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Check monotonic_cst Before Fit #82

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merged 2 commits into from
Sep 2, 2024
Merged

Check monotonic_cst Before Fit #82

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910675c
Check monotonic-cst before fit
reidjohnson Sep 2, 2024
e385f02
Update _quantile_forest.py
reidjohnson Sep 2, 2024
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17 changes: 10 additions & 7 deletions quantile_forest/_quantile_forest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,6 +160,16 @@ def fit(self, X, y, sample_weight=None, sparse_pickle=False):
"max_samples_leaf must be of integer, float, or None type, got "
f"{self.max_samples_leaf}."
)
if self.monotonic_cst is not None:
if (
not isinstance(self.max_samples_leaf, (Integral, np.integer))
or self.max_samples_leaf != 1
):
raise ValueError(
"Monotonicity constraints are not supported with multiple values per leaf. "
"To apply monotonicity constraints, set `max_samples_leaf=1`."
)

super(BaseForestQuantileRegressor, self).fit(X, y, sample_weight=sample_weight)
X, y = self._validate_data(
X, y, multi_output=True, accept_sparse="csc", dtype=DTYPE, force_all_finite=False
Expand Down Expand Up @@ -343,13 +353,6 @@ def _get_y_train_leaves(self, X, y, sorter=None, sample_weight=None):
f"got {type(self.max_samples_leaf)}."
)

if self.monotonic_cst is not None:
if max_samples_leaf != 1:
raise ValueError(
"Monotonicity constraints are not supported with multiple values per leaf. "
"To apply monotonicity constraints, set `max_samples_leaf=1`."
)

with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning)
X_leaves = self.apply(X)
Expand Down
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