def _select_features(self,
problem,
percent_features_to_select,
algorithm,
features_to_keep=None):
# Initialize FeatureSelector.
fs = FeatureSelector(problem=problem,
algorithm=algorithm,
random_state=self._random_state)
fs.set_input_matrix(self._X_train, column_or_1d(self._y_train))
num_features_to_select = int(percent_features_to_select *
len(self._X_train.columns.values))
# Parse features_to_keep.
if features_to_keep is None:
features_to_keep = []
# Select features.
fs.select(k=num_features_to_select)
# Enumerate eliminated features pre-transformation.
feature_ranks = fs.compute_ranks()
for i in range(len(feature_ranks)):
if feature_ranks[i] > num_features_to_select:
# If in features_to_keep, pretend it wasn't eliminated.
if self._X_train.columns[i] not in features_to_keep:
self._eliminated_features.append(self._X_train.columns[i])
# Hack: rather than making FeatureSelector handle the concept of
# kept features, just copy the data here and add it back to the
# transformed matrices.
# Rather than looping, do this individually so that we can skip if
# transformed X already has the feature.
for feature in features_to_keep:
kept_X_train_feature = self._X_train[[feature]].copy()
log.debug('kept_X_train_feature.shape: %s' %
str(kept_X_train_feature.shape))
self._X_train = fs.transform_matrix(self._X_train)
if feature not in self._X_train:
self._X_train = self._X_train.merge(kept_X_train_feature,
left_index=True,
right_index=True)
kept_X_test_feature = self._X_test[[feature]].copy()
log.debug('kept_X_test_feature.shape: %s' %
str(kept_X_test_feature.shape))
self._X_test = fs.transform_matrix(self._X_test)
if feature not in self._X_test:
self._X_test = self._X_test.merge(kept_X_test_feature,
left_index=True,
right_index=True)
if not features_to_keep:
# Even if there is no feature to keep, still need to
# perform transform_matrix to drop most low-rank features
self._X_train = fs.transform_matrix(self._X_train)
self._X_test = fs.transform_matrix(self._X_test)
def _select_features(self):
# Use FeatureSelector to prune all but 100 variables.
fs = FeatureSelector(algorithm=FeatureSelector.RECURSIVE_ELIMINATION, \
problem=FeatureSelector.CLASSIFICATION)
fs.set_input_matrix(self._X_train, column_or_1d(self._y_train))
num_features_to_select = int(0.01 * len(self._X_train.columns.values))
fs.select(k=num_features_to_select)
# Enumerate eliminated features pre-transformation.
self._feature_ranks = fs.compute_ranks()
for i in range(len(self._feature_ranks)):
if self._feature_ranks[i] > num_features_to_select:
self._eliminated_features.append(self._X_train.columns[i])
self._X_train = fs.transform_matrix(self._X_train)
self._X_test = fs.transform_matrix(self._X_test)