In [9]:

    

%matplotlib inline
from matplotlib import pylab as pl
import cPickle as pickle
import pandas as pd
import numpy as np
import os


    

In [10]:

    

df = pd.DataFrame()
df['gb'] = pd.read_csv('../submissions/140928-predict.1.csv', index_col='clip', squeeze=True) #64
df['rf'] = pd.read_csv('../submissions/141106-predict.1.csv', index_col='clip', squeeze=True)
df['rf1'] = pd.read_csv('../submissions/140926-predict.2.csv', index_col='clip', squeeze=True)
df['dbn'] = pd.read_csv('../submissions/140930-predict.5.csv', index_col='clip', squeeze=True)
df['rftime'] = pd.read_csv('../submissions/141111-predict.5.csv', index_col='clip', squeeze=True)
df['best'] = pd.read_csv('../submissions/141112-predict.1.csv', index_col='clip', squeeze=True)


    

In [11]:

    

pd.scatter_matrix(df,figsize=(6, 6), diagonal='kde');


    

In [12]:

    

w_gb  = 0.4
w_rf = 1.7
w_rf1 = 0
w_dbn = 0.6
w_rftime = 0.

s = w_gb + w_rf + w_rf1 + w_dbn + w_rftime

w_gb /= s
w_rf /= s
w_rf1 /= s
w_dbn /= s
w_rftime /= s


    

In [13]:

    

w_gb, w_rf, w_rf1, w_dbn, w_rftime


    

    
Out[13]:





(0.14814814814814814, 0.6296296296296295, 0.0, 0.2222222222222222, 0.0)

In [14]:

    

df['preictal'] = w_gb * df['gb'] + w_rf * df['rf'] + w_rf1 * df['rf1'] + w_dbn * df['dbn'] + w_rftime * df['rftime']


    

In [15]:

    

df['preictal'].to_csv('../submissions/141112-predict.9.csv', header=True)


    

In [16]:

    

pd.scatter_matrix(df[['best','preictal']])


    

    
Out[16]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11abc2050>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x115de2c10>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x116116a10>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x118fe9a90>]], dtype=object)






    

In [16]: