def predict_variant(X):
np.random.seed(42)
return np.array(
[1 if r > 0.0 else -1 for r in np.random.normal(0, 1, len(X))])
def class_based(dataf, sex="male", pclass=1):
predicate = (dataf["sex"] == sex) & (dataf["pclass"] == pclass)
return np.array(predicate).astype(int) * 2 - 1
@pytest.mark.parametrize(
"test_fn",
select_tests(
include=flatten([general_checks, outlier_checks, nonmeta_checks]),
exclude=[
"check_outliers_train",
"check_estimators_nan_inf",
"check_estimators_empty_data_messages",
"check_complex_data",
"check_dtype_object",
"check_classifier_data_not_an_array",
"check_fit1d",
"check_methods_subset_invariance",
"check_fit2d_predict1d",
"check_estimator_sparse_data",
],
),
)
def test_estimator_checks(test_fn):
def predict(X):
np.random.seed(42)
return np.array(
[1 if r > 0.5 else 0 for r in np.random.normal(0, 1, len(X))])
def predict_variant(X):
np.random.seed(42)
return np.array(
[1 if r > 0.0 else 0 for r in np.random.normal(0, 1, len(X))])
@pytest.mark.parametrize(
"test_fn",
select_tests(
include=flatten([general_checks, regressor_checks, nonmeta_checks]),
exclude=[
"check_methods_subset_invariance",
"check_fit2d_1sample",
"check_fit2d_1feature",
"check_regressors_train",
"check_fit2d_predict1d",
"check_fit1d",
"check_regressor_data_not_an_array",
"check_supervised_y_2d",
"check_supervised_y_no_nan",
"check_dtype_object",
"check_complex_data",
"check_estimators_empty_data_messages",
"check_estimators_nan_inf",
"check_estimator_sparse_data",
from tests.conftest import (
select_tests,
general_checks,
transformer_checks,
nonmeta_checks,
)
def double(x, factor=2):
return x * factor
@pytest.mark.parametrize(
"test_fn",
select_tests(
include=flatten([general_checks, transformer_checks, nonmeta_checks]),
exclude=[
"check_estimators_nan_inf",
"check_estimators_empty_data_messages",
"check_transformer_data_not_an_array",
"check_dtype_object",
"check_complex_data",
"check_fit1d",
],
),
)
def test_estimator_checks(test_fn):
clf = PipeTransformer(func=double)
test_fn(PipeTransformer.__name__, clf)
from hulearn.preprocessing import PipeTransformer
from hulearn.outlier import InteractiveOutlierDetector
from hulearn.common import flatten
from tests.conftest import (
select_tests,
general_checks,
classifier_checks,
nonmeta_checks,
)
@pytest.mark.parametrize(
"test_fn",
select_tests(
include=flatten([general_checks, classifier_checks, nonmeta_checks]),
exclude=[
"check_estimators_pickle",
"check_estimator_sparse_data",
"check_estimators_nan_inf",
"check_pipeline_consistency",
"check_complex_data",
"check_fit2d_predict1d",
"check_methods_subset_invariance",
"check_fit1d",
"check_dict_unchanged",
"check_classifier_data_not_an_array",
"check_classifiers_one_label",
"check_classifiers_classes",
"check_classifiers_train",
"check_supervised_y_2d",
