def test(self, classifier, testData, batchSize=None):
log.start('Testing classifier')
inputData, labels = testData
batchSize = batchSize if batchSize is not None else inputData.shape[0]
batchesCount = inputData.shape[0] / batchSize + 1
predictions = None
for batchIndex in xrange(batchesCount):
inputBatch = inputData[batchIndex * batchSize:(batchIndex + 1) * batchSize]
if predictions is None:
predictions = classifier.classify(inputBatch)
else:
p = classifier.classify(inputBatch)
if len(p):
predictions = numpy.concatenate([predictions, classifier.classify(inputBatch)])
log.progress('Testing classifier: {0}%'.format((batchIndex + 1) * 100 / batchesCount))
performance = Case.roc_auc_truncated(labels, predictions)
testMetrics = TestMetrics(performance)
log.done(testMetrics)
return testMetrics
def verify(self, classifier):
log.start('Verifying classifier')
self.checkAgreementData = pandas.read_csv(self.checkAgreementDataFile)
columns = self.checkAgreementData.columns[1:-2]
checkAgreementData = self.checkAgreementData[columns][goodFeatures].as_matrix()
agreementPredictions = classifier.classify(checkAgreementData)
realPredictions = agreementPredictions[self.checkAgreementData['signal'].values == 0]
monteCarloPrediction = agreementPredictions[self.checkAgreementData['signal'].values == 1]
realWeights = self.checkAgreementData[self.checkAgreementData['signal'] == 0]['weight'].values
monteCarloWeights = self.checkAgreementData[self.checkAgreementData['signal'] == 1]['weight'].values
agreementCoefficient = Case.getKolmogorovSmirnovDistance(realPredictions, monteCarloPrediction, realWeights, monteCarloWeights)
self.checkCorrelationData = pandas.read_csv(self.checkCorrelationDataFile)
columns = self.checkCorrelationData.columns[1:-1]
checkCorrelationData = self.checkCorrelationData[columns][goodFeatures].as_matrix()
masses = self.checkCorrelationData['mass']
correlationPrediction = classifier.classify(checkCorrelationData)
correlationCoefficient = Case.getCramerVonNeimanCoefficient(correlationPrediction, masses)
verificationMetrics = VerificationMetrics(agreementCoefficient, self.agreementCutoff, correlationCoefficient, self.correlationCutoff)
log.done(verificationMetrics)
return verificationMetrics
def dump(self, submission):
log.start('Dumping data')
fileName = '{0}/{1}.csv'.format(self.submissionsDirectory, self.seed)
submission.to_csv(fileName, index=False)
log.done()
def createSubmission(self, classifier, testData, batchSize=None):
log.start('Creating submission')
batchSize = batchSize if batchSize is not None else input.shape[0]
batchesCount = testData.shape[0] / batchSize + 1
predictions = None
for batchIndex in xrange(batchesCount):
inputBatch = testData[batchIndex * batchSize:(batchIndex + 1) * batchSize]
if predictions is None:
predictions = classifier.classify(inputBatch)
elif len(inputBatch):
predictions = numpy.concatenate([predictions, classifier.classify(inputBatch)])
log.progress('Creating submission: {0}%'.format((batchIndex + 1) * 100 / batchesCount))
submission = pandas.DataFrame({"id": self.testData["id"], "prediction": predictions})
log.done('submission' + str(submission.shape))
return submission
def loadData(self, minified=False):
log.start('Loading data')
self.trainingData = pandas.read_csv(self.trainingDataFile)
columns = self.trainingData.columns[1:-4]
trainingInput = self.trainingData[columns][goodFeatures].as_matrix()
trainingLabels = self.trainingData['signal'].as_matrix()
trainingData = trainingInput, trainingLabels
self.validationData = pandas.read_csv(self.checkAgreementDataFile)
columns = self.validationData.columns[1:-2]
validationInput = self.validationData[columns][goodFeatures].as_matrix()
validationLabels = self.validationData['signal'].as_matrix()
validationData = validationInput, validationLabels
self.testData = pandas.read_csv(self.testDataFile)
columns = self.testData.columns[1:]
testData = self.testData[columns][goodFeatures].as_matrix()
message = 'trainingData{0}, testData{1}'.format(trainingInput.shape, testData.shape)
log.done(message)
return trainingData, validationData, testData
def train(classifier, trainingData, validationData, batchSize=None):
log.start('Training classifier')
inputData, labels = trainingData
batchSize = batchSize if batchSize is not None else inputData.shape[0]
batchesCount = inputData.shape[0] / batchSize
start = time.time()
for batchIndex in xrange(batchesCount):
inputBatch = inputData[batchIndex * batchSize:(batchIndex + 1) * batchSize]
labelsBatch = labels[batchIndex * batchSize:(batchIndex + 1) * batchSize]
classifier.fit(inputBatch, labelsBatch)
log.progress('Training classifier: {0}%'.format((batchIndex + 1) * 100 / batchesCount))
end = time.time()
elapsed = end - start
trainingMetrics = TrainingMetrics(elapsed)
log.done(trainingMetrics)
return trainingMetrics
def main():
"""Main procedure of DFA minimization problem generator.
Parses command-line arguments and builds solution and task DFA accordingly.
Saves result and cleans up.
"""
# add and check parameters
class MyFormatter(argparse.ArgumentDefaultsHelpFormatter,
argparse.MetavarTypeHelpFormatter,
argparse.RawTextHelpFormatter):
pass
parser = argparse.ArgumentParser(
description='Command-line tool to generate DFA minimization problems.',
formatter_class=MyFormatter,
epilog=_EPILOG)
for groupName in _ARGUMENTS:
group = parser.add_argument_group(groupName)
for option in _ARGUMENTS[groupName]:
if len(option) == 4:
group.add_argument(option[0],
type=option[1],
default=option[2],
help=option[3])
else:
group.add_argument(option[0],
type=option[1],
default=option[2],
help=option[3],
choices=option[4])
args = parser.parse_args()
strToBool = lambda x: x == 'yes'
args.ps = strToBool(args.ps)
args.c = strToBool(args.c)
args.pt = strToBool(args.pt)
args.dfa = strToBool(args.dfa)
args.tex = strToBool(args.tex)
args.pdf = strToBool(args.pdf)
args.shuf = strToBool(args.shuf)
args.out = pathlib.Path(args.out)
if args.k > args.n:
log.k_too_big()
return
if args.n < args.f:
log.f_too_big()
return
if args.pt and not args.ps:
log.invalid_p_options()
return
if args.k == 0 and args.e > 0:
log.not_extendable()
return
if any(
map(lambda x: x < 0, (args.k, args.n, args.f, args.dmin, args.dmax,
args.e, args.u))):
log.neg_value()
return
if not args.out.exists() or not args.out.is_dir():
log.creating_output_dir()
args.out.mkdir()
log.done()
log.start(args)
# construct solution dfa
log.building_solution(args)
build = next_min_dfa if args.b == 'enum' else rand_min_dfa
solDFA = build(args.k, args.n, args.f, args.dmin, args.dmax, args.ps,
args.out)
if solDFA is None and args.b == 'enum':
log.done()
log.enum_finished()
return
log.done()
# extend dfa
log.extending_solution(args)
for i in range(10):
try:
reachDFA, taskDFA = extend_dfa(solDFA, args.e, args.u, args.pt,
args.c)
except DFANotExtendable:
log.failed()
log.dfa_not_extendable(args)
return
except PygraphIndexErrorBug:
log.failed()
log.pygraph_bug('extending')
if i == 9:
log.pygraph_bug_abort(args)
return
else:
log.done()
break
# generate graphical representation of solution and task dfa
if args.dfa or args.tex or args.pdf:
log.saving()
save_exercise(solDFA, reachDFA, taskDFA, args.out, args.dfa, args.tex,
args.pdf, args.shuf)
log.done()
else:
log.no_saving()
# clean up working directory
log.cleaning()
for f in args.out.iterdir():
if f.suffix in ('.toc', '.aux', '.log', '.gz', '.bbl', '.blg', '.out'):
f.unlink()
log.done()