From b7f3f4cccdb2f26a6d78b474a317bfcf4e37422f Mon Sep 17 00:00:00 2001 From: JiaZhou-PU Date: Fri, 3 May 2019 11:22:46 -0600 Subject: [PATCH] Adding ARMA reseeding option (#958) * add arma test to raven * change tests name * adding pk unit tests * save draft * pk possible * unit test draft working * seed fix * final check * add signal.csv * add engine test * change int into void in randomclass cpp * change signal length * revised * change segmeted gold * change npseed * change reseed copies as bool * change reseed on varma * fix varmatest * fix test * unordered xml * test more data on varma * change maxtime on varma test * change into org tests * change segmented xml tests * change segmented period --- crow/include/distributions/randomClass.h | 7 +- crow/src/distributions/randomClass.cxx | 21 +- framework/Models/ROM.py | 2 +- framework/SupervisedLearning/ARMA.py | 77 +- .../SupervisedLearning/SupervisedLearning.py | 22 + framework/utils/randomUtils.py | 3 +- rook/XMLDiff.py | 6 +- .../ARMA/gold/PreserveCdf/input_durs_0.csv | 18 +- .../ARMA/gold/PreserveCdf/metric.csv | 2 +- .../ARMA/gold/Segmented/rommeta.csv | 54 + .../ARMA/gold/Segmented/rommeta.xml | 1912 ++++++++ .../TimeSeries/ARMA/gold/Segmented/stats.csv | 2 +- .../ARMA/gold/SingleFourier/synthetic_0.csv | 4034 ++++++++--------- .../ROM/TimeSeries/ARMA/gold/VARMA/stats.csv | 2 +- .../ROM/TimeSeries/ARMA/segmented.xml | 20 +- tests/framework/ROM/TimeSeries/ARMA/tests | 29 +- tests/framework/ROM/TimeSeries/ARMA/varma.xml | 15 +- .../unit_tests/SupervisedLearning/ARMA.py | 74 +- .../unit_tests/SupervisedLearning/signal.csv | 101 + .../unit_tests/SupervisedLearning/tests | 6 + 20 files changed, 4324 insertions(+), 2083 deletions(-) create mode 100644 tests/framework/ROM/TimeSeries/ARMA/gold/Segmented/rommeta.csv create mode 100644 tests/framework/ROM/TimeSeries/ARMA/gold/Segmented/rommeta.xml create mode 100644 tests/framework/unit_tests/SupervisedLearning/signal.csv create mode 100644 tests/framework/unit_tests/SupervisedLearning/tests diff --git a/crow/include/distributions/randomClass.h b/crow/include/distributions/randomClass.h index 965d06ea82..114c3eff1b 100644 --- a/crow/include/distributions/randomClass.h +++ b/crow/include/distributions/randomClass.h @@ -26,11 +26,16 @@ class RandomClassImpl; class RandomClass { RandomClassImpl *_rng; const double _range; + unsigned int _counter; + unsigned int _seed; public: RandomClass(); ~RandomClass(); - void seed(unsigned int seed); + void seed(unsigned long int seed); double random(); + int get_rng_state(); + void forward_seed(unsigned int counts); + int get_rng_seed(); }; #endif /* RANDOMCLASS_H */ diff --git a/crow/src/distributions/randomClass.cxx b/crow/src/distributions/randomClass.cxx index e9b5273b2a..85b2ae74bb 100644 --- a/crow/src/distributions/randomClass.cxx +++ b/crow/src/distributions/randomClass.cxx @@ -24,16 +24,35 @@ class RandomClassImpl { }; RandomClass::RandomClass() : _rng(new RandomClassImpl()), _range(_rng->_backend.max() - _rng->_backend.min()) { + _counter=0; + _seed=0; } -void RandomClass::seed(unsigned int seed) { +void RandomClass::seed(unsigned long int seed) { + + _counter = 0; + _seed = seed; _rng->_backend.seed(seed); } double RandomClass::random() { + _counter++; return (_rng->_backend()-_rng->_backend.min())/_range; } +int RandomClass::get_rng_state() { + return _counter; +} + +void RandomClass::forward_seed(unsigned int counts){ + _counter = counts; + _rng->_backend.discard(counts); +} + +int RandomClass::get_rng_seed(){ + return _seed; +} + RandomClass::~RandomClass(){ delete _rng; } diff --git a/framework/Models/ROM.py b/framework/Models/ROM.py index ded022e056..d4551f6dc4 100644 --- a/framework/Models/ROM.py +++ b/framework/Models/ROM.py @@ -191,7 +191,7 @@ class cls. This one seems a bit excessive, are all of these for this class? inputSpecification.addSub(InputData.parameterInputFactory("P", InputData.IntegerType)) inputSpecification.addSub(InputData.parameterInputFactory("Q", InputData.IntegerType)) inputSpecification.addSub(InputData.parameterInputFactory("seed", InputData.IntegerType)) - inputSpecification.addSub(InputData.parameterInputFactory("reseedCopies", InputData.StringType)) + inputSpecification.addSub(InputData.parameterInputFactory("reseedCopies", InputData.BoolType)) inputSpecification.addSub(InputData.parameterInputFactory("Fourier", contentType=InputData.FloatListType)) inputSpecification.addSub(InputData.parameterInputFactory("preserveInputCDF", contentType=InputData.BoolType)) ### ARMA zero filter diff --git a/framework/SupervisedLearning/ARMA.py b/framework/SupervisedLearning/ARMA.py index d93dc74efc..715a601589 100644 --- a/framework/SupervisedLearning/ARMA.py +++ b/framework/SupervisedLearning/ARMA.py @@ -29,13 +29,14 @@ import collections import numpy as np import statsmodels.api as sm # VARMAX is in sm.tsa +import functools from statsmodels.tsa.arima_model import ARMA as smARMA from scipy.linalg import solve_discrete_lyapunov from sklearn import linear_model #External Modules End-------------------------------------------------------------------------------- #Internal Modules------------------------------------------------------------------------------------ -from utils import randomUtils, xmlUtils, mathUtils +from utils import randomUtils, xmlUtils, mathUtils,utils import Distributions from .SupervisedLearning import supervisedLearning #Internal Modules End-------------------------------------------------------------------------------- @@ -73,7 +74,8 @@ def __init__(self,messageHandler,**kwargs): self.Q = kwargs.get('Q', 3) # moving average lag self.segments = kwargs.get('segments', 1) # data manipulation - self.reseedCopies = kwargs.get('reseedCopies',True) + reseed=kwargs.get('reseedCopies',str(True)).lower() + self.reseedCopies = reseed not in utils.stringsThatMeanFalse() self.outTruncation = {'positive':set(),'negative':set()} # store truncation requests self.pivotParameterID = kwargs['pivotParameter'] self.pivotParameterValues = None # In here we store the values of the pivot parameter (e.g. Time) @@ -107,14 +109,18 @@ def __init__(self,messageHandler,**kwargs): self.normEngine.lowerBoundUsed = False self.normEngine.initializeDistribution() - # check for correlation - correlated = kwargs.get('correlate',None) - if correlated is not None: - # FIXME set the numpy seed + self.setEngine(randomUtils.newRNG(),seed=self.seed,count=0) + + # FIXME set the numpy seed ## we have to do this because VARMA.simulate does not accept a random number generator, ## but instead uses numpy directly. As a result, for now, we have to seed numpy. ## Because we use our RNG to set the seed, though, it should follow the global seed still. - self.raiseADebug('Setting Numpy seed to',self.seed) + self.raiseADebug('Setting ARMA seed to',self.seed) + randomUtils.randomSeed(self.seed,engine=self.randomEng) + + # check for correlation + correlated = kwargs.get('correlate',None) + if correlated is not None: np.random.seed(self.seed) # store correlated targets corVars = [x.strip() for x in correlated.split(',')] @@ -196,11 +202,10 @@ def __getstate__(self): @ In, None @ Out, d, dict, stateful dictionary """ - d = copy.copy(self.__dict__) - # set up a seed for the next pickled iteration - if self.reseedCopies: - rand = randomUtils.randomIntegers(1,int(2**20),self) - d['random seed'] = rand + d = supervisedLearning.__getstate__(self) + eng=d.pop("randomEng") + randCounts = eng.get_rng_state() + d['crow_rng_counts'] = randCounts return d def __setstate__(self,d): @@ -209,13 +214,12 @@ def __setstate__(self,d): @ In, d, dict, stateful dictionary @ Out, None """ - seed = d.pop('random seed',None) - if seed is not None: - self.reseed(seed) - self.__dict__ = d - # set VARMA numpy seed - self.raiseADebug('Setting Numpy seed to',self.seed) - np.random.seed(self.seed) + rngCounts = d.pop('crow_rng_counts') + self.__dict__.update(d) + self.setEngine(randomUtils.newRNG(),seed=None,count=rngCounts) + if self.reseedCopies: + randd = np.random.randint(1,2e9) + self.reseed(randd) def __trainLocal__(self,featureVals,targetVals): """ @@ -375,7 +379,7 @@ def __evaluateLocal__(self,featureVals): result = self.varmaResult[1] sample = self._generateARMASignal(result, numSamples = self.notZeroFilterMask.sum(), - randEngine = self.normEngine.rvs) + randEngine = self.randomEng) zeroedSample = np.zeros((self.notZeroFilterMask.sum(),1)) zeroedSample[:,0] = sample correlatedSample = True # placeholder, signifies we've sampled the correlated distribution @@ -408,7 +412,8 @@ def __evaluateLocal__(self,featureVals): if target == self.zeroFilterTarget: sample = self._generateARMASignal(result, numSamples = self.zeroFilterMask.sum(), - randEngine = self.normEngine.rvs) + randEngine = self.randomEng) + ## if so, then expand result into signal space (functionally, put back in all the zeros) signal = np.zeros(len(self.pivotParameterValues)) signal[self.zeroFilterMask] = sample @@ -416,7 +421,8 @@ def __evaluateLocal__(self,featureVals): ## if not, no extra work to be done here! sample = self._generateARMASignal(result, numSamples = len(self.pivotParameterValues), - randEngine = self.normEngine.rvs) + randEngine = self.randomEng) + signal = sample # END creating base signal # DEBUG adding arbitrary variables for debugging, TODO find a more elegant way, leaving these here as markers @@ -473,7 +479,8 @@ def reseed(self,seed): @ In, seed, int, new seed to use @ Out, None """ - randomUtils.randomSeed(seed) + randomUtils.randomSeed(seed,engine=self.randomEng) + self.seed=seed ### UTILITY METHODS ### def _computeNumberOfBins(self,data): @@ -498,7 +505,7 @@ def _denormalizeThroughCDF(self, data, params): denormed = self._sampleICDF(denormed, params) return denormed - def _generateARMASignal(self, model, numSamples=None, randEngine=None): + def _generateARMASignal(self, model, numSamples=None,randEngine=None): """ Generates a synthetic history from fitted parameters. @ In, model, statsmodels.tsa.arima_model.ARMAResults, fitted ARMA such as otained from _trainARMA @@ -508,10 +515,14 @@ def _generateARMASignal(self, model, numSamples=None, randEngine=None): """ if numSamples is None: numSamples = len(self.pivotParameterValues) + if randEngine is None: + randEngine=self.randomEng hist = sm.tsa.arma_generate_sample(ar = np.append(1., -model.arparams), ma = np.append(1., model.maparams), nsample = numSamples, - distrvs = randEngine, + distrvs = functools.partial(randomUtils.randomNormal,engine=randEngine), + # functool.partial provide the random number generator as a function + # with normal distribution and take engine as the positional arguments keywords. sigma = np.sqrt(model.sigma2), burnin = 2*max(self.P,self.Q)) # @epinas, 2018 return hist @@ -1129,3 +1140,19 @@ def __returnCurrentSettingLocal__(self): """ pass + def setEngine(self,eng,seed=None,count=None): + """ + Set up the random engine for arma + @ In, eng, instance, random number generator + @ In, seed, int, optional, the seed, if None then use the global seed from ARMA + @ In, count, int, optional, advances the state of the generator, if None then use the current ARMA.randomEng count + @ Out, None + """ + if seed is None: + seed=self.seed + seed=abs(seed) + eng.seed(seed) + if count is None: + count=self.randomEng.get_rng_state() + eng.forward_seed(count) + self.randomEng=eng diff --git a/framework/SupervisedLearning/SupervisedLearning.py b/framework/SupervisedLearning/SupervisedLearning.py index 7983f67bc1..9ee0ac39cc 100644 --- a/framework/SupervisedLearning/SupervisedLearning.py +++ b/framework/SupervisedLearning/SupervisedLearning.py @@ -118,6 +118,28 @@ def __init__(self,messageHandler,**kwargs): #these need to be declared in the child classes!!!! self.amITrained = False + def __getstate__(self): + """ + This function return the state of the ROM + @ In, None + @ Out, state, dict, it contains all the information needed by the ROM to be initialized + """ + state = copy.copy(self.__dict__) + state['initOptionDict'].pop('paramInput',None) + ## capture what is normally pickled + if not self.amITrained: + supervisedEngineObj = state.pop("supervisedContainer",None) + del supervisedEngineObj + return state + + def __setstate__(self, d): + """ + Initialize the ROM with the data contained in newstate + @ In, d, dict, it contains all the information needed by the ROM to be initialized + @ Out, None + """ + self.__dict__.update(d) + def initialize(self,idict): """ Initialization method diff --git a/framework/utils/randomUtils.py b/framework/utils/randomUtils.py index d9b8efa95b..df5b1bb0cb 100644 --- a/framework/utils/randomUtils.py +++ b/framework/utils/randomUtils.py @@ -117,7 +117,8 @@ def randomSeed(value,seedBoth=False,engine=None): np.random.seed(value+1) # +1 just to prevent identical seed sets if stochasticEnv== 'numpy' and replaceGlobalEnv: npStochEnv= engine - print('randomUtils: Global random number seed has been changed to',value) + if replaceGlobalEnv: + print('randomUtils: Global random number seed has been changed to',value) def random(dim=1,samples=1,keepMatrix=False,engine=None): """ diff --git a/rook/XMLDiff.py b/rook/XMLDiff.py index fff3c0a37d..d2c4605952 100644 --- a/rook/XMLDiff.py +++ b/rook/XMLDiff.py @@ -204,7 +204,11 @@ def fail_message(*args): note = '' for unmatched, close in matchvals.items(): #print the path without a match - note += 'No match for '+'/'.join(list(m.tag for m in unmatched))+'\n' + path = '/'.join(list(m.tag for m in unmatched)) + note += 'No match for gold node {}\n'.format(path) + note += ' tag: {}\n'.format(unmatched[-1].tag) + note += ' attr: {}\n'.format(unmatched[-1].attrib) + note += ' text: {}\n'.format(unmatched[-1].text) #print the tree of the nearest match note += ' Nearest unused match: ' close = sorted(list(close.items()), key=lambda x: x[1], reverse=True) diff --git a/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/input_durs_0.csv b/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/input_durs_0.csv index 01a3801f67..07800108a5 100644 --- a/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/input_durs_0.csv +++ b/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/input_durs_0.csv @@ -1,11 +1,11 @@ bins_Signal,counts_Signal -14.283117998,3 -11.2435880846,12 -8.20405817128,27 -5.16452825794,43 +14.28311799796,3 +11.243588084619999,12 +8.204058171279998,27 +5.164528257939997,43 2.1249983446,55 --0.91453156874,65 --3.95406148208,73 --6.99359139542,90 --10.0331213088,95 --13.0726512221,100 +-0.9145315687400011,65 +-3.954061482080002,73 +-6.993591395420001,90 +-10.033121308760002,95 +-13.072651222100001,100 diff --git a/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/metric.csv b/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/metric.csv index d7e4f05f54..7cd859c908 100644 --- a/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/metric.csv +++ b/tests/framework/ROM/TimeSeries/ARMA/gold/PreserveCdf/metric.csv @@ -1,2 +1,2 @@ metric_counts_Gold_counts_Signal -0.82 +0.6300000000000001 diff --git a/tests/framework/ROM/TimeSeries/ARMA/gold/Segmented/rommeta.csv b/tests/framework/ROM/TimeSeries/ARMA/gold/Segmented/rommeta.csv new file mode 100644 index 0000000000..ab4d1707e0 --- /dev/null +++ b/tests/framework/ROM/TimeSeries/ARMA/gold/Segmented/rommeta.csv @@ -0,0 +1,54 @@ +RAVEN_sample_ID,segment_number,seg_index_start,seg_index_end,seg_Time_start,seg_Time_end +0,0,0,167,0.0,601200.0 +0,1,168,335,604800.0,1206000.0 +0,2,336,503,1209600.0,1810800.0 +0,3,504,671,1814400.0,2415600.0 +0,4,672,839,2419200.0,3020400.0 +0,5,840,1007,3024000.0,3625200.0 +0,6,1008,1175,3628800.0,4230000.0 +0,7,1176,1343,4233600.0,4834800.0 +0,8,1344,1511,4838400.0,5439600.0 +0,9,1512,1679,5443200.0,6044400.0 +0,10,1680,1847,6048000.0,6649200.0 +0,11,1848,2015,6652800.0,7254000.0 +0,12,2016,2183,7257600.0,7858800.0 +0,13,2184,2351,7862400.0,8463600.0 +0,14,2352,2519,8467200.0,9068400.0 +0,15,2520,2687,9072000.0,9673200.0 +0,16,2688,2855,9676800.0,10278000.0 +0,17,2856,3023,10281600.0,10882800.0 +0,18,3024,3191,10886400.0,11487600.0 +0,19,3192,3359,11491200.0,12092400.0 +0,20,3360,3527,12096000.0,12697200.0 +0,21,3528,3695,12700800.0,13302000.0 +0,22,3696,3863,13305600.0,13906800.0 +0,23,3864,4031,13910400.0,14511600.0 +0,24,4032,4199,14515200.0,15116400.0 +0,25,4200,4367,15120000.0,15721200.0 +0,26,4368,4535,15724800.0,16326000.0 +0,27,4536,4703,16329600.0,16930800.0 +0,28,4704,4871,16934400.0,17535600.0 +0,29,4872,5039,17539200.0,18140400.0 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+1203000.0,1.8641861508348614 +1203600.0,1.7991359467662298 +1204200.0,1.9872186266982792 +1204800.0,1.6914559532098592 +1205400.0,2.032354711935212 +1206000.0,1.352554380905517 +1206600.0,2.446305992076962 +1207200.0,2.5907120037211366 +1207800.0,2.3015274709492464 +1208400.0,1.6923940610954076 +1209000.0,2.284838441326748 +1209600.0,2.4437897695227373 diff --git a/tests/framework/ROM/TimeSeries/ARMA/gold/VARMA/stats.csv b/tests/framework/ROM/TimeSeries/ARMA/gold/VARMA/stats.csv index 05949da4d9..98eba1d770 100644 --- a/tests/framework/ROM/TimeSeries/ARMA/gold/VARMA/stats.csv +++ b/tests/framework/ROM/TimeSeries/ARMA/gold/VARMA/stats.csv @@ -1,2 +1,2 @@ mean_A,var_A,mean_B,var_B,mean_C,var_C,mean_D,var_D,covar_A_B,covar_A_C,covar_A_D,covar_B_A,covar_B_C,covar_B_D,covar_C_A,covar_C_B,covar_C_D,covar_D_A,covar_D_B,covar_D_C -41.8926725776,26.2689134742,41.615008525,25.0785805596,41.7482936319,17.2727018904,30.4913308369,0.0837154059599,21.1481460475,-11.5623703287,-0.0789170391123,21.1481460475,-5.27246626335,-0.0682666779464,-11.5623703287,-5.27246626335,0.0487321626288,-0.0789170391123,-0.0682666779464,0.0487321626288 +41.88451002112952,26.229639509734415,41.61240072157775,25.110270642952994,41.75088495238037,17.233434833039198,30.502231702539405,0.08474805149852443,21.149079879541873,-11.495647127475273,-0.0403282915329323,21.149079879541873,-5.226194746433587,-0.015361513105005893,-11.495647127475273,-5.226194746433587,0.07057758849844753,-0.0403282915329323,-0.015361513105005893,0.07057758849844753 diff --git a/tests/framework/ROM/TimeSeries/ARMA/segmented.xml b/tests/framework/ROM/TimeSeries/ARMA/segmented.xml index 1899c42e7f..cf3a91901e 100644 --- a/tests/framework/ROM/TimeSeries/ARMA/segmented.xml +++ b/tests/framework/ROM/TimeSeries/ARMA/segmented.xml @@ -16,12 +16,16 @@ In this case, only the user requested variables as specified in the DataObjects are printed out. Added a pickling step to check serialization of segmented ARMA. + + Deleted the longest Fourier period for this test, because the phase of this period would have results between + PI and negative PI( a result of arctan(infinity) ), and cannnot be detected from the xml check. + Segmented - load,train,dump,unpickle,sample,print,reload,stats + load,train,rommeta,dump,unpickle,sample,print,reload,stats @@ -29,6 +33,11 @@ input input + + arma + rommeta + rommeta + input arma @@ -95,6 +104,7 @@ Time + @@ -103,9 +113,11 @@ Demand,Time scaling Time + 901017

0

0 - 1e200, 31556952, 15778476, 10518984, 7889238, 2592000, 1296000, 604800, 302400, 86400, 43200, 28800, 21600 + False + 31556952, 15778476, 10518984, 7889238, 2592000, 1296000, 604800, 302400, 86400, 43200, 28800, 21600 Time @@ -132,6 +144,10 @@ synthetic input, output + + csv + rommeta + csv stats diff --git a/tests/framework/ROM/TimeSeries/ARMA/tests b/tests/framework/ROM/TimeSeries/ARMA/tests index ba929a12fb..3009a8d4b0 100644 --- a/tests/framework/ROM/TimeSeries/ARMA/tests +++ b/tests/framework/ROM/TimeSeries/ARMA/tests @@ -62,10 +62,13 @@ [./VARMA] type = 'RavenFramework' input = 'varma.xml' - output = 'VARMA/plot_VARMA.png VARMA/plot_input.png VARMA/samples.csv VARMA/samples_0.csv VARMA/stats.xml' - csv = 'VARMA/stats.csv' - rel_err = 1e-1 + output = 'VARMA/plot_VARMA.png VARMA/plot_input.png VARMA/stats.xml VARMA/rommeta.xml VARMA/rommeta.csv VARMA/samples_0.csv' max_time = 500 + [./stats] + type = OrderedCSV + output = 'VARMA/stats.csv' + rel_err = 1e-1 + [../] [../] [./ZeroFilter] @@ -80,9 +83,23 @@ [./Segmented] type = 'RavenFramework' input = 'segmented.xml' - output = 'Segmented/synthetic.csv Segmented/synthetic_0.csv Segmented/synthetic.xml Segmented/stats.xml' - csv = 'Segmented/stats.csv' - rel_err = 1e-2 + output = 'Segmented/synthetic.csv Segmented/synthetic.xml Segmented/synthetic_0.csv' + [./stats] + type = OrderedCSV + output = 'Segmented/stats.csv' + rel_err = 1e-2 + [../] + [./romcsv] + type = OrderedCSV + output = 'Segmented/rommeta.csv' + rel_err = 1e-5 + [../] + [./romxml] + type = XML + unordered = true + output = 'Segmented/rommeta.xml' + rel_err = 1e-5 + [../] [../] [./Clustered] type = 'RavenFramework' diff --git a/tests/framework/ROM/TimeSeries/ARMA/varma.xml b/tests/framework/ROM/TimeSeries/ARMA/varma.xml index fd398d8c89..c93dc82d08 100644 --- a/tests/framework/ROM/TimeSeries/ARMA/varma.xml +++ b/tests/framework/ROM/TimeSeries/ARMA/varma.xml @@ -29,7 +29,7 @@ VARMA - read,train,sample,write,load,stats + read,train,rommeta,sample,write,load,stats 1 @@ -43,6 +43,11 @@ training VARMA + + VARMA + rommeta + rommeta + VARMA pk @@ -117,6 +122,7 @@ Time + @@ -124,9 +130,10 @@ A,B,C,D,Time scaling - 84 + 901017 A,B,C Time + False

3

1 @@ -151,6 +158,10 @@ + + csv + rommeta + csv samples diff --git a/tests/framework/unit_tests/SupervisedLearning/ARMA.py b/tests/framework/unit_tests/SupervisedLearning/ARMA.py index 2f29851557..5cd390ee50 100644 --- a/tests/framework/unit_tests/SupervisedLearning/ARMA.py +++ b/tests/framework/unit_tests/SupervisedLearning/ARMA.py @@ -27,13 +27,17 @@ import pickle as pk import numpy as np import copy +import pandas as pd # find location of crow, message handler frameworkDir = os.path.abspath(os.path.join(*([os.path.dirname(__file__)]+[os.pardir]*4+['framework']))) + sys.path.append(frameworkDir) from utils.utils import find_crow find_crow(frameworkDir) +from utils import randomUtils + import MessageHandler # message handler @@ -46,6 +50,7 @@ # find location of ARMA sys.path.append(os.path.join(frameworkDir,'SupervisedLearning')) import ARMA + print('Module undergoing testing:') print(ARMA) print('') @@ -259,18 +264,15 @@ def checkFails(comment,errstr,function,update=True,args=None,kwargs=None): ###################################### def createARMAXml(targets, pivot, p, q, fourier=None): if fourier is None: - fourier = {} + fourier = [] xml = createElement('ROM',attrib={'name':'test', 'subType':'ARMA'}) xml.append(createElement('Target',text=','.join(targets+[pivot]))) xml.append(createElement('Features',text='scaling')) xml.append(createElement('pivotParameter',text=pivot)) - xml.append(createElement('Pmin',text=str(p))) - xml.append(createElement('Pmax',text=str(p))) - xml.append(createElement('Qmin',text=str(q))) - xml.append(createElement('Qmax',text=str(q))) + xml.append(createElement('P',text=str(p))) + xml.append(createElement('Q',text=str(q))) if len(fourier): - xml.append(createElement('Fourier',text=','.join(str(f) for f in fourier.keys()))) - xml.append(createElement('FourierOrder',text=','.join(str(f) for f in fourier.values()))) + xml.append(createElement('Fourier',text=','.join(str(f) for f in fourier))) return xml def createFromXML(xml): @@ -286,7 +288,7 @@ def createARMA(targets, pivot, p, q, fourier=None): rom, arma = createFromXML(xml) return rom, arma -rom, arma = createARMA(['a','b'], 't', 6, 3, {86400: 2}) +rom, arma = createARMA(['a','b'], 't', 6, 3, [86400,43200]) # TODO confirmation testing for correct construction @@ -339,7 +341,9 @@ def plotPDF(edges, bins, ax, label, color, s='.', alpha=1.0): ax2.plot(x,pdf,'k-',label='true beta', lw=3) # random samples -data = dist.rvs(N) +data=pd.read_csv("signal.csv") +data=data.e_demand.values + if plotting: opdf, ocdf = makeCDF(data) plotCDF(ocdf[0], ocdf[1], ax, 'data', 'C0') @@ -348,6 +352,7 @@ def plotPDF(edges, bins, ax, label, color, s='.', alpha=1.0): # characterize params = arma._trainCDF(data) if plotting: + ebins = params['bins'] ecdf = params['cdf'] epdf = params['pdf'] plotCDF(ebins, ecdf, ax, 'empirical', 'C1') @@ -388,7 +393,7 @@ def plotPDF(edges, bins, ax, label, color, s='.', alpha=1.0): plt.show() # train ARMA on data and check CDFs of results -rom, arma = createARMA(['a'], 't', 0, 0, {}) +rom, arma = createARMA(['a'], 't', 0, 0, []) featureVals = np.zeros(1) targetVals = np.zeros([1,len(data),2]) # "a" @@ -402,7 +407,6 @@ def plotPDF(edges, bins, ax, label, color, s='.', alpha=1.0): for n in range(nsamp): ev = arma.__evaluateLocal__(np.array([1.0])) samples[n,:] = ev['a'] - # Enabling plotting will help visualize the signals that are tested # in the event they fail tests. Plotting should not be enabled in # the regression system as this point. @@ -416,8 +420,8 @@ def plotPDF(edges, bins, ax, label, color, s='.', alpha=1.0): ostats = (np.average(data), np.std(data)) for n in range(nsamp): stats = (np.average(samples[n,:]), np.std(samples[n,:])) - checkFloat('Mean, sample {}'.format(n), ostats[0], stats[0], tol=1e-2) - checkFloat('Std, sample {}'.format(n), ostats[1], stats[1], tol=1e-2) + checkFloat('Mean, sample {}'.format(n), ostats[0], stats[0], tol=3e-1) + checkFloat('Std, sample {}'.format(n), ostats[1], stats[1], tol=6e-1) if plotting: ax.plot(t, samples[n,:], '-', color='C1', label='sample', alpha=0.2) pdf,cdf = makeCDF(samples[n,:]) @@ -433,6 +437,49 @@ def plotPDF(edges, bins, ax, label, color, s='.', alpha=1.0): plt.show() +############################################# +# RESEEDCOPIES, ENGINE # +############################################# + +testVal=arma._trainARMA(data) +arma.amITrained=True +signal1=arma._generateARMASignal(testVal) +signal2=arma._generateARMASignal(testVal) + +#Test the reseed = False + +armaReF=arma +armaReF.reseedCopies=False +pklReF=pk.dumps(armaReF) +unpkReF=pk.loads(pklReF) +#signal 3 and 4 should be the same +signal3=armaReF._generateARMASignal(testVal) +signal4=unpkReF._generateARMASignal(testVal) +for n in range(len(data)): + checkFloat('signal 3, signal 4 ind{}'.format(n), signal3[n], signal4[n], tol=1e-5) + +#Test the reseed = True + +arma.reseedCopies=True +pklReT=pk.dumps(arma) +unpkReT=pk.loads(pklReT) +#signal 5 and 6 should not be the same +signal5=arma._generateARMASignal(testVal) +signal6=unpkReT._generateARMASignal(testVal) +for n in range(len(data)): + checkTrue('signal 5, signal 6 ind{}'.format(n),signal5[n]!=signal6[n]) + +# Test the engine with seed + +eng=randomUtils.newRNG() +arma.setEngine(eng,seed=901017,count=0) +signal7=arma._generateARMASignal(testVal) + +sig7=[0.39975177, -0.14531468, 0.13138866, -0.56565224, 0.06020252, + 0.60752306, -0.29076173, -1.1758456, 0.41108591, -0.05735384] +for n in range(10): + checkFloat('signal 7, evaluation ind{}'.format(n), signal7[n], sig7[n], tol=1e-7) + ################# # TODO UNTESTED # ################# @@ -443,7 +490,6 @@ def plotPDF(edges, bins, ax, label, color, s='.', alpha=1.0): # - Signal Reconstruction - print(results) sys.exit(results["fail"]) diff --git a/tests/framework/unit_tests/SupervisedLearning/signal.csv b/tests/framework/unit_tests/SupervisedLearning/signal.csv new file mode 100644 index 0000000000..0c644d6a81 --- /dev/null +++ b/tests/framework/unit_tests/SupervisedLearning/signal.csv @@ -0,0 +1,101 @@ +Time,D_avail,e_demand +0.0,61.33284800199106,0.13893995777942517 +1.0101010101010102,61.09437725520658,-0.8309815041848343 +2.0202020202020203,59.41855520849285,0.6107676967606288 +3.0303030303030303,60.949271558155736,1.9422158209487888 +4.040404040404041,63.1727442270002,0.18701799391126678 +5.050505050505051,62.70253306537926,-0.20693982691847282 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