def test_str_repr_del(
sample_labels,
sample_predictions,
population_size,
population_flagged_count,
confidence_level,
exact_precision,
):
"""Unit test for str, repr, and del methods."""
classification_confidence_intervals = ClassificationConfidenceIntervals(
sample_labels=sample_labels,
sample_predictions=sample_predictions,
population_size=population_size,
population_flagged_count=population_flagged_count,
confidence_level=confidence_level,
exact_precision=exact_precision,
)
pos_rate_cis, _, _, _ = classification_confidence_intervals.get_cis()
assert (
classification_confidence_intervals.__str__()
== classification_confidence_intervals.__repr__()
)
del classification_confidence_intervals
assert pos_rate_cis.__str__() == pos_rate_cis.__repr__()
del pos_rate_cis
def test_get_cis(
sample_labels,
sample_predictions,
population_size,
population_flagged_count,
confidence_level,
exact_precision,
plot_filename,
):
"""Unit test for get_cis method in ClassificationConfidenceIntervals class."""
classification_confidence_intervals = ClassificationConfidenceIntervals(
sample_labels=sample_labels,
sample_predictions=sample_predictions,
population_size=population_size,
population_flagged_count=population_flagged_count,
confidence_level=confidence_level,
exact_precision=exact_precision,
)
classification_confidence_intervals.get_cis(n_iters=100, plot_filename=plot_filename)
def test_bad_get_cis(
sample_labels,
sample_predictions,
population_size,
population_flagged_count,
confidence_level,
exact_precision,
n_iters,
plot_filename,
):
"""Unit test for bad inputs to get_cis method in ClassificationConfidenceIntervals class."""
classification_confidence_intervals = ClassificationConfidenceIntervals(
sample_labels=sample_labels,
sample_predictions=sample_predictions,
population_size=population_size,
population_flagged_count=population_flagged_count,
confidence_level=confidence_level,
exact_precision=exact_precision,
)
with pytest.raises(Exception):
classification_confidence_intervals.get_cis(n_iters=n_iters, plot_filename=plot_filename)
def test_overwrite(
sample_labels,
sample_predictions,
population_size,
population_flagged_count,
confidence_level,
exact_precision,
):
"""Unit test for read_only_properties wrapper."""
classification_confidence_intervals = ClassificationConfidenceIntervals(
sample_labels=sample_labels,
sample_predictions=sample_predictions,
population_size=population_size,
population_flagged_count=population_flagged_count,
confidence_level=confidence_level,
exact_precision=exact_precision,
)
pos_rate_cis, ppv_cis, npv_cis, recall_cis = classification_confidence_intervals.get_cis()
with pytest.raises(Exception):
pos_rate_cis.tnorm_ci = tuple([0.5, 0.8])
def test_init(
sample_labels,
sample_predictions,
population_size,
population_flagged_count,
confidence_level,
exact_precision,
):
"""Unit test for __init__ method in ClassificationConfidenceIntervals class."""
ClassificationConfidenceIntervals(
sample_labels=sample_labels,
sample_predictions=sample_predictions,
population_size=population_size,
population_flagged_count=population_flagged_count,
confidence_level=confidence_level,
exact_precision=exact_precision,
)
def test_bad_init(
sample_labels,
sample_predictions,
population_size,
population_flagged_count,
confidence_level,
exact_precision,
):
"""Unit test for bad inputs to __init__ method in ClassificationConfidenceIntervals class."""
with pytest.raises(Exception):
ClassificationConfidenceIntervals(
sample_labels=sample_labels,
sample_predictions=sample_predictions,
population_size=population_size,
population_flagged_count=population_flagged_count,
confidence_level=confidence_level,
exact_precision=exact_precision,
)
def test_iris():
iris = datasets.load_iris()
y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)
le = LabelEncoder()
le.fit(np.unique(y_true))
y_true = le.transform(y_true)
y_pred = le.transform(y_pred)
enc = OneHotEncoder()
y_true_matrix = enc.fit_transform(y_true.reshape(-1, 1)).toarray()
y_pred_matrix = enc.transform(y_pred.reshape(-1, 1)).toarray()
pos_rate_cis, ppv_cis, npv_cis, recall_cis = [], [], [], []
ci = 0.95
for j in range(y_true_matrix.shape[1]):
class_ci = ClassificationConfidenceIntervals(
sample_labels=y_true_matrix[:, j],
sample_predictions=y_pred_matrix[:, j],
population_size=1000000,
population_flagged_count=50000,
confidence_level=ci).get_cis()
pos_rate_cis.append(class_ci[0].tnorm_ci)
ppv_cis.append(class_ci[1].tnorm_ci)
npv_cis.append(class_ci[2].tnorm_ci)
recall_cis.append(class_ci[3].tnorm_ci)