实现对'NB', 'KNN', 'LR', 'RF', 'DT', 'SVM','SVMCV', 'GBDT'模型的简单调用。# coding=gbk import time  from sklearn import metrics  import pickle as pickle  import pandas as pd   # Multinomial Naive Bayes Classifier  def naive_bayes_classifier(train_x, train_y):      from sklearn.naive_bayes import MultinomialNB      model = MultinomialNB(alpha=0.01)      model.fit(train_x, train_y)      return model      # KNN Classifier  def knn_classifier(train_x, train_y):      from sklearn.neighbors import KNeighborsClassifier      model = KNeighborsClassifier()      model.fit(train_x, train_y)      return model      # Logistic Regression Classifier  def logistic_regression_classifier(train_x, train_y):      from sklearn.linear_model import LogisticRegression      model = LogisticRegression(penalty='l2')      model.fit(train_x, train_y)      return model      # Random Forest Classifier  def random_forest_classifier(train_x, train_y):      from sklearn.ensemble import RandomForestClassifier      model = RandomForestClassifier(n_estimators=8)      model.fit(train_x, train_y)      return model      # Decision Tree Classifier  def decision_tree_classifier(train_x, train_y):      from sklearn import tree      model = tree.DecisionTreeClassifier()      model.fit(train_x, train_y)      return model      # GBDT(Gradient Boosting Decision Tree) Classifier  def gradient_boosting_classifier(train_x, train_y):      from sklearn.ensemble import GradientBoostingClassifier      model = GradientBoostingClassifier(n_estimators=200)      model.fit(train_x, train_y)      return model      # SVM Classifier  def svm_classifier(train_x, train_y):      from sklearn.svm import SVC      model = SVC(kernel='rbf', probability=True)      model.fit(train_x, train_y)      return model    # SVM Classifier using cross validation  def svm_cross_validation(train_x, train_y):      from sklearn.grid_search import GridSearchCV      from sklearn.svm import SVC      model = SVC(kernel='rbf', probability=True)      param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]}      grid_search = GridSearchCV(model, param_grid, n_jobs = 1, verbose=1)      grid_search.fit(train_x, train_y)      best_parameters = grid_search.best_estimator_.get_params()      for para, val in list(best_parameters.items()):          print(para, val)      model = SVC(kernel='rbf', C=best_parameters['C'], gamma=best_parameters['gamma'], probability=True)      model.fit(train_x, train_y)      return model    def read_data(data_file):      data = pd.read_csv(data_file)    train = data[:int(len(data)*0.9)]    test = data[int(len(data)*0.9):]    train_y = train.label    train_x = train.drop('label', axis=1)    test_y = test.label    test_x = test.drop('label', axis=1)    return train_x, train_y, test_x, test_y      if __name__ == '__main__':      data_file = "H:\\Research\\data\\trainCG.csv"      thresh = 0.5      model_save_file = None      model_save = {}         test_classifiers = ['NB', 'KNN', 'LR', 'RF', 'DT', 'SVM','SVMCV', 'GBDT']      classifiers = {'NB':naive_bayes_classifier,                     'KNN':knn_classifier,                     'LR':logistic_regression_classifier,                     'RF':random_forest_classifier,                     'DT':decision_tree_classifier,                    'SVM':svm_classifier,                  'SVMCV':svm_cross_validation,                   'GBDT':gradient_boosting_classifier      }            print('reading training and testing data...')      train_x, train_y, test_x, test_y = read_data(data_file)            for classifier in test_classifiers:          print('******************* %s ********************' % classifier)          start_time = time.time()          model = classifiers[classifier](train_x, train_y)          print('training took %fs!' % (time.time() - start_time))          predict = model.predict(test_x)          if model_save_file != None:              model_save[classifier] = model          precision = metrics.precision_score(test_y, predict)          recall = metrics.recall_score(test_y, predict)          print('precision: %.2f%%, recall: %.2f%%' % (100 * precision, 100 * recall))          accuracy = metrics.accuracy_score(test_y, predict)          print('accuracy: %.2f%%' % (100 * accuracy))         if model_save_file != None:          pickle.dump(model_save, open(model_save_file, 'wb'))