def test_benchmark_slm(self):
print('Test BenchmarkSLM()...')
algorithm = BenchmarkSLM(10, MaxGenerationsCriterion(10), 3, 0.01, 50,
Mutation2())
X = get_input_variables(self.training).as_matrix()
y = get_target_variable(self.training).as_matrix()
log = algorithm.fit(X, y, RootMeanSquaredError, verbose=True)
self.assertTrue(expr=log)
print()
def test_ols(self):
print('OLS tests of fit()...')
algorithm = SemanticLearningMachine(100, MaxGenerationsCriterion(200),
3, 'optimized', 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()...')
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_slm_ols_wo_edv(self):
print("testing fit for SLM (OLS) without EDV ()...")
base_learner = SemanticLearningMachine(50, MaxGenerationsCriterion(20),
2, 'optimized', 10, Mutation2())
ensemble_learner = EnsembleBagging(base_learner, 100)
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))
self.assertTrue(expr=ensemble_learner.learners)
print()
def test_predict(self):
print("testing predict()...")
base_learner = SemanticLearningMachine(
50, ErrorDeviationVariationCriterion(0.25), 2, 1, 10, Mutation2())
ensemble_learner = EnsembleBagging(base_learner, 100)
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 test_tie(self):
print('TIE tests of fit()...')
def time_seconds(): return default_timer()
start_time = time_seconds()
algorithm = SemanticLearningMachine(100, TrainingImprovementEffectivenessCriterion(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_ols(self):
print('OLS tests of fit()...')
def time_seconds(): return default_timer()
start_time = time_seconds()
algorithm = SemanticLearningMachine(100, MaxGenerationsCriterion(200), 3, 'optimized', 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_slm_fls(self):
print("testing fit() for SLM (FLS) ...")
base_learner = SemanticLearningMachine(50, MaxGenerationsCriterion(100), 2, 1, 10, Mutation2())
ensemble_learner = EnsembleRandomIndependentWeighting(base_learner, 100, weight_range=1)
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))
self.assertTrue(expr=ensemble_learner.learners)
print()
from algorithms.simple_genetic_algorithm.mutation_operator import MutationOperatorGaussian
from algorithms.simple_genetic_algorithm.crossover_operator import CrossoverOperatorArithmetic
from algorithms.semantic_learning_machine.algorithm import SemanticLearningMachine
from itertools import product
from numpy import mean
_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 = {
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('c_diabetes', 0)