def test_fix_convergence(self, logger):
"""
This methods checks the results of a ExperimentLogisticRegression to match a recorded result which was
generated with the same parameters.
"""
n = 8
k = 2
N = 255
seed_instance = 0xBAE55E
seed_model = 0x5C6AE1E
seed_challenge = 0xB0661E
seed_distance = 0xB0C
experiment = ExperimentLogisticRegression(
LOG_PATH + 'exp',
LRParameters(n=n,
k=k,
N=N,
seed_model=seed_model,
seed_distance=seed_distance,
seed_instance=seed_instance,
seed_challenge=seed_challenge,
transformation='soelter_lightweight_secure',
combiner='xor',
mini_batch_size=0,
shuffle=False,
convergence_decimals=2))
experiment.execute(logger.queue, logger.logger_name)
result_str = logger.read_result_log()
self.assertFalse(result_str == '', 'The result log was empty.')
error = 'LR learning results deviate from legacy learning results.'
self.assertEqual(experiment.result.iteration_count, 274, error)
self.assertEqual(experiment.result.epoch_count, 274, error)
self.assertEqual(experiment.result.gradient_step_count, 274, error)
self.assertEqual(experiment.result.accuracy, 0.98828125, error)
print(experiment.result.model)
assert_array_equal(
around(experiment.result.model, decimals=8),
around([
0.00351544,
-0.00504143,
0.01470355,
0.00481524,
0.00649157,
-0.00301955,
-0.0025765,
0.00841928,
0.,
0.03248558,
0.37524702,
-0.0683109,
0.40447738,
0.49995907,
0.52796785,
0.00060493,
-0.40093716,
0.,
],
decimals=8), error)
def test_lr_experiments(self):
"""This method runs the experimenter for four logistic regression experiments."""
lr16_4_1 = ExperimentLogisticRegression('test_lr_experiments1', 8, 2,
2**8, 0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_2 = ExperimentLogisticRegression('test_lr_experiments2', 8, 2,
2**8, 0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_3 = ExperimentLogisticRegression('test_lr_experiments3', 8, 2,
2**8, 0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_4 = ExperimentLogisticRegression('test_lr_experiments4', 8, 2,
2**8, 0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_5 = ExperimentLogisticRegression('test_lr_experiments5', 8, 2,
2**8, 0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
experiments = [lr16_4_1, lr16_4_2, lr16_4_3, lr16_4_4, lr16_4_5]
experimenter = Experimenter('log', experiments)
experimenter.run()
class TestExperimentLogisticRegression(unittest.TestCase):
def test_run_and_analyze(self):
self.lr16_4 = ExperimentLogisticRegression('exp1.log', 8, 2, 2**8,
0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
self.lr16_4.execute()
def test_run_and_analyze(self, logger):
"""
This method only runs the experiment.
"""
lr16_4 = ExperimentLogisticRegression(
LOG_PATH + 'exp1',
8,
2,
2**8,
0xbeef,
0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor,
)
lr16_4.execute(logger.queue, logger.logger_name)
def test_multiprocessing_logs(self):
"""
This test checks for the predicted amount for result.
"""
experiments = []
n = 28
for i in range(n):
log_name = 'test_multiprocessing_logs{0}'.format(i)
lr16_4_1 = ExperimentLogisticRegression(log_name, 8, 2, 2 ** 8, 0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
experiments.append(lr16_4_1)
for i in range(n):
log_name = 'test_multiprocessing_logs{0}'.format(i)
experiment = ExperimentMajorityVoteFindVotes(
log_name=log_name,
n=8,
k=2,
challenge_count=2 ** 8,
seed_instance=0xC0DEBA5E,
seed_instance_noise=0xdeadbeef,
transformation=LTFArray.transform_id,
combiner=LTFArray.combiner_xor,
mu=0,
sigma=1,
sigma_noise_ratio=NoisyLTFArray.sigma_noise_from_random_weights(n, 1, .5),
seed_challenges=0xf000 + i,
desired_stability=0.95,
overall_desired_stability=0.8,
minimum_vote_count=1,
iterations=2,
bias=False
)
experiments.append(experiment)
experimenter = Experimenter('test_multiprocessing_logs', experiments)
experimenter.run()
def line_count(file_object):
"""
:param file_object:
:return: number of lines
"""
count = 0
while file_object.readline() != '':
count = count + 1
return count
paths = list(glob.glob('*.log'))
# Check if the number of lines is greater than zero
for log_path in paths:
exp_log_file = open(log_path, 'r')
self.assertGreater(line_count(exp_log_file), 0, 'The experiment log is empty.')
exp_log_file.close()
# Check if the number of results is correct
log_file = open('test_multiprocessing_logs.log', 'r')
self.assertEqual(line_count(log_file), n*2, 'Unexpected number of results')
log_file.close()
def test_fix_convergence(self, logger):
"""
This methods checks the results of a ExperimentLogisticRegression to match a recorded result which was
generated with the same parameters.
"""
n = 8
k = 2
N = 255
seed_instance = 0xBAE55E
seed_model = 0x5C6AE1E
seed_challenge = 0xB0661E
seed_distance = 0xB0C
experiment = ExperimentLogisticRegression(
LOG_PATH + 'exp',
LRParameters(n=n,
k=k,
N=N,
seed_model=seed_model,
seed_distance=seed_distance,
seed_instance=seed_instance,
seed_challenge=seed_challenge,
transformation='soelter_lightweight_secure',
combiner='xor',
mini_batch_size=0,
shuffle=False,
convergence_decimals=2))
experiment.execute(logger.queue, logger.logger_name)
result_str = logger.read_result_log()
self.assertFalse(result_str == '', 'The result log was empty.')
error = 'LR learning results deviate from legacy learning results.'
self.assertEqual(experiment.result.iteration_count, 256, error)
self.assertEqual(experiment.result.epoch_count, 256, error)
self.assertEqual(experiment.result.gradient_step_count, 256, error)
self.assertEqual(experiment.result.accuracy, 0.98828125, error)
assert_array_equal(
around(experiment.result.model, decimals=8),
around([
3.99071615e-03, -5.65532862e-03, 1.63862406e-02,
5.37762262e-03, 7.29781422e-03, -3.35141930e-03,
-2.95642974e-03, 9.40114614e-03, 1.26572532e-07,
3.49183531e-02, 3.68758330e-01, -7.85028286e-02,
4.17595994e-01, 5.09973673e-01, 5.13855116e-01, 2.97216086e-04,
-3.96978992e-01, -5.41390228e-03
],
decimals=8), error)
def test_fix_convergence(self, logger):
"""
This methods checks the results of a ExperimentLogisticRegression to match a recorded result which was
generated with the same parameters.
"""
n = 8
k = 2
N = 255
seed_instance = 0xBAE55E
seed_model = 0x5C6AE1E
seed_challenge = 0xB0661E
seed_distance = 0xB0C
experiment = ExperimentLogisticRegression(
LOG_PATH + 'exp',
n,
k,
N,
seed_instance,
seed_model,
LTFArray.transform_soelter_lightweight_secure,
LTFArray.combiner_xor,
seed_challenge=seed_challenge,
seed_chl_distance=seed_distance,
)
experiment.execute(logger.queue, logger.logger_name)
legacy_result = [
'0xbae55e', '0x5c6ae1e', '0', '8', '2', '255',
'transform_soelter_lightweight_secure', 'combiner_xor', '363',
'1.000000', '0.00443419669755,-0.00616546911566,0.0186346081194,'
'0.0061619719475,0.00795284461334,-0.00443539877583,'
'-0.00316047872599,0.00993214368373,0.0507595729459,'
'0.415207373134,-0.0517173737839,0.285900582842,'
'0.467512016377,0.550102231366,-0.000739711610042,'
'-0.467757977178\n'
]
result_str = logger.read_result_log()
self.assertFalse(result_str == '', 'The result log was empty.')
experiment_result = result_str.split('\t')
# remove execution time
del experiment_result[9]
self.assertTrue(experiment_result == legacy_result,
'You changed the code significant.')
def test_run_and_analyze(self, logger):
"""
This method only runs the experiment.
"""
lr16_4 = ExperimentLogisticRegression(
LOG_PATH + 'exp1',
LRParameters(n=8,
k=2,
N=2**8,
seed_model=0xbeef,
seed_distance=0xbeef,
seed_instance=0xdead,
seed_challenge=0xdead,
transformation='id',
combiner='xor',
mini_batch_size=2,
shuffle=False,
convergence_decimals=2))
lr16_4.execute(logger.queue, logger.logger_name)
def test_lr_experiments(self):
lr16_4_1 = ExperimentLogisticRegression('exp1.log', 8, 2, 2**8, 0xbeef,
0xbeef, LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_2 = ExperimentLogisticRegression('exp2.log', 8, 2, 2**8, 0xbeef,
0xbeef, LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_3 = ExperimentLogisticRegression('exp3.log', 8, 2, 2**8, 0xbeef,
0xbeef, LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_4 = ExperimentLogisticRegression('exp4.log', 8, 2, 2**8, 0xbeef,
0xbeef, LTFArray.transform_id,
LTFArray.combiner_xor)
lr16_4_5 = ExperimentLogisticRegression('exp5.log', 8, 2, 2**8, 0xbeef,
0xbeef, LTFArray.transform_id,
LTFArray.combiner_xor)
experiments = [lr16_4_1, lr16_4_2, lr16_4_3, lr16_4_4, lr16_4_5]
experimenter = Experimenter('log', experiments)
experimenter.run()
def test_run_and_analyze(self):
logger_name = 'log'
# Setup multiprocessing logging
queue = multiprocessing.Queue(-1)
listener = multiprocessing.Process(target=log_listener,
args=(
queue,
setup_logger,
logger_name,
))
listener.start()
self.lr16_4 = ExperimentLogisticRegression('exp1', 8, 2, 2**8, 0xbeef,
0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
self.lr16_4.execute(queue, logger_name)
queue.put_nowait(None)
listener.join()
def get_exp(name, k, trans, comb):
"""Experiment creation shortcut
:param name: string
Name of the experiment
"""
return ExperimentLogisticRegression(
LOG_PATH + name,
n,
k,
N,
seed_instance,
seed_model,
trans,
comb,
seed_challenge=seed_challenge,
seed_chl_distance=seed_distance,
)
def get_exp(name, trans, comb):
"""Experiment creation shortcut
:param name: string
Name of the experiment
"""
return ExperimentLogisticRegression(
LOG_PATH + name,
LRParameters(n=n,
k=k,
N=N,
seed_model=seed_model,
seed_distance=seed_distance,
seed_instance=seed_instance,
seed_challenge=seed_challenge,
transformation=trans,
combiner=comb,
mini_batch_size=0,
shuffle=False,
convergence_decimals=1))
def test_lr_experiments(self):
"""This method runs the experimenter for five logistic regression experiments."""
experimenter = Experimenter(LOG_PATH + 'test_lr_experiments')
for i in range(5):
experimenter.queue(
ExperimentLogisticRegression(
LOG_PATH + 'test_lr_experiments{}'.format(i + 1),
LRParameters(n=8,
k=2,
N=2**8,
seed_model=0xbeef,
seed_distance=0xbeef,
seed_instance=0xdead,
seed_challenge=0xdead,
transformation='id',
combiner='xor',
mini_batch_size=2,
shuffle=False,
convergence_decimals=2)))
experimenter.run()
def experiments(self):
e = []
(n, k) = self.SIZE
for transformation in self.LR_TRANSFORMATIONS:
for i in range(self.SAMPLE_SIZE):
e.append(
ExperimentLogisticRegression(
progress_log_prefix=None,
parameters=LRParameters(
seed_instance=314159 + i,
seed_model=265358 + i,
seed_challenge=979323 + i,
seed_distance=846264 + i,
n=n,
k=k,
transformation=transformation,
combiner='xor',
N=self.CRPS,
mini_batch_size=0,
convergence_decimals=2,
shuffle=False,
)))
for i in range(self.SAMPLE_SIZE):
e.append(
ExperimentCorrelationAttack(progress_log_prefix=None,
parameters=CorrParameters(
seed_instance=314159 + i,
seed_model=265358 + i,
seed_challenge=979323 + i,
seed_distance=846264 + i,
n=n,
k=k,
N=self.CRPS,
lr_iteration_limit=1000,
mini_batch_size=0,
convergence_decimals=2,
shuffle=False,
)))
return e
def experiments(self):
experiments = []
for (n, k, training_set_sizes) in self.DEFINITIONS:
for shuffle in [True, False]:
filename = 'figures/lr-minibatch-' + \
('shuffle' if shuffle else 'noshuffle') + '-success_rate-%i-%i.pdf' % (n, k)
plot = SuccessRatePlot(
filename=filename,
group_by='mini_batch_size',
group_labels=self.GROUP_LABELS,
)
self.result_plots.append(plot)
for training_set_size in training_set_sizes:
for i in range(self.SAMPLES_PER_POINT):
for mini_batch_size in [None
] + self.MINI_BATCH_SIZES[k]:
if mini_batch_size and mini_batch_size >= training_set_size:
break
e = ExperimentLogisticRegression(
progress_log_prefix=None,
parameters=Parameters(
n=n,
k=k,
N=training_set_size,
seed_instance=314159 + i,
seed_model=265358 + i,
transformation='id',
combiner='xor',
seed_challenge=979323 + i,
seed_distance=846264 + i,
mini_batch_size=mini_batch_size or 0,
convergence_decimals=1.5
if not mini_batch_size else 2.7,
shuffle=False
if mini_batch_size is None else shuffle,
))
experiments.append(e)
plot.experiment_hashes.append(e.hash)
return experiments
def experiments(self):
experiments = []
for idx, transformation in self.INPUT_TRANSFORMATIONS.items():
for training_set_size in self.TRAINING_SET_SIZES[idx]:
for i in range(self.SAMPLES_PER_POINT):
experiments.append(
ExperimentLogisticRegression(
progress_log_prefix=None,
parameters=Parameters(
n=64,
k=4,
N=training_set_size,
seed_instance=314159 + i,
seed_model=265358 + i,
transformation=transformation,
combiner='xor',
seed_challenge=979323 + i,
seed_distance=846264 + i,
convergence_decimals=2,
mini_batch_size=0,
shuffle=False,
)))
return experiments
def experiments(self):
experiments = []
for i in range(self.SAMPLES_PER_ENTRY):
for p in self.PARAMETERS:
common_parameters = {
'n': p.n,
'k': p.k,
'N': p.N,
'seed_instance': 314159 + i,
'seed_model': 265358 + i,
'seed_challenge': 979323 + i,
'seed_distance': 846264 + i,
'mini_batch_size': 0,
'convergence_decimals': 2,
'shuffle': False
}
ex = ExperimentCorrelationAttack(
progress_log_prefix=self.name(),
parameters=CorrelationAttackParameters(
**common_parameters,
lr_iteration_limit=1000,
)
)
experiments.append(ex)
for transform in self.TRANSFORMATIONS:
ex = ExperimentLogisticRegression(
progress_log_prefix=self.name(),
parameters=LogisticRegressionParameters(
**common_parameters,
transformation=transform.__name__,
combiner=LTFArray.combiner_xor.__name__,
)
)
experiments.append(ex)
return experiments
def main(args):
"""
This method includes the main functionality of the module it parses the argument vector and executes the learning
attempts on the PUF instances.
"""
if len(args) < 10 or len(args) > 11:
stderr.write('LTF Array Simulator and Logistic Regression Learner\n')
stderr.write('Usage:\n')
stderr.write('sim_learn.py n k transformation combiner N restarts seed_instance seed_model [log_name]\n')
stderr.write(' n: number of bits per Arbiter chain\n')
stderr.write(' k: number of Arbiter chains\n')
stderr.write(' transformation: used to transform input before it is used in LTFs\n')
stderr.write(' currently available:\n')
stderr.write(' - id -- does nothing at all\n')
stderr.write(' - atf -- convert according to "natural" Arbiter chain\n')
stderr.write(' implementation\n')
stderr.write(' - mm -- designed to achieve maximum PTF expansion length\n')
stderr.write(' only implemented for k=2 and even n\n')
stderr.write(' - lightweight_secure -- design by Majzoobi et al. 2008\n')
stderr.write(' only implemented for even n\n')
stderr.write(' - shift_lightweight_secure -- design like Majzoobi\n')
stderr.write(' et al. 2008, but with the shift\n')
stderr.write(' operation executed first\n')
stderr.write(' only implemented for even n\n')
stderr.write(' - soelter_lightweight_secure -- design like Majzoobi\n')
stderr.write(' et al. 2008, but one bit different\n')
stderr.write(' only implemented for even n\n')
stderr.write(' - 1_n_bent -- one LTF gets "bent" input, the others id\n')
stderr.write(' - 1_1_bent -- one bit gets "bent" input, the others id,\n')
stderr.write(' this is proven to have maximum PTF\n')
stderr.write(' length for the model\n')
stderr.write(' - polynomial -- challenges are interpreted as polynomials\n')
stderr.write(' from GF(2^64). From the initial challenge c,\n')
stderr.write(' the i-th Arbiter chain gets the coefficients \n')
stderr.write(' of the polynomial c^(i+1) as challenge.\n')
stderr.write(' For now only challenges with length n=64 are accepted.\n')
stderr.write(
' - permutation_atf -- for each Arbiter chain first a pseudorandom permutation \n')
stderr.write(' is applied and thereafter the ATF transform.\n')
stderr.write(' - random -- Each Arbiter chain gets a random challenge derived from the\n')
stderr.write(' original challenge using a PRNG.\n')
stderr.write(' combiner: used to combine the output bits to a single bit\n')
stderr.write(' currently available:\n')
stderr.write(' - xor -- output the parity of all output bits\n')
stderr.write(' - ip_mod2 -- output the inner product mod 2 of all output\n')
stderr.write(' bits (even n only)\n')
stderr.write(' N: number of challenge response pairs in the training set\n')
stderr.write(' restarts: number of repeated initializations the learner\n')
stderr.write(' instances: number of repeated initializations the instance\n')
stderr.write(' The number total learning attempts is restarts*instances.\n')
stderr.write(' seed_instance: random seed used for LTF array instance\n')
stderr.write(' seed_model: random seed used for the model in first learning attempt\n')
stderr.write(' [log_name]: path to the logfile which contains results from all instances. The tool '
'will add a ".log" to log_name. The default path is ./sim_learn.log\n')
quit(1)
n = int(args[1])
k = int(args[2])
transformation_name = args[3]
combiner_name = args[4]
N = int(args[5])
restarts = int(args[6])
instances = int(args[7])
seed_instance = int(args[8], 16)
seed_model = int(args[9], 16)
transformation = None
combiner = None
try:
transformation = getattr(LTFArray, 'transform_%s' % transformation_name)
except AttributeError:
stderr.write('Transformation %s unknown or currently not implemented\n' % transformation_name)
quit()
try:
combiner = getattr(LTFArray, 'combiner_%s' % combiner_name)
except AttributeError:
stderr.write('Combiner %s unknown or currently not implemented\n' % combiner_name)
quit()
log_name = 'sim_learn'
if len(args) == 11:
log_name = args[10]
stderr.write('Learning %s-bit %s XOR Arbiter PUF with %s CRPs and %s restarts.\n\n' % (n, k, N, restarts))
stderr.write('Using\n')
stderr.write(' transformation: %s\n' % transformation)
stderr.write(' combiner: %s\n' % combiner)
stderr.write(' instance random seed: 0x%x\n' % seed_instance)
stderr.write(' model random seed: 0x%x\n' % seed_model)
stderr.write('\n')
# create different experiment instances
experiments = []
for j in range(instances):
for start_number in range(restarts):
l_name = '%s_%i_%i' % (log_name, j, start_number)
experiment = ExperimentLogisticRegression(
log_name=l_name,
n=n,
k=k,
N=N,
seed_instance=seed_instance + j,
seed_model=seed_model + j + start_number,
transformation=transformation,
combiner=combiner
)
experiments.append(experiment)
experimenter = Experimenter(log_name, experiments)
# run the instances
experimenter.run()
# output format
str_format = '{:<15}\t{:<10}\t{:<8}\t{:<8}\t{:<8}\t{:<8}\t{:<18}\t{:<15}\t{:<6}\t{:<8}\t{:<8}\t{:<8}'
headline = str_format.format(
'seed_instance', 'seed_model', 'i', 'n', 'k', 'N', 'trans', 'comb', 'iter', 'time', 'accuracy',
'model_values\n'
)
# print the result headline
stderr.write(headline)
log_file = open(log_name + '.log', 'r')
# print the results
result = log_file.readline()
while result != '':
stderr.write(str_format.format(*result.split('\t')))
result = log_file.readline()
log_file.close()
def main(args):
"""
This method includes the main functionality of the module it parses the argument vector and executes the learning
attempts on the PUF instances.
"""
parser = argparse.ArgumentParser(
prog='sim_learn',
description="LTF Array Simulator and Logistic Regression Learner",
)
parser.add_argument("n", help="number of bits per Arbiter chain", type=int)
parser.add_argument("k", help="number of Arbiter chains", type=int)
parser.add_argument(
"transformation",
help=
"used to transform input before it is used in LTFs. Currently available: "
'"atf,id",'
'"lightweight_secure",'
'"permutation_atf",'
'"polynomial,random",'
'"shift",'
'"soelter_lightweight_secure"',
type=str,
)
parser.add_argument(
'combiner',
help=
'used to combine the output bits to a single bit. Currently available: "ip_mod2", "xor"',
type=str,
)
parser.add_argument(
'N',
help='number of challenge response pairs in the training set',
type=int)
parser.add_argument('restarts',
help='number of repeated initializations the learner',
type=int)
parser.add_argument(
'instances',
help='number of repeated initializations the instance\n'
'The number total learning attempts is restarts*instances.',
type=int,
)
parser.add_argument('seed_instance',
help='random seed used for LTF array instance',
type=str)
parser.add_argument(
'seed_model',
help='random seed used for the model in first learning attempt',
type=str)
parser.add_argument(
'--log_name',
help=
'path to the logfile which contains results from all instances. The tool '
'will add a ".log" to log_name. The default path is ./sim_learn.log',
default='sim_learn',
type=str,
)
parser.add_argument(
'--seed_challenges',
help='random seed used to draw challenges for the training set',
type=str,
)
parser.add_argument('--seed_distance',
help='random seed used to calculate the accuracy',
type=str)
args = parser.parse_args(args)
n = args.n
k = args.k
transformation = args.transformation
combiner = args.combiner
N = args.N
restarts = args.restarts
instances = args.instances
seed_instance = int(args.seed_instance, 16)
seed_model = int(args.seed_model, 16)
seed_challenges = 0x5A551
if args.seed_challenges is not None:
seed_challenges = int(args.seed_challenges, 16)
seed_distance = 0xB055
if args.seed_distance is not None:
seed_distance = int(args.seed_distance, 16)
try:
getattr(LTFArray, 'transform_%s' % transformation)
except AttributeError:
sys.stderr.write(
'Transformation %s unknown or currently not implemented\n' %
transformation)
quit()
try:
getattr(LTFArray, 'combiner_%s' % combiner)
except AttributeError:
sys.stderr.write('Combiner %s unknown or currently not implemented\n' %
combiner)
quit()
log_name = args.log_name
sys.stderr.write(
'Learning %s-bit %s XOR Arbiter PUF with %s CRPs and %s restarts.\n\n'
% (n, k, N, restarts))
sys.stderr.write('Using\n')
sys.stderr.write(' transformation: %s\n' % transformation)
sys.stderr.write(' combiner: %s\n' % combiner)
sys.stderr.write(' instance random seed: 0x%x\n' % seed_instance)
sys.stderr.write(' model random seed: 0x%x\n' % seed_model)
sys.stderr.write('\n')
# create different experiment instances
experimenter = Experimenter(log_name)
for j in range(instances):
for start_number in range(restarts):
l_name = '%s_%i_%i' % (log_name, j, start_number)
experiment = ExperimentLogisticRegression(
progress_log_prefix=l_name,
parameters=Parameters(
n=n,
k=k,
N=N,
seed_instance=seed_instance + j,
seed_model=seed_model + j + start_number,
transformation=transformation,
combiner=combiner,
seed_challenge=seed_challenges,
seed_distance=seed_distance,
convergence_decimals=2,
mini_batch_size=0,
shuffle=False,
))
experimenter.queue(experiment)
# run the instances
experimenter.run()
def test_multiprocessing_logs(self):
"""
This test checks for the predicted amount for result.
"""
experimenter_log_name = LOG_PATH + 'test_multiprocessing_logs'
experimenter = Experimenter(experimenter_log_name)
n = 4
for i in range(n):
log_name = LOG_PATH + 'test_multiprocessing_logs{0}'.format(i)
experimenter.queue(
ExperimentLogisticRegression(
log_name,
LRParameters(
n=8,
k=2,
N=2**8,
seed_challenge=0xbeef,
seed_instance=0xbeef,
seed_distance=0xf00,
seed_model=0x1,
transformation='id',
combiner='xor',
convergence_decimals=2,
mini_batch_size=0,
shuffle=False,
)))
for i in range(n):
log_name = LOG_PATH + 'test_multiprocessing_logs{0}'.format(i)
experiment = ExperimentMajorityVoteFindVotes(
progress_log_prefix=log_name,
parameters=MVParameters(n=8,
k=2,
challenge_count=2**8,
seed_instance=0xC0DEBA5E,
seed_instance_noise=0xdeadbeef,
transformation='id',
combiner='xor',
mu=0,
sigma=1,
sigma_noise_ratio=NoisyLTFArray.
sigma_noise_from_random_weights(
n, 1, .5),
seed_challenges=0xf000 + i,
desired_stability=0.95,
overall_desired_stability=0.6,
minimum_vote_count=1,
iterations=2,
bias=None))
experimenter.queue(experiment)
experimenter.run()
def line_count(file_object):
"""
:param file_object:
:return: number of lines
"""
count = 0
while file_object.readline() != '':
count = count + 1
return count
paths = list(glob.glob('logs/' + LOG_PATH + '*.log'))
# Check if the number of lines is greater than zero
for log_path in paths:
exp_log_file = open(log_path, 'r')
self.assertGreater(
line_count(exp_log_file), 0,
'The experiment log {} is empty.'.format(log_path))
exp_log_file.close()
# Check if the number of results is correct
with open('logs/' + experimenter_log_name + '.log', 'r') as log_file:
self.assertEqual(line_count(log_file), 2 * n,
'Unexpected number of results')