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These first notebook aims to evaluate the Additive Noise Models over pairs of observated variables. The variables are continuous and the directions are known, so we can evaluate the accuracy of the ANM's and try to reproduce the benchmarks





In [1]:



    


import readline
import rpy2.robjects
import code.additive_noise as an
import numpy as np
import numpy.random as rn



    











In [2]:



    


# First we load the metadata and the right directions. X->Y will be represented as 1, while Y->X is represented as -1
metadata = np.loadtxt('./pairmeta.txt')
directions = metadata[:,3]-metadata[:,2]



    











In [3]:



    


#We iterate through all the pairs (we exlude only three that give an error with pyGPs) and predict the directionality
# of the causal relationship. For pairs with more than 500 datapoints we will only consider the first 500 for faster 
# computation
accuracy = list()
for i in range(105):
    pair = str(i+1)
    if len(pair)==1:
        pair = '000'+ pair
    if len(pair)==2:
        pair = '00'+ pair
    if len(pair)==3:
        pair = '0'+ pair
    if pair != '0047' and pair != '0060' and pair != '0061':
        data = np.loadtxt('./data/pair'+pair+'.txt')
        if data.shape[0]>500:
            data = data[0:500]
        if data.shape[1]==2:
            X = data[:,0]
            Y = data[:,1]
            dire = an.ANM_algorithm_pairwise(X,Y, hsic='py')
            if dire == directions[i]:
                accuracy.append(1)
            else:
                accuracy.append(0)    
        else:
            continue



    


















    






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Warning: adding jitter of 3.8851199094e+05 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 3.8851199094e+06 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 3.8851199094e+07 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 3.8851199094e+08 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 3.8851199094e+09 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.0622316715e+04 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.0622316715e+05 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.0622316715e+06 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.0622316715e+07 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.0622316715e+08 to diagnol of kernel matrix for numerical stability
Number of line searches 7
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Warning: adding jitter of 9.4528890679e+11 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.4528890679e+12 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.4528890679e+13 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.4528890679e+14 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 9.4528890679e+15 to diagnol of kernel matrix for numerical stability
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Number of line searches 40
Warning: adding jitter of 2.1208427653e+14 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.1208427653e+15 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.1208427653e+16 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.1208427653e+17 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.1208427653e+18 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 1.6614155846e+26 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 1.6614155846e+27 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 1.6614155846e+28 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 1.6614155846e+29 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 1.6614155846e+30 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.3962027823e+08 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.3962027823e+09 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.3962027823e+10 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.3962027823e+11 to diagnol of kernel matrix for numerical stability
Warning: adding jitter of 2.3962027823e+12 to diagnol of kernel matrix for numerical stability
Number of line searches 40
Number of line searches 18
Number of line searches 40
Number of line searches 40

















In [6]:



    


len(accuracy)



    


















    
Out[6]:








93

















In [8]:



    


np.array(accuracy).sum()/float(len(accuracy))



    


















    
Out[8]:








0.66666666666666663

The resulting accuracy is 63.44% vs. the 64% reported by Peters et al.2016





In [2]:



    


import pandas as pd
def parse_dataframe(df):
    parse_cell = lambda cell: np.fromstring(cell, dtype=np.float, sep=" ")
    df = df.applymap(parse_cell)
    return df

data = parse_dataframe(pd.read_csv('./data2/CEdata_train_pairs.csv', index_col="SampleID"))
data = data.reset_index()

metadata = pd.read_csv('./data2/CEdata_train_target.csv', delimiter=',')
metadata.columns = ['id', '', 'target']



    











In [8]:



    


accuracy = list()
for i , t in enumerate(metadata[0:150].target):
    print i
    print data.ix[i].SampleID
    if t==1 or t==2:
        X = data.ix[i].A
        Y = data.ix[i].B
        
        if X.shape[0]>1000:
            X = X[0:1000]
            Y = Y[0:1000]
        
        try:
            dire = an.ANM_algorithm_pairwise(X,Y, hsic='py')
            if dire == -1:
                dire = 2
            if dire == t:
                accuracy.append(1)
            else:
                accuracy.append(0)    

        except:
            continue
    else:
        continue



    


















    






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In [9]:



    


np.array(accuracy).sum()/float(len(accuracy))



    


















    
Out[9]:








0.58904109589041098

















In [3]:



    


accuracy



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-f67ae9ba6c58> in <module>()
----> 1 accuracy

NameError: name 'accuracy' is not defined
















In [ ]:

Content source: mvp291/dsga1005

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