def performance_report(y_test, y_pred):
try:
print(classification_report(y_test, y_pred, target_names=categories, zero_division=0))
except ValueError as e:
logger.critical(f'Classification report: Invalid param `target_names`.')
logger.error(e)
print(classification_report(y_test, y_pred, zero_division=0))
def save_obj(obj, filepath):
try:
with open(filepath, 'wb') as f:
pickle.dump(obj, f)
logger.debug(f'Pickled object `{filepath}` saved to disk.')
except Exception as e:
logger.critical(f'An unexpected error occurred while saving the object `{filepath}`.')
logger.error(e)
def get_all_tags():
try:
conn = sqlite3.connect(DBFILE)
c = conn.cursor()
c.execute('SELECT filepath, tag, text, hosts FROM tags')
res = c.fetchall()
return res
except Exception as e:
logger.critical(f'Unable to fetch tags from database.')
logger.error(e)
sys.exit(2)
def load_obj(filepath):
try:
with open(filepath, 'rb') as f:
obj = pickle.load(f, encoding='latin1')
logger.debug(f'Pickled object loaded from `{filepath}`.')
return obj
except FileNotFoundError:
logger.critical(f'Pickled object `{filepath}` was not found. Exiting.')
sys.exit(2)
except Exception as e:
logger.critical(f'An unexpected error occurred while loading the object `{filepath}`.')
logger.error(e)
sys.exit(2)
def trainv2(std=False, algo='mnb', n=None):
"""Train a model using Naive Bayes
Parameters
----------
std : bool
Standardize the data.
algo : str
The algorithm to use. Can be either `mnb` or `cnb`
n : int
Select n samples from each category. (Default: All)
Returns
-------
"""
df = create_dataframe(n=n)
counts, df = process_dataframe(df, algo=algo)
### Todo: Remove
save_obj(df, 'v2_dataframe.p')
save_obj(counts, 'v2_counts.p')
###
# messages_train, messages_test, labels_train, labels_test
x_train, x_test, y_train, y_test = train_test_split(counts,
df['label'],
test_size=0.3,
random_state=69)
if std:
x_train, x_test = standardize(x_train, x_test)
if algo == 'cnb':
model = ComplementNB()
elif algo == 'mnb':
model = MultinomialNB()
else:
logger.critical(
f'Parameter `algo` specifies unknown algorithm. Defaulting to `mnb`.'
)
model = MultinomialNB()
model.fit(x_train, y_train)
save_model(model, version='v2', algo=algo)
y_pred = model.predict(x_test)
print(f'Accuracy: {accuracy_score(y_test, y_pred)}')
measure.evaluate(model, x_test, y_test)
measure.performance_report(y_test, y_pred)
measure.plot_confusion_mat(model, x_test, y_test)
title = f'Learning Curves ({algo.upper()})'
learning_curve.plot(model, x_test, y_test, title=title)
def warn_failed(failed):
logger.critical(f'An error occurred with {len(failed)} files. See `failed.out` for filenames.')
try:
fail_cat = {
'legit': len([fn for fn in failed if fn.startswith('legit')]),
'spam': len([fn for fn in failed if fn.startswith('spam')]),
'phish': len([fn for fn in failed if fn.startswith('phish')]),
'malware': len([fn for fn in failed if fn.startswith('malware')]),
'fraud': len([fn for fn in failed if fn.startswith('fraud')])
}
logger.critical(f'Failed:\n{fail_cat}')
except:
pass
with open('failed.out', 'w') as f:
f.write('\n'.join(failed))
def train(std=False, algo='mnb'):
"""Train a model using Naive Bayes
Parameters
----------
std : bool
Standardize the data
algo : str
The algorithm to use. Can be either `mnb` or `cnb`
Returns
-------
"""
dictionary = make_dictionary()
features, labels = make_dataset(dictionary)
### Todo: Remove
save_obj(features, 'v1_features.p')
save_obj(labels, 'v1_labels.p')
###
x_train, x_test, y_train, y_test = train_test_split(features,
labels,
test_size=0.3,
random_state=69)
if std:
x_train, x_test = standardize(x_train, x_test)
if algo == 'cnb':
model = ComplementNB()
elif algo == 'mnb':
model = MultinomialNB()
else:
logger.critical(
f'Parameter `algo` specifies unknown algorithm. Defaulting to `mnb`.'
)
model = MultinomialNB()
model.fit(x_train, y_train)
save_model(model, version='v1', algo=algo)
y_pred = model.predict(x_test)
print(f'Accuracy: {accuracy_score(y_test, y_pred)}')
measure.evaluate(model, x_test, y_test)
measure.performance_report(y_test, y_pred)
measure.plot_confusion_mat(model, x_test, y_test)
title = f'Learning Curves ({algo.upper()})'
learning_curve.plot(model, x_test, y_test, title=title)
def get_n_tags(n):
cats = [0, 1, 2, 3, 4]
res = []
try:
conn = sqlite3.connect(DBFILE)
c = conn.cursor()
for cat in cats:
try:
c.execute('SELECT filepath, tag, text, hosts FROM tags WHERE tag=?', (cat,))
tags = c.fetchall()
if n <= len(tags):
res += random.sample(tags, n)
else:
logger.warn(f'n={n} is higher than the number of samples in {cat}. Selecting {len(tags)} (all) samples.')
res += random.sample(tags, len(tags))
except Exception as e:
logger.critical(f'Unable to fetch {n} tags for category {cat}.')
logger.error(e)
return res
except Exception as e:
logger.critical(f'Unable to fetch tags from database.')
logger.error(e)
sys.exit(2)
import sys
from katatasso.helpers.const import CATEGORIES
from katatasso.helpers.extraction import (get_tfidf_counts, make_dictionary,
process_dataframe)
from katatasso.helpers.logger import rootLogger as logger
from katatasso.helpers.utils import load_model
try:
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import juicer
import numpy as np
import pandas as pd
except ModuleNotFoundError as e:
logger.critical(f'Module `{e.name}` not found. Please install before proceeding.')
sys.exit(2)
def classify(text, algo='mnb'):
"""Classify the text using a Naive Bayes model with
word vector counts
Parameters
----------
text : str
The text input to classify
algo : str
The algorithm to use
`mnb` for Multinomial Naïve Bayes,
def main():
TEXT = None
CONFIG = {}
result = {}
if len(sys.argv) < 2:
print(HELPMSG)
logger.critical('No input specified')
sys.exit(2)
argv = sys.argv[1:]
try:
opts, args = getopt.getopt(argv, 'hf:st:c:na:l:o:d:v', [
'help', 'infile=', 'stdin', 'std', 'algo=', '--limit', 'train=',
'classify=', 'outfile=', 'format=', 'verbose', 'log-file'
])
except getopt.GetoptError:
print(HELPMSG)
sys.exit(2)
if not opts:
print(HELPMSG)
sys.exit(0)
"""
Increase verbosity
"""
opts_v = len(list(filter(lambda opt: opt == ('-v', ''), opts)))
if opts_v > 4:
opts_v = 4
v = 0
while v < opts_v:
increase_log_level()
v += 1
"""
Log to file
"""
if v > 0:
enable_logfile = list(
filter(lambda opt: opt[0] in ('--log-file'), opts))
if enable_logfile:
log_to_file()
for opt, arg in opts:
if opt == '--help':
print(HELPMSG)
sys.exit(0)
elif opt in ('-f', '--infile'):
file_path = arg
logger.debug(f'Using input file {file_path}')
try:
with open(file_path, 'r') as f:
TEXT = f.read()
except FileNotFoundError:
logger.critical(
f'The specified file {file_path} does not exist.')
sys.exit(2)
except Exception as e:
logger.critical(
f'An error occurred while reading the file `{file_path}`.')
logger.error(e)
sys.exit(2)
elif opt in ('-s', '--stdin'):
try:
logger.debug(f'Using input from STDIN')
TEXT = sys.stdin.read()
except Exception as e:
logger.critical(f'An error occurred while reading from stdin.')
logger.error(e)
sys.exit(2)
elif opt in ('-n', '--std'):
logger.debug(f'OPTION: Standardizing data.')
CONFIG['std'] = True
elif opt in ('-a', '--algo'):
logger.debug('OPTION: Using Complement Naïve Bayes algorithm')
if arg not in ['mnb', 'cnb']:
print(HELPMSG)
logger.critical(
f'The specified algorithm `{arg}` is not available.')
sys.exit(2)
else:
CONFIG['algo'] = arg
elif opt in ('-l', '--limit'):
if arg.isnumeric:
logger.debug(f'OPTION: Using n={arg} samples.')
CONFIG['n'] = int(arg)
else:
print(HELPMSG)
logger.critical(f'n={arg} is non-numeric.')
sys.exit(2)
elif opt in ('-t', '--train'):
logger.debug(f'ACTION: Creating model from dataset')
if arg == 'v1':
katatasso.train(std=CONFIG.get('std', False),
algo=CONFIG.get('algo', 'mnb'))
elif arg == 'v2':
katatasso.trainv2(std=CONFIG.get('std', False),
algo=CONFIG.get('algo', 'mnb'),
n=CONFIG.get('n', None))
else:
logger.critical(
f'Please specify either `v1` or `v2`. E.g. `katatasso -t v2`'
)
sys.exit(2)
elif opt in ('-c', '--classify'):
if TEXT:
logger.debug(f'ACTION: Classifying input')
if CONFIG.get('cnb'):
algo = 'cnb'
else:
algo = 'mnb'
if arg == 'v1':
category = katatasso.classify(TEXT, algo=algo)
elif arg == 'v2':
category = katatasso.classifyv2(TEXT, algo=algo)
else:
logger.critical(
f'Please specify either `v1` or `v2`. E.g. `katatasso -c v2`'
)
sys.exit(2)
result = {
'category': category,
'accuracy': 'n/a',
'alias': CATEGORIES.get(category)
}
else:
logger.critical(f'Missing input (specify using -f or -s)')
sys.exit(2)
elif opt in ('-o', '--outfile'):
logger.debug(f'CONFIG: Setting output file to {arg}')
CONFIG['outfile'] = arg
elif opt in ('-d', '--format'):
if arg in ['plain', 'json']:
logger.debug(f'CONFIG: Setting output file format to {arg}')
CONFIG['format'] = arg
else:
logger.critical('Invalid format. Must be one of [plain, json]')
sys.exit(2)
if result:
outformat = CONFIG.get('format')
outfile = CONFIG.get('outfile')
if outfile:
ext = 'json' if outformat == 'json' else 'txt'
fname = f'{outfile}.{ext}'
if outformat == 'plain':
with open(fname, 'w') as f:
f.write('\n'.join(list(result.values())))
elif outformat == 'json':
import json
with open(fname, 'w', encoding='utf-8') as f:
json.dump(result, f, ensure_ascii=False, indent=4)
logger.debug(f'Results saved to file `{fname}`')
sys.exit(0)
else:
for k, v in result.items():
print(f'{k}: {v}')
sys.exit(0)
