如何使用另一种估计撰写sklearn估计？(How to compose sklearn estim

我希望培养一个LogisticRegression和RandomForestClassifier和使用结合自己的分数GaussianNB ：

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB

X, y = make_classification(n_samples=1000, n_features=4,
                           n_informative=2, n_redundant=0,
                           random_state=0, shuffle=False)

logit = LogisticRegression(random_state=0)
logit.fit(X, y)

randf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)
randf.fit(X, y)

X1 = np.transpose([logit.predict_proba(X)[:,0], randf.predict_proba(X)[:,0]])

nb = GaussianNB()
nb.fit(X1, y)

如何做到这一点与管道，这样我可以将它传递给cross_validate和GridSearchCV ？

PS。我想我可以定义自己的类实现fit和predict_proba方法，但我认为应该有这样做的标准方式...

没有，没有什么sklearn内置到你想要做什么，而无需编写一些自定义代码。您可以通过使用并行代码的某些部分FeatureUnion和序列采用全任务Pipeline ，但你需要写一个可以转发的输出定制变压器predict_proba到transform方法。

事情是这样的：

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline, FeatureUnion

X, y = make_classification(n_samples=1000, n_features=4,
                           n_informative=2, n_redundant=0,
                           random_state=0, shuffle=False)

# This is the custom transformer that will convert 
# predict_proba() to pipeline friendly transform()
class PredictProbaTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, clf=None):
        self.clf = clf

    def fit(self, X, y):
        if self.clf is not None:
            self.clf.fit(X, y)

        return self

    def transform(self, X):

        if self.clf is not None:
            # Drop the 2nd column but keep 2d shape
            # because FeatureUnion wants that 
            return self.clf.predict_proba(X)[:,[0]]

        return X

    # This method is important for correct working of pipeline
    def fit_transform(self, X, y):
        return self.fit(X, y).transform(X)

logit = LogisticRegression(random_state=0)
randf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)

pipe = Pipeline([
                 ('stack',FeatureUnion([
                              ('logit', PredictProbaTransformer(logit)),
                              ('randf', PredictProbaTransformer(randf)),
                              #You can add more classifiers with custom wrapper like above
                                       ])),
                 ('nb',GaussianNB())])

pipe.fit(X, y)

现在，你可以简单地调用pipe.predict()所有的东西都会被正确地完成。

有关FeatureUnion的更多信息，你可以看看我的其他的答案在这里一个类似的问题： -