class Select_Features(TransformerMixin):
def __init__(self, random_state=0, features_by_type=None):
'''
TODO: if feature_collection is None, assume all features are numeric.
Args:
random_state:
feature_dict:
'''
self.fs = FeatureSelector(
problem=FeatureSelector.CLASSIFICATION,
algorithm=FeatureSelector.RECURSIVE_ELIMINATION,
random_state=random_state)
self.features_by_type = features_by_type
self.selected_features = []
def fit(self, X, y=None, features_to_keep=None, select_percent=0.05):
'''
TODO: Does this "select_percent" include those pre-set to keep?
features_to_keep includes both features wanted to keep + non-numeric features
Args:
X:
y:
features_to_keep:
select_percent:
Returns:
'''
if not features_to_keep:
features_to_keep = []
X_numeric = X[X.columns[X.columns.isin(
self.features_by_type['numeric_features'])]]
self.fs.set_input_matrix(X_numeric.values, column_or_1d(y.values))
num_features_to_select = int(
round(select_percent * len(X_numeric.columns.values)))
self.fs.select(k=num_features_to_select)
feature_ranks = self.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.
features_to_keep.append(X_numeric.columns[i])
self.selected_features = features_to_keep[:]
return self
def transform(self, X):
return X[self.selected_features]
def __init__(self, random_state=0, features_by_type=None):
'''
TODO: if feature_collection is None, assume all features are numeric.
Args:
random_state:
feature_dict:
'''
self.fs = FeatureSelector(
problem=FeatureSelector.CLASSIFICATION,
algorithm=FeatureSelector.RECURSIVE_ELIMINATION,
random_state=random_state)
self.features_by_type = features_by_type
self.selected_features = []
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)
def _get_test_feature_ranks(self, algorithm, problem, X, y, k=None, percentile=None):
# Set input features and values.
fs = FeatureSelector(algorithm=algorithm, problem=problem, random_state=12345)
fs.set_input_matrix(X, y)
# Select k best features.
fs.select(k=k, percentile=percentile)
feature_ranks = fs.compute_ranks()
return feature_ranks
def select_features(matrix, features, random_state=0):
select_params = features['select_params']
fs = FeatureSelector(problem=select_params['selection_problem'],
algorithm=select_params['selection_algorithm'],
random_state=random_state)
X, y = split_Xy(matrix, features['ylabel'])
fs.set_input_matrix(X, y)
num_features_to_select = int(select_params['percent_features_to_select'] *
len(matrix.columns.values))
fs.select(k=num_features_to_select)
feature_ranks = fs.compute_ranks()
features_to_keep = []
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.
# self._eliminated_features.append(self._X_train.columns[i])
features_to_keep.append(X.columns[i])
return matrix[features_to_keep].copy()
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[features_to_keep].copy()
log.debug('kept_X_train_feature.shape: %s' %
str(kept_X_train_feature.shape))
self._X_train = fs.transform_matrix(self._X_train)
for feature in features_to_keep:
if feature not in self._X_train:
self._X_train = self._X_train.merge(
kept_X_train_feature[[feature]],
left_index=True,
right_index=True)
kept_X_test_feature = self._X_test[features_to_keep].copy()
log.debug('kept_X_test_feature.shape: %s' %
str(kept_X_test_feature.shape))
self._X_test = fs.transform_matrix(self._X_test)
for feature in features_to_keep:
if feature not in self._X_test:
self._X_test = self._X_test.merge(
kept_X_test_feature[[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 setUp(self):
fs = FeatureSelector()