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ENH _hdbscan HIERARCHY data type introduction #25826

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Merged
merged 9 commits into from
Mar 28, 2023
Merged

ENH _hdbscan HIERARCHY data type introduction #25826

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e8a56a2
Updated hdbscan submodule with new HIERARCHY types
Micky774 Mar 12, 2023
8d53894
Reverted change to decorator
Micky774 Mar 12, 2023
728f9dd
Refactor based on feedback
Micky774 Mar 22, 2023
f1c0d3e
Created second dtype for clarification of tree semantics
Micky774 Mar 23, 2023
5cac761
Update sklearn/cluster/_hdbscan/_tree.pxd
Micky774 Mar 23, 2023
00b0530
Merge branch 'hdbscan' into hdbscan_tree_dtype
Micky774 Mar 24, 2023
83d9e48
Improved documentation for condensed dtype
Micky774 Mar 24, 2023
5e05644
Update sklearn/cluster/_hdbscan/_linkage.pyx
Micky774 Mar 27, 2023
4fdf2be
Update sklearn/cluster/_hdbscan/_linkage.pyx
Micky774 Mar 27, 2023
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20 changes: 10 additions & 10 deletions sklearn/cluster/_hdbscan/_linkage.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,8 @@ from libc.float cimport DBL_MAX
import numpy as np
from ...metrics._dist_metrics cimport DistanceMetric
from ...cluster._hierarchical_fast cimport UnionFind
from ...cluster._hdbscan._tree cimport HIERARCHY_t
from ...cluster._hdbscan._tree import HIERARCHY_dtype
from ...utils._typedefs cimport ITYPE_t, DTYPE_t
from ...utils._typedefs import ITYPE, DTYPE

Expand Down Expand Up @@ -188,7 +190,7 @@ cpdef cnp.ndarray[MST_edge_t, ndim=1, mode='c'] mst_from_data_matrix(

return mst

cpdef cnp.ndarray[cnp.float64_t, ndim=2, mode='c'] make_single_linkage(const MST_edge_t[::1] mst):
cpdef cnp.ndarray[HIERARCHY_t, ndim=1, mode="c"] make_single_linkage(const MST_edge_t[::1] mst):
"""Construct a single-linkage tree from an MST.

Parameters
Expand All @@ -209,16 +211,16 @@ cpdef cnp.ndarray[cnp.float64_t, ndim=2, mode='c'] make_single_linkage(const MST
- new cluster size
"""
cdef:
cnp.ndarray[cnp.float64_t, ndim=2, mode='c'] single_linkage
cnp.ndarray[HIERARCHY_t, ndim=1, mode="c"] single_linkage

# Note mst.shape[0] is one fewer than the number of samples
cnp.int64_t n_samples = mst.shape[0] + 1
cnp.int64_t current_node_cluster, next_node_cluster
cnp.intp_t current_node_cluster, next_node_cluster
cnp.int64_t current_node, next_node, index
cnp.float64_t distance
UnionFind U = UnionFind(n_samples)

single_linkage = np.zeros((n_samples - 1, 4), dtype=np.float64)
single_linkage = np.zeros(n_samples - 1, dtype=HIERARCHY_dtype)

for i in range(n_samples - 1):

Expand All @@ -229,12 +231,10 @@ cpdef cnp.ndarray[cnp.float64_t, ndim=2, mode='c'] make_single_linkage(const MST
current_node_cluster = U.fast_find(current_node)
next_node_cluster = U.fast_find(next_node)

# TODO: Update this to an array of structs (AoS).
# Should be done simultaneously in _tree.pyx to ensure compatability.
single_linkage[i][0] = <cnp.float64_t> current_node_cluster
single_linkage[i][1] = <cnp.float64_t> next_node_cluster
single_linkage[i][2] = distance
single_linkage[i][3] = U.size[current_node_cluster] + U.size[next_node_cluster]
single_linkage[i].left_node = current_node_cluster
single_linkage[i].right_node = next_node_cluster
single_linkage[i].value = distance
single_linkage[i].cluster_size = U.size[current_node_cluster] + U.size[next_node_cluster]

U.union(current_node_cluster, next_node_cluster)

Expand Down
16 changes: 16 additions & 0 deletions sklearn/cluster/_hdbscan/_tree.pxd
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Original file line number Diff line number Diff line change
@@ -0,0 +1,16 @@
cimport numpy as cnp

# This corresponds to the scipy.cluster.hierarchy format
ctypedef packed struct HIERARCHY_t:
cnp.intp_t left_node
cnp.intp_t right_node
cnp.float64_t value
cnp.intp_t cluster_size

# Effectively an edgelist encoding a parent/child pair, along with a value and
# the corresponding cluster_size in each row providing a tree structure.
ctypedef packed struct CONDENSED_t:
cnp.intp_t parent
cnp.intp_t child
cnp.float64_t value
cnp.intp_t cluster_size
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