Merge branch 'master' into feat_cluster_selection_epsilon_max

Author: joaopmatias

azure-pipelines.yml

@@ -112,8 +112,6 @@ stages:
         container: quay.io/pypa/manylinux2014_x86_64:latest
         strategy:
           matrix:
-            linux_py37:
-              python.version: 'cp37-cp37m'
             linux_py38:
               python.version: 'cp38-cp38'
             linux_py39:
@@ -122,6 +120,8 @@ stages:
               python.version: 'cp310-cp310'
             linux_py311:
               python.version: 'cp311-cp311'
+            linux_py312:
+              python.version: 'cp312-cp312'
         steps:
         - script: |
             "${PYBIN}/python" -m pip install --upgrade pip
@@ -157,13 +157,7 @@ stages:
       - job: BuildWindowsAndMacOSArtifacts
         displayName: Build source dists and wheels for windows and macOS
         strategy:
-          matrix:
-            mac_py37:
-              imageName: 'macOS-latest'
-              python.version: '3.7'
-            windows_py37:
-              imageName: 'windows-latest'
-              python.version: '3.7'            
+          matrix:        
             mac_py38:
               imageName: 'macOS-latest'
               python.version: '3.8'
@@ -188,6 +182,12 @@ stages:
             windows_py311:
               imageName: 'windows-latest'
               python.version: '3.11'
+            mac_py312:
+              imageName: 'macOS-latest'
+              python.version: '3.12'
+            windows_py312:
+              imageName: 'windows-latest'
+              python.version: '3.12'
         pool:
           vmImage: $(imageName) 
 
@@ -202,6 +202,7 @@ stages:
             pip install wheel
             pip install -r requirements.txt
             pip install cython
+            pip install setuptools
           displayName: 'Install dependencies'
 
         - script: |

hdbscan/hdbscan_.py

@@ -1464,13 +1464,21 @@ def prediction_data_(self):
             raise AttributeError("No prediction data was generated")
         else:
             return self._prediction_data
+        
+    @prediction_data_.setter
+    def prediction_data_(self, value):
+        self._prediction_data = value
 
     @property
     def branch_detection_data_(self):
         if self._branch_detection_data is None:
             raise AttributeError("No branch detection data was generated")
         else:
             return self._branch_detection_data
+        
+    @branch_detection_data_.setter
+    def branch_detection_data_(self, value):
+        self._branch_detection_data = value
 
     @property
     def outlier_scores_(self):
@@ -1484,6 +1492,10 @@ def outlier_scores_(self):
                 raise AttributeError(
                     "No condensed tree was generated; try running fit first."
                 )
+            
+    @outlier_scores_.setter
+    def outlier_scores_(self, value):
+        self._outlier_scores = value
 
     @property
     def condensed_tree_(self):
@@ -1497,6 +1509,10 @@ def condensed_tree_(self):
             raise AttributeError(
                 "No condensed tree was generated; try running fit first."
             )
+        
+    @condensed_tree_.setter
+    def condensed_tree_(self, value):
+        self._condensed_tree = value
 
     @property
     def single_linkage_tree_(self):
@@ -1506,6 +1522,10 @@ def single_linkage_tree_(self):
             raise AttributeError(
                 "No single linkage tree was generated; try running fit" " first."
             )
+        
+    @single_linkage_tree_.setter
+    def single_linkage_tree_(self, value):
+        self._single_linkage_tree = value
 
     @property
     def minimum_spanning_tree_(self):
@@ -1525,6 +1545,10 @@ def minimum_spanning_tree_(self):
                 "This may be due to optimized algorithm variations that skip"
                 " explicit generation of the spanning tree."
             )
+    
+    @minimum_spanning_tree_.setter
+    def minimum_spanning_tree_(self, value):
+        self._min_spanning_tree = value
 
     @property
     def exemplars_(self):
@@ -1540,6 +1564,10 @@ def exemplars_(self):
                 "future, but for now no exemplars can be provided"
             )
 
+    @exemplars_.setter
+    def exemplars_(self, value):
+        self._exemplars = value
+
     @property
     def relative_validity_(self):
         if self._relative_validity is not None:
@@ -1625,3 +1653,7 @@ def relative_validity_(self):
         )
         self._relative_validity = score
         return self._relative_validity
+    
+    @relative_validity_.setter
+    def relative_validity_(self, value):
+        self._relative_validity = value

setup.py

@@ -1,8 +1,10 @@
 import warnings
 
 try:
-    from Cython.Distutils import build_ext
+    # from Cython.Distutils import build_ext
+    from Cython.Build import cythonize
     from setuptools import setup, Extension
+    from setuptools.command.build_ext import build_ext
     HAVE_CYTHON = True
 except ImportError as e:
     warnings.warn(e.args[0])
@@ -51,7 +53,7 @@ def requirements():
 
 configuration = {
     'name': 'hdbscan',
-    'version': '0.8.38-1',
+    'version': '0.8.39',
     'description': 'Clustering based on density with variable density clusters',
     'long_description': readme(),
     'classifiers': [