class TestEvaluation(unittest.TestCase):
def setUp(self):
self.eval = Evaluation(None, [], [])
lat = np.array([10, 12, 14, 16, 18])
lon = np.array([100, 102, 104, 106, 108])
time = np.array([dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
value = flat_array.reshape(12, 5, 5)
self.variable = 'prec'
self.other_var = 'temp'
self.test_dataset = Dataset(lat, lon, time, value, self.variable)
self.another_test_dataset = Dataset(lat, lon, time, value,
self.other_var)
def test_init(self):
self.assertEquals(self.eval.ref_dataset, None)
self.assertEquals(self.eval.target_datasets, [])
self.assertEquals(self.eval.metrics, [])
self.assertEquals(self.eval.unary_metrics, [])
def test_full_init(self):
self.eval = Evaluation(
self.test_dataset,
[self.test_dataset, self.another_test_dataset],
[Bias(), Bias(), TemporalStdDev()])
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
# Make sure the two target datasets were added properly
self.assertEqual(self.eval.target_datasets[0].variable, self.variable)
self.assertEqual(self.eval.target_datasets[1].variable, self.other_var)
# Make sure the three metrics were added properly
# The two Bias metrics are "binary" metrics
self.assertEqual(len(self.eval.metrics), 2)
# TemporalStdDev is a "unary" metric and should be stored as such
self.assertEqual(len(self.eval.unary_metrics), 1)
def test_invalid_ref_dataset(self):
with self.assertRaises(TypeError):
self.eval.ref_dataset = "This isn't a Dataset object"
def test_valid_subregion(self):
bound = Bounds(
-10, 10,
-20, 20,
dt.datetime(2000, 1, 1), dt.datetime(2001, 1, 1))
self.eval.subregions = [bound, bound]
self.assertEquals(len(self.eval.subregions), 2)
def test_invalid_subregion_bound(self):
bound = "This is not a bounds object"
with self.assertRaises(TypeError):
self.eval.subregions = [bound]
def test_add_ref_dataset(self):
self.eval = Evaluation(self.test_dataset, [], [])
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
def test_add_dataset(self):
self.eval.add_dataset(self.test_dataset)
self.assertEqual(self.eval.target_datasets[0].variable,
self.variable)
def test_add_datasets(self):
self.eval.add_datasets([self.test_dataset, self.another_test_dataset])
self.assertEqual(len(self.eval.target_datasets), 2)
self.assertEqual(self.eval.target_datasets[0].variable,
self.variable)
self.assertEqual(self.eval.target_datasets[1].variable,
self.other_var)
def test_add_metric(self):
# Add a "binary" metric
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metric(Bias())
self.assertEqual(len(self.eval.metrics), 1)
# Add a "unary" metric
self.assertEqual(len(self.eval.unary_metrics), 0)
self.eval.add_metric(TemporalStdDev())
self.assertEqual(len(self.eval.unary_metrics), 1)
def test_add_metrics(self):
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metrics([Bias(), Bias()])
self.assertEqual(len(self.eval.metrics), 2)
def test_bias_output_shape(self):
bias_eval = Evaluation(self.test_dataset, [self.another_test_dataset], [Bias()])
bias_eval.run()
input_shape = tuple(self.test_dataset.values.shape)
bias_results_shape = tuple(bias_eval.results[0][0].shape)
self.assertEqual(input_shape, bias_results_shape)
class TestEvaluation(unittest.TestCase):
def setUp(self):
self.eval = Evaluation(None, [], [])
lat = np.array([10, 12, 14, 16, 18])
lon = np.array([100, 102, 104, 106, 108])
time = np.array([dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
value = flat_array.reshape(12, 5, 5)
self.variable = 'prec'
self.other_var = 'temp'
self.test_dataset = Dataset(lat, lon, time, value, self.variable)
self.another_test_dataset = Dataset(lat, lon, time, value,
self.other_var)
def test_init(self):
self.assertEquals(self.eval.ref_dataset, None)
self.assertEquals(self.eval.target_datasets, [])
self.assertEquals(self.eval.metrics, [])
self.assertEquals(self.eval.unary_metrics, [])
def test_full_init(self):
self.eval = Evaluation(
self.test_dataset,
[self.test_dataset, self.another_test_dataset],
[Bias(), Bias(), TemporalStdDev()])
ref_dataset = self.test_dataset
target_datasets = [self.test_dataset, self.another_test_dataset]
metrics = [Bias(), Bias()]
unary_metrics = [TemporalStdDev()]
self.eval = Evaluation(ref_dataset,
target_datasets,
metrics + unary_metrics)
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
# Make sure the two target datasets were added properly
self.assertEqual(self.eval.target_datasets[0].variable, self.variable)
self.assertEqual(self.eval.target_datasets[1].variable, self.other_var)
# Make sure the three metrics were added properly
# The two Bias metrics are "binary" metrics
self.assertEqual(len(self.eval.metrics), 2)
# TemporalStdDev is a "unary" metric and should be stored as such
self.assertEqual(len(self.eval.unary_metrics), 1)
self.eval.run()
out_str = (
"<Evaluation - ref_dataset: {}, "
"target_dataset(s): {}, "
"binary_metric(s): {}, "
"unary_metric(s): {}, "
"subregion(s): {}>"
).format(
str(self.test_dataset),
[str(ds) for ds in target_datasets],
[str(m) for m in metrics],
[str(u) for u in unary_metrics],
None
)
self.assertEqual(str(self.eval), out_str)
def test_valid_ref_dataset_setter(self):
self.eval.ref_dataset = self.another_test_dataset
self.assertEqual(self.eval.ref_dataset.variable,
self.another_test_dataset.variable)
def test_invalid_ref_dataset(self):
with self.assertRaises(TypeError):
self.eval.ref_dataset = "This isn't a Dataset object"
def test_valid_subregion(self):
bound = Bounds(
lat_min=-10, lat_max=10,
lon_min=-20, lon_max=20,
start=dt.datetime(2000, 1, 1), end=dt.datetime(2001, 1, 1))
self.eval.subregions = [bound, bound]
self.assertEquals(len(self.eval.subregions), 2)
def test_invalid_subregion_bound(self):
bound = "This is not a bounds object"
with self.assertRaises(TypeError):
self.eval.subregions = [bound]
def test_add_ref_dataset(self):
self.eval = Evaluation(self.test_dataset, [], [])
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
def test_add_valid_dataset(self):
self.eval.add_dataset(self.test_dataset)
self.assertEqual(self.eval.target_datasets[0].variable,
self.variable)
def test_add_invalid_dataset(self):
with self.assertRaises(TypeError):
self.eval.add_dataset('This is an invalid dataset')
def test_add_datasets(self):
self.eval.add_datasets([self.test_dataset, self.another_test_dataset])
self.assertEqual(len(self.eval.target_datasets), 2)
self.assertEqual(self.eval.target_datasets[0].variable,
self.variable)
self.assertEqual(self.eval.target_datasets[1].variable,
self.other_var)
def test_add_valid_metric(self):
# Add a "binary" metric
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metric(Bias())
self.assertEqual(len(self.eval.metrics), 1)
# Add a "unary" metric
self.assertEqual(len(self.eval.unary_metrics), 0)
self.eval.add_metric(TemporalStdDev())
self.assertEqual(len(self.eval.unary_metrics), 1)
def test_add_invalid_metric(self):
with self.assertRaises(TypeError):
self.eval.add_metric('This is an invalid metric')
def test_add_metrics(self):
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metrics([Bias(), Bias()])
self.assertEqual(len(self.eval.metrics), 2)
def test_invalid_evaluation_run(self):
self.eval = Evaluation(None, [], [])
self.assertEqual(self.eval.run(), None)
def test_bias_output_shape(self):
bias_eval = Evaluation(self.test_dataset, [
self.another_test_dataset], [Bias()])
bias_eval.run()
input_shape = tuple(self.test_dataset.values.shape)
bias_results_shape = tuple(bias_eval.results[0][0].shape)
self.assertEqual(input_shape, bias_results_shape)
def test_result_shape(self):
bias_eval = Evaluation(
self.test_dataset,
[self.another_test_dataset, self.another_test_dataset,
self.another_test_dataset],
[Bias(), Bias()]
)
bias_eval.run()
# Expected result shape is
# [bias, bias] where bias.shape[0] = number of datasets
self.assertTrue(len(bias_eval.results) == 2)
self.assertTrue(bias_eval.results[0].shape[0] == 3)
def test_unary_result_shape(self):
new_eval = Evaluation(
self.test_dataset,
[self.another_test_dataset, self.another_test_dataset,
self.another_test_dataset, self.another_test_dataset],
[TemporalStdDev()]
)
new_eval.run()
# Expected result shape is
# [stddev] where stddev.shape[0] = number of datasets
self.assertTrue(len(new_eval.unary_results) == 1)
self.assertTrue(new_eval.unary_results[0].shape[0] == 5)
def test_subregion_result_shape(self):
bound = Bounds(
lat_min=10, lat_max=18,
lon_min=100, lon_max=108,
start=dt.datetime(2000, 1, 1), end=dt.datetime(2000, 3, 1))
bias_eval = Evaluation(
self.test_dataset,
[self.another_test_dataset, self.another_test_dataset],
[Bias()],
[bound]
)
bias_eval.run()
# Expected result shape is
# [
# [ # Subregions cause this extra layer
# [number of targets, bias.run(reference, target1).shape]
# ]
# ],
self.assertTrue(len(bias_eval.results) == 1)
self.assertTrue(len(bias_eval.results[0]) == 1)
self.assertTrue(bias_eval.results[0][0].shape[0] == 2)
self.assertTrue(isinstance(bias_eval.results, type([])))
def test_subregion_unary_result_shape(self):
bound = Bounds(
lat_min=10, lat_max=18,
lon_min=100, lon_max=108,
start=dt.datetime(2000, 1, 1), end=dt.datetime(2000, 3, 1))
new_eval = Evaluation(
self.test_dataset,
[self.another_test_dataset, self.another_test_dataset],
[TemporalStdDev(), TemporalStdDev()],
[bound, bound, bound, bound, bound]
)
new_eval.run()
# Expected result shape is
# [
# [ # Subregions cause this extra layer
# [3, temporalstddev.run(reference).shape],
# ]
# ]
# 5 = number of subregions
self.assertTrue(len(new_eval.unary_results) == 5)
# number of metrics
self.assertTrue(len(new_eval.unary_results[0]) == 2)
self.assertTrue(isinstance(new_eval.unary_results, type([])))
# number of datasets (ref + target)
self.assertTrue(new_eval.unary_results[0][0].shape[0] == 3)
class TestEvaluation(unittest.TestCase):
def setUp(self):
self.eval = Evaluation(None, [], [])
lat = np.array([10, 12, 14, 16, 18])
lon = np.array([100, 102, 104, 106, 108])
time = np.array([dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
value = flat_array.reshape(12, 5, 5)
self.variable = 'prec'
self.other_var = 'temp'
self.test_dataset = Dataset(lat, lon, time, value, self.variable)
self.another_test_dataset = Dataset(lat, lon, time, value,
self.other_var)
def test_init(self):
self.assertEquals(self.eval.ref_dataset, None)
self.assertEquals(self.eval.target_datasets, [])
self.assertEquals(self.eval.metrics, [])
self.assertEquals(self.eval.unary_metrics, [])
def test_full_init(self):
self.eval = Evaluation(
self.test_dataset, [self.test_dataset, self.another_test_dataset],
[Bias(), Bias(), TemporalStdDev()])
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
# Make sure the two target datasets were added properly
self.assertEqual(self.eval.target_datasets[0].variable, self.variable)
self.assertEqual(self.eval.target_datasets[1].variable, self.other_var)
# Make sure the three metrics were added properly
# The two Bias metrics are "binary" metrics
self.assertEqual(len(self.eval.metrics), 2)
# TemporalStdDev is a "unary" metric and should be stored as such
self.assertEqual(len(self.eval.unary_metrics), 1)
def test_invalid_ref_dataset(self):
with self.assertRaises(TypeError):
self.eval.ref_dataset = "This isn't a Dataset object"
def test_valid_subregion(self):
bound = Bounds(-10, 10, -20, 20, dt.datetime(2000, 1, 1),
dt.datetime(2001, 1, 1))
self.eval.subregions = [bound, bound]
self.assertEquals(len(self.eval.subregions), 2)
def test_invalid_subregion_bound(self):
bound = "This is not a bounds object"
with self.assertRaises(TypeError):
self.eval.subregions = [bound]
def test_add_ref_dataset(self):
self.eval = Evaluation(self.test_dataset, [], [])
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
def test_add_dataset(self):
self.eval.add_dataset(self.test_dataset)
self.assertEqual(self.eval.target_datasets[0].variable, self.variable)
def test_add_datasets(self):
self.eval.add_datasets([self.test_dataset, self.another_test_dataset])
self.assertEqual(len(self.eval.target_datasets), 2)
self.assertEqual(self.eval.target_datasets[0].variable, self.variable)
self.assertEqual(self.eval.target_datasets[1].variable, self.other_var)
def test_add_metric(self):
# Add a "binary" metric
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metric(Bias())
self.assertEqual(len(self.eval.metrics), 1)
# Add a "unary" metric
self.assertEqual(len(self.eval.unary_metrics), 0)
self.eval.add_metric(TemporalStdDev())
self.assertEqual(len(self.eval.unary_metrics), 1)
def test_add_metrics(self):
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metrics([Bias(), Bias()])
self.assertEqual(len(self.eval.metrics), 2)
def test_bias_output_shape(self):
bias_eval = Evaluation(self.test_dataset, [self.another_test_dataset],
[Bias()])
bias_eval.run()
input_shape = tuple(self.test_dataset.values.shape)
bias_results_shape = tuple(bias_eval.results[0][0].shape)
self.assertEqual(input_shape, bias_results_shape)