def generate_random_slm_bls_tie_edv_configuration(
init_maximum_layers=5,
maximum_iterations=100,
maximum_learning_step=10,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
mutation_maximum_new_neurons_per_layer=3):
configuration = generate_random_slm_bls_configuration(
init_maximum_layers=init_maximum_layers,
maximum_iterations=maximum_iterations,
maximum_learning_step=maximum_learning_step,
maximum_neuron_connection_weight=maximum_neuron_connection_weight,
maximum_bias_connection_weight=maximum_bias_connection_weight,
mutation_maximum_new_neurons_per_layer=
mutation_maximum_new_neurons_per_layer)
stopping_crit = random.randint(1, 2)
# EDV
if stopping_crit == 1:
configuration['stopping_criterion'] = ErrorDeviationVariationCriterion(
maximum_iterations=maximum_iterations)
# TIE
else:
configuration[
'stopping_criterion'] = TrainingImprovementEffectivenessCriterion(
maximum_iterations=maximum_iterations)
configuration['population_size'] = 100
return configuration
def get_random_config_slm_ols_edv(init_maximum_layers=5,
maximum_iterations=200,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
mutation_maximum_new_neurons_per_layer=3):
config = {}
config['stopping_criterion'] = ErrorDeviationVariationCriterion(
maximum_iterations=maximum_iterations)
config['population_size'] = 100
config['layers'] = init_maximum_layers
config['learning_step'] = 'optimized'
config['maximum_neuron_connection_weight'] = random.uniform(
0.1, maximum_neuron_connection_weight)
config['maximum_bias_connection_weight'] = random.uniform(
0.1, maximum_bias_connection_weight)
config['mutation_operator'] = Mutation4(
maximum_new_neurons_per_layer=mutation_maximum_new_neurons_per_layer,
maximum_bias_connection_weight=config['maximum_bias_connection_weight']
)
config['random_sampling_technique'] = False
config['random_weighting_technique'] = False
return config
def generate_random_slm_ols_edv_configuration(init_maximum_layers=5, maximum_iterations=250, maximum_neuron_connection_weight=0.5, maximum_bias_connection_weight=1.0, mutation_maximum_new_neurons_per_layer=3):
#def generate_random_slm_ols_edv_configuration(init_maximum_layers=5, maximum_iterations=100, maximum_neuron_connection_weight=0.5, maximum_bias_connection_weight=1.0, mutation_maximum_new_neurons_per_layer=3):
configuration = generate_random_slm_ols_configuration(init_maximum_layers=init_maximum_layers, maximum_iterations=maximum_iterations, maximum_neuron_connection_weight=maximum_neuron_connection_weight, maximum_bias_connection_weight=maximum_bias_connection_weight, mutation_maximum_new_neurons_per_layer=mutation_maximum_new_neurons_per_layer)
configuration['stopping_criterion'] = ErrorDeviationVariationCriterion(maximum_iterations=maximum_iterations)
configuration['population_size'] = 100
return configuration
def test_edv(self):
print('EDV tests of fit()...')
algorithm = SemanticLearningMachine(
100, ErrorDeviationVariationCriterion(0.25), 3, 0.01, 50,
Mutation2())
X = get_input_variables(self.training).as_matrix()
y = get_target_variable(self.training).as_matrix()
algorithm.fit(X, y, RootMeanSquaredError, verbose=True)
self.assertTrue(expr=algorithm.champion)
print()
def test_edv(self):
print('EDV tests of fit()...')
def time_seconds(): return default_timer()
start_time = time_seconds()
algorithm = SemanticLearningMachine(100, ErrorDeviationVariationCriterion(0.25), 3, 0.01, 50, Mutation2(), RootMeanSquaredError, True)
X = get_input_variables(self.training).values
y = get_target_variable(self.training).values
start_time = time_seconds()
algorithm.fit(X, y, RootMeanSquaredError, verbose=False)
print("time to train algorithm: ", (time_seconds()-start_time))
self.assertTrue(expr=algorithm.champion)
print()
def test_predict(self):
print("testing predict()...")
base_learner = SemanticLearningMachine(50, ErrorDeviationVariationCriterion(0.25), 2, 1, 10, Mutation2())
ensemble_learner = EnsembleRandomIndependentWeighting(base_learner, 100, weight_range=2)
X = get_input_variables(self.training).values
y = get_target_variable(self.training).values
def time_seconds(): return default_timer()
start_time = time_seconds()
ensemble_learner.fit(X, y, RootMeanSquaredError, verbose=False)
print("time to train algorithm: ", (time_seconds()-start_time))
start_time = time_seconds()
prediction = ensemble_learner.predict(get_input_variables(self.validation).values)
print("time to predict algorithm: ", (time_seconds()-start_time))
self.assertTrue(expr=len(prediction) == len(get_target_variable(self.validation).values))
print()
def get_random_config_slm_fls_tie_edv(
init_maximum_layers=5,
maximum_iterations=200,
maximum_learning_step=1,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
mutation_maximum_new_neurons_per_layer=3):
config = {}
stopping_crit = random.randint(1, 2)
# EDV
if stopping_crit == 1:
config['stopping_criterion'] = ErrorDeviationVariationCriterion(
maximum_iterations=maximum_iterations)
# TIE
else:
config[
'stopping_criterion'] = TrainingImprovementEffectivenessCriterion(
maximum_iterations=maximum_iterations)
config['population_size'] = 100
config['layers'] = init_maximum_layers
# config['learning_step'] = random.uniform(0.1, maximum_learning_step)
config['learning_step'] = random.uniform(0.001, maximum_learning_step)
config['maximum_neuron_connection_weight'] = random.uniform(
0.1, maximum_neuron_connection_weight)
config['maximum_bias_connection_weight'] = random.uniform(
0.1, maximum_bias_connection_weight)
config['mutation_operator'] = Mutation4(
maximum_new_neurons_per_layer=mutation_maximum_new_neurons_per_layer,
maximum_bias_connection_weight=config['maximum_bias_connection_weight']
)
config['random_sampling_technique'] = False
config['random_weighting_technique'] = False
return config
_BASE_PARAMETERS = {'number_generations': 200, 'population_size': 100}
_SLM_FLS_PARAMETERS = {
'stopping_criterion':
[MaxGenerationsCriterion(_BASE_PARAMETERS.get('number_generations'))],
'population_size': [_BASE_PARAMETERS.get('population_size')],
'layers': [1, 2, 3],
'learning_step': [0.01],
'max_connections': [1, 10, 50],
'mutation_operator': [Mutation2()]
}
_SLM_OLS_PARAMETERS = {
'stopping_criterion': [
ErrorDeviationVariationCriterion(0.25),
ErrorDeviationVariationCriterion(0.5)
],
'population_size': [_BASE_PARAMETERS.get('population_size')],
'layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'learning_step': ['optimized'],
'max_connections': [1, 10, 50, 100],
'mutation_operator': [Mutation2()]
}
_NEAT_PARAMETERS = {
'stopping_criterion':
[MaxGenerationsCriterion(_BASE_PARAMETERS.get(('number_generations')))],
'population_size': [_BASE_PARAMETERS.get('population_size')],
'compatibility_threshold': [3, 4],
'compatibility_disjoint_coefficient': [1],
