def test3_misclass_rate_feature(dataset_categorical,
dataset_predictions_categorical):
"""Checks for assertions that should be true for specific parameter combinations."""
result_edges, result_rate, result_sd = misclass_rate_feature(
dataset_categorical,
dataset_predictions_categorical,
feature="Feature 1",
categorical=True,
)
result_edges_scenario = ["blue", "green", "red"]
result_rate_scenario = [0.6667, 0.0, 1.0]
result_sd_scenario = [0.8563, float("nan"), 2.0]
# All results should have the expected number of elements
assert len(result_edges) == len(result_edges_scenario)
assert len(result_rate) == len(result_rate_scenario)
assert len(result_sd) == len(result_sd_scenario)
# All the elements of the three results should agree with the written values
# up to 0,1% (10^-3).
np.testing.assert_array_equal(result_edges, result_edges_scenario)
np.testing.assert_allclose(result_rate,
result_rate_scenario,
rtol=1e-03,
equal_nan=True)
np.testing.assert_allclose(result_sd,
result_sd_scenario,
rtol=1e-03,
equal_nan=True)
def test1_misclass_rate_feature(dataset, dataset_predictions, feature_param,
bins_param, bins_type_param):
"""Checks for assertions that should always apply regardless of the dataset
and the parameters.
"""
result_edges, result_rate, result_sd = misclass_rate_feature(
dataset,
dataset_predictions,
feature=feature_param,
bins=bins_param,
bins_type=bins_type_param,
)
assert len(result_edges) == len(result_rate) + 1
if type(bins_param) == int:
assert len(result_rate) == bins_param
elif type(bins_param) == list:
assert len(result_rate) == len(bins_param) - 1
assert max(result_rate) <= 1.0
assert min(result_rate) >= 0.0
def test2_misclass_rate_feature(
dataset,
dataset_predictions,
feature_scenario,
categorical_scenario,
bins_scenario,
bins_type_scenario,
result_edges_scenario,
result_rate_scenario,
result_sd_scenario,
):
"""Checks for assertions that should be true for specific parameter combinations."""
result_edges, result_rate, result_sd = misclass_rate_feature(
dataset,
dataset_predictions,
feature_scenario,
categorical=categorical_scenario,
bins=bins_scenario,
bins_type=bins_type_scenario,
)
# All results should have the expected number of elements
assert len(result_edges) == len(result_edges_scenario)
assert len(result_rate) == len(result_rate_scenario)
assert len(result_sd) == len(result_sd_scenario)
# All the elements of the three results should agree with the written values
# up to 0,1% (10^-3).
np.testing.assert_allclose(result_edges,
result_edges_scenario,
rtol=1e-03,
equal_nan=True)
np.testing.assert_allclose(result_rate,
result_rate_scenario,
rtol=1e-03,
equal_nan=True)
np.testing.assert_allclose(result_sd,
result_sd_scenario,
rtol=1e-03,
equal_nan=True)