def test_sample_array_throws_for_nonpositive_input(self):
self.assertRaises(ValueError,
lambda: SamplingFunctions.sample_array(np.array(list(range(20))), -1))
self.assertRaises(ValueError,
lambda: SamplingFunctions.sample_array(np.array(list(range(20))), 0.0))
self.assertRaises(ValueError,
lambda: SamplingFunctions.sample_array(np.array(list(range(20))), -0.000001))
def get_init_population(self, num_features):
feature_idxs = np.array(range(num_features))
population = []
for k in range(self.init_population_size):
selected_features_idxs = SamplingFunctions.sample_array(feature_idxs, self.percent_features_first_selected)
features = np.zeros(num_features, dtype=int)
features[selected_features_idxs] = 1
individual = GPIndividual(random.choice(self.clfs), self.scorer, features, self.use_proba)
population.append(individual)
return population
def test_sample_array__handles_probability(self):
l = np.array(list(range(3, 33, 3)))
n = 0.3
l2 = SamplingFunctions.sample_array(l, n)
self.assertTrue(len(l2), 3)
