python中scikit-learn机器代码实例

我们给大家带来了关于学习python中scikit-learn机器代码的相关具体实例，以下就是全部代码内容：

# -*- coding: utf-8 -*- import numpyfrom sklearn import metricsfrom sklearn.svm import LinearSVCfrom sklearn.naive_bayes import MultinomialNBfrom sklearn import linear_modelfrom sklearn.datasets import load_irisfrom sklearn.cross_validation import train_test_splitfrom sklearn.preprocessing import OneHotEncoder, StandardScalerfrom sklearn import cross_validationfrom 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 numpyfrom sklearn import metricsfrom sklearn.feature_extraction.text import HashingVectorizerfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.feature_extraction.text import CountVectorizer,TfidfTransformerfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.svm import SVCfrom sklearn.svm import LinearSVCimport codecsfrom sklearn.ensemble import RandomForestClassifierfrom sklearn import cross_validationfrom 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()