Skip to content

MAINT Parameters validation for mean_pinball_loss() #25685

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

Merged
merged 3 commits into from
Feb 24, 2023
Merged

MAINT Parameters validation for mean_pinball_loss() #25685

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
46b35e9
added parameter validation for metrics.mean_pinball_loss
Feb 24, 2023
efd84b3
removed unnecessary paramter check for metrics.mean_pinball_loss in m…
Feb 24, 2023
599cefa
nitpick
jeremiedbb Feb 24, 2023
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
28 changes: 16 additions & 12 deletions sklearn/metrics/_regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -216,6 +216,15 @@ def mean_absolute_error(
return np.average(output_errors, weights=multioutput)


@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"alpha": [Interval(Real, 0, 1, closed="both")],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
}
)
def mean_pinball_loss(
y_true, y_pred, *, sample_weight=None, alpha=0.5, multioutput="uniform_average"
):
Expand Down Expand Up @@ -285,18 +294,13 @@ def mean_pinball_loss(
sign = (diff >= 0).astype(diff.dtype)
loss = alpha * sign * diff - (1 - alpha) * (1 - sign) * diff
output_errors = np.average(loss, weights=sample_weight, axis=0)
if isinstance(multioutput, str):
if multioutput == "raw_values":
return output_errors
elif multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None
else:
raise ValueError(
"multioutput is expected to be 'raw_values' "
"or 'uniform_average' but we got %r"
" instead." % multioutput
)

if isinstance(multioutput, str) and multioutput == "raw_values":
return output_errors

if isinstance(multioutput, str) and multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None

return np.average(output_errors, weights=multioutput)

Expand Down
6 changes: 0 additions & 6 deletions sklearn/metrics/tests/test_regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -344,12 +344,6 @@ def test_regression_multioutput_array():

mse = mean_squared_error(y_true, y_pred, multioutput="raw_values")
mae = mean_absolute_error(y_true, y_pred, multioutput="raw_values")
err_msg = (
"multioutput is expected to be 'raw_values' "
"or 'uniform_average' but we got 'variance_weighted' instead."
)
with pytest.raises(ValueError, match=err_msg):
mean_pinball_loss(y_true, y_pred, multioutput="variance_weighted")

pbl = mean_pinball_loss(y_true, y_pred, multioutput="raw_values")
mape = mean_absolute_percentage_error(y_true, y_pred, multioutput="raw_values")
Expand Down
1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -124,6 +124,7 @@ def _check_function_param_validation(
"sklearn.metrics.hamming_loss",
"sklearn.metrics.log_loss",
"sklearn.metrics.mean_absolute_error",
"sklearn.metrics.mean_pinball_loss",
"sklearn.metrics.mean_squared_error",
"sklearn.metrics.mean_tweedie_deviance",
"sklearn.metrics.median_absolute_error",
Expand Down