def partitionIndices(self, seed=42):
"""
Partitions list of two-tuples of train and test indices for each trial.
"""
if self.experimentType == "k-folds":
self.partitions = KFolds(self.folds).split(
range(len(self.samples)),
randomize=(not self.orderedSplit),
seed=seed)
else:
# TODO: use StandardSplit in data_split.py
length = len(self.samples)
if self.orderedSplit:
for split in self.trainSizes:
trainIndices = range(split)
testIndices = range(split, length)
self.partitions.append((trainIndices, testIndices))
else:
# randomly sampled, not repeated
random.seed(seed)
for split in self.trainSizes:
trainIndices = random.sample(xrange(length), split)
testIndices = [
i for i in xrange(length) if i not in trainIndices
]
self.partitions.append((trainIndices, testIndices))
def partitionIndices(self, _):
"""
Sets self.partitions for the number of tokens for each sample in the
training and test sets.
The order of sequences is already specified by the network data files; if
generated by the experiment, these are in order or randomized as specified
by the orderedSplit arg.
"""
if self.experimentType == "k-folds":
for fold in xrange(self.folds):
dataFile = self.dataFiles[fold]
numTokens = NetworkDataGenerator.getNumberOfTokens(dataFile)
self.partitions = KFolds(self.folds).split(numTokens,
randomize=False)
else:
for trial, split in enumerate(self.trainSizes):
dataFile = self.dataFiles[trial]
numTokens = NetworkDataGenerator.getNumberOfTokens(dataFile)
self.partitions.append((numTokens[:split], numTokens[split:]))
