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fix: guard optimize_refine_core against float32 noise oscillation #593

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fix: guard optimize_refine_core against float32 noise oscillation #593

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2 changes: 1 addition & 1 deletion sknetwork/clustering/leiden.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ def _optimize_refine(self, labels, adjacency, out_weights, in_weights):
labels_refined = np.arange(len(labels)).astype(np.int32)
return optimize_refine_core(labels, labels_refined, indices, indptr, data, out_weights, in_weights,
out_cluster_weights, in_cluster_weights, cluster_weights, self_loops,
self.resolution)
self.resolution, self.tol_optimization)

@staticmethod
def _aggregate_refine(labels, labels_refined, adjacency, out_weights, in_weights):
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7 changes: 5 additions & 2 deletions sknetwork/clustering/leiden_core.pyx
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Expand Up @@ -13,7 +13,8 @@ ctypedef fused int_or_long:
@cython.wraparound(False)
def optimize_refine_core(int_or_long[:] labels, int_or_long[:] labels_refined, int_or_long[:] indices,
int_or_long[:] indptr, float[:] data, float[:] out_weights, float[:] in_weights, float[:] out_cluster_weights,
float[:] in_cluster_weights, float[:] cluster_weights, float[:] self_loops, float resolution): # pragma: no cover
float[:] in_cluster_weights, float[:] cluster_weights, float[:] self_loops, float resolution,
float tol_optimization): # pragma: no cover
"""Refine clusters while maximizing modularity.

Parameters
Expand Down Expand Up @@ -42,6 +43,8 @@ def optimize_refine_core(int_or_long[:] labels, int_or_long[:] labels_refined, i
Weights of self loops.
resolution :
Resolution parameter (positive).
tol_optimization :
Minimum increase in modularity to enter a new optimization pass.

Returns
-------
Expand Down Expand Up @@ -102,7 +105,7 @@ def optimize_refine_core(int_or_long[:] labels, int_or_long[:] labels_refined, i
delta_local -= resolution * out_weight * in_cluster_weights[label_target]
delta_local -= resolution * in_weight * out_cluster_weights[label_target]
delta_local -= delta
if delta_local > 0:
if delta_local > tol_optimization:
label_target_set.insert(label_target)
cluster_weights[label_target] = 0

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