Printing confusion matrix to file produces illegal characters - scipy

Question

I am classifying a set of images stored as tuples in a csv file.
The confusion matrix that I get on terminal display is correct. But when I write that same conf. matrix to a file, it produces illegal characters (32bit hex).
Here's the code-
from sklearn.metrics import confusion_matrix
import numpy as np
import os
import csv
from sklearn import svm
from sklearn import cross_validation
from sklearn import linear_model
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
from sklearn import metrics
import cPickle
def prec(num):
return "%0.5f"%num
outfile = open("output/linear_svm_output.txt","a")
for dim in [20,30,40]:
images=[]
labels=[]
name = str(dim)+"x"+str(dim)+".csv"
with open(name,'r') as file:
reader = csv.reader(file,delimiter=',')
for line in file:
labels.append(line[0])
line=line[2:] # Remove the label
image=[int(pixel) for pixel in line.split(',')]
images.append(np.array(image))
clf = svm.LinearSVC()
print clf
kf = cross_validation.KFold(len(images),n_folds=10,indices=True, shuffle=True, random_state=4)
print "\nDividing dataset using `Kfold()` -:\n\nThe training dataset has been divided into " + str(len(kf)) + " parts\n"
for train, test in kf:
training_images=[]
training_labels=[]
for i in train:
training_images.append(images[i])
training_labels.append(labels[i])
testing_images=[]
testing_labels=[]
for i in test:
testing_images.append(images[i])
testing_labels.append(labels[i])
clf.fit(training_images,training_labels)
predicted = clf.predict(testing_images)
print prec(clf.score(testing_images, testing_labels))
outfile.write(prec(clf.score(testing_images, testing_labels)))
outfile.write(str(clf))
outfile.write(confusion_matrix(testing_labels, predicted))
print confusion_matrix(testing_labels, predicted)
# outfile.write(metrics.classification_report(testing_labels, predicted))
print "\nDividing dataset using `train_test_split()` -:\n"
training_images, testing_images, training_labels, testing_labels = cross_validation.train_test_split(images,labels, test_size=0.2, random_state=0)
clf = clf.fit(training_images,training_labels)
score = clf.score(testing_images,testing_labels)
predicted = clf.predict(testing_images)
print prec(score)
outfile.write(str(clf))
outfile.write(confusion_matrix(testing_labels, predicted))
print confusion_matrix(testing_labels, predicted)
# outfile.write(metrics.classification_report(testing_labels, predicted))
Output in file-
302e 3939 3338 374c 696e 6561 7253 5643
2843 3d31 2e30 2c20 636c 6173 735f 7765
...

Answer 1

Use the following to print the matrix to file properly:
with open(filename, 'w') as f:
f.write(np.array2string(confusion_matrix(y_test, pred), separator=', '))

Answer 2

Because outfile.write(confusion_matrix(testing_labels, predicted)) will write the matrix out in binary format. If you want write it in human readable text, try this if you are using python 2.x
print >> outfile, confusion_matrix(testing_labels, predicted)
It just redirect the stdout to outfile