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pwd_guess_unit.py
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pwd_guess_unit.py
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# -*- coding: utf-8 -*-
# author: William Melicher
from keras.models import Sequential, model_from_json
from keras.layers.core import Activation, Dense, RepeatVector
from keras.layers import recurrent
import unittest
from unittest.mock import MagicMock, Mock
import argparse
import tempfile
import shutil
import os.path
import gzip
import io
import json
import numpy as np
import csv
import time
import string
import random
import re
import logging
import itertools
import sys
import glob
from tensorflow.python.client import device_lib
import yaml
import pwd_guess
import generator
TMPDIR = '/tmp'
RUN_SLOW_TESTS = False
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--tmp-dir', help='directory temporary for tmp files', default=TMPDIR)
parser.add_argument(
'--run-slow', help='Also run slow tests', action='store_true')
ns, args = parser.parse_known_args(namespace=unittest)
return ns, sys.argv[:1] + args
if __name__ == '__main__':
args, argv = parse_args() # run this first before defining tests
sys.argv[:] = argv # create cleans argv for main()
TMPDIR = args.tmp_dir
RUN_SLOW_TESTS = args.run_slow
class CharacterTableTest(unittest.TestCase):
def test_table(self):
ctable = pwd_guess.CharacterTable('ab', 2)
np.testing.assert_array_equal(ctable.encode('aa'),
np.array([[True, False],
[True, False]]))
np.testing.assert_array_equal(ctable.encode('ba'),
np.array([[False, True],
[True, False]]))
self.assertEqual(ctable.get_char_index('a'), 0)
self.assertEqual(ctable.get_char_index('b'), 1)
np.testing.assert_array_equal(ctable.encode_many(['aa', 'ba']),
np.array([[[True, False],
[True, False]],
[[False, True],
[True, False]]]))
self.assertEqual(ctable.translate('aba'), 'aba')
self.assertEqual(ctable.translate('aba'), 'aba')
def test_embedding(self):
ctable = pwd_guess.CharacterTable('ab', 2, embedding=True)
np.testing.assert_array_equal(
ctable.encode('aa'), np.array([0, 0]))
np.testing.assert_array_equal(
ctable.encode('ba'), np.array([1, 0]))
self.assertEqual(ctable.get_char_index('a'), 0)
self.assertEqual(ctable.get_char_index('b'), 1)
np.testing.assert_array_equal(
ctable.encode_many(['aa', 'ba']), np.array([[0, 0], [1, 0]]))
self.assertEqual(ctable.translate('aba'), 'aba')
self.assertEqual(ctable.translate('aba'), 'aba')
def test_encode_many_chunks(self):
ctable = pwd_guess.CharacterTable('abc', 4, embedding=True,
sequence_model=pwd_guess.Sequence.MANY_TO_ONE)
indices, strings = ctable.encode_many_chunks(['abcabc', 'aaabbb'], 6)
np.testing.assert_array_equal(indices, np.array([[0, 1, 2, 0], [2, 0, 1, 2],
[0, 0, 0, 1], [0, 1, 1, 1]]))
self.assertListEqual(strings, ['abca', 'cabc', 'aaab', 'abbb'])
zero = [True, False, False]
one = [False, True, False]
two = [False, False, True]
ctable = pwd_guess.CharacterTable('abc', 4, embedding=False,
sequence_model=pwd_guess.Sequence.MANY_TO_ONE)
indices, strings = ctable.encode_many_chunks(['abcabc', 'aaabbb'], 6)
np.testing.assert_array_equal(indices, np.array([[zero, one, two, zero], [two, zero, one, two],
[zero, zero, zero, one], [zero, one, one, one]]))
self.assertListEqual(strings, ['abca', 'cabc', 'aaab', 'abbb'])
ctable = pwd_guess.CharacterTable('abc', 4, embedding=True,
sequence_model=pwd_guess.Sequence.MANY_TO_MANY)
indices, strings = ctable.encode_many_chunks(['abcabc', 'aaabbb'], 6)
np.testing.assert_array_equal(indices, np.array([[0, 1, 2, 0], [2, 0, 1, 2],
[0, 0, 0, 1], [0, 1, 1, 1]]))
self.assertListEqual(strings, ['abca', 'cabc', 'aaab', 'abbb'] )
class OptimizingTableTest(unittest.TestCase):
def test_table(self):
ctable = pwd_guess.OptimizingCharacterTable('abcd', 2, 'ab', False)
self.assertEqual(ctable.rare_character_preimage, {
'a' : ['a', 'b']
})
np.testing.assert_array_equal(ctable.encode('cc'),
np.array([[False, True, False],
[False, True, False]]))
np.testing.assert_array_equal(ctable.encode('dc'),
np.array([[False, False, True],
[False, True, False]]))
np.testing.assert_array_equal(ctable.encode('aa'),
np.array([[True, False, False],
[True, False, False]]))
self.assertEqual(ctable.get_char_index('a'), 0)
self.assertEqual(ctable.get_char_index('b'), 0)
self.assertEqual(ctable.get_char_index('c'), 1)
self.assertEqual(ctable.get_char_index('d'), 2)
self.assertEqual(ctable.translate('cdb'), 'cda')
self.assertEqual(ctable.translate('cda'), 'cda')
def test_table_upper(self):
ctable = pwd_guess.OptimizingCharacterTable('abcdABCD:', 2, ':', True)
self.assertEqual(ctable.rare_character_preimage, {
':' : [':'],
'a' : ['A', 'a'],
'b' : ['B', 'b'],
'c' : ['C', 'c'],
'd' : ['D', 'd']
})
np.testing.assert_array_equal(ctable.encode('cc'), np.array(
[[False, False, False, True, False],
[False, False, False, True, False]]))
np.testing.assert_array_equal(ctable.encode('dc'), np.array(
[[False, False, False, False, True],
[False, False, False, True, False]]))
np.testing.assert_array_equal(ctable.encode('dC'), np.array(
[[False, False, False, False, True],
[False, False, False, True, False]]))
np.testing.assert_array_equal(ctable.encode('AA'), np.array(
[[False, True, False, False, False],
[False, True, False, False, False]]))
self.assertEqual(ctable.get_char_index('a'), 1)
self.assertEqual(ctable.get_char_index('b'), 2)
self.assertEqual(ctable.get_char_index('c'), 3)
self.assertEqual(ctable.get_char_index('d'), 4)
self.assertEqual(ctable.get_char_index(':'), 0)
self.assertEqual(ctable.get_char_index('B'), 2)
self.assertEqual(ctable.get_char_index('D'), 4)
def test_table_upper_and_rare(self):
ctable = pwd_guess.OptimizingCharacterTable('abcdABCD:', 2, ':A', True)
self.assertEqual(ctable.rare_characters, ':')
def test_table_context(self):
ctable = pwd_guess.CharacterTable('abc\n', 2)
np.testing.assert_array_equal(
ctable.encode_many(['', 'a', 'ab', 'abc']),
[[[False, False, False, False],
[False, False, False, False]],
[[False, True, False, False],
[False, False, False, False]],
[[False, True, False, False],
[False, False, True, False]],
[[False, False, True, False],
[False, False, False, True]]])
class PreprocessorTest(unittest.TestCase):
def test_train_set(self):
p = pwd_guess.Preprocessor(pwd_guess.ModelDefaults(max_len = 40))
p.begin([('pass', 1)])
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(['', 'p', 'pa', 'pas', 'pass'],
['p', 'a', 's', 's', '\n'])),
set(zip(list(prefix), list(suffix))))
self.assertTrue(all(map(lambda x: x == 1, weight)))
def test_training_set_small(self):
t = pwd_guess.Preprocessor(
pwd_guess.ModelDefaults(max_len = 3, min_len = 3))
t.begin([('aaa', 1)])
prefix, suffix, _ = t.next_chunk()
self.assertEqual(set(zip(['', 'a', 'aa', 'aaa'],
['a', 'a', 'a', '\n'])),
set(zip(list(prefix), list(suffix))))
def test_training_set_small_multi_use(self):
t = pwd_guess.Preprocessor(
pwd_guess.ModelDefaults(max_len = 3, min_len = 3))
t.begin([('aaa', 1)])
prefix, suffix, _ = t.next_chunk()
self.assertEqual(set(zip(['', 'a', 'aa', 'aaa'],
['a', 'a', 'a', '\n'])),
set(zip(list(prefix), list(suffix))))
t.reset()
t.begin([('aaa', 1)])
prefix, suffix, _ = t.next_chunk()
self.assertEqual(set(zip(['', 'a', 'aa', 'aaa'],
['a', 'a', 'a', '\n'])),
set(zip(list(prefix), list(suffix))))
def test_train_construct_dict(self):
t = pwd_guess.Preprocessor(pwd_guess.ModelDefaults(
simulated_frequency_optimization = True))
t.begin({'pass' : 1}.items())
prefix, suffix, weight = t.next_chunk()
self.assertEqual(set(zip(['', 'p', 'pa', 'pas', 'pass'],
['p', 'a', 's', 's', '\n'],
[1, 1, 1, 1, 1])),
set(zip(list(prefix), list(suffix), list(weight))))
def test_resetable(self):
config = pwd_guess.ModelDefaults(
training_main_memory_chunksize = 2,
training_chunk = 2)
p = pwd_guess.Preprocessor(config)
resetable = Mock()
resetable.as_iterator = MagicMock(return_value = [
('pwd', 1), ('j', 1), ('y', 1), ('a', 1), ('b', 1)])
p.begin_resetable(resetable)
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([('', 'p', 1), ('p', 'w', 1), ('pw', 'd', 1),
('pwd', '\n', 1), ('', 'j', 1), ('j', '\n', 1)]))
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([('', 'y', 1), ('y', '\n', 1),
('', 'a', 1), ('a', '\n', 1)]))
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([('', 'b', 1), ('b', '\n', 1)]))
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([]))
p.reset()
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([('', 'p', 1), ('p', 'w', 1), ('pw', 'd', 1),
('pwd', '\n', 1), ('', 'j', 1), ('j', '\n', 1)]))
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([('', 'y', 1), ('y', '\n', 1),
('', 'a', 1), ('a', '\n', 1)]))
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([('', 'b', 1), ('b', '\n', 1)]))
prefix, suffix, weight = p.next_chunk()
self.assertEqual(set(zip(list(prefix), list(suffix), list(weight))),
set([]))
class TrainerTest(unittest.TestCase):
def test_accuracy(self):
config = pwd_guess.ModelDefaults(max_len = 5)
pre = pwd_guess.Preprocessor(config)
pre.begin([('pass', 1)])
t = pwd_guess.Trainer(pre, config=config)
mock_model = Mock()
mock_model.train_on_batch = MagicMock(return_value = (0.5, 0.5))
mock_model.test_on_batch = MagicMock(return_value = (0.5, 0.5))
t.model = mock_model
self.assertEqual((0.5, False), t.train_model_generation())
def test_train_model(self):
config = pwd_guess.ModelDefaults(max_len = 5, generations = 20)
config.sequence_model = pwd_guess.Sequence.MANY_TO_ONE
pre = pwd_guess.Preprocessor(config)
pre.begin([('pass', 1)])
t = pwd_guess.Trainer(pre, config=config)
mock_model = Mock()
mock_model.train_on_batch = MagicMock(return_value = (0.5, 0.5))
mock_model.test_on_batch = MagicMock(return_value = (0.5, 0.5))
t.model = mock_model
t.train_model(pwd_guess.ModelSerializer())
self.assertEqual(t.generation, 20)
def test_char_table_no_error(self):
t = pwd_guess.Trainer(None)
self.assertNotEqual(None, t.ctable)
t.ctable.encode('1234' + ('\n' * 36), 40)
def test_output_as_np_many_one_no_embedding(self):
config = pwd_guess.ModelDefaults(max_len=5, generations=20)
config.sequence_model = pwd_guess.Sequence.MANY_TO_ONE
config.embedding_layer = False
pre = pwd_guess.Preprocessor(config)
pre.begin([('pass', 1)])
t = pwd_guess.Trainer(pre, config=config)
x_vec, y_vec, weight_vec = t.next_train_set_as_np()
self.assertTupleEqual(x_vec.shape, (5, t.config.max_len, len(t.config.char_bag)))
self.assertTupleEqual(y_vec.shape, (5, len(t.config.char_bag)))
def test_output_as_np_many_one_embedding(self):
config = pwd_guess.ModelDefaults(max_len=5, generations=20)
config.sequence_model = pwd_guess.Sequence.MANY_TO_ONE
config.embedding_layer = True
config.embedding_size = 20
pre = pwd_guess.Preprocessor(config)
pre.begin([('pass', 1)])
t = pwd_guess.Trainer(pre, config=config)
x_vec, y_vec, weight_vec = t.next_train_set_as_np()
self.assertTupleEqual(x_vec.shape, (5, t.config.max_len))
self.assertTupleEqual(y_vec.shape, (5, len(t.config.char_bag)))
def test_output_as_np_many_many_embedding(self):
config = pwd_guess.ModelDefaults(max_len=5, generations=20)
config.sequence_model = pwd_guess.Sequence.MANY_TO_MANY
config.embedding_layer = True
config.embedding_size = 20
config.sequence_model_updates()
pre = pwd_guess.ManyToManyPreprocessor(config)
pre.begin([('pass', 1)])
t = pwd_guess.ManyToManyTrainer(pre, config=config)
x_vec, y_vec, weight_vec = t.next_train_set_as_np()
self.assertTupleEqual(x_vec.shape, (1, t.config.max_len))
self.assertTupleEqual(y_vec.shape, (1, t.config.max_len, len(t.config.char_bag)))
def test_output_as_np_many_many_no_embedding(self):
config = pwd_guess.ModelDefaults(max_len=5, generations=20)
config.sequence_model = pwd_guess.Sequence.MANY_TO_MANY
config.embedding_layer = False
config.sequence_model_updates()
pre = pwd_guess.ManyToManyPreprocessor(config)
pre.begin([('pass', 1)])
t = pwd_guess.ManyToManyTrainer(pre, config=config)
x_vec, y_vec, weight_vec = t.next_train_set_as_np()
self.assertTupleEqual(x_vec.shape, (1, t.config.max_len, len(t.config.char_bag)))
self.assertTupleEqual(y_vec.shape, (1, t.config.max_len, len(t.config.char_bag)))
def test_build_model(self):
t = pwd_guess.Trainer(['pass'], config=pwd_guess.ModelDefaults(
hidden_size = 12, layers = 1))
t.build_model()
self.assertNotEqual(None, t.model)
def test_build_model_conv(self):
t = pwd_guess.Trainer(['pass'], config=pwd_guess.ModelDefaults(
layers = 2,
dense_layers = 1,
context_length = 10,
convolutional_kernel_size = 3,
model_type = 'Conv1D'))
t.build_model()
self.assertNotEqual(None, t.model)
def test_build_model_deep(self):
t = pwd_guess.Trainer(['pass'], config=pwd_guess.ModelDefaults(
hidden_size = 12, layers = 1, deep_model = True))
t.build_model()
self.assertNotEqual(None, t.model)
def test_build_model_deep_no_layers(self):
t = pwd_guess.Trainer(['pass'], config=pwd_guess.ModelDefaults(
hidden_size = 12, layers = 0, deep_model = True))
t.build_model()
self.assertNotEqual(None, t.model)
def test_train_set_np_two(self):
config = pwd_guess.ModelDefaults()
pre = pwd_guess.Preprocessor(config)
pre.begin([('pass', 1), ('word', 1)])
t = pwd_guess.Trainer(pre, config=config)
t.next_train_set_as_np()
def test_test_set(self):
config = pwd_guess.ModelDefaults(train_test_ratio = 10)
t = pwd_guess.Trainer(pwd_guess.Preprocessor(config), config=config)
a = np.zeros((10, 1, 1), dtype = np.bool)
b = np.zeros((10, 1, 1), dtype = np.bool)
w = np.zeros((10, 1))
x_t, x_v, y_t, y_v, w_t, w_v = t.test_set(a, b, w)
self.assertEqual(9, len(x_t))
self.assertEqual(1, len(x_v))
self.assertEqual(9, len(y_t))
self.assertEqual(1, len(y_v))
self.assertEqual(9, len(w_t))
self.assertEqual(1, len(w_v))
def test_test_set_small(self):
config = pwd_guess.ModelDefaults(train_test_ratio = 10)
t = pwd_guess.Trainer(pwd_guess.Preprocessor(config), config=config)
a = np.zeros((5, 1, 1), dtype = np.bool)
b = np.zeros((5, 1, 1), dtype = np.bool)
w = np.zeros((5, 1), dtype = np.bool)
x_t, x_v, y_t, y_v, w_t, w_v = t.test_set(a, b, w)
self.assertEqual(4, len(x_t))
self.assertEqual(1, len(x_v))
self.assertEqual(4, len(y_t))
self.assertEqual(1, len(y_v))
self.assertEqual(4, len(w_t))
self.assertEqual(1, len(w_v))
def test_early_stopping(self):
config = pwd_guess.ModelDefaults(early_stopping=True, early_stopping_patience=100)
t = pwd_guess.Trainer(pwd_guess.Preprocessor(config), config=config)
serializer = Mock()
serializer.save_model = MagicMock(return_value=None)
loss_function = [(x/1000)**2 - (x/1000) + 1 for x in range(0,1500)]
for i, loss in enumerate(loss_function):
stop = t.early_stopping(loss,serializer)
if i == 601:
self.assertTrue(stop)
break
else:
self.assertFalse(stop)
class ModelDefaultsTest(unittest.TestCase):
def test_get_default(self):
m = pwd_guess.ModelDefaults()
self.assertEqual(pwd_guess.ModelDefaults.hidden_size, m.hidden_size)
def test_get_set(self):
m = pwd_guess.ModelDefaults(hidden_size = 8)
self.assertEqual(8, m.hidden_size)
m = pwd_guess.ModelDefaults()
self.assertEqual(pwd_guess.ModelDefaults.hidden_size, m.hidden_size)
def test_get_set_dict(self):
m = pwd_guess.ModelDefaults({'hidden_size' : 8})
self.assertEqual(8, m.hidden_size)
def test_as_dict(self):
m = pwd_guess.ModelDefaults()
self.assertTrue(m.as_dict()['hidden_size'],
pwd_guess.ModelDefaults.hidden_size)
def test_serialize_dict(self):
m = pwd_guess.ModelDefaults()
self.assertTrue(json.dumps(m.as_dict()) is not None)
def test_set(self):
m = pwd_guess.ModelDefaults()
m.test = 1
m.hidden_size = 444
self.assertEqual(1, m.test)
other = pwd_guess.ModelDefaults()
self.assertFalse(hasattr(other, 'test'))
self.assertNotEqual(other.hidden_size, 444)
def test_intermediate_files(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as intermediate_file:
m = pwd_guess.ModelDefaults(
intermediate_fname = intermediate_file.name)
m.set_intermediate_info('test', 8)
self.assertEqual(m.get_intermediate_info('test'), 8)
m = pwd_guess.ModelDefaults(
intermediate_fname = intermediate_file.name)
self.assertEqual(m.get_intermediate_info('test'), 8)
def test_init(self):
self.assertTrue(pwd_guess.ModelDefaults().context_length, 40)
self.assertTrue(
pwd_guess.ModelDefaults(context_length = 20).context_length, 20)
self.assertTrue(
pwd_guess.ModelDefaults(max_len = 20).context_length, 20)
self.assertTrue(pwd_guess.ModelDefaults(
max_len = 20, context_length = 15).context_length, 20)
def test_override_from_commandline_one(self):
args = pwd_guess.ModelDefaults(context_length=20)
self.assertEqual(20, args.context_length)
args.override_from_commandline('context_length=10')
self.assertEqual(10, args.context_length)
def test_override_from_commandline_two(self):
args = pwd_guess.ModelDefaults(context_length=20, freq_format='hex')
self.assertEqual(20, args.context_length)
self.assertEqual('hex', args.freq_format)
args.override_from_commandline('context_length=10;freq_format=normal')
self.assertEqual(10, args.context_length)
self.assertEqual('normal', args.freq_format)
def test_override_from_commandline_two_ending_semi(self):
args = pwd_guess.ModelDefaults(context_length=20, freq_format='hex')
self.assertEqual(20, args.context_length)
self.assertEqual('hex', args.freq_format)
args.override_from_commandline('context_length=10;freq_format=normal;')
self.assertEqual(10, args.context_length)
self.assertEqual('normal', args.freq_format)
def test_concurrent_mod(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tfile:
m = pwd_guess.ModelDefaults(intermediate_fname=tfile.name)
m.set_intermediate_info('test', ['value'])
self.assertListEqual(m.get_intermediate_info('test'), ['value'])
time.sleep(.01)
tfile.write('{"test" : ["value2"]}'.encode('utf8'))
tfile.flush()
self.assertListEqual(m.get_intermediate_info('test'), ['value2'])
def test_load_infer_type(self):
with tempfile.NamedTemporaryFile(
dir=TMPDIR, suffix='.json', mode='w') as tfile:
json.dump({'hidden_size' : 42}, tfile)
tfile.flush()
m = pwd_guess.ModelDefaults.fromFile(tfile.name)
self.assertEqual(m.hidden_size, 42)
with tempfile.NamedTemporaryFile(
dir=TMPDIR, suffix='.yaml', mode='w') as tfile:
yaml.dump({'hidden_size' : 42}, tfile)
tfile.flush()
m = pwd_guess.ModelDefaults.fromFile(tfile.name)
self.assertEqual(m.hidden_size, 42)
with tempfile.NamedTemporaryFile(
dir=TMPDIR, suffix='.cfg', mode='w') as tfile:
json.dump({'hidden_size' : 42}, tfile)
tfile.flush()
m = pwd_guess.ModelDefaults.fromFile(tfile.name)
self.assertEqual(m.hidden_size, 42)
class PwdListTest(unittest.TestCase):
def setUp(self):
self.tempdir = tempfile.mkdtemp(dir=TMPDIR)
self.fcontent = 'pass \nword\n'
def make_file(self, fname, opener):
self.fname = os.path.join(self.tempdir, fname)
t = opener(self.fname, 'wt')
t.write(self.fcontent)
t.close()
def tearDown(self):
shutil.rmtree(self.tempdir)
def test_gzip_file(self):
self.make_file('test.txt.gz', gzip.open)
pwd = pwd_guess.PwdList(self.fname)
self.assertEqual([('pass ', 1), ('word', 1)], list(pwd.as_list()))
def test_as_list(self):
self.make_file('test.txt', open)
pwd = pwd_guess.PwdList(self.fname)
self.assertEqual([('pass ', 1), ('word', 1)], list(pwd.as_list()))
def test_tsv(self):
self.fcontent = 'pass \t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
pwd = pwd_guess.TsvList(self.fname)
self.assertEqual([('pass ', 1), ('word', 1)], list(pwd.as_list()))
def test_tsv_multiplier(self):
self.fcontent = 'pass \t2\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
pwd = pwd_guess.TsvList(self.fname)
self.assertEqual([('pass ', 1), ('pass ', 1), ('word', 1)],
list(pwd.as_list()))
def test_tsv_quote_char(self):
self.fcontent = 'pass"\t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
pwd = pwd_guess.TsvList(self.fname)
self.assertEqual([('pass"', 1), ('word', 1)], list(pwd.as_list()))
def test_tsv_simulated(self):
self.fcontent = 'pass"\t1\tR\nword\t2\tR\n'
self.make_file('test.tsv', open)
pwd = pwd_guess.TsvSimulatedList(self.fname)
self.assertEqual([('pass"', 1), ('word', 2)], list(pwd.as_list()))
def test_factory(self):
self.assertEqual(
type(pwd_guess.PwdList.getFactory(['tsv'], pwd_guess.ModelDefaults(
simulated_frequency_optimization = True))(['stuff'])),
pwd_guess.TsvSimulatedList)
self.assertEqual(
type(pwd_guess.PwdList.getFactory(['tsv'], pwd_guess.ModelDefaults(
simulated_frequency_optimization = False))(['stuff'])),
pwd_guess.TsvList)
self.assertEqual(
type(pwd_guess.PwdList.getFactory(['list'], pwd_guess.ModelDefaults(
simulated_frequency_optimization = False))(['stuff'])),
pwd_guess.PwdList)
self.assertEqual(
type(pwd_guess.PwdList.getFactory(
['list', 'list'], pwd_guess.ModelDefaults(
simulated_frequency_optimization = False))([
'stuff', 'stuff'])),
pwd_guess.ConcatenatingList)
def test_concat(self):
fact = pwd_guess.PwdList.getFactory(
['tsv', 'list'], pwd_guess.ModelDefaults(
simulated_frequency_optimization = False))
concat_list = fact([os.path.join(self.tempdir, 'test.tsv'),
os.path.join(self.tempdir, 'test.txt')])
self.assertEqual(type(concat_list), pwd_guess.ConcatenatingList)
self.fcontent = 'pass \t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
self.fcontent = 'pass\nword\n'
self.make_file('test.txt', open)
self.assertEqual(list(concat_list.as_list()), [
('pass ', 1), ('word', 1), ('pass', 1), ('word', 1)])
def test_concat_second_time(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tf:
fnames = [os.path.join(self.tempdir, 'test.tsv'),
os.path.join(self.tempdir, 'test.txt')]
config = pwd_guess.ModelDefaults(
intermediate_fname = tf.name,
simulated_frequency_optimization = False)
config.set_intermediate_info(
pwd_guess.ConcatenatingList.CONFIG_IM_KEY, {
fnames[0] : 1,
fnames[1] : .2
})
fact = pwd_guess.PwdList.getFactory(['tsv', 'list'], config)
concat_list = fact(fnames)
self.assertEqual(type(concat_list), pwd_guess.ConcatenatingList)
self.fcontent = 'pass \t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
self.fcontent = 'pass\nword9\n'
self.make_file('test.txt', open)
self.assertEqual(list(concat_list.as_list()), [
('pass ', 1), ('word', 1), ('pass', .2), ('word9', .2)])
def test_concat_twice_same_weights(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tf:
config = pwd_guess.ModelDefaults(
intermediate_fname = tf.name,
simulated_frequency_optimization = False)
fnames = [os.path.join(self.tempdir, 'test.tsv'),
os.path.join(self.tempdir, 'test.txt')]
fact = pwd_guess.PwdList.getFactory(['tsv', 'list'], config)
concat_list = fact(fnames)
self.assertEqual(type(concat_list), pwd_guess.ConcatenatingList)
self.fcontent = 'pass \t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
self.fcontent = 'pass\nword9\n'
self.make_file('test.txt', open)
self.assertEqual(list(concat_list.as_list()), [
('pass ', 1), ('word', 1), ('pass', 1), ('word9', 1)])
concat_list.finish()
self.assertEqual(list(fact(fnames).as_list()), [
('pass ', 1), ('word', 1), ('pass', 1), ('word9', 1)])
def test_concat_twice_diff_weights(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tf:
fnames = [os.path.join(self.tempdir, 'test.tsv'),
os.path.join(self.tempdir, 'test.txt')]
config = pwd_guess.ModelDefaults(
intermediate_fname = tf.name,
pwd_list_weights = {
fnames[0] : 1,
fnames[1] : .2
},
simulated_frequency_optimization = False)
fact = pwd_guess.PwdList.getFactory(['tsv', 'list'], config)
concat_list = fact(fnames)
self.assertEqual(type(concat_list), pwd_guess.ConcatenatingList)
self.fcontent = 'pass \t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
self.fcontent = 'pass\nword9\n'
self.make_file('test.txt', open)
self.assertEqual(list(concat_list.as_list()), [
('pass ', 1), ('word', 1), ('pass', 1), ('word9', 1)])
concat_list.finish()
self.assertEqual(list(fact(fnames).as_list()), [
('pass ', 1), ('word', 1), ('pass', .2), ('word9', .2)])
def test_concat_three_diff_weights(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tf:
fnames = [os.path.join(self.tempdir, 'test.tsv'),
os.path.join(self.tempdir, 'test.txt'),
os.path.join(self.tempdir, 'test1.txt')]
config = pwd_guess.ModelDefaults(
intermediate_fname = tf.name,
pwd_list_weights = {
fnames[0] : 1,
fnames[1] : 1,
fnames[2] : 1
},
simulated_frequency_optimization = False)
fact = pwd_guess.PwdList.getFactory(['tsv', 'list', 'list'], config)
concat_list = fact(fnames)
self.assertEqual(type(concat_list), pwd_guess.ConcatenatingList)
self.fcontent = 'pass \t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
self.fcontent = 'pass\nword9\n'
self.make_file('test.txt', open)
self.fcontent = 'pass\nword9\n'
self.make_file('test1.txt', open)
self.assertEqual(list(concat_list.as_list()), [
('pass ', 1), ('word', 1), ('pass', 1), ('word9', 1),
('pass', 1), ('word9', 1)])
concat_list.finish()
self.assertEqual(list(fact(fnames).as_list()), [
('pass ', 1), ('word', 1), ('pass', 1), ('word9', 1),
('pass', 1), ('word9', 1)])
def test_concat_twice_lopside(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tf:
fnames = [os.path.join(self.tempdir, 'test.tsv'),
os.path.join(self.tempdir, 'test.txt')]
config = pwd_guess.ModelDefaults(
intermediate_fname = tf.name,
pwd_list_weights = {
fnames[0] : 1,
fnames[1] : .2
},
simulated_frequency_optimization = False)
fact = pwd_guess.PwdList.getFactory(['tsv', 'list'], config)
concat_list = fact(fnames)
self.assertEqual(type(concat_list), pwd_guess.ConcatenatingList)
self.fcontent = 'pass \t1\tR\nword\t1\tR\n'
self.make_file('test.tsv', open)
self.fcontent = 'pass\nword9\nppppp\n'
self.make_file('test.txt', open)
self.assertEqual(list(concat_list.as_list()), [
('pass ', 1), ('word', 1), ('pass', 1),
('word9', 1), ('ppppp', 1)])
concat_list.finish()
self.assertEqual(list(fact(fnames).as_list()), [
('pass ', 1), ('word', 1), ('pass', 0.2),
('word9', 0.2), ('ppppp', 0.2)])
class FiltererTest(unittest.TestCase):
def test_pwd_is_valid(self):
f = pwd_guess.Filterer(pwd_guess.ModelDefaults())
# Normal characters are good
self.assertTrue(f.pwd_is_valid('pass'))
self.assertTrue(f.pwd_is_valid('passWord'))
self.assertTrue(f.pwd_is_valid('pass$@!@#Word'))
self.assertTrue(f.pwd_is_valid('1234'))
self.assertTrue(f.pwd_is_valid('u' * 40))
# No funky characters
self.assertFalse(f.pwd_is_valid('£jfiei'))
# No long passwords
self.assertFalse(f.pwd_is_valid('u' * 1000))
# No short passwords
self.assertFalse(f.pwd_is_valid('u'))
self.assertEqual(8, f.total)
self.assertEqual(3, f.filtered_out)
def test_filter(self):
f = pwd_guess.Filterer(pwd_guess.ModelDefaults())
self.assertEqual([('pass', 1)],
list(f.filter([('asdf£jfj', 1), ('pass', 1)])))
def test_filter_uniquify(self):
f = pwd_guess.Filterer(pwd_guess.ModelDefaults(), True)
self.assertTrue(f.uniquify)
self.assertEqual([('pass', 1)], list(f.filter(
[('asdf£jfj', 1), ('pass', 1), ('pass', 1)])))
f = pwd_guess.Filterer(pwd_guess.ModelDefaults(), False)
self.assertEqual([('pass', 1), ('pass', 1)], list(f.filter(
[('asdf£jfj', 1), ('pass', 1), ('pass', 1)])))
def test_filter_twice(self):
f = pwd_guess.Filterer(pwd_guess.ModelDefaults())
values = [('a', 1), ('a' * 44, 1), ('pass', 1), ('asdf£', 1)]
self.assertEqual(list(f.filter(values)), [('pass', 1)])
f.finish()
self.assertEqual(list(f.filter(values)), [('pass', 1)])
def test_filter_small(self):
f = pwd_guess.Filterer(pwd_guess.ModelDefaults(
min_len = 3, max_len = 3))
self.assertEqual([('aaa', 1)], list(f.filter([('aaa', 1)])))
def test_filter_freqs(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tf:
config = pwd_guess.ModelDefaults(
rare_character_lowest_threshold = 2, char_bag = 'pass\n',
intermediate_fname = tf.name,
rare_character_optimization = True)
f = pwd_guess.Filterer(config)
self.assertEqual([('pass', 1)],
list(f.filter([('asdf£jfj', 1), ('pass', 1)])))
self.assertEqual({'p' : 1, 'a' : 1, 's': 2}, dict(f.frequencies))
f.finish()
self.assertEqual(
set(config.get_intermediate_info('rare_character_bag')),
set(['a', 'p']))
def test_filter_freqs_non_appearing_rare_characters(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tf:
config = pwd_guess.ModelDefaults(
rare_character_lowest_threshold = 2, char_bag = 'pass12\n',
intermediate_fname = tf.name,
rare_character_optimization = True)
f = pwd_guess.Filterer(config)
self.assertEqual([('pass', 1)],
list(f.filter([('asdf£jfj', 1), ('pass', 1)])))
self.assertEqual({'p' : 1, 'a' : 1, 's': 2}, dict(f.frequencies))
f.finish()
self.assertEqual(
set(config.get_intermediate_info('rare_character_bag')),
set(['1', '2']))
def test_filter_dict(self):
f = pwd_guess.Filterer(pwd_guess.ModelDefaults(max_len = 6))
self.assertEqual([('pass', 1)],
list(f.filter([('pass', 1), ('passssss', 2)])))
class ModelSerializerTest(unittest.TestCase):
def test_model_serializer(self):
mock = Mock()
write_value = '{}'
mock.to_json = MagicMock(return_value = write_value)
mock.save_weights = MagicMock()
mock.load_weights = MagicMock()
with tempfile.NamedTemporaryFile(dir=TMPDIR) as fp:
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tp:
serializer = pwd_guess.ModelSerializer(fp.name, tp.name)
serializer.save_model(mock)
self.assertEqual(write_value, fp.read().decode('utf8'))
serializer = pwd_guess.ModelSerializer(fp.name, tp.name)
serializer.model_creator_from_json = MagicMock(
return_value = mock)
serializer.load_model()
def test_model_serializer_versioned(self):
mock = Mock()
write_value = '{}'
mock.to_json = MagicMock(return_value = write_value)
mock.save_weights = MagicMock()
mock.load_weights = MagicMock()
with tempfile.NamedTemporaryFile(dir=TMPDIR) as fp:
with tempfile.NamedTemporaryFile(dir=TMPDIR) as tp:
serializer = pwd_guess.ModelSerializer(fp.name, tp.name,
versioned = True)
serializer.save_model(mock)
mock.save_weights.assert_called_once_with(
tp.name + '.1', overwrite = True)
mock.save_weights.reset_mock()
serializer.save_model(mock)
mock.save_weights.assert_called_once_with(
tp.name + '.2', overwrite = True)
class GuesserTest(unittest.TestCase):
def mock_model(self, config, distribution):
def smart_mock_predict(str_list, **kwargs):
answer = []
for i in range(len(str_list)):
answer.append([distribution.copy()])
return answer
mock_model = Mock()
mock_model.predict = smart_mock_predict
return mock_model
def make(self, config, distribution):
ostream = io.StringIO()
guesser = pwd_guess.Guesser(self.mock_model(config, distribution),
config, ostream)
return guesser, ostream
def test_guesser(self):
config = pwd_guess.ModelDefaults(
min_len = 3, max_len = 3, char_bag = 'a\n',
lower_probability_threshold = 10**-2,
relevel_not_matching_passwords = False)
guesser, ostream = self.make(config, [0.5, 0.5])
guesser.guess()
self.assertEqual(""" 0.5
a 0.25
aa 0.125
aaa 0.0625
""", ostream.getvalue())
def test_guesser_small_batch(self):
config = pwd_guess.ModelDefaults(
min_len = 3, max_len = 3, char_bag = 'abcd\n',
lower_probability_threshold = 10**-3,
max_gpu_prediction_size = 3,
relevel_not_matching_passwords = False)
guesser, ostream = self.make(config, [0.5, 0.1, 0.1, 0.1, 0.2])
guesser.guess()
def test_guesser_small_chunk(self):
config = pwd_guess.ModelDefaults(
min_len = 3, max_len = 3, char_bag = 'a\n',
lower_probability_threshold = 10**-2,
relevel_not_matching_passwords = False,
chunk_size_guesser = 2)
guesser, ostream = self.make(config, [0.5, 0.5])
guesser.guess()
self.assertEqual(""" 0.5
a 0.25
aa 0.125
aaa 0.0625
""", ostream.getvalue())
def test_guesser_bigger_rare_c(self):
with tempfile.NamedTemporaryFile(dir=TMPDIR) as intermediatef:
config = pwd_guess.ModelDefaults(
parallel_guessing = False, char_bag = 'abAB\n', min_len = 3,
max_len = 5, uppercase_character_optimization = True,
random_walk_seed_num = 10000,
intermediate_fname = intermediatef.name,
relevel_not_matching_passwords = True,
rare_character_optimization_guessing = True,
guess_serialization_method = 'human')
config.set_intermediate_info(
'rare_character_bag', [])
freqs = {
'a' : .4, 'b' : .4, 'A' : .1, 'B' : .1,
}
config.set_intermediate_info('character_frequencies', freqs)
config.set_intermediate_info(
'beginning_character_frequencies', freqs)
config.set_intermediate_info(
'end_character_frequencies', freqs)
self.assertTrue(pwd_guess.Filterer(config).pwd_is_valid('aaa'))
builder = pwd_guess.GuesserBuilder(config)
mock_model = Mock()
mock_model.predict = mock_predict_smart_parallel_skewed
ostream = io.StringIO()
builder.add_model(mock_model).add_stream(ostream)
guesser = builder.build()
guesser.guess()
found = list(csv.reader(io.StringIO(
ostream.getvalue()), delimiter = '\t', quotechar = None))
found = sorted(found, key = lambda row: (float(row[1]), row[0]),
reverse = True)
with open('test_data/test_skewed.sorted.txt', 'r') as expected_data:
expected = csv.reader(
expected_data, delimiter = '\t', quotechar = None)
for i, row in enumerate(expected):
for item in range(len(row)):
value = row[item]
try:
self.assertAlmostEqual(float(row[item]),
float(found[i][item]))
except ValueError:
self.assertEqual(row[item], found[i][item])
def test_guessing_with_relevel(self):
config = pwd_guess.ModelDefaults(
min_len = 3, max_len = 3, char_bag = 'a\n',
lower_probability_threshold = 10**-1)
guesser, ostream = self.make(config, [0.5, 0.5])
guesser.guess()
self.assertEqual("""aaa 1.0
""", ostream.getvalue())
def test_predict(self):
config = pwd_guess.ModelDefaults(
min_len = 3, max_len = 3, char_bag = 'a\n',
lower_probability_threshold = 10**-1,
relevel_not_matching_passwords = False)
guesser, ostream = self.make(config, [0.5, 0.5])
np.testing.assert_array_equal(
[0.5, 0.5], guesser.conditional_probs(''))
np.testing.assert_array_equal(
[0.5, 0.5], guesser.conditional_probs('a'))
np.testing.assert_array_equal(
[0.5, 0.5], guesser.conditional_probs('aa'))
np.testing.assert_array_equal(
[0.5, 0.5], guesser.conditional_probs('aaa'))
def test_relevel(self):
config = pwd_guess.ModelDefaults(
min_len = 3, max_len = 3, char_bag = 'a\n',
lower_probability_threshold = 10**-1)
guesser, ostream = self.make(config, [0.5, 0.5])
np.testing.assert_array_equal([0.0, 1.0], guesser.conditional_probs(''))
np.testing.assert_array_equal(
[0.0, 1.0], guesser.conditional_probs('a'))
np.testing.assert_array_equal(
[0.0, 1.0], guesser.conditional_probs('aa'))
np.testing.assert_array_equal(
[1.0, 0.0], guesser.conditional_probs('aaa'))
def test_relevel_tri_alpha(self):
config = pwd_guess.ModelDefaults(
min_len = 3, max_len = 3, char_bag = 'ab\n',
lower_probability_threshold = 10**-1)
guesser, ostream = self.make(config, [0.5, 0.2, 0.3])
np.testing.assert_array_equal(
[0.0, .4, .6], guesser.conditional_probs(''))
np.testing.assert_array_equal(
[0.0, .4, .6], guesser.conditional_probs('a'))
np.testing.assert_array_equal(
[0.0, .4, .6], guesser.conditional_probs('aa'))
np.testing.assert_array_equal(
[0.0, .4, .6], guesser.conditional_probs('ab'))
np.testing.assert_array_equal(
[1.0, 0.0, 0.0], guesser.conditional_probs('aaa'))
def test_relevel_tri_alpha_calculator(self):
distribution = [0.5, 0.2, 0.3]
def smart_mock_predict(str_list, **kwargs):