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In [1]:



    


import graphlab

读取一些房屋销售数据





In [2]:



    


sales = graphlab.SFrame('home_data.gl/')



    


















    






[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1496394257.log










    






This non-commercial license of GraphLab Create for academic use is assigned to lbotong@gmail.com and will expire on May 31, 2018.

















In [3]:



    


sales



    


















    
Out[3]:








    

        id
        date
        price
        bedrooms
        bathrooms
        sqft_living
        sqft_lot
        floors
        waterfront
    

    

        7129300520
        2014-10-13 00:00:00+00:00
        221900
        3
        1
        1180
        5650
        1
        0
    

    

        6414100192
        2014-12-09 00:00:00+00:00
        538000
        3
        2.25
        2570
        7242
        2
        0
    

    

        5631500400
        2015-02-25 00:00:00+00:00
        180000
        2
        1
        770
        10000
        1
        0
    

    

        2487200875
        2014-12-09 00:00:00+00:00
        604000
        4
        3
        1960
        5000
        1
        0
    

    

        1954400510
        2015-02-18 00:00:00+00:00
        510000
        3
        2
        1680
        8080
        1
        0
    

    

        7237550310
        2014-05-12 00:00:00+00:00
        1225000
        4
        4.5
        5420
        101930
        1
        0
    

    

        1321400060
        2014-06-27 00:00:00+00:00
        257500
        3
        2.25
        1715
        6819
        2
        0
    

    

        2008000270
        2015-01-15 00:00:00+00:00
        291850
        3
        1.5
        1060
        9711
        1
        0
    

    

        2414600126
        2015-04-15 00:00:00+00:00
        229500
        3
        1
        1780
        7470
        1
        0
    

    

        3793500160
        2015-03-12 00:00:00+00:00
        323000
        3
        2.5
        1890
        6560
        2
        0
    





    

        view
        condition
        grade
        sqft_above
        sqft_basement
        yr_built
        yr_renovated
        zipcode
        lat
    

    

        0
        3
        7
        1180
        0
        1955
        0
        98178
        47.51123398
    

    

        0
        3
        7
        2170
        400
        1951
        1991
        98125
        47.72102274
    

    

        0
        3
        6
        770
        0
        1933
        0
        98028
        47.73792661
    

    

        0
        5
        7
        1050
        910
        1965
        0
        98136
        47.52082
    

    

        0
        3
        8
        1680
        0
        1987
        0
        98074
        47.61681228
    

    

        0
        3
        11
        3890
        1530
        2001
        0
        98053
        47.65611835
    

    

        0
        3
        7
        1715
        0
        1995
        0
        98003
        47.30972002
    

    

        0
        3
        7
        1060
        0
        1963
        0
        98198
        47.40949984
    

    

        0
        3
        7
        1050
        730
        1960
        0
        98146
        47.51229381
    

    

        0
        3
        7
        1890
        0
        2003
        0
        98038
        47.36840673
    





    

        long
        sqft_living15
        sqft_lot15
    

    

        -122.25677536
        1340.0
        5650.0
    

    

        -122.3188624
        1690.0
        7639.0
    

    

        -122.23319601
        2720.0
        8062.0
    

    

        -122.39318505
        1360.0
        5000.0
    

    

        -122.04490059
        1800.0
        7503.0
    

    

        -122.00528655
        4760.0
        101930.0
    

    

        -122.32704857
        2238.0
        6819.0
    

    

        -122.31457273
        1650.0
        9711.0
    

    

        -122.33659507
        1780.0
        8113.0
    

    

        -122.0308176
        2390.0
        7570.0
    



[21613 rows x 21 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.


















In [4]:



    


graphlab.canvas.set_target('ipynb')



    











In [5]:



    


sales.show(view='Scatter Plot',x='sqft_living',y='price')

分离出训练集和测试集





In [7]:



    


train_data,test_data = sales.random_split(.8,seed=0)

构建回归模型





In [8]:



    


sqft_model = graphlab.linear_regression.create(train_data,target='price',features=['sqft_living'])



    


















    






PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.











    







Linear regression:










    







--------------------------------------------------------










    







Number of examples          : 16453










    







Number of features          : 1










    







Number of unpacked features : 1










    







Number of coefficients    : 2










    







Starting Newton Method










    







--------------------------------------------------------










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| Iteration | Passes   | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| 1         | 2        | 1.077630     | 4337152.589192     | 2130030.539048       | 263182.612955 | 258732.479191   |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







SUCCESS: Optimal solution found.










    
































评估这个简单的线性回归模型
















In [46]:



    


print test_data['price'].mean()



    



















In [11]:



    


print sqft_model.evaluate(test_data)



    


















    






{'max_error': 4133892.8897543834, 'rmse': 255224.119123975}

























看看我们的预测是什么样子的
















In [16]:



    


import sys
reload(sys)
sys.setdefaultencoding('utf8')



    



















In [17]:



    


import matplotlib.pyplot as plt



    



















In [18]:



    


%matplotlib inline



    



















In [20]:



    


plt.plot(test_data['sqft_living'],test_data['price'],'.',
        test_data['sqft_living'],sqft_model.predict(test_data),'-')



    


















    
Out[20]:








[<matplotlib.lines.Line2D at 0x11b1cde10>,
 <matplotlib.lines.Line2D at 0x11b1cded0>]










    
































In [21]:



    


sqft_model.get('coefficients')



    


















    
Out[21]:








    

        name
        index
        value
        stderr
    

    

        (intercept)
        None
        -49870.469645
        5065.20305649
    

    

        sqft_living
        None
        283.213102112
        2.22707695617
    



[2 rows x 4 columns]



























探索数据中的其他特征
















In [29]:



    


my_features = ['bedrooms','bathrooms','sqft_living','sqft_lot','floors','zipcode']



    



















In [30]:



    


sales[my_features].show()



    


















    



































In [32]:



    


sales.show(view='BoxWhisker Plot',x='zipcode',y='price')



    


















    



































构建更多特征的回归模型
















In [42]:



    


my_features_model = graphlab.linear_regression.create(train_data,target='price',features=my_features)



    


















    







Linear regression:










    







--------------------------------------------------------










    







Number of examples          : 16507










    







Number of features          : 6










    







Number of unpacked features : 6










    







Number of coefficients    : 115










    







Starting Newton Method










    







--------------------------------------------------------










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| Iteration | Passes   | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| 1         | 2        | 0.036949     | 2572899.278587     | 5335774.506934       | 177327.132487 | 285242.205320   |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







SUCCESS: Optimal solution found.










    
































In [43]:



    


print my_features



    



















In [44]:



    


print sqft_model.evaluate(test_data)



    



















In [40]:



    






    



















In [41]:



    






    



















In [ ]:



    






    
















    


    

        

            

            

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