--- /dev/null
+from .timeseries import load_clusters, load_densities, build_cluster_timeseries
+
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
--- /dev/null
+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=<csv> --dampings=<csv> --preference_quantiles=<csv>
+
+ Keword arguments:
+ savefile: path to save the metadata and diagnostics
+ inpath: path to feather data containing a labeled matrix of subreddit similarities.
+ outpath: path to output fit kmeans clusterings.
+ dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering.
+ preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
+ convergence_iters:one or more integers of number of iterations without improvement before stopping.
+ max_iters: one or more numbers of different maximum interations.
+ """
+ obj = affinity_grid_sweep(inpath,
+ outpath,
+ map(float,dampings),
+ map(int,max_iters),
+ map(int,convergence_iters),
+ map(float,preference_quantiles))
+ obj.run(n_cores)
+ obj.save(savefile)
+
+def test_select_affinity_clustering():
+ # select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
+ # "test_hdbscan_author30k",
+ # min_cluster_sizes=[2],
+ # min_samples=[1,2],
+ # cluster_selection_epsilons=[0,0.05,0.1,0.15],
+ # cluster_selection_methods=['eom','leaf'],
+ # lsi_dimensions='all')
+ inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
+ outpath = "test_affinity";
+ dampings=[0.8,0.9]
+ max_iters=[100000]
+ convergence_iters=[15]
+ preference_quantiles=[0.5,0.7]
+
+ gs = affinity_lsi_grid_sweep(inpath, 'all', outpath, dampings, max_iters, convergence_iters, preference_quantiles)
+ gs.run(20)
+ gs.save("test_affinity/lsi_sweep.csv")
+
+
+if __name__ == "__main__":
+ fire.Fire(run_affinity_grid_sweep)
--- /dev/null
+import fire
+from affinity_clustering import affinity_clustering_result, affinity_job, affinity_grid_sweep
+from grid_sweep import grid_sweep
+from lsi_base import lsi_result_mixin, lsi_grid_sweep, lsi_mixin
+from dataclasses import dataclass
+
+@dataclass
+class affinity_clustering_result_lsi(affinity_clustering_result, lsi_result_mixin):
+ pass
+
+
+class affinity_lsi_job(affinity_job, lsi_mixin):
+ def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+ super().__init__(infile,
+ outpath,
+ name,
+ *args,
+ **kwargs)
+ super().set_lsi_dims(lsi_dims)
+
+ def get_info(self):
+ result = super().get_info()
+ self.result = affinity_clustering_result_lsi(**result.__dict__,
+ lsi_dimensions=self.lsi_dims)
+ return self.result
+
+class affinity_lsi_grid_sweep(lsi_grid_sweep):
+ def __init__(self,
+ inpath,
+ lsi_dims,
+ outpath,
+ dampings=[0.9],
+ max_iters=[10000],
+ convergence_iters=[30],
+ preference_quantiles=[0.5]):
+
+ super().__init__(affinity_lsi_job,
+ _affinity_lsi_grid_sweep,
+ inpath,
+ lsi_dims,
+ outpath,
+ dampings,
+ max_iters,
+ convergence_iters,
+ preference_quantiles)
+
+
+class _affinity_lsi_grid_sweep(grid_sweep):
+ def __init__(self,
+ inpath,
+ outpath,
+ lsi_dim,
+ *args,
+ **kwargs):
+ self.lsi_dim = lsi_dim
+ self.jobtype = affinity_lsi_job
+ super().__init__(self.jobtype,
+ inpath,
+ outpath,
+ self.namer,
+ [self.lsi_dim],
+ *args,
+ **kwargs)
+
+ def namer(self, *args, **kwargs):
+ s = affinity_grid_sweep.namer(self, *args[1:], **kwargs)
+ s += f"_lsi-{self.lsi_dim}"
+ return s
+
+def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5], lsi_dimensions='all',n_cores=30):
+ """Run affinity clustering once or more with different parameters.
+
+ Usage:
+ affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<csv> --dampings=<csv> --preference_quantiles=<csv> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+
+ Keword arguments:
+ savefile: path to save the metadata and diagnostics
+ inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+ outpath: path to output fit kmeans clusterings.
+ dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering.
+ preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
+ convergence_iters:one or more integers of number of iterations without improvement before stopping.
+ max_iters: one or more numbers of different maximum interations.
+ lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+ """
+
+ obj = affinity_lsi_grid_sweep(inpath,
+ lsi_dimensions,
+ outpath,
+ map(float,dampings),
+ map(int,max_iters),
+ map(int,convergence_iters),
+ map(float,preference_quantiles))
+
+ obj.run(n_cores)
+ obj.save(savefile)
+
+if __name__ == "__main__":
+ fire.Fire(run_affinity_lsi_grid_sweep)
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)
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)
--- /dev/null
+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
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
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)
--- /dev/null
+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)
--- /dev/null
+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=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=<csv "eom"|"leaf">
+
+ Keword arguments:
+ savefile: path to save the metadata and diagnostics
+ inpath: path to feather data containing a labeled matrix of subreddit similarities.
+ outpath: path to output fit kmeans clusterings.
+ min_cluster_sizes: one or more integers indicating the minumum cluster size
+ min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
+ cluster_selection_epsilon: one or more similarity thresholds for transition from dbscan to hdbscan
+ cluster_selection_method: "eom" or "leaf" eom gives larger clusters.
+ """
+ obj = hdbscan_grid_sweep(inpath,
+ outpath,
+ map(int,min_cluster_sizes),
+ map(int,min_samples),
+ map(float,cluster_selection_epsilons),
+ 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)
--- /dev/null
+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=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=[eom]> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+
+ Keword arguments:
+ savefile: path to save the metadata and diagnostics
+ inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+ outpath: path to output fit clusterings.
+ min_cluster_sizes: one or more integers indicating the minumum cluster size
+ min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
+ cluster_selection_epsilons: one or more similarity thresholds for transition from dbscan to hdbscan
+ cluster_selection_methods: one or more of "eom" or "leaf" eom gives larger clusters.
+ lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+ """
+
+ obj = hdbscan_lsi_grid_sweep(inpath,
+ lsi_dimensions,
+ outpath,
+ list(map(int,min_cluster_sizes)),
+ list(map(int,min_samples)),
+ list(map(float,cluster_selection_epsilons)),
+ cluster_selection_methods
+ )
+
+ obj.run(10)
+ obj.save(savefile)
+
+
+if __name__ == "__main__":
+ fire.Fire(run_hdbscan_lsi_grid_sweep)
--- /dev/null
+from sklearn.cluster import KMeans
+import fire
+from pathlib import Path
+from dataclasses import dataclass
+from clustering_base import clustering_result, clustering_job
+from grid_sweep import grid_sweep
+
+@dataclass
+class kmeans_clustering_result(clustering_result):
+ n_clusters:int
+ n_init:int
+ max_iter:int
+
+class kmeans_job(clustering_job):
+ def __init__(self, infile, outpath, name, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
+ super().__init__(infile,
+ outpath,
+ name,
+ call=kmeans_job._kmeans_clustering,
+ n_clusters=n_clusters,
+ n_init=n_init,
+ max_iter=max_iter,
+ random_state=random_state,
+ verbose=verbose)
+
+ self.n_clusters=n_clusters
+ self.n_init=n_init
+ self.max_iter=max_iter
+
+ def _kmeans_clustering(mat, *args, **kwargs):
+
+ clustering = KMeans(*args,
+ **kwargs,
+ ).fit(mat)
+
+ return clustering
+
+
+ def get_info(self):
+ result = super().get_info()
+ self.result = kmeans_clustering_result(**result.__dict__,
+ n_init=self.n_init,
+ max_iter=self.max_iter)
+ return self.result
+
+
+class kmeans_grid_sweep(grid_sweep):
+
+ def __init__(self,
+ inpath,
+ outpath,
+ *args,
+ **kwargs):
+ super().__init__(kmeans_job, inpath, outpath, self.namer, *args, **kwargs)
+
+ def namer(self,
+ n_clusters,
+ n_init,
+ max_iter):
+ return f"nclusters-{n_clusters}_nit-{n_init}_maxit-{max_iter}"
+
+def test_select_kmeans_clustering():
+ inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
+ outpath = "test_kmeans";
+ n_clusters=[200,300,400];
+ n_init=[1,2,3];
+ max_iter=[100000]
+
+ gs = kmeans_lsi_grid_sweep(inpath, 'all', outpath, n_clusters, n_init, max_iter)
+ gs.run(1)
+
+ cluster_selection_epsilons=[0,0.1,0.3,0.5];
+ cluster_selection_methods=['eom'];
+ lsi_dimensions='all'
+ gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
+ gs.run(20)
+ gs.save("test_hdbscan/lsi_sweep.csv")
+
+def run_kmeans_grid_sweep(savefile, inpath, outpath, n_clusters=[500], n_inits=[1], max_iters=[3000]):
+ """Run kmeans clustering once or more with different parameters.
+
+ Usage:
+ kmeans_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
+
+ Keword arguments:
+ savefile: path to save the metadata and diagnostics
+ inpath: path to feather data containing a labeled matrix of subreddit similarities.
+ outpath: path to output fit kmeans clusterings.
+ n_clusters: one or more numbers of kmeans clusters to select.
+ n_inits: one or more numbers of different initializations to use for each clustering.
+ max_iters: one or more numbers of different maximum interations.
+ """
+
+ obj = kmeans_grid_sweep(inpath,
+ outpath,
+ map(int,n_clusters),
+ map(int,n_inits),
+ map(int,max_iters))
+
+
+ obj.run(1)
+ obj.save(savefile)
+
+if __name__ == "__main__":
+ fire.Fire(run_kmeans_grid_sweep)
--- /dev/null
+import fire
+from dataclasses import dataclass
+from kmeans_clustering import kmeans_job, kmeans_clustering_result, kmeans_grid_sweep
+from lsi_base import lsi_mixin, lsi_result_mixin, lsi_grid_sweep
+from grid_sweep import grid_sweep
+
+@dataclass
+class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
+ pass
+
+class kmeans_lsi_job(kmeans_job, lsi_mixin):
+ def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+ super().__init__(infile,
+ outpath,
+ name,
+ *args,
+ **kwargs)
+ super().set_lsi_dims(lsi_dims)
+
+ def get_info(self):
+ result = super().get_info()
+ self.result = kmeans_clustering_result_lsi(**result.__dict__,
+ lsi_dimensions=self.lsi_dims)
+ return self.result
+
+class _kmeans_lsi_grid_sweep(grid_sweep):
+ def __init__(self,
+ inpath,
+ outpath,
+ lsi_dim,
+ *args,
+ **kwargs):
+ print(args)
+ print(kwargs)
+ self.lsi_dim = lsi_dim
+ self.jobtype = kmeans_lsi_job
+ super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
+
+ def namer(self, *args, **kwargs):
+ s = kmeans_grid_sweep.namer(self, *args[1:], **kwargs)
+ s += f"_lsi-{self.lsi_dim}"
+ return s
+
+class kmeans_lsi_grid_sweep(lsi_grid_sweep):
+
+ def __init__(self,
+ inpath,
+ lsi_dims,
+ outpath,
+ n_clusters,
+ n_inits,
+ max_iters
+ ):
+
+ super().__init__(kmeans_lsi_job,
+ _kmeans_lsi_grid_sweep,
+ inpath,
+ lsi_dims,
+ outpath,
+ n_clusters,
+ n_inits,
+ max_iters)
+
+def run_kmeans_lsi_grid_sweep(savefile, inpath, outpath, n_clusters=[500], n_inits=[1], max_iters=[3000], lsi_dimensions="all"):
+ """Run kmeans clustering once or more with different parameters.
+
+ Usage:
+ kmeans_clustering_lsi.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH d--lsi_dimensions=<"all"|csv number of LSI dimensions to use> --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
+
+ Keword arguments:
+ savefile: path to save the metadata and diagnostics
+ inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+ outpath: path to output fit kmeans clusterings.
+ lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+ n_clusters: one or more numbers of kmeans clusters to select.
+ n_inits: one or more numbers of different initializations to use for each clustering.
+ max_iters: one or more numbers of different maximum interations.
+ """
+
+ obj = kmeans_lsi_grid_sweep(inpath,
+ lsi_dimensions,
+ outpath,
+ list(map(int,n_clusters)),
+ list(map(int,n_inits)),
+ list(map(int,max_iters))
+ )
+
+ obj.run(1)
+ obj.save(savefile)
+
+
+if __name__ == "__main__":
+ fire.Fire(run_kmeans_lsi_grid_sweep)
--- /dev/null
+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)))
--- /dev/null
+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)
-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)
/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
#!/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
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
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
# 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
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'):
# 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)
# 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:
-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 ${tfidf_weekly_data}/comment_authors.parquet
# start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet
-/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
- start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
+# /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
+# start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
#!/usr/bin/bash
start_spark_cluster.sh
-spark-submit --master spark://$(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
--- /dev/null
+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})
+
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
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):
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):
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
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()
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')
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):
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))
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)
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,
--- /dev/null
+from .choose_clusters import load_clusters, load_densities
+from .cluster_timeseries import build_cluster_timeseries
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",
ts.write.parquet(output, mode='overwrite')
if __name__ == "__main__":
- fire.Fire(main)
+ fire.Fire(build_cluster_timeseries)
#
# 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"))
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})
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']]
# 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')
projects / cdsc_reddit.git / commitdiff