Pin Python venv and conda dependencies

.github/workflows/internal-check-python-venv-support.yml

@@ -32,10 +32,17 @@ jobs:
       env: 
         USE_VIRTUAL_PYTHON_ENVIRONMENT_VENV: "true"
       # 1.  Run the script under test to create, activate and install the virtual environment
-      # 2a. Run pip in dry-run mode without installing or resolving dependencies
-      # 2b. Suppress all pip output (stderr)
-      # 2c. Check if pip *would install* anything using grep
-      # 2d. If there are missing dependencies and the environment is incomplete, return 1 (indicates all requirements already satisfied)
+      # 2.  Run pip install for the current environment in dry-mode with full output first to get all infos and fail on missing packages.
+      # 3a. The leading exclamation mark tells the shell to invert the returned exit code. 
+      #     Failing because "Would install" is missing leads to a successful execution. 
+      # 3b. Run pip install for the current environment in dry-run mode without installing or resolving dependencies
+      # 3c. Suppress all pip output (stderr)
+      # 3d. Check if pip would install anything using grep
+      # 3e. If there are missing dependencies and the environment is incomplete, 
+      #     fail with code 1 (indicates all requirements already satisfied)
+      #
+      # Output installed dependencies for troubleshooting purposes:.venv/bin/pip freeze
       run: |
-        ./scripts/activatePythonEnvironment.sh
-        pip install --dry-run --no-deps --requirement "./requirements.txt" 2>/dev/null | grep -q "Would install" || return 1
+        source ./scripts/activatePythonEnvironment.sh
+        .venv/bin/pip install --dry-run --quiet --requirement requirements.txt
+        ! .venv/bin/pip install --dry-run --requirement requirements.txt 2>/dev/null | grep -q "Would install"

conda-environment.yml

@@ -5,26 +5,26 @@ channels:
   - plotly
   - conda-forge
 dependencies:
-  - python=3.12.*
-  - _python_rc=* # Needed on Mac since Python >= 3.12
-  - jupyter=1.1.*
-  - matplotlib=3.10.*
-  - nbconvert=7.16.*
-  - nbconvert-webpdf=7.16.*
-  - numpy=1.26.*
-  - pandas=2.2.*
-  - pip=25.0.*
-  - setuptools=80.9.* # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?) 
-  - typing-extensions=4.12.* # Also needed for opentsne and Python >= 3.12
-  - opentsne=1.0.* # to visualize node embeddings in 2D (t-SNE dimensionality reduction)
-  - wordcloud=1.9.*
-  - monotonic=1.*
-  - plotly=6.0.*
-  - python-kaleido=0.2.* # To render plotly plots. Static image export for web-based visualization libraries.
-  - scikit-learn=1.6.* # To try out this HDBSCAN implementation 
-  - seaborn=0.13.* # To visualize clustering results
-  - optuna=4.5.*
-  - umap-learn=0.5.* # to visualize node embeddings in 2D (UMAP dimensionality reduction)
-  - shap=0.48.*
+  - python=3.12.9
+  - _python_rc=1 # Needed on Mac since Python >= 3.12
+  - jupyter=1.1.1
+  - matplotlib=3.10.5
+  - nbconvert=7.16.6
+  - nbconvert-webpdf=7.16.6
+  - numpy=1.26.4
+  - pandas=2.2.3
+  - 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)
+  - wordcloud=1.9.4
+  - monotonic=1.6
+  - plotly=6.2.0
+  - 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
+  - umap-learn=0.5.9 # to visualize node embeddings in 2D (UMAP dimensionality reduction)
+  - shap=0.48.0
   - pip:
-      - neo4j==5.28.*
+      - neo4j==5.28.2

requirements.txt

@@ -4,30 +4,30 @@
 # NOTE: Keep this file in sync with conda-environment.yml
 
 # --- Core tools ---
-jupyter==1.1.*
-matplotlib==3.10.*
-nbconvert[webpdf]==7.16.*
-numpy==1.26.*
-pandas==2.2.*
-pip==25.0.*
-setuptools==80.9.* # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?) 
-typing-extensions==4.12.* # Needed for opentsne and Python >= 3.12
+jupyter==1.1.1
+matplotlib==3.10.5
+nbconvert[webpdf]==7.16.6
+numpy==1.26.4
+pandas==2.2.3
+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 # Needed for opentsne and Python >= 3.12
 
 # --- Visualization ---
-wordcloud==1.9.*
-monotonic==1.*
-plotly[kaleido]==6.2.*
-seaborn==0.13.* # To visualize clustering results
+wordcloud==1.9.4
+monotonic==1.6
+plotly[kaleido]==6.2.0
+seaborn==0.13.2 # To visualize clustering results
 
 # --- Machine Learning / Optimization ---
-scikit-learn==1.6.*
-optuna==4.5.*
-umap-learn==0.5.* # Dimensionality reduction to visualize node embeddings in 2D
+scikit-learn==1.6.1
+optuna==4.5.0
+umap-learn==0.5.8 # Dimensionality reduction to visualize node embeddings in 2D
 
 # --- Database connector ---
-neo4j==5.28.*
+neo4j==5.28.2
 
 # --- Native/scientific packages (may require compilation) ---
 # These are included but may cause install errors in pip/venv
-opentsne==1.0.*       # Dimensionality reduction to visualize node embeddings in 2D. Might get replaced by umap.
-shap==0.48.*          # For e.g. explaining anomaly detection results
+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