def fit(self, X, y=None):
self.selector = get_feature_selection_model_from_name(self.type_of_estimator, self.feature_selection_model)
if self.selector == 'KeepAll':
if scipy.sparse.issparse(X):
num_cols = X.shape[0]
else:
num_cols = len(X[0])
self.support_mask = [True for col_idx in range(num_cols) ]
else:
if self.feature_selection_model == 'SelectFromModel':
num_cols = X.shape[1]
num_rows = X.shape[0]
if self.type_of_estimator == 'regressor':
self.estimator = RandomForestRegressor(n_jobs=-1, max_depth=10, n_estimators=15)
else:
self.estimator = RandomForestClassifier(n_jobs=-1, max_depth=10, n_estimators=15)
self.estimator.fit(X, y)
feature_importances = self.estimator.feature_importances_
# Two ways of doing feature selection
# 1. Any feature with a feature importance of at least 1/100th of our max feature
max_feature_importance = max(feature_importances)
threshold_by_relative_importance = 0.01 * max_feature_importance
# 2. 1/4 the number of rows (so 100 rows means 25 columns)
sorted_importances = sorted(feature_importances, reverse=True)
max_cols = int(num_rows * 0.25)
try:
threshold_by_max_cols = sorted_importances[max_cols]
except IndexError:
threshold_by_max_cols = sorted_importances[-1]
threshold = max(threshold_by_relative_importance, threshold_by_max_cols)
self.support_mask = [True if x > threshold else False for x in feature_importances]
else:
self.selector.fit(X, y)
self.support_mask = self.selector.get_support()
# Get a mask of which indices it is we want to keep
self.index_mask = [idx for idx, val in enumerate(self.support_mask) if val == True]
return self
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