From: Nathan TeBlunthuis Date: Tue, 3 Aug 2021 22:02:08 +0000 (-0700) Subject: Merge branch 'master' of code:cdsc_reddit into excise_reindex X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/2d21ff1137dfaf83c5a51fdcd8900503c50a06ab?hp=c6122bb429d18d12418f58a8d7c3c820332f26ed Merge branch 'master' of code:cdsc_reddit into excise_reindex --- diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..dbb8061 --- /dev/null +++ b/__init__.py @@ -0,0 +1,2 @@ +from .timeseries import load_clusters, load_densities, build_cluster_timeseries + diff --git a/clustering/Makefile b/clustering/Makefile index adaa8fe..9643f52 100644 --- a/clustering/Makefile +++ b/clustering/Makefile @@ -2,26 +2,173 @@ srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh similarity_data=/gscratch/comdata/output/reddit_similarity clustering_data=/gscratch/comdata/output/reddit_clustering -selection_grid="--max_iter=10000 --convergence_iter=15,30,100 --preference_quantile=0.85 --damping=0.5,0.6,0.7,0.8,0.85,0.9,0.95,0.97,0.99, --preference_quantile=0.1,0.3,0.5,0.7,0.9" -all:$(clustering_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_authors-tf_similarities_30k.feather $(clustering_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_authors-tf_similarities_10k.feather $(clustering_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_10k.feather +kmeans_selection_grid=--max_iters=[3000] --n_inits=[10] --n_clusters=[100,500,1000,1250,1500,1750,2000] +hdbscan_selection_grid=--min_cluster_sizes=[2,3,4,5] --min_samples=[2,3,4,5] --cluster_selection_epsilons=[0,0.01,0.05,0.1,0.15,0.2] --cluster_selection_methods=[eom,leaf] +affinity_selection_grid=--dampings=[0.5,0.6,0.7,0.8,0.95,0.97,0.99] --preference_quantiles=[0.1,0.3,0.5,0.7,0.9] --convergence_iters=[15] -$(clustering_data)/subreddit_comment_authors_10k.feather:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py - $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k $(selection_grid) -J 20 +authors_10k_input=$(similarity_data)/subreddit_comment_authors_10k.feather +authors_10k_input_lsi=$(similarity_data)/subreddit_comment_authors_10k_LSI +authors_10k_output=$(clustering_data)/subreddit_comment_authors_10k +authors_10k_output_lsi=$(clustering_data)/subreddit_comment_authors_10k_LSI -$(clustering_data)/subreddit_comment_terms_10k.feather:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py - $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k $(selection_grid) -J 20 +authors_tf_10k_input=$(similarity_data)/subreddit_comment_authors-tf_10k.feather +authors_tf_10k_input_lsi=$(similarity_data)/subreddit_comment_authors-tf_10k_LSI +authors_tf_10k_output=$(clustering_data)/subreddit_comment_authors-tf_10k +authors_tf_10k_output_lsi=$(clustering_data)/subreddit_comment_authors-tf_10k_LSI -$(clustering_data)/subreddit_authors-tf_similarities_10k.feather:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather - $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k $(selection_grid) -J 20 +terms_10k_input=$(similarity_data)/subreddit_comment_terms_10k.feather +terms_10k_input_lsi=$(similarity_data)/subreddit_comment_terms_10k_LSI +terms_10k_output=$(clustering_data)/subreddit_comment_terms_10k +terms_10k_output_lsi=$(clustering_data)/subreddit_comment_terms_10k_LSI -$(clustering_data)/subreddit_comment_authors_30k.feather:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py - $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10 +all:terms_10k authors_10k authors_tf_10k terms_10k_lsi authors_10k_lsi authors_tf_10k_lsi -$(clustering_data)/subreddit_comment_terms_30k.feather:selection.py $(similarity_data)/subreddit_comment_terms_30k.feather clustering.py - $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_30k $(selection_grid) -J 10 +terms_10k:${terms_10k_output}/kmeans/selection_data.csv ${terms_10k_output}/affinity/selection_data.csv ${terms_10k_output}/hdbscan/selection_data.csv -$(clustering_data)/subreddit_authors-tf_similarities_30k.feather:clustering.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather - $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather $(clustering_data)/subreddit_comment_authors-tf_30k $(selection_grid) -J 8 +authors_10k:${authors_10k_output}/kmeans/selection_data.csv ${authors_10k_output}/hdbscan/selection_data.csv ${authors_10k_output}/affinity/selection_data.csv + +authors_tf_10k:${authors_tf_10k_output}/kmeans/selection_data.csv ${authors_tf_10k_output}/hdbscan/selection_data.csv ${authors_tf_10k_output}/affinity/selection_data.csv + +terms_10k_lsi:${terms_10k_output_lsi}/kmeans/selection_data.csv ${terms_10k_output_lsi}/affinity/selection_data.csv ${terms_10k_output_lsi}/hdbscan/selection_data.csv + +authors_10k_lsi:${authors_10k_output_lsi}/kmeans/selection_data.csv ${authors_10k_output_lsi}/hdbscan/selection_data.csv ${authors_10k_output_lsi}/affinity/selection_data.csv + +authors_tf_10k_lsi:${authors_tf_10k_output_lsi}/kmeans/selection_data.csv ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv ${authors_tf_10k_output_lsi}/affinity/selection_data.csv + +${authors_10k_output}/kmeans/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py kmeans_clustering.py + $(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/kmeans --savefile=${authors_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid) + +${terms_10k_output}/kmeans/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py kmeans_clustering.py + $(srun_singularity) python3 kmeans_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/kmeans --savefile=${terms_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid) + +${authors_tf_10k_output}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py kmeans_clustering.py + $(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/kmeans --savefile=${authors_tf_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid) + +${authors_10k_output}/affinity/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py affinity_clustering.py + $(srun_singularity) python3 affinity_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/affinity --savefile=${authors_10k_output}/affinity/selection_data.csv $(affinity_selection_grid) + +${terms_10k_output}/affinity/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py affinity_clustering.py + $(srun_singularity) python3 affinity_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/affinity --savefile=${terms_10k_output}/affinity/selection_data.csv $(affinity_selection_grid) + +${authors_tf_10k_output}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py affinity_clustering.py + $(srun_singularity) python3 affinity_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/affinity --savefile=${authors_tf_10k_output}/affinity/selection_data.csv $(affinity_selection_grid) + +${authors_10k_output}/hdbscan/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py hdbscan_clustering.py + $(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/hdbscan --savefile=${authors_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid) + +${terms_10k_output}/hdbscan/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py hdbscan_clustering.py + $(srun_singularity) python3 hdbscan_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/hdbscan --savefile=${terms_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid) + +${authors_tf_10k_output}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py hdbscan_clustering.py + $(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/hdbscan --savefile=${authors_tf_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid) + + +## LSI Models +${authors_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py kmeans_clustering.py + $(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/kmeans --savefile=${authors_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid) + +${terms_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py kmeans_clustering.py + $(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/kmeans --savefile=${terms_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid) + +${authors_tf_10k_output_lsi}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py kmeans_clustering.py + $(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/kmeans --savefile=${authors_tf_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid) + +${authors_10k_output_lsi}/affinity/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py affinity_clustering.py + $(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/affinity --savefile=${authors_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid) + +${terms_10k_output_lsi}/affinity/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py affinity_clustering.py + $(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/affinity --savefile=${terms_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid) + +${authors_tf_10k_output_lsi}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py affinity_clustering.py + $(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/affinity --savefile=${authors_tf_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid) + +${authors_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py hdbscan_clustering.py + $(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/hdbscan --savefile=${authors_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid) + +${terms_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py hdbscan_clustering.py + $(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/hdbscan --savefile=${terms_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid) + +${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py hdbscan_clustering.py + $(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/hdbscan --savefile=${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid) + +${terms_10k_output_lsi}/best_hdbscan.feather:${terms_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py + $(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2 + +${authors_tf_10k_output_lsi}/best_hdbscan.feather:${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py + $(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2 + +clean_affinity: + rm -f ${authors_10k_output}/affinity/selection_data.csv + rm -f ${authors_tf_10k_output}/affinity/selection_data.csv + rm -f ${terms_10k_output}/affinity/selection_data.csv + +clean_kmeans: + rm -f ${authors_10k_output}/kmeans/selection_data.csv + rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv + rm -f ${terms_10k_output}/kmeans/selection_data.csv + +clean_hdbscan: + rm -f ${authors_10k_output}/hdbscan/selection_data.csv + rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv + rm -f ${terms_10k_output}/hdbscan/selection_data.csv + +clean_authors: + rm -f ${authors_10k_output}/affinity/selection_data.csv + rm -f ${authors_10k_output}/kmeans/selection_data.csv + rm -f ${authors_10k_output}/hdbscan/selection_data.csv + +clean_authors_tf: + rm -f ${authors_tf_10k_output}/affinity/selection_data.csv + rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv + rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv + +clean_terms: + rm -f ${terms_10k_output}/affinity/selection_data.csv + rm -f ${terms_10k_output}/kmeans/selection_data.csv + rm -f ${terms_10k_output}/hdbscan/selection_data.csv + +clean_lsi_affinity: + rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv + rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv + rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv + +clean_lsi_kmeans: + rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv + rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv + rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv + +clean_lsi_hdbscan: + rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv + rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv + rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv + +clean_lsi_authors: + rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv + rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv + rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv + +clean_lsi_authors_tf: + rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv + rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv + rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv + +clean_lsi_terms: + rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv + rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv + rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv + +clean: clean_affinity clean_kmeans clean_hdbscan + +PHONY: clean clean_affinity clean_kmeans clean_hdbscan clean_authors clean_authors_tf clean_terms terms_10k authors_10k authors_tf_10k + +# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py +# $(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 + +# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_terms_30k.feather clustering.py +# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS + +# $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS:clustering.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather +# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather $(clustering_data)/subreddit_comment_authors-tf_30k $(selection_grid) -J 8 && touch $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS # $(clustering_data)/subreddit_comment_authors_100k.feather:clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather diff --git a/clustering/affinity_clustering.py b/clustering/affinity_clustering.py new file mode 100644 index 0000000..737967e --- /dev/null +++ b/clustering/affinity_clustering.py @@ -0,0 +1,129 @@ +from sklearn.cluster import AffinityPropagation +from dataclasses import dataclass +from clustering_base import clustering_result, clustering_job +from grid_sweep import grid_sweep +from pathlib import Path +from itertools import product, starmap +import fire +import sys +import numpy as np + +# 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 + preference:float + max_iter:int + +class affinity_job(clustering_job): + def __init__(self, infile, outpath, name, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True): + super().__init__(infile, + outpath, + name, + call=self._affinity_clustering, + preference_quantile=preference_quantile, + damping=damping, + max_iter=max_iter, + convergence_iter=convergence_iter, + random_state=1968, + 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= --dampings= --preference_quantiles= + + 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) diff --git a/clustering/affinity_clustering_lsi.py b/clustering/affinity_clustering_lsi.py new file mode 100644 index 0000000..983e861 --- /dev/null +++ b/clustering/affinity_clustering_lsi.py @@ -0,0 +1,99 @@ +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= --dampings= --preference_quantiles= --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) diff --git a/clustering/clustering.py b/clustering/clustering.py index cac5730..6ee7842 100755 --- a/clustering/clustering.py +++ b/clustering/clustering.py @@ -6,25 +6,24 @@ import numpy as np from sklearn.cluster import AffinityPropagation import fire from pathlib import Path +from multiprocessing import cpu_count +from dataclasses import dataclass +from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat -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) - -def affinity_clustering(similarities, *args, **kwargs): +def affinity_clustering(similarities, output, *args, **kwargs): subreddits, mat = read_similarity_mat(similarities) - return _affinity_clustering(mat, subreddits, *args, **kwargs) + clustering = _affinity_clustering(mat, *args, **kwargs) + cluster_data = process_clustering_result(clustering, subreddits) + cluster_data['algorithm'] = 'affinity' + return(cluster_data) def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True): ''' - similarities: feather file with a dataframe of similarity scores + similarities: matrix of similarity scores preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits. damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author. ''' - print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantilne}") + print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}") preference = np.quantile(mat,preference_quantile) @@ -40,25 +39,14 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, verbose=verbose, random_state=random_state).fit(mat) - - print(f"clustering took {clustering.n_iter_} iterations") - clusters = clustering.labels_ - - print(f"found {len(set(clusters))} clusters") - - cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_}) - - cluster_sizes = cluster_data.groupby("cluster").count() - print(f"the largest cluster has {cluster_sizes.subreddit.max()} members") - - print(f"the median cluster has {cluster_sizes.subreddit.median()} members") - - print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member") - - sys.stdout.flush() + cluster_data = process_clustering_result(clustering, subreddits) + output = Path(output) + output.parent.mkdir(parents=True,exist_ok=True) cluster_data.to_feather(output) print(f"saved {output}") return clustering + + if __name__ == "__main__": fire.Fire(affinity_clustering) diff --git a/clustering/clustering_base.py b/clustering/clustering_base.py new file mode 100644 index 0000000..3778fc3 --- /dev/null +++ b/clustering/clustering_base.py @@ -0,0 +1,105 @@ +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 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): + + 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") + + cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_}) + + cluster_sizes = cluster_data.groupby("cluster").count().reset_index() + 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") + + 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 + + return cluster_data + +@dataclass +class clustering_result: + outpath:Path + silhouette_score:float + name:str + n_clusters:int + n_isolates:int + silhouette_samples:str diff --git a/clustering/fit_tsne.py b/clustering/fit_tsne.py index c9f45f6..55d7239 100644 --- a/clustering/fit_tsne.py +++ b/clustering/fit_tsne.py @@ -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) diff --git a/clustering/grid_sweep.py b/clustering/grid_sweep.py new file mode 100644 index 0000000..c0365d0 --- /dev/null +++ b/clustering/grid_sweep.py @@ -0,0 +1,33 @@ +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) diff --git a/clustering/hdbscan_clustering.py b/clustering/hdbscan_clustering.py new file mode 100644 index 0000000..e533808 --- /dev/null +++ b/clustering/hdbscan_clustering.py @@ -0,0 +1,159 @@ +from clustering_base import clustering_result, clustering_job +from grid_sweep import grid_sweep +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 +import pandas as pd +from multiprocessing import cpu_count +import fire + +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/output/reddit_similarity/subreddit_comment_authors-tf_10k_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=['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): + min_cluster_size:int + min_samples:int + cluster_selection_epsilon:float + cluster_selection_method: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, + outpath, + name, + call=hdbscan_job._hdbscan_clustering, + min_cluster_size=min_cluster_size, + min_samples=min_samples, + cluster_selection_epsilon=cluster_selection_epsilon, + cluster_selection_method=cluster_selection_method + ) + + 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 + + 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, + **kwargs, + ) + + clustering = clusterer.fit(mat.astype('double')) + + 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= --min_samples= --cluster_selection_epsilons= --cluster_selection_methods= + + 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), + map(float,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) + d2 = distances[:,-1] + df = pd.DataFrame({'dist':d2}) + df = df.sort_values("dist",ascending=False) + df['idx'] = np.arange(0,d2.shape[0]) + 1 + p = pn.qplot(x='idx',y='dist',data=df,geom='line') + pn.scales.scale_y_continuous(minor_breaks = np.arange(0,50)/50, + breaks = np.arange(0,10)/10) + p.save(outname,width=16,height=10) + +def make_KNN_plots(): + similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10k.feather" + subreddits, mat = read_similarity_mat(similarities) + mat = sim_to_dist(mat) + + KNN_distances_plot(mat,k=2,outname='terms_knn_dist2.png') + + similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10k.feather" + subreddits, mat = read_similarity_mat(similarities) + mat = sim_to_dist(mat) + KNN_distances_plot(mat,k=2,outname='authors_knn_dist2.png') + + similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k.feather" + subreddits, mat = read_similarity_mat(similarities) + mat = sim_to_dist(mat) + 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) diff --git a/clustering/hdbscan_clustering_lsi.py b/clustering/hdbscan_clustering_lsi.py new file mode 100644 index 0000000..cbd44bd --- /dev/null +++ b/clustering/hdbscan_clustering_lsi.py @@ -0,0 +1,101 @@ +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=['eom'],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= --min_samples= --cluster_selection_epsilons= --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) diff --git a/clustering/kmeans_clustering.py b/clustering/kmeans_clustering.py new file mode 100644 index 0000000..211b666 --- /dev/null +++ b/clustering/kmeans_clustering.py @@ -0,0 +1,105 @@ +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= --n_inits= --max_iters= + + 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) diff --git a/clustering/kmeans_clustering_lsi.py b/clustering/kmeans_clustering_lsi.py new file mode 100644 index 0000000..bb006f3 --- /dev/null +++ b/clustering/kmeans_clustering_lsi.py @@ -0,0 +1,93 @@ +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= --n_inits= --max_iters= + + 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) diff --git a/clustering/lsi_base.py b/clustering/lsi_base.py new file mode 100644 index 0000000..f07bca6 --- /dev/null +++ b/clustering/lsi_base.py @@ -0,0 +1,28 @@ +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("*")) + else: + lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions] + + 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))) diff --git a/clustering/pick_best_clustering.py b/clustering/pick_best_clustering.py new file mode 100644 index 0000000..c541d23 --- /dev/null +++ b/clustering/pick_best_clustering.py @@ -0,0 +1,28 @@ +import fire +import pandas as pd +from pathlib import Path +import shutil +selection_data="/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv" + +outpath = 'test_best.feather' +min_clusters=50; max_isolates=5000; 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)].iloc[df.shape[1]] + + print(best_cluster.to_dict()) + best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather") + shutil.copy(best_path,output) + +if __name__ == "__main__": + fire.Fire(pick_best_clustering) diff --git a/clustering/selection.py b/clustering/selection.py index bfa1c31..81641db 100644 --- a/clustering/selection.py +++ b/clustering/selection.py @@ -1,87 +1,38 @@ -from sklearn.metrics import silhouette_score -from sklearn.cluster import AffinityPropagation -from functools import partial -from clustering import _affinity_clustering, read_similarity_mat -from dataclasses import dataclass -from multiprocessing import Pool, cpu_count, Array, Process -from pathlib import Path -from itertools import product, starmap 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 clustering_result: - outpath:Path - damping:float - max_iter:int - convergence_iter:int - preference_quantile:float - silhouette_score:float - alt_silhouette_score:float - name:str - -def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat): - if name is None: - name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{convergence_iter}" - print(name) - sys.stdout.flush() - outpath = outdir / (str(name) + ".feather") - print(outpath) - clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose) - score = silhouette_score(clustering.affinity_matrix_, clustering.labels_, metric='precomputed') - alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed') - - 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, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None): +import plotnine as pn +from pathlib import Path +from clustering.fit_tsne import fit_tsne +from visualization.tsne_vis import build_visualization - damping = list(map(float,damping)) - convergence_iter = convergence_iter = list(map(int,convergence_iter)) - preference_quantile = list(map(float,preference_quantile)) +df = pd.read_csv("/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv",index_col=0) - if type(outdir) is str: - outdir = Path(outdir) +# plot silhouette_score as a function of isolates +df = df.sort_values("silhouette_score") - outdir.mkdir(parents=True,exist_ok=True) +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") - subreddits, mat = read_similarity_mat(similarities,use_threads=True) +p = pn.ggplot(df,pn.aes(y='n_clusters',x='n_isolates',color='silhouette_score')) + pn.geom_point() +p.save("clusters_x_isolates.png") - if alt_similarities is not None: - alt_mat = read_similarity_mat(alt_similarities,use_threads=True) - else: - alt_mat = None +# 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]] - if J is None: - J = cpu_count() - pool = Pool(J) +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]] - # 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)) +tsne_data = Path("./clustering/authors-tf_lsi850_tsne.feather") - _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) +if not tnse_data.exists(): + fit_tsne("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather", + tnse_data) - # 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)) - return clustering_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") -if __name__ == "__main__": - fire.Fire(select_affinity_clustering) diff --git a/density/Makefile b/density/Makefile index d223399..90eba82 100644 --- a/density/Makefile +++ b/density/Makefile @@ -8,3 +8,9 @@ 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 diff --git a/density/job_script.sh b/density/job_script.sh index 7dfac14..e411ba7 100755 --- a/density/job_script.sh +++ b/density/job_script.sh @@ -1,4 +1,4 @@ #!/usr/bin/bash start_spark_cluster.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 +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 diff --git a/density/overlap_density.py b/density/overlap_density.py index 5a8e91a..2036824 100644 --- a/density/overlap_density.py +++ b/density/overlap_density.py @@ -1,11 +1,12 @@ 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, reindex_tfidf_time_interval +from similarities.similarities_helper import reindex_tfidf # this is the mean of the ratio of the overlap to the focal size. # mean shared membership per focal community member @@ -13,10 +14,12 @@ from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_i 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 @@ -25,6 +28,8 @@ 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 diff --git a/ngrams/tf_comments.py b/ngrams/tf_comments.py index f86548a..a40e5d9 100755 --- a/ngrams/tf_comments.py +++ b/ngrams/tf_comments.py @@ -13,10 +13,7 @@ from nltk.corpus import stopwords from nltk.util import ngrams import string from random import random - -# 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()@:%_\+.~#?&//=]*)") +from redditcleaner import clean # compute term frequencies for comments in each subreddit by week def weekly_tf(partition, mwe_pass = 'first'): @@ -95,8 +92,8 @@ def weekly_tf(partition, mwe_pass = 'first'): # lowercase text = text.lower() - # remove urls - text = urlregex.sub("", text) + # redditcleaner removes reddit markdown(newlines, quotes, bullet points, links, strikethrough, spoiler, code, superscript, table, headings) + text = clean(text) # sentence tokenize sentences = sent_tokenize(text) @@ -107,14 +104,13 @@ 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: diff --git a/similarities/Makefile b/similarities/Makefile index 0ec0342..f578fd5 100644 --- a/similarities/Makefile +++ b/similarities/Makefile @@ -1,25 +1,130 @@ -all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms.parquet +#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 +similarity_data=${base_data}/reddit_similarity +tfidf_data=${similarity_data}/tfidf +tfidf_weekly_data=${similarity_data}/tfidf_weekly +similarity_weekly_data=${similarity_data}/weekly +lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500] + +lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI + +all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet + +#${tfidf_weekly_data}/comment_terms_100k.parquet ${tfidf_weekly_data}/comment_authors_100k.parquet ${tfidf_weekly_data}/comment_terms_30k.parquet ${tfidf_weekly_data}/comment_authors_30k.parquet ${similarity_weekly_data}/comment_terms_100k.parquet ${similarity_weekly_data}/comment_authors_100k.parquet ${similarity_weekly_data}/comment_terms_30k.parquet ${similarity_weekly_data}/comment_authors_30k.parquet + +# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_weekly_130k.parquet # all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet +${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms.parquet + ${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet + +${similarity_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000 + +${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py + ${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200 + +${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py + ${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200 + +${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000 + +${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000 + +${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000 + +${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py + ${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2 + +${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py + ${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2 + +${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000 + +${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000 + +${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py + ${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2 + +${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py + ${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2 + +${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000 + +${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000 + +${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py + ${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000 + +${tfidf_data}/comment_terms_100k.feather/: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv + mkdir -p ${tfidf_data}/ + start_spark_and_run.sh 4 tfidf.py terms --topN=100000 --outpath=${tfidf_data}/comment_terms_100k.feather + +${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv + mkdir -p ${tfidf_data}/ + start_spark_and_run.sh 4 tfidf.py terms --topN=30000 --outpath=${tfidf_data}/comment_terms_30k.feather + +${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv + mkdir -p ${tfidf_data}/ + start_spark_and_run.sh 4 tfidf.py terms --topN=10000 --outpath=${tfidf_data}/comment_terms_10k.feather + +${tfidf_data}/comment_authors_100k.feather: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv + mkdir -p ${tfidf_data}/ + start_spark_and_run.sh 4 tfidf.py authors --topN=100000 --outpath=${tfidf_data}/comment_authors_100k.feather + +${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv + mkdir -p ${tfidf_data}/ + start_spark_and_run.sh 4 tfidf.py authors --topN=10000 --outpath=${tfidf_data}/comment_authors_10k.parquet + +${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv + mkdir -p ${tfidf_data}/ + start_spark_and_run.sh 4 tfidf.py authors --topN=30000 --outpath=${tfidf_data}/comment_authors_30k.parquet + +${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv + start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet + +${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv + start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${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 + +${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 + +${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet + ${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet + +${similarity_weekly_data}/comment_authors_100k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_100k.parquet + ${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet -# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet -# start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.feather +${similarity_weekly_data}/comment_terms_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms_30k.parquet + ${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet -/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv - start_spark_and_run.sh 1 tfidf.py terms --topN=10000 +${similarity_weekly_data}/comment_authors_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_30k.parquet + ${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet -/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv - start_spark_and_run.sh 1 tfidf.py authors --topN=10000 +# ${tfidf_weekly_data}/comment_authors_130k.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv +# start_spark_and_run.sh 1 tfidf.py authors_weekly --topN=130000 -/gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet - start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather +# /gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet +# start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather -/gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet - start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather +# /gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet +# start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather -# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet +# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py ${tfidf_weekly_data}/comment_authors.parquet # start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet -/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet - start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet +# /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet +# start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet diff --git a/similarities/job_script.sh b/similarities/job_script.sh index 03e77de..0c37103 100755 --- a/similarities/job_script.sh +++ b/similarities/job_script.sh @@ -1,4 +1,4 @@ #!/usr/bin/bash start_spark_cluster.sh -spark-submit --master spark://$(hostname):18899 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather -stop-all.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 stop-all.sh diff --git a/similarities/lsi_similarities.py b/similarities/lsi_similarities.py new file mode 100644 index 0000000..7ab7e8c --- /dev/null +++ b/similarities/lsi_similarities.py @@ -0,0 +1,61 @@ +import pandas as pd +import fire +from pathlib import Path +from similarities_helper import similarities, lsi_column_similarities +from functools import partial + +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) + + 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(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'): + + return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', + 'term', + outfile, + min_df, + max_df, + included_subreddits, + topN, + from_date, + to_date, + n_components=n_components + ) + +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, + max_df, + included_subreddits, + topN, + from_date=from_date, + to_date=to_date, + n_components=n_components + ) + +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, + max_df, + included_subreddits, + topN, + from_date=from_date, + to_date=to_date, + tfidf_colname='relative_tf', + n_components=n_components + ) + + +if __name__ == "__main__": + fire.Fire({'term':term_lsi_similarities, + 'author':author_lsi_similarities, + 'author-tf':author_tf_similarities}) + diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py index 3ace8f2..1492983 100644 --- a/similarities/similarities_helper.py +++ b/similarities/similarities_helper.py @@ -2,11 +2,14 @@ from pyspark.sql import SparkSession from pyspark.sql import Window from pyspark.sql import functions as f from enum import Enum +from multiprocessing import cpu_count, Pool from pyspark.mllib.linalg.distributed import CoordinateMatrix from tempfile import TemporaryDirectory import pyarrow import pyarrow.dataset as ds +from sklearn.metrics import pairwise_distances from scipy.sparse import csr_matrix, issparse +from sklearn.decomposition import TruncatedSVD import pandas as pd import numpy as np import pathlib @@ -17,128 +20,147 @@ class tf_weight(Enum): MaxTF = 1 Norm05 = 2 -infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.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 reindex_tfidf_time_interval(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None): - term = term_colname - term_id = term + '_id' - term_id_new = term + '_id_new' - - spark = SparkSession.builder.getOrCreate() - conf = spark.sparkContext.getConf() - print(exclude_phrases) - tfidf_weekly = spark.read.parquet(infile) - - # create the time interval - if from_date is not None: - if type(from_date) is str: - from_date = datetime.fromisoformat(from_date) - - tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date) - - if to_date is not None: - if type(to_date) is str: - to_date = datetime.fromisoformat(to_date) - tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date) - - tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf")) - tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05) - tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits) - tfidf = spark.read_parquet(tempdir.name) - subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas() - subreddit_names = subreddit_names.sort_values("subreddit_id_new") - subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1 - return(tempdir, subreddit_names) - -def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False): - spark = SparkSession.builder.getOrCreate() - conf = spark.sparkContext.getConf() - print(exclude_phrases) - - tfidf = spark.read.parquet(infile) +# subreddits missing after this step don't have any terms that have a high enough idf +# try rewriting without merges +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,map(str.lower,open(included_subreddits)))) - if exclude_phrases == True: - tfidf = tfidf.filter(~f.col(term_colname).contains("_")) + ds_filter = ds.field("subreddit").isin(included_subreddits) + + if min_df is not None: + ds_filter &= ds.field("count") >= min_df + + if max_df is not None: + ds_filter &= ds.field("count") <= max_df + + if week is not None: + ds_filter &= ds.field("week") == week + + if from_date is not None: + ds_filter &= ds.field("week") >= from_date - print("creating temporary parquet with matrix indicies") - tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits) + if to_date is not None: + ds_filter &= ds.field("week") <= to_date - tfidf = spark.read.parquet(tempdir.name) - subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas() + term = term_colname + term_id = term + '_id' + term_id_new = term + '_id_new' + + projection = { + 'subreddit_id':ds.field('subreddit_id'), + term_id:ds.field(term_id), + 'relative_tf':ds.field("relative_tf").cast('float32') + } + + if not rescale_idf: + projection = { + 'subreddit_id':ds.field('subreddit_id'), + term_id:ds.field(term_id), + 'relative_tf':ds.field('relative_tf').cast('float32'), + 'tf_idf':ds.field('tf_idf').cast('float32')} + + 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) + df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + grouped = df.groupby(term_id) + df[term_id_new] = grouped.ngroup() + + if rescale_idf: + print("computing idf", flush=True) + df['new_count'] = grouped[term_id].transform('count') + N_docs = df.subreddit_id_new.max() + 1 + df['idf'] = np.log(N_docs/(1+df.new_count),dtype='float32') + 1 + if tf_family == tf_weight.MaxTF: + df["tf_idf"] = df.relative_tf * df.idf + else: # tf_fam = tf_weight.Norm05 + df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf + + 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") - subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1 - spark.stop() - return (tempdir, subreddit_names) + return(df, subreddit_names) +def pull_names(batch): + return(batch.to_pandas().drop_duplicates()) -def similarities(infile, simfunc, 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'): +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. ''' - if from_date is not None or to_date is not None: - tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False, from_date=from_date, to_date=to_date) - - else: - tempdir, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False) - - print("loading matrix") - # mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname) - mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname) - print(f'computing similarities on mat. mat.shape:{mat.shape}') - print(f"size of mat is:{mat.data.nbytes}") - sims = simfunc(mat) - del mat - if issparse(sims): - sims = sims.todense() + def proc_sims(sims, outfile): + if issparse(sims): + sims = sims.todense() - print(f"shape of sims:{sims.shape}") - print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}") - sims = pd.DataFrame(sims) - sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1) - sims['subreddit'] = subreddit_names.subreddit.values + print(f"shape of sims:{sims.shape}") + print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}",flush=True) + sims = pd.DataFrame(sims) + sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1) + sims['_subreddit'] = subreddit_names.subreddit.values - p = Path(outfile) + p = Path(outfile) - 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")) + 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")) + outfile.parent.mkdir(exist_ok=True, parents=True) - sims.to_feather(outfile) - tempdir.cleanup() + sims.to_feather(outfile) -def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'): term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' - dataset = ds.dataset(path,format='parquet') - entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas() - return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1)))) + 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))) -def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'): - term = term_colname - term_id = term + '_id' - term_id_new = term + '_id_new' - dataset = ds.dataset(path,format='parquet') - print(f"tfidf_colname:{tfidf_colname}") - entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas() - return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))) - + print("loading matrix") + + # mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname) + + print(f'computing similarities on mat. mat.shape:{mat.shape}') + print(f"size of mat is:{mat.data.nbytes}",flush=True) + sims = simfunc(mat) + del mat + + if hasattr(sims,'__next__'): + for simmat, name in sims: + proc_sims(simmat, Path(outfile)/(str(name) + ".feather")) + else: + proc_sims(simmat, outfile) def write_weekly_similarities(path, sims, week, names): sims['week'] = week p = pathlib.Path(path) if not p.is_dir(): - p.mkdir() + p.mkdir(exist_ok=True,parents=True) # reformat as a pairwise list - sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values) + sims = sims.melt(id_vars=['_subreddit','week'],value_vars=names.subreddit.values) sims.to_parquet(p / week.isoformat()) def column_overlaps(mat): @@ -150,136 +172,62 @@ def column_overlaps(mat): return intersection / den -def column_similarities(mat): - norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32)) - mat = mat.multiply(1/norm) - sims = mat.T @ mat - return(sims) - - -def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits): - term = term_colname - term_id = term + '_id' - term_id_new = term + '_id_new' - - if min_df is None: - min_df = 0.1 * len(included_subreddits) - tfidf = tfidf.filter(f.col('count') >= min_df) - if max_df is not None: - tfidf = tfidf.filter(f.col('count') <= max_df) - - tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits)) - - # we might not have the same terms or subreddits each week, so we need to make unique ids for each week. - sub_ids = tfidf.select(['subreddit_id','week']).distinct() - sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id"))) - tfidf = tfidf.join(sub_ids,['subreddit_id','week']) - - # only use terms in at least min_df included subreddits in a given week - new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count')) - tfidf = tfidf.join(new_count,[term_id,'week'],how='inner') - - # reset the term ids - term_ids = tfidf.select([term_id,'week']).distinct() - term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id))) - tfidf = tfidf.join(term_ids,[term_id,'week']) - - tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") - tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float')) - - tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.') - - tfidf = tfidf.repartition('week') - - tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy') - return(tempdir) - - -def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits): - term = term_colname +def test_lsi_sims(): + term = "term" term_id = term + '_id' term_id_new = term + '_id_new' - if min_df is None: - min_df = 0.1 * len(included_subreddits) - tfidf = tfidf.filter(f.col('count') >= min_df) - if max_df is not None: - tfidf = tfidf.filter(f.col('count') <= max_df) - - tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits)) - - # reset the subreddit ids - sub_ids = tfidf.select('subreddit_id').distinct() - sub_ids = sub_ids.withColumn("subreddit_id_new", f.row_number().over(Window.orderBy("subreddit_id"))) - tfidf = tfidf.join(sub_ids,'subreddit_id') - - # only use terms in at least min_df included subreddits - new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) - tfidf = tfidf.join(new_count,term_id,how='inner') - - # reset the term ids - term_ids = tfidf.select([term_id]).distinct() - term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id))) - tfidf = tfidf.join(term_ids,term_id) - - tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") - tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float')) + t1 = time.perf_counter() + entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet", + term_colname='term', + min_df=2000, + topN=10000 + ) + t2 = time.perf_counter() + print(f"first load took:{t2 - t1}s") + + entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet", + term_colname='term', + min_df=2000, + topN=10000 + ) + t3=time.perf_counter() + + print(f"second load took:{t3 - t2}s") + + mat = csr_matrix((entries['tf_idf'],(entries[term_id_new], entries.subreddit_id_new))) + sims = list(lsi_column_similarities(mat, [10,50])) + sims_og = sims + sims_test = list(lsi_column_similarities(mat,[10,50],algorithm='randomized',n_iter=10)) + +# n_components is the latent dimensionality. sklearn recommends 100. More might be better +# 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'): + # 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] + + n_components = sorted(n_components,reverse=True) - tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.') + svd_components = n_components[0] + 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) + for n_dims in n_components: + sims = column_similarities(lsimat[:,np.arange(n_dims)]) + if len(n_components) > 1: + yield (sims, n_dims) + else: + return sims - tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy') - return tempdir - - -# try computing cosine similarities using spark -def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold): - term = term_colname - term_id = term + '_id' - term_id_new = term + '_id_new' - if min_df is None: - min_df = 0.1 * len(included_subreddits) - - tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits)) - tfidf = tfidf.cache() - - # reset the subreddit ids - sub_ids = tfidf.select('subreddit_id').distinct() - sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id"))) - tfidf = tfidf.join(sub_ids,'subreddit_id') - - # only use terms in at least min_df included subreddits - new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) - tfidf = tfidf.join(new_count,term_id,how='inner') - - # reset the term ids - term_ids = tfidf.select([term_id]).distinct() - term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id))) - tfidf = tfidf.join(term_ids,term_id) - - tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") - tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf) - - # step 1 make an rdd of entires - # sorted by (dense) spark subreddit id - n_partitions = int(len(included_subreddits)*2 / 5) - - entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions) - - # put like 10 subredis in each partition - - # step 2 make it into a distributed.RowMatrix - coordMat = CoordinateMatrix(entries) - - coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions)) - - # this needs to be an IndexedRowMatrix() - mat = coordMat.toRowMatrix() - - #goal: build a matrix of subreddit columns and tf-idfs rows - sim_dist = mat.columnSimilarities(threshold=similarity_threshold) - - return (sim_dist, tfidf) +def column_similarities(mat): + return 1 - pairwise_distances(mat,metric='cosine') def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05): @@ -331,7 +279,9 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig else: # tf_fam = tf_weight.Norm05 df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf) - return df + df = df.repartition(400,'subreddit','week') + dfwriter = df.write.partitionBy("week") + return dfwriter def _calc_tfidf(df, term_colname, tf_family): term = term_colname @@ -342,7 +292,7 @@ def _calc_tfidf(df, term_colname, tf_family): df = df.join(max_subreddit_terms, on='subreddit') - df = df.withColumn("relative_tf", df.tf / df.sr_max_tf) + df = df.withColumn("relative_tf", (df.tf / df.sr_max_tf)) # group by term. term is unique idf = df.groupby([term]).count() @@ -385,10 +335,28 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf')) df = _calc_tfidf(df, term_colname, tf_family) - - return df + df = df.repartition('subreddit') + dfwriter = df.write + return dfwriter def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"): rankdf = pd.read_csv(path) included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values) return included_subreddits + + +def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet", + outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"): + spark = SparkSession.builder.getOrCreate() + df = spark.read.parquet(inpath) + df = df.repartition(400,'subreddit') + df.write.parquet(outpath,mode='overwrite') + + +def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet", + outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"): + spark = SparkSession.builder.getOrCreate() + df = spark.read.parquet(inpath) + df = df.repartition(400,'subreddit','week') + dfwriter = df.write.partitionBy("week") + dfwriter.parquet(outpath,mode='overwrite') diff --git a/similarities/tfidf.py b/similarities/tfidf.py index 7f579fa..110536e 100644 --- a/similarities/tfidf.py +++ b/similarities/tfidf.py @@ -15,10 +15,9 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_ else: include_subs = select_topN_subreddits(topN) - df = func(df, include_subs, term_colname) - - df.write.parquet(outpath,mode='overwrite',compression='snappy') + dfwriter = func(df, include_subs, term_colname) + dfwriter.parquet(outpath,mode='overwrite',compression='snappy') spark.stop() def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits): diff --git a/similarities/top_subreddits_by_comments.py b/similarities/top_subreddits_by_comments.py index 1197b51..ff9293c 100644 --- a/similarities/top_subreddits_by_comments.py +++ b/similarities/top_subreddits_by_comments.py @@ -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.csv', index=False) +df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nsfw.csv', index=False) diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py index 044ee75..e24ceee 100644 --- a/similarities/weekly_cosine_similarities.py +++ b/similarities/weekly_cosine_similarities.py @@ -3,78 +3,78 @@ from pyspark.sql import SparkSession from pyspark.sql import Window import numpy as np import pyarrow +import pyarrow.dataset as ds import pandas as pd import fire -from itertools import islice +from itertools import islice, chain from pathlib import Path from similarities_helper import * from multiprocessing import Pool, cpu_count +from functools import partial -def _week_similarities(tempdir, term_colname, week): - print(f"loading matrix: {week}") - mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week) - print('computing similarities') - sims = column_similarities(mat) - del mat - names = subreddit_names.loc[subreddit_names.week == week] - sims = pd.DataFrame(sims.todense()) +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, 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) + mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new))) + print('computing similarities') + sims = column_similarities(mat) + del mat + sims = pd.DataFrame(sims.todense()) + sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1) + sims['_subreddit'] = names.subreddit.values + outfile = str(Path(outdir) / str(week)) + write_weekly_similarities(outfile, sims, week, names) - sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1) - sims['_subreddit'] = names.subreddit.values - - write_weekly_similarities(outfile, sims, week, names) +def pull_weeks(batch): + return set(batch.to_pandas()['week']) #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet') -def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500): - spark = SparkSession.builder.getOrCreate() - conf = spark.sparkContext.getConf() +def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500): print(outfile) - tfidf = spark.read.parquet(tfidf_path) - - if included_subreddits is None: - included_subreddits = select_topN_subreddits(topN) - else: - included_subreddits = set(open(included_subreddits)) - - print(f"computing weekly similarities for {len(included_subreddits)} subreddits") - - print("creating temporary parquet with matrix indicies") - tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df=None, included_subreddits=included_subreddits) - - tfidf = spark.read.parquet(tempdir.name) + tfidf_ds = ds.dataset(tfidf_path) + tfidf_ds = tfidf_ds.to_table(columns=["week"]) + batches = tfidf_ds.to_batches() - # the ids can change each week. - subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas() - subreddit_names = subreddit_names.sort_values("subreddit_id_new") - subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1 - spark.stop() + with Pool(cpu_count()) as pool: + weeks = set(chain( * pool.imap_unordered(pull_weeks,batches))) - weeks = sorted(list(subreddit_names.week.drop_duplicates())) + weeks = sorted(weeks) # do this step in parallel if we have the memory for it. # should be doable with pool.map - def week_similarities_helper(week): - _week_similarities(tempdir, term_colname, week) + print(f"computing weekly similarities") + 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) 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)) -def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500): +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, + max_df, included_subreddits, topN) -def term_cosine_similarities_weekly(outfile, min_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, - included_subreddits, - topN) +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, + max_df, + included_subreddits, + topN) if __name__ == "__main__": fire.Fire({'authors':author_cosine_similarities_weekly, diff --git a/timeseries/__init__.py b/timeseries/__init__.py new file mode 100644 index 0000000..c023c66 --- /dev/null +++ b/timeseries/__init__.py @@ -0,0 +1,2 @@ +from .choose_clusters import load_clusters, load_densities +from .cluster_timeseries import build_cluster_timeseries diff --git a/timeseries/cluster_timeseries.py b/timeseries/cluster_timeseries.py index 07507d7..91fa705 100644 --- a/timeseries/cluster_timeseries.py +++ b/timeseries/cluster_timeseries.py @@ -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 main(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather", +def build_cluster_timeseries(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 main(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_ ts.write.parquet(output, mode='overwrite') if __name__ == "__main__": - fire.Fire(main) + fire.Fire(build_cluster_timeseries) diff --git a/visualization/tsne_vis.py b/visualization/tsne_vis.py index c39a740..eb6a6be 100644 --- a/visualization/tsne_vis.py +++ b/visualization/tsne_vis.py @@ -22,8 +22,12 @@ 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=alt.Scale(scheme='category10')), + alt.Color(field='color',type='nominal',scale=base_scale), alt.value("lightgray")) @@ -84,6 +88,11 @@ 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}) @@ -120,15 +129,17 @@ 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)): - knn = indices[i] - knn_colors = color_assignments[knn] - available_colors = color_ids[list(set(color_ids) - set(knn_colors))] - - if(len(available_colors) > 0): - color_assignments[i] = available_colors[0] + if (centroids.iloc[i].name == -1) or (i in singletons): + color_assignments[i] = isolate_color else: - raise Exception("Can't color this many neighbors with this many colors") + knn = indices[i] + knn_colors = color_assignments[knn] + available_colors = color_ids[list(set(color_ids) - set(knn_colors))] + if(len(available_colors) > 0): + color_assignments[i] = available_colors[0] + else: + raise Exception("Can't color this many neighbors with this many colors") centroids = centroids.reset_index() colors = centroids.loc[:,['cluster']] @@ -143,12 +154,13 @@ 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')