# does not distinguish between emails where an attribute appears more than

# once as opposed to those where said attribute appears only once.

# note that we're not using equiprobabilities for the priors.

# we'll simulate the population priors using the sample priors.

accuracy_in_each_turn = list()

precision_in_each_turn_spam = list()

recall_in_each_turn_spam = list()

precision_in_each_turn_ham = list()

recall_in_each_turn_ham = list()

m = np.loadtxt(open("resources/binarized_data.csv","rb"),delimiter=',')

shuffled = np.random.permutation(m)

for i in xrange(NUMBER_OF_ROUNDS):

# we're using cross-validation so each iteration we take a different

# slice of the data to serve as test set

train_set,test_set = prep.split_sets(shuffled,TRAIN_TEST_RATIO,i)

p_attribute_spam = nb.take_p_attribute_spam(train_set,index,SPAM_ATTR_INDEX)

p_spam = nb.take_p_spam(train_set,SPAM_ATTR_INDEX)

p_attribute = nb.take_p_attribute(train_set,index,SPAM_ATTR_INDEX)

hits = 0.0

misses = 0.0

#number of instances corretcly evaluated as spam

correctly_is_spam = 0.0

#total number of spam instances

is_spam = 0.0

#total number of instances evaluated as spam

guessed_spam = 0.0

#number of instances correctly evaluated as ham

correctly_is_ham = 0.0

#total number of ham instances

is_ham = 0.0

#total number of instances evaluated as ham

guessed_ham = 0.0

# whichever is greater - that will be our prediction

p_spam_attribute = (p_attribute_spam * p_spam) / p_attribute

p_ham_attribute = 1 - p_spam_attribute

if p_spam_attribute > p_ham_attribute:

guess = 1

else:

guess = 0

# now we test the hypothesis against the test set

for row in test_set:

# if the attribute isn't there, then our actual guess is the opposite of the calculated guess

if(row[index] == 0) and (guess == 1):

actual_guess = 0

elif(row[index] == 0) and (guess == 0):

actual_guess = 1

else:

actual_guess = guess

if (row[SPAM_ATTR_INDEX] == 0) and (actual_guess == 0):

is_ham += 1

guessed_ham += 1

correctly_is_ham += 1

hits += 1

elif (row[SPAM_ATTR_INDEX] == 0) and (actual_guess ==1):

is_ham += 1

guessed_spam += 1

misses += 1

elif (row[SPAM_ATTR_INDEX] == 1) and (actual_guess ==0):

is_spam += 1

guessed_ham += 1

misses += 1

elif (row[SPAM_ATTR_INDEX] == 1) and (actual_guess ==1):

is_spam += 1

guessed_spam += 1

hits += 1

correctly_is_spam += 1

#accuracy = number of correctly evaluated instances/

# number of instances

#

#

accuracy = hits/(hits+misses)

#precision_spam = number of correctly evaluated instances as spam/

# number of spam instances

#

#

# in order to avoid divisions by zero in case nothing was found

if(is_spam == 0):

precision_spam = 0

else:

precision_spam = correctly_is_spam/is_spam

#recall_spam = number of correctly evaluated instances as spam/

# number of evaluated instances como spam

#

#

# in order to avoid divisions by zero in case nothing was found

if(guessed_spam == 0):

recall_spam = 0

else:

recall_spam = correctly_is_spam/guessed_spam

#precision_ham = number of correctly evaluated instances as ham/

# number of ham instances

#

#

# in order to avoid divisions by zero in case nothing was found

if(is_ham == 0):

precision_ham = 0

else:

precision_ham = correctly_is_ham/is_ham

#recall_ham = number of correctly evaluated instances as ham/

# number of evaluated instances como ham

#

#

# in order to avoid divisions by zero in case nothing was found

if(guessed_ham == 0):

recall_ham = 0

else:

recall_ham = correctly_is_ham/guessed_ham

accuracy_in_each_turn.append(accuracy)

precision_in_each_turn_spam.append(precision_spam)

recall_in_each_turn_spam.append(recall_spam)

precision_in_each_turn_ham.append(precision_ham)

recall_in_each_turn_ham.append(recall_ham)

# calculation of means for each metric at the end

mean_accuracy = np.mean(accuracy_in_each_turn)

std_dev_accuracy = np.std(accuracy_in_each_turn)

variance_accuracy = np.var(accuracy_in_each_turn)

mean_precision_spam = np.mean(precision_in_each_turn_spam)

std_dev_precision_spam = np.std(precision_in_each_turn_spam)

variance_precision_spam = np.var(precision_in_each_turn_spam)

mean_recall_spam = np.mean(recall_in_each_turn_spam)

std_dev_recall_spam = np.std(recall_in_each_turn_spam)

variance_recall_spam = np.var(recall_in_each_turn_spam)

mean_precision_ham = np.mean(precision_in_each_turn_ham)

std_dev_precision_ham = np.std(precision_in_each_turn_ham)

variance_precision_ham = np.var(precision_in_each_turn_ham)

mean_recall_ham = np.mean(recall_in_each_turn_ham)

std_dev_recall_ham = np.std(recall_in_each_turn_ham)

variance_recall_ham = np.var(recall_in_each_turn_ham)

if output:

print "\033[1;32m"

print '============================================='

print 'CASE 1 - ONE ATTRIBUTE - USING BERNOULLI MODEL'

print '============================================='

print "\033[00m"

print 'MEAN ACCURACY: '+str(round(mean_accuracy,5))

print 'STD. DEV. OF ACCURACY: '+str(round(std_dev_accuracy,5))

print 'VARIANCE OF ACCURACY: '+str(round(variance_accuracy,8))

print ''

print 'MEAN PRECISION FOR SPAM: '+str(round(mean_precision_spam,5))

print 'STD. DEV. OF PRECISION FOR SPAM: '+str(round(std_dev_precision_spam,5))

print 'VARIANCE OF PRECISION FOR SPAM: '+str(round(variance_precision_spam,8))

print ''

print 'MEAN RECALL FOR SPAM: '+str(round(mean_recall_spam,5))

print 'STD. DEV. OF RECALL FOR SPAM: '+str(round(std_dev_recall_spam,5))

print 'VARIANCE OF RECALL FOR SPAM: '+str(round(variance_recall_spam,8))

print ''

print 'MEAN PRECISION FOR HAM: '+str(round(mean_precision_ham,5))

print 'STD. DEV. OF PRECISION FOR HAM: '+str(round(std_dev_precision_ham,5))

print 'VARIANCE OF PRECISION FOR HAM: '+str(round(variance_precision_ham,8))

print ''

print 'MEAN RECALL FOR HAM: '+str(round(mean_recall_ham,5))

print 'STD. DEV. OF RECALL FOR HAM: '+str(round(std_dev_recall_ham,5))

print 'VARIANCE OF RECALL FOR HAM: '+str(round(variance_recall_ham,8))

# we'll only use these return values to compute rankings

# for example in script which_attribute_case_1

if ret == 'utility':

return mean_accuracy * mean_precision_ham

elif ret =='accuracy':

return mean_accuracy

else:

print 'UNKNOWN METRIC: '+ret