def test_save_load_exdir(self):
self.data = Data(logger_level="error", backend="exdir")
self.setup_mock_data(self.data)
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock")
filename = os.path.join(self.output_test_dir, "test_save_mock")
self.data.save(filename)
new_data = Data(filename, backend="exdir")
for statistical_metric in self.statistical_metrics:
new_data["feature1d"][statistical_metric] = np.array([1., 2.])
new_data["TestingModel1d"][statistical_metric] = np.array([3., 4.])
new_data["feature1d"]["labels"] = ["xlabel", "ylabel"]
new_data["TestingModel1d"]["labels"] = ["xlabel", "ylabel"]
new_data.model_name = "TestingModel1d"
new_data.uncertain_parameters = ["a", "b"]
new_data.method = "mock"
new_data.seed = 10
new_data.incomplete = ["a", "b"]
new_data.error = ["feature1d"]
def custom(self, argument):
self.convert_uncertain_parameters()
data = Data()
data.argument = argument
return data
def test_set_data(self):
data = Data()
data.load(os.path.join(self.test_data_dir, "test_save_mock"))
self.plot.data = data
self.assert_data()
def test_seed(self):
data = Data()
self.assertEqual(data.seed, "")
data.seed = 10
self.assertEqual(data.seed, 10)
data.seed = None
self.assertEqual(data.seed, "")
def test_save_empty(self):
data = Data()
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_empty")
filename = os.path.join(self.output_test_dir, "test_save_empty")
data.save(filename)
result = subprocess.call(["h5diff", filename, compare_file])
self.assertEqual(result, 0)
def test_empty_data(self):
self.plot.data = Data()
self.plot.data.model_name = "1D"
self.plot.data.add_features("0D")
self.plot.data.add_features("1D")
self.plot.data.add_features("2D")
with self.assertRaises(AttributeError):
self.plot.evaluations()
self.plot.data["0D"].evaluations = [1, 2, 3]
self.plot.data["1D"].evaluations = [[1], [2], [3]]
self.plot.data["2D"].evaluations = [[[1]], [[2]], [[3]]]
self.assertIsNone(self.plot.attribute_feature_1d("1D"))
self.assertIsNone(self.plot.attribute_feature_2d("2D"))
self.assertIsNone(self.plot.mean_variance_1d("1D"))
self.assertIsNone(self.plot.prediction_interval_1d("1D"))
self.assertIsNone(self.plot.sensitivity_1d("1D"))
self.assertIsNone(self.plot.sensitivity_1d_grid("1D"))
self.assertIsNone(self.plot.sensitivity_1d_combined("1D"))
self.assertIsNone(self.plot.feature_0d("0D"))
self.assertIsNone(self.plot.sensitivity_sum("1D"))
self.assertIsNone(self.plot.sensitivity_sum_grid())
with self.assertRaises(ValueError):
self.plot.attribute_feature_1d(attribute="not_existing")
def test_evaluations(self):
self.plot.data = Data(
os.path.join(self.test_data_dir, "TestingModel1d.h5"))
self.plot.all_evaluations()
plot_count = len(
glob.glob(
os.path.join(self.output_test_dir,
"evaluations/feature0d_var_evaluations/*.png")))
self.assertEqual(plot_count, 1)
plot_count = len(
glob.glob(
os.path.join(self.output_test_dir,
"evaluations/feature1d_var_evaluations/*.png")))
self.assertEqual(plot_count, 32)
plot_count = len(
glob.glob(
os.path.join(self.output_test_dir,
"evaluations/feature2d_var_evaluations/*.png")))
self.assertEqual(plot_count, 32)
plot_count = len(
glob.glob(
os.path.join(self.output_test_dir,
"evaluations/TestingModel1d_evaluations/*.png")))
self.assertEqual(plot_count, 32)
def test_evaluations_2d(self):
self.plot.data = Data(os.path.join(self.test_data_dir, "TestingModel2d.h5"))
self.plot.evaluations(feature="TestingModel2d")
plot_count = 0
for plot in glob.glob(os.path.join(self.output_test_dir, "TestingModel2d_evaluations/*.png")):
plot_count += 1
self.assertEqual(plot_count, 22)
def setUp(self):
self.output_test_dir = ".tests/"
self.seed = 10
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
os.makedirs(self.output_test_dir)
self.data = Data(logger_level="error")
self.statistical_metrics = [
"evaluations", "time", "mean", "variance", "percentile_5",
"percentile_95", "sobol_first", "sobol_first_average",
"sobol_total", "sobol_total_average"
]
self.data_information = [
"uncertain_parameters", "model_name", "incomplete", "method",
"version"
]
def test_evaluations_1d_feature(self):
self.plot.data = Data(
os.path.join(self.test_data_dir, "TestingModel1d.h5"))
self.plot.evaluations_1d(feature="feature1d_var")
plot_count = 0
for plot in glob.glob(
os.path.join(self.output_test_dir,
"feature1d_var_evaluations/*.png")):
plot_count += 1
self.assertEqual(plot_count, 32)
def test_evaluations_1d_error(self):
with self.assertRaises(ValueError):
self.plot.evaluations_1d(feature="TestingModel1d")
self.plot.data = Data()
self.plot.evaluations_1d(feature="TestingModel1d")
self.plot.data.add_features("TestingModel1d")
self.plot.data["TestingModel1d"].evaluations = [1, 1, 1, 1, 1]
self.plot.data.model_name = "TestingModel1d"
with self.assertRaises(ValueError):
self.plot.evaluations_1d(feature="TestingModel1d")
def test_evaluations_2d_error(self):
with self.assertRaises(ValueError):
self.plot.evaluations_2d(feature="TestingModel1d")
self.plot.data = Data()
self.plot.evaluations_2d(feature="TestingModel1d")
self.plot.data.add_features("TestingModel1d")
self.plot.data["TestingModel1d"].labels = ["x", "y"]
self.plot.data["TestingModel1d"].time = np.load(os.path.join(self.folder, "data/t_test.npy"))
values = np.load(os.path.join(self.folder, "data/U_test.npy"))
self.plot.data["TestingModel1d"].evaluations = [values, values, values, values, values]
self.plot.data.model_name = "TestingModel1d"
with self.assertRaises(ValueError):
self.plot.evaluations_2d(feature="TestingModel1d")
def test_evaluations_with_error_results(self):
self.plot.data = Data(
os.path.join(self.test_data_dir, "TestingModel1d.h5"))
self.plot.data.error = ["feature1d_var"]
del self.plot.data.data["feature0d_var"]
del self.plot.data.data["feature2d_var"]
del self.plot.data.data["TestingModel1d"]
self.plot.all_evaluations()
plot_count = len(
glob.glob(
os.path.join(self.output_test_dir,
"evaluations/feature1d_var_evaluations/*.png")))
self.assertEqual(plot_count, 32)
def test_evaluations_1d(self):
self.plot.data = Data()
self.plot.data.add_features("TestingModel1d")
self.plot.data["TestingModel1d"].labels = ["x", "y"]
self.plot.data["TestingModel1d"].time = np.load(os.path.join(self.folder, "data/t_test.npy"))
values = np.load(os.path.join(self.folder, "data/U_test.npy"))
self.plot.data["TestingModel1d"].evaluations = [values, values, values, values, values]
self.plot.data.model_name = "TestingModel1d"
self.plot.evaluations_1d(feature="TestingModel1d")
plot_count = 0
for plot in glob.glob(os.path.join(self.output_test_dir, "TestingModel1d_evaluations/*.png")):
plot_count += 1
self.assertEqual(plot_count, 5)
def create_PCE_custom(self, uncertain_parameters=None):
data = Data()
q0, q1 = cp.variable(2)
parameter_space = [cp.Uniform(), cp.Uniform()]
distribution = cp.J(*parameter_space)
data.uncertain_parameters = ["a", "b"]
data.test_value = "custom PCE method"
data.add_features(
["TestingModel1d", "feature0d", "feature1d", "feature2d"])
U_hat = {}
U_hat["TestingModel1d"] = cp.Poly([q0, q1 * q0, q1])
U_hat["feature0d"] = cp.Poly([q0, q1 * q0, q1])
U_hat["feature1d"] = cp.Poly([q0, q1 * q0, q1])
U_hat["feature2d"] = cp.Poly([q0, q1 * q0, q1])
return U_hat, distribution, data
class TestData(unittest.TestCase):
def setUp(self):
self.output_test_dir = ".tests/"
self.seed = 10
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
os.makedirs(self.output_test_dir)
self.data = Data()
self.statistical_metrics = [
"evaluations", "time", "mean", "variance", "percentile_5",
"percentile_95", "sobol_first", "sobol_first_sum", "sobol_total",
"sobol_total_sum"
]
self.data_information = [
"uncertain_parameters", "model_name", "incomplete", "method",
"version"
]
def tearDown(self):
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
# def test_features_0d(self):
# self.data.features_0d = ["feature0d"]
# self.assertEqual(self.data.feature_list, ["feature0d"])
# self.assertEqual(self.data.features_0d, ["feature0d"])
# def test_features_1d(self):
# self.data.features_1d = ["feature1d"]
# self.assertEqual(self.data.feature_list, ["feature1d"])
# self.assertEqual(self.data.features_1d, ["feature1d"])
def test_add_features(self):
self.data.add_features("feature1")
self.assertEqual(self.data.data, {"feature1": DataFeature("feature1")})
self.data.add_features(["feature2", "feature3"])
self.assertEqual(
self.data.data, {
"feature1": DataFeature("feature1"),
"feature2": DataFeature("feature2"),
"feature3": DataFeature("feature3")
})
# def test_nan_to_none(self):
# a = np.array([0, 1, 2, None, 4, None, None])
# b = np.array([0, 1, 2, np.nan, 4, np.nan, np.nan])
# result = self.data.nan_to_none(b)
# self.assertTrue(np.array_equal(a, result))
# def test_none_to_nan(self):
# a = [0, 1, 2, None, 4, None, None]
# b = np.array([0, 1, 2, np.nan, 4, np.nan, np.nan])
# result = self.data.none_to_nan(a)
# self.assertTrue(np.array_equal(b[~np.isnan(b)], result[~np.isnan(result)]))
# self.assertTrue(np.array_equal(np.isnan(b), np.isnan(result)))
# def test_features_2d(self):
# self.data.features_2d = ["feature2d"]
# self.assertEqual(self.data.feature_list, ["feature2d"])
# self.assertEqual(self.data.features_2d, ["feature2d"])
# def test_update_feature_list(self):
# self.data._features_1d = ["b"]
# self.data._features_2d = ["a"]
# self.data._update_feature_list()
# self.assertEqual(self.data.feature_list, ["a", "b"])
# def test_is_adaptive_false(self):
# self.data.evaluations = {"feature1d": [np.arange(1, 4), np.arange(1, 4), np.arange(1, 4)],
# "TestingModel1d": [np.arange(1, 4), np.arange(1, 4), np.arange(1, 4)]}
# self.data.features_1d = ["feature1d", "TestingModel1d"]
# self.assertFalse(self.data.is_adaptive())
# def test_is_adaptive_true(self):
# self.data.evaluations = {"feature1d": [np.arange(1, 4), np.arange(1, 4), np.arange(1, 5)],
# "TestingModel1d": [np.arange(1, 4), np.arange(1, 4), np.arange(1, 4)]}
# self.data.features_1d = ["feature1d", "TestingModel1d"]
# self.assertTrue(self.data.is_adaptive())
def test_save(self):
self.data.add_features(["feature1d", "TestingModel1d"])
for statistical_metric in self.statistical_metrics:
self.data["feature1d"][statistical_metric] = [1., 2.]
self.data["TestingModel1d"][statistical_metric] = [3., 4.]
self.data["feature1d"]["labels"] = ["xlabel", "ylabel"]
self.data["TestingModel1d"]["labels"] = ["xlabel", "ylabel"]
self.data.model_name = "TestingModel1d"
self.data.uncertain_parameters = ["a", "b"]
self.data.method = "mock"
self.data.seed = 10
self.data.incomplete = ["a", "b"]
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock")
filename = os.path.join(self.output_test_dir, "test_save_mock")
self.data.save(filename)
result = subprocess.call(["h5diff", filename, compare_file])
self.assertEqual(result, 0)
# TODO add this check when changing to python 3
# def test_loadError(self):
# compare_file = "this_file_should_not_exist"
#
# with self.assertRaises(FileNotFoundError):
# self.data.load(compare_file)
def test_seed(self):
data = Data()
self.assertEqual(data.seed, "")
data.seed = 10
self.assertEqual(data.seed, 10)
data.seed = None
self.assertEqual(data.seed, "")
def test_save_empty(self):
data = Data()
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_empty")
filename = os.path.join(self.output_test_dir, "test_save_empty")
data.save(filename)
result = subprocess.call(["h5diff", filename, compare_file])
self.assertEqual(result, 0)
def test_load(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock")
self.data.load(compare_file)
for statistical_metric in self.statistical_metrics:
self.assertTrue(
np.array_equal(self.data["feature1d"][statistical_metric],
[1., 2.]))
self.assertTrue(
np.array_equal(self.data["TestingModel1d"][statistical_metric],
[3., 4.]))
self.assertEqual(self.data.uncertain_parameters, ["a", "b"])
self.assertEqual(self.data.incomplete, ["a", "b"])
self.assertEqual(self.data.model_name, "TestingModel1d")
self.assertEqual(self.data.method, "mock")
self.assertEqual(self.data.seed, 10)
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["labels"],
["xlabel", "ylabel"]))
self.assertTrue(
np.array_equal(self.data["feature1d"]["labels"],
["xlabel", "ylabel"]))
def test_load_empty(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_empty")
self.data.load(compare_file)
self.assertEqual(self.data.data, {})
self.assertEqual(self.data.uncertain_parameters, [])
self.assertEqual(self.data.model_name, "")
self.assertEqual(self.data.uncertain_parameters, [])
self.assertEqual(self.data.incomplete, [])
self.assertEqual(self.data.method, "")
self.assertEqual(self.data.seed, "")
def test_get_labels(self):
self.data.add_features(["model_name", "feature", "feature2"])
self.data["model_name"].labels = ["x", "y"]
self.data["feature"].labels = ["x", "y"]
self.data["model_name"].evaluations = [[1, 2], [1, 2]]
self.data["feature"].evaluations = [[1, 2], [1, 2]]
self.data["feature2"].evaluations = [[1, 2], [1, 2]]
self.data.model_name = "model_name"
self.assertEqual(self.data.get_labels("feature"), ["x", "y"])
self.assertEqual(self.data.get_labels("feature2"), ["x", "y"])
self.data["feature2"].evaluations = [[[1], [2]], [[1], [2]]]
self.assertEqual(self.data.get_labels("feature2"), ["", "", ""])
self.data["feature"]["labels"] = ["x"]
self.assertEqual(self.data.get_labels("feature"), ["x"])
def test_getitem(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
self.assertEqual(self.data["test1"], 1)
self.assertEqual(self.data["test2"], 2)
def test_iter(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
result = []
for feature in self.data:
result.append(feature)
self.assertEqual(result, ["test1", "test2"])
def test_len(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
self.assertEqual(len(self.data), 2)
def test_delitem(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
del self.data["test2"]
self.assertTrue("test1" in self.data)
self.assertFalse("test2" in self.data)
def test_remove_only_invalid_features(self):
self.data.add_features(["feature1d", "TestingModel1d"])
self.data["feature1d"]["evaluations"] = np.array([[1, 2], [2, 3]])
self.data["TestingModel1d"]["evaluations"] = np.array([[3, 4],
[np.nan]])
self.data["feature1d"]["time"] = np.array([1, 2])
self.data["TestingModel1d"]["time"] = np.array([3, 4])
self.data.remove_only_invalid_features()
self.assertTrue(
np.array_equal(self.data["feature1d"]["evaluations"],
np.array([[1, 2], [2, 3]])))
self.assertTrue(
np.array_equal(self.data["feature1d"]["time"], np.array([1, 2])))
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["evaluations"],
np.array([[3, 4], [np.nan]])))
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["time"],
np.array([3, 4])))
def test_remove_only_invalid_features_error(self):
self.data.add_features(["feature1d", "TestingModel1d"])
self.data["feature1d"]["evaluations"] = np.array([[1, 2], [2, 3]])
self.data["TestingModel1d"]["evaluations"] = np.array([[np.nan],
[np.nan]])
self.data["feature1d"]["time"] = np.array([1, 2])
self.data["TestingModel1d"]["time"] = np.array([3, 4])
self.data.remove_only_invalid_features()
self.assertTrue(
np.array_equal(self.data["feature1d"]["evaluations"],
np.array([[1, 2], [2, 3]])))
self.assertTrue(
np.array_equal(self.data["feature1d"]["time"], np.array([1, 2])))
self.assertFalse("TestingModel1d" in self.data)
def test_str(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/TestingModel1d.h5")
self.data.load(compare_file)
# TODO Test that the content of the data string is correct
self.assertIsInstance(str(self.data), str)
def test_clear(self):
self.data.uncertain_parameters = -1
self.data.model_name = -1
self.data.data = -1
self.data.incomplete = -1
self.data.method = -1
self.data.seed = -1
self.data.clear()
self.assertEqual(self.data.model_name, "")
self.assertEqual(self.data.data, {})
self.assertEqual(self.data.uncertain_parameters, [])
self.assertEqual(self.data.incomplete, [])
self.assertEqual(self.data.model_name, "")
self.assertEqual(self.data.method, "")
self.assertEqual(self.data.seed, "")
def test_ndim(self):
self.data.add_features(
["feature0d", "feature1d", "feature2d", "feature_invalid"])
self.data["feature0d"].evaluations = [1]
self.data["feature1d"].evaluations = [np.arange(0, 10)]
self.data["feature2d"].evaluations = [
np.array([np.arange(0, 10), np.arange(0, 10)])
]
self.data["feature_invalid"].evaluations = [np.nan]
self.assertEqual(self.data.ndim("feature0d"), 0)
self.assertEqual(self.data.ndim("feature1d"), 1)
self.assertEqual(self.data.ndim("feature2d"), 2)
self.assertEqual(self.data.ndim("feature_invalid"), 0)
class TestData(unittest.TestCase):
def setUp(self):
self.output_test_dir = ".tests/"
self.seed = 10
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
os.makedirs(self.output_test_dir)
self.data = Data(logger_level="error")
self.statistical_metrics = [
"evaluations", "time", "mean", "variance", "percentile_5",
"percentile_95", "sobol_first", "sobol_first_average",
"sobol_total", "sobol_total_average"
]
self.data_information = [
"uncertain_parameters", "model_name", "incomplete", "method",
"version"
]
def tearDown(self):
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
def test_add_features(self):
self.data.add_features("feature1")
self.assertEqual(self.data.data, {"feature1": DataFeature("feature1")})
self.data.add_features(u"feature1")
self.assertEqual(self.data.data, {"feature1": DataFeature("feature1")})
self.data.add_features(["feature2", "feature3"])
self.assertEqual(
self.data.data, {
"feature1": DataFeature("feature1"),
"feature2": DataFeature("feature2"),
"feature3": DataFeature("feature3")
})
def test_save(self):
self.setup_mock_data(self.data)
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock")
filename = os.path.join(self.output_test_dir, "test_save_mock")
self.data.save(filename)
result = subprocess.call(["h5diff", filename, compare_file])
self.assertEqual(result, 0)
def test_backend_error(self):
with self.assertRaises(ValueError):
self.data = Data(backend="not a backend")
def test_save_load_exdir(self):
self.data = Data(logger_level="error", backend="exdir")
self.setup_mock_data(self.data)
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock")
filename = os.path.join(self.output_test_dir, "test_save_mock")
self.data.save(filename)
new_data = Data(filename, backend="exdir")
for statistical_metric in self.statistical_metrics:
new_data["feature1d"][statistical_metric] = np.array([1., 2.])
new_data["TestingModel1d"][statistical_metric] = np.array([3., 4.])
new_data["feature1d"]["labels"] = ["xlabel", "ylabel"]
new_data["TestingModel1d"]["labels"] = ["xlabel", "ylabel"]
new_data.model_name = "TestingModel1d"
new_data.uncertain_parameters = ["a", "b"]
new_data.method = "mock"
new_data.seed = 10
new_data.incomplete = ["a", "b"]
new_data.error = ["feature1d"]
def setup_mock_data(self, data):
data.add_features(["feature1d", "TestingModel1d"])
for statistical_metric in self.statistical_metrics:
data["feature1d"][statistical_metric] = np.array([1., 2.])
data["TestingModel1d"][statistical_metric] = np.array([3., 4.])
data["feature1d"]["labels"] = ["xlabel", "ylabel"]
data["TestingModel1d"]["labels"] = ["xlabel", "ylabel"]
data.model_name = "TestingModel1d"
data.uncertain_parameters = ["a", "b"]
data.method = "mock"
data.seed = 10
data.incomplete = ["a", "b"]
data.error = ["feature1d"]
return data
def test_save_irregular(self):
self.data.add_features(["feature1d", "TestingModel1d"])
self.setup_mock_data(self.data)
self.data.model_ignore = True
self.data["TestingModel1d"].evaluations = [[1, 2], [np.nan],
[1, [2, 3], 3], [1], 3,
[3, 4, 5], [1, 2], [],
[3, 4, 5], [], [3, 4, 5]]
self.data["TestingModel1d"].time = [[1, 2], [np.nan], [1, [2, 3], 3],
[1], 3, [3, 4, 5], [1, 2], [],
[3, 4, 5], [], [3, 4, 5]]
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock_irregular")
filename = os.path.join(self.output_test_dir,
"test_save_mock_irregular")
self.data.save(filename)
result = subprocess.call(["h5diff", filename, compare_file])
self.assertEqual(result, 0)
def test_load_irregular(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock_irregular")
self.data.load(compare_file)
for statistical_metric in self.statistical_metrics:
if statistical_metric in ["evaluations", "time"]:
self.assertTrue(
np.array_equal(self.data["feature1d"][statistical_metric],
[1., 2.]))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][0],
[1., 2.]))
self.assertTrue(
np.isnan(
self.data["TestingModel1d"][statistical_metric][1]))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][2][0],
1))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][2][1],
np.array([2, 3])))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][2][2],
3))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][3],
np.array([1])))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][4], 3))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][5],
[3, 4, 5]))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][6],
[1., 2.]))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][7],
np.array([])))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][8],
[3, 4, 5]))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][9],
np.array([])))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric][10],
[3, 4, 5]))
else:
self.assertTrue(
np.array_equal(self.data["feature1d"][statistical_metric],
[1., 2.]))
self.assertTrue(
np.array_equal(
self.data["TestingModel1d"][statistical_metric],
[3., 4.]))
self.assertEqual(self.data.uncertain_parameters, ["a", "b"])
self.assertEqual(self.data.incomplete, ["a", "b"])
self.assertEqual(self.data.error, ["feature1d"])
self.assertEqual(self.data.model_name, "TestingModel1d")
self.assertEqual(self.data.method, "mock")
self.assertEqual(self.data.seed, 10)
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["labels"],
["xlabel", "ylabel"]))
self.assertTrue(
np.array_equal(self.data["feature1d"]["labels"],
["xlabel", "ylabel"]))
# # TODO add this check when changing to python 3
# # def test_loadError(self):
# # compare_file = "this_file_should_not_exist"
# #
# # with self.assertRaises(FileNotFoundError):
# # self.data.load(compare_file)
def test_seed(self):
data = Data()
self.assertEqual(data.seed, "")
data.seed = 10
self.assertEqual(data.seed, 10)
data.seed = None
self.assertEqual(data.seed, "")
def test_save_empty(self):
data = Data()
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_empty")
filename = os.path.join(self.output_test_dir, "test_save_empty")
data.save(filename)
result = subprocess.call(["h5diff", filename, compare_file])
self.assertEqual(result, 0)
def test_load(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock")
self.data.load(compare_file)
for statistical_metric in self.statistical_metrics:
self.assertTrue(
np.array_equal(self.data["feature1d"][statistical_metric],
[1., 2.]))
self.assertTrue(
np.array_equal(self.data["TestingModel1d"][statistical_metric],
[3., 4.]))
self.assertEqual(self.data.uncertain_parameters, ["a", "b"])
self.assertEqual(self.data.incomplete, ["a", "b"])
self.assertEqual(self.data.error, ["feature1d"])
self.assertEqual(self.data.model_name, "TestingModel1d")
self.assertEqual(self.data.method, "mock")
self.assertEqual(self.data.seed, 10)
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["labels"],
["xlabel", "ylabel"]))
self.assertTrue(
np.array_equal(self.data["feature1d"]["labels"],
["xlabel", "ylabel"]))
def test_load_missing(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_mock_missing")
self.data.load(compare_file)
for statistical_metric in self.statistical_metrics:
self.assertTrue(
np.array_equal(self.data["feature1d"][statistical_metric],
[1., 2.]))
self.assertTrue(
np.array_equal(self.data["TestingModel1d"][statistical_metric],
[3., 4.]))
self.assertEqual(self.data.uncertain_parameters, ["a", "b"])
self.assertEqual(self.data.incomplete, [])
self.assertEqual(self.data.error, ["feature1d"])
self.assertEqual(self.data.model_name, "TestingModel1d")
self.assertEqual(self.data.method, "mock")
self.assertEqual(self.data.seed, "")
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["labels"],
["xlabel", "ylabel"]))
self.assertTrue(
np.array_equal(self.data["feature1d"]["labels"],
["xlabel", "ylabel"]))
def test_load_empty(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/test_save_empty")
self.data.load(compare_file)
self.assertEqual(self.data.data, {})
self.assertEqual(self.data.uncertain_parameters, [])
self.assertEqual(self.data.model_name, "")
self.assertEqual(self.data.uncertain_parameters, [])
self.assertEqual(self.data.incomplete, [])
self.assertEqual(self.data.method, "")
self.assertEqual(self.data.seed, "")
def test_get_labels(self):
self.data.add_features(["model_name", "feature", "feature2"])
self.data["model_name"].labels = ["x", "y"]
self.data["feature"].labels = ["x", "y"]
self.data["model_name"].evaluations = [[1, 2], [1, 2]]
self.data["feature"].evaluations = [[1, 2], [1, 2]]
self.data["feature2"].evaluations = [[1, 2], [1, 2]]
self.data.model_name = "model_name"
self.assertEqual(self.data.get_labels("feature"), ["x", "y"])
self.assertEqual(self.data.get_labels("feature2"), ["x", "y"])
self.data["feature2"].evaluations = [[[1], [2]], [[1], [2]]]
self.assertEqual(self.data.get_labels("feature2"), ["", "", ""])
self.data["feature"]["labels"] = ["x"]
self.assertEqual(self.data.get_labels("feature"), ["x"])
def test_getitem(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
self.assertEqual(self.data["test1"], 1)
self.assertEqual(self.data["test2"], 2)
def test_iter(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
result = []
for feature in self.data:
result.append(feature)
self.assertEqual(result, ["test1", "test2"])
def test_iter_error(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
self.data.error = ["test2"]
result = []
for feature in self.data:
result.append(feature)
self.assertEqual(result, ["test1"])
def test_iter_error_all(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
self.data.error = ["test1", "test2"]
result = []
for feature in self.data:
result.append(feature)
self.assertEqual(result, [])
def test_len(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
self.data.error = ["test2"]
self.assertEqual(len(self.data), 1)
def test_delitem(self):
self.data.data["test1"] = 1
self.data.data["test2"] = 2
del self.data["test2"]
self.assertTrue("test1" in self.data)
self.assertFalse("test2" in self.data)
def test_remove_only_invalid_features(self):
self.data.add_features(["feature1d", "TestingModel1d"])
self.data["feature1d"]["evaluations"] = np.array([[1, 2], [2, 3]])
self.data["TestingModel1d"]["evaluations"] = np.array([[3, 4],
[np.nan]])
self.data["feature1d"]["time"] = np.array([1, 2])
self.data["TestingModel1d"]["time"] = np.array([3, 4])
self.data.remove_only_invalid_features()
self.assertTrue(
np.array_equal(self.data["feature1d"]["evaluations"],
np.array([[1, 2], [2, 3]])))
self.assertTrue(
np.array_equal(self.data["feature1d"]["time"], np.array([1, 2])))
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["evaluations"],
np.array([[3, 4], [np.nan]])))
self.assertTrue(
np.array_equal(self.data["TestingModel1d"]["time"],
np.array([3, 4])))
def test_remove_only_invalid_features_error(self):
self.data.add_features(["feature1d", "TestingModel1d"])
self.data["feature1d"]["evaluations"] = np.array([[1, 2], [2, 3]])
self.data["TestingModel1d"]["evaluations"] = np.array([[np.nan],
[np.nan]])
self.data["feature1d"]["time"] = np.array([1, 2])
self.data["TestingModel1d"]["time"] = np.array([3, 4])
self.data.remove_only_invalid_features()
self.assertTrue(
np.array_equal(self.data["feature1d"]["evaluations"],
np.array([[1, 2], [2, 3]])))
self.assertTrue(
np.array_equal(self.data["feature1d"]["time"], np.array([1, 2])))
self.assertFalse("TestingModel1d" in self.data)
def test_str(self):
folder = os.path.dirname(os.path.realpath(__file__))
compare_file = os.path.join(folder, "data/TestingModel1d.h5")
self.data.load(compare_file)
# TODO Test that the content of the data string is correct
self.assertIsInstance(str(self.data), str)
def test_clear(self):
self.data.uncertain_parameters = -1
self.data.model_name = -1
self.data.data = -1
self.data.incomplete = -1
self.data.method = -1
self.data.seed = -1
self.data.clear()
self.assertEqual(self.data.model_name, "")
self.assertEqual(self.data.data, {})
self.assertEqual(self.data.uncertain_parameters, [])
self.assertEqual(self.data.incomplete, [])
self.assertEqual(self.data.model_name, "")
self.assertEqual(self.data.method, "")
self.assertEqual(self.data.seed, "")
def test_ndim(self):
self.data.add_features([
"feature0d", "feature1d", "feature2d", "feature_invalid", "empty",
"test"
])
self.data["feature0d"].evaluations = [1]
self.data["feature1d"].evaluations = [np.arange(0, 10)]
self.data["feature2d"].evaluations = [
np.array([np.arange(0, 10), np.arange(0, 10)])
]
self.data["feature_invalid"].evaluations = [np.nan]
self.data["test"].evaluations = [np.nan, np.arange(0, 10)]
self.assertEqual(self.data.ndim("feature0d"), 0)
self.assertEqual(self.data.ndim("feature1d"), 1)
self.assertEqual(self.data.ndim("feature2d"), 2)
self.assertIsNone(self.data.ndim("feature_invalid"))
self.assertIsNone(self.data.ndim("empty"))
def test_backend_error(self):
with self.assertRaises(ValueError):
self.data = Data(backend="not a backend")
