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 pd
import numpy as np

column_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_split
x_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 StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier

lr = 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_report
print('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: 0.988304093567precision recall f1-score supportBenign 0.99 0.99 0.99 100Malignant 0.99 0.99 0.99 71avg / total 0.99 0.99 0.99 171Accuracy of SGD Classifier: 0.970760233918precision recall f1-score supportBenign 0.97 0.98 0.98 100Malignant 0.97 0.96 0.96 71avg / total 0.97 0.97 0.97 171