refactor similarities to use submodule.

update pushshift dumps.
lsi support for weekly similarities
2022-01-19 15:05:49 -08:00 · 2021-12-10 21:23:32 -08:00 · 2021-08-11 22:48:33 -07:00 · 2021-08-03 15:13:39 -07:00 · 2021-08-03 15:13:21 -07:00 · 2021-08-03 15:06:48 -07:00
36 changed files with 1743 additions and 488 deletions
--- a/.gitmodules
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "cdsc_ecology_utils"]
+	path = cdsc_ecology_utils
+	url = code:cdsc_ecology_utils
--- a/init.py
+++ b/init.py
@@ -0,0 +1,2 @@
+from timeseries import load_clusters, load_densities, build_cluster_timeseries
+from cdsc_ecology_utils import similarity_functions
--- a/1
+++ b/1
--- a/clustering/Makefile
+++ b/clustering/Makefile
@@ -2,20 +2,164 @@
 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=3000 --convergence_iter=15,30,100 --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"
-#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
-all:$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv
-# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
-# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
+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/selection_data.csv: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 $(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(selection_grid) -J 20
+authors_10k_input=$(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/selection_data.csv: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  $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv $(selection_grid) -J 20 
+authors_tf_10k_input=$(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_comment_authors-tf_10k/selection_data.csv: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  $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(selection_grid) -J 20
+terms_10k_input=$(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=$(clustering_data)/subreddit_comment_terms_10k_LSI
+
+all:terms_10k authors_10k authors_tf_10k terms_10k_lsi authors_10k_lsi authors_tf_10k_lsi
+
+terms_10k:${terms_10k_output}/kmeans/selection_data.csv ${terms_10k_output}/affinity/selection_data.csv ${terms_10k_output}/hdbscan/selection_data.csv
+
+authors_10k:${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
--- a/clustering/affinity_clustering.py
+++ b/clustering/affinity_clustering.py
@@ -0,0 +1,129 @@
+from sklearn.cluster import AffinityPropagation
+from dataclasses import dataclass
+from clustering_base import clustering_result, clustering_job
+from grid_sweep import grid_sweep
+from pathlib import Path
+from itertools import product, starmap
+import fire
+import sys
+import numpy as np
+
+# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
+@dataclass
+class affinity_clustering_result(clustering_result):
+    damping:float
+    convergence_iter:int
+    preference_quantile:float
+    preference:float
+    max_iter:int
+
+class affinity_job(clustering_job):
+    def __init__(self, infile, outpath, name, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         call=self._affinity_clustering,
+                         preference_quantile=preference_quantile,
+                         damping=damping,
+                         max_iter=max_iter,
+                         convergence_iter=convergence_iter,
+                         random_state=1968,
+                         verbose=verbose)
+        self.damping=damping
+        self.max_iter=max_iter
+        self.convergence_iter=convergence_iter
+        self.preference_quantile=preference_quantile
+
+    def _affinity_clustering(self, mat, preference_quantile, *args, **kwargs):
+        mat = 1-mat
+        preference = np.quantile(mat, preference_quantile)
+        self.preference = preference
+        print(f"preference is {preference}")
+        print("data loaded")
+        sys.stdout.flush()
+        clustering = AffinityPropagation(*args,
+                                         preference=preference,
+                                         affinity='precomputed',
+                                         copy=False,
+                                         **kwargs).fit(mat)
+        return clustering
+
+    def get_info(self):
+        result = super().get_info()
+        self.result=affinity_clustering_result(**result.__dict__,
+                                               damping=self.damping,
+                                               max_iter=self.max_iter,
+                                               convergence_iter=self.convergence_iter,
+                                               preference_quantile=self.preference_quantile,
+                                               preference=self.preference)
+
+        return self.result
+
+class affinity_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 *args,
+                 **kwargs):
+
+        super().__init__(affinity_job,
+                         _afffinity_grid_sweep,
+                         inpath,
+                         outpath,
+                         self.namer,
+                         *args,
+                         **kwargs)
+    def namer(self,
+              damping,
+              max_iter,
+              convergence_iter,
+              preference_quantile):
+
+        return f"damp-{damping}_maxit-{max_iter}_convit-{convergence_iter}_prefq-{preference_quantile}"
+
+def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5],n_cores=10):
+    """Run affinity clustering once or more with different parameters.
+    
+    Usage:
+    affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<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)
--- a/clustering/affinity_clustering_lsi.py
+++ b/clustering/affinity_clustering_lsi.py
@@ -0,0 +1,99 @@
+import fire
+from affinity_clustering import affinity_clustering_result, affinity_job, affinity_grid_sweep
+from grid_sweep import grid_sweep
+from lsi_base import lsi_result_mixin, lsi_grid_sweep, lsi_mixin
+from dataclasses import dataclass
+
+@dataclass
+class affinity_clustering_result_lsi(affinity_clustering_result, lsi_result_mixin):
+    pass
+
+
+class affinity_lsi_job(affinity_job, lsi_mixin):
+    def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         *args,
+                         **kwargs)
+        super().set_lsi_dims(lsi_dims)
+
+    def get_info(self):
+        result = super().get_info()
+        self.result = affinity_clustering_result_lsi(**result.__dict__,
+                                                     lsi_dimensions=self.lsi_dims)
+        return self.result
+    
+class affinity_lsi_grid_sweep(lsi_grid_sweep):
+    def __init__(self,
+                 inpath,
+                 lsi_dims,
+                 outpath,
+                 dampings=[0.9],
+                 max_iters=[10000],
+                 convergence_iters=[30],
+                 preference_quantiles=[0.5]):
+
+        super().__init__(affinity_lsi_job,
+                         _affinity_lsi_grid_sweep,
+                         inpath,
+                         lsi_dims,
+                         outpath,
+                         dampings,
+                         max_iters,
+                         convergence_iters,
+                         preference_quantiles)
+    
+
+class _affinity_lsi_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 lsi_dim,
+                 *args,
+                 **kwargs):
+        self.lsi_dim = lsi_dim
+        self.jobtype = affinity_lsi_job
+        super().__init__(self.jobtype,
+                         inpath,
+                         outpath,
+                         self.namer,
+                         [self.lsi_dim],
+                         *args,
+                         **kwargs)
+
+    def namer(self, *args, **kwargs):
+        s = affinity_grid_sweep.namer(self, *args[1:], **kwargs)
+        s += f"_lsi-{self.lsi_dim}"
+        return s
+                         
+def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5], lsi_dimensions='all',n_cores=30):
+    """Run affinity clustering once or more with different parameters.
+    
+    Usage:
+    affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<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)
--- a/clustering/clustering.py
+++ b/clustering/clustering.py
@@ -6,21 +6,20 @@ import numpy as np
 from sklearn.cluster import AffinityPropagation
 import fire
 from pathlib import Path
+from multiprocessing import cpu_count
+from dataclasses import dataclass
+from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat

-def read_similarity_mat(similarities, use_threads=True):
-    df = pd.read_feather(similarities, use_threads=use_threads)
-    mat = np.array(df.drop('_subreddit',1))
-    n = mat.shape[0]
-    mat[range(n),range(n)] = 1
-    return (df._subreddit,mat)
-
-def affinity_clustering(similarities, *args, **kwargs):
+def affinity_clustering(similarities, output, *args, **kwargs):
    subreddits, mat = read_similarity_mat(similarities)
-    return _affinity_clustering(mat, subreddits, *args, **kwargs)
+    clustering = _affinity_clustering(mat, *args, **kwargs)
+    cluster_data = process_clustering_result(clustering, subreddits)
+    cluster_data['algorithm'] = 'affinity'
+    return(cluster_data)

 def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
    '''
-    similarities: feather file with a dataframe of similarity scores
+    similarities: matrix of similarity scores
    preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
    damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author. 
    '''
@@ -40,25 +39,14 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
                                     verbose=verbose,
                                     random_state=random_state).fit(mat)

-
-    print(f"clustering took {clustering.n_iter_} iterations")
-    clusters = clustering.labels_
-
-    print(f"found {len(set(clusters))} clusters")
-
-    cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
-
-    cluster_sizes = cluster_data.groupby("cluster").count()
-    print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
-
-    print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
-
-    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
-
-    sys.stdout.flush()
+    cluster_data = process_clustering_result(clustering, subreddits)
+    output = Path(output)
+    output.parent.mkdir(parents=True,exist_ok=True)
    cluster_data.to_feather(output)
    print(f"saved {output}")
    return clustering

+
+
 if __name__ == "__main__":
    fire.Fire(affinity_clustering)
--- a/clustering/clustering_base.py
+++ b/clustering/clustering_base.py
@@ -0,0 +1,105 @@
+from pathlib import Path
+import numpy as np
+import pandas as pd
+from dataclasses import dataclass
+from sklearn.metrics import silhouette_score, silhouette_samples
+from collections import Counter
+
+# this is meant to be an interface, not created directly
+class clustering_job:
+    def __init__(self, infile, outpath, name, call, *args, **kwargs):
+        self.outpath = Path(outpath)
+        self.call = call
+        self.args = args
+        self.kwargs = kwargs
+        self.infile = Path(infile)
+        self.name = name
+        self.hasrun = False
+
+    def run(self):
+        self.subreddits, self.mat = self.read_distance_mat(self.infile)
+        self.clustering = self.call(self.mat, *self.args, **self.kwargs)
+        self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
+        self.score = self.silhouette()
+        self.outpath.mkdir(parents=True, exist_ok=True)
+        self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
+        self.hasrun = True
+        
+    def get_info(self):
+        if not self.hasrun:
+            self.run()
+
+        self.result = clustering_result(outpath=str(self.outpath.resolve()),
+                                        silhouette_score=self.score,
+                                        name=self.name,
+                                        n_clusters=self.n_clusters,
+                                        n_isolates=self.n_isolates,
+                                        silhouette_samples = self.silsampout
+                                        )
+        return self.result
+
+    def silhouette(self):
+        counts = Counter(self.clustering.labels_)
+        singletons = [key for key, value in counts.items() if value == 1]
+        isolates = (self.clustering.labels_ == -1) | (np.isin(self.clustering.labels_,np.array(singletons)))
+        scoremat = self.mat[~isolates][:,~isolates]
+        if self.n_clusters > 1:
+            score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
+            silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
+            silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
+            self.outpath.mkdir(parents=True, exist_ok=True)
+            silsampout = self.outpath / ("silhouette_samples-" + self.name +  ".feather")
+            self.silsampout = silsampout.resolve()
+            silhouette_samp.to_feather(self.silsampout)
+        else:
+            score = None
+            self.silsampout = None
+        return score
+
+    def read_distance_mat(self, similarities, use_threads=True):
+        df = pd.read_feather(similarities, use_threads=use_threads)
+        mat = np.array(df.drop('_subreddit',1))
+        n = mat.shape[0]
+        mat[range(n),range(n)] = 1
+        return (df._subreddit,1-mat)
+
+    def process_clustering(self, clustering, subreddits):
+
+        if hasattr(clustering,'n_iter_'):
+            print(f"clustering took {clustering.n_iter_} iterations")
+
+        clusters = clustering.labels_
+        self.n_clusters = len(set(clusters))
+
+        print(f"found {self.n_clusters} clusters")
+
+        cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
+
+        cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
+        print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
+
+        print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+        n_isolates1 = (cluster_sizes.subreddit==1).sum()
+
+        print(f"{n_isolates1} clusters have 1 member")
+
+        n_isolates2 = cluster_sizes.loc[cluster_sizes.cluster==-1,:]['subreddit'].to_list()
+        if len(n_isolates2) > 0:
+            n_isloates2 = n_isolates2[0]
+        print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
+
+        if n_isolates1 == 0:
+            self.n_isolates = n_isolates2
+        else:
+            self.n_isolates = n_isolates1
+
+        return cluster_data
+
+@dataclass
+class clustering_result:
+    outpath:Path
+    silhouette_score:float
+    name:str
+    n_clusters:int
+    n_isolates:int
+    silhouette_samples:str
--- a/clustering/fit_tsne.py
+++ b/clustering/fit_tsne.py
@@ -17,7 +17,7 @@ def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=1000
    df = pd.read_feather(similarities)

    n = df.shape[0]
-    mat = np.array(df.drop('subreddit',1),dtype=np.float64)
+    mat = np.array(df.drop('_subreddit',1),dtype=np.float64)
    mat[range(n),range(n)] = 1
    mat[mat > 1] = 1
    dist = 2*np.arccos(mat)/np.pi
@@ -26,7 +26,7 @@ def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=1000

    tsne_fit_whole = tsne_fit_model.fit_transform(dist)

-    plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':df.subreddit})
+    plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], '_subreddit':df['_subreddit']})

    plot_data.to_feather(output)

--- a/clustering/grid_sweep.py
+++ b/clustering/grid_sweep.py
@@ -0,0 +1,33 @@
+from pathlib import Path
+from multiprocessing import Pool, cpu_count
+from itertools import product, chain
+import pandas as pd
+
+class grid_sweep:
+    def __init__(self, jobtype, inpath, outpath, namer, *args):
+        self.jobtype = jobtype
+        self.namer = namer
+        print(*args)
+        grid = list(product(*args))
+        inpath = Path(inpath)
+        outpath = Path(outpath)
+        self.hasrun = False
+        self.grid = [(inpath,outpath,namer(*g)) + g for g in grid]
+        self.jobs = [jobtype(*g) for g in self.grid]
+
+    def run(self, cores=20):
+        if cores is not None and cores > 1:
+            with Pool(cores) as pool:
+                infos = pool.map(self.jobtype.get_info, self.jobs)
+        else:
+            infos = map(self.jobtype.get_info, self.jobs)
+
+        self.infos = pd.DataFrame(infos)
+        self.hasrun = True
+
+    def save(self, outcsv):
+        if not self.hasrun:
+            self.run()
+        outcsv = Path(outcsv)
+        outcsv.parent.mkdir(parents=True, exist_ok=True)
+        self.infos.to_csv(outcsv)
--- a/clustering/hdbscan_clustering.py
+++ b/clustering/hdbscan_clustering.py
@@ -0,0 +1,159 @@
+from clustering_base import clustering_result, clustering_job
+from grid_sweep import grid_sweep
+from dataclasses import dataclass
+import hdbscan
+from sklearn.neighbors import NearestNeighbors
+import plotnine as pn
+import numpy as np
+from itertools import product, starmap, chain
+import pandas as pd
+from multiprocessing import cpu_count
+import fire
+
+def test_select_hdbscan_clustering():
+    # select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
+    #                           "test_hdbscan_author30k",
+    #                           min_cluster_sizes=[2],
+    #                           min_samples=[1,2],
+    #                           cluster_selection_epsilons=[0,0.05,0.1,0.15],
+    #                           cluster_selection_methods=['eom','leaf'],
+    #                           lsi_dimensions='all')
+    inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI"
+    outpath = "test_hdbscan";
+    min_cluster_sizes=[2,3,4];
+    min_samples=[1,2,3];
+    cluster_selection_epsilons=[0,0.1,0.3,0.5];
+    cluster_selection_methods=[1];
+    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),
+                             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)  
--- a/clustering/hdbscan_clustering_lsi.py
+++ b/clustering/hdbscan_clustering_lsi.py
@@ -0,0 +1,101 @@
+from hdbscan_clustering import hdbscan_job, hdbscan_grid_sweep, hdbscan_clustering_result
+from lsi_base import lsi_grid_sweep, lsi_mixin, lsi_result_mixin
+from grid_sweep import grid_sweep
+import fire
+from dataclasses import dataclass
+
+@dataclass
+class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
+    pass 
+
+class hdbscan_lsi_job(hdbscan_job, lsi_mixin):
+    def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+        super().__init__(
+                         infile,
+                         outpath,
+                         name,
+                         *args,
+                         **kwargs)
+        super().set_lsi_dims(lsi_dims)
+
+    def get_info(self):
+        partial_result = super().get_info()
+        self.result = hdbscan_clustering_result_lsi(**partial_result.__dict__,
+                                                    lsi_dimensions=self.lsi_dims)
+        return self.result
+
+class hdbscan_lsi_grid_sweep(lsi_grid_sweep):
+    def __init__(self,
+                 inpath,
+                 lsi_dims,
+                 outpath,
+                 min_cluster_sizes,
+                 min_samples,
+                 cluster_selection_epsilons,
+                 cluster_selection_methods
+                 ):
+
+        super().__init__(hdbscan_lsi_job,
+                         _hdbscan_lsi_grid_sweep,
+                         inpath,
+                         lsi_dims,
+                         outpath,
+                         min_cluster_sizes,
+                         min_samples,
+                         cluster_selection_epsilons,
+                         cluster_selection_methods)
+        
+
+
+class _hdbscan_lsi_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 lsi_dim,
+                 *args,
+                 **kwargs):
+        print(args)
+        print(kwargs)
+
+        self.lsi_dim = lsi_dim
+        self.jobtype = hdbscan_lsi_job
+        super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
+
+
+    def namer(self, *args, **kwargs):
+        s = hdbscan_grid_sweep.namer(self, *args[1:], **kwargs)
+        s += f"_lsi-{self.lsi_dim}"
+        return s
+
+def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath,  min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'):
+    """Run hdbscan clustering once or more with different parameters.
+    
+    Usage:
+    hdbscan_clustering_lsi --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=[eom]> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+    outpath: path to output fit clusterings.
+    min_cluster_sizes: one or more integers indicating the minumum cluster size
+    min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
+    cluster_selection_epsilons: one or more similarity thresholds for transition from dbscan to hdbscan
+    cluster_selection_methods: one or more of "eom" or "leaf" eom gives larger clusters. 
+    lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+    """    
+
+    obj = hdbscan_lsi_grid_sweep(inpath,
+                                 lsi_dimensions,
+                                 outpath,
+                                 list(map(int,min_cluster_sizes)),
+                                 list(map(int,min_samples)),
+                                 list(map(float,cluster_selection_epsilons)),
+                                 cluster_selection_methods)
+                                 
+
+    obj.run(10)
+    obj.save(savefile)
+
+
+if __name__ == "__main__":
+    fire.Fire(run_hdbscan_lsi_grid_sweep)
--- a/clustering/kmeans_clustering.py
+++ b/clustering/kmeans_clustering.py
@@ -0,0 +1,105 @@
+from sklearn.cluster import KMeans
+import fire
+from pathlib import Path
+from dataclasses import dataclass
+from clustering_base import clustering_result, clustering_job
+from grid_sweep import grid_sweep
+
+@dataclass
+class kmeans_clustering_result(clustering_result):
+    n_clusters:int
+    n_init:int
+    max_iter:int
+
+class kmeans_job(clustering_job):
+    def __init__(self, infile, outpath, name, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         call=kmeans_job._kmeans_clustering,
+                         n_clusters=n_clusters,
+                         n_init=n_init,
+                         max_iter=max_iter,
+                         random_state=random_state,
+                         verbose=verbose)
+
+        self.n_clusters=n_clusters
+        self.n_init=n_init
+        self.max_iter=max_iter
+
+    def _kmeans_clustering(mat, *args, **kwargs):
+
+        clustering = KMeans(*args,
+                            **kwargs,
+                            ).fit(mat)
+
+        return clustering
+
+
+    def get_info(self):
+        result = super().get_info()
+        self.result = kmeans_clustering_result(**result.__dict__,
+                                               n_init=self.n_init,
+                                               max_iter=self.max_iter)
+        return self.result
+
+
+class kmeans_grid_sweep(grid_sweep):
+       
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 *args,
+                 **kwargs):
+        super().__init__(kmeans_job, inpath, outpath, self.namer, *args, **kwargs)
+
+    def namer(self,
+             n_clusters,
+             n_init,
+             max_iter):
+        return f"nclusters-{n_clusters}_nit-{n_init}_maxit-{max_iter}"
+
+def test_select_kmeans_clustering():
+    inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
+    outpath = "test_kmeans";
+    n_clusters=[200,300,400];
+    n_init=[1,2,3];
+    max_iter=[100000]
+
+    gs = kmeans_lsi_grid_sweep(inpath, 'all', outpath, n_clusters, n_init, max_iter)
+    gs.run(1)
+
+    cluster_selection_epsilons=[0,0.1,0.3,0.5];
+    cluster_selection_methods=['eom'];
+    lsi_dimensions='all'
+    gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
+    gs.run(20)
+    gs.save("test_hdbscan/lsi_sweep.csv")
+
+def run_kmeans_grid_sweep(savefile, inpath, outpath,  n_clusters=[500], n_inits=[1], max_iters=[3000]):
+    """Run kmeans clustering once or more with different parameters.
+    
+    Usage:
+    kmeans_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --n_clusters=<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)
--- a/clustering/kmeans_clustering_lsi.py
+++ b/clustering/kmeans_clustering_lsi.py
@@ -0,0 +1,93 @@
+import fire
+from dataclasses import dataclass
+from kmeans_clustering import kmeans_job, kmeans_clustering_result, kmeans_grid_sweep
+from lsi_base import lsi_mixin, lsi_result_mixin, lsi_grid_sweep
+from grid_sweep import grid_sweep
+
+@dataclass
+class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
+    pass
+
+class kmeans_lsi_job(kmeans_job, lsi_mixin):
+    def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         *args,
+                         **kwargs)
+        super().set_lsi_dims(lsi_dims)
+
+    def get_info(self):
+        result = super().get_info()
+        self.result = kmeans_clustering_result_lsi(**result.__dict__,
+                                                   lsi_dimensions=self.lsi_dims)
+        return self.result
+
+class _kmeans_lsi_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 lsi_dim,
+                 *args,
+                 **kwargs):
+        print(args)
+        print(kwargs)
+        self.lsi_dim = lsi_dim
+        self.jobtype = kmeans_lsi_job
+        super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
+
+    def namer(self, *args, **kwargs):
+        s = kmeans_grid_sweep.namer(self, *args[1:], **kwargs)
+        s += f"_lsi-{self.lsi_dim}"
+        return s
+
+class kmeans_lsi_grid_sweep(lsi_grid_sweep):
+
+    def __init__(self,
+                 inpath,
+                 lsi_dims,
+                 outpath,
+                 n_clusters,
+                 n_inits,
+                 max_iters
+                 ):
+
+        super().__init__(kmeans_lsi_job,
+                         _kmeans_lsi_grid_sweep,
+                         inpath,
+                         lsi_dims,
+                         outpath,
+                         n_clusters,
+                         n_inits,
+                         max_iters)
+
+def run_kmeans_lsi_grid_sweep(savefile, inpath, outpath,  n_clusters=[500], n_inits=[1], max_iters=[3000], lsi_dimensions="all"):
+    """Run kmeans clustering once or more with different parameters.
+    
+    Usage:
+    kmeans_clustering_lsi.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH d--lsi_dimensions=<"all"|csv number of LSI dimensions to use> --n_clusters=<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)
--- a/clustering/lsi_base.py
+++ b/clustering/lsi_base.py
@@ -0,0 +1,29 @@
+from clustering_base import clustering_job, clustering_result
+from grid_sweep import grid_sweep
+from dataclasses import dataclass
+from itertools import chain
+from pathlib import Path
+
+class lsi_mixin():
+    def set_lsi_dims(self, lsi_dims):
+        self.lsi_dims = lsi_dims
+
+@dataclass
+class lsi_result_mixin:
+    lsi_dimensions:int
+
+class lsi_grid_sweep(grid_sweep):
+    def __init__(self, jobtype, subsweep, inpath, lsi_dimensions, outpath, *args, **kwargs):
+        self.jobtype = jobtype
+        self.subsweep = subsweep
+        inpath = Path(inpath)
+        if lsi_dimensions == 'all':
+            lsi_paths = list(inpath.glob("*.feather"))
+        else:
+            lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
+
+        print(lsi_paths)
+        lsi_nums = [int(p.stem) for p in lsi_paths]
+        self.hasrun = False
+        self.subgrids = [self.subsweep(lsi_path, outpath,  lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
+        self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))
--- a/clustering/pick_best_clustering.py
+++ b/clustering/pick_best_clustering.py
@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+import fire
+import pandas as pd
+from pathlib import Path
+import shutil
+selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv"
+
+outpath = 'test_best.feather'
+min_clusters=50; max_isolates=7500; min_cluster_size=2
+
+# pick the best clustering according to silhouette score subject to contraints
+def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size):
+    df = pd.read_csv(selection_data,index_col=0)
+    df = df.sort_values("silhouette_score",ascending=False)
+
+    # not sure I fixed the bug underlying this fully or not.
+    df['n_isolates_str'] = df.n_isolates.str.strip("[]")
+    df['n_isolates_0'] = df['n_isolates_str'].apply(lambda l: len(l) == 0)
+    df.loc[df.n_isolates_0,'n_isolates'] = 0
+    df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
+    
+    best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)]
+
+    best_cluster = best_cluster.iloc[0]
+
+    best_lsi_dimensions = best_cluster.lsi_dimensions
+    print(best_cluster.to_dict())
+    best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
+    shutil.copy(best_path,output)
+    print(f"lsi dimensions:{best_lsi_dimensions}")
+    
+if __name__ == "__main__":
+    fire.Fire(pick_best_clustering)
--- a/clustering/selection.py
+++ b/clustering/selection.py
@@ -1,101 +1,38 @@
-from sklearn.metrics import silhouette_score
-from sklearn.cluster import AffinityPropagation
-from functools import partial
-from clustering import _affinity_clustering, read_similarity_mat
-from dataclasses import dataclass
-from multiprocessing  import Pool, cpu_count, Array, Process
-from pathlib import Path
-from itertools import product, starmap
-import numpy as np
 import pandas as pd
-import fire
-import sys
+import plotnine as pn
+from pathlib import Path
+from clustering.fit_tsne import fit_tsne
+from visualization.tsne_vis import build_visualization

-# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
+df = pd.read_csv("/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv",index_col=0)

-@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
+# plot silhouette_score as a function of isolates
+df = df.sort_values("silhouette_score")

+df['n_isolates'] = df.n_isolates.str.split("\n0").apply(lambda rg: int(rg[1]))
+p = pn.ggplot(df,pn.aes(x='n_isolates',y='silhouette_score')) + pn.geom_point()
+p.save("isolates_x_score.png")

-def sim_to_dist(mat):
-    dist = 1-mat
-    dist[dist < 0] = 0
-    np.fill_diagonal(dist,0)
-    return dist
+p = pn.ggplot(df,pn.aes(y='n_clusters',x='n_isolates',color='silhouette_score')) + pn.geom_point()
+p.save("clusters_x_isolates.png")

-def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat, overwrite=False):
-    if name is None:
-        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
-    print(name)
-    sys.stdout.flush()
-    outpath = outdir / (str(name) + ".feather")
-    print(outpath)
-    clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
-    mat = sim_to_dist(clustering.affinity_matrix_)
+# 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]]

-    score = silhouette_score(mat, clustering.labels_, metric='precomputed')
+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]]

-    if alt_mat is not None:
-        alt_distances = sim_to_dist(alt_mat)
-        alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
+tsne_data = Path("./clustering/authors-tf_lsi850_tsne.feather")

-    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))
+if not tnse_data.exists():
+    fit_tsne("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather",
+             tnse_data)

-    return res
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_eom.outpath)/(best_eom['name']+'.feather'),
+                    "./authors-tf_lsi850_best_eom.html")

-# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_leaf.outpath)/(best_leaf['name']+'.feather'),
+                    "./authors-tf_lsi850_best_leaf.html")

-def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
-
-    damping = list(map(float,damping))
-    convergence_iter = convergence_iter = list(map(int,convergence_iter))
-    preference_quantile = list(map(float,preference_quantile))
-
-    if type(outdir) is str:
-        outdir = Path(outdir)
-
-    outdir.mkdir(parents=True,exist_ok=True)
-
-    subreddits, mat = read_similarity_mat(similarities,use_threads=True)
-
-    if alt_similarities is not None:
-        alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
-    else:
-        alt_mat = None
-
-    if J is None:
-        J = cpu_count()
-    pool = Pool(J)
-
-    # get list of tuples: the combinations of hyperparameters
-    hyper_grid = product(damping, convergence_iter, preference_quantile)
-    hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
-
-    _do_clustering = partial(do_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
-
-    #    similarities = Array('d', mat)
-    # call pool.starmap
-    print("running clustering selection")
-    clustering_data = pool.starmap(_do_clustering, hyper_grid)
-    clustering_data = pd.DataFrame(list(clustering_data))
-    clustering_data.to_csv(outinfo)
-    
-    return clustering_data
-
-if __name__ == "__main__":
-    x = fire.Fire(select_affinity_clustering)
--- a/density/Makefile
+++ b/density/Makefile
@@ -8,3 +8,9 @@ all: /gscratch/comdata/output/reddit_density/comment_terms_10000.feather /gscrat

 /gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
 	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather" --agg=pd.DataFrame.sum
+
+/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather
+	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather" --agg=pd.DataFrame.sum
+
+/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather
+	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather" --agg=pd.DataFrame.sum
--- a/density/job_script.sh
+++ b/density/job_script.sh
@@ -1,4 +1,4 @@
 #!/usr/bin/bash
 start_spark_cluster.sh
-spark-submit --master spark://$(hostname):18899 overlap_density.py authors --inpath=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --outpath=/gscratch/comdata/output/reddit_density/comment_authors_10000.feather --agg=pd.DataFrame.sum
-stop-all.sh
+singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 overlap_density.py authors --inpath=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather --outpath=/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather --agg=pd.DataFrame.sum
+singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
--- a/density/overlap_density.py
+++ b/density/overlap_density.py
@@ -1,11 +1,12 @@
 import pandas as pd
 from pandas.core.groupby import DataFrameGroupBy as GroupBy
+from pathlib import Path
 import fire
 import numpy as np
 import sys
 sys.path.append("..")
 sys.path.append("../similarities")
-from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_interval
+from similarities.similarities_helper import reindex_tfidf

 # this is the mean of the ratio of the overlap to the focal size.
 # mean shared membership per focal community member
@@ -13,10 +14,12 @@ from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_i

 def overlap_density(inpath, outpath, agg = pd.DataFrame.sum):
    df = pd.read_feather(inpath)
-    df = df.drop('subreddit',1)
+    df = df.drop('_subreddit',1)
    np.fill_diagonal(df.values,0)
    df = agg(df, 0).reset_index()
    df = df.rename({0:'overlap_density'},axis='columns')
+    outpath = Path(outpath)
+    outpath.parent.mkdir(parents=True, exist_ok = True)
    df.to_feather(outpath)
    return df

@@ -25,6 +28,8 @@ def overlap_density_weekly(inpath, outpath, agg = GroupBy.sum):
    # exclude the diagonal
    df = df.loc[df.subreddit != df.variable]
    res = agg(df.groupby(['subreddit','week'])).reset_index()
+    outpath = Path(outpath)
+    outpath.parent.mkdir(parents=True, exist_ok = True)
    res.to_feather(outpath)
    return res

--- a/dumps/check_comments_shas.py
+++ b/dumps/check_comments_shas.py
@@ -6,7 +6,7 @@ from os import path
 import hashlib

 shasums1 = requests.get("https://files.pushshift.io/reddit/comments/sha256sum.txt").text
-shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
+#shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text

 shasums = shasums1 + shasums2
 dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
--- a/dumps/pull_pushshift_comments.sh
+++ b/dumps/pull_pushshift_comments.sh
@@ -1,12 +1,12 @@
 #!/bin/bash

-user_agent='nathante teblunthuis <nathante@uw.edu>'
+user_agent='"nathante teblunthuis <nathante@uw.edu>"'
 output_dir='/gscratch/comdata/raw_data/reddit_dumps/comments'
 base_url='https://files.pushshift.io/reddit/comments/'

-wget -r --no-parent -A 'RC_201*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RC_201*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RC_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RC_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RC_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url


 ./check_comments_shas.py
--- a/dumps/pull_pushshift_submissions.sh
+++ b/dumps/pull_pushshift_submissions.sh
@@ -1,14 +1,14 @@
 #!/bin/bash

-user_agent='nathante teblunthuis <nathante@uw.edu>'
+user_agent='"nathante teblunthuis <nathante@uw.edu>"'
 output_dir='/gscratch/comdata/raw_data/reddit_dumps/submissions'
 base_url='https://files.pushshift.io/reddit/submissions/'

-wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
-wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
-wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
+wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
+wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
+wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/

 ./check_submission_shas.py
--- a/ngrams/tf_comments.py
+++ b/ngrams/tf_comments.py
@@ -13,10 +13,7 @@ from nltk.corpus import stopwords
 from nltk.util import ngrams
 import string
 from random import random
-
-# remove urls
-# taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
-urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)")
+from redditcleaner import clean

 # compute term frequencies for comments in each subreddit by week
 def weekly_tf(partition, mwe_pass = 'first'):
@@ -95,8 +92,8 @@ def weekly_tf(partition, mwe_pass = 'first'):
        # lowercase        
        text = text.lower()

-        # remove urls
-        text = urlregex.sub("", text)
+        # redditcleaner removes reddit markdown(newlines, quotes, bullet points, links, strikethrough, spoiler, code, superscript, table, headings)
+        text = clean(text)

        # sentence tokenize
        sentences = sent_tokenize(text)
@@ -107,14 +104,13 @@ def weekly_tf(partition, mwe_pass = 'first'):
        # remove punctuation
                        
        sentences = map(remove_punct, sentences)
-
-        # remove sentences with less than 2 words
-        sentences = filter(lambda sentence: len(sentence) > 2, sentences)
-
        # datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase.
        # they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms
        # here we take a 10 percent sample of sentences 
        if mwe_pass == 'first':
+
+            # remove sentences with less than 2 words
+            sentences = filter(lambda sentence: len(sentence) > 2, sentences)
            sentences = list(sentences)
            for sentence in sentences:
                if random() <= 0.1:
--- a/similarities/Makefile
+++ b/similarities/Makefile
@@ -1,25 +1,130 @@
-all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms.parquet
+#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
+srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
+srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
+base_data=/gscratch/comdata/output
+similarity_data=${base_data}/reddit_similarity
+tfidf_data=${similarity_data}/tfidf
+tfidf_weekly_data=${similarity_data}/tfidf_weekly
+similarity_weekly_data=${similarity_data}/weekly
+lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
+
+lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI
+
+all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet
+
+#${tfidf_weekly_data}/comment_terms_100k.parquet ${tfidf_weekly_data}/comment_authors_100k.parquet ${tfidf_weekly_data}/comment_terms_30k.parquet ${tfidf_weekly_data}/comment_authors_30k.parquet ${similarity_weekly_data}/comment_terms_100k.parquet ${similarity_weekly_data}/comment_authors_100k.parquet  ${similarity_weekly_data}/comment_terms_30k.parquet ${similarity_weekly_data}/comment_authors_30k.parquet
+
+# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_weekly_130k.parquet

 # all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet

+${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms.parquet
+	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet

-# /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_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

-/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_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

-/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
+${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

-/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
+${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

-/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
+${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

-# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet
+${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
+
+${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
+
+${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
+
+# ${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_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 ${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
--- a/similarities/TODO
+++ b/similarities/TODO
@@ -1 +0,0 @@
-Try normalizing tf by the mean or std instead of the max to avoid penalizing subreddits with very active users.
--- a/similarities/cosine_similarities.py
+++ b/similarities/cosine_similarities.py
@@ -1,12 +1,15 @@
 import pandas as pd
 import fire
 from pathlib import Path
-from similarities_helper import similarities, column_similarities
+from cdsc_ecology_utils.similarity import similarities, column_similarities
+from functools import partial

 def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):

-    return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+    return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_communities=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)

+# change so that these take in an input as an optional argument (for speed, but also for idf).
+def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):

 def term_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):

--- a/similarities/job_script.sh
+++ b/similarities/job_script.sh
@@ -1,4 +1,4 @@
 #!/usr/bin/bash
 start_spark_cluster.sh
-spark-submit --master spark://$(hostname):18899 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
-stop-all.sh
+singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname):7077 top_subreddits_by_comments.py 
+singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
--- a/similarities/lsi_similarities.py
+++ b/similarities/lsi_similarities.py
@@ -0,0 +1,85 @@
+import pandas as pd
+import fire
+from pathlib import Path
+from cdsc_ecology_utils.similarity.similarity_functions import lsi_column_similarities, similarities, 
+#from similarities_helper import similarities, lsi_column_similarities
+from functools import partial
+
+inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
+term_colname='term'
+outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
+n_components=[10,50,100]
+included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+n_iter=5
+random_state=1968
+algorithm='arpack'
+topN = None
+from_date=None
+to_date=None
+min_df=None
+max_df=None
+def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
+    print(n_components,flush=True)
+
+        
+    if lsi_model is None:
+        if type(n_components) == list:
+            lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl'
+        else:
+            lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl'
+
+    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
+
+    return similarities(inpath=inpath, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_communities=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+
+# change so that these take in an input as an optional argument (for speed, but also for idf).
+def term_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',outfile=None, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, algorithm='arpack', n_components=300,n_iter=5,random_state=1968):
+
+    res =  lsi_similarities(inpath,
+                            'term',
+                            outfile,
+                            min_df,
+                            max_df,
+                            included_subreddits,
+                            topN,
+                            from_date,
+                            to_date,
+                            n_components=n_components,
+                            algorithm = algorithm
+                            )
+    return res
+
+def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
+    return lsi_similarities(inpath,
+                            '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(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968):
+    return lsi_similarities(inpath,
+                            '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,
+                            algorithm=algorithm
+                            )
+
+
+if __name__ == "__main__":
+    fire.Fire({'term':term_lsi_similarities,
+               'author':author_lsi_similarities,
+               'author-tf':author_tf_similarities})
+
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -2,143 +2,199 @@ from pyspark.sql import SparkSession
 from pyspark.sql import Window
 from pyspark.sql import functions as f
 from enum import Enum
+from multiprocessing import cpu_count, Pool
 from pyspark.mllib.linalg.distributed import CoordinateMatrix
 from tempfile import TemporaryDirectory
 import pyarrow
 import pyarrow.dataset as ds
+from sklearn.metrics import pairwise_distances
 from scipy.sparse import csr_matrix, issparse
+from sklearn.decomposition import TruncatedSVD
 import pandas as pd
 import numpy as np
 import pathlib
 from datetime import datetime
 from pathlib import Path
+import pickle

 class tf_weight(Enum):
    MaxTF = 1
    Norm05 = 2

-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_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'
+# subreddits missing after this step don't have any terms that have a high enough idf
+# try rewriting without merges

-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(exclude_phrases)
-    tfidf_weekly = spark.read.parquet(infile)
+# does reindex_tfidf, but without reindexing.
+def reindex_tfidf(*args, **kwargs):
+    df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)

-    # create the time interval
-    if from_date is not None:
-        if type(from_date) is str:
-            from_date = datetime.fromisoformat(from_date)
+    print("assigning names")
+    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
+    batches = subreddit_names.to_batches()
    
-        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
+    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")

-    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()
+    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
-    return(tempdir, subreddit_names)
+    return(df, 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)
+def pull_tfidf(*args, **kwargs):
+    df, _, _ =  _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
+    return df

-    tfidf = spark.read.parquet(infile)
+def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
+    print(f"loading tfidf {infile}", flush=True)
+    if week is not None:
+        tfidf_ds = ds.dataset(infile, partitioning='hive')
+    else: 
+        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))))
+        included_subreddits = set(map(str.strip,open(included_subreddits)))

-    if exclude_phrases == True:
-        tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
+    ds_filter = ds.field("subreddit").isin(included_subreddits)

-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
+    if min_df is not None:
+        ds_filter &= ds.field("count") >= min_df

-    tfidf = spark.read.parquet(tempdir.name)
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
+    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
+
+    if to_date is not None:
+        ds_filter &= ds.field("week") <= to_date
+
+    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')}
+
+        print(projection)
+
+    df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
+
+    df = df.to_pandas(split_blocks=True,self_destruct=True)
+    print("assigning indexes",flush=True)
+    if reindex:
+        df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+    else:
+        df['subreddit_id_new'] = df['subreddit_id']
+
+    if reindex:
+        grouped = df.groupby(term_id)
+        df[term_id_new] = grouped.ngroup()
+    else:
+        df[term_id_new] = df[term_id]
+
+    if rescale_idf:
+        print("computing idf", flush=True)
+        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
+
+    return (df, tfidf_ds, ds_filter)
+
+    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(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
    '''
    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

+    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)}")
+        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
+        sims['_subreddit'] = subreddit_names.subreddit.values

        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"))
+        p.parent.mkdir(exist_ok=True, parents=True)

        sims.to_feather(outfile)
-    tempdir.cleanup()

-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(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
+    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(sims, outfile)

 def write_weekly_similarities(path, sims, week, names):
    sims['week'] = week
    p = pathlib.Path(path)
    if not p.is_dir():
-        p.mkdir()
+        p.mkdir(exist_ok=True,parents=True)
        
    # reformat as a pairwise list
-    sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
+    sims = sims.melt(id_vars=['_subreddit','week'],value_vars=names.subreddit.values)
    sims.to_parquet(p / week.isoformat())

 def column_overlaps(mat):
@@ -150,136 +206,75 @@ def column_overlaps(mat):

    return intersection / den
    
+def test_lsi_sims():
+    term = "term"
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    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',lsi_model_save=None,lsi_model_load=None):
+    # first compute the lsi of the matrix
+    # then take the column similarities
+
+    if type(n_components) is int:
+        n_components = [n_components]
+
+    n_components = sorted(n_components,reverse=True)
+    
+    svd_components = n_components[0]
+    
+    if lsi_model_load is not None and Path(lsi_model_load).exists():
+        print("loading LSI")
+        mod = pickle.load(open(lsi_model_load ,'rb'))
+        lsi_model_save = lsi_model_load
+
+    else:
+        print("running LSI",flush=True)
+
+        svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
+        mod = svd.fit(tfidfmat.T)
+
+    lsimat = mod.transform(tfidfmat.T)
+    if lsi_model_save is not None:
+        pickle.dump(mod, open(lsi_model_save,'wb'))
+
+    sims_list = []
+    for n_dims in n_components:
+        sims = column_similarities(lsimat[:,np.arange(n_dims)])
+        if len(n_components) > 1:
+            yield (sims, n_dims)
+        else:
+            return sims
+    
+
 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
-    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'))
-    
-    tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
-    
-    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)
+    return 1 - pairwise_distances(mat,metric='cosine')


 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
@@ -306,20 +301,20 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
    idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)

    # collect the dictionary to make a pydict of terms to indexes
-    terms = idf.select([term,'week']).distinct() # terms are distinct
+    terms = idf.select([term]).distinct() # terms are distinct

-    terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
+    terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct

    # make subreddit ids
-    subreddits = df.select(['subreddit','week']).distinct()
-    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
+    subreddits = df.select(['subreddit']).distinct()
+    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))

-    df = df.join(subreddits,on=['subreddit','week'])
+    df = df.join(subreddits,on=['subreddit'])

    # map terms to indexes in the tfs and the idfs
-    df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
+    df = df.join(terms,on=[term]) # subreddit-term-id is unique

-    idf = idf.join(terms,on=[term,'week'])
+    idf = idf.join(terms,on=[term])

    # join on subreddit/term to create tf/dfs indexed by term
    df = df.join(idf, on=[term_id, term,'week'])
@@ -331,7 +326,9 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
    else: # tf_fam = tf_weight.Norm05
        df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)

-    return df
+    df = df.repartition(400,'subreddit','week')
+    dfwriter = df.write.partitionBy("week")
+    return dfwriter

 def _calc_tfidf(df, term_colname, tf_family):
    term = term_colname
@@ -342,7 +339,7 @@ def _calc_tfidf(df, term_colname, tf_family):

    df = df.join(max_subreddit_terms, on='subreddit')

-    df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
+    df = df.withColumn("relative_tf", (df.tf / df.sr_max_tf))

    # group by term. term is unique
    idf = df.groupby([term]).count()
@@ -377,7 +374,7 @@ def _calc_tfidf(df, term_colname, tf_family):
    return df
    

-def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
    term = term_colname
    term_id = term + '_id'
    # aggregate counts by week. now subreddit-term is distinct
@@ -385,10 +382,28 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm
    df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))

    df = _calc_tfidf(df, term_colname, tf_family)
-
-    return df
+    df = df.repartition('subreddit')
+    dfwriter = df.write
+    return dfwriter

 def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
    rankdf = pd.read_csv(path)
    included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
    return included_subreddits
+
+
+def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
+                      outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"):
+    spark = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(inpath)
+    df = df.repartition(400,'subreddit')
+    df.write.parquet(outpath,mode='overwrite')
+
+    
+def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet",
+                      outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"):
+    spark = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(inpath)
+    df = df.repartition(400,'subreddit','week')
+    dfwriter = df.write.partitionBy("week")
+    dfwriter.parquet(outpath,mode='overwrite')
--- a/similarities/tfidf.py
+++ b/similarities/tfidf.py
@@ -1,7 +1,8 @@
 import fire
 from pyspark.sql import SparkSession
 from pyspark.sql import functions as f
-from similarities_helper import build_tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
+from cdsc_ecology_utils.similarity.similarity_functions import tfidf_dataset, \
+    build_weekly_tfidf_dataset, select_topN_communities

 def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
    spark = SparkSession.builder.getOrCreate()
@@ -11,27 +12,27 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
    df = df.filter(~ f.col(term_colname).isin(exclude))

    if included_subreddits is not None:
-        include_subs = set(map(str.strip,map(str.lower, open(included_subreddits))))
+        include_subs = set(map(str.strip,open(included_subreddits)))
    else:
-        include_subs = select_topN_subreddits(topN)
+        include_subs = select_topN_communities(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):
-    return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
+    return _tfidf_wrapper(tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)

 def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
    return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)

-def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
-                  topN=25000,
+def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+                  outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+                  topN=None,
                  included_subreddits=None):

-    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+    return tfidf(inpath,
                 outpath,
                 topN,
                 'author',
@@ -39,11 +40,12 @@ def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comm
                 included_subreddits=included_subreddits
                 )

-def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
-                topN=25000,
+def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+                outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+                topN=None,
                included_subreddits=None):

-    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+    return tfidf(inpath,
                 outpath,
                 topN,
                 'term',
@@ -51,11 +53,12 @@ def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/commen
                 included_subreddits=included_subreddits
                 )

-def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
-                         topN=25000,
+def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+                         outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+                         topN=None,
                         included_subreddits=None):

-    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+    return tfidf_weekly(inpath,
                        outpath,
                        topN,
                        'author',
@@ -63,12 +66,13 @@ def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfi
                        included_subreddits=included_subreddits
                        )

-def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
-                       topN=25000,
+def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+                       outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+                       topN=None,
                       included_subreddits=None):


-    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+    return tfidf_weekly(inpath,
                        outpath,
                        topN,
                        'term',
--- a/similarities/top_subreddits_by_comments.py
+++ b/similarities/top_subreddits_by_comments.py
@@ -17,7 +17,7 @@ df = df.filter(~df.subreddit.like("u_%"))
 df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))

 df = df.join(prop_nsfw,on='subreddit')
-df = df.filter(df.prop_nsfw < 0.5)
+#df = df.filter(df.prop_nsfw < 0.5)

 win = Window.orderBy(f.col('n_comments').desc())
 df = df.withColumn('comments_rank', f.rank().over(win))
@@ -26,4 +26,4 @@ df = df.toPandas()

 df = df.sort_values("n_comments")

-df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv', index=False)
+df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nsfw.csv', index=False)
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -1,81 +1,143 @@
+#!/usr/bin/env python3
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
 import numpy as np
 import pyarrow
+import pyarrow.dataset as ds
 import pandas as pd
 import fire
-from itertools import islice
+from itertools import islice, chain
 from pathlib import Path
-from similarities_helper import *
+from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
+from scipy.sparse import csr_matrix
 from multiprocessing import Pool, cpu_count
+from functools import partial

-def _week_similarities(tempdir, term_colname, week):
+infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
+tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
+min_df=None
+included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+max_df = None
+topN=100
+term_colname='author'
+# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
+# included_subreddits=None
+
+def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
    print(f"loading matrix: {week}")
-        mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
+
+    entries = pull_tfidf(infile = tfidf_path,
+                         term_colname=term_colname,
+                         min_df=min_df,
+                         max_df=max_df,
+                         included_subreddits=included_subreddits,
+                         topN=topN,
+                         week=week,
+                         rescale_idf=False)
+    
+    tfidf_colname='tf_idf'
+    # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
+
    print('computing similarities')
-        sims = column_similarities(mat)
+    sims = simfunc(mat)
    del mat
+    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
+    outfile = str(Path(outdir) / str(week))
+    write_weekly_similarities(outfile, sims, week, subreddit_names)

-        names = subreddit_names.loc[subreddit_names.week == week]
-        sims = pd.DataFrame(sims.todense())
+def pull_weeks(batch):
+    return set(batch.to_pandas()['week'])

-        sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
-        sims['_subreddit'] = names.subreddit.values
+# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week. 
+def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs):
+    term_colname= kwargs.get('term_colname')
+    #lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"

-        write_weekly_similarities(outfile, sims, week, names)
+    # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
+
+    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model)
+
+    return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)

 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, 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, simfunc=column_similarities):
    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)
-
-    # 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()
-
-    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
    # 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)
+    spark = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(tfidf_path)

-    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))
+    # load subreddits + topN
        
-def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+    subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
+    subreddit_names = subreddit_names.sort_values("subreddit_id")
+    nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
+    weeks = df.select(f.col("week")).distinct().toPandas().week.values
+    spark.stop()
+
+    print(f"computing weekly similarities")
+    week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
+
+    pool = Pool(cpu_count())
+    
+    list(pool.imap(week_similarities_helper,weeks))
+    pool.close()
+    #    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
+
+
+def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
+    return cosine_similarities_weekly(infile,
                                      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',
+def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
+        return cosine_similarities_weekly(infile,
                                          outfile,
                                          'term',
                                          min_df,
+                                          max_df,
                                          included_subreddits,
                                          topN)

+
+def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None):
+    return cosine_similarities_weekly_lsi(infile,
+                                          outfile,
+                                          'author',
+                                          min_df,
+                                          max_df,
+                                          included_subreddits,
+                                          topN,
+                                          n_components=n_components,
+                                          lsi_model=lsi_model)
+
+
+def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
+        return cosine_similarities_weekly_lsi(infile,
+                                              outfile,
+                                              'term',
+                                              min_df,
+                                              max_df,
+                                              included_subreddits,
+                                              topN,
+                                              n_components=n_components,
+                                              lsi_model=lsi_model)
+
 if __name__ == "__main__":
    fire.Fire({'authors':author_cosine_similarities_weekly,
-               'terms':term_cosine_similarities_weekly})
+               'terms':term_cosine_similarities_weekly,
+               'authors-lsi':author_cosine_similarities_weekly_lsi,
+               'terms-lsi':term_cosine_similarities_weekly
+               })
--- a/timeseries/init.py
+++ b/timeseries/init.py
@@ -0,0 +1,2 @@
+from .choose_clusters import load_clusters, load_densities
+from .cluster_timeseries import build_cluster_timeseries
--- a/timeseries/cluster_timeseries.py
+++ b/timeseries/cluster_timeseries.py
@@ -2,11 +2,11 @@ import pandas as pd
 import numpy as np
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
-from choose_clusters import load_clusters, load_densities
+from .choose_clusters import load_clusters, load_densities
 import fire
 from pathlib import Path

-def main(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
+def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
         author_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather",
         term_densities_path="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
         author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
@@ -34,4 +34,4 @@ def main(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_
    ts.write.parquet(output, mode='overwrite')

 if __name__ == "__main__":
-    fire.Fire(main)
+    fire.Fire(build_cluster_timeseries)
--- a/visualization/tsne_vis.py
+++ b/visualization/tsne_vis.py
@@ -22,8 +22,12 @@ def base_plot(plot_data):
    #
    #    subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
    
+    base_scale = alt.Scale(scheme={"name":'category10',
+                                   "extent":[0,100],
+                                   "count":10})
+
    color = alt.condition(cluster_click_select ,
-                          alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')),
+                          alt.Color(field='color',type='nominal',scale=base_scale),
                          alt.value("lightgray"))
  
    
@@ -84,6 +88,11 @@ def viewport_plot(plot_data):
    return chart

 def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
+    isolate_color = 101
+
+    cluster_sizes = clusters.groupby('cluster').count()
+    singletons = set(cluster_sizes.loc[cluster_sizes.subreddit == 1].reset_index().cluster)
+
    tsne_data = tsne_data.merge(clusters,on='subreddit')
    
    centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
@@ -120,6 +129,9 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
    color_assignments = np.repeat(-1,len(centroids))

    for i in range(len(centroids)):
+        if (centroids.iloc[i].name == -1) or (i in singletons):
+            color_assignments[i] = isolate_color
+        else:
            knn = indices[i]
            knn_colors = color_assignments[knn]
            available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
@@ -129,7 +141,6 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
            else:
                raise Exception("Can't color this many neighbors with this many colors")

-
    centroids = centroids.reset_index()
    colors = centroids.loc[:,['cluster']]
    colors['color'] = color_assignments
@@ -143,12 +154,13 @@ def build_visualization(tsne_data, clusters, output):
    # clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"

    tsne_data = pd.read_feather(tsne_data)
+    tsne_data = tsne_data.rename(columns={'_subreddit':'subreddit'})
    clusters = pd.read_feather(clusters)

    tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)

-    # sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
-    # sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
+    sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
+    sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})

    tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')
Author	SHA1	Message	Date
Nathan TeBlunthuis	930ee47d2b	refactor similarities to use submodule.	2022-01-19 15:05:49 -08:00
Nathan TeBlunthuis	98c1317af5	update pushshift dumps.	2021-12-10 21:23:32 -08:00
Nathan TeBlunthuis	541e125b28	lsi support for weekly similarities	2021-08-11 22:48:33 -07:00
Nathan TeBlunthuis	b7c39a3494	Merge branch 'master' of code:cdsc_reddit into excise_reindex	2021-08-03 15:13:39 -07:00
Nathan TeBlunthuis	ce549c6c97	Merge branch 'excise_reindex' of code:cdsc_reddit into excise_reindex	2021-08-03 15:13:21 -07:00
Nathan TeBlunthuis	6e43294a41	Updates to similarities code for smap project.	2021-08-03 15:06:48 -07:00
Nathan TeBlunthuis	2d21ff1137	Merge branch 'master' of code:cdsc_reddit into excise_reindex	2021-08-03 15:02:08 -07:00
Nate E TeBlunthuis	cf86c7492c	update clustering scripts	2021-08-03 14:55:02 -07:00
Nate E TeBlunthuis	87ffaa6858	script for picking the best clustering given constraints	2021-05-14 19:10:36 -07:00
Nate E TeBlunthuis	7b14db67de	Merge branch 'excise_reindex' of code:cdsc_reddit into excise_reindex	2021-05-13 22:28:31 -07:00
Nate E TeBlunthuis	0b95bea30e	support isolates in visualization	2021-05-13 22:26:58 -07:00
Nate E TeBlunthuis	582cf263ea	bug fix in affinity clustering	2021-05-13 22:26:15 -07:00
Nate E TeBlunthuis	8a2248fae1	Merge remote-tracking branch 'origin/excise_reindex' into temp	2021-05-10 18:32:03 -07:00
Nate E TeBlunthuis	47ba04aa97	add script for pulling cluster timeseries	2021-05-10 18:24:22 -07:00
Nate E TeBlunthuis	4cb7eeec80	Refactor to make a decent api.	2021-05-10 13:46:49 -07:00
Nate E TeBlunthuis	f05cb962e0	refactor clustring in object oriented style	2021-05-07 22:33:26 -07:00
Nate E TeBlunthuis	8d1df5b26e	refactor clustering.py into method-specific files.	2021-05-03 11:28:48 -07:00
Nate E TeBlunthuis	e1c9d9af6f	Remove 'exclude phrases' parameter.	2021-05-03 10:37:09 -07:00
Nate E TeBlunthuis	7df8436067	Use Latent semantic indexing and hdbscan	2021-05-02 23:39:55 -07:00
Nate E TeBlunthuis	36b24ee933	reindex tfidf in memory instead of using spark	2021-04-30 12:48:19 -07:00
Nate E TeBlunthuis	a013f6718b	export timeseries functions	2021-03-24 17:18:30 -07:00
				`@@ -1 +0,0 @@`
				`Try normalizing tf by the mean or std instead of the max to avoid penalizing subreddits with very active users.`