python中scikit-learn机器的示例分析

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代码：

# -*- coding: utf-8 -*-   import numpy from sklearn import metrics from sklearn.svm import LinearSVC from sklearn.naive_bayes import MultinomialNB from sklearn import linear_model from sklearn.datasets import load_iris from sklearn.cross_validation import train_test_split from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn import cross_validation from sklearn import preprocessing #import iris_data   def load_data():   iris = load_iris()   x, y = iris.data, iris.target   x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)   return x_train,y_train,x_test,y_test   def train_clf3(train_data, train_tags):   clf = LinearSVC(C=1100.0)#default with 'rbf'    clf.fit(train_data,train_tags)   return clf   def train_clf(train_data, train_tags):   clf = MultinomialNB(alpha=0.01)   print numpy.asarray(train_tags)   clf.fit(train_data, numpy.asarray(train_tags))   return clf   def evaluate(actual, pred):   m_precision = metrics.precision_score(actual, pred)   m_recall = metrics.recall_score(actual, pred)   print 'precision:{0:.3f}'.format(m_precision)   print 'recall:{0:0.3f}'.format(m_recall)   print 'f1-score:{0:.8f}'.format(metrics.f1_score(actual,pred));   x_train,y_train,x_test,y_test = load_data()   clf = train_clf(x_train, y_train)   pred = clf.predict(x_test) evaluate(numpy.asarray(y_test), pred) print metrics.classification_report(y_test, pred)     使用自定义数据 # coding: utf-8   import numpy from sklearn import metrics from sklearn.feature_extraction.text import HashingVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.feature_extraction.text import CountVectorizer,TfidfTransformer from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.svm import LinearSVC import codecs from sklearn.ensemble import RandomForestClassifier from sklearn import cross_validation from sklearn import linear_model   train_corpus = [    '我们 我们 好孩子 认证 。 就是',    '我们 好孩子 认证 。 中国',    '我们 好孩子 认证 。 孤独',    '我们 好孩子 认证 。',  ]   test_corpus = [    '我 菲律宾 韩国',    '我们 好孩子 认证 。 中国',  ]   def input_data(train_file, test_file):   train_words = []   train_tags = []   test_words = []   test_tags = []   f1 = codecs.open(train_file,'r','utf-8','ignore')   for line in f1:     tks = line.split(':', 1)     word_list = tks[1]     word_array = word_list[1:(len(word_list)-3)].split(", ")     train_words.append(" ".join(word_array))     train_tags.append(tks[0])   f2 = codecs.open(test_file,'r','utf-8','ignore')   for line in f2:     tks = line.split(':', 1)     word_list = tks[1]     word_array = word_list[1:(len(word_list)-3)].split(", ")     test_words.append(" ".join(word_array))     test_tags.append(tks[0])   return train_words, train_tags, test_words, test_tags     def vectorize(train_words, test_words):   #v = HashingVectorizer(n_features=25000, non_negative=True)   v = HashingVectorizer(non_negative=True)   #v = CountVectorizer(min_df=1)   train_data = v.fit_transform(train_words)   test_data = v.fit_transform(test_words)   return train_data, test_data   def vectorize1(train_words, test_words):   tv = TfidfVectorizer(sublinear_tf = False,use_idf=True);   train_data = tv.fit_transform(train_words);   tv2 = TfidfVectorizer(vocabulary = tv.vocabulary_);   test_data = tv2.fit_transform(test_words);   return train_data, test_data    def vectorize2(train_words, test_words):   count_v1= CountVectorizer(stop_words = 'english', max_df = 0.5);    counts_train = count_v1.fit_transform(train_words);        count_v2 = CountVectorizer(vocabulary=count_v1.vocabulary_);   counts_test = count_v2.fit_transform(test_words);       tfidftransformer = TfidfTransformer();       train_data = tfidftransformer.fit(counts_train).transform(counts_train);    test_data = tfidftransformer.fit(counts_test).transform(counts_test);   return train_data, test_data   def evaluate(actual, pred):   m_precision = metrics.precision_score(actual, pred)   m_recall = metrics.recall_score(actual, pred)   print 'precision:{0:.3f}'.format(m_precision)   print 'recall:{0:0.3f}'.format(m_recall)   print 'f1-score:{0:.8f}'.format(metrics.f1_score(actual,pred));     def train_clf(train_data, train_tags):   clf = MultinomialNB(alpha=0.01)   clf.fit(train_data, numpy.asarray(train_tags))   return clf     def train_clf1(train_data, train_tags):   #KNN Classifier   clf = KNeighborsClassifier()#default with k=5    clf.fit(train_data, numpy.asarray(train_tags))    return clf   def train_clf2(train_data, train_tags):   clf = linear_model.LogisticRegression(C=1e5)    clf.fit(train_data,train_tags)   return clf   def train_clf3(train_data, train_tags):   clf = LinearSVC(C=1100.0)#default with 'rbf'    clf.fit(train_data,train_tags)   return clf   def train_clf4(train_data, train_tags):   """   随机森林，不可使用稀疏矩阵   """   clf = RandomForestClassifier(n_estimators=10)   clf.fit(train_data.todense(),train_tags)   return clf   #使用codecs逐行读取 def codecs_read_label_line(filename):   label_list=[]   f = codecs.open(filename,'r','utf-8','ignore')   line = f.readline()   while line:     #label_list.append(line[0:len(line)-2])     label_list.append(line[0:len(line)-1])     line = f.readline()   f.close()   return label_list   def save_test_features(test_url, test_label):   test_feature_list = codecs_read_label_line('test.dat')   fw = open('test_labeded.dat',"w+")      for (url,label) in zip(test_feature_list,test_label):     fw.write(url+'\t'+label)     fw.write('\n')   fw.close()   def main():   train_file = u'..\\file\\py_train.txt'   test_file = u'..\\file\\py_test.txt'   train_words, train_tags, test_words, test_tags = input_data(train_file, test_file)   #print len(train_words), len(train_tags), len(test_words), len(test_words),       train_data, test_data = vectorize1(train_words, test_words)   print type(train_data)   print train_data.shape   print test_data.shape   print test_data[0].shape   print numpy.asarray(test_data[0])      clf = train_clf3(train_data, train_tags)      scores = cross_validation.cross_val_score(   clf, train_data, train_tags, cv=5, scoring="f1_weighted")   print scores     #predicted = cross_validation.cross_val_predict(clf, train_data,train_tags, cv=5)     '''      '''   pred = clf.predict(test_data)   error_list=[]   for (true_tag,predict_tag) in zip(test_tags,pred):     if true_tag != predict_tag:       print true_tag,predict_tag       error_list.append(true_tag+' '+predict_tag)   print len(error_list)   evaluate(numpy.asarray(test_tags), pred)   '''   #输出打标签结果   test_feature_list = codecs_read_label_line('test.dat')   save_test_features(test_feature_list, pred)   '''      if __name__ == '__main__':   main()

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