sklearn学习笔记-《模型验证方法》

模型验证方法

一.学习率曲线

# import numpy as np
# from sklearn.model_selection import learning_curve, ShuffleSplit
# from sklearn.datasets import load_digits
# from sklearn.naive_bayes import GaussianNB
# from sklearn import svm
# import matplotlib.pyplot as plt
# def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,n_jobs=1, train_size=np.linspace(.1, 1.0, 5 )):
#     if __name__ == '__main__':
#         plt.figure()
#         plt.title(title)
#         if ylim is not None:
#             plt.ylim(*ylim)
#         plt.xlabel('Training example')
#         plt.ylabel('score')
#         train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_size)
#         train_scores_mean = np.mean(train_scores, axis=1)
#         train_scores_std = np.std(train_scores, axis=1)
#         test_scores_mean = np.mean(test_scores, axis=1)
#         test_scores_std = np.std(test_scores, axis=1)
#         plt.grid()#区域
#         plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
#                          train_scores_mean + train_scores_std, alpha=0.1,
#                          color="r")
#         plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
#                          test_scores_mean + test_scores_std, alpha=0.1,
#                          color="g")
#         plt.plot(train_sizes, train_scores_mean, 'o-', color='r',
#                  label="Training score")
#         plt.plot(train_sizes, test_scores_mean,'o-',color="g",
#                  label="Cross-validation score")
#         plt.legend(loc="best")
#         return plt
# digits = load_digits()
# X = digits.data
# y = digits.target
# cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)#切割100ci
# estimator = GaussianNB()
# title = "Learning Curves(naive_bayes)"
# plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=4)
# title = "Learning Curves(SVM,RBF kernel, $\gamma=0.001$)"
# cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)#交叉验证传入别的方法，而不是默认的k折交叉验证
# estimator = svm.SVC(gamma=0.001)
# plot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv, n_jobs=4)
# plt.show()

二.交叉验证得分

# import matplotlib.pyplot as plt
# from sklearn.model_selection import cross_val_score
# import numpy as np
# from sklearn import datasets, svm
# digits = datasets.load_digits()
# x = digits.data
# y = digits.target
# vsc = svm.SVC(kernel='linear')
# if __name__=='__main__':
#     c_S = np.logspace(-10, 0, 10)#在范围内取是个对数
#     # print ("length", len(c_S))
#     scores = list()
#     scores_std = list()
#     for c in c_S:
#         vsc.C = c
#         this_scores = cross_val_score(vsc, x, y, n_jobs=4)#多线程 n_jobs，默认三次交叉验证
#         scores.append(np.mean(this_scores))
#         scores_std.append(np.std(this_scores))
#     plt.figure(1, figsize=(4, 3))#绘图
#     plt.clf()
#     plt.semilogx(c_S, scores)#划线
#     plt.semilogx(c_S, np.array(scores)+np.array(scores_std), 'b--')
#     plt.semilogx(c_S, np.array(scores)-np.array(scores_std), 'b--')
#     locs, labels = plt.yticks()
#     plt.yticks(locs, list(map(lambda X: "%g" % X, locs)))#阶段点
#     plt.ylabel('CV score')
#     plt.xlabel('parameter C')
#     plt.ylim(0, 1.1)#范围
#     plt.show()

三.验证曲线

# from sklearn import svm
# from sklearn.model_selection import validation_curve
# from sklearn.datasets import load_digits
# import numpy as np
# import matplotlib.pyplot as plt
# digits = load_digits()
# X = digits.data
# y = digits.target
# param_range = np.logspace(-6, -1, 5)
# vsc = svm.SVC()
# train_score, test_score = validation_curve(vsc, X, y, param_name='gamma', param_range=param_range, cv=10, scoring="accuracy", n_jobs=1)
# train_score_mean = np.mean(train_score, axis=1)
# train_score_std = np.std(train_score, axis=1)
# test_score_mean = np.mean(test_score, axis=1)
# test_score_std = np.std(test_score, axis=1)
# plt.title("validation curve with SVM")
# plt.xlabel("$\gamma%")
# plt.ylabel("Score")
# plt.ylim()
# lw = 2
# plt.semilogx(param_range, train_score_mean,label="training score", color="darkorange", lw=lw)
# plt.fill_between(param_range, train_score_mean-train_score_std, train_score_mean+train_score_std, alpha=0.2, color="navy", lw=lw)
# plt.legend(loc="best")
# plt.show()