sklearn pipeline簡介

Pipeline可以將許多算法模型串聯起來，比如將特徵提取、歸一化、分類組織在一起形成一個典型的機器學習問題工作流。主要帶來兩點好處：
1. 直接調用fit和predict方法來對pipeline中的所有算法模型進行訓練和預測。
2. 可以結合grid search對參數進行選擇

下面是一個官方文檔的示例：

>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
>>> estimators = [('reduce_dim', PCA()), ('svm', SVC())]
>>> clf = Pipeline(estimators)
>>> clf
Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None,
    whiten=False)), ('svm', SVC(C=1.0, cache_size=200, class_weight=None,
    coef0=0.0, decision_function_shape=None, degree=3, gamma='auto',
    kernel='rbf', max_iter=-1, probability=False, random_state=None,
    shrinking=True, tol=0.001, verbose=False))])

estimators中定義了兩個模型，一個是PCA、另一個是SVC。

>>> clf.set_params(svm__C=10)

可以通過set_params函數對pipeline中的某個模型設定參數，上面是將svm參數C設置爲10

另外一個例子：

>>> from sklearn import svm
>>> from sklearn.datasets import samples_generator
>>> from sklearn.feature_selection import SelectKBest
>>> from sklearn.feature_selection import f_regression
>>> from sklearn.pipeline import Pipeline
>>> # generate some data to play with
>>> X, y = samples_generator.make_classification(
...     n_informative=5, n_redundant=0, random_state=42)
>>> # ANOVA SVM-C
>>> anova_filter = SelectKBest(f_regression, k=5)
>>> clf = svm.SVC(kernel='linear')
>>> anova_svm = Pipeline([('anova', anova_filter), ('svc', clf)])
>>> # You can set the parameters using the names issued
>>> # For instance, fit using a k of 10 in the SelectKBest
>>> # and a parameter 'C' of the svm
>>> anova_svm.set_params(anova__k=10, svc__C=.1).fit(X, y)
...                                              
Pipeline(steps=[...])
>>> prediction = anova_svm.predict(X)
>>> anova_svm.score(X, y)                        
0.77...
>>> # getting the selected features chosen by anova_filter
>>> anova_svm.named_steps['anova'].get_support()
...
array([ True,  True,  True, False, False,  True, False,  True,  True, True,
       False, False,  True, False,  True, False, False, False, False,
       True], dtype=bool)