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[Refactor] make faster _convert_tree_to_points use np.vectorize instead of merge in xbooster/lgb_constructor.py #11

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[Refactor] make faster _convert_tree_to_points use np.vectorize instead of merge in xbooster/lgb_constructor.py #11

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82fb47c
use vectorize instead of merge
RektPunk Dec 20, 2025
71078fb
use map instead of np vectorize
RektPunk Dec 21, 2025
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32 changes: 14 additions & 18 deletions xbooster/lgb_constructor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -494,27 +494,23 @@ def _convert_tree_to_points(self, X: pd.DataFrame) -> pd.DataFrame: # pylint: d

# Get leaf indices for all trees
X_leaf_indices = self.get_leafs(X, output_type="leaf_index")

result = pd.DataFrame()
for col in X_leaf_indices.columns:
tree_number = col.split("_")[1]
n_samples, n_trees = X_leaf_indices.shape
points_matrix = np.zeros((n_samples, n_trees))
leaf_idx_values = X_leaf_indices.values
for t in range(n_trees):
# Get points for this tree
subset_points_df = self.lgb_scorecard_with_points[
self.lgb_scorecard_with_points["Tree"] == int(tree_number)
].copy()

# Merge leaf indices with points
merged_df = pd.merge(
X_leaf_indices[[col]].round(4),
subset_points_df[["Node", "Points"]],
left_on=col,
right_on="Node",
how="left",
)
result[f"Score_{tree_number}"] = merged_df["Points"]
tree_points = self.lgb_scorecard_with_points[
self.lgb_scorecard_with_points["Tree"] == t
]
# Mapping dictionary instead of merge
mapping_dict = dict(zip(tree_points["Node"], tree_points["Points"]))
points_matrix[:, t] = pd.Series(leaf_idx_values[:, t]).map(mapping_dict).to_numpy()

result = pd.DataFrame(
points_matrix, index=X.index, columns=[f"Score_{i}" for i in range(n_trees)]
)
# Add total score
result = pd.concat([result, result.sum(axis=1).rename("Score")], axis=1)
result["Score"] = points_matrix.sum(axis=1)
return result

def predict_score(self, X: pd.DataFrame) -> pd.Series: # pylint: disable=C0103
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