python sklearn 分类算法简单调用

 scikit-learn已经包含在Anaconda中。也可以在官方下载源码包进行安装。本文代码里封装了如下机器学习算法，我们修改数据加载函数，即可一键测试：

# 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'))  

测试结果如下：

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%