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charliepat
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f728292461 | ||
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95905cfc8b | ||
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46623927fe |
3
.gitmodules
vendored
3
.gitmodules
vendored
@@ -1,3 +0,0 @@
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[submodule "cdsc_ecology_utils"]
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path = cdsc_ecology_utils
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url = code:cdsc_ecology_utils
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@@ -1,2 +0,0 @@
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from timeseries import load_clusters, load_densities, build_cluster_timeseries
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from cdsc_ecology_utils import similarity_functions
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Submodule cdsc_ecology_utils deleted from 5b5fdbb3c0
@@ -2,164 +2,41 @@
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srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
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similarity_data=/gscratch/comdata/output/reddit_similarity
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clustering_data=/gscratch/comdata/output/reddit_clustering
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kmeans_selection_grid=--max_iters=[3000] --n_inits=[10] --n_clusters=[100,500,1000,1250,1500,1750,2000]
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hdbscan_selection_grid=--min_cluster_sizes=[2,3,4,5] --min_samples=[2,3,4,5] --cluster_selection_epsilons=[0,0.01,0.05,0.1,0.15,0.2] --cluster_selection_methods=[eom,leaf]
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affinity_selection_grid=--dampings=[0.5,0.6,0.7,0.8,0.95,0.97,0.99] --preference_quantiles=[0.1,0.3,0.5,0.7,0.9] --convergence_iters=[15]
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kmeans_selection_grid="--max_iter=3000 --n_init=[10] --n_clusters=[100,500,1000,1500,2000,2500,3000,2350,3500,3570,4000]"
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#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
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all:$(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv
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# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
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# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
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authors_10k_input=$(similarity_data)/subreddit_comment_authors_10k.feather
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authors_10k_input_lsi=$(similarity_data)/subreddit_comment_authors_10k_LSI
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authors_10k_output=$(clustering_data)/subreddit_comment_authors_10k
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authors_10k_output_lsi=$(clustering_data)/subreddit_comment_authors_10k_LSI
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$(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
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$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/kmeans $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
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authors_tf_10k_input=$(similarity_data)/subreddit_comment_authors-tf_10k.feather
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authors_tf_10k_input_lsi=$(similarity_data)/subreddit_comment_authors-tf_10k_LSI
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authors_tf_10k_output=$(clustering_data)/subreddit_comment_authors-tf_10k
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authors_tf_10k_output_lsi=$(clustering_data)/subreddit_comment_authors-tf_10k_LSI
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$(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
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$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/kmeans $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
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terms_10k_input=$(similarity_data)/subreddit_comment_terms_10k.feather
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terms_10k_input_lsi=$(similarity_data)/subreddit_comment_terms_10k_LSI
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terms_10k_output=$(clustering_data)/subreddit_comment_terms_10k
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terms_10k_output_lsi=$(clustering_data)/subreddit_comment_terms_10k_LSI
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all:terms_10k authors_10k authors_tf_10k terms_10k_lsi authors_10k_lsi authors_tf_10k_lsi
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terms_10k:${terms_10k_output}/kmeans/selection_data.csv ${terms_10k_output}/affinity/selection_data.csv ${terms_10k_output}/hdbscan/selection_data.csv
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authors_10k:${authors_10k_output}/kmeans/selection_data.csv ${authors_10k_output}/hdbscan/selection_data.csv ${authors_10k_output}/affinity/selection_data.csv
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authors_tf_10k:${authors_tf_10k_output}/kmeans/selection_data.csv ${authors_tf_10k_output}/hdbscan/selection_data.csv ${authors_tf_10k_output}/affinity/selection_data.csv
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terms_10k_lsi:${terms_10k_output_lsi}/kmeans/selection_data.csv ${terms_10k_output_lsi}/affinity/selection_data.csv ${terms_10k_output_lsi}/hdbscan/selection_data.csv
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authors_10k_lsi:${authors_10k_output_lsi}/kmeans/selection_data.csv ${authors_10k_output_lsi}/hdbscan/selection_data.csv ${authors_10k_output_lsi}/affinity/selection_data.csv
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authors_tf_10k_lsi:${authors_tf_10k_output_lsi}/kmeans/selection_data.csv ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
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${authors_10k_output}/kmeans/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/kmeans --savefile=${authors_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${terms_10k_output}/kmeans/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/kmeans --savefile=${terms_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${authors_tf_10k_output}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/kmeans --savefile=${authors_tf_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${authors_10k_output}/affinity/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/affinity --savefile=${authors_10k_output}/affinity/selection_data.csv $(affinity_selection_grid)
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${terms_10k_output}/affinity/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/affinity --savefile=${terms_10k_output}/affinity/selection_data.csv $(affinity_selection_grid)
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${authors_tf_10k_output}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/affinity --savefile=${authors_tf_10k_output}/affinity/selection_data.csv $(affinity_selection_grid)
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${authors_10k_output}/hdbscan/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/hdbscan --savefile=${authors_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${terms_10k_output}/hdbscan/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/hdbscan --savefile=${terms_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${authors_tf_10k_output}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/hdbscan --savefile=${authors_tf_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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$(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
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$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
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## LSI Models
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${authors_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/kmeans --savefile=${authors_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
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affinity_selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
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$(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
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$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/affinity $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
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${terms_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/kmeans --savefile=${terms_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
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$(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
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$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/affinity $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
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${authors_tf_10k_output_lsi}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/kmeans --savefile=${authors_tf_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
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$(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
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$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/affinity $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
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${authors_10k_output_lsi}/affinity/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/affinity --savefile=${authors_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
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clean:
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rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv
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rm -f $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv
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rm -f $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv
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rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv
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rm -f $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv
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rm -f $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv
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${terms_10k_output_lsi}/affinity/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/affinity --savefile=${terms_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
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${authors_tf_10k_output_lsi}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/affinity --savefile=${authors_tf_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
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${authors_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/hdbscan --savefile=${authors_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${terms_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/hdbscan --savefile=${terms_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/hdbscan --savefile=${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${terms_10k_output_lsi}/best_hdbscan.feather:${terms_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
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$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
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${authors_tf_10k_output_lsi}/best_hdbscan.feather:${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
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$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
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clean_affinity:
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rm -f ${authors_10k_output}/affinity/selection_data.csv
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rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
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rm -f ${terms_10k_output}/affinity/selection_data.csv
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clean_kmeans:
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rm -f ${authors_10k_output}/kmeans/selection_data.csv
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rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
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rm -f ${terms_10k_output}/kmeans/selection_data.csv
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clean_hdbscan:
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rm -f ${authors_10k_output}/hdbscan/selection_data.csv
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rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
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rm -f ${terms_10k_output}/hdbscan/selection_data.csv
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clean_authors:
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rm -f ${authors_10k_output}/affinity/selection_data.csv
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rm -f ${authors_10k_output}/kmeans/selection_data.csv
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rm -f ${authors_10k_output}/hdbscan/selection_data.csv
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clean_authors_tf:
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rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
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rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
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rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
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clean_terms:
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rm -f ${terms_10k_output}/affinity/selection_data.csv
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rm -f ${terms_10k_output}/kmeans/selection_data.csv
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rm -f ${terms_10k_output}/hdbscan/selection_data.csv
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clean_lsi_affinity:
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rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
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rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
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rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
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clean_lsi_kmeans:
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rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
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rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
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rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
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clean_lsi_hdbscan:
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rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
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rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
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rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
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clean_lsi_authors:
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rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
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rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
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rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
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clean_lsi_authors_tf:
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rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
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rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
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rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
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clean_lsi_terms:
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rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
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rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
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rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
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clean: clean_affinity clean_kmeans clean_hdbscan
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PHONY: clean clean_affinity clean_kmeans clean_hdbscan clean_authors clean_authors_tf clean_terms terms_10k authors_10k authors_tf_10k
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PHONY: clean
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# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py
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# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS
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@@ -1,129 +0,0 @@
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from sklearn.cluster import AffinityPropagation
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from dataclasses import dataclass
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from clustering_base import clustering_result, clustering_job
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from grid_sweep import grid_sweep
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from pathlib import Path
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from itertools import product, starmap
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import fire
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import sys
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import numpy as np
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# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying.
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@dataclass
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class affinity_clustering_result(clustering_result):
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damping:float
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convergence_iter:int
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preference_quantile:float
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preference:float
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max_iter:int
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class affinity_job(clustering_job):
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def __init__(self, infile, outpath, name, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
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super().__init__(infile,
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outpath,
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name,
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call=self._affinity_clustering,
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preference_quantile=preference_quantile,
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damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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random_state=1968,
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||||
verbose=verbose)
|
||||
self.damping=damping
|
||||
self.max_iter=max_iter
|
||||
self.convergence_iter=convergence_iter
|
||||
self.preference_quantile=preference_quantile
|
||||
|
||||
def _affinity_clustering(self, mat, preference_quantile, *args, **kwargs):
|
||||
mat = 1-mat
|
||||
preference = np.quantile(mat, preference_quantile)
|
||||
self.preference = preference
|
||||
print(f"preference is {preference}")
|
||||
print("data loaded")
|
||||
sys.stdout.flush()
|
||||
clustering = AffinityPropagation(*args,
|
||||
preference=preference,
|
||||
affinity='precomputed',
|
||||
copy=False,
|
||||
**kwargs).fit(mat)
|
||||
return clustering
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result=affinity_clustering_result(**result.__dict__,
|
||||
damping=self.damping,
|
||||
max_iter=self.max_iter,
|
||||
convergence_iter=self.convergence_iter,
|
||||
preference_quantile=self.preference_quantile,
|
||||
preference=self.preference)
|
||||
|
||||
return self.result
|
||||
|
||||
class affinity_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
*args,
|
||||
**kwargs):
|
||||
|
||||
super().__init__(affinity_job,
|
||||
_afffinity_grid_sweep,
|
||||
inpath,
|
||||
outpath,
|
||||
self.namer,
|
||||
*args,
|
||||
**kwargs)
|
||||
def namer(self,
|
||||
damping,
|
||||
max_iter,
|
||||
convergence_iter,
|
||||
preference_quantile):
|
||||
|
||||
return f"damp-{damping}_maxit-{max_iter}_convit-{convergence_iter}_prefq-{preference_quantile}"
|
||||
|
||||
def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5],n_cores=10):
|
||||
"""Run affinity clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<csv> --dampings=<csv> --preference_quantiles=<csv>
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to feather data containing a labeled matrix of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering.
|
||||
preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
|
||||
convergence_iters:one or more integers of number of iterations without improvement before stopping.
|
||||
max_iters: one or more numbers of different maximum interations.
|
||||
"""
|
||||
obj = affinity_grid_sweep(inpath,
|
||||
outpath,
|
||||
map(float,dampings),
|
||||
map(int,max_iters),
|
||||
map(int,convergence_iters),
|
||||
map(float,preference_quantiles))
|
||||
obj.run(n_cores)
|
||||
obj.save(savefile)
|
||||
|
||||
def test_select_affinity_clustering():
|
||||
# select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
|
||||
# "test_hdbscan_author30k",
|
||||
# min_cluster_sizes=[2],
|
||||
# min_samples=[1,2],
|
||||
# cluster_selection_epsilons=[0,0.05,0.1,0.15],
|
||||
# cluster_selection_methods=['eom','leaf'],
|
||||
# lsi_dimensions='all')
|
||||
inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
|
||||
outpath = "test_affinity";
|
||||
dampings=[0.8,0.9]
|
||||
max_iters=[100000]
|
||||
convergence_iters=[15]
|
||||
preference_quantiles=[0.5,0.7]
|
||||
|
||||
gs = affinity_lsi_grid_sweep(inpath, 'all', outpath, dampings, max_iters, convergence_iters, preference_quantiles)
|
||||
gs.run(20)
|
||||
gs.save("test_affinity/lsi_sweep.csv")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_affinity_grid_sweep)
|
||||
@@ -1,99 +0,0 @@
|
||||
import fire
|
||||
from affinity_clustering import affinity_clustering_result, affinity_job, affinity_grid_sweep
|
||||
from grid_sweep import grid_sweep
|
||||
from lsi_base import lsi_result_mixin, lsi_grid_sweep, lsi_mixin
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class affinity_clustering_result_lsi(affinity_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
|
||||
class affinity_lsi_job(affinity_job, lsi_mixin):
|
||||
def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
*args,
|
||||
**kwargs)
|
||||
super().set_lsi_dims(lsi_dims)
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = affinity_clustering_result_lsi(**result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
class affinity_lsi_grid_sweep(lsi_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
dampings=[0.9],
|
||||
max_iters=[10000],
|
||||
convergence_iters=[30],
|
||||
preference_quantiles=[0.5]):
|
||||
|
||||
super().__init__(affinity_lsi_job,
|
||||
_affinity_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
dampings,
|
||||
max_iters,
|
||||
convergence_iters,
|
||||
preference_quantiles)
|
||||
|
||||
|
||||
class _affinity_lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
*args,
|
||||
**kwargs):
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = affinity_lsi_job
|
||||
super().__init__(self.jobtype,
|
||||
inpath,
|
||||
outpath,
|
||||
self.namer,
|
||||
[self.lsi_dim],
|
||||
*args,
|
||||
**kwargs)
|
||||
|
||||
def namer(self, *args, **kwargs):
|
||||
s = affinity_grid_sweep.namer(self, *args[1:], **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5], lsi_dimensions='all',n_cores=30):
|
||||
"""Run affinity clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<csv> --dampings=<csv> --preference_quantiles=<csv> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering.
|
||||
preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
|
||||
convergence_iters:one or more integers of number of iterations without improvement before stopping.
|
||||
max_iters: one or more numbers of different maximum interations.
|
||||
lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
"""
|
||||
|
||||
obj = affinity_lsi_grid_sweep(inpath,
|
||||
lsi_dimensions,
|
||||
outpath,
|
||||
map(float,dampings),
|
||||
map(int,max_iters),
|
||||
map(int,convergence_iters),
|
||||
map(float,preference_quantiles))
|
||||
|
||||
obj.run(n_cores)
|
||||
obj.save(savefile)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_affinity_lsi_grid_sweep)
|
||||
@@ -3,7 +3,7 @@
|
||||
import sys
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.cluster import AffinityPropagation
|
||||
from sklearn.cluster import AffinityPropagation, KMeans
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from multiprocessing import cpu_count
|
||||
@@ -46,6 +46,24 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
|
||||
print(f"saved {output}")
|
||||
return clustering
|
||||
|
||||
def kmeans_clustering(similarities, *args, **kwargs):
|
||||
subreddits, mat = read_similarity_mat(similarities)
|
||||
mat = sim_to_dist(mat)
|
||||
clustering = _kmeans_clustering(mat, *args, **kwargs)
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
return(cluster_data)
|
||||
|
||||
def _kmeans_clustering(mat, output, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
|
||||
|
||||
clustering = KMeans(n_clusters=n_clusters,
|
||||
n_init=n_init,
|
||||
max_iter=max_iter,
|
||||
random_state=random_state,
|
||||
verbose=verbose
|
||||
).fit(mat)
|
||||
|
||||
return clustering
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -2,76 +2,21 @@ from pathlib import Path
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from dataclasses import dataclass
|
||||
from sklearn.metrics import silhouette_score, silhouette_samples
|
||||
from collections import Counter
|
||||
|
||||
# this is meant to be an interface, not created directly
|
||||
class clustering_job:
|
||||
def __init__(self, infile, outpath, name, call, *args, **kwargs):
|
||||
self.outpath = Path(outpath)
|
||||
self.call = call
|
||||
self.args = args
|
||||
self.kwargs = kwargs
|
||||
self.infile = Path(infile)
|
||||
self.name = name
|
||||
self.hasrun = False
|
||||
def sim_to_dist(mat):
|
||||
dist = 1-mat
|
||||
dist[dist < 0] = 0
|
||||
np.fill_diagonal(dist,0)
|
||||
return dist
|
||||
|
||||
def run(self):
|
||||
self.subreddits, self.mat = self.read_distance_mat(self.infile)
|
||||
self.clustering = self.call(self.mat, *self.args, **self.kwargs)
|
||||
self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
|
||||
self.score = self.silhouette()
|
||||
self.outpath.mkdir(parents=True, exist_ok=True)
|
||||
self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
|
||||
self.hasrun = True
|
||||
|
||||
def get_info(self):
|
||||
if not self.hasrun:
|
||||
self.run()
|
||||
|
||||
self.result = clustering_result(outpath=str(self.outpath.resolve()),
|
||||
silhouette_score=self.score,
|
||||
name=self.name,
|
||||
n_clusters=self.n_clusters,
|
||||
n_isolates=self.n_isolates,
|
||||
silhouette_samples = self.silsampout
|
||||
)
|
||||
return self.result
|
||||
|
||||
def silhouette(self):
|
||||
counts = Counter(self.clustering.labels_)
|
||||
singletons = [key for key, value in counts.items() if value == 1]
|
||||
isolates = (self.clustering.labels_ == -1) | (np.isin(self.clustering.labels_,np.array(singletons)))
|
||||
scoremat = self.mat[~isolates][:,~isolates]
|
||||
if self.n_clusters > 1:
|
||||
score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
|
||||
silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
|
||||
silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
|
||||
self.outpath.mkdir(parents=True, exist_ok=True)
|
||||
silsampout = self.outpath / ("silhouette_samples-" + self.name + ".feather")
|
||||
self.silsampout = silsampout.resolve()
|
||||
silhouette_samp.to_feather(self.silsampout)
|
||||
else:
|
||||
score = None
|
||||
self.silsampout = None
|
||||
return score
|
||||
|
||||
def read_distance_mat(self, similarities, use_threads=True):
|
||||
df = pd.read_feather(similarities, use_threads=use_threads)
|
||||
mat = np.array(df.drop('_subreddit',1))
|
||||
n = mat.shape[0]
|
||||
mat[range(n),range(n)] = 1
|
||||
return (df._subreddit,1-mat)
|
||||
|
||||
def process_clustering(self, clustering, subreddits):
|
||||
def process_clustering_result(clustering, subreddits):
|
||||
|
||||
if hasattr(clustering,'n_iter_'):
|
||||
print(f"clustering took {clustering.n_iter_} iterations")
|
||||
|
||||
clusters = clustering.labels_
|
||||
self.n_clusters = len(set(clusters))
|
||||
|
||||
print(f"found {self.n_clusters} clusters")
|
||||
print(f"found {len(set(clusters))} clusters")
|
||||
|
||||
cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
|
||||
|
||||
@@ -79,27 +24,26 @@ class clustering_job:
|
||||
print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
|
||||
|
||||
print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
|
||||
n_isolates1 = (cluster_sizes.subreddit==1).sum()
|
||||
|
||||
print(f"{n_isolates1} clusters have 1 member")
|
||||
print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
|
||||
|
||||
n_isolates2 = cluster_sizes.loc[cluster_sizes.cluster==-1,:]['subreddit'].to_list()
|
||||
if len(n_isolates2) > 0:
|
||||
n_isloates2 = n_isolates2[0]
|
||||
print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
|
||||
|
||||
if n_isolates1 == 0:
|
||||
self.n_isolates = n_isolates2
|
||||
else:
|
||||
self.n_isolates = n_isolates1
|
||||
print(f"{(cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])} subreddits are in cluster -1",flush=True)
|
||||
|
||||
return cluster_data
|
||||
|
||||
|
||||
@dataclass
|
||||
class clustering_result:
|
||||
outpath:Path
|
||||
max_iter:int
|
||||
silhouette_score:float
|
||||
alt_silhouette_score:float
|
||||
name:str
|
||||
n_clusters:int
|
||||
n_isolates:int
|
||||
silhouette_samples:str
|
||||
|
||||
def read_similarity_mat(similarities, use_threads=True):
|
||||
df = pd.read_feather(similarities, use_threads=use_threads)
|
||||
mat = np.array(df.drop('_subreddit',1))
|
||||
n = mat.shape[0]
|
||||
mat[range(n),range(n)] = 1
|
||||
return (df._subreddit,mat)
|
||||
|
||||
@@ -17,7 +17,7 @@ def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=1000
|
||||
df = pd.read_feather(similarities)
|
||||
|
||||
n = df.shape[0]
|
||||
mat = np.array(df.drop('_subreddit',1),dtype=np.float64)
|
||||
mat = np.array(df.drop('subreddit',1),dtype=np.float64)
|
||||
mat[range(n),range(n)] = 1
|
||||
mat[mat > 1] = 1
|
||||
dist = 2*np.arccos(mat)/np.pi
|
||||
@@ -26,7 +26,7 @@ def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=1000
|
||||
|
||||
tsne_fit_whole = tsne_fit_model.fit_transform(dist)
|
||||
|
||||
plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], '_subreddit':df['_subreddit']})
|
||||
plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':df.subreddit})
|
||||
|
||||
plot_data.to_feather(output)
|
||||
|
||||
|
||||
@@ -1,33 +0,0 @@
|
||||
from pathlib import Path
|
||||
from multiprocessing import Pool, cpu_count
|
||||
from itertools import product, chain
|
||||
import pandas as pd
|
||||
|
||||
class grid_sweep:
|
||||
def __init__(self, jobtype, inpath, outpath, namer, *args):
|
||||
self.jobtype = jobtype
|
||||
self.namer = namer
|
||||
print(*args)
|
||||
grid = list(product(*args))
|
||||
inpath = Path(inpath)
|
||||
outpath = Path(outpath)
|
||||
self.hasrun = False
|
||||
self.grid = [(inpath,outpath,namer(*g)) + g for g in grid]
|
||||
self.jobs = [jobtype(*g) for g in self.grid]
|
||||
|
||||
def run(self, cores=20):
|
||||
if cores is not None and cores > 1:
|
||||
with Pool(cores) as pool:
|
||||
infos = pool.map(self.jobtype.get_info, self.jobs)
|
||||
else:
|
||||
infos = map(self.jobtype.get_info, self.jobs)
|
||||
|
||||
self.infos = pd.DataFrame(infos)
|
||||
self.hasrun = True
|
||||
|
||||
def save(self, outcsv):
|
||||
if not self.hasrun:
|
||||
self.run()
|
||||
outcsv = Path(outcsv)
|
||||
outcsv.parent.mkdir(parents=True, exist_ok=True)
|
||||
self.infos.to_csv(outcsv)
|
||||
@@ -1,57 +1,32 @@
|
||||
from clustering_base import clustering_result, clustering_job
|
||||
from grid_sweep import grid_sweep
|
||||
from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
|
||||
from dataclasses import dataclass
|
||||
import hdbscan
|
||||
from sklearn.neighbors import NearestNeighbors
|
||||
import plotnine as pn
|
||||
import numpy as np
|
||||
from itertools import product, starmap, chain
|
||||
from itertools import product, starmap
|
||||
import pandas as pd
|
||||
from multiprocessing import cpu_count
|
||||
from sklearn.metrics import silhouette_score, silhouette_samples
|
||||
from pathlib import Path
|
||||
from multiprocessing import Pool, cpu_count
|
||||
import fire
|
||||
from pyarrow.feather import write_feather
|
||||
|
||||
def test_select_hdbscan_clustering():
|
||||
# select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
|
||||
# "test_hdbscan_author30k",
|
||||
# min_cluster_sizes=[2],
|
||||
# min_samples=[1,2],
|
||||
# cluster_selection_epsilons=[0,0.05,0.1,0.15],
|
||||
# cluster_selection_methods=['eom','leaf'],
|
||||
# lsi_dimensions='all')
|
||||
inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI"
|
||||
select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
|
||||
"test_hdbscan_author30k",
|
||||
min_cluster_sizes=[2],
|
||||
min_samples=[1,2],
|
||||
cluster_selection_epsilons=[0,0.05,0.1,0.15],
|
||||
cluster_selection_methods=['eom','leaf'],
|
||||
lsi_dimensions='all')
|
||||
inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI"
|
||||
outpath = "test_hdbscan";
|
||||
min_cluster_sizes=[2,3,4];
|
||||
min_samples=[1,2,3];
|
||||
cluster_selection_epsilons=[0,0.1,0.3,0.5];
|
||||
cluster_selection_methods=[1];
|
||||
cluster_selection_methods=['eom'];
|
||||
lsi_dimensions='all'
|
||||
gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
|
||||
gs.run(20)
|
||||
gs.save("test_hdbscan/lsi_sweep.csv")
|
||||
# job1 = hdbscan_lsi_job(infile=inpath, outpath=outpath, name="test", lsi_dims=500, min_cluster_size=2, min_samples=1,cluster_selection_epsilon=0,cluster_selection_method='eom')
|
||||
# job1.run()
|
||||
# print(job1.get_info())
|
||||
|
||||
# df = pd.read_csv("test_hdbscan/selection_data.csv")
|
||||
# test_select_hdbscan_clustering()
|
||||
# check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
|
||||
# silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
|
||||
# c = check_clusters.merge(silscores,on='subreddit')# fire.Fire(select_hdbscan_clustering)
|
||||
class hdbscan_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
*args,
|
||||
**kwargs):
|
||||
|
||||
super().__init__(hdbscan_job, inpath, outpath, self.namer, *args, **kwargs)
|
||||
|
||||
def namer(self,
|
||||
min_cluster_size,
|
||||
min_samples,
|
||||
cluster_selection_epsilon,
|
||||
cluster_selection_method):
|
||||
return f"mcs-{min_cluster_size}_ms-{min_samples}_cse-{cluster_selection_epsilon}_csm-{cluster_selection_method}"
|
||||
|
||||
@dataclass
|
||||
class hdbscan_clustering_result(clustering_result):
|
||||
@@ -59,31 +34,101 @@ class hdbscan_clustering_result(clustering_result):
|
||||
min_samples:int
|
||||
cluster_selection_epsilon:float
|
||||
cluster_selection_method:str
|
||||
lsi_dimensions:int
|
||||
n_isolates:int
|
||||
silhouette_samples:str
|
||||
|
||||
class hdbscan_job(clustering_job):
|
||||
def __init__(self, infile, outpath, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
|
||||
super().__init__(infile,
|
||||
def select_hdbscan_clustering(inpath,
|
||||
outpath,
|
||||
name,
|
||||
call=hdbscan_job._hdbscan_clustering,
|
||||
outfile=None,
|
||||
min_cluster_sizes=[2],
|
||||
min_samples=[1],
|
||||
cluster_selection_epsilons=[0],
|
||||
cluster_selection_methods=['eom'],
|
||||
lsi_dimensions='all'
|
||||
):
|
||||
|
||||
inpath = Path(inpath)
|
||||
outpath = Path(outpath)
|
||||
outpath.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
if lsi_dimensions == 'all':
|
||||
lsi_paths = list(inpath.glob("*"))
|
||||
|
||||
else:
|
||||
lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
|
||||
|
||||
lsi_nums = [p.stem for p in lsi_paths]
|
||||
grid = list(product(lsi_nums,
|
||||
min_cluster_sizes,
|
||||
min_samples,
|
||||
cluster_selection_epsilons,
|
||||
cluster_selection_methods))
|
||||
|
||||
# fix the output file names
|
||||
names = list(map(lambda t:'_'.join(map(str,t)),grid))
|
||||
|
||||
grid = [(inpath/(str(t[0])+'.feather'),outpath/(name + '.feather'), t[0], name) + t[1:] for t, name in zip(grid, names)]
|
||||
|
||||
with Pool(int(cpu_count()/4)) as pool:
|
||||
mods = starmap(hdbscan_clustering, grid)
|
||||
|
||||
res = pd.DataFrame(mods)
|
||||
if outfile is None:
|
||||
outfile = outpath / "selection_data.csv"
|
||||
|
||||
res.to_csv(outfile)
|
||||
|
||||
def hdbscan_clustering(similarities, output, lsi_dim, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
|
||||
subreddits, mat = read_similarity_mat(similarities)
|
||||
mat = sim_to_dist(mat)
|
||||
clustering = _hdbscan_clustering(mat,
|
||||
min_cluster_size=min_cluster_size,
|
||||
min_samples=min_samples,
|
||||
cluster_selection_epsilon=cluster_selection_epsilon,
|
||||
cluster_selection_method=cluster_selection_method
|
||||
cluster_selection_method=cluster_selection_method,
|
||||
metric='precomputed',
|
||||
core_dist_n_jobs=cpu_count()
|
||||
)
|
||||
|
||||
self.min_cluster_size = min_cluster_size
|
||||
self.min_samples = min_samples
|
||||
self.cluster_selection_epsilon = cluster_selection_epsilon
|
||||
self.cluster_selection_method = cluster_selection_method
|
||||
# self.mat = 1 - self.mat
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
isolates = clustering.labels_ == -1
|
||||
scoremat = mat[~isolates][:,~isolates]
|
||||
score = silhouette_score(scoremat, clustering.labels_[~isolates], metric='precomputed')
|
||||
cluster_data.to_feather(output)
|
||||
|
||||
def _hdbscan_clustering(mat, *args, **kwargs):
|
||||
silhouette_samp = silhouette_samples(mat, clustering.labels_, metric='precomputed')
|
||||
silhouette_samp = pd.DataFrame({'subreddit':subreddits,'score':silhouette_samp})
|
||||
silsampout = output.parent / ("silhouette_samples" + output.name)
|
||||
silhouette_samp.to_feather(silsampout)
|
||||
|
||||
result = hdbscan_clustering_result(outpath=output,
|
||||
max_iter=None,
|
||||
silhouette_samples=silsampout,
|
||||
silhouette_score=score,
|
||||
alt_silhouette_score=score,
|
||||
name=name,
|
||||
min_cluster_size=min_cluster_size,
|
||||
min_samples=min_samples,
|
||||
cluster_selection_epsilon=cluster_selection_epsilon,
|
||||
cluster_selection_method=cluster_selection_method,
|
||||
lsi_dimensions=lsi_dim,
|
||||
n_isolates=isolates.sum(),
|
||||
n_clusters=len(set(clustering.labels_))
|
||||
)
|
||||
|
||||
|
||||
|
||||
return(result)
|
||||
|
||||
# for all runs we should try cluster_selection_epsilon = None
|
||||
# for terms we should try cluster_selection_epsilon around 0.56-0.66
|
||||
# for authors we should try cluster_selection_epsilon around 0.98-0.99
|
||||
def _hdbscan_clustering(mat, *args, **kwargs):
|
||||
print(f"running hdbscan clustering. args:{args}. kwargs:{kwargs}")
|
||||
|
||||
print(mat)
|
||||
clusterer = hdbscan.HDBSCAN(metric='precomputed',
|
||||
core_dist_n_jobs=cpu_count(),
|
||||
*args,
|
||||
clusterer = hdbscan.HDBSCAN(*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -91,39 +136,6 @@ class hdbscan_job(clustering_job):
|
||||
|
||||
return(clustering)
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = hdbscan_clustering_result(**result.__dict__,
|
||||
min_cluster_size=self.min_cluster_size,
|
||||
min_samples=self.min_samples,
|
||||
cluster_selection_epsilon=self.cluster_selection_epsilon,
|
||||
cluster_selection_method=self.cluster_selection_method)
|
||||
return self.result
|
||||
|
||||
def run_hdbscan_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom']):
|
||||
"""Run hdbscan clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
hdbscan_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=<csv "eom"|"leaf">
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to feather data containing a labeled matrix of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
min_cluster_sizes: one or more integers indicating the minumum cluster size
|
||||
min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
|
||||
cluster_selection_epsilon: one or more similarity thresholds for transition from dbscan to hdbscan
|
||||
cluster_selection_method: "eom" or "leaf" eom gives larger clusters.
|
||||
"""
|
||||
obj = hdbscan_grid_sweep(inpath,
|
||||
outpath,
|
||||
map(int,min_cluster_sizes),
|
||||
map(int,min_samples),
|
||||
map(float,cluster_selection_epsilons),
|
||||
cluster_selection_methods)
|
||||
obj.run()
|
||||
obj.save(savefile)
|
||||
|
||||
def KNN_distances_plot(mat,outname,k=2):
|
||||
nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
|
||||
distances, indices = nbrs.kneighbors(mat)
|
||||
@@ -153,7 +165,8 @@ def make_KNN_plots():
|
||||
KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_hdbscan_grid_sweep)
|
||||
|
||||
# test_select_hdbscan_clustering()
|
||||
#fire.Fire(select_hdbscan_clustering)
|
||||
df = pd.read_csv("test_hdbscan/selection_data.csv")
|
||||
test_select_hdbscan_clustering()
|
||||
check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
|
||||
silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
|
||||
c = check_clusters.merge(silscores,on='subreddit')# fire.Fire(select_hdbscan_clustering)
|
||||
|
||||
@@ -1,101 +0,0 @@
|
||||
from hdbscan_clustering import hdbscan_job, hdbscan_grid_sweep, hdbscan_clustering_result
|
||||
from lsi_base import lsi_grid_sweep, lsi_mixin, lsi_result_mixin
|
||||
from grid_sweep import grid_sweep
|
||||
import fire
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
class hdbscan_lsi_job(hdbscan_job, lsi_mixin):
|
||||
def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
|
||||
super().__init__(
|
||||
infile,
|
||||
outpath,
|
||||
name,
|
||||
*args,
|
||||
**kwargs)
|
||||
super().set_lsi_dims(lsi_dims)
|
||||
|
||||
def get_info(self):
|
||||
partial_result = super().get_info()
|
||||
self.result = hdbscan_clustering_result_lsi(**partial_result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
class hdbscan_lsi_grid_sweep(lsi_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
min_cluster_sizes,
|
||||
min_samples,
|
||||
cluster_selection_epsilons,
|
||||
cluster_selection_methods
|
||||
):
|
||||
|
||||
super().__init__(hdbscan_lsi_job,
|
||||
_hdbscan_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
min_cluster_sizes,
|
||||
min_samples,
|
||||
cluster_selection_epsilons,
|
||||
cluster_selection_methods)
|
||||
|
||||
|
||||
|
||||
class _hdbscan_lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
*args,
|
||||
**kwargs):
|
||||
print(args)
|
||||
print(kwargs)
|
||||
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = hdbscan_lsi_job
|
||||
super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
|
||||
|
||||
|
||||
def namer(self, *args, **kwargs):
|
||||
s = hdbscan_grid_sweep.namer(self, *args[1:], **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'):
|
||||
"""Run hdbscan clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
hdbscan_clustering_lsi --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=[eom]> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
|
||||
outpath: path to output fit clusterings.
|
||||
min_cluster_sizes: one or more integers indicating the minumum cluster size
|
||||
min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
|
||||
cluster_selection_epsilons: one or more similarity thresholds for transition from dbscan to hdbscan
|
||||
cluster_selection_methods: one or more of "eom" or "leaf" eom gives larger clusters.
|
||||
lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
"""
|
||||
|
||||
obj = hdbscan_lsi_grid_sweep(inpath,
|
||||
lsi_dimensions,
|
||||
outpath,
|
||||
list(map(int,min_cluster_sizes)),
|
||||
list(map(int,min_samples)),
|
||||
list(map(float,cluster_selection_epsilons)),
|
||||
cluster_selection_methods)
|
||||
|
||||
|
||||
obj.run(10)
|
||||
obj.save(savefile)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_hdbscan_lsi_grid_sweep)
|
||||
@@ -1,105 +0,0 @@
|
||||
from sklearn.cluster import KMeans
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from dataclasses import dataclass
|
||||
from clustering_base import clustering_result, clustering_job
|
||||
from grid_sweep import grid_sweep
|
||||
|
||||
@dataclass
|
||||
class kmeans_clustering_result(clustering_result):
|
||||
n_clusters:int
|
||||
n_init:int
|
||||
max_iter:int
|
||||
|
||||
class kmeans_job(clustering_job):
|
||||
def __init__(self, infile, outpath, name, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
call=kmeans_job._kmeans_clustering,
|
||||
n_clusters=n_clusters,
|
||||
n_init=n_init,
|
||||
max_iter=max_iter,
|
||||
random_state=random_state,
|
||||
verbose=verbose)
|
||||
|
||||
self.n_clusters=n_clusters
|
||||
self.n_init=n_init
|
||||
self.max_iter=max_iter
|
||||
|
||||
def _kmeans_clustering(mat, *args, **kwargs):
|
||||
|
||||
clustering = KMeans(*args,
|
||||
**kwargs,
|
||||
).fit(mat)
|
||||
|
||||
return clustering
|
||||
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = kmeans_clustering_result(**result.__dict__,
|
||||
n_init=self.n_init,
|
||||
max_iter=self.max_iter)
|
||||
return self.result
|
||||
|
||||
|
||||
class kmeans_grid_sweep(grid_sweep):
|
||||
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
*args,
|
||||
**kwargs):
|
||||
super().__init__(kmeans_job, inpath, outpath, self.namer, *args, **kwargs)
|
||||
|
||||
def namer(self,
|
||||
n_clusters,
|
||||
n_init,
|
||||
max_iter):
|
||||
return f"nclusters-{n_clusters}_nit-{n_init}_maxit-{max_iter}"
|
||||
|
||||
def test_select_kmeans_clustering():
|
||||
inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
|
||||
outpath = "test_kmeans";
|
||||
n_clusters=[200,300,400];
|
||||
n_init=[1,2,3];
|
||||
max_iter=[100000]
|
||||
|
||||
gs = kmeans_lsi_grid_sweep(inpath, 'all', outpath, n_clusters, n_init, max_iter)
|
||||
gs.run(1)
|
||||
|
||||
cluster_selection_epsilons=[0,0.1,0.3,0.5];
|
||||
cluster_selection_methods=['eom'];
|
||||
lsi_dimensions='all'
|
||||
gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
|
||||
gs.run(20)
|
||||
gs.save("test_hdbscan/lsi_sweep.csv")
|
||||
|
||||
def run_kmeans_grid_sweep(savefile, inpath, outpath, n_clusters=[500], n_inits=[1], max_iters=[3000]):
|
||||
"""Run kmeans clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
kmeans_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to feather data containing a labeled matrix of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
n_clusters: one or more numbers of kmeans clusters to select.
|
||||
n_inits: one or more numbers of different initializations to use for each clustering.
|
||||
max_iters: one or more numbers of different maximum interations.
|
||||
"""
|
||||
|
||||
obj = kmeans_grid_sweep(inpath,
|
||||
outpath,
|
||||
map(int,n_clusters),
|
||||
map(int,n_inits),
|
||||
map(int,max_iters))
|
||||
|
||||
|
||||
obj.run(1)
|
||||
obj.save(savefile)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_kmeans_grid_sweep)
|
||||
@@ -1,93 +0,0 @@
|
||||
import fire
|
||||
from dataclasses import dataclass
|
||||
from kmeans_clustering import kmeans_job, kmeans_clustering_result, kmeans_grid_sweep
|
||||
from lsi_base import lsi_mixin, lsi_result_mixin, lsi_grid_sweep
|
||||
from grid_sweep import grid_sweep
|
||||
|
||||
@dataclass
|
||||
class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
class kmeans_lsi_job(kmeans_job, lsi_mixin):
|
||||
def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
*args,
|
||||
**kwargs)
|
||||
super().set_lsi_dims(lsi_dims)
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = kmeans_clustering_result_lsi(**result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
class _kmeans_lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
*args,
|
||||
**kwargs):
|
||||
print(args)
|
||||
print(kwargs)
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = kmeans_lsi_job
|
||||
super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
|
||||
|
||||
def namer(self, *args, **kwargs):
|
||||
s = kmeans_grid_sweep.namer(self, *args[1:], **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
class kmeans_lsi_grid_sweep(lsi_grid_sweep):
|
||||
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
n_clusters,
|
||||
n_inits,
|
||||
max_iters
|
||||
):
|
||||
|
||||
super().__init__(kmeans_lsi_job,
|
||||
_kmeans_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
n_clusters,
|
||||
n_inits,
|
||||
max_iters)
|
||||
|
||||
def run_kmeans_lsi_grid_sweep(savefile, inpath, outpath, n_clusters=[500], n_inits=[1], max_iters=[3000], lsi_dimensions="all"):
|
||||
"""Run kmeans clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
kmeans_clustering_lsi.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH d--lsi_dimensions=<"all"|csv number of LSI dimensions to use> --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
n_clusters: one or more numbers of kmeans clusters to select.
|
||||
n_inits: one or more numbers of different initializations to use for each clustering.
|
||||
max_iters: one or more numbers of different maximum interations.
|
||||
"""
|
||||
|
||||
obj = kmeans_lsi_grid_sweep(inpath,
|
||||
lsi_dimensions,
|
||||
outpath,
|
||||
list(map(int,n_clusters)),
|
||||
list(map(int,n_inits)),
|
||||
list(map(int,max_iters))
|
||||
)
|
||||
|
||||
obj.run(1)
|
||||
obj.save(savefile)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_kmeans_lsi_grid_sweep)
|
||||
@@ -1,29 +0,0 @@
|
||||
from clustering_base import clustering_job, clustering_result
|
||||
from grid_sweep import grid_sweep
|
||||
from dataclasses import dataclass
|
||||
from itertools import chain
|
||||
from pathlib import Path
|
||||
|
||||
class lsi_mixin():
|
||||
def set_lsi_dims(self, lsi_dims):
|
||||
self.lsi_dims = lsi_dims
|
||||
|
||||
@dataclass
|
||||
class lsi_result_mixin:
|
||||
lsi_dimensions:int
|
||||
|
||||
class lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self, jobtype, subsweep, inpath, lsi_dimensions, outpath, *args, **kwargs):
|
||||
self.jobtype = jobtype
|
||||
self.subsweep = subsweep
|
||||
inpath = Path(inpath)
|
||||
if lsi_dimensions == 'all':
|
||||
lsi_paths = list(inpath.glob("*.feather"))
|
||||
else:
|
||||
lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
|
||||
|
||||
print(lsi_paths)
|
||||
lsi_nums = [int(p.stem) for p in lsi_paths]
|
||||
self.hasrun = False
|
||||
self.subgrids = [self.subsweep(lsi_path, outpath, lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
|
||||
self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))
|
||||
@@ -1,33 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
import fire
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv"
|
||||
|
||||
outpath = 'test_best.feather'
|
||||
min_clusters=50; max_isolates=7500; min_cluster_size=2
|
||||
|
||||
# pick the best clustering according to silhouette score subject to contraints
|
||||
def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size):
|
||||
df = pd.read_csv(selection_data,index_col=0)
|
||||
df = df.sort_values("silhouette_score",ascending=False)
|
||||
|
||||
# not sure I fixed the bug underlying this fully or not.
|
||||
df['n_isolates_str'] = df.n_isolates.str.strip("[]")
|
||||
df['n_isolates_0'] = df['n_isolates_str'].apply(lambda l: len(l) == 0)
|
||||
df.loc[df.n_isolates_0,'n_isolates'] = 0
|
||||
df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
|
||||
|
||||
best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)]
|
||||
|
||||
best_cluster = best_cluster.iloc[0]
|
||||
|
||||
best_lsi_dimensions = best_cluster.lsi_dimensions
|
||||
print(best_cluster.to_dict())
|
||||
best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
|
||||
shutil.copy(best_path,output)
|
||||
print(f"lsi dimensions:{best_lsi_dimensions}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(pick_best_clustering)
|
||||
132
clustering/select_affinity.py
Normal file
132
clustering/select_affinity.py
Normal file
@@ -0,0 +1,132 @@
|
||||
from sklearn.metrics import silhouette_score
|
||||
from sklearn.cluster import AffinityPropagation
|
||||
from functools import partial
|
||||
from dataclasses import dataclass
|
||||
from clustering import _affinity_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
|
||||
from multiprocessing import Pool, cpu_count, Array, Process
|
||||
from pathlib import Path
|
||||
from itertools import product, starmap
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import fire
|
||||
import sys
|
||||
|
||||
# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying.
|
||||
@dataclass
|
||||
class affinity_clustering_result(clustering_result):
|
||||
damping:float
|
||||
convergence_iter:int
|
||||
preference_quantile:float
|
||||
|
||||
def do_affinity_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
|
||||
if name is None:
|
||||
name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
|
||||
print(name)
|
||||
sys.stdout.flush()
|
||||
outpath = outdir / (str(name) + ".feather")
|
||||
outpath.parent.mkdir(parents=True,exist_ok=True)
|
||||
print(outpath)
|
||||
clustering = _affinity_clustering(mat, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
mat = sim_to_dist(clustering.affinity_matrix_)
|
||||
|
||||
try:
|
||||
score = silhouette_score(mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
score = None
|
||||
|
||||
if alt_mat is not None:
|
||||
alt_distances = sim_to_dist(alt_mat)
|
||||
try:
|
||||
alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
alt_score = None
|
||||
|
||||
res = affinity_clustering_result(outpath=outpath,
|
||||
damping=damping,
|
||||
max_iter=max_iter,
|
||||
convergence_iter=convergence_iter,
|
||||
preference_quantile=preference_quantile,
|
||||
silhouette_score=score,
|
||||
alt_silhouette_score=score,
|
||||
name=str(name))
|
||||
|
||||
return res
|
||||
|
||||
def do_affinity_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
|
||||
if name is None:
|
||||
name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
|
||||
print(name)
|
||||
sys.stdout.flush()
|
||||
outpath = outdir / (str(name) + ".feather")
|
||||
outpath.parent.mkdir(parents=True,exist_ok=True)
|
||||
print(outpath)
|
||||
clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
|
||||
mat = sim_to_dist(clustering.affinity_matrix_)
|
||||
|
||||
try:
|
||||
score = silhouette_score(mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
score = None
|
||||
|
||||
if alt_mat is not None:
|
||||
alt_distances = sim_to_dist(alt_mat)
|
||||
try:
|
||||
alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
alt_score = None
|
||||
|
||||
res = clustering_result(outpath=outpath,
|
||||
damping=damping,
|
||||
max_iter=max_iter,
|
||||
convergence_iter=convergence_iter,
|
||||
preference_quantile=preference_quantile,
|
||||
silhouette_score=score,
|
||||
alt_silhouette_score=score,
|
||||
name=str(name))
|
||||
|
||||
return res
|
||||
|
||||
|
||||
# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
|
||||
|
||||
def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
|
||||
|
||||
damping = list(map(float,damping))
|
||||
convergence_iter = convergence_iter = list(map(int,convergence_iter))
|
||||
preference_quantile = list(map(float,preference_quantile))
|
||||
|
||||
if type(outdir) is str:
|
||||
outdir = Path(outdir)
|
||||
|
||||
outdir.mkdir(parents=True,exist_ok=True)
|
||||
|
||||
subreddits, mat = read_similarity_mat(similarities,use_threads=True)
|
||||
|
||||
if alt_similarities is not None:
|
||||
alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
|
||||
else:
|
||||
alt_mat = None
|
||||
|
||||
if J is None:
|
||||
J = cpu_count()
|
||||
pool = Pool(J)
|
||||
|
||||
# get list of tuples: the combinations of hyperparameters
|
||||
hyper_grid = product(damping, convergence_iter, preference_quantile)
|
||||
hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
|
||||
|
||||
_do_clustering = partial(do_affinity_clustering, mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
|
||||
|
||||
# similarities = Array('d', mat)
|
||||
# call pool.starmap
|
||||
print("running clustering selection")
|
||||
clustering_data = pool.starmap(_do_clustering, hyper_grid)
|
||||
clustering_data = pd.DataFrame(list(clustering_data))
|
||||
clustering_data.to_csv(outinfo)
|
||||
|
||||
|
||||
return clustering_data
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = fire.Fire(select_affinity_clustering)
|
||||
92
clustering/select_kmeans.py
Normal file
92
clustering/select_kmeans.py
Normal file
@@ -0,0 +1,92 @@
|
||||
from sklearn.metrics import silhouette_score
|
||||
from sklearn.cluster import AffinityPropagation
|
||||
from functools import partial
|
||||
from clustering import _kmeans_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
|
||||
from dataclasses import dataclass
|
||||
from multiprocessing import Pool, cpu_count, Array, Process
|
||||
from pathlib import Path
|
||||
from itertools import product, starmap
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import fire
|
||||
import sys
|
||||
|
||||
@dataclass
|
||||
class kmeans_clustering_result(clustering_result):
|
||||
n_clusters:int
|
||||
n_init:int
|
||||
|
||||
|
||||
# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying.
|
||||
|
||||
def do_clustering(n_clusters, n_init, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
|
||||
if name is None:
|
||||
name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
|
||||
print(name)
|
||||
sys.stdout.flush()
|
||||
outpath = outdir / (str(name) + ".feather")
|
||||
print(outpath)
|
||||
mat = sim_to_dist(mat)
|
||||
clustering = _kmeans_clustering(mat, outpath, n_clusters, n_init, max_iter, random_state, verbose)
|
||||
|
||||
outpath.parent.mkdir(parents=True,exist_ok=True)
|
||||
cluster_data.to_feather(outpath)
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
|
||||
try:
|
||||
score = silhouette_score(mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
score = None
|
||||
|
||||
if alt_mat is not None:
|
||||
alt_distances = sim_to_dist(alt_mat)
|
||||
try:
|
||||
alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
alt_score = None
|
||||
|
||||
res = kmeans_clustering_result(outpath=outpath,
|
||||
max_iter=max_iter,
|
||||
n_clusters=n_clusters,
|
||||
n_init = n_init,
|
||||
silhouette_score=score,
|
||||
alt_silhouette_score=score,
|
||||
name=str(name))
|
||||
|
||||
return res
|
||||
|
||||
|
||||
# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
|
||||
def select_kmeans_clustering(similarities, outdir, outinfo, n_clusters=[1000], max_iter=100000, n_init=10, random_state=1968, verbose=True, alt_similarities=None):
|
||||
|
||||
n_clusters = list(map(int,n_clusters))
|
||||
n_init = list(map(int,n_init))
|
||||
|
||||
if type(outdir) is str:
|
||||
outdir = Path(outdir)
|
||||
|
||||
outdir.mkdir(parents=True,exist_ok=True)
|
||||
|
||||
subreddits, mat = read_similarity_mat(similarities,use_threads=True)
|
||||
|
||||
if alt_similarities is not None:
|
||||
alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
|
||||
else:
|
||||
alt_mat = None
|
||||
|
||||
# get list of tuples: the combinations of hyperparameters
|
||||
hyper_grid = product(n_clusters, n_init)
|
||||
hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
|
||||
|
||||
_do_clustering = partial(do_clustering, mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
|
||||
|
||||
# call starmap
|
||||
print("running clustering selection")
|
||||
clustering_data = starmap(_do_clustering, hyper_grid)
|
||||
clustering_data = pd.DataFrame(list(clustering_data))
|
||||
clustering_data.to_csv(outinfo)
|
||||
|
||||
return clustering_data
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = fire.Fire(select_kmeans_clustering)
|
||||
@@ -1,38 +1,7 @@
|
||||
import pandas as pd
|
||||
import plotnine as pn
|
||||
from pathlib import Path
|
||||
from clustering.fit_tsne import fit_tsne
|
||||
from visualization.tsne_vis import build_visualization
|
||||
|
||||
df = pd.read_csv("/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv",index_col=0)
|
||||
|
||||
# plot silhouette_score as a function of isolates
|
||||
df = df.sort_values("silhouette_score")
|
||||
|
||||
df['n_isolates'] = df.n_isolates.str.split("\n0").apply(lambda rg: int(rg[1]))
|
||||
p = pn.ggplot(df,pn.aes(x='n_isolates',y='silhouette_score')) + pn.geom_point()
|
||||
p.save("isolates_x_score.png")
|
||||
|
||||
p = pn.ggplot(df,pn.aes(y='n_clusters',x='n_isolates',color='silhouette_score')) + pn.geom_point()
|
||||
p.save("clusters_x_isolates.png")
|
||||
|
||||
# the best result for hdbscan seems like this one: it has a decent number of
|
||||
# i think I prefer the 'eom' clustering style because larger clusters are less likely to suffer from ommitted variables
|
||||
best_eom = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='eom')&(df.min_cluster_size==2)].iloc[df.shape[1]]
|
||||
|
||||
best_lsi = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='leaf')&(df.min_cluster_size==2)].iloc[df.shape[1]]
|
||||
|
||||
tsne_data = Path("./clustering/authors-tf_lsi850_tsne.feather")
|
||||
|
||||
if not tnse_data.exists():
|
||||
fit_tsne("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather",
|
||||
tnse_data)
|
||||
|
||||
build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
|
||||
Path(best_eom.outpath)/(best_eom['name']+'.feather'),
|
||||
"./authors-tf_lsi850_best_eom.html")
|
||||
|
||||
build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
|
||||
Path(best_leaf.outpath)/(best_leaf['name']+'.feather'),
|
||||
"./authors-tf_lsi850_best_leaf.html")
|
||||
import fire
|
||||
from select_affinity import select_affinity_clustering
|
||||
from select_kmeans import select_kmeans_clustering
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({"kmeans":select_kmeans_clustering,
|
||||
"affinity":select_affinity_clustering})
|
||||
|
||||
@@ -8,9 +8,3 @@ all: /gscratch/comdata/output/reddit_density/comment_terms_10000.feather /gscrat
|
||||
|
||||
/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
|
||||
start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather" --agg=pd.DataFrame.sum
|
||||
|
||||
/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather
|
||||
start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather" --agg=pd.DataFrame.sum
|
||||
|
||||
/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather
|
||||
start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather" --agg=pd.DataFrame.sum
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
#!/usr/bin/bash
|
||||
start_spark_cluster.sh
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 overlap_density.py authors --inpath=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather --outpath=/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather --agg=pd.DataFrame.sum
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
|
||||
spark-submit --master spark://$(hostname):18899 overlap_density.py authors --inpath=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --outpath=/gscratch/comdata/output/reddit_density/comment_authors_10000.feather --agg=pd.DataFrame.sum
|
||||
stop-all.sh
|
||||
|
||||
@@ -1,12 +1,11 @@
|
||||
import pandas as pd
|
||||
from pandas.core.groupby import DataFrameGroupBy as GroupBy
|
||||
from pathlib import Path
|
||||
import fire
|
||||
import numpy as np
|
||||
import sys
|
||||
sys.path.append("..")
|
||||
sys.path.append("../similarities")
|
||||
from similarities.similarities_helper import reindex_tfidf
|
||||
from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_interval
|
||||
|
||||
# this is the mean of the ratio of the overlap to the focal size.
|
||||
# mean shared membership per focal community member
|
||||
@@ -14,12 +13,10 @@ from similarities.similarities_helper import reindex_tfidf
|
||||
|
||||
def overlap_density(inpath, outpath, agg = pd.DataFrame.sum):
|
||||
df = pd.read_feather(inpath)
|
||||
df = df.drop('_subreddit',1)
|
||||
df = df.drop('subreddit',1)
|
||||
np.fill_diagonal(df.values,0)
|
||||
df = agg(df, 0).reset_index()
|
||||
df = df.rename({0:'overlap_density'},axis='columns')
|
||||
outpath = Path(outpath)
|
||||
outpath.parent.mkdir(parents=True, exist_ok = True)
|
||||
df.to_feather(outpath)
|
||||
return df
|
||||
|
||||
@@ -28,8 +25,6 @@ def overlap_density_weekly(inpath, outpath, agg = GroupBy.sum):
|
||||
# exclude the diagonal
|
||||
df = df.loc[df.subreddit != df.variable]
|
||||
res = agg(df.groupby(['subreddit','week'])).reset_index()
|
||||
outpath = Path(outpath)
|
||||
outpath.parent.mkdir(parents=True, exist_ok = True)
|
||||
res.to_feather(outpath)
|
||||
return res
|
||||
|
||||
|
||||
@@ -6,7 +6,7 @@ from os import path
|
||||
import hashlib
|
||||
|
||||
shasums1 = requests.get("https://files.pushshift.io/reddit/comments/sha256sum.txt").text
|
||||
#shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
|
||||
shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
|
||||
|
||||
shasums = shasums1 + shasums2
|
||||
dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
|
||||
|
||||
@@ -1,12 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
user_agent='"nathante teblunthuis <nathante@uw.edu>"'
|
||||
user_agent='nathante teblunthuis <nathante@uw.edu>'
|
||||
output_dir='/gscratch/comdata/raw_data/reddit_dumps/comments'
|
||||
base_url='https://files.pushshift.io/reddit/comments/'
|
||||
|
||||
wget -r --no-parent -A 'RC_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_201*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_201*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
|
||||
# starting in 2020 we use daily dumps not monthly dumps
|
||||
wget -r --no-parent -A 'RC_202*.gz' -U $user_agent -P $output_dir -nd -nc $base_url/daily/
|
||||
|
||||
./check_comments_shas.py
|
||||
|
||||
@@ -1,14 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
user_agent='"nathante teblunthuis <nathante@uw.edu>"'
|
||||
user_agent='nathante teblunthuis <nathante@uw.edu>'
|
||||
output_dir='/gscratch/comdata/raw_data/reddit_dumps/submissions'
|
||||
base_url='https://files.pushshift.io/reddit/submissions/'
|
||||
|
||||
wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
|
||||
./check_submission_shas.py
|
||||
|
||||
@@ -13,7 +13,10 @@ from nltk.corpus import stopwords
|
||||
from nltk.util import ngrams
|
||||
import string
|
||||
from random import random
|
||||
from redditcleaner import clean
|
||||
|
||||
# remove urls
|
||||
# taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
|
||||
urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)")
|
||||
|
||||
# compute term frequencies for comments in each subreddit by week
|
||||
def weekly_tf(partition, mwe_pass = 'first'):
|
||||
@@ -92,8 +95,8 @@ def weekly_tf(partition, mwe_pass = 'first'):
|
||||
# lowercase
|
||||
text = text.lower()
|
||||
|
||||
# redditcleaner removes reddit markdown(newlines, quotes, bullet points, links, strikethrough, spoiler, code, superscript, table, headings)
|
||||
text = clean(text)
|
||||
# remove urls
|
||||
text = urlregex.sub("", text)
|
||||
|
||||
# sentence tokenize
|
||||
sentences = sent_tokenize(text)
|
||||
@@ -104,13 +107,14 @@ def weekly_tf(partition, mwe_pass = 'first'):
|
||||
# remove punctuation
|
||||
|
||||
sentences = map(remove_punct, sentences)
|
||||
|
||||
# remove sentences with less than 2 words
|
||||
sentences = filter(lambda sentence: len(sentence) > 2, sentences)
|
||||
|
||||
# datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase.
|
||||
# they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms
|
||||
# here we take a 10 percent sample of sentences
|
||||
if mwe_pass == 'first':
|
||||
|
||||
# remove sentences with less than 2 words
|
||||
sentences = filter(lambda sentence: len(sentence) > 2, sentences)
|
||||
sentences = list(sentences)
|
||||
for sentence in sentences:
|
||||
if random() <= 0.1:
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
|
||||
srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
|
||||
srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
|
||||
base_data=/gscratch/comdata/output
|
||||
base_data=/gscratch/comdata/output/
|
||||
similarity_data=${base_data}/reddit_similarity
|
||||
tfidf_data=${similarity_data}/tfidf
|
||||
tfidf_weekly_data=${similarity_data}/tfidf_weekly
|
||||
@@ -97,7 +97,7 @@ ${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/outpu
|
||||
start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
|
||||
|
||||
${tfidf_weekly_data}/comment_terms_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
|
||||
start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
|
||||
start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
|
||||
|
||||
${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
|
||||
start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
|
||||
|
||||
@@ -1,16 +1,14 @@
|
||||
import pandas as pd
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from cdsc_ecology_utils.similarity import similarities, column_similarities
|
||||
from similarities_helper import similarities, column_similarities
|
||||
from functools import partial
|
||||
|
||||
def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
|
||||
return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_communities=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
|
||||
return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
|
||||
|
||||
# change so that these take in an input as an optional argument (for speed, but also for idf).
|
||||
def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
|
||||
|
||||
def term_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
|
||||
|
||||
return cosine_similarities(infile,
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
#!/usr/bin/bash
|
||||
start_spark_cluster.sh
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname):7077 top_subreddits_by_comments.py
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 lsi_similarities.py author --outfile=/gscratch/comdata/output//reddit_similarity/subreddit_comment_authors_10k_LSI.feather --topN=10000
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
|
||||
|
||||
@@ -1,41 +1,20 @@
|
||||
import pandas as pd
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from cdsc_ecology_utils.similarity.similarity_functions import lsi_column_similarities, similarities,
|
||||
#from similarities_helper import similarities, lsi_column_similarities
|
||||
from similarities_helper import similarities, lsi_column_similarities
|
||||
from functools import partial
|
||||
|
||||
inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
|
||||
term_colname='term'
|
||||
outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
|
||||
n_components=[10,50,100]
|
||||
included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
|
||||
n_iter=5
|
||||
random_state=1968
|
||||
algorithm='arpack'
|
||||
topN = None
|
||||
from_date=None
|
||||
to_date=None
|
||||
min_df=None
|
||||
max_df=None
|
||||
def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
|
||||
def lsi_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
print(n_components,flush=True)
|
||||
|
||||
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm)
|
||||
|
||||
if lsi_model is None:
|
||||
if type(n_components) == list:
|
||||
lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl'
|
||||
else:
|
||||
lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl'
|
||||
|
||||
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
|
||||
|
||||
return similarities(inpath=inpath, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_communities=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
|
||||
return similarities(infile=infile, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
|
||||
|
||||
# change so that these take in an input as an optional argument (for speed, but also for idf).
|
||||
def term_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',outfile=None, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, algorithm='arpack', n_components=300,n_iter=5,random_state=1968):
|
||||
def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
|
||||
res = lsi_similarities(inpath,
|
||||
return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
|
||||
'term',
|
||||
outfile,
|
||||
min_df,
|
||||
@@ -44,13 +23,11 @@ def term_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfi
|
||||
topN,
|
||||
from_date,
|
||||
to_date,
|
||||
n_components=n_components,
|
||||
algorithm = algorithm
|
||||
n_components=n_components
|
||||
)
|
||||
return res
|
||||
|
||||
def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
|
||||
return lsi_similarities(inpath,
|
||||
def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
|
||||
'author',
|
||||
outfile,
|
||||
min_df,
|
||||
@@ -62,8 +39,8 @@ def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/t
|
||||
n_components=n_components
|
||||
)
|
||||
|
||||
def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968):
|
||||
return lsi_similarities(inpath,
|
||||
def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
|
||||
'author',
|
||||
outfile,
|
||||
min_df,
|
||||
@@ -73,8 +50,7 @@ def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tf
|
||||
from_date=from_date,
|
||||
to_date=to_date,
|
||||
tfidf_colname='relative_tf',
|
||||
n_components=n_components,
|
||||
algorithm=algorithm
|
||||
n_components=n_components
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -15,53 +15,27 @@ import numpy as np
|
||||
import pathlib
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
import pickle
|
||||
|
||||
class tf_weight(Enum):
|
||||
MaxTF = 1
|
||||
Norm05 = 2
|
||||
|
||||
# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
|
||||
# cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
|
||||
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
|
||||
cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
|
||||
|
||||
def termauthor_tfidf(term_tfidf_callable, author_tfidf_callable):
|
||||
|
||||
|
||||
# subreddits missing after this step don't have any terms that have a high enough idf
|
||||
# try rewriting without merges
|
||||
|
||||
# does reindex_tfidf, but without reindexing.
|
||||
def reindex_tfidf(*args, **kwargs):
|
||||
df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
|
||||
|
||||
print("assigning names")
|
||||
subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
|
||||
batches = subreddit_names.to_batches()
|
||||
|
||||
with Pool(cpu_count()) as pool:
|
||||
chunks = pool.imap_unordered(pull_names,batches)
|
||||
subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
|
||||
subreddit_names = subreddit_names.set_index("subreddit_id")
|
||||
|
||||
new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
|
||||
new_ids = new_ids.set_index('subreddit_id')
|
||||
subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
|
||||
subreddit_names = subreddit_names.drop("subreddit_id",1)
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
return(df, subreddit_names)
|
||||
|
||||
def pull_tfidf(*args, **kwargs):
|
||||
df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
|
||||
return df
|
||||
|
||||
def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
|
||||
print(f"loading tfidf {infile}", flush=True)
|
||||
if week is not None:
|
||||
tfidf_ds = ds.dataset(infile, partitioning='hive')
|
||||
else:
|
||||
def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF):
|
||||
print("loading tfidf", flush=True)
|
||||
tfidf_ds = ds.dataset(infile)
|
||||
|
||||
if included_subreddits is None:
|
||||
included_subreddits = select_topN_subreddits(topN)
|
||||
else:
|
||||
included_subreddits = set(map(str.strip,open(included_subreddits)))
|
||||
included_subreddits = set(map(str.strip,map(str.lower,open(included_subreddits))))
|
||||
|
||||
ds_filter = ds.field("subreddit").isin(included_subreddits)
|
||||
|
||||
@@ -97,22 +71,15 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
|
||||
'relative_tf':ds.field('relative_tf').cast('float32'),
|
||||
'tf_idf':ds.field('tf_idf').cast('float32')}
|
||||
|
||||
print(projection)
|
||||
tfidf_ds = ds.dataset(infile)
|
||||
|
||||
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
|
||||
|
||||
df = df.to_pandas(split_blocks=True,self_destruct=True)
|
||||
print("assigning indexes",flush=True)
|
||||
if reindex:
|
||||
df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
|
||||
else:
|
||||
df['subreddit_id_new'] = df['subreddit_id']
|
||||
|
||||
if reindex:
|
||||
grouped = df.groupby(term_id)
|
||||
df[term_id_new] = grouped.ngroup()
|
||||
else:
|
||||
df[term_id_new] = df[term_id]
|
||||
|
||||
if rescale_idf:
|
||||
print("computing idf", flush=True)
|
||||
@@ -124,7 +91,9 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
|
||||
else: # tf_fam = tf_weight.Norm05
|
||||
df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
|
||||
|
||||
return (df, tfidf_ds, ds_filter)
|
||||
print("assigning names")
|
||||
subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
|
||||
batches = subreddit_names.to_batches()
|
||||
|
||||
with Pool(cpu_count()) as pool:
|
||||
chunks = pool.imap_unordered(pull_names,batches)
|
||||
@@ -141,7 +110,7 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
|
||||
def pull_names(batch):
|
||||
return(batch.to_pandas().drop_duplicates())
|
||||
|
||||
def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
'''
|
||||
tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
|
||||
'''
|
||||
@@ -161,7 +130,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
|
||||
output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
|
||||
output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
|
||||
output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
|
||||
p.parent.mkdir(exist_ok=True, parents=True)
|
||||
outfile.parent.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
sims.to_feather(outfile)
|
||||
|
||||
@@ -169,7 +138,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
entries, subreddit_names = reindex_tfidf(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
|
||||
entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
|
||||
|
||||
print("loading matrix")
|
||||
@@ -185,7 +154,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
|
||||
for simmat, name in sims:
|
||||
proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
|
||||
else:
|
||||
proc_sims(sims, outfile)
|
||||
proc_sims(simmat, outfile)
|
||||
|
||||
def write_weekly_similarities(path, sims, week, names):
|
||||
sims['week'] = week
|
||||
@@ -238,9 +207,10 @@ def test_lsi_sims():
|
||||
# if n_components is a list we'll return a list of similarities with different latent dimensionalities
|
||||
# if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
|
||||
# this function takes the svd and then the column similarities of it
|
||||
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
|
||||
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
|
||||
# first compute the lsi of the matrix
|
||||
# then take the column similarities
|
||||
print("running LSI",flush=True)
|
||||
|
||||
if type(n_components) is int:
|
||||
n_components = [n_components]
|
||||
@@ -248,23 +218,9 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
|
||||
n_components = sorted(n_components,reverse=True)
|
||||
|
||||
svd_components = n_components[0]
|
||||
|
||||
if lsi_model_load is not None and Path(lsi_model_load).exists():
|
||||
print("loading LSI")
|
||||
mod = pickle.load(open(lsi_model_load ,'rb'))
|
||||
lsi_model_save = lsi_model_load
|
||||
|
||||
else:
|
||||
print("running LSI",flush=True)
|
||||
|
||||
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
|
||||
mod = svd.fit(tfidfmat.T)
|
||||
|
||||
lsimat = mod.transform(tfidfmat.T)
|
||||
if lsi_model_save is not None:
|
||||
pickle.dump(mod, open(lsi_model_save,'wb'))
|
||||
|
||||
sims_list = []
|
||||
for n_dims in n_components:
|
||||
sims = column_similarities(lsimat[:,np.arange(n_dims)])
|
||||
if len(n_components) > 1:
|
||||
@@ -301,20 +257,20 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
|
||||
idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
|
||||
|
||||
# collect the dictionary to make a pydict of terms to indexes
|
||||
terms = idf.select([term]).distinct() # terms are distinct
|
||||
terms = idf.select([term,'week']).distinct() # terms are distinct
|
||||
|
||||
terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
|
||||
terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
|
||||
|
||||
# make subreddit ids
|
||||
subreddits = df.select(['subreddit']).distinct()
|
||||
subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
|
||||
subreddits = df.select(['subreddit','week']).distinct()
|
||||
subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
|
||||
|
||||
df = df.join(subreddits,on=['subreddit'])
|
||||
df = df.join(subreddits,on=['subreddit','week'])
|
||||
|
||||
# map terms to indexes in the tfs and the idfs
|
||||
df = df.join(terms,on=[term]) # subreddit-term-id is unique
|
||||
df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
|
||||
|
||||
idf = idf.join(terms,on=[term])
|
||||
idf = idf.join(terms,on=[term,'week'])
|
||||
|
||||
# join on subreddit/term to create tf/dfs indexed by term
|
||||
df = df.join(idf, on=[term_id, term,'week'])
|
||||
@@ -327,7 +283,7 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
|
||||
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
|
||||
|
||||
df = df.repartition(400,'subreddit','week')
|
||||
dfwriter = df.write.partitionBy("week")
|
||||
dfwriter = df.write.partitionBy("week").sortBy("subreddit")
|
||||
return dfwriter
|
||||
|
||||
def _calc_tfidf(df, term_colname, tf_family):
|
||||
@@ -374,7 +330,7 @@ def _calc_tfidf(df, term_colname, tf_family):
|
||||
return df
|
||||
|
||||
|
||||
def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
|
||||
def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
# aggregate counts by week. now subreddit-term is distinct
|
||||
@@ -383,7 +339,7 @@ def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
|
||||
|
||||
df = _calc_tfidf(df, term_colname, tf_family)
|
||||
df = df.repartition('subreddit')
|
||||
dfwriter = df.write
|
||||
dfwriter = df.write.sortBy("subreddit","tf")
|
||||
return dfwriter
|
||||
|
||||
def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
|
||||
|
||||
@@ -1,8 +1,7 @@
|
||||
import fire
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql import functions as f
|
||||
from cdsc_ecology_utils.similarity.similarity_functions import tfidf_dataset, \
|
||||
build_weekly_tfidf_dataset, select_topN_communities
|
||||
from similarities_helper import build_tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
|
||||
|
||||
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
@@ -12,9 +11,9 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
|
||||
df = df.filter(~ f.col(term_colname).isin(exclude))
|
||||
|
||||
if included_subreddits is not None:
|
||||
include_subs = set(map(str.strip,open(included_subreddits)))
|
||||
include_subs = set(map(str.strip,map(str.lower, open(included_subreddits))))
|
||||
else:
|
||||
include_subs = select_topN_communities(topN)
|
||||
include_subs = select_topN_subreddits(topN)
|
||||
|
||||
dfwriter = func(df, include_subs, term_colname)
|
||||
|
||||
@@ -22,17 +21,16 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
|
||||
spark.stop()
|
||||
|
||||
def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
|
||||
return _tfidf_wrapper(tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
|
||||
return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
|
||||
|
||||
def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
|
||||
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
|
||||
|
||||
def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
|
||||
topN=None,
|
||||
def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
|
||||
topN=25000,
|
||||
included_subreddits=None):
|
||||
|
||||
return tfidf(inpath,
|
||||
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
|
||||
outpath,
|
||||
topN,
|
||||
'author',
|
||||
@@ -40,12 +38,11 @@ def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors
|
||||
included_subreddits=included_subreddits
|
||||
)
|
||||
|
||||
def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
|
||||
topN=None,
|
||||
def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
|
||||
topN=25000,
|
||||
included_subreddits=None):
|
||||
|
||||
return tfidf(inpath,
|
||||
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
outpath,
|
||||
topN,
|
||||
'term',
|
||||
@@ -53,12 +50,11 @@ def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.par
|
||||
included_subreddits=included_subreddits
|
||||
)
|
||||
|
||||
def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
|
||||
topN=None,
|
||||
def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
|
||||
topN=25000,
|
||||
included_subreddits=None):
|
||||
|
||||
return tfidf_weekly(inpath,
|
||||
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
|
||||
outpath,
|
||||
topN,
|
||||
'author',
|
||||
@@ -66,13 +62,12 @@ def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_
|
||||
included_subreddits=included_subreddits
|
||||
)
|
||||
|
||||
def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
topN=None,
|
||||
def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
topN=25000,
|
||||
included_subreddits=None):
|
||||
|
||||
|
||||
return tfidf_weekly(inpath,
|
||||
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
outpath,
|
||||
topN,
|
||||
'term',
|
||||
|
||||
@@ -17,7 +17,7 @@ df = df.filter(~df.subreddit.like("u_%"))
|
||||
df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))
|
||||
|
||||
df = df.join(prop_nsfw,on='subreddit')
|
||||
#df = df.filter(df.prop_nsfw < 0.5)
|
||||
df = df.filter(df.prop_nsfw < 0.5)
|
||||
|
||||
win = Window.orderBy(f.col('n_comments').desc())
|
||||
df = df.withColumn('comments_rank', f.rank().over(win))
|
||||
@@ -26,4 +26,4 @@ df = df.toPandas()
|
||||
|
||||
df = df.sort_values("n_comments")
|
||||
|
||||
df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nsfw.csv', index=False)
|
||||
df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv', index=False)
|
||||
|
||||
114
similarities/weekly_cosine_similarities.py
Executable file → Normal file
114
similarities/weekly_cosine_similarities.py
Executable file → Normal file
@@ -1,4 +1,3 @@
|
||||
#!/usr/bin/env python3
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql import Window
|
||||
@@ -9,92 +8,58 @@ import pandas as pd
|
||||
import fire
|
||||
from itertools import islice, chain
|
||||
from pathlib import Path
|
||||
from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
|
||||
from scipy.sparse import csr_matrix
|
||||
from similarities_helper import *
|
||||
from multiprocessing import Pool, cpu_count
|
||||
from functools import partial
|
||||
|
||||
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
|
||||
tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
|
||||
min_df=None
|
||||
included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
|
||||
max_df = None
|
||||
topN=100
|
||||
term_colname='author'
|
||||
# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
|
||||
# included_subreddits=None
|
||||
|
||||
def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
|
||||
def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
print(f"loading matrix: {week}")
|
||||
|
||||
entries = pull_tfidf(infile = tfidf_path,
|
||||
entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
|
||||
term_colname=term_colname,
|
||||
min_df=min_df,
|
||||
max_df=max_df,
|
||||
included_subreddits=included_subreddits,
|
||||
topN=topN,
|
||||
week=week,
|
||||
rescale_idf=False)
|
||||
|
||||
tfidf_colname='tf_idf'
|
||||
# if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
|
||||
|
||||
week=week)
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
|
||||
print('computing similarities')
|
||||
sims = simfunc(mat)
|
||||
sims = column_similarities(mat)
|
||||
del mat
|
||||
sims = pd.DataFrame(sims)
|
||||
sims = pd.DataFrame(sims.todense())
|
||||
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
|
||||
sims['_subreddit'] = subreddit_names.subreddit.values
|
||||
sims['_subreddit'] = names.subreddit.values
|
||||
outfile = str(Path(outdir) / str(week))
|
||||
write_weekly_similarities(outfile, sims, week, subreddit_names)
|
||||
write_weekly_similarities(outfile, sims, week, names)
|
||||
|
||||
def pull_weeks(batch):
|
||||
return set(batch.to_pandas()['week'])
|
||||
|
||||
# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
|
||||
def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs):
|
||||
term_colname= kwargs.get('term_colname')
|
||||
#lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
|
||||
|
||||
# simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
|
||||
|
||||
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model)
|
||||
|
||||
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
|
||||
|
||||
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
|
||||
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500, simfunc=column_similarities):
|
||||
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
|
||||
print(outfile)
|
||||
tfidf_ds = ds.dataset(tfidf_path)
|
||||
tfidf_ds = tfidf_ds.to_table(columns=["week"])
|
||||
batches = tfidf_ds.to_batches()
|
||||
|
||||
with Pool(cpu_count()) as pool:
|
||||
weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
|
||||
|
||||
weeks = sorted(weeks)
|
||||
# do this step in parallel if we have the memory for it.
|
||||
# should be doable with pool.map
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.parquet(tfidf_path)
|
||||
|
||||
# load subreddits + topN
|
||||
|
||||
subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id")
|
||||
nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
|
||||
weeks = df.select(f.col("week")).distinct().toPandas().week.values
|
||||
spark.stop()
|
||||
|
||||
print(f"computing weekly similarities")
|
||||
week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
|
||||
week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN)
|
||||
|
||||
pool = Pool(cpu_count())
|
||||
with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
|
||||
list(pool.map(week_similarities_helper,weeks))
|
||||
|
||||
list(pool.imap(week_similarities_helper,weeks))
|
||||
pool.close()
|
||||
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
|
||||
|
||||
|
||||
def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly(infile,
|
||||
def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
|
||||
outfile,
|
||||
'author',
|
||||
min_df,
|
||||
@@ -102,8 +67,8 @@ def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/
|
||||
included_subreddits,
|
||||
topN)
|
||||
|
||||
def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
|
||||
return cosine_similarities_weekly(infile,
|
||||
def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
outfile,
|
||||
'term',
|
||||
min_df,
|
||||
@@ -111,33 +76,6 @@ def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/re
|
||||
included_subreddits,
|
||||
topN)
|
||||
|
||||
|
||||
def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None):
|
||||
return cosine_similarities_weekly_lsi(infile,
|
||||
outfile,
|
||||
'author',
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
n_components=n_components,
|
||||
lsi_model=lsi_model)
|
||||
|
||||
|
||||
def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
|
||||
return cosine_similarities_weekly_lsi(infile,
|
||||
outfile,
|
||||
'term',
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
n_components=n_components,
|
||||
lsi_model=lsi_model)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({'authors':author_cosine_similarities_weekly,
|
||||
'terms':term_cosine_similarities_weekly,
|
||||
'authors-lsi':author_cosine_similarities_weekly_lsi,
|
||||
'terms-lsi':term_cosine_similarities_weekly
|
||||
})
|
||||
'terms':term_cosine_similarities_weekly})
|
||||
|
||||
@@ -1,2 +0,0 @@
|
||||
from .choose_clusters import load_clusters, load_densities
|
||||
from .cluster_timeseries import build_cluster_timeseries
|
||||
@@ -2,11 +2,11 @@ import pandas as pd
|
||||
import numpy as np
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import SparkSession
|
||||
from .choose_clusters import load_clusters, load_densities
|
||||
from choose_clusters import load_clusters, load_densities
|
||||
import fire
|
||||
from pathlib import Path
|
||||
|
||||
def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
|
||||
def main(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
|
||||
author_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather",
|
||||
term_densities_path="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
|
||||
author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
|
||||
@@ -34,4 +34,4 @@ def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit
|
||||
ts.write.parquet(output, mode='overwrite')
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(build_cluster_timeseries)
|
||||
fire.Fire(main)
|
||||
|
||||
@@ -22,12 +22,8 @@ def base_plot(plot_data):
|
||||
#
|
||||
# subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
|
||||
|
||||
base_scale = alt.Scale(scheme={"name":'category10',
|
||||
"extent":[0,100],
|
||||
"count":10})
|
||||
|
||||
color = alt.condition(cluster_click_select ,
|
||||
alt.Color(field='color',type='nominal',scale=base_scale),
|
||||
alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')),
|
||||
alt.value("lightgray"))
|
||||
|
||||
|
||||
@@ -88,11 +84,6 @@ def viewport_plot(plot_data):
|
||||
return chart
|
||||
|
||||
def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
|
||||
isolate_color = 101
|
||||
|
||||
cluster_sizes = clusters.groupby('cluster').count()
|
||||
singletons = set(cluster_sizes.loc[cluster_sizes.subreddit == 1].reset_index().cluster)
|
||||
|
||||
tsne_data = tsne_data.merge(clusters,on='subreddit')
|
||||
|
||||
centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
|
||||
@@ -129,9 +120,6 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
|
||||
color_assignments = np.repeat(-1,len(centroids))
|
||||
|
||||
for i in range(len(centroids)):
|
||||
if (centroids.iloc[i].name == -1) or (i in singletons):
|
||||
color_assignments[i] = isolate_color
|
||||
else:
|
||||
knn = indices[i]
|
||||
knn_colors = color_assignments[knn]
|
||||
available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
|
||||
@@ -141,6 +129,7 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
|
||||
else:
|
||||
raise Exception("Can't color this many neighbors with this many colors")
|
||||
|
||||
|
||||
centroids = centroids.reset_index()
|
||||
colors = centroids.loc[:,['cluster']]
|
||||
colors['color'] = color_assignments
|
||||
@@ -154,13 +143,12 @@ def build_visualization(tsne_data, clusters, output):
|
||||
# clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
|
||||
|
||||
tsne_data = pd.read_feather(tsne_data)
|
||||
tsne_data = tsne_data.rename(columns={'_subreddit':'subreddit'})
|
||||
clusters = pd.read_feather(clusters)
|
||||
|
||||
tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
|
||||
|
||||
sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
|
||||
sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
|
||||
# sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
|
||||
# sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
|
||||
|
||||
tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')
|
||||
|
||||
|
||||
Reference in New Issue
Block a user