examples/feature_selection/plot_select_from_model_diabetes.py - main - githubmirror/scikit-learn

examples/feature_selection/plot_select_from_model_diabetes.py
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"""
============================================
Model-based and sequential feature selection
============================================

This example illustrates and compares two approaches for feature selection:
:class:`~sklearn.feature_selection.SelectFromModel` which is based on feature
importance, and
:class:`~sklearn.feature_selection.SequentialFeatureSelector` which relies
on a greedy approach.

We use the Diabetes dataset, which consists of 10 features collected from 442
diabetes patients.
"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Loading the data
# ----------------
#
# We first load the diabetes dataset which is available from within
# scikit-learn, and print its description:
from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target
print(diabetes.DESCR)

# %%
# Feature importance from coefficients
# ------------------------------------
#
# To get an idea of the importance of the features, we are going to use the
# :class:`~sklearn.linear_model.RidgeCV` estimator. The features with the
# highest absolute `coef_` value are considered the most important.
# We can observe the coefficients directly without needing to scale them (or
# scale the data) because from the description above, we know that the features
# were already standardized.
# For a more complete example on the interpretations of the coefficients of
# linear models, you may refer to
# :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.  # noqa: E501
import matplotlib.pyplot as plt
import numpy as np

from sklearn.linear_model import RidgeCV

ridge = RidgeCV(alphas=np.logspace(-6, 6, num=5)).fit(X, y)
importance = np.abs(ridge.coef_)
feature_names = np.array(diabetes.feature_names)
plt.bar(height=importance, x=feature_names)
plt.title("Feature importances via coefficients")
plt.show()

# %%
# Selecting features based on importance
# --------------------------------------
#
# Now we want to select the two features which are the most important according
# to the coefficients. The :class:`~sklearn.feature_selection.SelectFromModel`
# is meant just for that. :class:`~sklearn.feature_selection.SelectFromModel`
# accepts a `threshold` parameter and will select the features whose importance
# (defined by the coefficients) are above this threshold.
#
# Since we want to select only 2 features, we will set this threshold slightly
# above the coefficient of third most important feature.
from time import time

from sklearn.feature_selection import SelectFromModel

threshold = np.sort(importance)[-3] + 0.01

tic = time()
sfm = SelectFromModel(ridge, threshold=threshold).fit(X, y)
toc = time()
print(f"Features selected by SelectFromModel: {feature_names[sfm.get_support()]}")
print(f"Done in {toc - tic:.3f}s")

# %%
# Selecting features with Sequential Feature Selection
# ----------------------------------------------------
#
# Another way of selecting features is to use
# :class:`~sklearn.feature_selection.SequentialFeatureSelector`
# (SFS). SFS is a greedy procedure where, at each iteration, we choose the best
# new feature to add to our selected features based a cross-validation score.
# That is, we start with 0 features and choose the best single feature with the
# highest score. The procedure is repeated until we reach the desired number of
# selected features.
#
# We can also go in the reverse direction (backward SFS), *i.e.* start with all
# the features and greedily choose features to remove one by one. We illustrate
# both approaches here.

from sklearn.feature_selection import SequentialFeatureSelector

tic_fwd = time()
sfs_forward = SequentialFeatureSelector(
    ridge, n_features_to_select=2, direction="forward"
).fit(X, y)
toc_fwd = time()

tic_bwd = time()
sfs_backward = SequentialFeatureSelector(
    ridge, n_features_to_select=2, direction="backward"
).fit(X, y)
toc_bwd = time()

print(
    "Features selected by forward sequential selection: "
    f"{feature_names[sfs_forward.get_support()]}"
)
print(f"Done in {toc_fwd - tic_fwd:.3f}s")
print(
    "Features selected by backward sequential selection: "
    f"{feature_names[sfs_backward.get_support()]}"
)
print(f"Done in {toc_bwd - tic_bwd:.3f}s")

# %%
# Interestingly, forward and backward selection have selected the same set of
# features. In general, this isn't the case and the two methods would lead to
# different results.
#
# We also note that the features selected by SFS differ from those selected by
# feature importance: SFS selects `bmi` instead of `s1`. This does sound
# reasonable though, since `bmi` corresponds to the third most important
# feature according to the coefficients. It is quite remarkable considering
# that SFS makes no use of the coefficients at all.
#
# To finish with, we should note that
# :class:`~sklearn.feature_selection.SelectFromModel` is significantly faster
# than SFS. Indeed, :class:`~sklearn.feature_selection.SelectFromModel` only
# needs to fit a model once, while SFS needs to cross-validate many different
# models for each of the iterations. SFS however works with any model, while
# :class:`~sklearn.feature_selection.SelectFromModel` requires the underlying
# estimator to expose a `coef_` attribute or a `feature_importances_`
# attribute. The forward SFS is faster than the backward SFS because it only
# needs to perform `n_features_to_select = 2` iterations, while the backward
# SFS needs to perform `n_features - n_features_to_select = 8` iterations.
#
# Using negative tolerance values
# -------------------------------
#
# :class:`~sklearn.feature_selection.SequentialFeatureSelector` can be used
# to remove features present in the dataset and return a
# smaller subset of the original features with `direction="backward"`
# and a negative value of `tol`.
#
# We begin by loading the Breast Cancer dataset, consisting of 30 different
# features and 569 samples.
import numpy as np

from sklearn.datasets import load_breast_cancer

breast_cancer_data = load_breast_cancer()
X, y = breast_cancer_data.data, breast_cancer_data.target
feature_names = np.array(breast_cancer_data.feature_names)
print(breast_cancer_data.DESCR)

# %%
# We will make use of the :class:`~sklearn.linear_model.LogisticRegression`
# estimator with :class:`~sklearn.feature_selection.SequentialFeatureSelector`
# to perform the feature selection.
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

for tol in [-1e-2, -1e-3, -1e-4]:
    start = time()
    feature_selector = SequentialFeatureSelector(
        LogisticRegression(),
        n_features_to_select="auto",
        direction="backward",
        scoring="roc_auc",
        tol=tol,
        n_jobs=2,
    )
    model = make_pipeline(StandardScaler(), feature_selector, LogisticRegression())
    model.fit(X, y)
    end = time()
    print(f"\ntol: {tol}")
    print(f"Features selected: {feature_names[model[1].get_support()]}")
    print(f"ROC AUC score: {roc_auc_score(y, model.predict_proba(X)[:, 1]):.3f}")
    print(f"Done in {end - start:.3f}s")

# %%
# We can see that the number of features selected tend to increase as negative
# values of `tol` approach to zero. The time taken for feature selection also
# decreases as the values of `tol` come closer to zero.