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Shield against breaking changes from scikit-learn 1.3.0 release #598

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Shield against breaking changes from scikit-learn 1.3.0 release #598

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d80a3d0
fix: workaround scikit-learn 1.3.0 release changes to KDTree and Ball…
exs-avianello Jul 2, 2023
cdfc24e
fix: missing direct reference to KDTree.valid_metrics
exs-avianello Jul 2, 2023
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25 changes: 17 additions & 8 deletions hdbscan/hdbscan_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,10 @@
of Applications with Noise
"""

import sklearn
import numpy as np

from packaging.version import Version
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics import pairwise_distances
from scipy.sparse import issparse
Expand Down Expand Up @@ -37,7 +39,14 @@
from .plots import CondensedTree, SingleLinkageTree, MinimumSpanningTree
from .prediction import PredictionData

FAST_METRICS = KDTree.valid_metrics + BallTree.valid_metrics + ["cosine", "arccos"]
if Version(sklearn.__version__) >= Version("1.3.0"):
kdtree_valid_metrics = KDTree.valid_metrics()
balltree_valid_metrics = BallTree.valid_metrics()
else:
kdtree_valid_metrics = KDTree.valid_metrics
balltree_valid_metrics = BallTree.valid_metrics

FAST_METRICS = kdtree_valid_metrics + balltree_valid_metrics + ["cosine", "arccos"]

# Author: Leland McInnes <leland.mcinnes@gmail.com>
# Steve Astels <sastels@gmail.com>
Expand Down Expand Up @@ -742,19 +751,19 @@ def hdbscan(
_hdbscan_generic
)(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
elif algorithm == "prims_kdtree":
if metric not in KDTree.valid_metrics:
if metric not in kdtree_valid_metrics:
raise ValueError("Cannot use Prim's with KDTree for this" " metric!")
(single_linkage_tree, result_min_span_tree) = memory.cache(
_hdbscan_prims_kdtree
)(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
elif algorithm == "prims_balltree":
if metric not in BallTree.valid_metrics:
if metric not in balltree_valid_metrics:
raise ValueError("Cannot use Prim's with BallTree for this" " metric!")
(single_linkage_tree, result_min_span_tree) = memory.cache(
_hdbscan_prims_balltree
)(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
elif algorithm == "boruvka_kdtree":
if metric not in BallTree.valid_metrics:
if metric not in balltree_valid_metrics:
raise ValueError("Cannot use Boruvka with KDTree for this" " metric!")
(single_linkage_tree, result_min_span_tree) = memory.cache(
_hdbscan_boruvka_kdtree
Expand All @@ -771,7 +780,7 @@ def hdbscan(
**kwargs
)
elif algorithm == "boruvka_balltree":
if metric not in BallTree.valid_metrics:
if metric not in balltree_valid_metrics:
raise ValueError("Cannot use Boruvka with BallTree for this" " metric!")
if (X.shape[0] // leaf_size) > 16000:
warn(
Expand Down Expand Up @@ -802,7 +811,7 @@ def hdbscan(
(single_linkage_tree, result_min_span_tree) = memory.cache(
_hdbscan_generic
)(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
elif metric in KDTree.valid_metrics:
elif metric in kdtree_valid_metrics:
# TO DO: Need heuristic to decide when to go to boruvka;
# still debugging for now
if X.shape[1] > 60:
Expand Down Expand Up @@ -1237,9 +1246,9 @@ def generate_prediction_data(self):

if self.metric in FAST_METRICS:
min_samples = self.min_samples or self.min_cluster_size
if self.metric in KDTree.valid_metrics:
if self.metric in kdtree_valid_metrics:
tree_type = "kdtree"
elif self.metric in BallTree.valid_metrics:
elif self.metric in balltree_valid_metrics:
tree_type = "balltree"
else:
warn("Metric {} not supported for prediction data!".format(self.metric))
Expand Down
13 changes: 11 additions & 2 deletions hdbscan/robust_single_linkage_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,10 @@
"""
Robust Single Linkage: Density based single linkage clustering.
"""
import sklearn
import numpy as np

from packaging.version import Version
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics import pairwise_distances
from scipy.sparse import issparse
Expand All @@ -24,7 +26,14 @@
#
# License: BSD 3 clause

FAST_METRICS = KDTree.valid_metrics + BallTree.valid_metrics
if Version(sklearn.__version__) >= Version("1.3.0"):
kdtree_valid_metrics = KDTree.valid_metrics()
balltree_valid_metrics = BallTree.valid_metrics()
else:
kdtree_valid_metrics = KDTree.valid_metrics
balltree_valid_metrics = BallTree.valid_metrics

FAST_METRICS = kdtree_valid_metrics + balltree_valid_metrics


def _rsl_generic(X, k=5, alpha=1.4142135623730951, metric='euclidean',
Expand Down Expand Up @@ -266,7 +275,7 @@ def robust_single_linkage(X, cut, k=5, alpha=1.4142135623730951,
# We can't do much with sparse matrices ...
single_linkage_tree = memory.cache(_rsl_generic)(
X, k, alpha, metric, **kwargs)
elif metric in KDTree.valid_metrics:
elif metric in kdtree_valid_metrics:
# Need heuristic to decide when to go to boruvka;
# still debugging for now
if X.shape[1] > 128:
Expand Down
3 changes: 2 additions & 1 deletion requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
cython>=0.27
numpy>=1.20
packaging
scipy>= 1.0
scikit-learn>=0.20
joblib>=1.0
joblib>=1.0