"""
Feature selection based on the scikit-learn
ExtraTreesClassifier and SelectFromModel solution.
>>> from sklearn.ensemble import ExtraTreesClassifier
>>> from sklearn.datasets import load_iris
>>> from sklearn.feature_selection import SelectFromModel
>>> iris = load_iris()
>>> X, y = iris.data, iris.target
>>> X.shape
(150, 4)
>>> clf = ExtraTreesClassifier()
>>> clf = clf.fit(X, y)
>>> clf.feature_importances_
array([ 0.04..., 0.05..., 0.4..., 0.4...])
>>> model = SelectFromModel(clf, prefit=True)
>>> X_new = model.transform(X)
>>> X_new.shape
(150, 2)
"""
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import Lasso
class EstimatorFeatureSelector(object):
"""Feature Selector based on sklearn.feature_selection.SelectFromModel.
Attributes:
ESTIMATOR (class): Estimator class to use. To be defined in subclasses.
selector (SelectFromModel): sklearn.ensemble.SelectFromModel that will
perform the actual feature selection.
Example:
This example below shows simple usage case using an ExtraTreesClassifier
as the estimator, and shows the output when passing both a pandas.DataFrame
and a numpy.ndarray.
>>> import pandas as pd
>>> df = pd.DataFrame([
... {'a': 1, 'b': 1, 'c': 1},
... {'a': 1, 'b': 2, 'c': 1},
... {'a': 2, 'b': 1, 'c': 2},
... {'a': 2, 'b': 2, 'c': 2}
... ])
>>> X = df[['a', 'b']]
>>> y = df.c
>>> from mlblocks.primitives.custom.preprocessors.feature_selection \
... import EstimatorFeatureSelector
>>> from sklearn.ensemble import ExtraTreesClassifier
>>> efs = EstimatorFeatureSelector(ExtraTreesClassifier)
>>> efs.fit(X, y)
>>> efs.transform(X)
a
0 1
1 1
2 2
3 2
>>> efs.transform(X.values)
array([[1],
[1],
[2],
[2]])
"""
ESTIMATOR = None
def __init__(self, estimator_class=None, bypass=False, threshold=None,
norm_order=1, *args, **kwargs):
self.bypass = bypass
if not bypass:
estimator = (self.ESTIMATOR or estimator_class)(*args, **kwargs)
self.selector = SelectFromModel(estimator, threshold, False, norm_order)
def fit(self, X, y):
if not self.bypass:
self.selector.fit(X, y)
def _get_df(self, X, new_X):
columns_idx = self.selector.get_support()
columns = X.columns[columns_idx]
return pd.DataFrame(new_X, index=X.index, columns=columns)
def transform(self, X):
if self.bypass:
return X
new_X = self.selector.transform(X)
if isinstance(X, pd.DataFrame):
new_X = self._get_df(X, new_X)
return new_X
def fit_transform(self, X, y):
self.fit(X, y)
return self.transform(X)
class ExtraTreesClassifierFeatureSelector(EstimatorFeatureSelector):
"""EstimatorFeatureSelector based on ExtraTreesClassifier."""
ESTIMATOR = ExtraTreesClassifier
class ExtraTreesRegressorFeatureSelector(EstimatorFeatureSelector):
"""EstimatorFeatureSelector based on ExtraTreesRegressor."""
ESTIMATOR = ExtraTreesRegressor
class LassoFeatureSelector(EstimatorFeatureSelector):
"""EstimatorFeatureSelector based on Lasso."""
ESTIMATOR = Lasso
