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keep the output of linear tree-based model as probability estimates #2

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Dec 10, 2024
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keep the output of linear tree-based model as probability estimates #2

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8 changes: 4 additions & 4 deletions libmultilabel/linear/tree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ def predict_values(
x: sparse.csr_matrix,
beam_width: int = 10,
) -> np.ndarray:
"""Calculates the decision values associated with x.
"""Calculates the probability estimates associated with x.

Args:
x (sparse.csr_matrix): A matrix with dimension number of instances * number of features.
Expand All @@ -72,10 +72,10 @@ def predict_values(
return np.vstack([self._beam_search(all_preds[i], beam_width) for i in range(all_preds.shape[0])])

def _beam_search(self, instance_preds: np.ndarray, beam_width: int) -> np.ndarray:
"""Predict with beam search using cached decision values for a single instance.
"""Predict with beam search using cached probability estimates for a single instance.

Args:
instance_preds (np.ndarray): A vector of cached decision values of each node, has dimension number of labels + total number of metalabels.
instance_preds (np.ndarray): A vector of cached probability estimates of each node, has dimension number of labels + total number of metalabels.
beam_width (int): Number of candidates considered.

Returns:
Expand All @@ -101,7 +101,7 @@ def _beam_search(self, instance_preds: np.ndarray, beam_width: int) -> np.ndarra
next_level = []

num_labels = len(self.root.label_map)
scores = np.full(num_labels, -np.inf)
scores = np.full(num_labels, 0)
for node, score in cur_level:
slice = np.s_[self.weight_map[node.index] : self.weight_map[node.index + 1]]
pred = instance_preds[slice]
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