本文实例讲述了Python基于sklearn库的分类算法简单应用。分享给大家供大家参考，具体如下：
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：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
|
# coding=gbk'''Created on 2016年6月4日@author: bryan'''import timefrom sklearn import metricsimport pickle as pickleimport pandas as pd# Multinomial Naive Bayes Classifierdef 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 Classifierdef knn_classifier(train_x, train_y): from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier() model.fit(train_x, train_y) return model# Logistic Regression Classifierdef 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 Classifierdef 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 Classifierdef 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) Classifierdef 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 Classifierdef 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 validationdef 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 modeldef 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_yif __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%
希望本文所述对大家Python程序设计有所帮助。
原文链接：https://blog.csdn.net/Bryan__/article/details/51288953
