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 [6]:

    

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/140926-predict.2.csv', index_col='clip', squeeze=True)


    

In [7]:

    

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


    

In [8]:

    

w_gb  = 0.4
w_rf = 1

s = w_gb + w_rf

w_gb /= s
w_rf /= s


    

In [9]:

    

w_gb, w_rf


    

    
Out[9]:





(0.28571428571428575, 0.7142857142857143)

In [10]:

    

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


    

In [11]:

    

df['preictal'].to_csv('../submissions/140928-predict.2.csv', header=True)


    

In [12]:

    

!head ../submissions/140928-predict.2.csv


    

    




clip,preictal
Dog_1_test_segment_0001.mat,0.4133467551486389
Dog_1_test_segment_0002.mat,0.09224539061095215
Dog_1_test_segment_0003.mat,0.131194537821495
Dog_1_test_segment_0004.mat,0.18514265287316242
Dog_1_test_segment_0005.mat,0.1418730267251374
Dog_1_test_segment_0006.mat,0.14158106558901232
Dog_1_test_segment_0007.mat,0.05703624983301842
Dog_1_test_segment_0008.mat,0.194975158567898
Dog_1_test_segment_0009.mat,0.07618983261021842

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