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)