyecDkcAwIA14DfgCaJ1dIqWUUkoVF56fJFTQQ2PDgTJAHxE5D6w2xgQBscaY121hWRhjWgJ3AG1F5Ftb2GHgB2NMRxFZU0DlV0oppVRhKQYLKnYFVmZq8MzDahy1zSHd8fRGEICIbAH22s4ppZRSSuVZQTeE6gMJGQNE5ACQZDuX63Q28TmkU0oppZRyqaAbQiHAOSfhZ23nPJ1OKaWUUsqlwnh83tmKhcZFuNvpjDEPAQ/ZXqYYY37LdQlVQagAnCrsQig7rY+iReuj6NE6KVoiPZVRQTeEzgLlnIQH47zHJ2O6MCfh5VylE5GpwFQAY8w2EWmet6Kq/KR1UrRofRQtWh9Fj9ZJ0WKM2eapvAp6aCyBTHN6jDE3AAE4nwPkMp2Nq7lDSimllFI5KuiG0HLgDmNM2Qxh9wCXgA05pKtkjLk9PcAY0xyoZTunlFJKKZVnBd0Q+hBIARYbYzra5vHEAhMzPlJvjPnTGDM9/bWIbAZWArONMX2MMb2Az4Dvc7mG0FRP3oTyCK2TokXro2jR+ih6tE6KFo/Vhyno3daNMQ2Bd4GWWPN7PgJiRSQ1Q5x9wHoRGZwhrBwwCeiN1YD7GnhMRHTymlJKKaXcUuANIaWUUkqpoqLAN131JN3Atehxp06MMTfb6uNPW7o/jDHjjDF+BVXu4srdz0iG9KWMMduNMWKM6Z6fZS0prqVObFMDthpjLhljThtjVhhjAvK7zMXZNXyPNDfGrLLVwxljzBpjTIuCKHNxZoypY4yZYoz5xRiTaoxZn8t0bn+vF8Y6Qh6hG7gWPddQJ/fY4r4G7AJuBF6y/eybj0Uu1q7xM5JuGFA1XwpYAl1LnRhjhmFNK3gdeBJrMdn2XMf/jxc2d+vD9rTzGuBH4AFb8JPAKmPMjSKyPz/LXcw1Au4E/gf45CGd+9/rInJdHsAzWOsLBWUIewpru46gbNK1xFqEsU2GsFtsYR0L+76u5+Ma6iTMSdhDtjqpUdj3db0e7tZHhrghwElgqK0uuhf2PV3vxzV8RioAicCDhX0Pxem4hvoYDqQC5TKEhdjC/l3Y93U9H0CpDP9eiDVfOKc01/S9fj0PjekGrkWPW3UiIiedBP9k+1nRc8Urcdz9jKR7CdgIrM2HspVU7tbJ3bafs/KrYCWUu/XhDVwFLmQIu2ALy4f90UsOEUlzI9k1fa9fzw0h3cC16HG3Tpy5Dat78w/PFK1Ecrs+jDE3AkOAMflWupLJ3TppgfVZGGqMOWSMuWKM+cEYc1v+FbVEcLc+FtnivGWMqWiMqYj1VPNZYEE+lVW5dk3f69dzQ0g3cC16PPLeGmMqAc8Bn2T6S03lzbXUx2TgPRH50+OlKtncrZNKWPMfxgJPAz2Ai8AKY0y4pwtZgrhVHyJyBGiHNYfxuO3oA9zhoodb5a9r+u65nhtCUEAbuKo8uab31hjjA8RhdTOP8mC5Sqo814cxZgDWl+7L+VWoEs6dz0gpIBAYKiKficgKoBfWnJRHPV/EEsWdz0hlrPkr27GGXrra/r3UGFM9PwqpcuT2d8/13BC6lg1cnaVzuYGryjV36wQAY4wBZmN7akBEznq2eCVOnuvDGOMNvIH1xEUp20KmQbbTAZm2x1F55+5n5Izt5/r0AFtv6XagoacKVwK5Wx9PYj2t109EVtgapn2xGqY6nFzwrul7/XpuCOkGrkWPu3WSbhLWI6w9RUTr4tq5Ux8BQDVgItZ/LmeBX2zn5vH3JHblHnc/I/FYf9lmnohrsObSKfe4Wx/1gR0iciU9QEQuAzuwHsFXBeuavtev54aQbuBa9LhbJxhjngFGAPeJyPf5V8QSxZ36uIA19yHjMdB27llgUP4UtcRw9zPyNVajp116gDEmGGjG3w1VlXfu1sd+IMo2lA+AMcYXiAL25UM5Vfau7Xu9sNcMuIa1BkKAo8BqoCPWujMXgJczxfsTmJ4pbAWwB2tyWy+spzG+K+x7ut4Pd+sEuBfrr90ZwK2ZjixrDOmRv/XhJJ8IdB2hQq8TrMXhjgIxQDesL+qTQEhh39f1elzD/1nNgCvAUltddLd94V4B/lHY93U9H4A/0M92bMbqZUt/7e+sPmxhbn+vF/pNX+Mb1hD4Bqv1fhRr3ZPSmeLsA2ZmCitn+9I9B5wH5gAVCvt+isPhTp0AM21ftM6OwYV9T9fz4e5nJNN5bQgVgTrBmiz9AXDalnYN0Liw7+d6P66hPjoA32LN3zqD1TCNLuz7ud6PDP/fODsisqkPt7/XddNVpZRSSpVY1/McIaWUUkqpa6INIaWUUkqVWNoQUkoppVSJpQ0hpZRSSpVY2hBSSimlVImlDSGllFJKlVjaEFKqCDDGDDbGbDfGJBpjzhpjfjLGTMxwPsIYI8aY7oVcRjHGBHogrwbGmO+MMRdteUYYY0oZY94zxhy3hcW6c01jzD5jzJvXWsaCZoypaoy5YIypZbt3yeFYb0u3PkPYVWPMbmPMJGNMUIa8M8fZZ4yZYowJy1SGJ40xawv41pUqVLqOkFKFzLa9yEvA68A6wA9r5dr7RKSOLY4v0ARIEJFC2RzYGDMYa8GysiJy4Rrz+hqoAYwELmLtYdYDWAAMBX4HDgEpWHs3bRGRXO2pZYxpApwWkQPXUsaCZoz5ACgnIgONMdWw9nxLNwJoD/TOEHZeRH63NYjSsLZA8QJuxvp9WiEi/Wx5Z47TFHgZ633tmKEMZYEDQG8RWZ8Pt6lUkeNV2AVQSvEoMEVEns0QtsQY80L6CxFJAf5X4CXLP/WBr0TE3vtgjKkPnBWRjzPFPZmXjEXkutsY1tZ7E4O16TAicgirIZh+vh+QIiKufgfOZDj3vTEmAHjJGBMmIiddxPEHXjXGVBGRI7brJhpjFmE1vNZ78BaVKrJ0aEypwlcOOJY5UDJ01zobGjPG+BpjPjDGnDPGnDbGvGGMGWmMyZgu2pYu2hizwDb0sscY83DGaxljWhpjvjLGHLENV/1sjHFrg1VjTHVjzDxjzBljTJIxZqUxJjLjfWD18oxKH+Kx9Vi8BIRkGMKJcDY0ZowpY4x53Riz3xiTYozZa4x5NcP5LENjxpjbjTEbbOU5bYyZZjJstJnhOo2NMatt70GCMaaPk/vrbYzZYoy5ZMtrmTGmhjGmkS2PtpniB9re98eyedvuxtri4Zu8vNfZ2G77GZFNnPTNWm/IFL4I6G6MKe+hsihVpGlDSKnC9yMwwhgTY4wJzUO614HBwAtYu8JXB55wEXca1hdfb6y/9N8zxtyS4XwNYCMwDGuIahEwwxgzkDywfXl+D0QCw7G+4AOANcaYMlh7ObXEavjNsf37YdsxHfjLFtbSFjdz/gb4Evg38B5wJzAOqJBNmVoBa23X7Ic1HHcn1jBfZnOAr7Dep13APNswVXpe9wOLgd22exsC7MTaHHgHVq/dkEx59ge8bXm70gFrmCo1mzh5EWH7maWBnUF1rOGy/ZnCN2GVt7WHyqJU0VbYG6zpoUdJP4AbsXZNFqwvph3Ai0BQhjgRZNj4FAjF6kF4MkMcY0srGcKibelezBDmjTXcNMFFeQzWsPkU4JsM4YNteQVmcy8vYW0KWj5DWAhWA+eRDGH7gDczpY0FTmUKc7gmcIft9V3ZlMEhb+A7YF2mOO1t+URlus4/M8QJBa4Cw22vSwGHgcXZXHsY1u7lgRnCvgUW5vA7sBN4I5vzbwL7XJxbj9Vw9QJ8sRowh4Ct/D0PNHOc22zv0wfZvIfjC/uzoYceBXFoj5BShUxE/h/QALgLeB+rIfI8sM24flqqMdak6q8y5CPAEhfxV2WIdwWrtyNjT0eIMeYdY8x+4IrteAiol8fb6QisBs4bY7yMMV5AItZQTfM85uVMe6y5Ll/lGBOwzYNpCcSll8dWpu+x7rFZpiQZ36fTwAn+fp8igSo470lKN8/2s7/t+rWB23NIA1AJOJXjDbnWB+t+krEaXvuAQbbfCWdxNgLHAVfDdadsZVKq2NOGkFJFgIikiMgSEXlURBpi9SzUxXqCypn0L6nME4ldTSzO/KTZZayGVLqZwD3AG0BnrCePPs4UJzcq2PK5kuloR9a5KO4IxcmQWTZCgNJYDcyM5UnB6hnLXKbs3qf0YUuX1xfrabo4/h4eG4w1PLUih3L62crkrm+w6qwJVm/c7SKy00Wc24HXgFuwnhxzJoW8171S1yV9akypIkhEphtjXsd6usqZ9LkfYcCZDOFhTuJmyxjjB3QDHhWRDzOEu/OH0hmsXqqXnJxLdCO/zE4DlfMQ/xzWkFcssMzJ+SN5vDa5uP5HwEZjTF3gAWC25Dz35wzWpHl3nRWRbXmIs9G2htBIY8y7InIwU9xyOP5eKVVsaY+QUoXMGFPRSVgYEIw1fOHMr1hDHD0zpDFYE53zyher18TeI2F7ououN/JaCzQCdojItkzHH27k5yz/8iaXC0uKyEWsCcyRTsqzTWyPjefSH1hzhGJyuOYmIAGrR606Vm9bbvKumYeyeMI4289RGQNtDeDqWPOWlCr2tEdIqcL3qzHmS6z5KSewnuAaAyQBs5wlEJHTxphpwAvGmCtAPNZwTBBWD0iuichfxpitwH+MMeexJmz/H9YE56BsE2c1EbgP+MYYMxmr4RAOtAW+F5G5ecwvs9XASmCOMeZFrCfuKgNtRORfLtI8Baw1xqQBC7F6pqpj9YI952QIySkRSTPGPAV8Zoz5DJiL9V63B+Zm6pGZjjXMuFlEEnKR/Ubca3i6TUQOGWNmAQ8aY16UvxfqjAQCbWVSqtjTHiGlCt+LWE+FvYPVGHoJ6+mvW0RkbzbpnsLqbYjF+lI+jvUFfN6NMtwL7AVmA29jPWE0O6+ZiMgp4FasHpFJWPfzOlbv1v9zo1yZ8xesR9unYj0GvxxrnovLicYi8j3QBmvY8BOsCeVPAQdx3ePmKq85QF+sIcuFWO9RfbLOzfrC9jPz4pCuLAYaGmOq56U8HjABay7QvzOEdcH6XbjuFqZUyh26xYZSxYgxZg3gLSJtc4ys8o1twcrXgSoikquGqTHmZ+AzEXkjXwuXczk2A0tFxNVEaqWKFR0aU+o6ZYxpB7TAGh7yxnpaqwO2R7dVwTPGRGAtOfAsMDO3jSCb8cAbxphJInI1H4qXI2NMC6werq6FcX2lCoM2hJS6fl0AegHPYA1v7AIGi8jCQi1VyRaLNcy4AWstqLxYCNQCqpJ1teeCUh6IkULa2FepwqBDY0oppZQqsXSytFJKKaVKLG0IKaWUUqrE0oaQUkoppUosbQgppZRSqsTShpBSSimlSixtCCmllFKqxPr/BAWV+8SoWhwAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "utils.plot_roc_curves(proba, validation_data.Class.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "utils.plot_signal_efficiency_on_p(proba, validation_data.Class.values, validation_data.TrackP.values, 60, 50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "utils.plot_signal_efficiency_on_pt(proba, validation_data.Class.values, validation_data.TrackPt.values, 60, 50)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prepare submission\n", + "\n", + "Select your best classifier and prepare submission file." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "test = pandas.read_csv('test.csv.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "best_model = nn" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "test_scale = scaler.transform(test[features])" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# predict test sample\n", + "submit_proba = best_model.predict_proba(test_scale)\n", + "submit_ids = test.ID" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "submission_file.csv.gz
" + ], + "text/plain": [ + "E:\\GitHub\\Advanced-Machine-Learning-Specialization\\Addressing Large Hadron Collider Challenges by Machine Learning\\Week2\\submission_file.csv.gz" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import FileLink\n", + "utils.create_solution(submit_ids, submit_proba, filename='submission_file.csv.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Addressing Large Hadron Collider Challenges by Machine Learning/Week2/README.md b/Addressing Large Hadron Collider Challenges by Machine Learning/Week2/README.md new file mode 100644 index 0000000..f6a8f7f --- /dev/null +++ b/Addressing Large Hadron Collider Challenges by Machine Learning/Week2/README.md @@ -0,0 +1,12 @@ +In this programming assignment you will train a classifier to identify type of a particle. +There are six particle types: electron, proton, muon, kaon, pion and ghost. +Ghost is a particle with other type than the first five or a detector noise. +Different particle types remain different responses in the detector systems or subdetectors. +Thre are five systems: tracking system, ring imaging Cherenkov detector (RICH), electromagnetic and hadron calorimeters, +and muon system. 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zp(M6);#$v*tCi0Lqh2Euj57M#RWFRdb#bVuY68rsErrWkCX`m28WmHZ=uH`v2?J?Z?&ejbg^aC%tXVnQX;qk~(^1^&FRD{J>|m3$gD`ElKgx$e8> ewY!Z1h}T@NHY`4bY;jl=O|i59t5WITC;k_4?>XTB literal 0 HcmV?d00001 diff --git a/Addressing Large Hadron Collider Challenges by Machine Learning/Week2/utils.py b/Addressing Large Hadron Collider Challenges by Machine Learning/Week2/utils.py new file mode 100644 index 0000000..64108b4 --- /dev/null +++ b/Addressing Large Hadron Collider Challenges by Machine Learning/Week2/utils.py @@ -0,0 +1,228 @@ +import matplotlib.pyplot as plt +import numpy +import pandas +from sklearn.metrics import roc_curve, roc_auc_score + + +label_class_correspondence = {'Electron': 0, 'Ghost': 1, 'Kaon': 2, 'Muon': 3, 'Pion': 4, 'Proton': 5} +class_label_correspondence = {0: 'Electron', 1: 'Ghost', 2: 'Kaon', 3: 'Muon', 4: 'Pion', 5: 'Proton'} + + +def get_class_ids(labels): + """ + Convert particle type names into class ids. + + Parameters: + ----------- + labels : array_like + Array of particle type names ['Electron', 'Muon', ...]. + + Return: + ------- + class ids : array_like + Array of class ids [1, 0, 3, ...]. + """ + return numpy.array([label_class_correspondence[alabel] for alabel in labels]) + + +def plot_roc_curves(predictions, labels): + """ + Plot ROC curves. + + Parameters: + ----------- + predictions : array_like + Array of particle type predictions with shape=(n_particles, n_types). + labels : array_like + Array of class ids [1, 0, 3, ...]. + """ + plt.figure(figsize=(9, 6)) + u_labels = numpy.unique(labels) + for lab in u_labels: + y_true = labels == lab + y_pred = predictions[:, lab] + fpr, tpr, _ = roc_curve(y_true, y_pred) + auc = roc_auc_score(y_true, y_pred) + plt.plot(tpr, 1-fpr, linewidth=3, label=class_label_correspondence[lab] + ', AUC = ' + str(numpy.round(auc, 4))) + plt.xlabel('Signal efficiency (TPR)', size=15) + plt.ylabel("Background rejection (1 - FPR)", size=15) + plt.xticks(size=15) + plt.yticks(size=15) + plt.xlim(0., 1) + plt.ylim(0., 1) + plt.legend(loc='lower left', fontsize=15) + plt.title('One particle vs rest ROC curves', loc='right', size=15) + plt.grid(b=1) + + +def my_percentile(arr, w, q): + + left = 0. + right = (w).sum() + sort_inds = numpy.argsort(arr, axis=0) + if left/right >= q/100.: + return arr[0] + for i in sort_inds: + left += w[i] + if left/right >= q/100.: + return arr[i] + +def base_plot(prediction, spectator, cut, percentile=True, weights=None, n_bins=100, + color='b', marker='o', ms=4, label="MVA", fmt='o', markeredgecolor='b', markeredgewidth=2, ecolor='b'): + """ + Base plot for signal efficiency. + + Parameters: + ----------- + prediction : array_like + Array of predictions for signal for a selected particle type with shape=(n_particles, ). + spectator : array_like + To plot dependence of signal efficiency on this feature. + cut : float + Global efficiency value. + bins : int + Number of bin for plot. + """ + if weights is None: + weights = numpy.ones(len(prediction)) + + if percentile: + if weights is None: + cut = numpy.percentile(prediction, 100-cut) + else: + cut = my_percentile(prediction, weights, 100-cut) + + edges = numpy.linspace(spectator.min(), spectator.max(), n_bins) + + xx = [] + yy = [] + xx_err = [] + yy_err = [] + + for i_edge in range(len(edges)-1): + + left = edges[i_edge] + right = edges[i_edge + 1] + + N_tot_bin = weights[((spectator >= left) * (spectator < right))].sum() + N_cut_bin = weights[((spectator >= left) * (spectator < right) * (prediction >= cut))].sum() + + if N_tot_bin != 0: + + x = 0.5 * (right + left) + y = 1. * N_cut_bin / N_tot_bin + + if y > 1.: + y = 1. + if y < 0: + y = 0 + + xx.append(x) + yy.append(y) + + x_err = 0.5 * (right - left) + y_err = numpy.sqrt(y*(1-y)/N_tot_bin) + + xx_err.append(x_err) + yy_err.append(y_err) + + else: + pass + + plt.errorbar(xx, yy, yerr=yy_err, xerr=xx_err, fmt=fmt, color=color, marker=marker, ms=ms, label=label, markeredgecolor=markeredgecolor, markeredgewidth=markeredgewidth, ecolor=ecolor) + + return cut + +def plot_signal_efficiency(predictions, labels, spectator, eff=60, n_bins=20, xlabel='Spectator'): + """ + Plot dependence of signal efficiency from spectator feature for all particle types. + + Parameters: + ----------- + prediction : array_like + Array of predictions for signal for a selected particle type with shape=(n_particles, ). + labels : array_like + Array of class ids [1, 0, 3, ...]. + spectator : array_like + To plot dependence of signal efficiency on this feature. + cut : float + Global efficiency value. + bins : int + Number of bin for plot. + xlabel : string + Label of x-axis. + """ + + plt.figure(figsize=(5.5*2, 3.5*3)) + u_labels = numpy.unique(labels) + for lab in u_labels: + y_true = labels == lab + pred = predictions[y_true, lab] + spec = spectator[y_true] + plt.subplot(3, 2, lab+1) + base_plot(pred, spec, cut=eff, percentile=True, weights=None, n_bins=n_bins, color='1', marker='o', + ms=7, label=class_label_correspondence[lab], fmt='o') + + plt.plot([spec.min(), spec.max()], [eff / 100., eff / 100.], label='Global signal efficiecny', color='r', linewidth=3) + plt.legend(loc='best', fontsize=12) + plt.xticks(size=12) + plt.yticks(size=12) + plt.ylabel('Signal efficiency (TPR)', size=12) + plt.xlabel(xlabel,size=12) + plt.ylim(0, 1) + plt.xlim(spec.min(), spec.max()) + plt.grid(b=1) + plt.tight_layout() + + +def plot_signal_efficiency_on_p(predictions, labels, spectator, eff=60, n_bins=20): + """ + Plot dependence of signal efficiency from particle momentum feature for all particle types. + + Parameters: + ----------- + prediction : array_like + Array of predictions for signal for a selected particle type with shape=(n_particles, ). + labels : array_like + Array of class ids [1, 0, 3, ...]. + spectator : array_like + To plot dependence of signal efficiency on this feature. + cut : float + Global efficiency value. + bins : int + Number of bin for plot. + """ + sel = spectator < 200 * 10**3 + plot_signal_efficiency(predictions[sel], labels[sel], spectator[sel] / 10**3, eff, n_bins, 'Momentum, GeV/c') + + +def plot_signal_efficiency_on_pt(predictions, labels, spectator, eff=60, n_bins=20): + """ + Plot dependence of signal efficiency from particle transverse momentum feature for all particle types. + + Parameters: + ----------- + prediction : array_like + Array of predictions for signal for a selected particle type with shape=(n_particles, ). + labels : array_like + Array of class ids [1, 0, 3, ...]. + spectator : array_like + To plot dependence of signal efficiency on this feature. + cut : float + Global efficiency value. + bins : int + Number of bin for plot. + """ + sel = spectator < 10 * 10**3 + plot_signal_efficiency(predictions[sel], labels[sel], spectator[sel] / 10**3, eff, n_bins, 'Transverse momentum, GeV/c') + + + +from IPython.display import FileLink +def create_solution(ids, proba, filename='submission_file.csv.zip'): + """saves predictions to file and provides a link for downloading """ + solution = pandas.DataFrame({'ID': ids}) + for name in ['Ghost', 'Electron', 'Muon', 'Pion', 'Kaon', 'Proton']: + solution[name] = proba[:, label_class_correspondence[name]] + solution.to_csv('{}'.format(filename), index=False, float_format='%.5f', compression="gzip") + return FileLink('{}'.format(filename)) \ No newline at end of file diff --git a/Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_ml-checkpoint.ipynb b/Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_ml-checkpoint.ipynb deleted file mode 100644 index e524b36..0000000 --- a/Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_ml-checkpoint.ipynb +++ /dev/null @@ -1,3051 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A crash course in\n", - "
Surviving Titanic
\n", - "\n", - "
(with numpy and matplotlib)
\n", - "\n", - "---\n", - "\n", - "This notebook's gonna teach you to use the basic data science stack for python: jupyter, numpy, matplotlib and sklearn." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part I: Jupyter notebooks in a nutshell\n", - "* You are reading this line in a jupyter notebook.\n", - "* A notebook consists of cells. A cell can contain either code or hypertext. \n", - " * This cell contains hypertext. The next cell contains code.\n", - "* You can __run a cell__ with code by selecting it (click) and pressing `Ctrl + Enter` to execute the code and display output(if any).\n", - "* If you're running this on a device with no keyboard, ~~you are doing it wrong~~ use topbar (esp. play/stop/restart buttons) to run code.\n", - "* Behind the curtains, there's a python interpreter that runs that code and remembers anything you defined.\n", - "\n", - "Run these cells to get started" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "a = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10\n" - ] - } - ], - "source": [ - "print(a * 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* `Ctrl + S` to save changes (or use the button that looks like a floppy disk)\n", - "* Top menu -> Kernel -> Interrupt (or Stop button) if you want it to stop running cell midway.\n", - "* Top menu -> Kernel -> Restart (or cyclic arrow button) if interrupt doesn't fix the problem (you will lose all variables).\n", - "* For shortcut junkies like us: Top menu -> Help -> Keyboard Shortcuts\n", - "\n", - "\n", - "* More: [Hacker's guide](http://arogozhnikov.github.io/2016/09/10/jupyter-features.html), [Beginner's guide'](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/), [Datacamp tutorial](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook)\n", - "\n", - "Now __the most important feature__ of jupyter notebooks for this course: \n", - "* if you're typing something, press `Tab` to see automatic suggestions, use arrow keys + enter to pick one.\n", - "* if you move your cursor inside some function and press `__Shift + Tab__`, you'll get a help window. `Shift + (Tab , Tab)` will expand it." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# run this first\n", - "import math" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.5707963267948966" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# place your cursor at the end of the unfinished line below to find a function \n", - "# that computes arctangent from two parameters (should have 2 in it's name)\n", - "# once you chose it, press shift + tab + tab(again) to see the docs\n", - "\n", - "math.acos(0) # <---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part II: Loading data with Pandas\n", - "Pandas is a library that helps you load the data, prepare it and perform some lightweight analysis. The god object here is the `pandas.DataFrame` - a 2d table with batteries included. \n", - "\n", - "In the cell below we use it to read the data on the infamous titanic shipwreck.\n", - "\n", - "__please keep running all the code cells as you read__" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "data = pd.read_csv(\"train.csv\", index_col='PassengerId') # this yields a pandas.DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen, Mr. William Henrymale35.0003734508.0500NaNS
603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
1012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "3 1 3 \n", - "4 1 1 \n", - "5 0 3 \n", - "6 0 3 \n", - "7 0 1 \n", - "8 0 3 \n", - "9 1 3 \n", - "10 1 2 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund, Mr. Owen Harris male 22.0 \n", - "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", - "3 Heikkinen, Miss. Laina female 26.0 \n", - "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "5 Allen, Mr. William Henry male 35.0 \n", - "6 Moran, Mr. James male NaN \n", - "7 McCarthy, Mr. Timothy J male 54.0 \n", - "8 Palsson, Master. Gosta Leonard male 2.0 \n", - "9 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 \n", - "10 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", - "4 1 0 113803 53.1000 C123 S \n", - "5 0 0 373450 8.0500 NaN S \n", - "6 0 0 330877 8.4583 NaN Q \n", - "7 0 0 17463 51.8625 E46 S \n", - "8 3 1 349909 21.0750 NaN S \n", - "9 0 2 347742 11.1333 NaN S \n", - "10 1 0 237736 30.0708 NaN C " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Selecting rows\n", - "head = data[:10]\n", - "\n", - "head #if you leave an expression at the end of a cell, jupyter will \"display\" it automatically" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### About the data\n", - "Here's some of the columns\n", - "* Name - a string with person's full name\n", - "* Survived - 1 if a person survived the shipwreck, 0 otherwise.\n", - "* Pclass - passenger class. Pclass == 3 is cheap'n'cheerful, Pclass == 1 is for moneybags.\n", - "* Sex - a person's gender (in those good ol' times when there were just 2 of them)\n", - "* Age - age in years, if available\n", - "* Sibsp - number of siblings on a ship\n", - "* Parch - number of parents on a ship\n", - "* Fare - ticket cost\n", - "* Embarked - port where the passenger embarked\n", - " * C = Cherbourg; Q = Queenstown; S = Southampton" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "len(data) = 891\n", - "data.shape = (891, 11)\n" - ] - } - ], - "source": [ - "# table dimensions\n", - "print(\"len(data) = \",len(data))\n", - "print(\"data.shape = \",data.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Survived 1\n", - "Pclass 1\n", - "Name Futrelle, Mrs. Jacques Heath (Lily May Peel)\n", - "Sex female\n", - "Age 35\n", - "SibSp 1\n", - "Parch 0\n", - "Ticket 113803\n", - "Fare 53.1\n", - "Cabin C123\n", - "Embarked S\n", - "Name: 4, dtype: object\n" - ] - } - ], - "source": [ - "# select a single row\n", - "print(data.loc[4])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PassengerId\n", - "1 22.0\n", - "2 38.0\n", - "3 26.0\n", - "4 35.0\n", - "5 35.0\n", - "6 NaN\n", - "7 54.0\n", - "8 2.0\n", - "9 27.0\n", - "10 14.0\n", - "Name: Age, dtype: float64\n" - ] - } - ], - "source": [ - "# select a single column.\n", - "ages = data[\"Age\"]\n", - "print(ages[:10]) # alternatively: data.Age" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Fare Pclass\n", - "PassengerId \n", - "5 8.0500 3\n", - "6 8.4583 3\n", - "7 51.8625 1\n", - "8 21.0750 3\n", - "9 11.1333 3\n", - "10 30.0708 2" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# select several columns and rows at once\n", - "data.loc[5:10, (\"Fare\", \"Pclass\")] # alternatively: data[[\"Fare\",\"Pclass\"]].loc[5:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your turn:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
1303Saundercock, Mr. William Henrymale20.000A/5. 21518.05NaNS
66602Hickman, Mr. Lewismale32.020S.O.C. 1487973.50NaNS
\n", - "
" - ], - "text/plain": [ - " Survived Pclass Name Sex Age \\\n", - "PassengerId \n", - "13 0 3 Saundercock, Mr. William Henry male 20.0 \n", - "666 0 2 Hickman, Mr. Lewis male 32.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "13 0 0 A/5. 2151 8.05 NaN S \n", - "666 2 0 S.O.C. 14879 73.50 NaN S " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# select passengers number 13 and 666 - did they survive?\n", - "\n", - "data.loc[[13, 666], :]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3838383838383838" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# compute the overall survival rate (what fraction of passengers survived the shipwreck)\n", - "\n", - "len(data[data.Survived == 1]) / len(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas also has some basic data analysis tools. For one, you can quickly display statistical aggregates for each column using `.describe()`" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SurvivedPclassAgeSibSpParchFare
count891.000000891.000000714.000000891.000000891.000000891.000000
mean0.3838382.30864229.6991180.5230080.38159432.204208
std0.4865920.83607114.5264971.1027430.80605749.693429
min0.0000001.0000000.4200000.0000000.0000000.000000
25%0.0000002.00000020.1250000.0000000.0000007.910400
50%0.0000003.00000028.0000000.0000000.00000014.454200
75%1.0000003.00000038.0000001.0000000.00000031.000000
max1.0000003.00000080.0000008.0000006.000000512.329200
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" - ], - "text/plain": [ - " Survived Pclass Age SibSp Parch Fare\n", - "count 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000\n", - "mean 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208\n", - "std 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429\n", - "min 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000\n", - "25% 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400\n", - "50% 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200\n", - "75% 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000\n", - "max 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some columns contain __NaN__ values - this means that there is no data there. For example, passenger `#5` has unknown age. To simplify the future data analysis, we'll replace NaN values by using pandas `fillna` function.\n", - "\n", - "_Note: we do this so easily because it's a tutorial. In general, you think twice before you modify data like this._" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Survived 0\n", - "Pclass 3\n", - "Name Moran, Mr. James\n", - "Sex male\n", - "Age NaN\n", - "SibSp 0\n", - "Parch 0\n", - "Ticket 330877\n", - "Fare 8.4583\n", - "Cabin NaN\n", - "Embarked Q\n", - "Name: 6, dtype: object" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.iloc[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "data['Age'] = data['Age'].fillna(value=data['Age'].mean())\n", - "data['Fare'] = data['Fare'].fillna(value=data['Fare'].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Survived 0\n", - "Pclass 3\n", - "Name Moran, Mr. James\n", - "Sex male\n", - "Age 29.6991\n", - "SibSp 0\n", - "Parch 0\n", - "Ticket 330877\n", - "Fare 8.4583\n", - "Cabin NaN\n", - "Embarked Q\n", - "Name: 6, dtype: object" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.iloc[5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More pandas: \n", - "* A neat [tutorial](http://pandas.pydata.org/) from pydata\n", - "* Official [tutorials](https://pandas.pydata.org/pandas-docs/stable/tutorials.html), including this [10 minutes to pandas](https://pandas.pydata.org/pandas-docs/stable/10min.html#min)\n", - "* Bunch of cheat sheets awaits just one google query away from you (e.g. [basics](http://blog.yhat.com/static/img/datacamp-cheat.png), [combining datasets](https://pbs.twimg.com/media/C65MaMpVwAA3v0A.jpg) and so on). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part III: Numpy and vectorized computing\n", - "\n", - "Almost any machine learning model requires some computational heavy lifting usually involving linear algebra problems. Unfortunately, raw python is terrible at this because each operation is interpreted at runtime. \n", - "\n", - "So instead, we'll use `numpy` - a library that lets you run blazing fast computation with vectors, matrices and other tensors. Again, the god oject here is `numpy.ndarray`:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a = [1 2 3 4 5]\n", - "b = [5 4 3 2 1]\n", - "a + 1 = [2 3 4 5 6]\n", - "a * 2 = [ 2 4 6 8 10]\n", - "a == 2 [False True False False False]\n", - "a + b = [6 6 6 6 6]\n", - "a * b = [5 8 9 8 5]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "a = np.array([1,2,3,4,5])\n", - "b = np.array([5,4,3,2,1])\n", - "print(\"a = \",a)\n", - "print(\"b = \",b)\n", - "\n", - "# math and boolean operations can applied to each element of an array\n", - "print(\"a + 1 =\", a + 1)\n", - "print(\"a * 2 =\", a * 2)\n", - "print(\"a == 2\", a == 2)\n", - "# ... or corresponding elements of two (or more) arrays\n", - "print(\"a + b =\",a + b)\n", - "print(\"a * b =\",a * b)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.5, 4. , 4.5, 4. , 2.5])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Your turn: compute half-products of a and b elements (halves of products)\n", - "a * b / 2" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.16666667, 0.4 , 0.75 , 1.33333333, 2.5 ])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# compute elementwise quoient between squared a and (b plus 1)\n", - "a / (b + 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "\n", - "### How fast is it, harry?\n", - "![img](https://img.buzzfeed.com/buzzfeed-static/static/2015-11/6/7/enhanced/webdr10/enhanced-buzz-22847-1446811476-0.jpg)\n", - "\n", - "Let's compare computation time for python and numpy\n", - "* Two arrays of 10^6 elements\n", - " * first - from 0 to 1 000 000\n", - " * second - from 99 to 1 000 099\n", - " \n", - "* Computing:\n", - " * elemwise sum\n", - " * elemwise product\n", - " * square root of first array\n", - " * sum of all elements in the first array\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 987 ms\n" - ] - } - ], - "source": [ - "%%time \n", - "# ^-- this \"magic\" measures and prints cell computation time\n", - "\n", - "# Option I: pure python\n", - "arr_1 = range(1000000)\n", - "arr_2 = range(99,1000099)\n", - "\n", - "\n", - "a_sum = []\n", - "a_prod = []\n", - "sqrt_a1 = []\n", - "for i in range(len(arr_1)):\n", - " a_sum.append(arr_1[i]+arr_2[i])\n", - " a_prod.append(arr_1[i]*arr_2[i])\n", - " a_sum.append(arr_1[i]**0.5)\n", - " \n", - "arr_1_sum = sum(arr_1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 301 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "# Option II: start from python, convert to numpy\n", - "arr_1 = range(1000000)\n", - "arr_2 = range(99,1000099)\n", - "\n", - "arr_1, arr_2 = np.array(arr_1) , np.array(arr_2)\n", - "\n", - "\n", - "a_sum = arr_1 + arr_2\n", - "a_prod = arr_1 * arr_2\n", - "sqrt_a1 = arr_1 ** .5\n", - "arr_1_sum = arr_1.sum()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 36.9 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "# Option III: pure numpy\n", - "arr_1 = np.arange(1000000)\n", - "arr_2 = np.arange(99,1000099)\n", - "\n", - "a_sum = arr_1 + arr_2\n", - "a_prod = arr_1 * arr_2\n", - "sqrt_a1 = arr_1 ** .5\n", - "arr_1_sum = arr_1.sum()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want more serious benchmarks, take a look at [this](http://brilliantlywrong.blogspot.ru/2015/01/benchmarks-of-speed-numpy-vs-all.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "\n", - "There's also a bunch of pre-implemented operations including logarithms, trigonometry, vector/matrix products and aggregations." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "numpy.sum(a) = 15\n", - "numpy.mean(a) = 3.0\n", - "numpy.min(a) = 1\n", - "numpy.argmin(b) = 4\n", - "numpy.dot(a,b) = 35\n", - "numpy.unique(['male','male','female','female','male']) = ['female' 'male']\n" - ] - } - ], - "source": [ - "a = np.array([1,2,3,4,5])\n", - "b = np.array([5,4,3,2,1])\n", - "print(\"numpy.sum(a) = \", np.sum(a))\n", - "print(\"numpy.mean(a) = \", np.mean(a))\n", - "print(\"numpy.min(a) = \", np.min(a))\n", - "print(\"numpy.argmin(b) = \", np.argmin(b)) # index of minimal element\n", - "print(\"numpy.dot(a,b) = \", np.dot(a, b)) # dot product. Also used for matrix/tensor multiplication\n", - "print(\"numpy.unique(['male','male','female','female','male']) = \", np.unique(['male','male','female','female','male']))\n", - "\n", - "# and tons of other stuff. see http://bit.ly/2u5q430 ." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The important part: all this functionality works with dataframes:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max ticket price: 512.3292\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "module 'numpy' has no attribute 'idxmax'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Max ticket price: \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Fare\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\nThe guy who paid the most:\\n\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Fare\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'idxmax'" - ] - } - ], - "source": [ - "print(\"Max ticket price: \", np.max(data[\"Fare\"]))\n", - "print(\"\\nThe guy who paid the most:\\n\", data.loc[np.idxmax(data[\"Fare\"])])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean passenger age: 29.69911764705882\n", - "\n", - "Oldest guy on the ship:\n", - " Survived 1\n", - "Pclass 1\n", - "Name Barkworth, Mr. Algernon Henry Wilson\n", - "Sex male\n", - "Age 80\n", - "SibSp 0\n", - "Parch 0\n", - "Ticket 27042\n", - "Fare 30\n", - "Cabin A23\n", - "Embarked S\n", - "Name: 631, dtype: object\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\jiadai\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\numpy\\core\\fromnumeric.py:52: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n", - "will be corrected to return the positional maximum in the future.\n", - "Use 'series.values.argmax' to get the position of the maximum now.\n", - " return getattr(obj, method)(*args, **kwds)\n" - ] - } - ], - "source": [ - "# your code: compute mean passenger age and the oldest guy on the ship\n", - "print(\"Mean passenger age: \", np.mean(data[\"Age\"]))\n", - "print(\"\\nOldest guy on the ship:\\n\", data.loc[np.argmax(data[\"Age\"])])" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Boolean operations\n", - "a = [1 2 3 4 5]\n", - "b = [5 4 3 2 1]\n", - "a > 2 [False False True True True]\n", - "numpy.logical_not(a>2) = [ True True False False False]\n", - "numpy.logical_and(a>2,b>2) = [False False True False False]\n", - "numpy.logical_or(a>4,b<3) = [False False True True True]\n", - "\n", - " shortcuts\n", - "~(a > 2) = [ True True False False False]\n", - "(a > 2) & (b > 2) = [False False True False False]\n", - "(a > 2) | (b < 3) = [False False True True True]\n" - ] - } - ], - "source": [ - "print(\"Boolean operations\")\n", - "\n", - "print('a = ', a)\n", - "print('b = ', b)\n", - "print(\"a > 2\", a > 2)\n", - "print(\"numpy.logical_not(a>2) = \", np.logical_not(a>2))\n", - "print(\"numpy.logical_and(a>2,b>2) = \", np.logical_and(a > 2,b > 2))\n", - "print(\"numpy.logical_or(a>4,b<3) = \", np.logical_or(a > 2, b < 3))\n", - "\n", - "print(\"\\n shortcuts\")\n", - "print(\"~(a > 2) = \", ~(a > 2)) #logical_not(a > 2)\n", - "print(\"(a > 2) & (b > 2) = \", (a > 2) & (b > 2)) #logical_and\n", - "print(\"(a > 2) | (b < 3) = \", (a > 2) | (b < 3)) #logical_or" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final numpy feature we'll need is indexing: selecting elements from an array. \n", - "Aside from python indexes and slices (e.g. a[1:4]), numpy also allows you to select several elements at once." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a = [ 0 1 4 9 16 25]\n", - "Select by element index\n", - "a[[1,2,5]] = [1 4 9]\n", - "\n", - "Select by boolean mask\n", - "a[a > 5] = [ 9 16 25]\n", - "(a % 2 == 0) = [ True False True False True False]\n", - "a[a % 2 == 0] = [ 0 4 16]\n", - "data[(data['Age'] < 18) & (data['Sex'] == 'male')] = (below)\n" - ] - }, - { - "data": { - "text/html": [ - "
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SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
803Palsson, Master. Gosta Leonardmale2.003134990921.0750NaNS
1703Rice, Master. Eugenemale2.004138265229.1250NaNQ
5103Panula, Master. Juha Niilomale7.0041310129539.6875NaNS
6003Goodwin, Master. William Frederickmale11.0052CA 214446.9000NaNS
6403Skoog, Master. Haraldmale4.003234708827.9000NaNS
7912Caldwell, Master. Alden Gatesmale0.830224873829.0000NaNS
8703Ford, Mr. William Nealmale16.0013W./C. 660834.3750NaNS
12613Nicola-Yarred, Master. Eliasmale12.0010265111.2417NaNC
13903Osen, Mr. Olaf Elonmale16.000075349.2167NaNS
16403Calic, Mr. Jovomale17.00003150938.6625NaNS
16503Panula, Master. Eino Viljamimale1.0041310129539.6875NaNS
16613Goldsmith, Master. Frank John William \"Frankie\"male9.000236329120.5250NaNS
17203Rice, Master. Arthurmale4.004138265229.1250NaNQ
18303Asplund, Master. Clarence Gustaf Hugomale9.004234707731.3875NaNS
18412Becker, Master. Richard Fmale1.002123013639.0000F4S
19412Navratil, Master. Michel Mmale3.001123008026.0000F2S
22113Sunderland, Mr. Victor Francismale16.0000SOTON/OQ 3920898.0500NaNS
26213Asplund, Master. Edvin Rojj Felixmale3.004234707731.3875NaNS
26703Panula, Mr. Ernesti Arvidmale16.0041310129539.6875NaNS
27903Rice, Master. Ericmale7.004138265229.1250NaNQ
28303de Pelsmaeker, Mr. Alfonsmale16.00003457789.5000NaNS
30611Allison, Master. Hudson Trevormale0.9212113781151.5500C22 C26S
33403Vander Planke, Mr. Leo Edmondusmale16.002034576418.0000NaNS
34112Navratil, Master. Edmond Rogermale2.001123008026.0000F2S
34913Coutts, Master. William Loch \"William\"male3.0011C.A. 3767115.9000NaNS
35303Elias, Mr. Tannousmale15.001126957.2292NaNC
38703Goodwin, Master. Sidney Leonardmale1.0052CA 214446.9000NaNS
40812Richards, Master. William Rowemale3.00112910618.7500NaNS
43403Kallio, Mr. Nikolai Erlandmale17.0000STON/O 2. 31012747.1250NaNS
44611Dodge, Master. Washingtonmale4.00023363881.8583A34S
48103Goodwin, Master. Harold Victormale9.0052CA 214446.9000NaNS
49013Coutts, Master. Eden Leslie \"Neville\"male9.0011C.A. 3767115.9000NaNS
50103Calic, Mr. Petarmale17.00003150868.6625NaNS
53303Elias, Mr. Joseph Jrmale17.001126907.2292NaNC
55012Davies, Master. John Morgan Jrmale8.0011C.A. 3311236.7500NaNS
55111Thayer, Mr. John Borland Jrmale17.000217421110.8833C70C
57503Rush, Mr. Alfred George Johnmale16.0000A/4. 205898.0500NaNS
68403Goodwin, Mr. Charles Edwardmale14.0052CA 214446.9000NaNS
68703Panula, Mr. Jaako Arnoldmale14.0041310129539.6875NaNS
72203Jensen, Mr. Svend Lauritzmale17.00103500487.0542NaNS
73203Hassan, Mr. Houssein G Nmale11.0000269918.7875NaNC
74703Abbott, Mr. Rossmore Edwardmale16.0011C.A. 267320.2500NaNS
75213Moor, Master. Meiermale6.000139209612.4750E121S
75612Hamalainen, Master. Viljomale0.671125064914.5000NaNS
76503Eklund, Mr. Hans Linusmale16.00003470747.7750NaNS
78803Rice, Master. George Hughmale8.004138265229.1250NaNQ
78913Dean, Master. Bertram Veremale1.0012C.A. 231520.5750NaNS
79202Gaskell, Mr. Alfredmale16.000023986526.0000NaNS
80311Carter, Master. William Thornton IImale11.0012113760120.0000B96 B98S
80413Thomas, Master. Assad Alexandermale0.420126258.5167NaNC
82003Skoog, Master. Karl Thorstenmale10.003234708827.9000NaNS
82503Panula, Master. Urho Abrahammale2.0041310129539.6875NaNS
82812Mallet, Master. Andremale1.0002S.C./PARIS 207937.0042NaNC
83212Richards, Master. George Sibleymale0.83112910618.7500NaNS
84202Mudd, Mr. Thomas Charlesmale16.0000S.O./P.P. 310.5000NaNS
84503Culumovic, Mr. Jesomale17.00003150908.6625NaNS
85103Andersson, Master. Sigvard Harald Eliasmale4.004234708231.2750NaNS
87013Johnson, Master. Harold Theodormale4.001134774211.1333NaNS
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "8 0 3 \n", - "17 0 3 \n", - "51 0 3 \n", - "60 0 3 \n", - "64 0 3 \n", - "79 1 2 \n", - "87 0 3 \n", - "126 1 3 \n", - "139 0 3 \n", - "164 0 3 \n", - "165 0 3 \n", - "166 1 3 \n", - "172 0 3 \n", - "183 0 3 \n", - "184 1 2 \n", - "194 1 2 \n", - "221 1 3 \n", - "262 1 3 \n", - "267 0 3 \n", - "279 0 3 \n", - "283 0 3 \n", - "306 1 1 \n", - "334 0 3 \n", - "341 1 2 \n", - "349 1 3 \n", - "353 0 3 \n", - "387 0 3 \n", - "408 1 2 \n", - "434 0 3 \n", - "446 1 1 \n", - "481 0 3 \n", - "490 1 3 \n", - "501 0 3 \n", - "533 0 3 \n", - "550 1 2 \n", - "551 1 1 \n", - "575 0 3 \n", - "684 0 3 \n", - "687 0 3 \n", - "722 0 3 \n", - "732 0 3 \n", - "747 0 3 \n", - "752 1 3 \n", - "756 1 2 \n", - "765 0 3 \n", - "788 0 3 \n", - "789 1 3 \n", - "792 0 2 \n", - "803 1 1 \n", - "804 1 3 \n", - "820 0 3 \n", - "825 0 3 \n", - "828 1 2 \n", - "832 1 2 \n", - "842 0 2 \n", - "845 0 3 \n", - "851 0 3 \n", - "870 1 3 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "8 Palsson, Master. Gosta Leonard male 2.00 \n", - "17 Rice, Master. Eugene male 2.00 \n", - "51 Panula, Master. Juha Niilo male 7.00 \n", - "60 Goodwin, Master. William Frederick male 11.00 \n", - "64 Skoog, Master. Harald male 4.00 \n", - "79 Caldwell, Master. Alden Gates male 0.83 \n", - "87 Ford, Mr. William Neal male 16.00 \n", - "126 Nicola-Yarred, Master. Elias male 12.00 \n", - "139 Osen, Mr. Olaf Elon male 16.00 \n", - "164 Calic, Mr. Jovo male 17.00 \n", - "165 Panula, Master. Eino Viljami male 1.00 \n", - "166 Goldsmith, Master. Frank John William \"Frankie\" male 9.00 \n", - "172 Rice, Master. Arthur male 4.00 \n", - "183 Asplund, Master. Clarence Gustaf Hugo male 9.00 \n", - "184 Becker, Master. Richard F male 1.00 \n", - "194 Navratil, Master. Michel M male 3.00 \n", - "221 Sunderland, Mr. Victor Francis male 16.00 \n", - "262 Asplund, Master. Edvin Rojj Felix male 3.00 \n", - "267 Panula, Mr. Ernesti Arvid male 16.00 \n", - "279 Rice, Master. Eric male 7.00 \n", - "283 de Pelsmaeker, Mr. Alfons male 16.00 \n", - "306 Allison, Master. Hudson Trevor male 0.92 \n", - "334 Vander Planke, Mr. Leo Edmondus male 16.00 \n", - "341 Navratil, Master. Edmond Roger male 2.00 \n", - "349 Coutts, Master. William Loch \"William\" male 3.00 \n", - "353 Elias, Mr. Tannous male 15.00 \n", - "387 Goodwin, Master. Sidney Leonard male 1.00 \n", - "408 Richards, Master. William Rowe male 3.00 \n", - "434 Kallio, Mr. Nikolai Erland male 17.00 \n", - "446 Dodge, Master. Washington male 4.00 \n", - "481 Goodwin, Master. Harold Victor male 9.00 \n", - "490 Coutts, Master. Eden Leslie \"Neville\" male 9.00 \n", - "501 Calic, Mr. Petar male 17.00 \n", - "533 Elias, Mr. Joseph Jr male 17.00 \n", - "550 Davies, Master. John Morgan Jr male 8.00 \n", - "551 Thayer, Mr. John Borland Jr male 17.00 \n", - "575 Rush, Mr. Alfred George John male 16.00 \n", - "684 Goodwin, Mr. Charles Edward male 14.00 \n", - "687 Panula, Mr. Jaako Arnold male 14.00 \n", - "722 Jensen, Mr. Svend Lauritz male 17.00 \n", - "732 Hassan, Mr. Houssein G N male 11.00 \n", - "747 Abbott, Mr. Rossmore Edward male 16.00 \n", - "752 Moor, Master. Meier male 6.00 \n", - "756 Hamalainen, Master. Viljo male 0.67 \n", - "765 Eklund, Mr. Hans Linus male 16.00 \n", - "788 Rice, Master. George Hugh male 8.00 \n", - "789 Dean, Master. Bertram Vere male 1.00 \n", - "792 Gaskell, Mr. Alfred male 16.00 \n", - "803 Carter, Master. William Thornton II male 11.00 \n", - "804 Thomas, Master. Assad Alexander male 0.42 \n", - "820 Skoog, Master. Karl Thorsten male 10.00 \n", - "825 Panula, Master. Urho Abraham male 2.00 \n", - "828 Mallet, Master. Andre male 1.00 \n", - "832 Richards, Master. George Sibley male 0.83 \n", - "842 Mudd, Mr. Thomas Charles male 16.00 \n", - "845 Culumovic, Mr. Jeso male 17.00 \n", - "851 Andersson, Master. Sigvard Harald Elias male 4.00 \n", - "870 Johnson, Master. Harold Theodor male 4.00 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "8 3 1 349909 21.0750 NaN S \n", - "17 4 1 382652 29.1250 NaN Q \n", - "51 4 1 3101295 39.6875 NaN S \n", - "60 5 2 CA 2144 46.9000 NaN S \n", - "64 3 2 347088 27.9000 NaN S \n", - "79 0 2 248738 29.0000 NaN S \n", - "87 1 3 W./C. 6608 34.3750 NaN S \n", - "126 1 0 2651 11.2417 NaN C \n", - "139 0 0 7534 9.2167 NaN S \n", - "164 0 0 315093 8.6625 NaN S \n", - "165 4 1 3101295 39.6875 NaN S \n", - "166 0 2 363291 20.5250 NaN S \n", - "172 4 1 382652 29.1250 NaN Q \n", - "183 4 2 347077 31.3875 NaN S \n", - "184 2 1 230136 39.0000 F4 S \n", - "194 1 1 230080 26.0000 F2 S \n", - "221 0 0 SOTON/OQ 392089 8.0500 NaN S \n", - "262 4 2 347077 31.3875 NaN S \n", - "267 4 1 3101295 39.6875 NaN S \n", - "279 4 1 382652 29.1250 NaN Q \n", - "283 0 0 345778 9.5000 NaN S \n", - "306 1 2 113781 151.5500 C22 C26 S \n", - "334 2 0 345764 18.0000 NaN S \n", - "341 1 1 230080 26.0000 F2 S \n", - "349 1 1 C.A. 37671 15.9000 NaN S \n", - "353 1 1 2695 7.2292 NaN C \n", - "387 5 2 CA 2144 46.9000 NaN S \n", - "408 1 1 29106 18.7500 NaN S \n", - "434 0 0 STON/O 2. 3101274 7.1250 NaN S \n", - "446 0 2 33638 81.8583 A34 S \n", - "481 5 2 CA 2144 46.9000 NaN S \n", - "490 1 1 C.A. 37671 15.9000 NaN S \n", - "501 0 0 315086 8.6625 NaN S \n", - "533 1 1 2690 7.2292 NaN C \n", - "550 1 1 C.A. 33112 36.7500 NaN S \n", - "551 0 2 17421 110.8833 C70 C \n", - "575 0 0 A/4. 20589 8.0500 NaN S \n", - "684 5 2 CA 2144 46.9000 NaN S \n", - "687 4 1 3101295 39.6875 NaN S \n", - "722 1 0 350048 7.0542 NaN S \n", - "732 0 0 2699 18.7875 NaN C \n", - "747 1 1 C.A. 2673 20.2500 NaN S \n", - "752 0 1 392096 12.4750 E121 S \n", - "756 1 1 250649 14.5000 NaN S \n", - "765 0 0 347074 7.7750 NaN S \n", - "788 4 1 382652 29.1250 NaN Q \n", - "789 1 2 C.A. 2315 20.5750 NaN S \n", - "792 0 0 239865 26.0000 NaN S \n", - "803 1 2 113760 120.0000 B96 B98 S \n", - "804 0 1 2625 8.5167 NaN C \n", - "820 3 2 347088 27.9000 NaN S \n", - "825 4 1 3101295 39.6875 NaN S \n", - "828 0 2 S.C./PARIS 2079 37.0042 NaN C \n", - "832 1 1 29106 18.7500 NaN S \n", - "842 0 0 S.O./P.P. 3 10.5000 NaN S \n", - "845 0 0 315090 8.6625 NaN S \n", - "851 4 2 347082 31.2750 NaN S \n", - "870 1 1 347742 11.1333 NaN S " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.array([0, 1, 4, 9, 16, 25])\n", - "ix = np.array([1,2,3])\n", - "print(\"a = \", a)\n", - "print(\"Select by element index\")\n", - "print(\"a[[1,2,5]] = \", a[ix])\n", - "\n", - "print(\"\\nSelect by boolean mask\")\n", - "print(\"a[a > 5] = \", a[a > 5]) # select all elementts in a that are greater than 5\n", - "print(\"(a % 2 == 0) =\",a % 2 == 0) # True for even, False for odd\n", - "print(\"a[a % 2 == 0] =\", a[a % 2 == 0]) # select all elements in a that are even\n", - "\n", - "\n", - "print(\"data[(data['Age'] < 18) & (data['Sex'] == 'male')] = (below)\") # select male children\n", - "data[(data['Age'] < 18) & (data['Sex'] == 'male')]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Your turn\n", - "\n", - "Use numpy and pandas to answer a few questions about data" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "25.523893414211443 44.47981783439491\n" - ] - } - ], - "source": [ - "# who on average paid more for their ticket, men or women?\n", - "\n", - "mean_fare_men = np.mean(data[data['Sex'] == 'male']['Fare'])\n", - "mean_fare_women = np.mean(data[data['Sex'] == 'female']['Fare'])\n", - "\n", - "print(mean_fare_men, mean_fare_women)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.12682379349046016 0.8731762065095399\n" - ] - } - ], - "source": [ - "# who is more likely to survive: a child (<18 yo) or an adult?\n", - "\n", - "child_survival_rate = len(data[data['Age'] < 18]) / len(data)\n", - "adult_survival_rate = len(data[data['Age'] >= 18]) / len(data)\n", - "\n", - "print(child_survival_rate, adult_survival_rate)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Part IV: plots and matplotlib\n", - "\n", - "Using python to visualize the data is covered by yet another library: `matplotlib`.\n", - "\n", - "Just like python itself, matplotlib has an awesome tendency of keeping simple things simple while still allowing you to write complicated stuff with convenience (e.g. super-detailed plots or custom animations)." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline \n", - "# ^-- this \"magic\" tells all future matplotlib plots to be drawn inside notebook and not in a separate window.\n", - "\n", - "# line plot\n", - "plt.plot([0,1,2,3,4,5],[0,1,4,9,16,25])" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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C\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#scatter-plot\n", - "plt.scatter([0,1,2,3,4,5],[0,1,4,9,16,25])\n", - "\n", - "plt.show() # show the first plot and begin drawing next one" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '# Layers in state of the art image classifier')" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# draw a scatter plot with custom markers and colors\n", - "plt.scatter([1,1,2,3,4,4.5],[3,2,2,5,15,24],\n", - " c = [\"red\",\"blue\",\"orange\",\"green\",\"cyan\",\"gray\"], marker = \"x\")\n", - "\n", - "# without .show(), several plots will be drawn on top of one another\n", - "plt.plot([0,1,2,3,4,5],[0,1,4,9,16,25],c = \"black\")\n", - "\n", - "# adding more sugar\n", - "plt.title(\"Conspiracy theory proven!!!\")\n", - "plt.xlabel(\"Per capita alcohol consumption\")\n", - "plt.ylabel(\"# Layers in state of the art image classifier\")\n", - "\n", - "# fun with correlations: http://bit.ly/1FcNnWF" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(array([4., 7., 5., 3., 3.]),\n", - " array([ 0., 2., 4., 6., 8., 10.]),\n", - " )" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# histogram - showing data density\n", - "plt.hist([0,1,1,1,2,2,3,3,3,3,3,4,4,5,5,5,6,7,7,8,9,10])\n", - "plt.show()\n", - "\n", - "plt.hist([0,1,1,1,2,2,3,3,3,3,3,4,4,5,5,5,6,7,7,8,9,10], bins = 5)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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UluR1SR5P8qVW1wda+yVJHmt1fbx9iH/aJTkryReTPDhjdR1O8uUk+5IstrZZ2M/OS/KJJF9t+9ovTruuJD/Tvk/HH99O8r5p19Vq+2dtv9+f5N728zDxfeyMC/oZvL3CR4BrT2jbBTxSVduAR9ry6fYK8P6qegtwJXBr+z5Nu7aXgaur6q3AduDaJFcCHwTubHV9C7jlNNd13HuBg0PLs1IXwC9V1fahU/Gm/V4C/Dvgj6vqZ4G3MvjeTbWuqnq6fZ+2A38b+B7w6WnXlWQz8E+Bhar6eQYnqtzI6djHquqMegC/CPzJ0PJtwG1Trmke2D+0/DSwqc1vAp6ege/bA8A/mKXagDcATzK4avobwNkrvcensZ4tDALgauBBBhf+Tb2utu3DwIUntE31vQT+GvAs7bO+WanrhFp+Gfjvs1AXr90x4AIGJ8I8CLzjdOxjZ9wRPSvfXmHzlGo5mYur6ihAm140zWKSzAOXAY8xA7W14ZF9wDHgYeAvgBer6pXWZVrv6b8F/gXwg7b8EzNSFwyuLP/TJE+0q8ph+u/lTwHLwB+24a7/mOSNM1DXsBuBe9v8VOuqqq8Dvwc8BxwFXgKe4DTsY2di0I+8vYJek+RNwCeB91XVt6ddD0BVvVqDP6u3MLgB3ltW6nY6a0ryD4FjVfXEcPMKXae1r11VVZczGLK8Ncnfn1Idw84GLgfuqqrLgP/NdIaPVtTGun8F+M/TrgWgfSawA7gE+EngjQzezxNt+D52Jgb9yNsrzIAXkmwCaNNj0ygiyTkMQv5jVfWpWaoNoKpeBD7H4DOE85Icv65jGu/pVcCvJDnM4G6rVzM4wp92XQBU1ZE2PcZgvPkKpv9eLgFLVfVYW/4Eg+Cfdl3HXQc8WVUvtOVp1/V24NmqWq6q7wOfAv4up2EfOxOD/ky4vcJe4OY2fzOD8fHTKkmAu4GDVfWhWaktyVyS89r86xns/AeBR4Ffm1ZdVXVbVW2pqnkG+9Rnq+ofT7sugCRvTPJjx+cZjDvvZ8rvZVX9L+D5JD/Tmq4BvjLtuobcxGvDNjD9up4Drkzyhvbzefz7Nfl9bFofkqzzQ43rgf/BYGz3X065lnsZjLd9n8ERzi0MxnYfAZ5p0wumUNffY/An4FPAvva4ftq1AX8L+GKraz/wr1r7TwGPA4cY/Kl97hTf07cBD85KXa2GL7XHgeP7/LTfy1bDdmCxvZ//BTh/Rup6A/CXwI8Ptc1CXR8Avtr2/T8Czj0d+5hXxkpS587EoRtJ0ioY9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kde7/AY1oaiRdc8CVAAAAAElFTkSuQmCC\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot a histogram of age and a histogram of ticket fares on separate plots\n", - "plt.hist(data['Age'])\n", - "plt.show()\n", - "\n", - "plt.hist(data['Fare'])\n", - "plt.show()\n", - "\n", - "plt.hist2d(data['Age'], data['Fare'])\n", - "plt.show()\n", - "#bonus: use tab shift-tab to see if there is a way to draw a 2D histogram of age vs fare." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# make a scatter plot of passenger age vs ticket fare\n", - "\n", - "plt.scatter(data[data['Sex'] == 'male']['Age'], data[data['Sex'] == 'male']['Fare'])\n", - "plt.scatter(data[data['Sex'] == 'female']['Age'], data[data['Sex'] == 'female']['Fare'], c='red')\n", - "plt.show()\n", - "\n", - "# kudos if you add separate colors for men and women" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Extended [tutorial](https://matplotlib.org/2.0.2/users/pyplot_tutorial.html)\n", - "* A [cheat sheet](http://bit.ly/2koHxNF)\n", - "* Other libraries for more sophisticated stuff: [Plotly](https://plot.ly/python/) and [Bokeh](https://bokeh.pydata.org/en/latest/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part V (final): machine learning with scikit-learn\n", - "\n", - "\n", - "\n", - "Scikit-learn is _the_ tool for simple machine learning pipelines. \n", - "\n", - "It's a single library that unites a whole bunch of models under the common interface:\n", - "* Create:__ `model = sklearn.whatever.ModelNameHere(parameters_if_any)`__\n", - "* Train:__ `model.fit(X,y)`__\n", - "* Predict:__ `model.predict(X_test)`__\n", - "\n", - "It also contains utilities for feature extraction, quality estimation or cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test accuracy: 0.67\n" - ] - } - ], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "features = data[[\"Fare\", \"SibSp\"]].copy()\n", - "answers = data[\"Survived\"]\n", - "\n", - "model = RandomForestClassifier(n_estimators=100)\n", - "model.fit(features[:-100], answers[:-100])\n", - "\n", - "test_predictions = model.predict(features[-100:])\n", - "print(\"Test accuracy:\", accuracy_score(answers[-100:], test_predictions))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Final quest: add more features to achieve accuracy of at least 0.80\n", - "\n", - "__Hint:__ for string features like \"Sex\" or \"Embarked\" you will have to compute some kind of numeric representation.\n", - "For example, 1 if male and 0 if female or vice versa \n", - "\n", - "__Hint II:__ you can use `model.feature_importances_` to get a hint on how much did it rely each of your features." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "features['Sex'] = (data['Sex'] == 'male').astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "features['Age'] = data['Age']" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
FareSibSpSexAge
PassengerId
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\n", - "
" - ], - "text/plain": [ - " Fare SibSp Sex Age\n", - "PassengerId \n", - "1 7.2500 1 1 22.0\n", - "2 71.2833 1 0 38.0\n", - "3 7.9250 0 0 26.0\n", - "4 53.1000 1 0 35.0\n", - "5 8.0500 0 1 35.0" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test accuracy: 0.83\n" - ] - } - ], - "source": [ - "model.fit(features[:-100], answers[:-100])\n", - "\n", - "test_predictions = model.predict(features[-100:])\n", - "print(\"Test accuracy:\", accuracy_score(answers[-100:], test_predictions))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Sklearn [tutorials](http://scikit-learn.org/stable/tutorial/index.html)\n", - "* Sklearn [examples](http://scikit-learn.org/stable/auto_examples/index.html)\n", - "* SKlearn [cheat sheet](http://scikit-learn.org/stable/_static/ml_map.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "\n", - "Okay, what we learned: to survive a shipwreck you need to become an underaged girl with parents on the ship. Try this next time you'll find yourself in a shipwreck" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_tensorflow-checkpoint.ipynb b/Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_tensorflow-checkpoint.ipynb deleted file mode 100644 index e0dd142..0000000 --- a/Practical Reinforcement Learning/Week1_intro/primer/.ipynb_checkpoints/recap_tensorflow-checkpoint.ipynb +++ /dev/null @@ -1,1204 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "# Going deeper with Tensorflow\n", - "\n", - "In this seminar, we're going to play with [Tensorflow](https://www.tensorflow.org/) and see how it helps us build deep learning models.\n", - "\n", - "If you're running this notebook outside the course environment, you'll need to install tensorflow:\n", - "* `pip install tensorflow` should install cpu-only TF on Linux & Mac OS\n", - "* If you want GPU support from offset, see [TF install page](https://www.tensorflow.org/install/)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "gpu_options = tf.GPUOptions(allow_growth=True, per_process_gpu_memory_fraction=0.1)\n", - "s = tf.InteractiveSession(config=tf.ConfigProto(gpu_options=gpu_options))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Warming up\n", - "For starters, let's implement a python function that computes the sum of squares of numbers from 0 to N-1.\n", - "* Use numpy or python\n", - "* An array of numbers 0 to N - numpy.arange(N)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "def sum_squares(N):\n", - " return np.sum(np.arange(N) ** 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 400 ms, sys: 827 ms, total: 1.23 s\n", - "Wall time: 2.52 s\n" - ] - }, - { - "data": { - "text/plain": [ - "662921401752298880" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "sum_squares(10**8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tensorflow teaser\n", - "\n", - "Doing the very same thing" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#I gonna be your function parameter\n", - "N = tf.placeholder('int64', name=\"input_to_your_function\")\n", - "\n", - "#i am a recipe on how to produce sum of squares of arange of N given N\n", - "result = tf.reduce_sum((tf.range(N)**2))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "662921401752298880\n", - "CPU times: user 514 ms, sys: 451 ms, total: 964 ms\n", - "Wall time: 2 s\n" - ] - } - ], - "source": [ - "%%time\n", - "#example of computing the same as sum_squares\n", - "print(result.eval({N:10**8}))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How does it work?\n", - "1. define placeholders where you'll send inputs;\n", - "2. make symbolic graph: a recipe for mathematical transformation of those placeholders;\n", - "3. compute outputs of your graph with particular values for each placeholder\n", - " * output.eval({placeholder:value}) \n", - " * s.run(output, {placeholder:value})\n", - "\n", - "* So far there are two main entities: \"placeholder\" and \"transformation\"\n", - "* Both can be numbers, vectors, matrices, tensors, etc.\n", - "* Both can be int32/64, floats of booleans (uint8) of various size.\n", - "\n", - "* You can define new transformations as an arbitrary operation on placeholders and other transformations\n", - " * tf.reduce_sum(tf.arange(N)\\**2) are 3 sequential transformations of placeholder N\n", - " * There's a tensorflow symbolic version for every numpy function\n", - " * `a+b, a/b, a**b, ...` behave just like in numpy\n", - " * np.mean -> tf.reduce_mean\n", - " * np.arange -> tf.range\n", - " * np.cumsum -> tf.cumsum\n", - " * If if you can't find the op you need, see the [docs](https://www.tensorflow.org/api_docs/python).\n", - " \n", - " \n", - "Still confused? We gonna fix that." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#Default placeholder that can be arbitrary float32 scalar, vector, matrix, etc.\n", - "arbitrary_input = tf.placeholder('float32')\n", - "\n", - "#Input vector of arbitrary length\n", - "input_vector = tf.placeholder('float32',shape=(None,))\n", - "\n", - "#Input vector that _must_ have 10 elements and integer type\n", - "fixed_vector = tf.placeholder('int32',shape=(10,))\n", - "\n", - "#Matrix of arbitrary n_rows and 15 columns (e.g. a minibatch your data table)\n", - "input_matrix = tf.placeholder('float32',shape=(None,15))\n", - "\n", - "#You can generally use None whenever you don't need a specific shape\n", - "input1 = tf.placeholder('float64',shape=(None,100,None))\n", - "input2 = tf.placeholder('int32',shape=(None,None,3,224,224))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#elementwise multiplication\n", - "double_the_vector = input_vector*2\n", - "\n", - "#elementwise cosine\n", - "elementwise_cosine = tf.cos(input_vector)\n", - "\n", - "#difference between squared vector and vector itself\n", - "vector_squares = input_vector**2 - input_vector\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#Practice time: create two vectors of type float32\n", - "my_vector = tf.placeholder('float32', shape=(None,))\n", - "my_vector2 = tf.placeholder('float32', shape=(None,))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#Write a transformation(recipe):\n", - "#(vec1)*(vec2) / (sin(vec1) +1)\n", - "my_transformation = my_vector * my_vector2 / (tf.sin(my_vector) + 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tensor(\"truediv:0\", shape=(?,), dtype=float32)\n" - ] - } - ], - "source": [ - "print(my_transformation)\n", - "#it's okay, it's a symbolic graph" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 1.6291324, 2.0950115, 2.6289961, 0. ],\n", - " dtype=float32)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#\n", - "dummy = np.arange(5).astype('float32')\n", - "\n", - "my_transformation.eval({my_vector:dummy,my_vector2:dummy[::-1]})" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "### Visualizing graphs\n", - "\n", - "It's often useful to visualize the computation graph when debugging or optimizing. \n", - "Interactive visualization is where tensorflow really shines as compared to other frameworks. \n", - "\n", - "There's a special instrument for that, called Tensorboard. You can launch it from console:\n", - "\n", - "```tensorboard --logdir=/tmp/tboard --port=7007```\n", - "\n", - "If you're pathologically afraid of consoles, try this:\n", - "\n", - "```os.system(\"tensorboard --logdir=/tmp/tboard --port=7007 &\"```\n", - "\n", - "_(but don't tell anyone we taught you that)_" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Port: 7000\n" - ] - } - ], - "source": [ - "# launch tensorflow the ugly way, uncomment if you need that\n", - "import os\n", - "port = 6000 + os.getuid()\n", - "print(\"Port: %d\" % port)\n", - "#!killall tensorboard\n", - "os.system(\"tensorboard --logdir=./tboard --port=%d &\" % port)\n", - "\n", - "# show graph to tensorboard\n", - "writer = tf.summary.FileWriter(\"./tboard\", graph=tf.get_default_graph())\n", - "writer.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One basic functionality of tensorboard is drawing graphs. Once you've run the cell above, go to `localhost:7007` in your browser and switch to _graphs_ tab in the topbar. \n", - "\n", - "Here's what you should see:\n", - "\n", - "\n", - "\n", - "Tensorboard also allows you to draw graphs (e.g. learning curves), record images & audio ~~and play flash games~~. This is useful when monitoring learning progress and catching some training issues.\n", - "\n", - "One researcher said:\n", - "```\n", - "If you spent last four hours of your worktime watching as your algorithm prints numbers and draws figures, you're probably doing deep learning wrong.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can read more on tensorboard usage [here](https://www.tensorflow.org/get_started/graph_viz)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Do It Yourself\n", - "\n", - "__[2 points max]__" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Quest #1 - implement a function that computes a mean squared error of two input vectors\n", - "# Your function has to take 2 vectors and return a single number\n", - "\n", - "vec1 = tf.placeholder('float32', shape=(None,))\n", - "vec2 = tf.placeholder('float32', shape=(None,))\n", - "\n", - "mse = tf.reduce_mean((vec1 - vec2) ** 2)\n", - "\n", - "compute_mse = lambda vector1, vector2: mse.eval({vec1:vector1, vec2:vector2})" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All tests passed\n" - ] - } - ], - "source": [ - "# Tests\n", - "from sklearn.metrics import mean_squared_error\n", - "\n", - "for n in [1,5,10,10**3]:\n", - " \n", - " elems = [np.arange(n),np.arange(n,0,-1), np.zeros(n),\n", - " np.ones(n),np.random.random(n),np.random.randint(100,size=n)]\n", - " \n", - " for el in elems:\n", - " for el_2 in elems:\n", - " true_mse = np.array(mean_squared_error(el,el_2))\n", - " my_mse = compute_mse(el,el_2)\n", - " if not np.allclose(true_mse,my_mse):\n", - " print('Wrong result:')\n", - " print('mse(%s,%s)' % (el,el_2))\n", - " print(\"should be: %f, but your function returned %f\" % (true_mse,my_mse))\n", - " raise ValueError(\"Что-то не так\")\n", - "\n", - "print(\"All tests passed\") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# variables\n", - "\n", - "The inputs and transformations have no value outside function call. This isn't too comfortable if you want your model to have parameters (e.g. network weights) that are always present, but can change their value over time.\n", - "\n", - "Tensorflow solves this with `tf.Variable` objects.\n", - "* You can assign variable a value at any time in your graph\n", - "* Unlike placeholders, there's no need to explicitly pass values to variables when `s.run(...)`-ing\n", - "* You can use variables the same way you use transformations \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#creating shared variable\n", - "shared_vector_1 = tf.Variable(initial_value=np.ones(5))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "initial value [1. 1. 1. 1. 1.]\n" - ] - } - ], - "source": [ - "#initialize variable(s) with initial values\n", - "s.run(tf.global_variables_initializer())\n", - "\n", - "#evaluating shared variable (outside symbolic graph)\n", - "print(\"initial value\", s.run(shared_vector_1))\n", - "\n", - "# within symbolic graph you use them just as any other inout or transformation, not \"get value\" needed" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "new value [0. 1. 2. 3. 4.]\n" - ] - } - ], - "source": [ - "#setting new value\n", - "s.run(shared_vector_1.assign(np.arange(5)))\n", - "\n", - "#getting that new value\n", - "print(\"new value\", s.run(shared_vector_1))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# tf.gradients - why graphs matter\n", - "* Tensorflow can compute derivatives and gradients automatically using the computation graph\n", - "* Gradients are computed as a product of elementary derivatives via chain rule:\n", - "\n", - "$$ {\\partial f(g(x)) \\over \\partial x} = {\\partial f(g(x)) \\over \\partial g(x)}\\cdot {\\partial g(x) \\over \\partial x} $$\n", - "\n", - "It can get you the derivative of any graph as long as it knows how to differentiate elementary operations" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "my_scalar = tf.placeholder('float32')\n", - "\n", - "scalar_squared = my_scalar**2\n", - "\n", - "#a derivative of scalar_squared by my_scalar\n", - "derivative = tf.gradients(scalar_squared, my_scalar)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "x = np.linspace(-3,3)\n", - "x_squared, x_squared_der = s.run([scalar_squared,derivative],\n", - " {my_scalar:x})\n", - "\n", - "plt.plot(x, x_squared,label=\"x^2\")\n", - "plt.plot(x, x_squared_der, label=\"derivative\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Why that rocks" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "my_vector = tf.placeholder('float32',[None])\n", - "\n", - "#Compute the gradient of the next weird function over my_scalar and my_vector\n", - "#warning! Trying to understand the meaning of that function may result in permanent brain damage\n", - "\n", - "weird_psychotic_function = tf.reduce_mean((my_vector+my_scalar)**(1+tf.nn.moments(my_vector,[0])[1]) + 1./ tf.atan(my_scalar))/(my_scalar**2 + 1) + 0.01*tf.sin(2*my_scalar**1.5)*(tf.reduce_sum(my_vector)* my_scalar**2)*tf.exp((my_scalar-4)**2)/(1+tf.exp((my_scalar-4)**2))*(1.-(tf.exp(-(my_scalar-4)**2))/(1+tf.exp(-(my_scalar-4)**2)))**2\n", - "\n", - "der_by_scalar = tf.gradients(weird_psychotic_function, my_scalar)[0]\n", - "der_by_vector = tf.gradients(weird_psychotic_function, my_vector)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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PJCUuitT4KNJs0fSxRQX35rrW0FgL9gp0bAoldgsHy+s4XGmntLqBkup6quub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\nP4azf2NGiN7O529qgFULzMK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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Plotting your derivative\n", - "scalar_space = np.linspace(1, 7, 100)\n", - "\n", - "y = [s.run(weird_psychotic_function, {my_scalar:x, my_vector:[1, 2, 3]})\n", - " for x in scalar_space]\n", - "\n", - "plt.plot(scalar_space, y, label='function')\n", - "\n", - "y_der_by_scalar = [s.run(der_by_scalar, {my_scalar:x, my_vector:[1, 2, 3]})\n", - " for x in scalar_space]\n", - "\n", - "plt.plot(scalar_space, y_der_by_scalar, label='derivative')\n", - "plt.grid()\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Almost done - optimizers\n", - "\n", - "While you can perform gradient descent by hand with automatic grads from above, tensorflow also has some optimization methods implemented for you. Recall momentum & rmsprop?" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "y_guess = tf.Variable(np.zeros(2,dtype='float32'))\n", - "y_true = tf.range(1,3,dtype='float32')\n", - "\n", - "loss = tf.reduce_mean((y_guess - y_true + tf.random_normal([2]))**2) \n", - "\n", - "optimizer = tf.train.MomentumOptimizer(0.01,0.9).minimize(loss,var_list=y_guess)\n", - "\n", - "#same, but more detailed:\n", - "#updates = [[tf.gradients(loss,y_guess)[0], y_guess]]\n", - "#optimizer = tf.train.MomentumOptimizer(0.01,0.9).apply_gradients(updates)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import clear_output\n", - "\n", - "s.run(tf.global_variables_initializer())\n", - "\n", - "guesses = [s.run(y_guess)]\n", - "\n", - "for _ in range(100):\n", - " s.run(optimizer)\n", - " guesses.append(s.run(y_guess))\n", - " \n", - " clear_output(True)\n", - " plt.plot(*zip(*guesses),marker='.')\n", - " plt.scatter(*s.run(y_true),c='red')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Logistic regression example\n", - "Implement the regular logistic regression training algorithm\n", - "\n", - "Tips:\n", - "* Use a shared variable for weights\n", - "* X and y are potential inputs\n", - "* Compile 2 functions:\n", - " * `train_function(X, y)` - returns error and computes weights' new values __(through updates)__\n", - " * `predict_fun(X)` - just computes probabilities (\"y\") given data\n", - " \n", - " \n", - "We shall train on a two-class MNIST dataset\n", - "* please note that target `y` are `{0,1}` and not `{-1,1}` as in some formulae" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "y [shape - (360,)]: [0 1 0 1 0 1 0 0 1 1]\n", - "X [shape - (360, 64)]:\n" - ] - } - ], - "source": [ - "from sklearn.datasets import load_digits\n", - "mnist = load_digits(2)\n", - "\n", - "X,y = mnist.data, mnist.target\n", - "\n", - "print(\"y [shape - %s]:\" % (str(y.shape)), y[:10])\n", - "print(\"X [shape - %s]:\" % (str(X.shape)))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X:\n", - " [[ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 12. 13. 5. 0. 0. 0. 0.]\n", - " [ 0. 0. 1. 9. 15. 11. 0. 0. 0. 0.]]\n", - "y:\n", - " [0 1 0 1 0 1 0 0 1 1]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAACylJREFUeJzt3f+rlvUdx/HXa0ftTLMc2So8MmuU\nEItlOYc4guk2bEUFG0uhxmJwYFAUyaJGY9s/EO6HEYTVglzSrCBaXxaraIEzv+QqOzpMGp6sNPru\nSD353g/nFpw7230d7+vb/e75gIPnPt6cz/tGnl7Xuc99Xx9HhADk9IWmBwBQHQIHEiNwIDECBxIj\ncCAxAgcSI3AgMQIHEiNwILEpVXzTaT4pBjWjim/dqLHZ9T6mM898r7a13jwwq7a1BkcP17ZWHB6r\nba06faoDOhQH3e1+lQQ+qBn6ppdV8a0b9e4PFte63s9XrattrV9uubK2tc67+a3a1hp7+53a1qrT\nxvhLoftxig4kRuBAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYoUCt73c9k7bu2zfWvVQAMrRNXDbA5J+\nJ+lSSedLWmn7/KoHA9C7IkfwRZJ2RcTuiDgkaZ2k+l7XCOCEFQl8jqQ9x9we7XwNQMsVebPJRO9Y\n+a+LqdseljQsSYOa3uNYAMpQ5Ag+KmnuMbeHJO09/k4RcVdELIyIhVN1UlnzAehBkcA3STrX9tm2\np0laIenRascCUIaup+gRMWb7eklPSRqQdE9EbK98MgA9K3TBh4h4XNLjFc8CoGS8kg1IjMCBxAgc\nSIzAgcQIHEiMwIHECBxIjMCBxCrZ2SSrOncakaQVM9+vba3Vsz6pba0/bX2qtrUu/vXPaltLkmbf\ntaHW9brhCA4kRuBAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYgQOJFZkZ5N7bO+z/WodAwEoT5Ej+O8l\nLa94DgAV6Bp4RDwv6b0aZgFQMn4GBxIr7d1kbF0EtE9pR3C2LgLah1N0ILEivyZ7QNIGSfNtj9r+\nafVjAShDkb3JVtYxCIDycYoOJEbgQGIEDiRG4EBiBA4kRuBAYgQOJEbgQGJ9v3XR2NKLa1trxcxt\nta0lSZcuX1HbWqe+vKO2tX70wrLa1npvwWe1rSVJs2tdrTuO4EBiBA4kRuBAYgQOJEbgQGIEDiRG\n4EBiBA4kRuBAYgQOJFbkootzbT9re8T2dts31jEYgN4VeS36mKRVEbHV9kxJW2w/HRGvVTwbgB4V\n2ZvsrYjY2vn8Y0kjkuZUPRiA3k3q3WS250laIGnjBH/H1kVAyxR+ks32yZIeknRTRHx0/N+zdRHQ\nPoUCtz1V43GvjYiHqx0JQFmKPItuSXdLGomIO6ofCUBZihzBl0i6VtJS29s6H9+veC4AJSiyN9kL\nklzDLABKxivZgMQIHEiMwIHECBxIjMCBxAgcSIzAgcQIHEis7/cm+/S0+h7C7fsuqG0tSTpS435h\nddr0ylebHuFzgyM4kBiBA4kROJAYgQOJETiQGIEDiRE4kBiBA4kROJBYkYsuDtp+0fbfO1sX/aaO\nwQD0rsjrPA9KWhoRn3Qun/yC7Sci4m8VzwagR0UuuhiSPuncnNr5iCqHAlCOohsfDNjeJmmfpKcj\nYsKti2xvtr35sA6WPSeAE1Ao8Ij4LCIulDQkaZHtr01wH7YuAlpmUs+iR8QHkp6TtLySaQCUqsiz\n6KfbntX5/IuSviMp5xuVgWSKPIt+lqT7bA9o/D+EByPisWrHAlCGIs+iv6zxPcEB9BleyQYkRuBA\nYgQOJEbgQGIEDiRG4EBiBA4kRuBAYv2/ddGX6vs/au2GxbWtJUnn6cVa16vLlFMP1bbW2IfTalur\njTiCA4kROJAYgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJFQ68c230l2xzPTagT0zmCH6jpJGqBgFQ\nvqI7mwxJukzSmmrHAVCmokfw1ZJukXSkwlkAlKzIxgeXS9oXEVu63I+9yYCWKXIEXyLpCttvSFon\naant+4+/E3uTAe3TNfCIuC0ihiJinqQVkp6JiGsqnwxAz/g9OJDYpK7oEhHPaXx3UQB9gCM4kBiB\nA4kROJAYgQOJETiQGIEDiRE4kBiBA4n1/dZFg+/X9wa3b1zwem1rSdKHNa415cwzalvr6vP/7/uW\nSvXgE9+qba024ggOJEbgQGIEDiRG4EBiBA4kRuBAYgQOJEbgQGIEDiRW6JVsnSuqfizpM0ljEbGw\nyqEAlGMyL1X9dkS8W9kkAErHKTqQWNHAQ9KfbW+xPVzlQADKU/QUfUlE7LX9ZUlP294REc8fe4dO\n+MOSNKjpJY8J4EQUOoJHxN7On/skPSJp0QT3YesioGWKbD44w/bMo59L+p6kV6seDEDvipyinyHp\nEdtH7/+HiHiy0qkAlKJr4BGxW9LXa5gFQMn4NRmQGIEDiRE4kBiBA4kROJAYgQOJETiQGIEDifX9\n1kWn7Kxvg59fDT1W21qS9OPhm2tba+pV+2tbq05n37ah6REaxREcSIzAgcQIHEiMwIHECBxIjMCB\nxAgcSIzAgcQIHEisUOC2Z9leb3uH7RHbi6seDEDvir5U9beSnoyIH9qeJnHhc6AfdA3c9imSLpH0\nE0mKiEOSDlU7FoAyFDlFP0fSfkn32n7J9prO9dEBtFyRwKdIukjSnRGxQNIBSbcefyfbw7Y32958\nWAdLHhPAiSgS+Kik0YjY2Lm9XuPB/we2LgLap2vgEfG2pD2253e+tEzSa5VOBaAURZ9Fv0HS2s4z\n6LslXVfdSADKUijwiNgmaWHFswAoGa9kAxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCAxAgcS6/u9\nyY68vKO2ta6+c1Vta0nS7aseqG2t1a8vq22tTRcO1LbW5x1HcCAxAgcSI3AgMQIHEiNwIDECBxIj\ncCAxAgcSI3Agsa6B255ve9sxHx/ZvqmO4QD0putLVSNip6QLJcn2gKQ3JT1S8VwASjDZU/Rlkl6P\niH9WMQyAck32zSYrJE34Dgjbw5KGJWmQzUeBVih8BO9senCFpD9O9PdsXQS0z2RO0S+VtDUi3qlq\nGADlmkzgK/U/Ts8BtFOhwG1Pl/RdSQ9XOw6AMhXdm+xfkk6reBYAJeOVbEBiBA4kRuBAYgQOJEbg\nQGIEDiRG4EBiBA4k5ogo/5va+yVN9i2lsyW9W/ow7ZD1sfG4mvOViDi9250qCfxE2N4cEQubnqMK\nWR8bj6v9OEUHEiNwILE2BX5X0wNUKOtj43G1XGt+BgdQvjYdwQGUrBWB215ue6ftXbZvbXqeMtie\na/tZ2yO2t9u+semZymR7wPZLth9repYy2Z5le73tHZ1/u8VNz9SLxk/RO9da/4fGrxgzKmmTpJUR\n8Vqjg/XI9lmSzoqIrbZnStoi6ap+f1xH2b5Z0kJJp0TE5U3PUxbb90n6a0Ss6VxodHpEfND0XCeq\nDUfwRZJ2RcTuiDgkaZ2kKxueqWcR8VZEbO18/rGkEUlzmp2qHLaHJF0maU3Ts5TJ9imSLpF0tyRF\nxKF+jltqR+BzJO055vaokoRwlO15khZI2tjsJKVZLekWSUeaHqRk50jaL+nezo8fa2zPaHqoXrQh\ncE/wtTRP7ds+WdJDkm6KiI+anqdXti+XtC8itjQ9SwWmSLpI0p0RsUDSAUl9/ZxQGwIflTT3mNtD\nkvY2NEupbE/VeNxrIyLLFWmXSLrC9hsa/3Fqqe37mx2pNKOSRiPi6JnWeo0H37faEPgmSefaPrvz\npMYKSY82PFPPbFvjP8uNRMQdTc9Tloi4LSKGImKexv+tnomIaxoeqxQR8bakPbbnd760TFJfPyk6\n2b3JShcRY7avl/SUpAFJ90TE9obHKsMSSddKesX2ts7XfhERjzc4E7q7QdLazsFmt6TrGp6nJ43/\nmgxAddpwig6gIgQOJEbgQGIEDiRG4EBiBA4kRuBAYgQOJPZvavih6sahAwsAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print('X:\\n',X[:3,:10])\n", - "print('y:\\n',y[:10])\n", - "plt.imshow(X[0].reshape([8,8]))" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# inputs and shareds\n", - "weights = tf.Variable(tf.zeros((64, 1), dtype='float32'))\n", - "b = tf.Variable(np.zeros([1], dtype='float32'))\n", - "input_X = tf.placeholder('float32', shape=(None, 64))\n", - "input_y = tf.placeholder('float32', shape=(None,))" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "predicted_y = tf.sigmoid(tf.matmul(input_X, weights) + b)\n", - "loss = tf.reduce_mean(-tf.reduce_sum(input_y * tf.log(predicted_y), axis=1))\n", - "\n", - "optimizer = tf.train.AdamOptimizer().minimize(loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "train_function = lambda X, y : s.run([optimizer, loss, weights], feed_dict={input_X:X, input_y:y})\n", - "predict_function = lambda X : s.run(predicted_y, feed_dict={input_X:X})" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss at iter 0:93.5749\n", - "train auc: 0.48345679012345677\n", - "test auc: 0.4388916378030678\n", - "loss at iter 50:0.5273\n", - "train auc: 0.49377229080932783\n", - "test auc: 0.4477981197427016\n", - "loss at iter 100:0.3210\n", - "train auc: 0.49909465020576127\n", - "test auc: 0.45670460168233545\n", - "loss at iter 150:0.2243\n", - "train auc: 0.5032921810699589\n", - "test auc: 0.46313706086095996\n", - "loss at iter 200:0.1659\n", - "train auc: 0.506721536351166\n", - "test auc: 0.46561108362196935\n", - "loss at iter 250:0.1282\n", - "train auc: 0.5095198902606309\n", - "test auc: 0.4685799109351806\n", - "loss at iter 300:0.1025\n", - "train auc: 0.5120164609053498\n", - "test auc: 0.4720435428005938\n", - "loss at iter 350:0.0841\n", - "train auc: 0.514238683127572\n", - "test auc: 0.47402276100940127\n", - "loss at iter 400:0.0705\n", - "train auc: 0.516241426611797\n", - "test auc: 0.47550717466600695\n", - "loss at iter 450:0.0601\n", - "train auc: 0.5175857338820302\n", - "test auc: 0.4764967837704107\n", - "resulting weights:\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.metrics import roc_auc_score\n", - "\n", - "s.run(tf.global_variables_initializer())\n", - "for i in range(500):\n", - " _, loss_i, shared_weights = train_function(X_train, y_train)\n", - " \n", - " if i % 50 == 0:\n", - " print(\"loss at iter %i:%.4f\" % (i, loss_i))\n", - "\n", - " print(\"train auc:\",roc_auc_score(y_train, predict_function(X_train)))\n", - " print(\"test auc:\",roc_auc_score(y_test, predict_function(X_test)))\n", - "\n", - " \n", - "print (\"resulting weights:\")\n", - "plt.imshow(shared_weights.reshape(8, -1))\n", - "plt.colorbar();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bonus: my1stNN\n", - "Your ultimate task for this week is to build your first neural network [almost] from scratch and pure tensorflow.\n", - "\n", - "This time you will same digit recognition problem, but at a larger scale\n", - "* images are now 28x28\n", - "* 10 different digits\n", - "* 50k samples\n", - "\n", - "Note that you are not required to build 152-layer monsters here. A 2-layer (one hidden, one output) NN should already have ive you an edge over logistic regression.\n", - "\n", - "__[bonus score]__\n", - "If you've already beaten logistic regression with a two-layer net, but enthusiasm still ain't gone, you can try improving the test accuracy even further! The milestones would be 95%/97.5%/98.5% accuraсy on test set.\n", - "\n", - "__SPOILER!__\n", - "At the end of the notebook you will find a few tips and frequently made mistakes. If you feel enough might to shoot yourself in the foot without external assistance, we encourage you to do so, but if you encounter any unsurpassable issues, please do look there before mailing us." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import keras\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def load_dataset(flatten=False):\n", - " (X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()\n", - "\n", - " # normalize x\n", - " X_train = X_train.astype(float) / 255.\n", - " X_test = X_test.astype(float) / 255.\n", - "\n", - " # we reserve the last 10000 training examples for validation\n", - " X_train, X_val = X_train[:-10000], X_train[-10000:]\n", - " y_train, y_val = y_train[:-10000], y_train[-10000:]\n", - "\n", - " if flatten:\n", - " X_train = X_train.reshape([X_train.shape[0], -1])\n", - " X_val = X_val.reshape([X_val.shape[0], -1])\n", - " X_test = X_test.reshape([X_test.shape[0], -1])\n", - "\n", - " return X_train, y_train, X_val, y_val, X_test, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n" - ] - }, - { - "ename": "Exception", - "evalue": "URL fetch failure on https://s3.amazonaws.com/img-datasets/mnist.npz: None -- [Errno 110] Connection timed out", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTimeoutError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1318\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1233\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1234\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 935\u001b[0m self.sock = self._create_connection(\n\u001b[0;32m--> 936\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 937\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 713\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 714\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTimeoutError\u001b[0m: [Errno 110] Connection timed out", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget_file\u001b[0;34m(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_progress\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mURLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 249\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 543\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 544\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 545\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 505\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1360\u001b[0m return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0;32m-> 1361\u001b[0;31m context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[1;32m 1362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1321\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mURLError\u001b[0m: ", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# _val can be used to evaluate out-of-sample error, compare models or perform early-stopping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# _test should be hidden under a rock untill final evaluation... But we both know it is near impossible to catch you evaluating on it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(flatten)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# normalize x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/datasets/mnist.py\u001b[0m in \u001b[0;36mload_data\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 21\u001b[0m path = get_file(path,\n\u001b[1;32m 22\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'https://s3.amazonaws.com/img-datasets/mnist.npz'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m file_hash='8a61469f7ea1b51cbae51d4f78837e45')\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x_train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y_train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget_file\u001b[0;34m(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_progress\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mURLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 225\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mException\u001b[0m: URL fetch failure on https://s3.amazonaws.com/img-datasets/mnist.npz: None -- [Errno 110] Connection timed out" - ] - } - ], - "source": [ - "#[down]loading the original MNIST dataset.\n", - "#Please note that you should only train your NN on _train sample,\n", - "# _val can be used to evaluate out-of-sample error, compare models or perform early-stopping\n", - "# _test should be hidden under a rock untill final evaluation... But we both know it is near impossible to catch you evaluating on it.\n", - "X_train,y_train,X_val,y_val,X_test,y_test = load_dataset()\n", - "\n", - "print (X_train.shape,y_train.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "plt.imshow(X_train[0,0])" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def reset_tf_session():\n", - " curr_session = tf.get_default_session()\n", - " # close current session\n", - " if curr_session is not None:\n", - " curr_session.close()\n", - " # reset graph\n", - " K.clear_session()\n", - " # create new session\n", - " config = tf.ConfigProto()\n", - " config.gpu_options.allow_growth = True\n", - " s = tf.InteractiveSession(config=config)\n", - " K.set_session(s)\n", - " return s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "batch_size = 128\n", - "num_classes = 10\n", - "epochs = 12" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "callback = keras.callbacks.EarlyStopping(monitor='val_loss',\n", - " min_delta=0,\n", - " patience=2,\n", - " verbose=0, mode='auto')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3),\n", - " activation='relu',\n", - " input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy,\n", - " optimizer=keras.optimizers.Adadelta(),\n", - " metrics=['accuracy'])\n", - "\n", - "model.fit(X_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(X_val, y_val),\n", - " callback=callback)\n", - "score = model.evaluate(X_test, y_test, verbose=0)\n", - "print('Test loss:', score[0])\n", - "print('Test accuracy:', score[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "```\n", - "\n", - "\n", - "# SPOILERS!\n", - "\n", - "Recommended pipeline\n", - "\n", - "* Adapt logistic regression from previous assignment to classify some number against others (e.g. zero vs nonzero)\n", - "* Generalize it to multiclass logistic regression.\n", - " - Either try to remember lecture 0 or google it.\n", - " - Instead of weight vector you'll have to use matrix (feature_id x class_id)\n", - " - softmax (exp over sum of exps) can implemented manually or as T.nnet.softmax (stable)\n", - " - probably better to use STOCHASTIC gradient descent (minibatch)\n", - " - in which case sample should probably be shuffled (or use random subsamples on each iteration)\n", - "* Add a hidden layer. Now your logistic regression uses hidden neurons instead of inputs.\n", - " - Hidden layer uses the same math as output layer (ex-logistic regression), but uses some nonlinearity (sigmoid) instead of softmax\n", - " - You need to train both layers, not just output layer :)\n", - " - Do not initialize layers with zeros (due to symmetry effects). A gaussian noize with small sigma will do.\n", - " - 50 hidden neurons and a sigmoid nonlinearity will do for a start. Many ways to improve. \n", - " - In ideal casae this totals to 2 .dot's, 1 softmax and 1 sigmoid\n", - " - __make sure this neural network works better than logistic regression__\n", - " \n", - "* Now's the time to try improving the network. Consider layers (size, neuron count), nonlinearities, optimization methods, initialization - whatever you want, but please avoid convolutions for now." - ] - } - ], - "metadata": { - "celltoolbar": "Slideshow", - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -}

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