Update python-machine-learning-libs

conda-environment.yml

@@ -16,14 +16,14 @@ dependencies:
   - pip=25.0.1
   - setuptools=80.9.0 # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?) 
   - typing-extensions=4.12.2 # Also needed for opentsne and Python >= 3.12
-  - opentsne=1.0.2 # to visualize node embeddings in 2D (t-SNE dimensionality reduction)
+  - opentsne=1.0.4 # to visualize node embeddings in 2D (t-SNE dimensionality reduction)
   - wordcloud=1.9.4
   - monotonic=1.6
   - plotly=6.3.1
   - python-kaleido=1.0.0 # To render plotly plots. Static image export for web-based visualization libraries.
   - scikit-learn=1.6.1 # For anomaly detection HDBSCAN clustering, scores like AMI, and others 
   - seaborn=0.13.2 # To visualize clustering results
-  - optuna=4.5.0
+  - optuna=4.6.0
   - umap-learn=0.5.9 # to visualize node embeddings in 2D (UMAP dimensionality reduction)
   - shap=0.48.0
   - pip:

requirements.txt

@@ -21,13 +21,13 @@ seaborn==0.13.2 # To visualize clustering results
 
 # --- Machine Learning / Optimization ---
 scikit-learn==1.7.2
-optuna==4.5.0
+optuna==4.6.0
 umap-learn==0.5.9.post2 # Dimensionality reduction to visualize node embeddings in 2D
 
 # --- Database connector ---
 neo4j==5.28.2
 
 # --- Native/scientific packages (may require compilation) ---
 # These are included but may cause install errors in pip/venv
-opentsne==1.0.2       # Dimensionality reduction to visualize node embeddings in 2D. Might get replaced by umap.
-shap==0.48.0          # For e.g. explaining anomaly detection results
+opentsne==1.0.4       # Dimensionality reduction to visualize node embeddings in 2D. Might get replaced by umap.
+shap==0.49.1          # For e.g. explaining anomaly detection results