Merge remote-tracking branch 'refs/remotes/origin/master' into master

author_cosine_similarity.py

@@ -71,8 +71,8 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
     similarities = similarities.join(df, on='j')
     similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
 
-    similarities.write_feather(output_feather)
+    similarities.to_feather(output_feather)
-    similarities.write_csv(output_csv)
+    similarities.to_csv(output_csv)
     return similarities
     
 if __name__ == '__main__':

clustering.py

@@ -0,0 +1,45 @@
+import pandas as pd
+import numpy as np
+from sklearn.cluster import AffinityPropagation
+import fire
+
+def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968):
+    '''
+    similarities: feather file with a dataframe 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.
+    '''
+
+    df = pd.read_feather(similarities)
+    n = df.shape[0]
+    mat = np.array(df.drop('subreddit',1))
+    mat[range(n),range(n)] = 1
+
+    preference = np.quantile(mat,preference_quantile)
+
+    clustering = AffinityPropagation(damping=damping,
+                                     max_iter=max_iter,
+                                     convergence_iter=convergence_iter,
+                                     copy=False,
+                                     preference=preference,
+                                     affinity='precomputed',
+                                     random_state=random_state).fit(mat)
+
+
+    print(f"clustering took {clustering.n_iter_} iterations")
+    clusters = clustering.labels_
+
+    print(f"found {len(set(clusters))} clusters")
+
+    cluster_data = pd.DataFrame({'subreddit': df.subreddit,'cluster':clustering.labels_})
+
+    cluster_sizes = cluster_data.groupby("cluster").count()
+    print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
+
+    print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+
+    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
+
+    cluster_data.to_feather(output)
+
+if __name__ == "__main__":
+    fire.Fire(affinity_clustering)

fit_tsne.py

@@ -1,35 +1,34 @@
+import fire
 import pyarrow
 import pandas as pd
 from numpy import random
 import numpy as np
 from sklearn.manifold import TSNE
 
-df = pd.read_feather("reddit_term_similarity_3000.feather")
+similarities = "term_similarities_10000.feather"
-df = df.sort_values(['i','j'])
 
-n = max(df.i.max(),df.j.max())
+def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=10000, early_exaggeration=20):
+    '''
+    similarities: feather file with a dataframe of similarity scores
+    learning_rate: parameter controlling how fast the model converges. Too low and you get outliers. Too high and you get a ball.
+    perplexity: number of neighbors to use. the default of 50 is often good.
 
-def zero_pad(grp):
+    '''
-    p = grp.shape[0]
+    df = pd.read_feather(similarities)
-    grp = grp.sort_values('j')
-    return np.concatenate([np.zeros(n-p),np.ones(1),np.array(grp.value)])
 
-col_names = df.sort_values('j').loc[:,['subreddit_j']].drop_duplicates()
+    n = df.shape[0]
-first_name = list(set(df.subreddit_i) - set(df.subreddit_j))[0]
+    mat = np.array(df.drop('subreddit',1),dtype=np.float64)
-col_names = [first_name] + list(col_names.subreddit_j)
+    mat[range(n),range(n)] = 1
-mat = df.groupby('i').apply(zero_pad)
+    mat[mat > 1] = 1
-mat.loc[n] = np.concatenate([np.zeros(n),np.ones(1)])
+    dist = 2*np.arccos(mat)/np.pi
-mat = np.stack(mat)
+    tsne_model = TSNE(2,learning_rate=750,perplexity=50,n_iter=10000,metric='precomputed',early_exaggeration=20,n_jobs=-1)
+    tsne_fit_model = tsne_model.fit(dist)
 
-mat = mat + np.tril(mat.transpose(),k=-1)
+    tsne_fit_whole = tsne_fit_model.fit_transform(dist)
-dist = 2*np.arccos(mat)/np.pi
 
-tsne_model = TSNE(2,learning_rate=750,perplexity=50,n_iter=10000,metric='precomputed',early_exaggeration=20,n_jobs=-1)
+    plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':df.subreddit})
 
-tsne_fit_model = tsne_model.fit(dist)
+    plot_data.to_feather(output)
 
-tsne_fit_whole = tsne_fit_model.fit_transform(dist)
+if __name__ == "__main__":
-
+    fire.Fire(fit_tsne)
-plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':col_names})
-
-plot_data.to_feather("tsne_subreddit_fit.feather")

similarities_helper.py

@@ -2,11 +2,67 @@ from pyspark.sql import Window
 from pyspark.sql import functions as f
 from enum import Enum
 from pyspark.mllib.linalg.distributed import CoordinateMatrix
+from tempfile import TemporaryDirectory
+import pyarrow
+import pyarrow.dataset as ds
+from scipy.sparse import csr_matrix
+import pandas as pd
+import numpy as np
 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 
+def read_tfidf_matrix(path,term_colname):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    dataset = ds.dataset(path,format='parquet')
+    entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new]).to_pandas()
+    return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+    
+def column_similarities(mat):
+    norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
+    mat = mat.multiply(1/norm)
+    sims = mat.T @ mat
+    return(sims)
+
+
+def prep_tfidf_entries(tfidf, term_colname, min_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("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'))
+#    new_count = new_count.filter(f.col('new_count') >= min_df)
+    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.withColumnRenamed("idf","idf_old")
+    # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
+    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
 
 def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
     term = term_colname

term_cosine_similarity.py

@@ -8,38 +8,23 @@ import pandas as pd
 import fire
 from itertools import islice
 from pathlib import Path
-from similarities_helper import cosine_similarities
+from similarities_helper import cosine_similarities, prep_tfidf_entries, read_tfidf_matrix, column_similarities
-
+import scipy
-spark = SparkSession.builder.getOrCreate()
+# outfile='test_similarities_500.feather';
-conf = spark.sparkContext.getConf()
+# min_df = None;
-
+# included_subreddits=None; topN=100; exclude_phrases=True;
-# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
-def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
-    '''
-    Compute similarities between subreddits based on tfi-idf vectors of comment texts 
-    
-    included_subreddits : string
-        Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
-
-    similarity_threshold : double (default = 0)
-        set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
-https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
-
-    min_df : int (default = 0.1 * (number of included_subreddits)
-         exclude terms that appear in fewer than this number of documents.
-
-    outfile: string
-         where to output csv and feather outputs
-'''
 
+def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, topN=500, exclude_phrases=False):
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
     print(outfile)
     print(exclude_phrases)
 
     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
 
     if included_subreddits is None:
-        included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
+        rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
-        included_subreddits = {s.strip('\n') for s in included_subreddits}
+        included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
 
     else:
         included_subreddits = set(open(included_subreddits))
@@ -47,7 +32,23 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
     if exclude_phrases == True:
         tfidf = tfidf.filter(~f.col(term).contains("_"))
 
-    sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries(tfidf, 'term', min_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
+    spark.stop()
+
+    print("loading matrix")
+    mat = read_tfidf_matrix(tempdir.name,'term')
+    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'] = subreddit_names.subreddit.values
 
     p = Path(outfile)
 
@@ -55,25 +56,72 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
 
-    sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
+    sims.to_feather(outfile)
+    tempdir.cleanup()
+    path = "term_tfidf_entriesaukjy5gv.parquet"
     
-    #instead of toLocalMatrix() why not read as entries and put strait into numpy
-    sim_entries = pd.read_parquet(output_parquet)
 
-    df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
+# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
-    spark.stop()
+# def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
-    df['subreddit_id_new'] = df['subreddit_id_new'] - 1
+#     '''
-    df = df.sort_values('subreddit_id_new').reset_index(drop=True)
+#     Compute similarities between subreddits based on tfi-idf vectors of comment texts 
-    df = df.set_index('subreddit_id_new')
     
-    similarities = sim_entries.join(df, on='i')
+#     included_subreddits : string
-    similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
+#         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
-    similarities = similarities.join(df, on='j')
-    similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
 
-    similarities.write_feather(output_feather)
+#     similarity_threshold : double (default = 0)
-    similarities.write_csv(output_csv)
+#         set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
-    return similarities
+# https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
+
+#     min_df : int (default = 0.1 * (number of included_subreddits)
+#          exclude terms that appear in fewer than this number of documents.
+
+#     outfile: string
+#          where to output csv and feather outputs
+# '''
+
+#     print(outfile)
+#     print(exclude_phrases)
+
+#     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
+
+#     if included_subreddits is None:
+#         included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
+#         included_subreddits = {s.strip('\n') for s in included_subreddits}
+
+#     else:
+#         included_subreddits = set(open(included_subreddits))
+
+#     if exclude_phrases == True:
+#         tfidf = tfidf.filter(~f.col(term).contains("_"))
+
+#     sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)
+
+#     p = Path(outfile)
+
+#     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
+#     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
+#     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
+
+#     sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
+    
+#     #instead of toLocalMatrix() why not read as entries and put strait into numpy
+#     sim_entries = pd.read_parquet(output_parquet)
+
+#     df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
+#     spark.stop()
+#     df['subreddit_id_new'] = df['subreddit_id_new'] - 1
+#     df = df.sort_values('subreddit_id_new').reset_index(drop=True)
+#     df = df.set_index('subreddit_id_new')
+
+#     similarities = sim_entries.join(df, on='i')
+#     similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
+#     similarities = similarities.join(df, on='j')
+#     similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
+
+#     similarities.to_feather(output_feather)
+#     similarities.to_csv(output_csv)
+#     return similarities
     
 if __name__ == '__main__':
     fire.Fire(term_cosine_similarities)

top_subreddits_by_comments.py

@@ -0,0 +1,30 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+from pyspark.mllib.linalg.distributed import RowMatrix, CoordinateMatrix
+import numpy as np
+import pyarrow
+import pandas as pd
+import fire
+from itertools import islice
+from pathlib import Path
+from similarities_helper import cosine_similarities
+
+spark = SparkSession.builder.getOrCreate()
+conf = spark.sparkContext.getConf()
+
+df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
+
+# remove /u/ pages
+df = df.filter(~df.subreddit.like("u_%"))
+
+df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))
+
+win = Window.orderBy(f.col('n_comments').desc())
+df = df.withColumn('comments_rank',f.rank().over(win))
+
+df = df.toPandas()
+
+df = df.sort_values("n_comments")
+
+df.to_csv('/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv',index=False)

visualization/data/term_affinityprop_10000.feather

@@ -0,0 +1 @@
+../../.git/annex/objects/Qk/wG/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784

visualization/data/term_affinityprop_3000.feather

@@ -0,0 +1 @@
+../../.git/annex/objects/w7/2f/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e

visualization/data/term_tsne_10000.feather

@@ -0,0 +1 @@
+../../.git/annex/objects/WX/v3/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543

visualization/data/term_tsne_3000.feather

@@ -0,0 +1 @@
+../../.git/annex/objects/mq/2z/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf