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Pin Python venv and conda dependencies
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conda-environment.yml

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- plotly
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- conda-forge
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dependencies:
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- python=3.12.*
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- _python_rc=* # Needed on Mac since Python >= 3.12
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- jupyter=1.1.*
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- matplotlib=3.10.*
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- nbconvert=7.16.*
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- nbconvert-webpdf=7.16.*
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- numpy=1.26.*
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- pandas=2.2.*
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- pip=25.0.*
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- setuptools=80.9.* # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
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- typing-extensions=4.12.* # Also needed for opentsne and Python >= 3.12
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- opentsne=1.0.* # to visualize node embeddings in 2D (t-SNE dimensionality reduction)
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- wordcloud=1.9.*
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- monotonic=1.*
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- plotly=6.0.*
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- python-kaleido=0.2.* # To render plotly plots. Static image export for web-based visualization libraries.
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- scikit-learn=1.6.* # To try out this HDBSCAN implementation
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- seaborn=0.13.* # To visualize clustering results
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- optuna=4.5.*
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- umap-learn=0.5.* # to visualize node embeddings in 2D (UMAP dimensionality reduction)
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- shap=0.48.*
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- python=3.12.9
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- _python_rc=1 # Needed on Mac since Python >= 3.12
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- jupyter=1.1.1
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- matplotlib=3.10.5
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- nbconvert=7.16.6
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- nbconvert-webpdf=7.16.6
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- numpy=1.26.4
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- pandas=2.2.3
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- pip=25.0.1
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- setuptools=80.9.0 # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
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- typing-extensions=4.12.2 # Also needed for opentsne and Python >= 3.12
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- opentsne=1.0.2 # to visualize node embeddings in 2D (t-SNE dimensionality reduction)
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- wordcloud=1.9.4
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- monotonic=1.6
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- plotly=6.2.0
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- python-kaleido=1.0.0 # To render plotly plots. Static image export for web-based visualization libraries.
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- scikit-learn=1.6.1 # For anomaly detection HDBSCAN clustering, scores like AMI, and others
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- seaborn=0.13.2 # To visualize clustering results
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- optuna=4.5.0
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- umap-learn=0.5.9 # to visualize node embeddings in 2D (UMAP dimensionality reduction)
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- shap=0.48.0
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- pip:
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- neo4j==5.28.*
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- neo4j==5.28.2

requirements.txt

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# NOTE: Keep this file in sync with conda-environment.yml
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# --- Core tools ---
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jupyter==1.1.*
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matplotlib==3.10.*
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nbconvert[webpdf]==7.16.*
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numpy==1.26.*
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pandas==2.2.*
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pip==25.0.*
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setuptools==80.9.* # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
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typing-extensions==4.12.* # Needed for opentsne and Python >= 3.12
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jupyter==1.1.1
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matplotlib==3.10.5
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nbconvert[webpdf]==7.16.6
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numpy==1.26.4
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pandas==2.2.3
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pip==25.0.1
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setuptools==80.9.0 # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
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typing-extensions==4.12.2 # Needed for opentsne and Python >= 3.12
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# --- Visualization ---
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wordcloud==1.9.*
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monotonic==1.*
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plotly[kaleido]==6.2.*
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seaborn==0.13.* # To visualize clustering results
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wordcloud==1.9.4
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monotonic==1.6
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plotly[kaleido]==6.2.0
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seaborn==0.13.2 # To visualize clustering results
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# --- Machine Learning / Optimization ---
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scikit-learn==1.6.*
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optuna==4.5.*
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umap-learn==0.5.* # Dimensionality reduction to visualize node embeddings in 2D
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scikit-learn==1.6.1
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optuna==4.5.0
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umap-learn==0.5.8 # Dimensionality reduction to visualize node embeddings in 2D
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# --- Database connector ---
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neo4j==5.28.*
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neo4j==5.28.2
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# --- Native/scientific packages (may require compilation) ---
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# These are included but may cause install errors in pip/venv
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opentsne==1.0.* # Dimensionality reduction to visualize node embeddings in 2D. Might get replaced by umap.
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shap==0.48.* # For e.g. explaining anomaly detection results
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opentsne==1.0.2 # Dimensionality reduction to visualize node embeddings in 2D. Might get replaced by umap.
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shap==0.48.0 # For e.g. explaining anomaly detection results

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