python sklearn常用分类算法模型的调用

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'))