python sklearn 分类算法简单调用（借鉴）

本文主要是介绍python sklearn 分类算法简单调用（借鉴），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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：

数据为近红外测试猕猴桃软硬和时间差异的数据，可以作为分类软硬以及前后时间差的分类。

[python]  view plain copy  
 # 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__':    
         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'))    

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%