针对python机器学习与实战代码在python3上运行出现的错误分析和warning的修改代码34—38

#导入model_selection进行数据分割from sklearn.model_selection import train_test_splitimport numpy as npx = boston.datay = boston.targetx_train, x_test, y_train, y_test = train_test_split(x, y , test_size=0.25, random_state=33)print("The max target value is", np.max(boston.target))print("The min target value is", np.min(boston.target))print("The average target value is", np.mean(boston.target))from sklearn.preprocessing import StandardScalerss_x = StandardScaler()ss_y = StandardScaler()x_train = ss_x.fit_transform(x_train)x_test = ss_x.transform(x_test)y_train = ss_y.fit_transform(y_train)y_test = ss_y.transform(y_test)from sklearn.linear_model import LinearRegressionlr = LinearRegression()lr.fit(x_train, y_train)lr_y_pred = lr.predict(x_test)from sklearn.linear_model import SGDRegressorsgdr = SGDRegressor()sgdr.fit(x_train, y_train)sgdr_y_pred = sgdr.predict(x_test)print('The value of default measurement of LinearRegression is', lr.score(x_train, y_train))from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_errorprint('The value of R-sqaured of LinearRegression is', r2_score(y_test, lr_y_pred))print('The mean squared error of LinearRegression is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_pred)))print('The mean absolute error of LinearRegression is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_pred)))print('The value of default of SDGRegressor is', sgdr.score(x_test, y_test))print('The vlue of R-squared of SGDRegressor is', r2_score(y_test, sgdr_y_pred))print('The mean squared error of SGDRegressor is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_pred)))print('The mean absolute error of SGDRegressor is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_pred)))

运行python机器学习及实践中利用线性回归对Boston房价进行预测的时候，会出现如下错误：

Traceback (most recent call last):  File "D:\Python362\a_机器学习及实战\LinearRegression.py", line 22, in <module>    y_train = ss_y.fit_transform(y_train)  File "D:\Python362\lib\site-packages\sklearn\base.py", line 518, in fit_transform    return self.fit(X, **fit_params).transform(X)  File "D:\Python362\lib\site-packages\sklearn\preprocessing\data.py", line 590, in fit    return self.partial_fit(X, y)  File "D:\Python362\lib\site-packages\sklearn\preprocessing\data.py", line 612, in partial_fit    warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)  File "D:\Python362\lib\site-packages\sklearn\utils\validation.py", line 410, in check_array    "if it contains a single sample.".format(array))ValueError: Expected 2D array, got 1D array instead:array=[ 33.8  20.3  10.2  22.   21.2  24.2  29.   22.7  21.8  34.9  25.2  20.9  19.4  20.   14.   30.1  33.1  20.6  22.6  33.4  20.1  10.5  15.6  16.8  22.6  34.6  19.8  17.8  22.   17.4  15.4  16.7  22.6  15.1  21.4  15.3   7.4  13.9  17.6  25.   46.7  17.1  23.1  18.7  21.9  18.9  26.7  22.3  25.   14.6  42.8  17.3  22.2  36.5  22.8  19.9  36.2  50.   25.   22.2  17.5  23.9  19.6  24.7  28.4   8.7  21.7  20.   19.9  24.5  15.    7.  15.2  20.4   8.5  17.1  30.1  15.   19.4  23.2  17.   18.9  50.   25.  46.    7.2  17.8  35.1  24.3   5.   16.6  21.8  28.5  22.   20.3  21.7  26.4  30.7  50.   17.2  26.6  21.   23.4  19.5  20.7  23.3  48.8  15.6  19.6  17.4  21.7  14.6  37.9   9.7  17.8  12.1  20.1  29.9  26.4  18.8  32.5  15.7  13.4  21.7  23.6  11.9  13.8  22.2  13.   33.2  50.   22.3  22.4  23.8  29.1  20.8  23.7  19.8  13.9  28.4  45.4  23.7  50.   18.  17.1  18.9  10.4  24.7  23.9  23.   20.2   8.5  14.2  20.3  18.5  12.  19.3  20.6  16.1  12.3  23.1  22.7  20.3  16.7  27.9  21.4   8.1  37.6  15.6  29.6  22.9  24.8  24.4  50.   28.7  50.   16.5  18.2  50.   16.2  14.1  21.2  18.4  25.   50.   21.2  20.4  15.2  22.   19.8  22.1  23.9  24.6  23.9  21.7  44.8   7.2  18.5  20.1  23.3  19.2  29.1  31.   22.9  27.5  39.8  22.   22.8  22.9  14.3  14.5  22.4  19.3  32.   20.1  18.3  24.5  18.4  23.1  22.6  20.2  17.8  31.6  43.5  36.4  11.3  20.5  23.2  29.8  20.6  24.3  18.1  19.1  21.4  31.5  19.2  14.3  24.8  21.1  18.2  48.3  19.4  21.2  10.9  27.5  34.7  14.4  22.8  17.8  50.   24.4  12.8  30.8  28.2  25.   33.1  27.5  12.7  43.1  13.4  21.5  33.4  23.8  21.  26.6  18.5  23.   24.1  20.5  32.2  14.4  11.8  19.5  23.7  13.2  29.  18.2  18.6  23.   42.3  17.2  16.2  20.   30.3  20.9  20.4  24.8  18.7  16.8  22.5  18.8  23.7  23.8  19.6  20.4  16.1  44.   19.3  17.4  10.2  11.7  37.2  11.   23.6  22.8  15.   34.9  17.9  24.4  24.5   6.3  29.4  10.4  38.7  20.   19.4  37.   50.   18.7  48.5  35.4  23.4   7.   50.  20.7  35.4   9.6  25.1  16.1  27.   16.6  13.3  25.   24.   19.6  29.6  21.7  19.1  22.   13.3  27.1  22.9  33.2  13.5  14.5   8.3  41.7  31.2  23.9  23.1  24.3  18.3  20.8  28.   19.5  21.5  13.1  12.5  31.7  13.1  23.1  14.5  22.2  13.1  37.3  22.   10.2   5.   19.3  16.   18.6  50.  31.6  24.1  15.6  19.4  23.3  23.2  13.6].Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

主要是工具包版本更新造成的，面对上面问题，我们根据上面错误的提示相应的找出出错的两行代码：

y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)

主要问题出现在上面的两行代码中，例如数据格式为[1,  2, 3, 4]就会出错，如果把这行数据转换成[[1], [2], [3], [4]]就不会出错了，所以要对上面导致出错的两行代码做出修改：
y_train = ss_y.fit_transform(y_train.reshape(-1, 1))
y_test = ss_y.transform(y_test.reshape(-1,1))

再运行程序，错误消除。但是代码还会出现一些提醒，如下：

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.SGDRegressor'> 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.Warning (from warnings module):  File "D:\Python362\lib\site-packages\sklearn\utils\validation.py", line 547    y = column_or_1d(y, warn=True)DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

第一个提醒是说版本不同，SGDRegressor所默认设置的迭代次数也不同，既然这样为了消除这个提醒，我们可以在sgdr = SGDRegressor()中把迭代次数重新设置一下sgdr = SGDRegressor(max_iter=5)本文设置最大迭代次数为5，也可以设置成1000，这个根据自己的需求设置，第一个warning消除。

第二个提醒是要对预测输出y做出ravel()转换，例如下面操作：

>>> x = np.array([[1, 2, 3], [4, 5, 6]])>>> print(np.ravel(x))[1 2 3 4 5 6]

也就是说我的y是2D的形式(shapes, 1),要把二维的形式改成1D的形式(shapes, ),，这就可以对fit输入的y_train作y_train.ravel()这样的转换，即把sgdr.fit(x_train, y_train)代码修改为sgdr.fit(x_train, y_train.ravel())，warning就会消失了

最终修改后的完整代码为：

from sklearn.datasets import load_bostonboston = load_boston()print(boston.DESCR)#导入model_selection进行数据分割from sklearn.model_selection import train_test_splitimport numpy as npx = boston.datay = boston.targetx_train, x_test, y_train, y_test = train_test_split(x, y , test_size=0.25, random_state=33)print("The max target value is", np.max(boston.target))print("The min target value is", np.min(boston.target))print("The average target value is", np.mean(boston.target))from sklearn.preprocessing import StandardScalerss_x = StandardScaler()ss_y = StandardScaler()x_train = ss_x.fit_transform(x_train)x_test = ss_x.transform(x_test)y_train = ss_y.fit_transform(y_train.reshape(-1, 1))#修改1y_test = ss_y.transform(y_test.reshape(-1,1))#修改2from sklearn.linear_model import LinearRegressionlr = LinearRegression()lr.fit(x_train, y_train)lr_y_pred = lr.predict(x_test)from sklearn.linear_model import SGDRegressorsgdr = SGDRegressor(max_iter=5)#修改3sgdr.fit(x_train, y_train.ravel())#修改4sgdr_y_pred= sgdr.predict(x_test)print('The value of default measurement of LinearRegression is', lr.score(x_train, y_train))from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_errorprint('The value of R-sqaured of LinearRegression is', r2_score(y_test, lr_y_pred))print('The mean squared error of LinearRegression is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_pred)))print('The mean absolute error of LinearRegression is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_pred)))print('The value of default of SDGRegressor is', sgdr.score(x_test, y_test))print('The vlue of R-squared of SGDRegressor is', r2_score(y_test, sgdr_y_pred))print('The mean squared error of SGDRegressor is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_pred)))print('The mean absolute error of SGDRegressor is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_pred)))

最终运行结果：

Boston House Prices dataset===========================Notes------Data Set Characteristics:      :Number of Instances: 506     :Number of Attributes: 13 numeric/categorical predictive        :Median Value (attribute 14) is usually the target    :Attribute Information (in order):        - CRIM     per capita crime rate by town        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.        - INDUS    proportion of non-retail business acres per town        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)        - NOX      nitric oxides concentration (parts per 10 million)        - RM       average number of rooms per dwelling        - AGE      proportion of owner-occupied units built prior to 1940        - DIS      weighted distances to five Boston employment centres        - RAD      index of accessibility to radial highways        - TAX      full-value property-tax rate per $10,000        - PTRATIO  pupil-teacher ratio by town        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town        - LSTAT    % lower status of the population        - MEDV     Median value of owner-occupied homes in $1000's    :Missing Attribute Values: None    :Creator: Harrison, D. and Rubinfeld, D.L.This is a copy of UCI ML housing dataset.http://archive.ics.uci.edu/ml/datasets/HousingThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonicprices and the demand for clean air', J. Environ. Economics & Management,vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics...', Wiley, 1980.   N.B. Various transformations are used in the table onpages 244-261 of the latter.The Boston house-price data has been used in many machine learning papers that address regressionproblems.        **References**   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)The max target value is 50.0The min target value is 5.0The average target value is 22.5328063241The value of default measurement of LinearRegression is 0.754922212481The value of R-sqaured of LinearRegression is 0.6763403831The mean squared error of LinearRegression is 25.0969856921The mean absolute error of LinearRegression is 3.5261239964The value of default of SDGRegressor is 0.655430817284The vlue of R-squared of SGDRegressor is 0.655430817284The mean squared error of SGDRegressor is 26.7183404942The mean absolute error of SGDRegressor is 3.53570681806