#featureLength

N = 1899

x = [0] * N

for i in range(len(word_indices)) :

x[ word_indices[i] ] = 1

path = os.getcwd()

path = os.path.join(path,'dataSets')

# =============== Part 1 ====================

# To use an SVM to classify emails into Spam v.s. Non-Spam, we first need

# to convert each email into a vector of features. In this part, we

# implement the preprocessing steps for each email.

f = open(os.path.join(path,"emailSample1.txt"),'r')

email_contents = f.read()

f.close()

print(email_contents)

word_indices = processEmail.processEmail(email_contents)

features = emailFeatures(word_indices)

print('Word Indices :\n')

print(word_indices, "\n")

#============= Part 2 =======================

# Print Stats

print('Length of feature vector: %d\n' % len(features))

print('Number of non-zero entries: %d\n' % sum(features))

# ============= Part 3 ======================

# In this section, we will train a linear classifier to determine if an

# email is Spam or Not-Spam.

print('\n\nRunning SVM on training set...')

mat = io.loadmat(os.path.join(path,'spamTrain.mat'))

X = mat['X']

y = mat['y']

y = numpy.ravel(y)

model = svm.SVC(C = 0.1, kernel='linear')

model.fit(X, y)

p = model.predict(X)

accuracy = model.score(X, y)

accuracy *= 100.0

print('\nTraining Accuracy: %.2f' % accuracy)

#================ Part 4 ========================

# Xtest and ytest are the env. variables

mat = io.loadmat(os.path.join(path,'spamTest.mat'))

XTest = mat['Xtest']

yTest = mat['ytest']

yTest = numpy.ravel(yTest)

p = model.predict(XTest)

accuracy = model.score(XTest,yTest)

accuracy *= 100.0

print('\nTest Accuracy: %.2f' % accuracy)

#================ Part 5 ============================

# Since the model we are training is a linear SVM, we can inspect the

# weights learned by the model to understand better how it is determining

# whether an email is spam or not. The following code finds the words with

# the highest weights in the classifier. Informally, the classifier

# 'thinks' that these words are the most likely indicators of spam.

print('\nTop spam predictors (keywords) \n')

z = model.coef_

z = numpy.ravel(z)

vocabList = getVocabList.getVocabList()

dic = {}

for i in range(len(z)) :

dic[ vocabList[i] ] = z[i]

cnt = 0

for w in sorted(dic, key=dic.get, reverse=True):

if cnt == 15 :

break

cnt = cnt + 1

print('{0:10} - {1:10f}'.format(w, dic[w]))

print('\n\n')

# ============ Part 6: Test a sample Email =====================

# Now that we have trained the spam classifier, we can use it on our own

# emails!

# The following code reads in one of these emails and then uses our

# learned SVM classifier to determine whether the email is Spam or

# Not Spam

f = open(os.path.join(path, "spamSample1.txt"),'r')

email_contents = f.read()

f.close()

print('Sample Email : ')

print(email_contents)

word_indices = processEmail.processEmail(email_contents)

features = emailFeatures(word_indices)

X = emailFeatures(word_indices);

p = model.predict(X)

print('\nEmail Processed\n\nSpam Classification: %d\n' % p);