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 [2]:

    

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/141104-predict.8.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['best'] = pd.read_csv('../submissions/141103-predict.16.csv', index_col='clip', squeeze=True)


    

In [4]:

    

pd.scatter_matrix(df[['gb','rf','rf1','dbn','best']],figsize=(6, 6), diagonal='kde');


    

In [5]:

    

w_gb  = 0.4
w_rf = 0.8
w_rf1 = 0.8
w_dbn = 0.6

s = w_gb + w_rf + w_rf1 + w_dbn

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


    

In [6]:

    

w_gb, w_rf, w_rf1, w_dbn


    

    
Out[6]:





(0.15384615384615385,
 0.3076923076923077,
 0.3076923076923077,
 0.23076923076923075)

In [8]:

    

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


    

In [9]:

    

df['preictal'].to_csv('../submissions/141104-predict.12.csv', header=True)


    

In [10]:

    

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


    

    
Out[10]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x109359ad0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x117e15a50>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x117eb9bd0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x117f1dcd0>]], dtype=object)






    

In [16]: