bayes.py

import os,glob,numpy
os.chdir('/Desktop/malimg_dataset') # the parent folder with sub-folders

list_fams = os.listdir(os.getcwd()) # vector of strings with family names

no_imgs = [] # No. of samples per family

for i in range(len(list_fams)):
 os.chdir(list_fams[i])
 len1 = len(glob.glob('*.png')) # assuming the images are stored as 'png'
 no_imgs.append(len1)
 os.chdir('..')

total = sum(no_imgs) # total number of all samples
y = numpy.zeros(total) # label vector

temp1 = numpy.zeros(len(no_imgs)+1)
temp1[1:len(temp1)]=no_imgs
temp2 = int(temp1[0]) # now temp2 is [0 no_imgs]

for jj in range(len(no_imgs)):
    temp3 = temp2 +int(temp1[jj+1])
    for ii in range(temp2,temp3):
       y[ii] = jj
    temp2 = temp2+ int(temp1[jj+1])

import Image, leargist

X = numpy.zeros((sum(no_imgs), 320))  # Feature Matrix
cnt = 0
for i in range(len(list_fams)):
    os.chdir(list_fams[i])
    img_list = glob.glob('*.png')  # Getting only 'png' files in a folder
    for j in range(len(img_list)):
        im = Image.open(img_list[j])
        im1 = im.resize((64, 64), Image.ANTIALIAS);  # for faster computation
        des = leargist.color_gist(im1)
        X[cnt] = des[0:320]
        cnt = cnt + 1
    os.chdir('..')
import random
from sklearn.cross_validation import StratifiedKFold
from sklearn.utils import shuffle

n_samples, n_features = X.shape
p = range(n_samples)  # an index array, 0:n_samples
random.seed(random.random())
random.shuffle(p)  # the index array is now shuffled

X, y = X[p], y[p]  # both the arrays are now shuffled

kfold = 10  # no. of folds (better to have this at the start of the code)

skf = StratifiedKFold(y, kfold) #indices='true'

 # Stratified KFold: This first divides the data into k folds. Then it also makes sure that the distribution of the data in each fold follows the original input distribution
# Note: in future versions of scikit.learn, this module will be fused with kfold

skfind = [None] * len(skf)  # indices
cnt = 0
for train_index in skf:
    skfind[cnt] = train_index
    cnt = cnt + 1


from sklearn.naive_bayes import MultinomialNB



import time

conf_mat = numpy.zeros((len(no_imgs), len(no_imgs)))  # Initializing the Confusion Matrix

n_neighbors = 1  # better to have this at the start of the code

# 10-fold Cross Validation

for i in range(kfold):
    train_indices = skfind[i][0]
    test_indices = skfind[i][1]
    clf = []
    clf = MultinomialNB()


    X_train = X[train_indices]
    y_train = y[train_indices]
    X_test = X[test_indices]
    y_test = y[test_indices]

    # Training
    tic = time.time()
    clf.fit(X_train, y_train)
    toc = time.time()
    print "training time= ", toc - tic  # roughly 2.5 secs

    # Testing
y_predict = []
tic = time.time()
y_predict = clf.predict(X_test)  # output is labels and not indices
toc = time.time()
print "testing time = ", toc - tic  # roughly 0.3 secs

# Compute confusion matrix
from sklearn.metrics import confusion_matrix
cm = []
cm = confusion_matrix(y_test, y_predict)
conf_mat = conf_mat + cm

conf_mat = conf_mat.T  # since rows and  cols are interchanged
avg_acc = numpy.trace(conf_mat) / sum(no_imgs)
conf_mat_norm = conf_mat / no_imgs  # Normalizing the confusion matrix

import matplotlib.pyplot as plt
plt.imshow(conf_mat_norm, interpolation='nearest')
plt.title('Confusion matrix')
plt.colorbar()
plt.show()
plt.savefig('confusion_matrix.png')

conf_mat2 = numpy.around(conf_mat_norm,decimals=2) # rounding to display in figure
plt.imshow(conf_mat2,interpolation='nearest')
for x in xrange(len(list_fams)):
  for y in xrange(len(list_fams)):
    plt.annotate(str(conf_mat2[x][y]),xy=(y,x),ha='center',va='center')

plt.xticks(range(len(list_fams)),list_fams,rotation=90,fontsize=11)
plt.yticks(range(len(list_fams)),list_fams,fontsize=11)
plt.title('multinomial Naive Bayes')
plt.colorbar()
plt.show()
plt.savefig('confusion_matrix.png')