sklearn 下常用模型分类算法简单调用对比（借鉴），SKlearn 中clf模型保存于调回

# coding=gbk  
''''' 
测试
'''  

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__':    
        datafilename = 'softunion20_21.csv'

    data_file = "L:\\Python\\output\\"+datafilename    
    thresh = 0.5    
    model_save_file = 1    
    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))




    import numpy as np
    model = classifiers['LR'](train_x, train_y)
    predict = model.predict(test_x)
    print "LR :"
    print "Predict：",test_x,predict.T


    if model_save_file != None:    
        pickle.dump(model_save, open(model_save_file, 'wb'))  

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 ********************
training took 0.061161s!
precision: 89.16%, recall: 92.50%
accuracy: 90.07%
******************* RF ********************
training took 0.040111s!
precision: 96.39%, recall: 100.00%
accuracy: 98.01%
******************* DT ********************
training took 0.004513s!
precision: 96.20%, recall: 95.00%
accuracy: 95.36%
******************* SVM ********************
training took 0.242145s!
precision: 97.53%, recall: 98.75%
accuracy: 98.01%
******************* SVMCV ********************
Fitting 3 folds for each of 14 candidates, totalling 42 fits
[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    6.8s finished
probability True
verbose False
coef0 0.0
degree 3
tol 0.001
shrinking True
cache_size 200
gamma 0.001
max_iter -1
C 1000
decision_function_shape None
random_state None
class_weight None
kernel rbf
training took 7.434668s!
precision: 98.75%, recall: 98.75%
accuracy: 98.68%
******************* GBDT ********************
training took 0.521916s!
precision: 97.56%, recall: 100.00%
accuracy: 98.68%

from sklearn.externals import joblib

joblib.dump(clf, "train_model.m")

 clf = joblib.load("train_model.m")