In [2]:

    

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


    

In [3]:

    

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 [4]:

    

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


    

In [5]:

    

w_gb  = 0.89
w_rf = 1

s = w_gb + w_rf

w_gb /= s
w_rf /= s


    

In [6]:

    

w_gb, w_rf


    

    
Out[6]:





(0.4708994708994709, 0.5291005291005291)

In [7]:

    

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


    

In [8]:

    

df['preictal'].to_csv('../submissions/140929-predict.3.csv', header=True)


    

In [9]:

    

!head ../submissions/140929-predict.3.csv


    

    




clip,preictal
Dog_1_test_segment_0001.mat,0.3101525965937809
Dog_1_test_segment_0002.mat,0.06842166383064048
Dog_1_test_segment_0003.mat,0.09718808109984112
Dog_1_test_segment_0004.mat,0.13715595130374966
Dog_1_test_segment_0005.mat,0.10523319205978315
Dog_1_test_segment_0006.mat,0.10488373427180674
Dog_1_test_segment_0007.mat,0.042254071469135825
Dog_1_test_segment_0008.mat,0.1445272727475574
Dog_1_test_segment_0009.mat,0.05649311659060416

In [10]:

    

df['best'] = pd.read_csv('../submissions/140928-predict.2.csv', index_col='clip', squeeze=True)


    

In [11]:

    

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


    

    
Out[11]:





array([[<matplotlib.axes.AxesSubplot object at 0x105e1cb50>,
        <matplotlib.axes.AxesSubplot object at 0x105ecf1d0>],
       [<matplotlib.axes.AxesSubplot object at 0x116442150>,
        <matplotlib.axes.AxesSubplot object at 0x116dc3050>]], dtype=object)






    

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