def setUp(self):
self.training, self.validation, self.testing = load_samples(
'r_music', 0)
topology = create_network_from_topology([2, 2])
self.sga = SimpleGeneticAlgorithm(100, MaxGenerationsCriterion(200),
topology,
SelectionOperatorTournament(5),
MutationOperatorGaussian(0.05),
CrossoverOperatorArithmetic(), 0.01,
0.5)
def __init__(self,
data_set_name,
metric=RootMeanSquaredError,
models=_MODELS):
"""Initializes benchmark environment."""
self.data_set_name = data_set_name
# Creates file name as combination of data set name and and date.
self.file_name = self.data_set_name + "__" + _now.strftime(
"%Y_%m_%d__%H_%M_%S")
# Loads samples into object.
self.samples = [
load_samples(data_set_name, index) for index in range(30)
]
self.metric = metric
self.models = models
# If data set is classification problem, remove regression models. Else, vice versa.
if is_classification(self.samples[0][0]):
if 'svr' in self.models.keys():
del self.models['svr']
if 'mlpr' in self.models.keys():
del self.models['mlpr']
if 'rfr' in self.models.keys():
del self.models['rfr']
else:
if 'svc' in self.models.keys():
del self.models['svc']
if 'mlpc' in self.models.keys():
del self.models['mlpc']
if 'rfc' in self.models.keys():
del self.models['rfc']
# Create results dictionary with models under study.
self.results = {
k: [None for i in self.samples]
for k in self.models.keys()
}
# Serialize benchmark environment.
benchmark_to_pickle(self)
def setUp(self):
self.training, self.validation, self.testing = load_samples(
'c_cancer', 0)
def setUp(self):
self.training, self.validation, self.testing = load_samples('c_cancer', 0)
self.neat = Neat(100, MaxGenerationsCriterion(200),
4, 1, 1, 0.1, 0.1, 0.1, 0.1, 0.5, 0.01)
def setUp(self):
base_learner = SemanticLearningMachine(50, MaxGenerationsCriterion(10),
2, 'optimized', 10, Mutation2())
self.ensemble_learner = Ensemble(base_learner, 50)
self.training, self.validation, self.testing = load_samples(
'r_concrete', 0)