lsi support for weekly similarities

clustering/hdbscan_clustering.py

@@ -18,12 +18,12 @@ def test_select_hdbscan_clustering():
     #                           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/"
+    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)
@@ -120,7 +120,7 @@ def run_hdbscan_grid_sweep(savefile, inpath, outpath,  min_cluster_sizes=[2], mi
                              map(int,min_cluster_sizes),
                              map(int,min_samples),
                              map(float,cluster_selection_epsilons),
-                             map(float,cluster_selection_methods))
+                             cluster_selection_methods)
     obj.run()
     obj.save(savefile)
 

clustering/hdbscan_clustering_lsi.py

@@ -67,7 +67,7 @@ class _hdbscan_lsi_grid_sweep(grid_sweep):
         s += f"_lsi-{self.lsi_dim}"
         return s
 
-def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath,  min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom'],lsi_dimensions='all'):
+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:
@@ -90,8 +90,8 @@ def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath,  min_cluster_sizes=[2]
                                  list(map(int,min_cluster_sizes)),
                                  list(map(int,min_samples)),
                                  list(map(float,cluster_selection_epsilons)),
-                                 cluster_selection_methods
+                                 cluster_selection_methods)
-                                 )
+                                 
 
     obj.run(10)
     obj.save(savefile)

clustering/lsi_base.py

@@ -18,10 +18,11 @@ class lsi_grid_sweep(grid_sweep):
         self.subsweep = subsweep
         inpath = Path(inpath)
         if lsi_dimensions == 'all':
-            lsi_paths = list(inpath.glob("*"))
+            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)]

clustering/pick_best_clustering.py

@@ -1,11 +1,12 @@
+#!/usr/bin/env python3
 import fire
 import pandas as pd
 from pathlib import Path
 import shutil
-selection_data="/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv"
+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=5000; min_cluster_size=2
+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):
@@ -18,11 +19,15 @@ def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min
     df.loc[df.n_isolates_0,'n_isolates'] = 0
     df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
     
-    best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)].iloc[df.shape[1]]
+    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)

similarities/similarities_helper.py

@@ -97,6 +97,7 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
             'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
+        print(projection)
 
     df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
 
@@ -240,7 +241,6 @@ def test_lsi_sims():
 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]
@@ -249,10 +249,14 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     
     svd_components = n_components[0]
     
-    if lsi_model_load is not None:
+    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)
 

similarities/tfidf.py

@@ -4,7 +4,7 @@ from pyspark.sql import functions as f
 from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
 
 def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
-    spark = SparkSession.builder.getOrCreate()
+    spark = SparkSession.builder.getOrCreate()y
 
     df = spark.read.parquet(inpath)
 
@@ -26,11 +26,12 @@ def tfidf(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',
+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',
@@ -38,11 +39,12 @@ def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comm
                  included_subreddits=included_subreddits
                  )
 
-def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+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',
@@ -50,11 +52,12 @@ def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/commen
                  included_subreddits=included_subreddits
                  )
 
-def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+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',
@@ -62,12 +65,13 @@ def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfi
                         included_subreddits=included_subreddits
                         )
 
-def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+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',

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
@@ -8,17 +9,18 @@ import pandas as pd
 import fire
 from itertools import islice, chain
 from pathlib import Path
-from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities
+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_test.parquet"
+infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
-# tfidf_path = infile 
+tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
-# min_df=None
+min_df=None
-# max_df = None
+included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
-# topN=100
+max_df = None
-# term_colname='author'
+topN=100
+term_colname='author'
 # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
 # included_subreddits=None
 
@@ -34,7 +36,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
                          max_df=max_df,
                          included_subreddits=included_subreddits,
                          topN=topN,
-                         week=week.isoformat(),
+                         week=week,
                          rescale_idf=False)
     
     tfidf_colname='tf_idf'
@@ -42,7 +44,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
     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 = simfunc(mat.T)
+    sims = simfunc(mat)
     del mat
     sims = pd.DataFrame(sims)
     sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
@@ -53,14 +55,28 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
 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)
     # 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
@@ -68,7 +84,7 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
     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, subreddit_names=subreddit_names,nterms=nterms)
+    week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
 
     pool = Pool(cpu_count())
     
@@ -77,8 +93,8 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
     #    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):
+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('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
+    return cosine_similarities_weekly(infile,
                                       outfile,
                                       'author',
                                       min_df,
@@ -86,8 +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):
+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('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+        return cosine_similarities_weekly(infile,
                                           outfile,
                                           'term',
                                           min_df,
@@ -95,6 +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
+               })