sklearn Pipeline 函数用法

0 导入包

from sklearn.pipeline import Pipeline

1 定义

• Pipeline，中文是管道。相当于将一系列的操作封装成一个函数，可以拿这个函数对其他数据进行相同的（流水线）操作。

class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False)

• Pipeline是用 (key, value) 的列表构造的，相当于[(key, value), (key, value), ……, (key, value)]的形式
• key是字符串，表示这个step的名字
• value是是一个estimator的object

2 用例

2.1 构造Pipeline

>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
>>> estimators = [('reduce_dim', PCA()), ('clf', SVC())]
>>> pipe = Pipeline(estimators)
>>> pipe
Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())])

2.2 设置estimator的参数

• 可以用__形式设置参数，第一个参数是estimator的名字，第二个参数是estimator的参数，中间是两个下划线

>>> pipe.set_params(clf__C=10)
Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))])

2.3 完整用例

>>> from sklearn.svm import SVC
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.pipeline import Pipeline
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
...                                                     random_state=0)
>>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
>>> # The pipeline can be used as any other estimator
>>> # and avoids leaking the test set into the train set
>>> pipe.fit(X_train, y_train)
Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])
>>> pipe.score(X_test, y_test)
0.88

2.4 结合GridSearchCV

>>> from sklearn.model_selection import GridSearchCV
>>> param_grid = dict(reduce_dim__n_components=[2, 5, 10],
...                   clf__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)

2.5 其他Method

sklearn官网 Pipeline 链接

……有空的时候再写写其他Merhod