refactor similarities to use submodule.

Also lowercases included subreddits.

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "cdsc_ecology_utils"]
+	path = cdsc_ecology_utils
+	url = code:cdsc_ecology_utils

__init__.py

@@ -0,0 +1,2 @@
+from timeseries import load_clusters, load_densities, build_cluster_timeseries
+from cdsc_ecology_utils import similarity_functions

clustering/Makefile

@@ -2,41 +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
-kmeans_selection_grid="--max_iter=3000 --n_init=[10] --n_clusters=[100,500,1000,1500,2000,2500,3000,2350,3500,3570,4000]"
-#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
-all:$(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv
-# $(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/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/kmeans $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
+authors_10k_input=$(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/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/kmeans  $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
+authors_tf_10k_input=$(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/kmeans/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
-	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans  $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
+terms_10k_input=$(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) 
 
 
-affinity_selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
-$(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/affinity $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
+## LSI Models
+${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)
 
-$(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/affinity  $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20 
+${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)
 
-$(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
-	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/affinity  $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
+${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)
 
-clean:
-	rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv
+${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)
 
-PHONY: clean
+${terms_10k_output_lsi}/affinity/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py affinity_clustering.py
+	$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/affinity  --savefile=${terms_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
+
+${authors_tf_10k_output_lsi}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py affinity_clustering.py
+	$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/affinity --savefile=${authors_tf_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
+
+${authors_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py hdbscan_clustering.py
+	$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/hdbscan --savefile=${authors_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
+
+${terms_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py hdbscan_clustering.py
+	$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/hdbscan  --savefile=${terms_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
+
+${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py hdbscan_clustering.py
+	$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/hdbscan --savefile=${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
+
+${terms_10k_output_lsi}/best_hdbscan.feather:${terms_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
+	$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
+
+${authors_tf_10k_output_lsi}/best_hdbscan.feather:${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
+	$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
+
+clean_affinity:
+	rm -f ${authors_10k_output}/affinity/selection_data.csv
+	rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
+	rm -f ${terms_10k_output}/affinity/selection_data.csv
+
+clean_kmeans:
+	rm -f ${authors_10k_output}/kmeans/selection_data.csv
+	rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
+	rm -f ${terms_10k_output}/kmeans/selection_data.csv
+
+clean_hdbscan:
+	rm -f ${authors_10k_output}/hdbscan/selection_data.csv
+	rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
+	rm -f ${terms_10k_output}/hdbscan/selection_data.csv
+
+clean_authors:
+	rm -f ${authors_10k_output}/affinity/selection_data.csv
+	rm -f ${authors_10k_output}/kmeans/selection_data.csv
+	rm -f ${authors_10k_output}/hdbscan/selection_data.csv
+
+clean_authors_tf:
+	rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
+	rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
+	rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
+
+clean_terms:
+	rm -f ${terms_10k_output}/affinity/selection_data.csv
+	rm -f ${terms_10k_output}/kmeans/selection_data.csv
+	rm -f ${terms_10k_output}/hdbscan/selection_data.csv
+
+clean_lsi_affinity:
+	rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
+	rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
+
+clean_lsi_kmeans:
+	rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
+	rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
+
+clean_lsi_hdbscan:
+	rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
+	rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
+
+clean_lsi_authors:
+	rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
+	rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
+	rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
+
+clean_lsi_authors_tf:
+	rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
+
+clean_lsi_terms:
+	rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
+	rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
+	rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
+
+clean: clean_affinity clean_kmeans clean_hdbscan
+
+PHONY: clean clean_affinity clean_kmeans clean_hdbscan clean_authors clean_authors_tf clean_terms terms_10k authors_10k authors_tf_10k
 
 # $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py
 # 	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS

clustering/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)

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)

clustering/clustering.py

@@ -3,7 +3,7 @@
 import sys
 import pandas as pd
 import numpy as np
-from sklearn.cluster import AffinityPropagation, KMeans
+from sklearn.cluster import AffinityPropagation
 import fire
 from pathlib import Path
 from multiprocessing import cpu_count
@@ -46,24 +46,6 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
     print(f"saved {output}")
     return clustering
 
-def kmeans_clustering(similarities, *args, **kwargs):
-    subreddits, mat = read_similarity_mat(similarities)
-    mat = sim_to_dist(mat)
-    clustering = _kmeans_clustering(mat, *args, **kwargs)
-    cluster_data = process_clustering_result(clustering, subreddits)
-    return(cluster_data)
-
-def _kmeans_clustering(mat, output, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
-
-    clustering = KMeans(n_clusters=n_clusters,
-                        n_init=n_init,
-                        max_iter=max_iter,
-                        random_state=random_state,
-                        verbose=verbose
-                        ).fit(mat)
-
-    return clustering
-
 
 
 if __name__ == "__main__":

clustering/clustering_base.py

@@ -2,48 +2,104 @@ 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
 
-def sim_to_dist(mat):
-    dist = 1-mat
-    dist[dist < 0] = 0
-    np.fill_diagonal(dist,0)
-    return dist
+# 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 process_clustering_result(clustering, subreddits):
+    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()
 
-    if hasattr(clustering,'n_iter_'):
-        print(f"clustering took {clustering.n_iter_} iterations")
+        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
 
-    clusters = clustering.labels_
+    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
 
-    print(f"found {len(set(clusters))} clusters")
+    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)
 
-    cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
+    def process_clustering(self, clustering, subreddits):
 
-    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")
+        if hasattr(clustering,'n_iter_'):
+            print(f"clustering took {clustering.n_iter_} iterations")
 
-    print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+        clusters = clustering.labels_
+        self.n_clusters = len(set(clusters))
 
-    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
+        print(f"found {self.n_clusters} clusters")
 
-    print(f"{(cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])} subreddits are in cluster -1",flush=True)
+        cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
 
-    return cluster_data
+        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
-    max_iter:int
     silhouette_score:float
-    alt_silhouette_score:float
     name:str
     n_clusters:int
-
-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)
+    n_isolates:int
+    silhouette_samples:str

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)
 

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)

clustering/hdbscan_clustering.py

@@ -1,32 +1,57 @@
-from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
+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
+from itertools import product, starmap, chain
 import pandas as pd
-from sklearn.metrics import silhouette_score, silhouette_samples
-from pathlib import Path
-from multiprocessing import Pool, cpu_count
+from multiprocessing import cpu_count
 import fire
-from pyarrow.feather import write_feather
 
 def test_select_hdbscan_clustering():
-    select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
-                              "test_hdbscan_author30k",
-                              min_cluster_sizes=[2],
-                              min_samples=[1,2],
-                              cluster_selection_epsilons=[0,0.05,0.1,0.15],
-                              cluster_selection_methods=['eom','leaf'],
-                              lsi_dimensions='all')
-    inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI"
+    # select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
+    #                           "test_hdbscan_author30k",
+    #                           min_cluster_sizes=[2],
+    #                           min_samples=[1,2],
+    #                           cluster_selection_epsilons=[0,0.05,0.1,0.15],
+    #                           cluster_selection_methods=['eom','leaf'],
+    #                           lsi_dimensions='all')
+    inpath = "/gscratch/comdata/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=['eom'];
+    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):
@@ -34,107 +59,70 @@ class hdbscan_clustering_result(clustering_result):
     min_samples:int
     cluster_selection_epsilon:float
     cluster_selection_method:str
-    lsi_dimensions:int
-    n_isolates:int
-    silhouette_samples:str
 
-def select_hdbscan_clustering(inpath,
-                              outpath,
-                              outfile=None,
-                              min_cluster_sizes=[2],
-                              min_samples=[1],
-                              cluster_selection_epsilons=[0],
-                              cluster_selection_methods=['eom'],
-                              lsi_dimensions='all'
-                              ):
+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
+                         )
 
-    inpath = Path(inpath)
-    outpath = Path(outpath)
-    outpath.mkdir(exist_ok=True, parents=True)
+        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,
+                                    )
     
-    if lsi_dimensions == 'all':
-        lsi_paths = list(inpath.glob("*"))
-
-    else:
-        lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
-
-    lsi_nums = [p.stem for p in lsi_paths]
-    grid = list(product(lsi_nums,
-                        min_cluster_sizes,
-                        min_samples,
-                        cluster_selection_epsilons,
-                        cluster_selection_methods))
-
-    # fix the output file names
-    names = list(map(lambda t:'_'.join(map(str,t)),grid))
-
-    grid = [(inpath/(str(t[0])+'.feather'),outpath/(name + '.feather'), t[0], name) + t[1:] for t, name in zip(grid, names)]
-        
-    with Pool(int(cpu_count()/4)) as pool:
-        mods = starmap(hdbscan_clustering, grid)
-
-    res = pd.DataFrame(mods)
-    if outfile is None:
-        outfile = outpath / "selection_data.csv"
-
-    res.to_csv(outfile)
-
-def hdbscan_clustering(similarities, output, lsi_dim, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
-    subreddits, mat = read_similarity_mat(similarities)
-    mat = sim_to_dist(mat)
-    clustering = _hdbscan_clustering(mat,
-                                     min_cluster_size=min_cluster_size,
-                                     min_samples=min_samples,
-                                     cluster_selection_epsilon=cluster_selection_epsilon,
-                                     cluster_selection_method=cluster_selection_method,
-                                     metric='precomputed',
-                                     core_dist_n_jobs=cpu_count()
-                                     )
-
-    cluster_data = process_clustering_result(clustering, subreddits)
-    isolates = clustering.labels_ == -1
-    scoremat = mat[~isolates][:,~isolates]
-    score = silhouette_score(scoremat, clustering.labels_[~isolates], metric='precomputed')
-    cluster_data.to_feather(output)
-
-    silhouette_samp = silhouette_samples(mat, clustering.labels_, metric='precomputed')
-    silhouette_samp = pd.DataFrame({'subreddit':subreddits,'score':silhouette_samp})
-    silsampout = output.parent / ("silhouette_samples" + output.name)
-    silhouette_samp.to_feather(silsampout)
-
-    result = hdbscan_clustering_result(outpath=output,
-                                       max_iter=None,
-                                       silhouette_samples=silsampout,
-                                       silhouette_score=score,
-                                       alt_silhouette_score=score,
-                                       name=name,
-                                       min_cluster_size=min_cluster_size,
-                                       min_samples=min_samples,
-                                       cluster_selection_epsilon=cluster_selection_epsilon,
-                                       cluster_selection_method=cluster_selection_method,
-                                       lsi_dimensions=lsi_dim,
-                                       n_isolates=isolates.sum(),
-                                       n_clusters=len(set(clustering.labels_))
-                                   )
-
-
-                                       
-    return(result)
-
-# for all runs we should try cluster_selection_epsilon = None
-# for terms we should try cluster_selection_epsilon around 0.56-0.66
-# for authors we should try cluster_selection_epsilon around 0.98-0.99
-def _hdbscan_clustering(mat, *args, **kwargs):
-    print(f"running hdbscan clustering. args:{args}. kwargs:{kwargs}")
-
-    print(mat)
-    clusterer = hdbscan.HDBSCAN(*args,
-                                **kwargs,
-                                )
+        clustering = clusterer.fit(mat.astype('double'))
     
-    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.
     
-    return(clustering)
+    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)
@@ -165,8 +153,7 @@ def make_KNN_plots():
     KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
 
 if __name__ == "__main__":
-    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)
+    fire.Fire(run_hdbscan_grid_sweep)
+    
+#    test_select_hdbscan_clustering()
+    #fire.Fire(select_hdbscan_clustering)  

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)

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)

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)

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)))

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)

clustering/select_affinity.py

@@ -1,132 +0,0 @@
-from sklearn.metrics import silhouette_score
-from sklearn.cluster import AffinityPropagation
-from functools import partial
-from dataclasses import dataclass
-from clustering import _affinity_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
-from multiprocessing  import Pool, cpu_count, Array, Process
-from pathlib import Path
-from itertools import product, starmap
-import numpy as np
-import pandas as pd
-import fire
-import sys
-
-# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
-@dataclass
-class affinity_clustering_result(clustering_result):
-    damping:float
-    convergence_iter:int
-    preference_quantile:float
-
-def do_affinity_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat, overwrite=False):
-    if name is None:
-        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
-    print(name)
-    sys.stdout.flush()
-    outpath = outdir / (str(name) + ".feather")
-    outpath.parent.mkdir(parents=True,exist_ok=True)
-    print(outpath)
-    clustering = _affinity_clustering(mat, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
-    cluster_data = process_clustering_result(clustering, subreddits)
-    mat = sim_to_dist(clustering.affinity_matrix_)
-
-    try: 
-        score = silhouette_score(mat, clustering.labels_, metric='precomputed')
-    except ValueError:
-        score = None
-
-    if alt_mat is not None:
-        alt_distances = sim_to_dist(alt_mat)
-        try:
-            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
-        except ValueError:
-            alt_score = None
-    
-    res = affinity_clustering_result(outpath=outpath,
-                                     damping=damping,
-                                     max_iter=max_iter,
-                                     convergence_iter=convergence_iter,
-                                     preference_quantile=preference_quantile,
-                                     silhouette_score=score,
-                                     alt_silhouette_score=score,
-                                     name=str(name))
-
-    return res
-
-def do_affinity_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat, overwrite=False):
-    if name is None:
-        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
-    print(name)
-    sys.stdout.flush()
-    outpath = outdir / (str(name) + ".feather")
-    outpath.parent.mkdir(parents=True,exist_ok=True)
-    print(outpath)
-    clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
-    mat = sim_to_dist(clustering.affinity_matrix_)
-
-    try: 
-        score = silhouette_score(mat, clustering.labels_, metric='precomputed')
-    except ValueError:
-        score = None
-
-    if alt_mat is not None:
-        alt_distances = sim_to_dist(alt_mat)
-        try:
-            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
-        except ValueError:
-            alt_score = None
-    
-    res = clustering_result(outpath=outpath,
-                            damping=damping,
-                            max_iter=max_iter,
-                            convergence_iter=convergence_iter,
-                            preference_quantile=preference_quantile,
-                            silhouette_score=score,
-                            alt_silhouette_score=score,
-                            name=str(name))
-
-    return res
-
-
-# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
-
-def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
-
-    damping = list(map(float,damping))
-    convergence_iter = convergence_iter = list(map(int,convergence_iter))
-    preference_quantile = list(map(float,preference_quantile))
-
-    if type(outdir) is str:
-        outdir = Path(outdir)
-
-    outdir.mkdir(parents=True,exist_ok=True)
-
-    subreddits, mat = read_similarity_mat(similarities,use_threads=True)
-
-    if alt_similarities is not None:
-        alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
-    else:
-        alt_mat = None
-
-    if J is None:
-        J = cpu_count()
-    pool = Pool(J)
-
-    # get list of tuples: the combinations of hyperparameters
-    hyper_grid = product(damping, convergence_iter, preference_quantile)
-    hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
-
-    _do_clustering = partial(do_affinity_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
-
-    #    similarities = Array('d', mat)
-    # call pool.starmap
-    print("running clustering selection")
-    clustering_data = pool.starmap(_do_clustering, hyper_grid)
-    clustering_data = pd.DataFrame(list(clustering_data))
-    clustering_data.to_csv(outinfo)
-
-    
-    return clustering_data
-
-if __name__ == "__main__":
-    x = fire.Fire(select_affinity_clustering)

clustering/select_kmeans.py

@@ -1,92 +0,0 @@
-from sklearn.metrics import silhouette_score
-from sklearn.cluster import AffinityPropagation
-from functools import partial
-from clustering import _kmeans_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
-from dataclasses import dataclass
-from multiprocessing  import Pool, cpu_count, Array, Process
-from pathlib import Path
-from itertools import product, starmap
-import numpy as np
-import pandas as pd
-import fire
-import sys
-
-@dataclass
-class kmeans_clustering_result(clustering_result):
-    n_clusters:int
-    n_init:int
-
-
-# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
-
-def do_clustering(n_clusters, n_init, name, mat, subreddits,  max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
-    if name is None:
-        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
-    print(name)
-    sys.stdout.flush()
-    outpath = outdir / (str(name) + ".feather")
-    print(outpath)
-    mat = sim_to_dist(mat)
-    clustering = _kmeans_clustering(mat, outpath, n_clusters, n_init, max_iter, random_state, verbose)
-
-    outpath.parent.mkdir(parents=True,exist_ok=True)
-    cluster_data.to_feather(outpath)
-    cluster_data = process_clustering_result(clustering, subreddits)
-
-    try: 
-        score = silhouette_score(mat, clustering.labels_, metric='precomputed')
-    except ValueError:
-        score = None
-
-    if alt_mat is not None:
-        alt_distances = sim_to_dist(alt_mat)
-        try:
-            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
-        except ValueError:
-            alt_score = None
-    
-    res = kmeans_clustering_result(outpath=outpath,
-                                   max_iter=max_iter,
-                                   n_clusters=n_clusters,
-                                   n_init = n_init,
-                                   silhouette_score=score,
-                                   alt_silhouette_score=score,
-                                   name=str(name))
-
-    return res
-
-
-# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
-def select_kmeans_clustering(similarities, outdir, outinfo, n_clusters=[1000], max_iter=100000, n_init=10, random_state=1968, verbose=True, alt_similarities=None):
-
-    n_clusters = list(map(int,n_clusters))
-    n_init  = list(map(int,n_init))
-
-    if type(outdir) is str:
-        outdir = Path(outdir)
-
-    outdir.mkdir(parents=True,exist_ok=True)
-
-    subreddits, mat = read_similarity_mat(similarities,use_threads=True)
-
-    if alt_similarities is not None:
-        alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
-    else:
-        alt_mat = None
-
-    # get list of tuples: the combinations of hyperparameters
-    hyper_grid = product(n_clusters, n_init)
-    hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
-
-    _do_clustering = partial(do_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
-
-    # call starmap
-    print("running clustering selection")
-    clustering_data = starmap(_do_clustering, hyper_grid)
-    clustering_data = pd.DataFrame(list(clustering_data))
-    clustering_data.to_csv(outinfo)
-    
-    return clustering_data
-
-if __name__ == "__main__":
-    x = fire.Fire(select_kmeans_clustering)

clustering/selection.py

@@ -1,7 +1,38 @@
-import fire
-from select_affinity import select_affinity_clustering
-from select_kmeans import select_kmeans_clustering
+import pandas as pd
+import plotnine as pn
+from pathlib import Path
+from clustering.fit_tsne import fit_tsne
+from visualization.tsne_vis import build_visualization
+
+df = pd.read_csv("/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv",index_col=0)
+
+# plot silhouette_score as a function of isolates
+df = df.sort_values("silhouette_score")
+
+df['n_isolates'] = df.n_isolates.str.split("\n0").apply(lambda rg: int(rg[1]))
+p = pn.ggplot(df,pn.aes(x='n_isolates',y='silhouette_score')) + pn.geom_point()
+p.save("isolates_x_score.png")
+
+p = pn.ggplot(df,pn.aes(y='n_clusters',x='n_isolates',color='silhouette_score')) + pn.geom_point()
+p.save("clusters_x_isolates.png")
+
+# the best result for hdbscan seems like this one: it has a decent number of 
+# i think I prefer the 'eom' clustering style because larger clusters are less likely to suffer from ommitted variables
+best_eom = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='eom')&(df.min_cluster_size==2)].iloc[df.shape[1]]
+
+best_lsi = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='leaf')&(df.min_cluster_size==2)].iloc[df.shape[1]]
+
+tsne_data = Path("./clustering/authors-tf_lsi850_tsne.feather")
+
+if not tnse_data.exists():
+    fit_tsne("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather",
+             tnse_data)
+
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_eom.outpath)/(best_eom['name']+'.feather'),
+                    "./authors-tf_lsi850_best_eom.html")
+
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_leaf.outpath)/(best_leaf['name']+'.feather'),
+                    "./authors-tf_lsi850_best_leaf.html")
 
-if __name__ == "__main__":
-    fire.Fire({"kmeans":select_kmeans_clustering,
-               "affinity":select_affinity_clustering})

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

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

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
 

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"

dumps/pull_pushshift_comments.sh

@@ -1,14 +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
 
-# starting in 2020 we use daily dumps not monthly dumps
-wget -r --no-parent -A 'RC_202*.gz' -U $user_agent -P $output_dir -nd -nc $base_url/daily/
 
 ./check_comments_shas.py

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

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:

similarities/Makefile

@@ -1,7 +1,7 @@
 #all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
 srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
 srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
-base_data=/gscratch/comdata/output/
+base_data=/gscratch/comdata/output
 similarity_data=${base_data}/reddit_similarity
 tfidf_data=${similarity_data}/tfidf
 tfidf_weekly_data=${similarity_data}/tfidf_weekly
@@ -97,7 +97,7 @@ ${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/outpu
 	start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
 
 ${tfidf_weekly_data}/comment_terms_30k.parquet:  /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
+	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

similarities/cosine_similarities.py

@@ -1,17 +1,19 @@
 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):
 
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
+def term_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
+
+    return cosine_similarities(infile,
                                'term',
                                outfile,
                                min_df,
@@ -23,8 +25,8 @@ def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subredd
                                to_date
                                )
 
-def author_cosine_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+def author_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
+    return cosine_similarities(infile,
                                'author',
                                outfile,
                                min_df,
@@ -36,8 +38,8 @@ def author_cosine_similarities(outfile, min_df=2, max_df=None, included_subreddi
                                to_date=to_date
                                )
 
-def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+def author_tf_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
+    return cosine_similarities(infile,
                                'author',
                                outfile,
                                min_df,

similarities/job_script.sh

@@ -1,4 +1,4 @@
 #!/usr/bin/bash
 start_spark_cluster.sh
-singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 lsi_similarities.py author --outfile=/gscratch/comdata/output//reddit_similarity/subreddit_comment_authors_10k_LSI.feather --topN=10000
+singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname):7077 top_subreddits_by_comments.py 
 singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh

similarities/lsi_similarities.py

@@ -1,20 +1,41 @@
 import pandas as pd
 import fire
 from pathlib import Path
-from similarities_helper import similarities, lsi_column_similarities
+from cdsc_ecology_utils.similarity.similarity_functions import lsi_column_similarities, similarities, 
+#from similarities_helper import similarities, lsi_column_similarities
 from functools import partial
 
-def lsi_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack'):
+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)
 
-    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm)
+        
+    if lsi_model is None:
+        if type(n_components) == list:
+            lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl'
+        else:
+            lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl'
 
-    return similarities(infile=infile, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+    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(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+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):
 
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
+    res =  lsi_similarities(inpath,
                             'term',
                             outfile,
                             min_df,
@@ -23,11 +44,13 @@ def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits
                             topN,
                             from_date,
                             to_date,
-                            n_components=n_components
+                            n_components=n_components,
+                            algorithm = algorithm
                             )
+    return res
 
-def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+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,
@@ -39,8 +62,8 @@ def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=
                             n_components=n_components
                                )
 
-def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+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,
@@ -50,7 +73,8 @@ def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=N
                             from_date=from_date,
                             to_date=to_date,
                             tfidf_colname='relative_tf',
-                            n_components=n_components
+                            n_components=n_components,
+                            algorithm=algorithm
                             )
 
 

similarities/similarities_helper.py

@@ -15,27 +15,53 @@ import numpy as np
 import pathlib
 from datetime import datetime
 from pathlib import Path
+import pickle
 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 
-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
-cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
-
-def termauthor_tfidf(term_tfidf_callable, author_tfidf_callable):
-    
+# 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"
 
 # subreddits missing after this step don't have any terms that have a high enough idf
 # try rewriting without merges
-def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF):
-    print("loading tfidf", flush=True)
-    tfidf_ds = ds.dataset(infile)
+
+# does reindex_tfidf, but without reindexing.
+def reindex_tfidf(*args, **kwargs):
+    df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
+
+    print("assigning names")
+    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
+    batches = subreddit_names.to_batches()
+    
+    with Pool(cpu_count()) as pool:
+        chunks = pool.imap_unordered(pull_names,batches) 
+        subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
+        subreddit_names = subreddit_names.set_index("subreddit_id")
+
+    new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
+    new_ids = new_ids.set_index('subreddit_id')
+    subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
+    subreddit_names = subreddit_names.drop("subreddit_id",1)
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    return(df, subreddit_names)
+
+def pull_tfidf(*args, **kwargs):
+    df, _, _ =  _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
+    return df
+
+def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
+    print(f"loading tfidf {infile}", flush=True)
+    if week is not None:
+        tfidf_ds = ds.dataset(infile, partitioning='hive')
+    else: 
+        tfidf_ds = ds.dataset(infile)
 
     if included_subreddits is None:
         included_subreddits = select_topN_subreddits(topN)
     else:
-        included_subreddits = set(open(included_subreddits))
+        included_subreddits = set(map(str.strip,open(included_subreddits)))
 
     ds_filter = ds.field("subreddit").isin(included_subreddits)
 
@@ -71,15 +97,22 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
             'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
-    tfidf_ds = ds.dataset(infile)
+        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)
-    df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
-    grouped = df.groupby(term_id)
-    df[term_id_new] = grouped.ngroup()
+    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)
@@ -91,9 +124,7 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
         else: # tf_fam = tf_weight.Norm05
             df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
 
-    print("assigning names")
-    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
-    batches = subreddit_names.to_batches()
+    return (df, tfidf_ds, ds_filter)
 
     with Pool(cpu_count()) as pool:
         chunks = pool.imap_unordered(pull_names,batches) 
@@ -110,7 +141,7 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
 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, 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.
     '''
@@ -130,7 +161,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
         output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
         output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
-        outfile.parent.mkdir(exist_ok=True, parents=True)
+        p.parent.mkdir(exist_ok=True, parents=True)
 
         sims.to_feather(outfile)
 
@@ -138,7 +169,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 
-    entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
+    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)))
 
     print("loading matrix")        
@@ -154,7 +185,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         for simmat, name in sims:
             proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
     else:
-        proc_sims(simmat, outfile)
+        proc_sims(sims, outfile)
 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week
@@ -207,10 +238,9 @@ def test_lsi_sims():
 # if n_components is a list we'll return a list of similarities with different latent dimensionalities
 # if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
 # this function takes the svd and then the column similarities of it
-def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
+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
-    print("running LSI",flush=True)
 
     if type(n_components) is int:
         n_components = [n_components]
@@ -218,9 +248,23 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     n_components = sorted(n_components,reverse=True)
     
     svd_components = n_components[0]
-    svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
-    mod = svd.fit(tfidfmat.T)
+    
+    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:
@@ -257,20 +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'])
@@ -283,7 +327,7 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 
     df = df.repartition(400,'subreddit','week')
-    dfwriter = df.write.partitionBy("week").sortBy("subreddit")
+    dfwriter = df.write.partitionBy("week")
     return dfwriter
 
 def _calc_tfidf(df, term_colname, tf_family):
@@ -330,7 +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
@@ -339,7 +383,7 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm
 
     df = _calc_tfidf(df, term_colname, tf_family)
     df = df.repartition('subreddit')
-    dfwriter = df.write.sortBy("subreddit","tf")
+    dfwriter = df.write
     return dfwriter
 
 def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):

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()
@@ -11,9 +12,9 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
     df = df.filter(~ f.col(term_colname).isin(exclude))
 
     if included_subreddits is not None:
-        include_subs = list(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)
 
     dfwriter = func(df, include_subs, term_colname)
 
@@ -21,54 +22,62 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
     spark.stop()
 
 def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
-    return _tfidf_wrapper(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',
                  ['[deleted]','AutoModerator'],
-                 included_subreddits=None
+                 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',
                  [],
-                 included_subreddits=None
+                 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',
                         ['[deleted]','AutoModerator'],
-                        included_subreddits=None
+                        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',
                         [],
-                        included_subreddits=None
+                        included_subreddits=included_subreddits
                         )
 
 

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)

similarities/weekly_cosine_similarities.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
@@ -8,58 +9,92 @@ import pandas as pd
 import fire
 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
 
+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):
+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}")
-    entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
-                                             term_colname=term_colname,
-                                             min_df=min_df,
-                                             max_df=max_df,
-                                             included_subreddits=included_subreddits,
-                                             topN=topN,
-                                             week=week)
-    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
+
+    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.todense())
+    sims = pd.DataFrame(sims)
     sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['_subreddit'] = names.subreddit.values
+    sims['_subreddit'] = subreddit_names.subreddit.values
     outfile = str(Path(outdir) / str(week))
-    write_weekly_similarities(outfile, sims, week, names)
+    write_weekly_similarities(outfile, sims, week, subreddit_names)
 
 def pull_weeks(batch):
     return set(batch.to_pandas()['week'])
 
+# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week. 
+def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs):
+    term_colname= kwargs.get('term_colname')
+    #lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
+
+    # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
+
+    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model)
+
+    return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
+
 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
+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_ds = ds.dataset(tfidf_path)
-    tfidf_ds = tfidf_ds.to_table(columns=["week"])
-    batches = tfidf_ds.to_batches()
-
-    with Pool(cpu_count()) as pool:
-        weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
-
-    weeks = sorted(weeks)
     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
 
+    spark = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(tfidf_path)
+
+    # load subreddits + topN
+        
+    subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
+    subreddit_names = subreddit_names.sort_values("subreddit_id")
+    nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
+    weeks = df.select(f.col("week")).distinct().toPandas().week.values
+    spark.stop()
+
     print(f"computing weekly similarities")
-    week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN)
+    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)
 
-    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))
+    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, min_df=2, max_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+
+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,
@@ -67,8 +102,8 @@ def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_s
                                       included_subreddits,
                                       topN)
 
-def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
-        return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+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,
@@ -76,6 +111,33 @@ def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_
                                           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
+               })

timeseries/__init__.py

@@ -0,0 +1,2 @@
+from .choose_clusters import load_clusters, load_densities
+from .cluster_timeseries import build_cluster_timeseries

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)

visualization/tsne_vis.py

@@ -22,8 +22,12 @@ def base_plot(plot_data):
     #
     #    subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
     
+    base_scale = alt.Scale(scheme={"name":'category10',
+                                   "extent":[0,100],
+                                   "count":10})
+
     color = alt.condition(cluster_click_select ,
-                          alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')),
+                          alt.Color(field='color',type='nominal',scale=base_scale),
                           alt.value("lightgray"))
   
     
@@ -84,6 +88,11 @@ def viewport_plot(plot_data):
     return chart
 
 def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
+    isolate_color = 101
+
+    cluster_sizes = clusters.groupby('cluster').count()
+    singletons = set(cluster_sizes.loc[cluster_sizes.subreddit == 1].reset_index().cluster)
+
     tsne_data = tsne_data.merge(clusters,on='subreddit')
     
     centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
@@ -120,15 +129,17 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
     color_assignments = np.repeat(-1,len(centroids))
 
     for i in range(len(centroids)):
-        knn = indices[i]
-        knn_colors = color_assignments[knn]
-        available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
-
-        if(len(available_colors) > 0):
-            color_assignments[i] = available_colors[0]
+        if (centroids.iloc[i].name == -1) or (i in singletons):
+            color_assignments[i] = isolate_color
         else:
-            raise Exception("Can't color this many neighbors with this many colors")
+            knn = indices[i]
+            knn_colors = color_assignments[knn]
+            available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
 
+            if(len(available_colors) > 0):
+                color_assignments[i] = available_colors[0]
+            else:
+                raise Exception("Can't color this many neighbors with this many colors")
 
     centroids = centroids.reset_index()
     colors = centroids.loc[:,['cluster']]
@@ -143,12 +154,13 @@ def build_visualization(tsne_data, clusters, output):
     # clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
 
     tsne_data = pd.read_feather(tsne_data)
+    tsne_data = tsne_data.rename(columns={'_subreddit':'subreddit'})
     clusters = pd.read_feather(clusters)
 
     tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
 
-    # sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
-    # sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
+    sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
+    sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
 
     tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')