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_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()
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 = EnsembleBoosting(base_learner, 100, meta_learner=median, learning_rate=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()
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 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)