From: Nate E TeBlunthuis <nathante@n3008.hyak.local>
Date: Mon, 3 May 2021 06:39:55 +0000 (-0700)
Subject: Use Latent semantic indexing and hdbscan
X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/7df8436067dba9a9e6867424002d01593e4bcd25?ds=sidebyside

Use Latent semantic indexing and hdbscan
---

diff --git a/clustering/Makefile b/clustering/Makefile
index 338f0a6..d09cfd9 100644
--- a/clustering/Makefile
+++ b/clustering/Makefile
@@ -2,20 +2,41 @@
 srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
 similarity_data=/gscratch/comdata/output/reddit_similarity
 clustering_data=/gscratch/comdata/output/reddit_clustering
-selection_grid="--max_iter=3000 --convergence_iter=15,30,100 --damping=0.5,0.6,0.7,0.8,0.85,0.9,0.95,0.97,0.99, --preference_quantile=0.1,0.3,0.5,0.7,0.9"
+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/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv
+all:$(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
 
-$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k $(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(selection_grid) -J 20
+$(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)
 
-$(clustering_data)/subreddit_comment_terms_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k  $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv $(selection_grid) -J 20 
+$(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)
 
-$(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k  $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(selection_grid) -J 20
+$(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 $(kmeans_selection_grid)
+
+
+affinity_selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
+$(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 $(affinity_selection_grid) -J 20
+
+$(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 $(affinity_selection_grid) -J 20 
+
+$(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
+
+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
+
+PHONY: clean
 
 # $(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
diff --git a/clustering/clustering.py b/clustering/clustering.py
index 153a5c9..85be3fe 100755
--- a/clustering/clustering.py
+++ b/clustering/clustering.py
@@ -3,24 +3,23 @@
 import sys
 import pandas as pd
 import numpy as np
-from sklearn.cluster import AffinityPropagation
+from sklearn.cluster import AffinityPropagation, KMeans
 import fire
 from pathlib import Path
+from multiprocessing import cpu_count
+from dataclasses import dataclass
+from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
 
-def read_similarity_mat(similarities, use_threads=True):
-    df = pd.read_feather(similarities, use_threads=use_threads)
-    mat = np.array(df.drop('_subreddit',1))
-    n = mat.shape[0]
-    mat[range(n),range(n)] = 1
-    return (df._subreddit,mat)
-
-def affinity_clustering(similarities, *args, **kwargs):
+def affinity_clustering(similarities, output, *args, **kwargs):
     subreddits, mat = read_similarity_mat(similarities)
-    return _affinity_clustering(mat, subreddits, *args, **kwargs)
+    clustering = _affinity_clustering(mat, *args, **kwargs)
+    cluster_data = process_clustering_result(clustering, subreddits)
+    cluster_data['algorithm'] = 'affinity'
+    return(cluster_data)
 
 def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
     '''
-    similarities: feather file with a dataframe of similarity scores
+    similarities: matrix of similarity scores
     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
     damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author. 
     '''
@@ -40,25 +39,32 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
                                      verbose=verbose,
                                      random_state=random_state).fit(mat)
 
+    cluster_data = process_clustering_result(clustering, subreddits)
+    output = Path(output)
+    output.parent.mkdir(parents=True,exist_ok=True)
+    cluster_data.to_feather(output)
+    print(f"saved {output}")
+    return clustering
 
-    print(f"clustering took {clustering.n_iter_} iterations")
-    clusters = clustering.labels_
-
-    print(f"found {len(set(clusters))} clusters")
+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)
 
-    cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
+def _kmeans_clustering(mat, output, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
 
-    cluster_sizes = cluster_data.groupby("cluster").count()
-    print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
+    clustering = KMeans(n_clusters=n_clusters,
+                        n_init=n_init,
+                        max_iter=max_iter,
+                        random_state=random_state,
+                        verbose=verbose
+                        ).fit(mat)
 
-    print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+    return clustering
 
-    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
 
-    sys.stdout.flush()
-    cluster_data.to_feather(output)
-    print(f"saved {output}")
-    return clustering
 
 if __name__ == "__main__":
     fire.Fire(affinity_clustering)
diff --git a/clustering/clustering_base.py b/clustering/clustering_base.py
new file mode 100644
index 0000000..1d86438
--- /dev/null
+++ b/clustering/clustering_base.py
@@ -0,0 +1,49 @@
+from pathlib import Path
+import numpy as np
+import pandas as pd
+from dataclasses import dataclass
+
+def sim_to_dist(mat):
+    dist = 1-mat
+    dist[dist < 0] = 0
+    np.fill_diagonal(dist,0)
+    return dist
+
+def process_clustering_result(clustering, subreddits):
+
+    if hasattr(clustering,'n_iter_'):
+        print(f"clustering took {clustering.n_iter_} iterations")
+
+    clusters = clustering.labels_
+
+    print(f"found {len(set(clusters))} clusters")
+
+    cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
+
+    cluster_sizes = cluster_data.groupby("cluster").count().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")
+
+    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
+
+    print(f"{(cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])} subreddits are in cluster -1",flush=True)
+
+    return cluster_data
+
+
+@dataclass
+class clustering_result:
+    outpath:Path
+    max_iter:int
+    silhouette_score:float
+    alt_silhouette_score:float
+    name:str
+    n_clusters:int
+
+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)
diff --git a/clustering/hdbscan_clustering.py b/clustering/hdbscan_clustering.py
new file mode 100644
index 0000000..888554a
--- /dev/null
+++ b/clustering/hdbscan_clustering.py
@@ -0,0 +1,172 @@
+from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
+from dataclasses import dataclass
+import hdbscan
+from sklearn.neighbors import NearestNeighbors
+import plotnine as pn
+import numpy as np
+from itertools import product, starmap
+import pandas as pd
+from sklearn.metrics import silhouette_score, silhouette_samples
+from pathlib import Path
+from multiprocessing import Pool, cpu_count
+import fire
+from pyarrow.feather import write_feather
+
+def test_select_hdbscan_clustering():
+    select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
+                              "test_hdbscan_author30k",
+                              min_cluster_sizes=[2],
+                              min_samples=[1,2],
+                              cluster_selection_epsilons=[0,0.05,0.1,0.15],
+                              cluster_selection_methods=['eom','leaf'],
+                              lsi_dimensions='all')
+    inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI"
+    outpath = "test_hdbscan";
+    min_cluster_sizes=[2,3,4];
+    min_samples=[1,2,3];
+    cluster_selection_epsilons=[0,0.1,0.3,0.5];
+    cluster_selection_methods=['eom'];
+    lsi_dimensions='all'
+
+@dataclass
+class hdbscan_clustering_result(clustering_result):
+    min_cluster_size:int
+    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'
+                              ):
+
+    inpath = Path(inpath)
+    outpath = Path(outpath)
+    outpath.mkdir(exist_ok=True, parents=True)
+    
+    if lsi_dimensions == 'all':
+        lsi_paths = list(inpath.glob("*"))
+
+    else:
+        lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
+
+    lsi_nums = [p.stem for p in lsi_paths]
+    grid = list(product(lsi_nums,
+                        min_cluster_sizes,
+                        min_samples,
+                        cluster_selection_epsilons,
+                        cluster_selection_methods))
+
+    # fix the output file names
+    names = list(map(lambda t:'_'.join(map(str,t)),grid))
+
+    grid = [(inpath/(str(t[0])+'.feather'),outpath/(name + '.feather'), t[0], name) + t[1:] for t, name in zip(grid, names)]
+        
+    with Pool(int(cpu_count()/4)) as pool:
+        mods = starmap(hdbscan_clustering, grid)
+
+    res = pd.DataFrame(mods)
+    if outfile is None:
+        outfile = outpath / "selection_data.csv"
+
+    res.to_csv(outfile)
+
+def hdbscan_clustering(similarities, output, lsi_dim, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
+    subreddits, mat = read_similarity_mat(similarities)
+    mat = sim_to_dist(mat)
+    clustering = _hdbscan_clustering(mat,
+                                     min_cluster_size=min_cluster_size,
+                                     min_samples=min_samples,
+                                     cluster_selection_epsilon=cluster_selection_epsilon,
+                                     cluster_selection_method=cluster_selection_method,
+                                     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'))
+    
+    return(clustering)
+
+def KNN_distances_plot(mat,outname,k=2):
+    nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
+    distances, indices = nbrs.kneighbors(mat)
+    d2 = distances[:,-1]
+    df = pd.DataFrame({'dist':d2})
+    df = df.sort_values("dist",ascending=False)
+    df['idx'] = np.arange(0,d2.shape[0]) + 1
+    p = pn.qplot(x='idx',y='dist',data=df,geom='line') + pn.scales.scale_y_continuous(minor_breaks = np.arange(0,50)/50,
+                                                                                      breaks = np.arange(0,10)/10)
+    p.save(outname,width=16,height=10)
+    
+def make_KNN_plots():
+    similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10k.feather"
+    subreddits, mat = read_similarity_mat(similarities)
+    mat = sim_to_dist(mat)
+
+    KNN_distances_plot(mat,k=2,outname='terms_knn_dist2.png')
+
+    similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10k.feather"
+    subreddits, mat = read_similarity_mat(similarities)
+    mat = sim_to_dist(mat)
+    KNN_distances_plot(mat,k=2,outname='authors_knn_dist2.png')
+
+    similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k.feather"
+    subreddits, mat = read_similarity_mat(similarities)
+    mat = sim_to_dist(mat)
+    KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
+
+if __name__ == "__main__":
+    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)
diff --git a/clustering/select_affinity.py b/clustering/select_affinity.py
index 520857d..b8bd13a 100644
--- a/clustering/select_affinity.py
+++ b/clustering/select_affinity.py
@@ -1,8 +1,8 @@
 from sklearn.metrics import silhouette_score
 from sklearn.cluster import AffinityPropagation
 from functools import partial
-from clustering import _affinity_clustering, read_similarity_mat
 from dataclasses import dataclass
+from 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
@@ -12,40 +12,69 @@ import fire
 import sys
 
 # silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
-
 @dataclass
-class clustering_result:
-    outpath:Path
+class affinity_clustering_result(clustering_result):
     damping:float
-    max_iter:int
     convergence_iter:int
     preference_quantile:float
-    silhouette_score:float
-    alt_silhouette_score:float
-    name:str
 
+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))
 
-def sim_to_dist(mat):
-    dist = 1-mat
-    dist[dist < 0] = 0
-    np.fill_diagonal(dist,0)
-    return dist
+    return res
 
-def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat, overwrite=False):
+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_)
 
-    score = silhouette_score(mat, clustering.labels_, metric='precomputed')
+    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)
-        alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
+        try:
+            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
+        except ValueError:
+            alt_score = None
     
     res = clustering_result(outpath=outpath,
                             damping=damping,
@@ -58,6 +87,7 @@ def do_clustering(damping, convergence_iter, preference_quantile, name, mat, sub
 
     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):
@@ -86,7 +116,7 @@ def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max
     hyper_grid = product(damping, convergence_iter, preference_quantile)
     hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
 
-    _do_clustering = partial(do_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
+    _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
@@ -94,6 +124,7 @@ def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max
     clustering_data = pool.starmap(_do_clustering, hyper_grid)
     clustering_data = pd.DataFrame(list(clustering_data))
     clustering_data.to_csv(outinfo)
+
     
     return clustering_data
 
diff --git a/clustering/select_kmeans.py b/clustering/select_kmeans.py
new file mode 100644
index 0000000..b07a108
--- /dev/null
+++ b/clustering/select_kmeans.py
@@ -0,0 +1,92 @@
+from sklearn.metrics import silhouette_score
+from sklearn.cluster import AffinityPropagation
+from functools import partial
+from clustering import _kmeans_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
+from dataclasses import dataclass
+from multiprocessing  import Pool, cpu_count, Array, Process
+from pathlib import Path
+from itertools import product, starmap
+import numpy as np
+import pandas as pd
+import fire
+import sys
+
+@dataclass
+class kmeans_clustering_result(clustering_result):
+    n_clusters:int
+    n_init:int
+
+
+# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
+
+def do_clustering(n_clusters, n_init, name, mat, subreddits,  max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
+    if name is None:
+        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
+    print(name)
+    sys.stdout.flush()
+    outpath = outdir / (str(name) + ".feather")
+    print(outpath)
+    mat = sim_to_dist(mat)
+    clustering = _kmeans_clustering(mat, outpath, n_clusters, n_init, max_iter, random_state, verbose)
+
+    outpath.parent.mkdir(parents=True,exist_ok=True)
+    cluster_data.to_feather(outpath)
+    cluster_data = process_clustering_result(clustering, subreddits)
+
+    try: 
+        score = silhouette_score(mat, clustering.labels_, metric='precomputed')
+    except ValueError:
+        score = None
+
+    if alt_mat is not None:
+        alt_distances = sim_to_dist(alt_mat)
+        try:
+            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
+        except ValueError:
+            alt_score = None
+    
+    res = kmeans_clustering_result(outpath=outpath,
+                                   max_iter=max_iter,
+                                   n_clusters=n_clusters,
+                                   n_init = n_init,
+                                   silhouette_score=score,
+                                   alt_silhouette_score=score,
+                                   name=str(name))
+
+    return res
+
+
+# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
+def select_kmeans_clustering(similarities, outdir, outinfo, n_clusters=[1000], max_iter=100000, n_init=10, random_state=1968, verbose=True, alt_similarities=None):
+
+    n_clusters = list(map(int,n_clusters))
+    n_init  = list(map(int,n_init))
+
+    if type(outdir) is str:
+        outdir = Path(outdir)
+
+    outdir.mkdir(parents=True,exist_ok=True)
+
+    subreddits, mat = read_similarity_mat(similarities,use_threads=True)
+
+    if alt_similarities is not None:
+        alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
+    else:
+        alt_mat = None
+
+    # get list of tuples: the combinations of hyperparameters
+    hyper_grid = product(n_clusters, n_init)
+    hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
+
+    _do_clustering = partial(do_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
+
+    # call starmap
+    print("running clustering selection")
+    clustering_data = starmap(_do_clustering, hyper_grid)
+    clustering_data = pd.DataFrame(list(clustering_data))
+    clustering_data.to_csv(outinfo)
+    
+    return clustering_data
+
+if __name__ == "__main__":
+    x = fire.Fire(select_kmeans_clustering)
diff --git a/clustering/selection.py b/clustering/selection.py
new file mode 100644
index 0000000..d2fa6de
--- /dev/null
+++ b/clustering/selection.py
@@ -0,0 +1,7 @@
+import fire
+from select_affinity import select_affinity_clustering
+from select_kmeans import select_kmeans_clustering
+
+if __name__ == "__main__":
+    fire.Fire({"kmeans":select_kmeans_clustering,
+               "affinity":select_affinity_clustering})
diff --git a/similarities/Makefile b/similarities/Makefile
index 0ec0342..cfe8a49 100644
--- a/similarities/Makefile
+++ b/similarities/Makefile
@@ -1,25 +1,130 @@
-all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms.parquet
+#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
+srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
+srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
+base_data=/gscratch/comdata/output/
+similarity_data=${base_data}/reddit_similarity
+tfidf_data=${similarity_data}/tfidf
+tfidf_weekly_data=${similarity_data}/tfidf_weekly
+similarity_weekly_data=${similarity_data}/weekly
+lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
+
+lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI
+
+all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet
+
+#${tfidf_weekly_data}/comment_terms_100k.parquet ${tfidf_weekly_data}/comment_authors_100k.parquet ${tfidf_weekly_data}/comment_terms_30k.parquet ${tfidf_weekly_data}/comment_authors_30k.parquet ${similarity_weekly_data}/comment_terms_100k.parquet ${similarity_weekly_data}/comment_authors_100k.parquet  ${similarity_weekly_data}/comment_terms_30k.parquet ${similarity_weekly_data}/comment_authors_30k.parquet
+
+# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_weekly_130k.parquet
 
 # all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
 
+${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms.parquet
+	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
+
+${similarity_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
+
+${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
+	${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
+
+${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
+	${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200
+
+${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000
+
+${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000
+
+${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000
+
+${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
+
+${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
+
+${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000
+
+${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
+
+${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
+
+${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
+
+${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
+
+${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
+
+${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
+
+${tfidf_data}/comment_terms_100k.feather/: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 4 tfidf.py terms --topN=100000 --outpath=${tfidf_data}/comment_terms_100k.feather 
+
+${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 4 tfidf.py terms --topN=30000 --outpath=${tfidf_data}/comment_terms_30k.feather
+
+${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 4 tfidf.py terms --topN=10000 --outpath=${tfidf_data}/comment_terms_10k.feather
+
+${tfidf_data}/comment_authors_100k.feather: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 4 tfidf.py authors --topN=100000 --outpath=${tfidf_data}/comment_authors_100k.feather
+
+${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 4 tfidf.py authors --topN=10000 --outpath=${tfidf_data}/comment_authors_10k.parquet
+
+${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 4 tfidf.py authors --topN=30000 --outpath=${tfidf_data}/comment_authors_30k.parquet
+
+${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet
+
+${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv
+	start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
+
+${tfidf_weekly_data}/comment_terms_30k.parquet:  /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
+
+${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
+	start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
+
+${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet
+	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
+
+${similarity_weekly_data}/comment_authors_100k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
+	${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
 
-# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
-# 	start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.feather
+${similarity_weekly_data}/comment_terms_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
+	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
 
-/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 1 tfidf.py terms --topN=10000
+${similarity_weekly_data}/comment_authors_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
+	${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
 
-/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 1 tfidf.py authors --topN=10000
+# ${tfidf_weekly_data}/comment_authors_130k.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
+# 	start_spark_and_run.sh 1 tfidf.py authors_weekly --topN=130000
 
-/gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet 
-	start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
+# /gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet 
+# 	start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
 
-/gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet
-	start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
+# /gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet
+# 	start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
 
-# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet
+# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py ${tfidf_weekly_data}/comment_authors.parquet
 # 	start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet
 
-/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
-	start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
+# /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
+# 	start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
diff --git a/similarities/cosine_similarities.py b/similarities/cosine_similarities.py
index 38b1d7c..0c9c986 100644
--- a/similarities/cosine_similarities.py
+++ b/similarities/cosine_similarities.py
@@ -2,12 +2,13 @@ import pandas as pd
 import fire
 from pathlib import Path
 from similarities_helper import similarities, column_similarities
+from functools import partial
 
 def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
 
     return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
 
-
+# change so that these take in an input as an optional argument (for speed, but also for idf).
 def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
 
     return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
diff --git a/similarities/job_script.sh b/similarities/job_script.sh
index 03e77de..1f363cd 100755
--- a/similarities/job_script.sh
+++ b/similarities/job_script.sh
@@ -1,4 +1,4 @@
 #!/usr/bin/bash
 start_spark_cluster.sh
-spark-submit --master spark://$(hostname):18899 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
-stop-all.sh
+singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 lsi_similarities.py author --outfile=/gscratch/comdata/output//reddit_similarity/subreddit_comment_authors_10k_LSI.feather --topN=10000
+singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
diff --git a/similarities/lsi_similarities.py b/similarities/lsi_similarities.py
new file mode 100644
index 0000000..7ab7e8c
--- /dev/null
+++ b/similarities/lsi_similarities.py
@@ -0,0 +1,61 @@
+import pandas as pd
+import fire
+from pathlib import Path
+from similarities_helper import similarities, lsi_column_similarities
+from functools import partial
+
+def lsi_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack'):
+    print(n_components,flush=True)
+
+    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm)
+
+    return similarities(infile=infile, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+
+# change so that these take in an input as an optional argument (for speed, but also for idf).
+def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+
+    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
+                            'term',
+                            outfile,
+                            min_df,
+                            max_df,
+                            included_subreddits,
+                            topN,
+                            from_date,
+                            to_date,
+                            n_components=n_components
+                            )
+
+def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+                            'author',
+                            outfile,
+                            min_df,
+                            max_df,
+                            included_subreddits,
+                            topN,
+                            from_date=from_date,
+                            to_date=to_date,
+                            n_components=n_components
+                               )
+
+def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+                            'author',
+                            outfile,
+                            min_df,
+                            max_df,
+                            included_subreddits,
+                            topN,
+                            from_date=from_date,
+                            to_date=to_date,
+                            tfidf_colname='relative_tf',
+                            n_components=n_components
+                            )
+
+
+if __name__ == "__main__":
+    fire.Fire({'term':term_lsi_similarities,
+               'author':author_lsi_similarities,
+               'author-tf':author_tf_similarities})
+
diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index fd532a9..7f8a639 100644
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -2,6 +2,7 @@ from pyspark.sql import SparkSession
 from pyspark.sql import Window
 from pyspark.sql import functions as f
 from enum import Enum
+from multiprocessing import cpu_count, Pool
 from pyspark.mllib.linalg.distributed import CoordinateMatrix
 from tempfile import TemporaryDirectory
 import pyarrow
@@ -19,46 +20,16 @@ class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 
-infile = "/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet"
+infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
 cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
 
-def reindex_tfidf_time_interval(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(exclude_phrases)
-    tfidf_weekly = spark.read.parquet(infile)
-
-    # create the time interval
-    if from_date is not None:
-        if type(from_date) is str:
-            from_date = datetime.fromisoformat(from_date)
-
-        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
-        
-    if to_date is not None:
-        if type(to_date) is str:
-            to_date = datetime.fromisoformat(to_date)
-        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)
-
-    tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
-    tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
-    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
-    tfidf = spark.read_parquet(tempdir.name)
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
-    return(tempdir, subreddit_names)
+def termauthor_tfidf(term_tfidf_callable, author_tfidf_callable):
+    
 
 # subreddits missing after this step don't have any terms that have a high enough idf
-def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500,  tf_family=tf_weight.MaxTF):
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(exclude_phrases)
-
+# try rewriting without merges
+def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF):
+    print("loading tfidf", flush=True)
     tfidf_ds = ds.dataset(infile)
 
     if included_subreddits is None:
@@ -74,94 +45,116 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
     if max_df is not None:
         ds_filter &= ds.field("count") <= max_df
 
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-
-    df = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id',term_id,'relative_tf']).to_pandas()
-
-    sub_ids = df.subreddit_id.drop_duplicates()
-    new_sub_ids = pd.DataFrame({'subreddit_id':old,'subreddit_id_new':new} for new, old in enumerate(sorted(sub_ids)))
-    df = df.merge(new_sub_ids,on='subreddit_id',how='inner',validate='many_to_one')
+    if week is not None:
+        ds_filter &= ds.field("week") == week
 
-    new_count = df.groupby(term_id)[term_id].aggregate(new_count='count').reset_index()
-    df = df.merge(new_count,on=term_id,how='inner',validate='many_to_one')
-
-    term_ids = df[term_id].drop_duplicates()
-    new_term_ids = pd.DataFrame({term_id:old,term_id_new:new} for new, old in enumerate(sorted(term_ids)))
+    if from_date is not None:
+        ds_filter &= ds.field("week") >= from_date
 
-    df = df.merge(new_term_ids, on=term_id, validate='many_to_one')
-    N_docs = sub_ids.shape[0]
+    if to_date is not None:
+        ds_filter &= ds.field("week") <= to_date
 
-    df['idf'] = np.log(N_docs/(1+df.new_count)) + 1
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+    
+    projection = {
+        'subreddit_id':ds.field('subreddit_id'),
+        term_id:ds.field(term_id),
+        'relative_tf':ds.field("relative_tf").cast('float32')
+        }
+
+    if not rescale_idf:
+        projection = {
+            'subreddit_id':ds.field('subreddit_id'),
+            term_id:ds.field(term_id),
+            'relative_tf':ds.field('relative_tf').cast('float32'),
+            'tf_idf':ds.field('tf_idf').cast('float32')}
 
-    # agg terms by subreddit to make sparse tf/df vectors
-    if tf_family == tf_weight.MaxTF:
-        df["tf_idf"] = df.relative_tf * df.idf
-    else: # tf_fam = tf_weight.Norm05
-        df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
+    tfidf_ds = ds.dataset(infile)
 
-    subreddit_names = df.loc[:,['subreddit','subreddit_id_new']].drop_duplicates()
+    df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
+
+    df = df.to_pandas(split_blocks=True,self_destruct=True)
+    print("assigning indexes",flush=True)
+    df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+    grouped = df.groupby(term_id)
+    df[term_id_new] = grouped.ngroup()
+
+    if rescale_idf:
+        print("computing idf", flush=True)
+        df['new_count'] = grouped[term_id].transform('count')
+        N_docs = df.subreddit_id_new.max() + 1
+        df['idf'] = np.log(N_docs/(1+df.new_count),dtype='float32') + 1
+        if tf_family == tf_weight.MaxTF:
+            df["tf_idf"] = df.relative_tf * df.idf
+        else: # tf_fam = tf_weight.Norm05
+            df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
+
+    print("assigning names")
+    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
+    batches = subreddit_names.to_batches()
+
+    with Pool(cpu_count()) as pool:
+        chunks = pool.imap_unordered(pull_names,batches) 
+        subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
+
+    subreddit_names = subreddit_names.set_index("subreddit_id")
+    new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
+    new_ids = new_ids.set_index('subreddit_id')
+    subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
+    subreddit_names = subreddit_names.drop("subreddit_id",1)
     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
     return(df, subreddit_names)
 
+def pull_names(batch):
+    return(batch.to_pandas().drop_duplicates())
 
-def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
+def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
     '''
     tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
     '''
-    if from_date is not None or to_date is not None:
-        tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False, from_date=from_date, to_date=to_date)
-        mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)        
-    else:
-        entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False)
-        mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
-
-    print("loading matrix")        
 
-    #    mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
+    def proc_sims(sims, outfile):
+        if issparse(sims):
+            sims = sims.todense()
 
-    print(f'computing similarities on mat. mat.shape:{mat.shape}')
-    print(f"size of mat is:{mat.data.nbytes}")
-    sims = simfunc(mat)
-    del mat
+        print(f"shape of sims:{sims.shape}")
+        print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}",flush=True)
+        sims = pd.DataFrame(sims)
+        sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
+        sims['_subreddit'] = subreddit_names.subreddit.values
 
-    if issparse(sims):
-        sims = sims.todense()
+        p = Path(outfile)
 
-    print(f"shape of sims:{sims.shape}")
-    print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
-    sims = pd.DataFrame(sims)
-    sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['_subreddit'] = subreddit_names.subreddit.values
+        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 = Path(outfile)
+        sims.to_feather(outfile)
 
-    output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
-    output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
-    output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
-
-    sims.to_feather(outfile)
-#    tempdir.cleanup()
-
-def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
     term = term_colname
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 
-    dataset = ds.dataset(path,format='parquet')
-    entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
-    return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
+    entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
 
-def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-    dataset = ds.dataset(path,format='parquet')
-    print(f"tfidf_colname:{tfidf_colname}")
-    entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
-    return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
-    
+    print("loading matrix")        
+
+    #    mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
+
+    print(f'computing similarities on mat. mat.shape:{mat.shape}')
+    print(f"size of mat is:{mat.data.nbytes}",flush=True)
+    sims = simfunc(mat)
+    del mat
+
+    if hasattr(sims,'__next__'):
+        for simmat, name in sims:
+            proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
+    else:
+        proc_sims(simmat, outfile)
 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week
@@ -182,155 +175,62 @@ def column_overlaps(mat):
 
     return intersection / den
     
+def test_lsi_sims():
+    term = "term"
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    t1 = time.perf_counter()
+    entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet",
+                                             term_colname='term',
+                                             min_df=2000,
+                                             topN=10000
+                                             )
+    t2 = time.perf_counter()
+    print(f"first load took:{t2 - t1}s")
+
+    entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
+                                             term_colname='term',
+                                             min_df=2000,
+                                             topN=10000
+                                             )
+    t3=time.perf_counter()
+
+    print(f"second load took:{t3 - t2}s")
+
+    mat = csr_matrix((entries['tf_idf'],(entries[term_id_new], entries.subreddit_id_new)))
+    sims = list(lsi_column_similarities(mat, [10,50]))
+    sims_og = sims
+    sims_test = list(lsi_column_similarities(mat,[10,50],algorithm='randomized',n_iter=10))
+
 # n_components is the latent dimensionality. sklearn recommends 100. More might be better
-# if algorithm is 'random' instead of 'arpack' then n_iter gives the number of iterations.
+# 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=5,random_state=1968,algorithm='arpack'):
+def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
     # first compute the lsi of the matrix
     # then take the column similarities
-    svd = TruncatedSVD(n_components=n_components,random_state=random_state,algorithm='arpack')
+    print("running LSI",flush=True)
+
+    if type(n_components) is int:
+        n_components = [n_components]
+
+    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)
     lsimat = mod.transform(tfidfmat.T)
-    sims = column_similarities(lsimat)
-    return sims
+    for n_dims in n_components:
+        sims = column_similarities(lsimat[:,np.arange(n_dims)])
+        if len(n_components) > 1:
+            yield (sims, n_dims)
+        else:
+            return sims
     
 
 def column_similarities(mat):
     return 1 - pairwise_distances(mat,metric='cosine')
-    # if issparse(mat):
-    #     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
-    #     mat = mat.multiply(1/norm)
-    # else:
-    #     norm = np.matrix(np.power(np.power(mat,2).sum(axis=0),0.5,dtype=np.float32))
-    #     mat = np.multiply(mat,1/norm)
-    # sims = mat.T @ mat
-    # return(sims)
-
-
-def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-
-    if min_df is None:
-        min_df = 0.1 * len(included_subreddits)
-        tfidf = tfidf.filter(f.col('count') >= min_df)
-    if max_df is not None:
-        tfidf = tfidf.filter(f.col('count') <= max_df)
-
-    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
-
-    # we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
-    sub_ids = tfidf.select(['subreddit_id','week']).distinct()
-    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
-    tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
-
-    # only use terms in at least min_df included subreddits in a given week
-    new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
-    tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
-
-    # reset the term ids
-    term_ids = tfidf.select([term_id,'week']).distinct()
-    term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
-    tfidf = tfidf.join(term_ids,[term_id,'week'])
-
-    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
-    tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
-
-    tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
-
-    tfidf = tfidf.repartition('week')
-
-    tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
-    return(tempdir)
-    
-
-def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-
-    if min_df is None:
-        min_df = 0.1 * len(included_subreddits)
-
-    tfidf = tfidf.filter(f.col('count') >= min_df)
-    if max_df is not None:
-        tfidf = tfidf.filter(f.col('count') <= max_df)
-
-    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
-
-    # reset the subreddit ids
-    sub_ids = tfidf.select('subreddit_id').distinct()
-    sub_ids = sub_ids.withColumn("subreddit_id_new", f.row_number().over(Window.orderBy("subreddit_id")))
-    tfidf = tfidf.join(sub_ids,'subreddit_id')
-
-    # only use terms in at least min_df included subreddits
-    new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
-    tfidf = tfidf.join(new_count,term_id,how='inner')
-    
-    # reset the term ids
-    term_ids = tfidf.select([term_id]).distinct()
-    term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
-    tfidf = tfidf.join(term_ids,term_id)
-
-    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
-    tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
-    
-    tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
-    
-    tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
-    return tempdir
-
-
-# try computing cosine similarities using spark
-def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-
-    if min_df is None:
-        min_df = 0.1 * len(included_subreddits)
-
-    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
-    tfidf = tfidf.cache()
-
-    # reset the subreddit ids
-    sub_ids = tfidf.select('subreddit_id').distinct()
-    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
-    tfidf = tfidf.join(sub_ids,'subreddit_id')
-
-    # only use terms in at least min_df included subreddits
-    new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
-    tfidf = tfidf.join(new_count,term_id,how='inner')
-    
-    # reset the term ids
-    term_ids = tfidf.select([term_id]).distinct()
-    term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
-    tfidf = tfidf.join(term_ids,term_id)
-
-    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
-    tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
-
-    # step 1 make an rdd of entires
-    # sorted by (dense) spark subreddit id
-    n_partitions = int(len(included_subreddits)*2 / 5)
-
-    entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
-
-    # put like 10 subredis in each partition
-
-    # step 2 make it into a distributed.RowMatrix
-    coordMat = CoordinateMatrix(entries)
-
-    coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
-
-    # this needs to be an IndexedRowMatrix()
-    mat = coordMat.toRowMatrix()
-
-    #goal: build a matrix of subreddit columns and tf-idfs rows
-    sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
-
-    return (sim_dist, tfidf)
 
 
 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
@@ -382,7 +282,9 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     else: # tf_fam = tf_weight.Norm05
         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 
-    return df
+    df = df.repartition(400,'subreddit','week')
+    dfwriter = df.write.partitionBy("week").sortBy("subreddit")
+    return dfwriter
 
 def _calc_tfidf(df, term_colname, tf_family):
     term = term_colname
@@ -393,7 +295,7 @@ def _calc_tfidf(df, term_colname, tf_family):
 
     df = df.join(max_subreddit_terms, on='subreddit')
 
-    df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
+    df = df.withColumn("relative_tf", (df.tf / df.sr_max_tf))
 
     # group by term. term is unique
     idf = df.groupby([term]).count()
@@ -436,8 +338,9 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm
     df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
 
     df = _calc_tfidf(df, term_colname, tf_family)
-
-    return df
+    df = df.repartition('subreddit')
+    dfwriter = df.write.sortBy("subreddit","tf")
+    return dfwriter
 
 def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
     rankdf = pd.read_csv(path)
@@ -445,3 +348,18 @@ def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarit
     return included_subreddits
 
 
+def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
+                      outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"):
+    spark = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(inpath)
+    df = df.repartition(400,'subreddit')
+    df.write.parquet(outpath,mode='overwrite')
+
+    
+def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet",
+                      outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"):
+    spark = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(inpath)
+    df = df.repartition(400,'subreddit','week')
+    dfwriter = df.write.partitionBy("week")
+    dfwriter.parquet(outpath,mode='overwrite')
diff --git a/similarities/tfidf.py b/similarities/tfidf.py
index 30033a8..002e89f 100644
--- a/similarities/tfidf.py
+++ b/similarities/tfidf.py
@@ -15,10 +15,9 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
     else:
         include_subs = select_topN_subreddits(topN)
 
-    df = func(df, include_subs, term_colname)
-
-    df.write.parquet(outpath,mode='overwrite',compression='snappy')
+    dfwriter = func(df, include_subs, term_colname)
 
+    dfwriter.parquet(outpath,mode='overwrite',compression='snappy')
     spark.stop()
 
 def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
index 044ee75..e24ceee 100644
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -3,78 +3,78 @@ from pyspark.sql import SparkSession
 from pyspark.sql import Window
 import numpy as np
 import pyarrow
+import pyarrow.dataset as ds
 import pandas as pd
 import fire
-from itertools import islice
+from itertools import islice, chain
 from pathlib import Path
 from similarities_helper import *
 from multiprocessing import Pool, cpu_count
+from functools import partial
 
-def _week_similarities(tempdir, term_colname, week):
-        print(f"loading matrix: {week}")
-        mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
-        print('computing similarities')
-        sims = column_similarities(mat)
-        del mat
 
-        names = subreddit_names.loc[subreddit_names.week == week]
-        sims = pd.DataFrame(sims.todense())
+def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+    print(f"loading matrix: {week}")
+    entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
+                                             term_colname=term_colname,
+                                             min_df=min_df,
+                                             max_df=max_df,
+                                             included_subreddits=included_subreddits,
+                                             topN=topN,
+                                             week=week)
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
+    print('computing similarities')
+    sims = column_similarities(mat)
+    del mat
+    sims = pd.DataFrame(sims.todense())
+    sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
+    sims['_subreddit'] = names.subreddit.values
+    outfile = str(Path(outdir) / str(week))
+    write_weekly_similarities(outfile, sims, week, names)
 
-        sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
-        sims['_subreddit'] = names.subreddit.values
-
-        write_weekly_similarities(outfile, sims, week, names)
+def pull_weeks(batch):
+    return set(batch.to_pandas()['week'])
 
 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
     print(outfile)
-    tfidf = spark.read.parquet(tfidf_path)
-    
-    if included_subreddits is None:
-        included_subreddits = select_topN_subreddits(topN)
-    else:
-        included_subreddits = set(open(included_subreddits))
-
-    print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
-
-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df=None, included_subreddits=included_subreddits)
-
-    tfidf = spark.read.parquet(tempdir.name)
+    tfidf_ds = ds.dataset(tfidf_path)
+    tfidf_ds = tfidf_ds.to_table(columns=["week"])
+    batches = tfidf_ds.to_batches()
 
-    # the ids can change each week.
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
-    spark.stop()
+    with Pool(cpu_count()) as pool:
+        weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
 
-    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
+    weeks = sorted(weeks)
     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
 
-    def week_similarities_helper(week):
-        _week_similarities(tempdir, term_colname, week)
+    print(f"computing weekly similarities")
+    week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN)
 
     with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
         list(pool.map(week_similarities_helper,weeks))
 
-def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500):
+def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
     return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
                                       outfile,
                                       'author',
                                       min_df,
+                                      max_df,
                                       included_subreddits,
                                       topN)
 
-def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
-                                      outfile,
-                                      'term',
-                                      min_df,
-                                      included_subreddits,
-                                      topN)
+def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
+        return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+                                          outfile,
+                                          'term',
+                                          min_df,
+                                          max_df,
+                                          included_subreddits,
+                                          topN)
 
 if __name__ == "__main__":
     fire.Fire({'authors':author_cosine_similarities_weekly,