def set_bounds(self): bounds = [] for key in sorted(self.config.matrix.keys()): value = self.config.matrix[key] self._features.append(key) # one hot encoding for categorical type if value.is_categorical: values = to_numpy(value) num_feasible = len(values) for _ in range(num_feasible): bounds.append((0, 1)) self._categorical_features[key] = { "values": values, "number": num_feasible, } self._dim += num_feasible elif value.is_discrete: self._dim = self._dim + 1 discrete_values = to_numpy(value) bounds.append((get_min(value), get_max(value))) self._discrete_features[key] = {"values": discrete_values} elif value.is_uniform: self._dim = self._dim + 1 bounds.append((float(get_min(value)), float(get_max(value)))) self._bounds = np.asarray(bounds)
def test_matrix_values_option(self): config_dict = {"kind": "choice", "value": [1, 2, 3]} config = MatrixChoiceConfig.from_dict(config_dict) assert config.to_dict() == config_dict assert to_numpy(config) == config_dict["value"] assert sample(config) in [1, 2, 3] assert get_length(config) == 3 assert config.is_categorical is False assert config.is_distribution is False assert config.is_range is False assert config.is_uniform is False assert config.is_discrete is True assert config.is_continuous is False assert get_min(config) == 1 assert get_max(config) == 3 config_dict["value"] = ["ok", "nook"] config = MatrixChoiceConfig.from_dict(config_dict) assert config.to_dict() == config_dict assert to_numpy(config) == config_dict["value"] assert sample(config) in ["ok", "nook"] assert get_length(config) == 2 assert config.is_categorical is True assert config.is_distribution is False assert config.is_range is False assert config.is_uniform is False assert config.is_discrete is True assert config.is_continuous is False assert get_min(config) is None assert get_max(config) is None config_dict["value"] = [[1, 2], [2, 4]] config = MatrixChoiceConfig.from_dict(config_dict) assert config.to_dict() == config_dict assert to_numpy(config) == config_dict["value"] assert sample(config) in [[1, 2], [2, 4]] assert get_length(config) == 2 assert config.is_categorical is True assert config.is_distribution is False assert config.is_range is False assert config.is_uniform is False assert config.is_discrete is True assert config.is_continuous is False assert get_min(config) is None assert get_max(config) is None
def assert_equal(config, v1, v2, v3): result = {"start": v1, "stop": v2, "num": v3} assert config.to_dict()["value"] == result np.testing.assert_array_equal(to_numpy(config), np.geomspace(**result)) assert get_length(config) == len(np.geomspace(**result)) assert sample(config) in np.geomspace(**result) assert config.is_categorical is False assert config.is_distribution is False assert config.is_range is True assert config.is_uniform is False assert config.is_discrete is True assert config.is_continuous is False assert get_min(config) == v1 assert get_max(config) == v2
def assert_equal(config, v1, v2, q, v3=None): result = {"loc": v1, "scale": v2, "q": q} if v3: result["size"] = v3 assert config.to_dict()["value"] == result with self.assertRaises(ValidationError): to_numpy(config) with self.assertRaises(ValidationError): get_length(config) assert isinstance(sample(config), float) assert config.is_categorical is False assert config.is_distribution is True assert config.is_range is False assert config.is_uniform is False assert config.is_discrete is False assert config.is_continuous is True assert get_min(config) is None assert get_max(config) is None
def assert_equal(config, v1, v2, v3=None): result = {"low": v1, "high": v2} if v3: result["size"] = v3 assert config.to_dict()["value"] == result with self.assertRaises(ValidationError): to_numpy(config) with self.assertRaises(ValidationError): to_numpy(config) with self.assertRaises(ValidationError): get_length(config) assert v1 <= sample(config) <= v2 assert config.is_categorical is False assert config.is_distribution is True assert config.is_range is False assert config.is_uniform is True assert config.is_discrete is False assert config.is_continuous is True assert get_min(config) == v1 assert get_max(config) == v2
def test_matrix_pchoice_option(self): config_dict = { "kind": "pchoice", "value": [(1, 0.1), (2, 0.3), (3, 6)] } with self.assertRaises(ValidationError): MatrixPChoiceConfig.from_dict(config_dict) config_dict["value"] = [(1, 0.1), (2, 0.3), (3, 0.8)] with self.assertRaises(ValidationError): MatrixPChoiceConfig.from_dict(config_dict) config_dict["value"] = [(1, 0.1), (2, 0.3), (3, -0.6)] with self.assertRaises(ValidationError): MatrixPChoiceConfig.from_dict(config_dict) config_dict["value"] = ["ok", "nook"] with self.assertRaises(ValidationError): MatrixPChoiceConfig.from_dict(config_dict) # Pass for correct config config_dict["value"] = [(1, 0.1), (2, 0.1), (3, 0.8)] config = MatrixPChoiceConfig.from_dict(config_dict) assert config.to_dict() == config_dict with self.assertRaises(ValidationError): to_numpy(config) assert sample(config) in [1, 2, 3] assert get_length(config) == 3 assert config.is_categorical is False assert config.is_distribution is True assert config.is_range is False assert config.is_uniform is False assert config.is_discrete is True assert config.is_continuous is False assert get_min(config) is None assert get_max(config) is None