def predict(iterable,
program=None,
precondition=precondition,
postcondition=postcondition,
parameters_priors=None):
"""Map a graph to an output data type."""
try:
# the wrapper provides the vectorization support
if precond_is_classifier(iterable=iterable, program=program):
wprogram = ClassifierWrapper(program=program)
elif precond_is_regressor(iterable=iterable, program=program):
wprogram = RegressorWrapper(program=program)
elif precond_is_knn(iterable=iterable, program=program):
wprogram = KNNWrapper(program=program)
elif precond_is_wrapped(iterable=iterable, program=program):
wprogram = program
else:
Exception('program type is unknown')
parameters = sample_parameters_uniformly_at_random(parameters_priors)
if parameters:
wprogram.set_params(**parameters)
if precondition(iterable=iterable, program=wprogram) is False:
raise Exception('precondition failed')
predictions = wprogram.predict(iterable)
if postcondition(iterable=predictions, program=wprogram) is False:
raise Exception('postcondition failed')
return predictions
except Exception as e:
logger.debug('Error. Reason: %s' % e)
logger.debug('Exception', exc_info=True)
def predict(iterable, program=None, precondition=precondition,
postcondition=postcondition, parameters_priors=None):
"""Map a graph to an output data type."""
try:
# the wrapper provides the vectorization support
if precond_is_classifier(iterable=iterable, program=program):
wprogram = ClassifierWrapper(program=program)
elif precond_is_regressor(iterable=iterable, program=program):
wprogram = RegressorWrapper(program=program)
elif precond_is_knn(iterable=iterable, program=program):
wprogram = KNNWrapper(program=program)
elif precond_is_wrapped(iterable=iterable, program=program):
wprogram = program
else:
Exception('program type is unknown')
parameters = sample_parameters_uniformly_at_random(parameters_priors)
if parameters:
wprogram.set_params(**parameters)
if precondition(iterable=iterable, program=wprogram) is False:
raise Exception('precondition failed')
predictions = wprogram.predict(iterable)
if postcondition(iterable=predictions, program=wprogram) is False:
raise Exception('postcondition failed')
return predictions
except Exception as e:
logger.debug('Error. Reason: %s' % e)
logger.debug('Exception', exc_info=True)
def model(iterable, program=None, precondition=precondition,
postcondition=postcondition, parameters_priors=None):
"""Induce a predictive model.
The induction is done by optimizing the parameters and the
hyper parameters.
Return a biased program that can be used in the other operators.
"""
try:
# the wrapper provides the vectorization support
if precond_is_classifier(iterable=iterable, program=program):
wprogram = ClassifierWrapper(program=program)
elif precond_is_regressor(iterable=iterable, program=program):
wprogram = RegressorWrapper(program=program)
elif precond_is_knn(iterable=iterable, program=program):
wprogram = KNNWrapper(program=program)
elif precond_is_wrapped(iterable=iterable, program=program):
wprogram = program
else:
Exception('program type is unknown')
parameters = sample_parameters_uniformly_at_random(parameters_priors)
if parameters:
wprogram.set_params(**parameters)
if precondition(iterable=iterable, program=wprogram) is False:
raise Exception('precondition failed')
wprogram = wprogram.fit(iterable)
if postcondition(iterable=None, program=wprogram) is False:
raise Exception('postcondition failed')
return wprogram
except Exception as e:
logger.debug('Error. Reason: %s' % e)
logger.debug('Exception', exc_info=True)
def model(iterable,
program=None,
precondition=precondition,
postcondition=postcondition,
parameters_priors=None):
"""Induce a predictive model.
The induction is done by optimizing the parameters and the
hyper parameters.
Return a biased program that can be used in the other operators.
"""
try:
# the wrapper provides the vectorization support
if precond_is_classifier(iterable=iterable, program=program):
wprogram = ClassifierWrapper(program=program)
elif precond_is_regressor(iterable=iterable, program=program):
wprogram = RegressorWrapper(program=program)
elif precond_is_knn(iterable=iterable, program=program):
wprogram = KNNWrapper(program=program)
elif precond_is_wrapped(iterable=iterable, program=program):
wprogram = program
else:
Exception('program type is unknown')
parameters = sample_parameters_uniformly_at_random(parameters_priors)
if parameters:
wprogram.set_params(**parameters)
if precondition(iterable=iterable, program=wprogram) is False:
raise Exception('precondition failed')
wprogram = wprogram.fit(iterable)
if postcondition(iterable=None, program=wprogram) is False:
raise Exception('postcondition failed')
return wprogram
except Exception as e:
logger.debug('Error. Reason: %s' % e)
logger.debug('Exception', exc_info=True)