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/141103-predict.12.csv', index_col='clip', squeeze=True)
df['rfpca'] = pd.read_csv('../submissions/141001-predict.1.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.1.csv', index_col='clip', squeeze=True)


    

In [3]:

    

df['rf'].hist()


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x113172090>






    

In [4]:

    

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


    

In [5]:

    

w_gb  = 0.4
w_rf = 0.8
w_rfpca = 0.4
w_dbn = 0.6

s = w_gb + w_rf + w_rfpca + w_dbn

w_gb /= s
w_rf /= s
w_rfpca /= s
w_dbn /= s


    

In [6]:

    

w_gb, w_rf, w_rfpca, w_dbn


    

    
Out[6]:





(0.18181818181818182,
 0.36363636363636365,
 0.18181818181818182,
 0.2727272727272727)

In [7]:

    

df['preictal'] = w_gb * df['gb'] + w_rf * df['rf'] + w_rfpca * df['rfpca'] + w_dbn * df['dbn']


    

In [8]:

    

df['preictal'].to_csv('../submissions/141103-predict.13.csv', header=True)


    

In [10]:

    

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


    

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