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如何使用另一个估计器组合sklearn估计器?

scikit-learn machine-learning

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sds Niraj Rajbhandari · 技术社区 · 7 年前

LogisticRegression 和 RandomForestClassifier 并使用 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)

我该怎么做呢 Pipeline 这样我就可以把它传给 cross_validate 和 GridSearchCV ?

我想我可以定义自己的类来实现 fit 和 predict_proba

1 回复 | 直到 7 年前

1

sds Niraj Rajbhandari 7 年前

不,SKL中没有内置任何东西,不需要编写自定义代码就可以学会做你想做的事情。您可以使用 FeatureUnion Pipeline predict_proba 到 transform 方法。

大概是这样的:

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)

现在你只需打电话 pipe.predict() 所有的事情都会正确地完成。

有关FeatureUnion的更多信息,请参阅我对类似问题的其他回答:-

Use predicted probability of one model to train another model and save as one single model