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Fix for Issues with CluStream clustering #1634

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Fix for Issues with CluStream clustering #1634

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00fbc31
CluStream: fix MC mismatch during prediction
PhilJahn Nov 15, 2024
e41d2b2
CluStream: guaranteed kMeans at time gap
PhilJahn Nov 15, 2024
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12 changes: 6 additions & 6 deletions river/cluster/clustream.py
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Original file line number Diff line number Diff line change
Expand Up @@ -236,12 +236,12 @@ def learn_one(self, x, w=1.0):

if closest_dist < radius:
closest_mc.insert(x, w, self._timestamp)
return
else:

# If the new point does not fit in the micro-cluster, micro-clusters
# whose relevance stamps are less than the threshold are deleted.
# Otherwise, closest micro-clusters are merged with each other.
self._maintain_micro_clusters(x=x, w=w)
# If the new point does not fit in the micro-cluster, micro-clusters
# whose relevance stamps are less than the threshold are deleted.
# Otherwise, closest micro-clusters are merged with each other.
self._maintain_micro_clusters(x=x, w=w)

# Apply incremental K-Means on micro-clusters after each time_gap
if self._timestamp % self.time_gap == self.time_gap - 1:
Expand All @@ -260,7 +260,7 @@ def learn_one(self, x, w=1.0):
def predict_one(self, x):
index, _ = self._get_closest_mc(x)
try:
return self._kmeans_mc.predict_one(self._mc_centers[index])
return self._kmeans_mc.predict_one(self.micro_clusters[index].center)
except (KeyError, AttributeError):
return 0

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