In [2]:

    

from sklearn.datasets import load_boston
boston=load_boston()
print boston.DESCR


    

    




 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/Housing


This 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. 'Hedonic
prices 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 on
pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression
problems.   
     
**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)

In [3]:

    

from sklearn.cross_validation import train_test_split
import numpy as np
X=boston.data
y=boston.target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25)
print 'max target value is ', np.max(boston.target)
print 'min target value is ', np.min(boston.target)
print 'avg target value is ', np.mean(boston.target)


    

    




max target value is  50.0
min target value is  5.0
avg target value is  22.5328063241






    




/Users/ifeng/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

In [4]:

    

from sklearn.preprocessing import StandardScaler
ss_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)


    

    




/Users/ifeng/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/Users/ifeng/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/Users/ifeng/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)

In [5]:

    

from sklearn.linear_model import LinearRegression
lr=LinearRegression()
lr.fit(X_train,y_train)
lr_y_predict=lr.predict(X_test)
from sklearn.linear_model import SGDRegressor
sgdr=SGDRegressor()
sgdr.fit(X_train,y_train)
sgdr_y_predict=sgdr.predict(X_test)


    

In [11]:

    

print 'LinearRegression自带评估模块的评估结果：',lr.score(X_test,y_test)
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error
print 'LinearRegression的R-squared评估结果：',r2_score(y_test,lr_y_predict)
print 'LinearRegression的mean squared error评估结果：',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predict))
print 'LinearRegression的mean absolute error评估结果：',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predict))
help(ss_y.inverse_transform)


    

    




LinearRegression自带评估模块的评估结果： 0.6763403831
LinearRegression的R-squared评估结果： 0.6763403831
LinearRegression的mean squared error评估结果： 25.0969856921
LinearRegression的mean absolute error评估结果： 3.5261239964
Help on method inverse_transform in module sklearn.preprocessing.data:

inverse_transform(self, X, copy=None) method of sklearn.preprocessing.data.StandardScaler instance
    Scale back the data to the original representation
    
    Parameters
    ----------
    X : array-like, shape [n_samples, n_features]
        The data used to scale along the features axis.

In [15]:

    

print 'SGDRegressor自带评估：',sgdr.score(X_test,y_test)
print 'R-squared评估SGDRegressor:',r2_score(y_test,sgdr_y_predict)
print 'SGDRegressor的mean squared error评估：',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(sgdr_y_predict))
print 'SGDRegressor的mean absolute error评估：',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(sgdr_y_predict))


    

    




SGDRegressor自带评估： 0.658614663816
R-squared评估SGDRegressor: 0.658614663816
SGDRegressor的mean squared error评估： 26.4714609124
SGDRegressor的mean absolute error评估： 3.51249288598