In [1]:

    

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


    

In [5]:

    

def prb2logit(x):
    return np.log(x/(1.-x))
def logit2prb(x):
    return 1./(1+np.exp(-x))


    

In [28]:

    

df = pd.DataFrame()
df['gb'] = pd.read_csv('../submissions/141107-predict.4.csv', index_col='clip', squeeze=True)
df['rf'] = pd.read_csv('../submissions/141107-predict.2.csv', index_col='clip', squeeze=True)
df['best'] = pd.read_csv('../submissions/141106-predict.3.csv', index_col='clip', squeeze=True)


    

In [46]:

    

df['gbl'] = prb2logit(df['gb'])
df['gbl'] = logit2prb(0.1*df['gbl'])


    

In [47]:

    

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


    

In [48]:

    

w_gb  = 0.5
w_rf = 0.5

s = w_gb + w_rf

w_gb /= s
w_rf /= s


    

In [49]:

    

w_gb, w_rf


    

    
Out[49]:





(0.5, 0.5)

In [50]:

    

df['preictal'] = w_gb * df['gbl'] + w_rf * df['rf']


    

In [51]:

    

df['preictal'].to_csv('../submissions/141107-predict.6.csv', header=True)


    

In [52]:

    

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


    

    
Out[52]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1174c65d0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x117496050>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x1135e7e50>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x113a50050>]], dtype=object)






    

In [16]: