# coding=gbk  
''''' 
Created on 2016年6月4日 
 
@author: bryan 
'''  
   
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'))    
 

結果：

reading training and testing data...
******************* NB ********************
training took 0.004986s!
precision: 78.08%, recall: 71.25%
accuracy: 74.17%
******************* KNN ********************
training took 0.017545s!
precision: 97.56%, recall: 100.00%
accuracy: 98.68%
******************* LR ********************

accuracy: 98.68%

轉載自：部落格https://blog.csdn.net/bryan__/article/details/51288953