def _training_vectors_classes(self):
"""Create vectors and classes used for training feature selection.
Args:
None
Returns:
result: Tuple of training (vectors, classes)
"""
# Split into input and output
_vectors = self._dataframe.drop(self._label2predict, axis=1)
_classes = self._dataclasses
# Get rid of the NaNs in the vectors and classes.
(nanless_vectors, nanless_classes) = _no_nans(_vectors, _classes,
self._shift_steps)
# Work only with training data
(vectors, _, __, classes, ___,
____) = general.train_validation_test_split(nanless_vectors,
nanless_classes,
self._test_size)
# Return
result = (vectors, classes)
return result
def _training_vectors_classes(self):
"""Create vectors and classes used for training feature selection.
Args:
None
Returns:
result: Tuple of training (vectors, classes)
"""
# Split into input and output
_vectors = self._dataframe
_classes = self._dataclasses
# Get rid of the NaNs in the vectors and classes.
(nanless_vectors, nanless_classes) = _no_nans(
_vectors, _classes, self._shift_steps)
# Work only with training data
(vectors, _, __,
classes, ___, ____) = general.train_validation_test_split(
nanless_vectors, nanless_classes, self._test_size)
# Return
result = (vectors, classes)
return result
def train_validation_test_split(self):
"""Create contiguous (not random) training and test data.
train_test_split in sklearn.model_selection does this randomly and is
not suited for time-series data. It also doesn't create a
validation-set
Args:
None
Returns:
result: Training or test vector numpy arrays
"""
# Return
vectors = self._vectors['NoNaNs']
classes = self._classes['NoNaNs']
result = general.train_validation_test_split(vectors, classes,
self._test_size)
return result
def train_validation_test_split(self):
"""Create contiguous (not random) training and test data.
train_test_split in sklearn.model_selection does this randomly and is
not suited for time-series data. It also doesn't create a
validation-set
Args:
None
Returns:
result: Training or test vector numpy arrays
"""
# Return
vectors = self._vectors['NoNaNs']
classes = self._classes['NoNaNs']
result = general.train_validation_test_split(
vectors, classes, self._test_size)
return result