单变量特征选择#

此笔记本是使用单变量特征选择来提高嘈杂数据集上分类精度的示例。

在此示例中，一些噪声（非信息性）特征被添加到 iris 数据集中。支持向量机 (SVM) 用于在应用单变量特征选择之前和之后对数据集进行分类。对于每个特征，我们绘制单变量特征选择的 p 值以及相应的 SVM 权重。通过此，我们将比较模型精度并检查单变量特征选择对模型权重的影响。

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

生成样本数据#

import numpy as np

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# The iris dataset
X, y = load_iris(return_X_y=True)

# Some noisy data not correlated
E = np.random.RandomState(42).uniform(0, 0.1, size=(X.shape[0], 20))

# Add the noisy data to the informative features
X = np.hstack((X, E))

# Split dataset to select feature and evaluate the classifier
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)

单变量特征选择#

使用 F 检验进行特征评分的单变量特征选择。我们使用默认选择函数来选择四个最重要的特征。

from sklearn.feature_selection import SelectKBest, f_classif

selector = SelectKBest(f_classif, k=4)
selector.fit(X_train, y_train)
scores = -np.log10(selector.pvalues_)
scores /= scores.max()

import matplotlib.pyplot as plt

X_indices = np.arange(X.shape[-1])
plt.figure(1)
plt.clf()
plt.bar(X_indices - 0.05, scores, width=0.2)
plt.title("Feature univariate score")
plt.xlabel("Feature number")
plt.ylabel(r"Univariate score ($-Log(p_{value})$)")
plt.show()

在全部特征集中，只有 4 个原始特征是显著的。我们可以看到它们在单变量特征选择中得分最高。

与 SVM 比较#

无单变量特征选择

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import LinearSVC

clf = make_pipeline(MinMaxScaler(), LinearSVC())
clf.fit(X_train, y_train)
print(
    "Classification accuracy without selecting features: {:.3f}".format(
        clf.score(X_test, y_test)
    )
)

svm_weights = np.abs(clf[-1].coef_).sum(axis=0)
svm_weights /= svm_weights.sum()

Classification accuracy without selecting features: 0.789

单变量特征选择后

clf_selected = make_pipeline(SelectKBest(f_classif, k=4), MinMaxScaler(), LinearSVC())
clf_selected.fit(X_train, y_train)
print(
    "Classification accuracy after univariate feature selection: {:.3f}".format(
        clf_selected.score(X_test, y_test)
    )
)

svm_weights_selected = np.abs(clf_selected[-1].coef_).sum(axis=0)
svm_weights_selected /= svm_weights_selected.sum()

Classification accuracy after univariate feature selection: 0.868

plt.bar(
    X_indices - 0.45, scores, width=0.2, label=r"Univariate score ($-Log(p_{value})$)"
)

plt.bar(X_indices - 0.25, svm_weights, width=0.2, label="SVM weight")

plt.bar(
    X_indices[selector.get_support()] - 0.05,
    svm_weights_selected,
    width=0.2,
    label="SVM weights after selection",
)

plt.title("Comparing feature selection")
plt.xlabel("Feature number")
plt.yticks(())
plt.axis("tight")
plt.legend(loc="upper right")
plt.show()

在没有单变量特征选择的情况下，SVM 将较大的权重赋予前 4 个原始显著特征，但也选择许多非信息性特征。在 SVM 之前应用单变量特征选择会增加 SVM 赋予显著特征的权重，从而提高分类精度。

脚本总运行时间：（0 分钟 0.190 秒）