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lines changed Original file line number Diff line number Diff line change @@ -5,26 +5,26 @@ channels:
55 - plotly
66 - conda-forge
77dependencies :
8- - python=3.12.*
9- - _python_rc=* # Needed on Mac since Python >= 3.12
10- - jupyter=1.1.*
11- - matplotlib=3.10.*
12- - nbconvert=7.16.*
13- - nbconvert-webpdf=7.16.*
14- - numpy=1.26.*
15- - pandas=2.2.*
16- - pip=25.0.*
17- - setuptools=80.9.* # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
18- - typing-extensions=4.12.* # Also needed for opentsne and Python >= 3.12
19- - opentsne=1.0.* # to visualize node embeddings in 2D (t-SNE dimensionality reduction)
20- - wordcloud=1.9.*
21- - monotonic=1.*
22- - plotly=6.0.*
23- - python-kaleido=0.2.* # To render plotly plots. Static image export for web-based visualization libraries.
24- - scikit-learn=1.6.* # To try out this HDBSCAN implementation
25- - seaborn=0.13.* # To visualize clustering results
26- - optuna=4.5.*
27- - umap-learn=0.5.* # to visualize node embeddings in 2D (UMAP dimensionality reduction)
28- - shap=0.48.*
8+ - python=3.12.9
9+ - _python_rc=1 # Needed on Mac since Python >= 3.12
10+ - jupyter=1.1.1
11+ - matplotlib=3.10.5
12+ - nbconvert=7.16.6
13+ - nbconvert-webpdf=7.16.6
14+ - numpy=1.26.4
15+ - pandas=2.2.3
16+ - pip=25.0.1
17+ - setuptools=80.9.0 # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
18+ - typing-extensions=4.12.2 # Also needed for opentsne and Python >= 3.12
19+ - opentsne=1.0.2 # to visualize node embeddings in 2D (t-SNE dimensionality reduction)
20+ - wordcloud=1.9.4
21+ - monotonic=1.6
22+ - plotly=6.2.0
23+ - python-kaleido=1.0.0 # To render plotly plots. Static image export for web-based visualization libraries.
24+ - scikit-learn=1.6.1 # For anomaly detection HDBSCAN clustering, scores like AMI, and others
25+ - seaborn=0.13.2 # To visualize clustering results
26+ - optuna=4.5.0
27+ - umap-learn=0.5.9 # to visualize node embeddings in 2D (UMAP dimensionality reduction)
28+ - shap=0.48.0
2929 - pip :
30- - neo4j==5.28.*
30+ - neo4j==5.28.2
Original file line number Diff line number Diff line change 44# NOTE: Keep this file in sync with conda-environment.yml
55
66# --- Core tools ---
7- jupyter == 1.1.*
8- matplotlib == 3.10.*
9- nbconvert [webpdf ]== 7.16.*
10- numpy == 1.26.*
11- pandas == 2.2.*
12- pip == 25.0.*
13- setuptools == 80.9.* # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
14- typing-extensions == 4.12.* # Needed for opentsne and Python >= 3.12
7+ jupyter == 1.1.1
8+ matplotlib == 3.10.5
9+ nbconvert [webpdf ]== 7.16.6
10+ numpy == 1.26.4
11+ pandas == 2.2.3
12+ pip == 25.0.1
13+ setuptools == 80.9.0 # opentsne uses sklearn.base uses joblib uses distutils missing in Python >= 12 (TODO use native openTSNE?)
14+ typing-extensions == 4.12.2 # Needed for opentsne and Python >= 3.12
1515
1616# --- Visualization ---
17- wordcloud == 1.9.*
18- monotonic == 1.*
19- plotly [kaleido ]== 6.2.*
20- seaborn == 0.13.* # To visualize clustering results
17+ wordcloud == 1.9.4
18+ monotonic == 1.6
19+ plotly [kaleido ]== 6.2.0
20+ seaborn == 0.13.2 # To visualize clustering results
2121
2222# --- Machine Learning / Optimization ---
23- scikit-learn == 1.6.*
24- optuna == 4.5.*
25- umap-learn == 0.5.* # Dimensionality reduction to visualize node embeddings in 2D
23+ scikit-learn == 1.6.1
24+ optuna == 4.5.0
25+ umap-learn == 0.5.8 # Dimensionality reduction to visualize node embeddings in 2D
2626
2727# --- Database connector ---
28- neo4j == 5.28.*
28+ neo4j == 5.28.2
2929
3030# --- Native/scientific packages (may require compilation) ---
3131# These are included but may cause install errors in pip/venv
32- opentsne == 1.0.* # Dimensionality reduction to visualize node embeddings in 2D. Might get replaced by umap.
33- shap == 0.48.* # For e.g. explaining anomaly detection results
32+ opentsne == 1.0.2 # Dimensionality reduction to visualize node embeddings in 2D. Might get replaced by umap.
33+ shap == 0.48.0 # For e.g. explaining anomaly detection results
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