<p>定制函数来实现您想要的功能是非常容易的。在</p>
<p>导入先决条件:</p>
<pre><code>import numpy as np
from sklearn.preprocessing import LabelEncoder
def fit_multiple_estimators(classifiers, X_list, y, sample_weights = None):
# Convert the labels `y` using LabelEncoder, because the predict method is using index-based pointers
# which will be converted back to original data later.
le_ = LabelEncoder()
le_.fit(y)
transformed_y = le_.transform(y)
# Fit all estimators with their respective feature arrays
estimators_ = [clf.fit(X, y) if sample_weights is None else clf.fit(X, y, sample_weights) for clf, X in zip([clf for _, clf in classifiers], X_list)]
return estimators_, le_
def predict_from_multiple_estimator(estimators, label_encoder, X_list, weights = None):
# Predict 'soft' voting with probabilities
pred1 = np.asarray([clf.predict_proba(X) for clf, X in zip(estimators, X_list)])
pred2 = np.average(pred1, axis=0, weights=weights)
pred = np.argmax(pred2, axis=1)
# Convert integer predictions to original labels:
return label_encoder.inverse_transform(pred)
</code></pre>
<p>逻辑取自<a href="https://github.com/scikit-learn/scikit-learn/blob/ab93d65/sklearn/ensemble/voting_classifier.py#L35" rel="noreferrer">VotingClassifier source</a>。在</p>
<p>现在测试上述方法。
首先获取一些数据:</p>
^{pr2}$
<p>将数据分成训练和测试:</p>
<pre><code>from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y)
</code></pre>
<p>将X划分为不同的特征数据:</p>
<pre><code>X_train1, X_train2 = X_train[:,:2], X_train[:,2:]
X_test1, X_test2 = X_test[:,:2], X_test[:,2:]
X_train_list = [X_train1, X_train2]
X_test_list = [X_test1, X_test2]
</code></pre>
<p>获取分类器列表:</p>
<pre><code>from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
# Make sure the number of estimators here are equal to number of different feature datas
classifiers = [('knn', KNeighborsClassifier(3)),
('svc', SVC(kernel="linear", C=0.025, probability=True))]
</code></pre>
<p>将分类器与数据匹配:</p>
<pre><code>fitted_estimators, label_encoder = fit_multiple_estimators(classifiers, X_train_list, y_train)
</code></pre>
<p>使用测试数据预测:</p>
<pre><code>y_pred = predict_from_multiple_estimator(fitted_estimators, label_encoder, X_test_list)
</code></pre>
<p>获得预测的准确性:</p>
<pre><code>from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))
</code></pre>
<p>如果有任何疑问,请随时询问。在</p>