python sklearn常用分类算法模型的调用

Python  /  管理员 发布于 7年前   392

本文实例为大家分享了python sklearn分类算法模型调用的具体代码，供大家参考，具体内容如下

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')) 

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。