In [17]:

    

%pylab inline
import pandas as pd
from sklearn.cross_validation import LeaveOneOut
from sklearn import tree


train_df = pd.read_csv('train.csv', parse_dates=[1],index_col='Id')
train_df = train_df[train_df['revenue']<10000000]

train_df = train_df.replace(0, np.nan)
train_df = train_df.dropna(axis=1,thresh=80)
train_df = train_df.replace('IL',0)
train_df = train_df.replace('FC',0)
train_df = train_df.replace('DT',0)
train_df['Open_Year'] = train_df['Open Date'].map( lambda x : x.year)

X = pd.DataFrame(train_df,columns = ['Open_Year','P2','P28'])
clf = tree.DecisionTreeRegressor()
clf = clf.fit(X.values, train_df['revenue'])
predicty = clf.predict(X.values)
errors = predicty - train_df['revenue']
RMSE = sqrt(np.mean(errors ** 2))
print '{:,}'.format(RMSE)

pylab.hist(errors,bins=20)
pylab.title('errors histogram')
pylab.xlabel('error')
pylab.ylabel('count')
pylab.show()


    

    




Populating the interactive namespace from numpy and matplotlib
1,037,619.7512






    




WARNING: pylab import has clobbered these variables: ['clf']
`%matplotlib` prevents importing * from pylab and numpy






    

In [16]:

    

print train_df.shape


    

    




(134, 26)

In [18]:

    

print X.shape


    

    




(134, 3)

In [31]:

    

loo = LeaveOneOut(134)
features = ['Open_Year','P2','P28']
test_1_predicts = []
errs = []
for train_idx, test_idx in loo:
    train_1 = train_df.iloc[ train_idx ]
    test_1 = train_df.iloc[ test_idx ]
    X = train_1[ features ]
    x_1 = test_1[ features ]
    clf = tree.DecisionTreeRegressor()
    clf = clf.fit(X.values, train_1['revenue'])
    
    y_1_hat = clf.predict(x_1)
    test_1_predicts.append(y_1_hat)
    
    y_1 = test_1.revenue.iloc[0]
    errs.append(y_1_hat - y_1)
    #test_1_pred = train_1.revenue.mean()
    #test_1_predicts.append(test_1_pred)
    #test_1_label = train_df_wo_outliers.iloc[ test ].revenue.iloc[0]
    #errs.append(test_1_pred - test_1_label)


    

In [32]:

    

pd.Series(data=test_1_predicts).hist()


    

    
Out[32]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fb2746eca50>






    

In [24]:

    

err_series = pd.Series(data=errs)


    

In [34]:

    

err_series.hist(bins=20)


    

    
Out[34]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fb276e45bd0>






    

In [28]:

    

test_rmse = sqrt( np.mean(err_series ** 2) )
print 'Cross validation estimated test error (RMSE) : \n'
print "{:,}".format(test_rmse[0])
#print test_rmse


    

    




Cross validation estimated test error (RMSE) : 

2,168,196.60605

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