VotingClassifier：不同的功能集

在我的例子中，我有两个不同的功能集（因此，行数相同且标签相同）DataFrames:

df1:

| A | B | C |
-------------
| 1 | 4 | 2 |
| 1 | 4 | 8 |
| 2 | 1 | 1 |
| 2 | 3 | 0 |
| 3 | 2 | 5 |

df2:

| E | F |
---------
| 6 | 1 |
| 1 | 3 |
| 8 | 1 |
| 2 | 8 |
| 5 | 2 |

labels:

| labels |
----------
|    5   |
|    5   |
|    1   |
|    7   |
|    3   |

我想用它们来训练VotingClassifier。但拟合步骤只允许指定单个特征集。目标是适合clf1 with df1 and clf2 with df2.

eclf = VotingClassifier(estimators=[('df1-clf', clf1), ('df2-clf', clf2)], voting='soft')
eclf.fit(...)

遇到这种情况我该怎么办？有什么简单的解决办法吗？

制作自定义函数来完成您想要实现的目标非常容易。

导入先决条件：

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)

逻辑取自VotingClassifier 源 https://github.com/scikit-learn/scikit-learn/blob/ab93d65/sklearn/ensemble/voting_classifier.py#L35.

现在测试一下上面的方法。 首先获取一些数据：

from sklearn.datasets import load_iris
data = load_iris()
X = data.data
y = []

#Convert int classes to string labels
for x in data.target:
    if x==0:
        y.append('setosa')
    elif x==1:
        y.append('versicolor')
    else:
        y.append('virginica')

将数据分为训练和测试：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y)

将X分为不同的特征数据：

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]

获取分类器列表：

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))]

用数据拟合分类器：

fitted_estimators, label_encoder = fit_multiple_estimators(classifiers, X_train_list, y_train)

使用测试数据进行预测：

y_pred = predict_from_multiple_estimator(fitted_estimators, label_encoder, X_test_list)

获得预测的准确性：

from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))

如有任何疑问，请随时询问。