def test_run_feature_no_time(self):
parallel = Parallel(
model=TestingModel1d(),
features=TestingFeatures(features_to_run="feature_no_time"))
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def setUp(self):
self.output_test_dir = ".tests/"
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
os.makedirs(self.output_test_dir)
self.features = TestingFeatures(features_to_run=[
"feature0d", "feature1d", "feature2d", "feature_invalid",
"feature_interpolate"
])
self.parallel = Parallel(model=TestingModel1d(),
features=self.features,
logger_level="error")
self.model_parameters = {"a": 0, "b": 1}
self.t = np.arange(0, 10)
self.values = np.arange(0, 10) + 1
def test_run_interpolate(self):
parallel = Parallel(
model=TestingModelAdaptive(),
features=TestingFeatures(features_to_run="feature_interpolate"))
results = parallel.run(self.model_parameters)
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["TestingModelAdaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
self.assertTrue(
np.array_equal(results["feature_interpolate"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["feature_interpolate"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["feature_interpolate"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_postprocess_error_value(self):
parallel = Parallel(model=PostprocessErrorValue())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_run_model_no_time(self):
parallel = Parallel(model=TestingModelNoTime())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_init(self):
Parallel(TestingModel1d())
class TestParallel(unittest.TestCase):
def setUp(self):
self.output_test_dir = ".tests/"
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
os.makedirs(self.output_test_dir)
self.features = TestingFeatures(features_to_run=[
"feature0d", "feature1d", "feature2d", "feature_invalid",
"feature_interpolate"
])
self.parallel = Parallel(model=TestingModel1d(),
features=self.features,
logger_level="error")
self.model_parameters = {"a": 0, "b": 1}
self.t = np.arange(0, 10)
self.values = np.arange(0, 10) + 1
def tearDown(self):
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
def test_init(self):
Parallel(TestingModel1d())
def test_feature(self):
self.parallel.features = Features
self.assertIsInstance(self.parallel.features, Features)
def test_feature_function(self):
def feature_function(time, values):
return "time", "values"
self.parallel.features = feature_function
self.assertIsInstance(self.parallel.features, Features)
time, values = self.parallel.features.feature_function(None, None)
self.assertEqual(time, "time")
self.assertEqual(values, "values")
self.assertEqual(self.parallel.features.features_to_run,
["feature_function"])
def test_feature_functions(self):
def feature_function(time, values):
return "time", "values"
def feature_function2(time, values):
return "time2", "values2"
self.parallel.features = [feature_function, feature_function2]
self.assertIsInstance(self.parallel.features, Features)
time, values = self.parallel.features.feature_function(None, None)
self.assertEqual(time, "time")
self.assertEqual(values, "values")
time, values = self.parallel.features.feature_function(None, None)
self.assertEqual(time, "time")
self.assertEqual(values, "values")
time, values = self.parallel.features.feature_function2(None, None)
self.assertEqual(time, "time2")
self.assertEqual(values, "values2")
self.assertEqual(self.parallel.features.features_to_run,
["feature_function", "feature_function2"])
def test_model(self):
self.parallel.model = model_function
self.assertIsInstance(self.parallel.model, Model)
self.assertEqual(self.parallel.model.name, "model_function")
# def test_sort_features(self):
# results = {"TestingModel1d": {"values": np.arange(0, 10) + 1,
# "time": np.arange(0, 10)},
# "feature1d": {"values": np.arange(0, 10),
# "time": np.arange(0, 10)},
# "feature0d": {"values": 1,
# "time": np.nan},
# "feature2d": {"values": np.array([np.arange(0, 10),
# np.arange(0, 10)]),
# "time": np.arange(0, 10)},
# "feature_interpolate": {"values": np.arange(0, 10) + 1,
# "time": np.arange(0, 10),
# "interpolation": "interpolation object"},
# "feature_invalid": {"values": np.nan,
# "time": np.nan}}
# features_0d, features_1d, features_2d = self.parallel.sort_features(results)
# self.assertEqual(features_0d, ["feature0d", "feature_invalid"])
# self.assertEqual(set(features_1d),
# set(["feature1d", "TestingModel1d", "feature_interpolate"]))
# self.assertEqual(features_2d, ["feature2d"])
def test_create_interpolations(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10) + 1,
"time": np.arange(0, 10)
},
"feature1d": {
"values": np.arange(0, 10),
"time": np.arange(0, 10)
},
"feature0d": {
"values": 1,
"time": np.nan
},
"feature2d": {
"values": np.array([np.arange(0, 10),
np.arange(0, 10)]),
"time": np.arange(0, 10)
},
"feature_interpolate": {
"values": np.arange(0, 10) + 1,
"time": np.arange(0, 10)
},
"feature_invalid": {
"values": np.nan,
"time": np.nan
}
}
results = self.parallel.create_interpolations(results)
self.assertTrue(
np.array_equal(results["TestingModel1d"]["time"], np.arange(0,
10)))
self.assertTrue(
np.array_equal(results["TestingModel1d"]["values"],
np.arange(0, 10) + 1))
self.assertTrue(
np.array_equal(results["feature1d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature1d"]["values"], np.arange(0, 10)))
self.assertTrue(np.isnan(results["feature0d"]["time"]))
self.assertEqual(results["feature0d"]["values"], 1)
self.assertTrue(
np.array_equal(results["feature2d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature2d"]["values"],
np.array([np.arange(0, 10),
np.arange(0, 10)])))
self.assertTrue(np.isnan(results["feature_invalid"]["time"]))
self.assertTrue(np.isnan(results["feature_invalid"]["values"]))
self.assertTrue(
np.array_equal(results["feature_interpolate"]["time"],
np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature_interpolate"]["values"],
np.arange(0, 10) + 1))
self.assertIsInstance(results["feature_interpolate"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_create_interpolations_irregular_values(self):
self.parallel.model.interpolate = True
results = {
"TestingModel1d": {
"values": [1, 3, [3, 4]],
"time": np.arange(0, 10)
}
}
with self.assertRaises(ValueError):
results = self.parallel.create_interpolations(results)
def test_create_interpolations_irregular_time(self):
self.parallel.model.interpolate = True
results = {
"TestingModel1d": {
"values": np.arange(0, 10),
"time": [1, 3, [3, 4]]
}
}
with self.assertRaises(ValueError):
results = self.parallel.create_interpolations(results)
def test_create_interpolations_ignore(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10) + 1,
"time": np.arange(0, 10)
}
}
self.parallel.model.interpolate = True
inter = self.parallel.create_interpolations(results)
self.assertIsInstance(inter["TestingModel1d"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
self.parallel.model.ignore = True
results = {
"TestingModel1d": {
"values": np.arange(0, 10) + 1,
"time": np.arange(0, 10)
}
}
inter = self.parallel.create_interpolations(results)
self.assertNotIn("interpolation", inter["TestingModel1d"])
def test_create_interpolations_feature_1d_no_t(self):
results = {
"feature_interpolate": {
"values": np.arange(0, 10),
"time": np.nan
}
}
with self.assertRaises(ValueError):
self.parallel.create_interpolations(results)
def test_create_interpolations_feature_1d_t_nan(self):
results = {
"feature_interpolate": {
"values": np.arange(0, 10),
"time": [np.nan] * 10
}
}
results = self.parallel.create_interpolations(results)
self.assertIsNone(results["feature_interpolate"]["interpolation"])
def test_create_interpolations_feature_1d_values_nan(self):
results = {
"feature_interpolate": {
"time": np.arange(0, 10),
"values": [np.nan] * 10
}
}
results = self.parallel.create_interpolations(results)
self.assertIsNone(results["feature_interpolate"]["interpolation"])
def test_create_interpolations_feature_0d(self):
results = {
"feature_interpolate": {
"values": 1,
"time": np.arange(0, 10)
}
}
# with self.assertRaises(AttributeError):
# self.parallel.create_interpolations(results)
results = self.parallel.create_interpolations(results)
self.assertTrue(
np.array_equal(results["feature_interpolate"]["time"],
np.arange(0, 10)))
self.assertEqual(results["feature_interpolate"]["values"], 1)
def test_create_interpolations_feature_2d(self):
results = {
"feature_interpolate": {
"values": np.array([np.arange(0, 10),
np.arange(0, 10)]),
"time": np.arange(0, 10)
}
}
with self.assertRaises(NotImplementedError):
self.parallel.create_interpolations(results)
def test_interpolation_1d(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10) + 1,
"time": np.arange(0, 10)
}
}
interpolation = self.parallel.interpolation_1d(results,
"TestingModel1d")
self.assertIsInstance(interpolation,
scipy.interpolate.fitpack2.UnivariateSpline)
def test_interpolation_1d_different_time_values(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 5),
"time": [[1, 3], [3, 4]]
}
}
with self.assertRaises(ValueError):
self.parallel.interpolation_1d(results, "TestingModel1d")
def test_interpolation_1d_values_nan(self):
results = {
"TestingModel1d": {
"values": [1, 2, 3, 4, np.nan],
"time": [1, 2, 3, 4, 5]
}
}
interpolation = self.parallel.interpolation_1d(results,
"TestingModel1d")
self.assertIsNone(interpolation)
def test_interpolation_1d_values_none(self):
results = {
"TestingModel1d": {
"values": [1, 2, 3, 4, None],
"time": [1, 2, 3, 4, 5]
}
}
interpolation = self.parallel.interpolation_1d(results,
"TestingModel1d")
self.assertIsNone(interpolation)
def test_interpolation_1d_time_nan(self):
results = {
"TestingModel1d": {
"time": [1, 2, 3, 4, np.nan],
"values": [1, 2, 3, 4, 5]
}
}
interpolation = self.parallel.interpolation_1d(results,
"TestingModel1d")
self.assertIsNone(interpolation)
def test_interpolation_1d_time_none(self):
results = {
"TestingModel1d": {
"time": [1, 2, 3, 4, None],
"values": [1, 2, 3, 4, 5]
}
}
interpolation = self.parallel.interpolation_1d(results,
"TestingModel1d")
self.assertIsNone(interpolation)
def test_interpolation_1d_feature_1d_no_t(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10),
"time": np.nan
}
}
with self.assertRaises(ValueError):
interpolation = self.parallel.interpolation_1d(
results, "TestingModel1d")
def test_interpolation_1d_feature_1d_t_nan(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10),
"time": [np.nan] * 10
}
}
interpolation = self.parallel.interpolation_1d(results,
"TestingModel1d")
self.assertIsNone(interpolation)
def test_interpolation_1d_irregular(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10),
"time": [1, 3, [3, 4]]
}
}
with self.assertRaises(TypeError):
self.parallel.interpolation_1d(results, "TestingModel1d")
def test_interpolation_1d_2d(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10),
"time": [[1, 3], [3, 4]]
}
}
with self.assertRaises(ValueError):
self.parallel.interpolation_1d(results, "TestingModel1d")
def test_create_interpolations_model_0d(self):
self.parallel.model.interpolate = True
results = {"TestingModel1d": {"values": 1, "time": np.arange(0, 10)}}
# with self.assertRaises(AttributeError):
# self.parallel.create_interpolations(results)
results = self.parallel.create_interpolations(results)
self.assertTrue(
np.array_equal(results["TestingModel1d"]["time"], np.arange(0,
10)))
self.assertEqual(results["TestingModel1d"]["values"], 1)
def test_create_interpolations_model_2d(self):
self.parallel.model.interpolate = True
results = {
"TestingModel1d": {
"values": np.array([np.arange(0, 10),
np.arange(0, 10)]),
"time": np.arange(0, 10)
}
}
with self.assertRaises(NotImplementedError):
self.parallel.create_interpolations(results)
def test_run(self):
results = self.parallel.run(self.model_parameters)
self.assertTrue(self.parallel.features.is_preprocess_run)
self.assertTrue(
np.array_equal(results["TestingModel1d"]["time"], np.arange(0,
10)))
self.assertTrue(
np.array_equal(results["TestingModel1d"]["values"],
np.arange(0, 10) + 1))
self.assertTrue(
np.array_equal(results["feature1d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature1d"]["values"], np.arange(0, 10)))
self.assertTrue(np.isnan(results["feature0d"]["time"]))
self.assertEqual(results["feature0d"]["values"], 1)
self.assertTrue(
np.array_equal(results["feature2d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature2d"]["values"],
np.array([np.arange(0, 10),
np.arange(0, 10)])))
self.assertTrue(np.isnan(results["feature_invalid"]["time"]))
self.assertTrue(np.isnan(results["feature_invalid"]["values"]))
self.assertTrue(
np.array_equal(results["feature_interpolate"]["time"],
np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature_interpolate"]["values"],
np.arange(0, 10) + 1))
self.assertIsInstance(results["feature_interpolate"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_run_kwargs(self):
def test_model(a=10, b=11, c=12):
return a + b, c
model = Model(test_model, c=22)
self.parallel.model = model
self.parallel.features = None
results = self.parallel.run(self.model_parameters)
self.assertEqual(results["test_model"]["time"], 1)
self.assertEqual(results["test_model"]["values"], 22)
def test_run_interpolate(self):
parallel = Parallel(
model=TestingModelAdaptive(),
features=TestingFeatures(features_to_run="feature_interpolate"))
results = parallel.run(self.model_parameters)
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["TestingModelAdaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
self.assertTrue(
np.array_equal(results["feature_interpolate"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["feature_interpolate"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["feature_interpolate"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_run_model_no_time(self):
parallel = Parallel(model=TestingModelNoTime())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_run_feature_no_time(self):
parallel = Parallel(
model=TestingModel1d(),
features=TestingFeatures(features_to_run="feature_no_time"))
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_postprocess_error_numpy(self):
parallel = Parallel(model=PostprocessErrorNumpy())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_postprocess_error_one(self):
parallel = Parallel(model=PostprocessErrorOne())
with self.assertRaises(TypeError):
parallel.run(self.model_parameters)
def test_postprocess_error_value(self):
parallel = Parallel(model=PostprocessErrorValue())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_use_info_arg(self):
def model_function(**model_parameters):
return 1, 2, True
def feature_function(time, values, info=False):
self.assertTrue(info)
return "time", "values"
self.parallel.model = model_function
self.parallel.features = feature_function
self.parallel.run(self.model_parameters)
def test_use_info_arg_dict(self):
def model_function(**model_parameters):
return 1, 2, {"1": 1, "2": 2}
def feature_function(time, values, info):
self.assertEqual(info["1"], 1)
self.assertEqual(info["2"], 2)
return "time", "values"
self.parallel.model = model_function
self.parallel.features = feature_function
self.parallel.run(self.model_parameters)
def test_use_model_feature_arguments_error(self):
def model_function(**model_parameters):
return 1, 2, 3
def feature_function(time, values):
return "time", "values"
self.parallel.model = model_function
self.parallel.features = feature_function
with self.assertRaises(TypeError):
self.parallel.run(self.model_parameters)