python机器学习及实战代码13-16，程序运行时出现提醒及修改

import pandas as pdimport numpy as npcolumn_names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']data = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', names=column_names)data = data.replace(to_replace='?', value=np.nan)data = data.dropna(how='any')print(data.shape)from sklearn.model_selection import train_test_splitx_train, x_test, y_train, y_test = train_test_split(data[column_names[1:10]], data[column_names[10]], test_size=0.25, random_state=33)print(y_train.value_counts())print(y_test.value_counts())from sklearn.preprocessing import StandardScalerfrom sklearn.linear_model import LogisticRegressionfrom sklearn.linear_model import SGDClassifierlr = LogisticRegression()sgdc = SGDClassifier()lr.fit(x_train, y_train)lr_y_predict = lr.predict(x_test)sgdc.fit(x_train, y_train)sgdc_y_predict = sgdc.predict(x_test)from sklearn.metrics import classification_reportprint('Accuracy of LR Classifier:', lr.score(x_test, y_test))print(classification_report(y_test, lr_y_predict, target_names=['Benign', 'Malignant']))print('Accuracy of SGD Classifier:', sgdc.score(x_test, y_test))print(classification_report(y_test, sgdc_y_predict, target_names=['Benign', 'Malignant']))

运行上面代码的时候发现有如下提醒：

Warning (from warnings module):  File "D:\Python362\lib\site-packages\sklearn\linear_model\stochastic_gradient.py", line 84    "and default tol will be 1e-3." % type(self), FutureWarning)FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.

意思大概是说在0.19中，SGDClassifier随机梯度下降分类器在sklearn.linear_model.stochastic_gradient.SGDClassifier模块中，而不是直接在模块sklearn.linear_model中，只需找到相应的模块导入代码from sklearn.linear_model import SGDClassifier将其修改为from sklearn.linear_model import stochastic_gradient，并且程序中相应的代码也要做修改，将sgdc = SGDClassifier()改为sgdc = stochastic_gradient.SGDClassifier()

运行结果如下：

2    3444    168Name: Class, dtype: int642    1004     71Name: Class, dtype: int64Accuracy of LR Classifier: 0.988304093567             precision    recall  f1-score   support     Benign       0.99      0.99      0.99       100  Malignant       0.99      0.99      0.99        71avg / total       0.99      0.99      0.99       171Accuracy of SGD Classifier: 0.970760233918             precision    recall  f1-score   support     Benign       0.97      0.98      0.98       100  Malignant       0.97      0.96      0.96        71avg / total       0.97      0.97      0.97       171