Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Regression errors failing with mixed data type combinations #4770

Merged
merged 5 commits into from
Jun 29, 2022
Merged

Regression errors failing with mixed data type combinations #4770

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
5243261
Fix: Allow mixed data types for regression errors
shaswat-indian Jun 6, 2022
a7073c7
Merge branch 'branch-22.08' of https://github.com/shaswat-indian/cuml…
shaswat-indian Jun 6, 2022
67ff7d2
Code styling changes
shaswat-indian Jun 6, 2022
db35872
Added unit test for mixed dtypes
shaswat-indian Jun 14, 2022
399a5bd
Removed redundant test: test_regression_metrics_random
shaswat-indian Jun 23, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 6 additions & 6 deletions python/cuml/metrics/regression.pyx
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -104,19 +104,19 @@ def _prepare_input_reg(y_true, y_pred, sample_weight, multioutput):
Helper function to avoid code duplication for regression metrics.
Converts inputs to CumlArray and check multioutput parameter validity.
"""
allowed_d_types = [np.float32, np.float64, np.int32, np.int64]
y_true = y_true.squeeze() if len(y_true.shape) > 1 else y_true
y_true, n_rows, n_cols, ytype = \
input_to_cuml_array(y_true, check_dtype=[np.float32, np.float64,
np.int32, np.int64])
input_to_cuml_array(y_true, check_dtype=allowed_d_types)

y_pred = y_pred.squeeze() if len(y_pred.shape) > 1 else y_pred
y_pred, _, _, _ = \
input_to_cuml_array(y_pred, check_dtype=ytype, check_rows=n_rows,
check_cols=n_cols)
input_to_cuml_array(y_pred, check_dtype=allowed_d_types,
check_rows=n_rows, check_cols=n_cols)

if sample_weight is not None:
sample_weight, _, _, _ = \
input_to_cuml_array(sample_weight, check_dtype=ytype,
input_to_cuml_array(sample_weight, check_dtype=allowed_d_types,
check_rows=n_rows, check_cols=n_cols)

raw_multioutput = False
Expand All @@ -134,7 +134,7 @@ def _prepare_input_reg(y_true, y_pred, sample_weight, multioutput):
multioutput = None
elif multioutput is not None:
multioutput, _, _, _ = \
input_to_cuml_array(multioutput, check_dtype=ytype)
input_to_cuml_array(multioutput, check_dtype=allowed_d_types)
if n_cols == 1:
raise ValueError("Custom weights are useful only in "
"multi-output cases.")
Expand Down
17 changes: 10 additions & 7 deletions python/cuml/tests/test_metrics.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -481,15 +481,18 @@ def test_regression_metrics():


@pytest.mark.parametrize('n_samples', [50, stress_param(500000)])
@pytest.mark.parametrize('dtype', [np.int32, np.int64, np.float32, np.float64])
@pytest.mark.parametrize('y_dtype',
[np.int32, np.int64, np.float32, np.float64])
@pytest.mark.parametrize('pred_dtype',
[np.int32, np.int64, np.float32, np.float64])
@pytest.mark.parametrize('function', ['mse', 'mae', 'msle'])
def test_regression_metrics_random(n_samples, dtype, function):
if dtype == np.float32 and n_samples == 500000:
# stress test for float32 fails because of floating point precision
pytest.xfail()
def test_regression_metrics_random_with_mixed_dtypes(n_samples, y_dtype,
pred_dtype, function):
y_true, _, _, _ = generate_random_labels(
lambda rng: rng.randint(0, 1000, n_samples).astype(y_dtype))

y_true, y_pred, _, _ = generate_random_labels(
lambda rng: rng.randint(0, 1000, n_samples).astype(dtype))
y_pred, _, _, _ = generate_random_labels(
lambda rng: rng.randint(0, 1000, n_samples).astype(pred_dtype))

cuml_reg, sklearn_reg = {
'mse': (mean_squared_error, sklearn_mse),
Expand Down