sklearn中pipeline的用法和FeatureUnion
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一、pipeline的用法
pipeline可以用于把多个estimators级联成一个estimator,这么 做的原因是考虑了数据处理过程中一系列前后相继的固定流程,比如feature selection->normalization->classification
pipeline提供了两种服务:
- Convenience:只需要调用一次fit和predict就可以在数据集上训练一组estimators
- Joint parameter selection可以把grid search 用在pipeline中所有的estimators参数的参数组合上面
注意:Pipleline中最后一个之外的所有estimators都必须是变换器(transformers),最后一个estimator可以是任意类型(transformer,classifier,regresser)
如果最后一个estimator是个分类器,则整个pipeline就可以作为分类器使用,如果最后一个estimator是个聚类器,则整个pipeline就可以作为聚类器使用。
from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline estimator=[(‘pca‘, PCA()), (‘clf‘, LogisticRegression()) ] pipe=Pipeline(estimator) print(pipe) #Pipeline(steps=[(‘pca‘, PCA(copy=True, iterated_power=‘auto‘, n_components=None, random_state=None,svd_solver=‘auto‘, tol=0.0, whiten=False)), (‘clf‘, LogisticRegression(C=1.0, class_weight=None, dual=False,fit_intercept=True,intercept_scaling=1, max_iter=100, multi_class=‘ovr‘, n_jobs=1,penalty=‘l2‘, random_state=None, solver=‘liblinear‘, tol=0.0001,verbose=0, warm_start=False))]) print(pipe.steps[0]) #(‘pca‘, PCA(copy=True, iterated_power=‘auto‘, n_components=None, random_state=None, svd_solver=‘auto‘, tol=0.0, whiten=False)) print(pipe.named_steps[‘pca‘]) #PCA(copy=True, iterated_power=‘auto‘, n_components=None, random_state=None, svd_solver=‘auto‘, tol=0.0, whiten=False)
在pipeline中estimator的参数通过使用<estimator>__<parameter>语法来获取
#修改参数并打印输出 print(pipe.set_params(clf__C=10)) #Pipeline(steps=[(‘pca‘, PCA(copy=True, iterated_power=‘auto‘, n_components=None, random_state=None,svd_solver=‘auto‘, tol=0.0, whiten=False)), (‘clf‘, LogisticRegression(C=10, class_weight=None, dual=False,fit_intercept=True,intercept_scaling=1, max_iter=100, multi_class=‘ovr‘, n_jobs=1,penalty=‘l2‘, random_state=None, solver=‘liblinear‘, tol=0.0001,verbose=0, warm_start=False))])
既然有参数的存在,就可以使用网格搜索方法来调节参数
from sklearn.model_selection import GridSearchCV params=dict(pca__n_components=[2,5,10],clf__C=[0,1,10,100]) grid_research=GridSearchCV(pipe,param_grid=params)
单个阶段(step)可以用参数替换,而且非最后阶段还可以将其设置为None来忽略:
from sklearn.linear_model import LogisticRegression params=dict(pca=[None,PCA(5),PCA(10)],clf=[SVC(),LogisticRegression()], clf_C=[0.1,10,100]) grid_research=GridSearchCV(pipe,param_grid=params)
函数make_pipeline是一个构造pipeline的简短工具,他接受可变数量的estimators并返回一个pipeline,每个estimator的名称自动填充。
from sklearn.pipeline import make_pipeline from sklearn.naive_bayes import MultinomialNB from sklearn.preprocessing import Binarizer print(make_pipeline(Binarizer(),MultinomialNB())) #Pipeline(steps=[(‘binarizer‘, Binarizer(copy=True, threshold=0.0)), (‘multinomialnb‘, MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])
FeatureUnion:composite(组合)feature spaces
FeatureUnion把若干个transformer objects组合成一个新的transformer,这个新的transformer组合了他们的输出,一个FeatureUnion对象接受一个transformer对象列表
二、FeatureUnion 的用法
from sklearn.pipeline import FeatureUnion from sklearn.decomposition import PCA from sklearn.decomposition import KernelPCA estimators=[(‘linear_pca‘,PCA()),(‘kernel_pca‘,KernelPCA())] combined=FeatureUnion(estimators) print(combined) #FeatureUnion(n_jobs=1, transformer_list=[(‘linear_pca‘, PCA(copy=True, iterated_power=‘auto‘, n_components=None, random_state=None, svd_solver=‘auto‘, tol=0.0, whiten=False)), (‘kernel_pca‘, KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver=‘auto‘, fit_inverse_transform=False, gamma=None, kernel=‘linear‘, kernel_params=None, max_iter=None, n_components=None, n_jobs=1, random_state=None, remove_zero_eig=False, tol=0))],transformer_weights=None)
与pipeline类似,feature union也有一种比较简单的构造方法:make_union,不需要显示的给每个estimator指定名称。
Featu热Union设置参数
#修改参数 print(combined.set_params(kernel_pca=None)) #FeatureUnion(n_jobs=1,transformer_list=[(‘linear_pca‘, PCA(copy=True, iterated_power=‘auto‘, n_components=None, random_state=None,svd_solver=‘auto‘, tol=0.0, whiten=False)), (‘kernel_pca‘, None)],transformer_weights=None)
另外一篇讲pipleline不错的文章:http://blog.csdn.net/lanchunhui/article/details/50521648
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