scikit learn 模块 调参 pipeline+girdsearch

#-*- coding: UTF-8 -*-import numpy as npfrom sklearn.pipeline import Pipelinefrom sklearn.linear_model import SGDClassifierfrom sklearn.grid_search import GridSearchCVfrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.feature_extraction.text import TfidfTransformerfrom sklearn.datasets import fetch_20newsgroupsfrom sklearn import metrics#加载数据源categories = ['comp.graphics', 'comp.os.ms-windows.misc','comp.sys.ibm.pc.hardware','comp.sys.mac.hardware','comp.windows.x'];newsgroup_data = fetch_20newsgroups(subset = 'train',categories = categories)X,Y=np.array(newsgroup_data.data),np.array(newsgroup_data.target)Xtrain,Ytrain,Xtest,Ytest =X[0:2400],Y[0:2400],X[2400:],Y[2400:]'''Pipeline主要用于将三个需要串行的模块串在一起，后一个模型处理前一个的结果'''''' vect主要用于去音调、转小写、去停顿词->tdidf主要用于计词频->clf分类模型'''pipeline_obj = Pipeline([('vect',CountVectorizer()),('tfidf',TfidfTransformer()),('clf',SGDClassifier()),])print "pipeline:",'\n', [name for name, _ in pipeline_obj.steps],'\n''''定义需要遍历的所有候选参数的字典，key_name需要用__分隔模型名和模型内部的参数名'''parameters = {    'vect__max_df': (0.5, 0.75),'vect__max_features': (None, 5000, 10000),    'tfidf__use_idf': (True, False),'tfidf__norm': ('l1', 'l2'),    'clf__alpha': (0.00001, 0.000001), 'clf__n_iter': (10, 50) }print "parameters:",'\n',parameters,'\n''''GridSearchCV用于寻找vectorizer词频统计, tfidftransformer特征变换和SGD classifier分类模型的最优参数'''grid_search = GridSearchCV( pipeline_obj, parameters, n_jobs = 1,verbose=1 )print 'grid_search','\n',grid_search,'\n' #输出所有参数名及参数候选值grid_search.fit(Xtrain,Ytrain),'\n'#遍历执行候选参数，寻找最优参数best_parameters = dict(grid_search.best_estimator_.get_params())#get实例中的最优参数for param_name in sorted(parameters.keys()):    print("\t%s: %r" % (param_name, best_parameters[param_name])),'\n'#输出最有参数结果'''将pipeline_obj实例中的参数重写为最优结果'''# pipeline_obj.set_params(clf__alpha = 1e-05,clf__n_iter = 50,tfidf__use_idf = True,vect__max_df = 0.5,vect__max_features = None)print pipeline_obj.named_steps'''用最优参数训练模型'''pipeline_obj.fit(Xtrain,Ytrain)pred = pipeline_obj.predict(Xtrain)print '\n',metrics.classification_report(Ytrain,pred)pred = pipeline_obj.predict(Xtest)print '\n',metrics.classification_report(Ytest,pred)

参考：

# http://blog.csdn.net/mmc2015/article/details/46991465
# http://blog.csdn.net/abcjennifer/article/details/23884761
# http://scikit-learn.org/stable/modules/pipeline.html
# http://blog.csdn.net/yuanyu5237/article/details/44278759