def compute_quality_for_corpus(corpus_dir):
truth = utils.read_classification_from_file(os.path.join(corpus_dir, '!truth.txt'))
prediction = utils.read_classification_from_file(os.path.join(corpus_dir, '!prediction.txt'))
mat = confmat.BinaryConfusionMatrix('SPAM', 'OK')
mat.compute_from_dicts(truth, prediction)
p = mat.as_dict()
return quality_score(p['tp'], p['tn'], p['fp'], p['fn'])
def compute_quality_for_corpus(corpus_dir):
path = os.getcwd()
os.chdir(corpus_dir)
truth_dict = read_classification_from_file('!truth.txt')
pred_dict = read_classification_from_file('!prediction.txt')
os.chdir(path)
cm = BinaryConfusionMatrix(pos_tag='SPAM', neg_tag='OK')
cm.compute_from_dicts(truth_dict, pred_dict)
return quality_score(**cm.as_dict())
def compute_quality_for_corpus(corpus_dir):
truth = utils.read_classification_from_file(corpus_dir + os.path.sep +
"!truth.txt")
predicted = utils.read_classification_from_file(corpus_dir + os.path.sep +
"!prediction.txt")
predMat = confmat.BinaryConfusionMatrix("SPAM", "OK")
predMat.compute_from_dicts(truth, predicted)
confusion_dict = predMat.as_dict()
return quality_score(confusion_dict["tp"], confusion_dict["tn"],
confusion_dict["fp"], confusion_dict["fn"])
def compute_quality_for_corpus(corpus_dir): """ Compute quality_score() for predictions in corpus """ matrix = BinaryConfusionMatrix(pos_tag="SPAM", neg_tag="OK") matrix.compute_from_dicts( dict(read_classification_from_file(os.path.join(corpus_dir, "!truth.txt"))), dict(read_classification_from_file(os.path.join(corpus_dir, "!prediction.txt"))) ) return quality_score(**matrix.as_dict())
def compute_quality_for_corpus(corpus_dir):
truth_dict = utils.read_classification_from_file(
os.path.join(corpus_dir, '!truth.txt'))
prediction_dict = utils.read_classification_from_file(
os.path.join(corpus_dir, '!prediction.txt'))
confusion_matrix = BinaryConfusionMatrix(pos_tag, neg_tag)
confusion_matrix.compute_from_dicts(truth_dict, prediction_dict)
conf_dict = confusion_matrix.as_dict()
return quality_score(**conf_dict)
def compute_quality_for_corpus(corpus_dir):
truth_file_path = corpus_dir + '/!truth.txt'
prediction_file_path = corpus_dir + '/!prediction.txt'
truth_dict = utils.read_classification_from_file(truth_file_path)
pred_dict = utils.read_classification_from_file(prediction_file_path)
confusion_matrix = compute_confusion_matrix(truth_dict, pred_dict)
tp = getattr(confusion_matrix, 'tp')
tn = getattr(confusion_matrix, 'tn')
fp = getattr(confusion_matrix, 'fp')
fn = getattr(confusion_matrix, 'fn')
quality = quality_score(tp, tn, fp, fn)
return quality
def compute_quality_for_corpus(corpus_dir):
"""
Computes quality for given filter based on its prediction for given data
:param corpus_dir: directory with emails and prediction
:return:
"""
truth_dict = utils.read_classification_from_file(os.path.join(corpus_dir, '!truth.txt')) # read truth
pred_dict = utils.read_classification_from_file(os.path.join(corpus_dir, '!prediction.txt')) # read prediction
bcm = confmat.BinaryConfusionMatrix("SPAM", "OK") # create bin confusion matrix where SPAM is true and HAM is neg
bcm.compute_from_dicts(truth_dict, pred_dict) # compute from dictionaries
dic = bcm.as_dict()
return quality_score(dic['tp'], dic['tn'], dic['fp'], dic['fn']) # get spam filter score
def compute_quality_for_corpus(corpus_dir,
fn_weight=1,
fp_weight=10,
pos_tag="SPAM",
neg_tag="OK"):
matrix = confmat.BinaryConfusionMatrix(pos_tag, neg_tag)
truth_dict = read_classification_from_file(corpus_dir + "/!truth.txt")
pred_dict = read_classification_from_file(corpus_dir + "/!prediction.txt")
matrix.compute_from_dicts(truth_dict, pred_dict)
print(
f"Fp: {matrix.fp}\nFn: {matrix.fn}\nTp: {matrix.tp}\nTn: {matrix.tn}")
return (matrix.tp + matrix.tn) / (
fn_weight * matrix.fn + fp_weight * matrix.fp + matrix.tp + matrix.tn)
def compute_quality_for_corpus(corpus_dir):
""" Compute quality_score() for predictions in corpus """
matrix = BinaryConfusionMatrix(pos_tag="SPAM", neg_tag="OK")
matrix.compute_from_dicts(
dict(
read_classification_from_file(
os.path.join(corpus_dir, "!truth.txt"))),
dict(
read_classification_from_file(
os.path.join(corpus_dir, "!prediction.txt"))))
return quality_score(**matrix.as_dict())
def compute_quality_for_corpus(corpus_dir):
'''
Calculates quality for corpus
:param corpus_dir: path to corpus
:return: Number (0 - 1) representing quality
'''
truth = utils.read_classification_from_file(corpus_dir + "/" + TRUTH_FILE)
prediction = utils.read_classification_from_file(corpus_dir + "/" +
PREDICTION_FILE)
bcm = confmat.BinaryConfusionMatrix(pos_tag=SPAM_TAG, neg_tag=HAM_TAG)
bcm.compute_from_dicts(truth_dict=truth, pred_dict=prediction)
return quality_score(bcm.tp, bcm.tn, bcm.fp, bcm.fn)
def train(self, dir):
classification = utils.read_classification_from_file(dir +
"/!truth.txt")
spam_total = 0
ham_total = 0
file_name_with_data = dict()
for filename in os.listdir(dir):
if filename[0] == "!": continue
f = open(dir + "/" + filename, 'r', encoding="utf8")
file_name_with_data.update({filename: f.read()})
for file_name, email_content in file_name_with_data.items():
cls = classification[file_name]
if cls == "SPAM":
spam_total += 1
else:
ham_total += 1
for word in set(self.get_tokens(email_content)):
if cls == "SPAM":
self.spams[word] += 1
else:
self.hams[word] += 1
spam_probability = spam_total / (spam_total + ham_total)
ham_probability = 1 - spam_probability
for word in (set(self.spams.keys()) | set(self.hams.keys())):
self.spamicity[word] = (self.spams[word] / spam_total * spam_probability) / \
(self.spams[word] / spam_total * spam_probability + self.hams[
word] / ham_total * ham_probability)
def is_tag(self, ename, whichtag):
rel_path = self.path_to_mails + '/'
is_ham = read_classification_from_file(rel_path + TRUTHFILE)
if (is_ham[ename] == whichtag):
return True
else:
return False
def test_atom_filter(initialized_filter, train_dir, test_dir):
train_corp = TrainingCorpus(train_dir)
test_corp = Corpus(test_dir)
filter = initialized_filter
filter.train(train_corp)
prediction = dict()
for name, mail in test_corp.emails():
result = filter.test(mail)
if result == -1:
continue
elif result > POSITIVITY_THRESHOLD:
prediction[name] = POSITIVE
else:
prediction[name] = NEGATIVE
truth = read_classification_from_file(test_dir + '/' + TRUTHFILE)
conf_matrix = BinaryConfusionMatrix(POSITIVE, NEGATIVE)
conf_matrix.compute_from_dicts(truth, prediction)
matrix_dict = conf_matrix.as_dict()
# For testing purposes
print(matrix_dict)
score = quality_score(matrix_dict['tp'], \
matrix_dict['tn'], \
matrix_dict['fp'], \
matrix_dict['fn'])
return score
def train(
self,
file_path,
batch_size=10,
learning_rate=0.1,
lr_decay=0.05,
epochs=1000,
momentum=0.0,
tuning=False
): # analogous to PLR_filter, tuning parameter plots out a graph of mean loss over epochs
if tuning:
og_lr = learning_rate # original learning rate
x_plt = [] # x-axis (epochs)
y_plt = [] # y-axis (mean loss)
corpus = Corpus(file_path)
truth_dict = utils.read_classification_from_file(file_path +
"/!truth.txt")
got_data = True
mails_getter = corpus.emails()
batches = []
while got_data:
batch = []
for i in range(batch_size):
try:
email = next(mails_getter)
batch.append(
(email[1],
1 if truth_dict[email[0]] == self.pos_tag else 0))
except StopIteration:
got_data = False
break
batches.append(batch)
for e in range(epochs):
if tuning:
steps = 0
print(learning_rate)
self.init_momentums()
loss = 0
for batch in batches:
batch_vectors = [(m[0].get_feature_vector_lr()) for m in batch]
y = [m[1] for m in batch]
loss += self.gradient_descent(y, batch_vectors, learning_rate,
momentum)
if tuning:
steps += 1
print(f"trained on epoch #{e +1}")
learning_rate *= 1 / (1 + lr_decay * e)
if tuning:
y_plt.append(loss / steps)
x_plt.append(e)
if tuning:
plt.plot(x_plt, y_plt)
plt.title(
f"lr:{og_lr} lrd:{lr_decay} bs:{batch_size} m: {momentum} e:{epochs}"
)
plt.xlabel("epochs")
plt.ylabel("mean loss")
plt.show()
def count_spams(email_adress):
truth = read_classification_from_file(str(email_adress +
'/!truth.txt'))
count_spam = 0
for email in truth:
if truth[email] == 'SPAM':
count_spam += 1
return count_spam
def is_ham(self, email_soubor):
truth = read_classification_from_file(
str(self.email_adress + '/!truth.txt'))
for email in truth:
if email == email_soubor and truth[email] == 'SPAM':
return False
if email == email_soubor and truth[email] == 'OK':
return True
def compute_quality_for_corpus(corpus_dir):
truth_clasf = read_classification_from_file(corpus_dir + '/' + TRUTHFILE)
pred_clasf = read_classification_from_file(corpus_dir + '/' + PREDFILE)
conf_matrix = BinaryConfusionMatrix(POSITIVE, NEGATIVE)
conf_matrix.compute_from_dicts(truth_clasf, pred_clasf)
matrix_dict = conf_matrix.as_dict()
# Testing purposes
print(matrix_dict)
score = quality_score(matrix_dict['tp'], \
matrix_dict['tn'], \
matrix_dict['fp'], \
matrix_dict['fn'])
return score
def assertPredictionFileExistsAndContainsClassificationFor(self, expected):
fpath = os.path.join(CORPUS_DIR, PREDICTION_FILENAME)
self.assertTrue(os.path.isfile(fpath),
"The test() method did not create the !prediction.txt file.")
observed = read_classification_from_file(fpath)
self.assertEqual(
sorted(expected.keys()), sorted(observed.keys()),
'The !prediction.txt file does not contain decisions for the files it should.')
self.assertTrue(
all(value in (SPAM_TAG,HAM_TAG) for value in observed.values()))
def assertPredictionFileExistsAndContainsClassificationFor(self, expected):
fpath = os.path.join(CORPUS_DIR, PREDICTION_FILENAME)
self.assertTrue(os.path.isfile(fpath),
"The test() method did not create the !prediction.txt file.")
observed = read_classification_from_file(fpath)
self.assertEqual(
sorted(expected.keys()), sorted(observed.keys()),
'The !prediction.txt file does not contain decisions for the files it should.')
self.assertTrue(
all(value in (SPAM_TAG,HAM_TAG) for value in observed.values()))
def test_correctlyFormattedFile(self):
# Prepare fixture
expected = create_classification()
save_classification_to_file(expected, FILENAME)
# Exercise the SUT
observed = read_classification_from_file(FILENAME)
# Validate results
self.assertDictEqual(
expected, observed,
'The read file contents are not equal to the expected contents.')
def test_returnEmptyDict_forEmptyFile(self):
# Prepare fixture
expected = dict()
save_classification_to_file(expected, FILENAME)
# Excercise the SUT
observed = read_classification_from_file(FILENAME)
# Validate results
self.assertDictEqual(
expected, observed,
'The read dictionary shall be empty for empty file.')
def test_correctlyFormattedFile(self):
# Prepare fixture
expected = create_classification()
save_classification_to_file(expected, FILENAME)
# Exercise the SUT
with replaced_open():
observed = read_classification_from_file(FILENAME)
# Validate results
self.assertDictEqual(
expected, observed,
'The read file contents are not equal to the expected contents.')
def compute_quality_for_corpus(corpus_dir):
dirs = os.listdir(corpus_dir)
for file in dirs:
if '!' in file:
if file == "!truth.txt":
truth_dict = read_classification_from_file(corpus_dir + '/' +
file)
elif file == "!prediction.txt":
pred_dict = read_classification_from_file(corpus_dir + '/' +
file)
else:
truth_dict = None
pred_dict = None
else:
pass
cm1 = BinaryConfusionMatrix(pos_tag="SPAM", neg_tag="OK")
cm1.compute_from_dicts(truth_dict, pred_dict)
final_dict = cm1.as_dict()
return quality_score(final_dict['tp'], final_dict['tn'], final_dict['fp'],
final_dict['fn'])
def train(self, train_corpus_dir):
"""
Train the corpus on given emails dataset
:param train_corpus_dir:
"""
self.truth = utils.read_classification_from_file(
train_corpus_dir) # load truth
train_corpus = Corpus(train_corpus_dir)
self.get_SPAM_percentage(train_corpus) # not in use now
for val in email_keys: # get values for parts of email header
self.classify_part(train_corpus, val.lower())
self.classify_payload(
train_corpus) # get values for email payload/body
def test_returnEmptyDict_forEmptyFile(self):
# Prepare fixture
expected = dict()
save_classification_to_file(expected, FILENAME)
# Excercise the SUT
with replaced_open(): # Insist on explicit use of encoding
observed = read_classification_from_file(FILENAME)
# Validate results
self.assertDictEqual(
expected, observed,
'The read dictionary shall be empty for empty file.')
def train(self,
file_path,
batch_size=10,
learning_rate=0.1,
lr_decay=0.05,
epochs=1000,
momentum=0.0):
corpus = Corpus(file_path)
truth_dict = utils.read_classification_from_file(file_path +
"/!truth.txt")
got_data = True
mails_getter = corpus.emails()
batches = []
# loads all data from directory in batches of given size
while got_data:
batch = []
# loads a batch of given size, a smaller one if out of data
for i in range(batch_size):
try:
email = next(mails_getter)
batch.append(
(email[1],
1 if truth_dict[email[0]] == self.pos_tag else 0))
except StopIteration:
got_data = False
break
batches.append(batch)
for e in range(epochs): # trains multiple times on all batches
self.init_momentums()
for batch in batches: # performs gradient descent on each bach
# gets feature vectors for batch
feature_vectors = [
(m[0].get_feature_vector_plr()) for m in batch
] # gets feature vectors of the batch
y = [m[1] for m in batch] # gets the truth vector of the batch
for i in range(
self.subvector_count
): # weights for each subvector are trained separately
subvector_batch = [
v[i] for v in feature_vectors
] # isolates a subvector from all vectors
self.gradient_descent(i, y, subvector_batch, learning_rate,
momentum)
print(f"trained on epoch #{e +1}")
learning_rate *= 1 / (1 + lr_decay * e)
def train(self, path):
"""
Trains the corpus on given emails dataset
:param path: directory with emails
"""
self.truth_dict = read_classification_from_file(path + "/!truth.txt")
emails = TrainingCorpus(path)
spam_words, num_of_spam_emails = self.list_spam_ham_words(emails, True)
ham_words, num_of_ham_emails = self.list_spam_ham_words(emails, False)
self.portion_of_spam_emails = num_of_spam_emails / (
num_of_spam_emails + num_of_ham_emails)
self.all_words = Counter(join_spam_and_ham_words(
spam_words, ham_words))
self.num_of_spam_words = len(spam_words)
self.num_of_ham_words = len(ham_words)
self.num_of_all_words = len(self.all_words)
def train(self, file_path):
self.content_spam_dict = {}
self.content_ham_dict = {}
class_dict = utils.read_classification_from_file(file_path +
'/!truth.txt')
corpus = Corpus(file_path)
email_generator = corpus.emails()
content_counter_spam = Counter()
content_counter_ham = Counter()
content_wordcount_spam = 0
content_wordcount_ham = 0
spam_count = 0
ham_count = 0
every_word_content = set()
for mail in email_generator:
content_words = self.string_to_words(mail[1].content_no_html)
content_counter = Counter(content_words)
for word in content_words:
every_word_content.add(word)
if class_dict[mail[0]] == self.pos_tag:
spam_count += 1
content_counter_spam += content_counter
content_wordcount_spam += len(content_words)
else:
ham_count += 1
content_counter_ham += content_counter
content_wordcount_ham += len(content_words)
for word in every_word_content:
content_counter_ham[word] += 1
content_counter_spam[word] += 1
self.content_spam_dict[
word] = content_counter_spam[word] / content_wordcount_spam
self.content_ham_dict[
word] = content_counter_ham[word] / content_wordcount_ham
self.spam_probability = spam_count / (spam_count + ham_count)
self.ham_probability = ham_count / (spam_count + ham_count)
self.trained = True
def train(self, train_dir):
self.train_files_dict = read_classification_from_file(train_dir +
'/!truth.txt')
total_emails = len(self.train_files_dict)
for file in self.train_files_dict:
train_file_path = train_dir
train_file_path += '/' + file
mail = self.get_email(train_file_path)
mail_words = self.get_email_message(mail)
mail_unique_words = set(mail_words)
"""Counting spam and ham word appearances"""
if self.train_files_dict[file] == self.decision_table[1]:
self.spam_words_counter.update(mail_words)
self.total_spam_emails += 1
else:
self.ham_words_counter.update(mail_words)
self.words_counter.update(mail_words)
self.vocabulary.update(mail_unique_words)
self.total_ham_emails = total_emails - self.total_spam_emails
"""Computing the probability that a message containing a given word is spam."""
for word in self.vocabulary:
if self.ham_words_counter.get(
word, 0) == 0 and self.spam_words_counter.get(word, 0) > 0:
self.word_spaminess[word] = 0.99
elif self.ham_words_counter.get(
word, 0) > 0 and self.spam_words_counter.get(word, 0) == 0:
self.word_spaminess[word] = 0.01
else:
spam_likelihood = self.spam_words_counter.get(
word, 0) / self.total_spam_emails
ham_likelihood = self.ham_words_counter.get(
word, 0) / self.total_ham_emails
self.word_spaminess[word] = max(
spam_likelihood / (spam_likelihood + ham_likelihood), 0.01)
def get_class(self, filename):
dic = read_classification_from_file(self.path + TRUTH)
return dic[filename]
def __init__(self, path_to_train):
self.path_to_train = path_to_train
self.path_to_truth = os.path.join(path_to_train, '!truth.txt')
self.truth_dic = utils.read_classification_from_file(self.path_to_truth)
def train(self, training_corpus_path):
self.dictionary = read_classification_from_file(training_corpus_path +
'/!truth.txt')
self.dictionary.fromkeys(self.dictionary, self.table[1])
def train(self, training_corpus_path):
self.dictionary = read_classification_from_file(training_corpus_path +
'/!truth.txt')
self.dictionary = {x: choice(self.table) for x in self.dictionary}
def __init__(self, path):
Corpus.__init__(self, path)
self.path = path
self.truth_dict = read_classification_from_file(self.path +
"/!truth.txt")
def get_class(self, filename):
dic = read_classification_from_file(self.path+TRUTH)
return dic[filename]
def train(self, path_to_training_corpus):
self.trained_data_dict = utils.read_classification_from_file(path_to_training_corpus)
def get_truth_class(self):
if (not os.path.isfile(os.path.join(self.path,TRUTH))):
raise FileExistsError("File " + os.path.join(self.path,TRUTH) + " does not exists!")
emails_class = utils.read_classification_from_file(os.path.join(self.path,TRUTH))
for name, body in self.emails():
yield (body,emails_class[name])
f.write(part + " " + key + " " +
str(self.classification[part][key]) + "\n")
def convert(file, out):
"""
I used this to convert the truth file for emails I found on internet, where the SPAM or HAM was before file name
:param file:
:param out:
"""
dic = {}
with open(file, 'r', encoding="utf-8") as f:
for line in f.readlines():
key, val = line.split()
dic[val] = key
with open(out, 'w', encoding="utf-8") as f:
for key in dic:
f.write(key + " " + dic[key] + "\n")
if __name__ == "__main__":
# used for testing and debugging
filter = MyFilter()
filter.test("SPAM-data/2/")
confusion_matrix = BinaryConfusionMatrix('SPAM', 'OK')
confusion_matrix.compute_from_dicts(
utils.read_classification_from_file("SPAM-data/2/"),
filter.predictions)
print("Quality: %.2f%%" % (confusion_matrix.quality_score() * 100))
utils.clean_up("SPAM-data/2/") # clean !truth
