本例展示怎样在一个管道中使用FunctionTransformer.如果你知道你的数据集的第一主成分与分类任务无关,你可以使用FunctionTransformer选取除PCA转化的数据的第一列之外的全部数据.

# coding:utf-8from pylab import *import numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.decomposition import PCAfrom sklearn.pipeline import make_pipelinefrom sklearn.preprocessing import FunctionTransformermyfont = matplotlib.font_manager.FontProperties(fname="Microsoft-Yahei-UI-Light.ttc")mpl.rcParams['axes.unicode_minus'] = Falsedef _generate_vector(shift=0.5, noise=15):    return np.arange(1000) + (np.random.rand(1000) - shift) * noisedef generate_dataset():    """    本数据集是两条斜率为1的直线,一个截距为0,一个截距为100    """    return np.vstack((        np.vstack((            _generate_vector(),            _generate_vector() + 100,        )).T,        np.vstack((            _generate_vector(),            _generate_vector(),        )).T,    )), np.hstack((np.zeros(1000), np.ones(1000)))def all_but_first_column(X):    return X[:, 1:]def drop_first_component(X, y):    """    创建一个具有PCA(主成分分析)和列选择器的管道,    并使用它转换数据集    """    pipeline = make_pipeline(        PCA(), FunctionTransformer(all_but_first_column),    )    X_train, X_test, y_train, y_test = train_test_split(X, y)    pipeline.fit(X_train, y_train)    return pipeline.transform(X_test), y_testif __name__ == '__main__':    X, y = generate_dataset()    lw = 0    plt.figure()    plt.scatter(X[:, 0], X[:, 1], c=y, lw=lw)    plt.title(u"FunctionTransformer选择数据列",fontproperties=myfont)    plt.figure()    X_transformed, y_transformed = drop_first_component(*generate_dataset())    plt.scatter(        X_transformed[:, 0],        np.zeros(len(X_transformed)),        c=y_transformed,        lw=lw,        s=60    )    plt.title(u"FunctionTransformer选择数据列",fontproperties=myfont)    plt.show()