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DOC Adds HDBSCAN.dbscan_clustering section to plot_hdbscan.py #25538

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Merged
merged 3 commits into from
Feb 7, 2023
Merged

DOC Adds HDBSCAN.dbscan_clustering section to plot_hdbscan.py #25538

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e27160c
DOC Adds `HDBSCAN.dbscan_clustering` section to `plot_hdbscan.py`
Micky774 Feb 4, 2023
d80d34e
Merge branch 'hdbscan' into hdbscan_plotting_update
Micky774 Feb 4, 2023
a05cf66
Update examples/cluster/plot_hdbscan.py
Micky774 Feb 7, 2023
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25 changes: 24 additions & 1 deletion examples/cluster/plot_hdbscan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,6 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=
# ^^^^^^^^^^^^^
# `min_samples` is the number of samples in a neighborhood for a point to
# be considered as a core point, including the point itself.

# `min_samples` defaults to `min_cluster_size`.
# Similarly to `min_cluster_size`, larger values for `min_samples` increase
# the model's robustness to noise, but risks ignoring or discarding
Expand All @@ -213,3 +212,27 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=
labels = hdb.labels_

plot(X, labels, hdb.probabilities_, param, ax=axes[i])

# %%
# `dbscan_clustering`
# ^^^^^^^^^^^^^^^^^^^
# During `fit`, `HDBSCAN` builds a single-linkage tree which encodes the
# clustering of all points across all values of :class:`~cluster.DBSCAN`'s
# `eps` parameter.
# We can thus plot and evaluate these clusterings efficiently without fully
# recomputing intermediate values such as core-distances, mutual-reachability,
# and the minimum spanning tree. All we need to do is specify the `cut_distance`
# (equivalent to `eps`) we want to cluster with.

PARAM = (
{"cut_distance": 0.1},
{"cut_distance": 0.5},
{"cut_distance": 1.0},
)
hdb = HDBSCAN()
hdb.fit(X)
fig, axes = plt.subplots(len(PARAM), 1, figsize=(10, 12))
for i, param in enumerate(PARAM):
labels = hdb.dbscan_clustering(**param)

plot(X, labels, hdb.probabilities_, param, ax=axes[i])