sklearn分类算法（逻辑回归、朴素贝叶斯、K近邻、支持向量机 、决策树、随机森林 ）的使用

        scikit-learn机器学习的分类算法包括逻辑回归、朴素贝叶斯、KNN、支持向量机、决策树和随机森林等。这些模块的调用形式基本一致，训练用fit方法，预测用predict方法。用joblib.dump方法可以保存训练的模型，用joblib.load方法可以载入模型。

测试程序。测试数据采用小麦种子数据集 （seeds）。（注意，该数据集有个别数据用多个\t分割，执行前要把多余的\t删除，或者使用正则表达式的re.split('\s')）

       小麦数据集合说明：

# -*- coding: utf-8 -*-  
  
import numpy as np    
from sklearn.cross_validation import KFold  
from sklearn.linear_model import LogisticRegression  
from sklearn.naive_bayes import GaussianNB  
from sklearn.neighbors import KNeighborsClassifier   
from sklearn import svm  
from sklearn.tree import DecisionTreeClassifier  
from sklearn.ensemble import RandomForestClassifier  
  
feature_names = [    
    'area',    
    'perimeter',    
    'compactness',    
    'length of kernel',    
    'width of kernel',    
    'asymmetry coefficien',    
    'length of kernel groove',    
]    
  
COLOUR_FIGURE = False    
  
def load_csv_data(filename):     #读取文件，文件最后一列当成标签
    data = []    
    labels = []    
    datafile = open(filename)    
    for line in datafile:    
        fields = line.strip().split('\t')    
        data.append([float(field) for field in fields[:-1]])    
        labels.append(fields[-1])     #文件最后一列当成标签
    data = np.array(data)    
    labels = np.array(labels)    
    return data, labels    
      
def accuracy(test_labels, pred_lables):    
    correct = np.sum(test_labels == pred_lables)  #相等   
    n = len(test_labels)    
    return float(correct) / n    
  
#------------------------------------------------------------------------------  
#K近邻  
#------------------------------------------------------------------------------  
def testKNN(features, labels):  
    kf = KFold(len(features), n_folds=3, shuffle=True) #len(features)这个参数的意思是把（0，len(features)-1）之间的序列随机拆分成3份。
    clf = KNeighborsClassifier(n_neighbors=3)    
    result_set = []
    for train, test in kf:
        clf.fit(features[train], labels[train])
        result=clf.predict(features[test])
        result_set.append((result, test))
        
    score=[]
    for result in result_set:
        print (labels[result[1]], result[0])
        a=accuracy(labels[result[1]], result[0])
        score.append(a)
    print(score)

#------------------------------------------------------------------------------  
#逻辑回归  
#------------------------------------------------------------------------------  
def testLR(features, labels):  
    kf = KFold(len(features), n_folds=3, shuffle=True)    
    clf = LogisticRegression()  
    result_set = [(clf.fit(features[train], labels[train]).predict(features[test]), test) for train, test in kf]    
    score = [accuracy(labels[result[1]], result[0]) for result in result_set]    
    print(score)  

  
#------------------------------------------------------------------------------  
#朴素贝叶斯  
#------------------------------------------------------------------------------  
def testNaiveBayes(features, labels):  
    kf = KFold(len(features), n_folds=3, shuffle=True)    
    clf = GaussianNB()  
    result_set = [(clf.fit(features[train], labels[train]).predict(features[test]), test) for train, test in kf]    
    score = [accuracy(labels[result[1]], result[0]) for result in result_set]    
    print(score)  
  
               
#------------------------------------------------------------------------------  
#--- 支持向量机  
#------------------------------------------------------------------------------  
def testSVM(features, labels):  
    kf = KFold(len(features), n_folds=3, shuffle=True)    
    clf = svm.SVC()  
    result_set = [(clf.fit(features[train], labels[train]).predict(features[test]), test) for train, test in kf]    
    score = [accuracy(labels[result[1]], result[0]) for result in result_set]    
    print(score)  

  
#------------------------------------------------------------------------------  
#--- 决策树  
#------------------------------------------------------------------------------  
def testDecisionTree(features, labels):  
    kf = KFold(len(features), n_folds=3, shuffle=True)    
    clf = DecisionTreeClassifier()  
    result_set = [(clf.fit(features[train], labels[train]).predict(features[test]), test) for train, test in kf]    
    score = [accuracy(labels[result[1]], result[0]) for result in result_set]    
    print(score)  

      
#------------------------------------------------------------------------------  
#--- 随机森林  
#------------------------------------------------------------------------------  
def testRandomForest(features, labels):  
    kf = KFold(len(features), n_folds=3, shuffle=True)    
    clf = RandomForestClassifier()  
    result_set = [(clf.fit(features[train], labels[train]).predict(features[test]), test) for train, test in kf]    
    score = [accuracy(labels[result[1]], result[0]) for result in result_set]    
    print(score)  
  
      
if __name__ == '__main__':  
#     features, labels = load_csv_data('/Users/yumh/Desktop/重要/第二课课程代码及数据文件/data./data/seeds_dataset.txt')
    features, labels = load_csv_data('/Users/yumh/Desktop/重要/第二课课程代码及数据文件/data/seeds_dataset.txt') 
 #   print(features)  
 #   print(labels)
      
    print('LogisticRegression: \r')  
    testLR(features, labels)  
      
    print('GaussianNB: \r')  
    testNaiveBayes(features, labels)  
      
    print('KNN: \r')  
    testKNN(features, labels)  
      
    print('SVM: \r')  
    testSVM(features, labels)  
      
    print('Decision Tree: \r')  
    testDecisionTree(features, labels)  
      
    print('Random Forest: \r')  
    testRandomForest(features, labels)

实验数据：seeds_dataset.txt

15.26	14.84	0.871	5.763	3.312	2.221	5.22	1
14.88	14.57	0.8811	5.554	3.333	1.018	4.956	1
14.29	14.09	0.905	5.291	3.337	2.699	4.825	1
13.84	13.94	0.8955	5.324	3.379	2.259	4.805	1
16.14	14.99	0.9034	5.658	3.562	1.355	5.175	1
14.38	14.21	0.8951	5.386	3.312	2.462	4.956	1
14.69	14.49	0.8799	5.563	3.259	3.586	5.219	1
14.11	14.1	0.8911	5.42	3.302	2.7	5	1
16.63	15.46	0.8747	6.053	3.465	2.04	5.877	1
16.44	15.25	0.888	5.884	3.505	1.969	5.533	1
15.26	14.85	0.8696	5.714	3.242	4.543	5.314	1
14.03	14.16	0.8796	5.438	3.201	1.717	5.001	1
13.89	14.02	0.888	5.439	3.199	3.986	4.738	1
13.78	14.06	0.8759	5.479	3.156	3.136	4.872	1
13.74	14.05	0.8744	5.482	3.114	2.932	4.825	1
14.59	14.28	0.8993	5.351	3.333	4.185	4.781	1
13.99	13.83	0.9183	5.119	3.383	5.234	4.781	1
15.69	14.75	0.9058	5.527	3.514	1.599	5.046	1
14.7	14.21	0.9153	5.205	3.466	1.767	4.649	1
12.72	13.57	0.8686	5.226	3.049	4.102	4.914	1
14.16	14.4	0.8584	5.658	3.129	3.072	5.176	1
14.11	14.26	0.8722	5.52	3.168	2.688	5.219	1
15.88	14.9	0.8988	5.618	3.507	0.7651	5.091	1
12.08	13.23	0.8664	5.099	2.936	1.415	4.961	1
15.01	14.76	0.8657	5.789	3.245	1.791	5.001	1
16.19	15.16	0.8849	5.833	3.421	0.903	5.307	1
13.02	13.76	0.8641	5.395	3.026	3.373	4.825	1
12.74	13.67	0.8564	5.395	2.956	2.504	4.869	1
14.11	14.18	0.882	5.541	3.221	2.754	5.038	1
13.45	14.02	0.8604	5.516	3.065	3.531	5.097	1
13.16	13.82	0.8662	5.454	2.975	0.8551	5.056	1
15.49	14.94	0.8724	5.757	3.371	3.412	5.228	1
14.09	14.41	0.8529	5.717	3.186	3.92	5.299	1
13.94	14.17	0.8728	5.585	3.15	2.124	5.012	1
15.05	14.68	0.8779	5.712	3.328	2.129	5.36	1
16.12	15	0.9	5.709	3.485	2.27	5.443	1
16.2	15.27	0.8734	5.826	3.464	2.823	5.527	1
17.08	15.38	0.9079	5.832	3.683	2.956	5.484	1
14.8	14.52	0.8823	5.656	3.288	3.112	5.309	1
14.28	14.17	0.8944	5.397	3.298	6.685	5.001	1
13.54	13.85	0.8871	5.348	3.156	2.587	5.178	1
13.5	13.85	0.8852	5.351	3.158	2.249	5.176	1
13.16	13.55	0.9009	5.138	3.201	2.461	4.783	1
15.5	14.86	0.882	5.877	3.396	4.711	5.528	1
15.11	14.54	0.8986	5.579	3.462	3.128	5.18	1
13.8	14.04	0.8794	5.376	3.155	1.56	4.961	1
15.36	14.76	0.8861	5.701	3.393	1.367	5.132	1
14.99	14.56	0.8883	5.57	3.377	2.958	5.175	1
14.79	14.52	0.8819	5.545	3.291	2.704	5.111	1
14.86	14.67	0.8676	5.678	3.258	2.129	5.351	1
14.43	14.4	0.8751	5.585	3.272	3.975	5.144	1
15.78	14.91	0.8923	5.674	3.434	5.593	5.136	1
14.49	14.61	0.8538	5.715	3.113	4.116	5.396	1
14.33	14.28	0.8831	5.504	3.199	3.328	5.224	1
14.52	14.6	0.8557	5.741	3.113	1.481	5.487	1
15.03	14.77	0.8658	5.702	3.212	1.933	5.439	1
14.46	14.35	0.8818	5.388	3.377	2.802	5.044	1
14.92	14.43	0.9006	5.384	3.412	1.142	5.088	1
15.38	14.77	0.8857	5.662	3.419	1.999	5.222	1
12.11	13.47	0.8392	5.159	3.032	1.502	4.519	1
11.42	12.86	0.8683	5.008	2.85	2.7	4.607	1
11.23	12.63	0.884	4.902	2.879	2.269	4.703	1
12.36	13.19	0.8923	5.076	3.042	3.22	4.605	1
13.22	13.84	0.868	5.395	3.07	4.157	5.088	1
12.78	13.57	0.8716	5.262	3.026	1.176	4.782	1
12.88	13.5	0.8879	5.139	3.119	2.352	4.607	1
14.34	14.37	0.8726	5.63	3.19	1.313	5.15	1
14.01	14.29	0.8625	5.609	3.158	2.217	5.132	1
14.37	14.39	0.8726	5.569	3.153	1.464	5.3	1
12.73	13.75	0.8458	5.412	2.882	3.533	5.067	1
17.63	15.98	0.8673	6.191	3.561	4.076	6.06	2
16.84	15.67	0.8623	5.998	3.484	4.675	5.877	2
17.26	15.73	0.8763	5.978	3.594	4.539	5.791	2
19.11	16.26	0.9081	6.154	3.93	2.936	6.079	2
16.82	15.51	0.8786	6.017	3.486	4.004	5.841	2
16.77	15.62	0.8638	5.927	3.438	4.92	5.795	2
17.32	15.91	0.8599	6.064	3.403	3.824	5.922	2
20.71	17.23	0.8763	6.579	3.814	4.451	6.451	2
18.94	16.49	0.875	6.445	3.639	5.064	6.362	2
17.12	15.55	0.8892	5.85	3.566	2.858	5.746	2
16.53	15.34	0.8823	5.875	3.467	5.532	5.88	2
18.72	16.19	0.8977	6.006	3.857	5.324	5.879	2
20.2	16.89	0.8894	6.285	3.864	5.173	6.187	2
19.57	16.74	0.8779	6.384	3.772	1.472	6.273	2
19.51	16.71	0.878	6.366	3.801	2.962	6.185	2
18.27	16.09	0.887	6.173	3.651	2.443	6.197	2
18.88	16.26	0.8969	6.084	3.764	1.649	6.109	2
18.98	16.66	0.859	6.549	3.67	3.691	6.498	2
21.18	17.21	0.8989	6.573	4.033	5.78	6.231	2
20.88	17.05	0.9031	6.45	4.032	5.016	6.321	2
20.1	16.99	0.8746	6.581	3.785	1.955	6.449	2
18.76	16.2	0.8984	6.172	3.796	3.12	6.053	2
18.81	16.29	0.8906	6.272	3.693	3.237	6.053	2
18.59	16.05	0.9066	6.037	3.86	6.001	5.877	2
18.36	16.52	0.8452	6.666	3.485	4.933	6.448	2
16.87	15.65	0.8648	6.139	3.463	3.696	5.967	2
19.31	16.59	0.8815	6.341	3.81	3.477	6.238	2
18.98	16.57	0.8687	6.449	3.552	2.144	6.453	2
18.17	16.26	0.8637	6.271	3.512	2.853	6.273	2
18.72	16.34	0.881	6.219	3.684	2.188	6.097	2
16.41	15.25	0.8866	5.718	3.525	4.217	5.618	2
17.99	15.86	0.8992	5.89	3.694	2.068	5.837	2
19.46	16.5	0.8985	6.113	3.892	4.308	6.009	2
19.18	16.63	0.8717	6.369	3.681	3.357	6.229	2
18.95	16.42	0.8829	6.248	3.755	3.368	6.148	2
18.83	16.29	0.8917	6.037	3.786	2.553	5.879	2
18.85	16.17	0.9056	6.152	3.806	2.843	6.2	2
17.63	15.86	0.88	6.033	3.573	3.747	5.929	2
19.94	16.92	0.8752	6.675	3.763	3.252	6.55	2
18.55	16.22	0.8865	6.153	3.674	1.738	5.894	2
18.45	16.12	0.8921	6.107	3.769	2.235	5.794	2
19.38	16.72	0.8716	6.303	3.791	3.678	5.965	2
19.13	16.31	0.9035	6.183	3.902	2.109	5.924	2
19.14	16.61	0.8722	6.259	3.737	6.682	6.053	2
20.97	17.25	0.8859	6.563	3.991	4.677	6.316	2
19.06	16.45	0.8854	6.416	3.719	2.248	6.163	2
18.96	16.2	0.9077	6.051	3.897	4.334	5.75	2
19.15	16.45	0.889	6.245	3.815	3.084	6.185	2
18.89	16.23	0.9008	6.227	3.769	3.639	5.966	2
20.03	16.9	0.8811	6.493	3.857	3.063	6.32	2
20.24	16.91	0.8897	6.315	3.962	5.901	6.188	2
18.14	16.12	0.8772	6.059	3.563	3.619	6.011	2
16.17	15.38	0.8588	5.762	3.387	4.286	5.703	2
18.43	15.97	0.9077	5.98	3.771	2.984	5.905	2
15.99	14.89	0.9064	5.363	3.582	3.336	5.144	2
18.75	16.18	0.8999	6.111	3.869	4.188	5.992	2
18.65	16.41	0.8698	6.285	3.594	4.391	6.102	2
17.98	15.85	0.8993	5.979	3.687	2.257	5.919	2
20.16	17.03	0.8735	6.513	3.773	1.91	6.185	2
17.55	15.66	0.8991	5.791	3.69	5.366	5.661	2
18.3	15.89	0.9108	5.979	3.755	2.837	5.962	2
18.94	16.32	0.8942	6.144	3.825	2.908	5.949	2
15.38	14.9	0.8706	5.884	3.268	4.462	5.795	2
16.16	15.33	0.8644	5.845	3.395	4.266	5.795	2
15.56	14.89	0.8823	5.776	3.408	4.972	5.847	2
15.38	14.66	0.899	5.477	3.465	3.6	5.439	2
17.36	15.76	0.8785	6.145	3.574	3.526	5.971	2
15.57	15.15	0.8527	5.92	3.231	2.64	5.879	2
15.6	15.11	0.858	5.832	3.286	2.725	5.752	2
16.23	15.18	0.885	5.872	3.472	3.769	5.922	2
13.07	13.92	0.848	5.472	2.994	5.304	5.395	3
13.32	13.94	0.8613	5.541	3.073	7.035	5.44	3
13.34	13.95	0.862	5.389	3.074	5.995	5.307	3
12.22	13.32	0.8652	5.224	2.967	5.469	5.221	3
11.82	13.4	0.8274	5.314	2.777	4.471	5.178	3
11.21	13.13	0.8167	5.279	2.687	6.169	5.275	3
11.43	13.13	0.8335	5.176	2.719	2.221	5.132	3
12.49	13.46	0.8658	5.267	2.967	4.421	5.002	3
12.7	13.71	0.8491	5.386	2.911	3.26	5.316	3
10.79	12.93	0.8107	5.317	2.648	5.462	5.194	3
11.83	13.23	0.8496	5.263	2.84	5.195	5.307	3
12.01	13.52	0.8249	5.405	2.776	6.992	5.27	3
12.26	13.6	0.8333	5.408	2.833	4.756	5.36	3
11.18	13.04	0.8266	5.22	2.693	3.332	5.001	3
11.36	13.05	0.8382	5.175	2.755	4.048	5.263	3
11.19	13.05	0.8253	5.25	2.675	5.813	5.219	3
11.34	12.87	0.8596	5.053	2.849	3.347	5.003	3
12.13	13.73	0.8081	5.394	2.745	4.825	5.22	3
11.75	13.52	0.8082	5.444	2.678	4.378	5.31	3
11.49	13.22	0.8263	5.304	2.695	5.388	5.31	3
12.54	13.67	0.8425	5.451	2.879	3.082	5.491	3
12.02	13.33	0.8503	5.35	2.81	4.271	5.308	3
12.05	13.41	0.8416	5.267	2.847	4.988	5.046	3
12.55	13.57	0.8558	5.333	2.968	4.419	5.176	3
11.14	12.79	0.8558	5.011	2.794	6.388	5.049	3
12.1	13.15	0.8793	5.105	2.941	2.201	5.056	3
12.44	13.59	0.8462	5.319	2.897	4.924	5.27	3
12.15	13.45	0.8443	5.417	2.837	3.638	5.338	3
11.35	13.12	0.8291	5.176	2.668	4.337	5.132	3
11.24	13	0.8359	5.09	2.715	3.521	5.088	3
11.02	13	0.8189	5.325	2.701	6.735	5.163	3
11.55	13.1	0.8455	5.167	2.845	6.715	4.956	3
11.27	12.97	0.8419	5.088	2.763	4.309	5	3
11.4	13.08	0.8375	5.136	2.763	5.588	5.089	3
10.83	12.96	0.8099	5.278	2.641	5.182	5.185	3
10.8	12.57	0.859	4.981	2.821	4.773	5.063	3
11.26	13.01	0.8355	5.186	2.71	5.335	5.092	3
10.74	12.73	0.8329	5.145	2.642	4.702	4.963	3
11.48	13.05	0.8473	5.18	2.758	5.876	5.002	3
12.21	13.47	0.8453	5.357	2.893	1.661	5.178	3
11.41	12.95	0.856	5.09	2.775	4.957	4.825	3
12.46	13.41	0.8706	5.236	3.017	4.987	5.147	3
12.19	13.36	0.8579	5.24	2.909	4.857	5.158	3
11.65	13.07	0.8575	5.108	2.85	5.209	5.135	3
12.89	13.77	0.8541	5.495	3.026	6.185	5.316	3
11.56	13.31	0.8198	5.363	2.683	4.062	5.182	3
11.81	13.45	0.8198	5.413	2.716	4.898	5.352	3
10.91	12.8	0.8372	5.088	2.675	4.179	4.956	3
11.23	12.82	0.8594	5.089	2.821	7.524	4.957	3
10.59	12.41	0.8648	4.899	2.787	4.975	4.794	3
10.93	12.8	0.839	5.046	2.717	5.398	5.045	3
11.27	12.86	0.8563	5.091	2.804	3.985	5.001	3
11.87	13.02	0.8795	5.132	2.953	3.597	5.132	3
10.82	12.83	0.8256	5.18	2.63	4.853	5.089	3
12.11	13.27	0.8639	5.236	2.975	4.132	5.012	3
12.8	13.47	0.886	5.16	3.126	4.873	4.914	3
12.79	13.53	0.8786	5.224	3.054	5.483	4.958	3
13.37	13.78	0.8849	5.32	3.128	4.67	5.091	3
12.62	13.67	0.8481	5.41	2.911	3.306	5.231	3
12.76	13.38	0.8964	5.073	3.155	2.828	4.83	3
12.38	13.44	0.8609	5.219	2.989	5.472	5.045	3
12.67	13.32	0.8977	4.984	3.135	2.3	4.745	3
11.18	12.72	0.868	5.009	2.81	4.051	4.828	3
12.7	13.41	0.8874	5.183	3.091	8.456	5	3
12.37	13.47	0.8567	5.204	2.96	3.919	5.001	3
12.19	13.2	0.8783	5.137	2.981	3.631	4.87	3
11.23	12.88	0.8511	5.14	2.795	4.325	5.003	3
13.2	13.66	0.8883	5.236	3.232	8.315	5.056	3
11.84	13.21	0.8521	5.175	2.836	3.598	5.044	3
12.3	13.34	0.8684	5.243	2.974	5.637	5.063	3