def load_parameters_with_filename(filepath):
f = open(filepath, 'r')
_ = f.readline()
settings = arg.processCommandLineArguments(f.readline().strip().split(" "),
None)
f.close()
return settings[0]
def load_from_pickle(f):
"""
Reads models from pickle with settings as header.
Settings are pre-populated with default values from tools.arguments.
"""
# Skip deprecated first line
_ = f.readline()
settings = arg.processCommandLineArguments(f.readline().strip().split(" "),
None)
try:
savedVars = pickle.load(f)
except IndexError:
return False
f.close()
return dict(savedVars), settings[0]
score_types['Inpt.size %d' % size] = 'Score by input size = %d:' % size
for symbolId in range(20):
score_types['Symbol %d' % symbolId] = 'Symbol %d:' % symbolId
for offset in range(20):
score_types['X offset %d' % offset] = 'X offset = %d:' % offset
for trueSize in range(20):
for nrCorrect in range(20):
score_types['T %d C %d' %
(trueSize,
nrCorrect)] = 'Prediction size %d nr correct %d' % (
trueSize, nrCorrect)
trackerreporter.init('http://rjbruin.nl/experimenttracker/api/', api_key)
cmdargs = sys.argv[1:]
# Check for experiment settings file argument and obtain new arguments
allparameters = processCommandLineArguments(cmdargs)
newparameters = []
if (allparameters[0]['debug']):
newparameters = allparameters
else:
for i in range(len(allparameters)):
iterative = False
# Ask for experiment base name
basename = raw_input("Experiment %d name (%s): " %
(i + 1, allparameters[i]['name']))
if (' ' in basename):
raise ValueError(
"Experiment name cannot contain whitespace! Offending name: \"%s\""
% basename)
allparameters[i]['basename'] = allparameters[i]['name']
if (basename != ''):
def writeToVerbose(verboseOutputter, s):
f = verboseOutputter['f']()
f.write(s + '\n')
f.close()
if (__name__ == '__main__'):
np.set_printoptions(precision=3, threshold=10000000)
# Specific settings - default name is time of experiment
name = time.strftime("%d-%m-%Y_%H-%M-%S")
saveModels = True
# Process parameters
parameters = processCommandLineArguments(sys.argv[1:])
# Set up extreme verbose output
# Can always be called but will not do anything if not needed
if (parameters['extreme_verbose']):
if (parameters['tensorflow']):
raise ValueError(
"Feature extreme_verbose = True not supported in combination with tensorflow = True!"
)
verboseOutputter = {
'name': './verbose_output/%s.debug' % name
}
verboseOutputter['f'] = lambda: open(verboseOutputter['name'], 'a')
verboseOutputter['write'] = lambda s: writeToVerbose(
verboseOutputter, s)
else:
@author: Robert-Jan
'''
from tools.arguments import processCommandLineArguments
from tools.model import constructModels
if __name__ == '__main__':
max_length = 17
intervention_range = 12
base_offset = 5
# Process parameters
cmd = "--dataset ./data/subsystems_deep_simple_topcause --finish_subsystems True"
#cmd += " --max_training_size 1000";
parameters = processCommandLineArguments(cmd.split(" "))
# Construct models
datasets, model = constructModels(parameters, 0, {})
dataset = datasets[0]
validCountsPerInterventionLocation = {}
for interventionLocation in range(
max_length - intervention_range - base_offset,
max_length - base_offset):
validCountsPerInterventionLocation[interventionLocation] = 0
result = dataset.expressionsByPrefix.get_next([])
while (result != False):
expression, path = result
print(" / ".join([expression]))
def testExists(self):
params = [
'--finish_subsystems', 'True', '--only_cause_expression', '1',
'--dataset', '../data/subsystems_shallow_simple_topcause',
"--sample_testing_size", "10000", "--n_max_digits", "17",
"--intervention_base_offset", "0", "--intervention_range", "17",
"--nesterov_optimizer", "True", "--decoder", "True",
"--learning_rate", "0.005", "--hidden_dim", "256"
]
params = processCommandLineArguments(params)
datasets, _ = constructModels(params, 1234, {})
dataset = datasets[0]
storage = dataset.expressionsByPrefix
expressions = [
"(3-9)*(0-3)=18", "(4-7)+(6*5)=27", "(0/6)+(2*8)=16",
"(1-4)+(3+6)=6", "(6+0)+(0-1)=5"
]
for i, expr in enumerate(expressions):
self.assertEqual(
storage.exists(expr), True,
"(exists) Failing exists lookup for sample %d" % i)
_, _, _, _, branch = storage.get(expr[:4], alsoGetStructure=True)
closest, _, _, _ = branch.get_closest(expr[4:])
self.assertNotEqual(
closest, False,
"(exists) Branch-based lookup failed with False for sample %d: %s"
% (i, closest))
self.assertEqual(
closest, expr,
"(exists) Failing branch-based lookup for sample %d: %s" %
(i, closest))
# Apply mutations and test if both methods get the same new label
for n in range(20):
intervention_location = np.random.randint(0, len(expr))
new_symbol = np.random.randint(dataset.data_dim)
new_expression = expr[:intervention_location] + dataset.findSymbol[
new_symbol] + expr[intervention_location + 1:]
print("Old: %s\tNew: %s" % (expr, new_expression))
_, _, valids, _, branch = storage.get(
new_expression[:intervention_location + 1],
alsoGetStructure=True)
if (new_expression not in valids and len(valids) > 0):
# Old method: compare all
profiler.start('old')
nearest = -1
nearest_score = 100000
for j, nexpr in enumerate(valids):
score = string_difference(new_expression, nexpr)
if (score < nearest_score):
nearest = j
nearest_score = score
closest_old = valids[nearest]
profiler.stop('old')
profiler.start('new')
# New method:
closest_new, _, _, _ = branch.get_closest(
new_expression[intervention_location + 1:])
profiler.stop('new')
if (closest_old != closest_new):
print(
"(exists) Intervened closest do not match for sample %d: loc %d / orig %s / int %s / old %s / new %s"
% (i, intervention_location, expr, new_expression,
closest_old, closest_new))
# self.assertEqual(closest_old, closest_new,
# "(exists) Intervened closest do not match for sample %d: loc %d / orig %s / int %s / old %s / new %s" %
# (i, intervention_location, expr, new_expression, closest_old, closest_new));
profiler.profile()
