similarities/similarities_helper.py

   1 from pyspark.sql import SparkSession
   2 from pyspark.sql import Window
   3 from pyspark.sql import functions as f
   4 from enum import Enum
   5 from pyspark.mllib.linalg.distributed import CoordinateMatrix
   6 from tempfile import TemporaryDirectory
   7 import pyarrow
   8 import pyarrow.dataset as ds
   9 from scipy.sparse import csr_matrix, issparse
  10 import pandas as pd
  11 import numpy as np
  12 import pathlib
  13 from datetime import datetime
  14 from pathlib import Path
  15
  16 class tf_weight(Enum):
  17     MaxTF = 1
  18     Norm05 = 2
  19
  20 infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet"
  21
  22 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):
  23     term = term_colname
  24     term_id = term + '_id'
  25     term_id_new = term + '_id_new'
  26
  27     spark = SparkSession.builder.getOrCreate()
  28     conf = spark.sparkContext.getConf()
  29     print(exclude_phrases)
  30     tfidf_weekly = spark.read.parquet(infile)
  31
  32     # create the time interval
  33     if from_date is not None:
  34         if type(from_date) is str:
  35             from_date = datetime.fromisoformat(from_date)
  36
  37         tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
  38
  39     if to_date is not None:
  40         if type(to_date) is str:
  41             to_date = datetime.fromisoformat(to_date)
  42         tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)
  43
  44     tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
  45     tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
  46     tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
  47     tfidf = spark.read_parquet(tempdir.name)
  48     subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
  49     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  50     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  51     return(tempdir, subreddit_names)
  52
  53 def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
  54     spark = SparkSession.builder.getOrCreate()
  55     conf = spark.sparkContext.getConf()
  56     print(exclude_phrases)
  57
  58     tfidf = spark.read.parquet(infile)
  59
  60     if included_subreddits is None:
  61         included_subreddits = select_topN_subreddits(topN)
  62     else:
  63         included_subreddits = set(open(included_subreddits))
  64
  65     if exclude_phrases == True:
  66         tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
  67
  68     print("creating temporary parquet with matrix indicies")
  69     tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
  70
  71     tfidf = spark.read.parquet(tempdir.name)
  72     subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
  73     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  74     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  75     spark.stop()
  76     return (tempdir, subreddit_names)
  77
  78
  79 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'):
  80     '''
  81     tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
  82     '''
  83     if from_date is not None or to_date is not None:
  84         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)
  85
  86     else:
  87         tempdir, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False)
  88
  89     print("loading matrix")
  90     #    mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
  91     mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)
  92     print('computing similarities')
  93     sims = simfunc(mat)
  94     del mat
  95
  96     if issparse(sims):
  97         sims = sims.todense()
  98
  99     print(f"shape of sims:{sims.shape}")
 100     print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
 101     sims = pd.DataFrame(sims)
 102     sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
 103     sims['subreddit'] = subreddit_names.subreddit.values
 104
 105     p = Path(outfile)
 106
 107     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
 108     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
 109     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
 110
 111     sims.to_feather(outfile)
 112     tempdir.cleanup()
 113
 114 def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
 115     term = term_colname
 116     term_id = term + '_id'
 117     term_id_new = term + '_id_new'
 118
 119     dataset = ds.dataset(path,format='parquet')
 120     entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
 121     return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
 122
 123 def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
 124     term = term_colname
 125     term_id = term + '_id'
 126     term_id_new = term + '_id_new'
 127     dataset = ds.dataset(path,format='parquet')
 128     print(f"tfidf_colname:{tfidf_colname}")
 129     entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
 130     return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
 131
 132
 133 def write_weekly_similarities(path, sims, week, names):
 134     sims['week'] = week
 135     p = pathlib.Path(path)
 136     if not p.is_dir():
 137         p.mkdir()
 138
 139     # reformat as a pairwise list
 140     sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
 141     sims.to_parquet(p / week.isoformat())
 142
 143 def column_overlaps(mat):
 144     non_zeros = (mat != 0).astype('double')
 145
 146     intersection = non_zeros.T @ non_zeros
 147     card1 = non_zeros.sum(axis=0)
 148     den = np.add.outer(card1,card1) - intersection
 149
 150     return intersection / den
 151
 152 def column_similarities(mat):
 153     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
 154     mat = mat.multiply(1/norm)
 155     sims = mat.T @ mat
 156     return(sims)
 157
 158
 159 def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits):
 160     term = term_colname
 161     term_id = term + '_id'
 162     term_id_new = term + '_id_new'
 163
 164     if min_df is None:
 165         min_df = 0.1 * len(included_subreddits)
 166         tfidf = tfidf.filter(f.col('count') >= min_df)
 167     if max_df is not None:
 168         tfidf = tfidf.filter(f.col('count') <= max_df)
 169
 170     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 171
 172     # we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
 173     sub_ids = tfidf.select(['subreddit_id','week']).distinct()
 174     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
 175     tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
 176
 177     # only use terms in at least min_df included subreddits in a given week
 178     new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
 179     tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
 180
 181     # reset the term ids
 182     term_ids = tfidf.select([term_id,'week']).distinct()
 183     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
 184     tfidf = tfidf.join(term_ids,[term_id,'week'])
 185
 186     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 187     tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
 188
 189     tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
 190
 191     tfidf = tfidf.repartition('week')
 192
 193     tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
 194     return(tempdir)
 195
 196
 197 def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits):
 198     term = term_colname
 199     term_id = term + '_id'
 200     term_id_new = term + '_id_new'
 201
 202     if min_df is None:
 203         min_df = 0.1 * len(included_subreddits)
 204         tfidf = tfidf.filter(f.col('count') >= min_df)
 205     if max_df is not None:
 206         tfidf = tfidf.filter(f.col('count') <= max_df)
 207
 208     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 209
 210     # reset the subreddit ids
 211     sub_ids = tfidf.select('subreddit_id').distinct()
 212     sub_ids = sub_ids.withColumn("subreddit_id_new", f.row_number().over(Window.orderBy("subreddit_id")))
 213     tfidf = tfidf.join(sub_ids,'subreddit_id')
 214
 215     # only use terms in at least min_df included subreddits
 216     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
 217     tfidf = tfidf.join(new_count,term_id,how='inner')
 218
 219     # reset the term ids
 220     term_ids = tfidf.select([term_id]).distinct()
 221     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
 222     tfidf = tfidf.join(term_ids,term_id)
 223
 224     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 225     tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
 226
 227     tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
 228
 229     tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
 230     return tempdir
 231
 232
 233 # try computing cosine similarities using spark
 234 def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
 235     term = term_colname
 236     term_id = term + '_id'
 237     term_id_new = term + '_id_new'
 238
 239     if min_df is None:
 240         min_df = 0.1 * len(included_subreddits)
 241
 242     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 243     tfidf = tfidf.cache()
 244
 245     # reset the subreddit ids
 246     sub_ids = tfidf.select('subreddit_id').distinct()
 247     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
 248     tfidf = tfidf.join(sub_ids,'subreddit_id')
 249
 250     # only use terms in at least min_df included subreddits
 251     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
 252     tfidf = tfidf.join(new_count,term_id,how='inner')
 253
 254     # reset the term ids
 255     term_ids = tfidf.select([term_id]).distinct()
 256     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
 257     tfidf = tfidf.join(term_ids,term_id)
 258
 259     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 260     tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
 261
 262     # step 1 make an rdd of entires
 263     # sorted by (dense) spark subreddit id
 264     n_partitions = int(len(included_subreddits)*2 / 5)
 265
 266     entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
 267
 268     # put like 10 subredis in each partition
 269
 270     # step 2 make it into a distributed.RowMatrix
 271     coordMat = CoordinateMatrix(entries)
 272
 273     coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
 274
 275     # this needs to be an IndexedRowMatrix()
 276     mat = coordMat.toRowMatrix()
 277
 278     #goal: build a matrix of subreddit columns and tf-idfs rows
 279     sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
 280
 281     return (sim_dist, tfidf)
 282
 283
 284 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 285     term = term_colname
 286     term_id = term + '_id'
 287
 288     # aggregate counts by week. now subreddit-term is distinct
 289     df = df.filter(df.subreddit.isin(include_subs))
 290     df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf'))
 291
 292     max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
 293     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 294     df = df.join(max_subreddit_terms, on=['subreddit','week'])
 295     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 296
 297     # group by term. term is unique
 298     idf = df.groupby([term,'week']).count()
 299
 300     N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
 301
 302     idf = idf.join(N_docs, on=['week'])
 303
 304     # add a little smoothing to the idf
 305     idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
 306
 307     # collect the dictionary to make a pydict of terms to indexes
 308     terms = idf.select([term,'week']).distinct() # terms are distinct
 309
 310     terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
 311
 312     # make subreddit ids
 313     subreddits = df.select(['subreddit','week']).distinct()
 314     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
 315
 316     df = df.join(subreddits,on=['subreddit','week'])
 317
 318     # map terms to indexes in the tfs and the idfs
 319     df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
 320
 321     idf = idf.join(terms,on=[term,'week'])
 322
 323     # join on subreddit/term to create tf/dfs indexed by term
 324     df = df.join(idf, on=[term_id, term,'week'])
 325
 326     # agg terms by subreddit to make sparse tf/df vectors
 327
 328     if tf_family == tf_weight.MaxTF:
 329         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 330     else: # tf_fam = tf_weight.Norm05
 331         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 332
 333     return df
 334
 335 def _calc_tfidf(df, term_colname, tf_family):
 336     term = term_colname
 337     term_id = term + '_id'
 338
 339     max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
 340     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 341
 342     df = df.join(max_subreddit_terms, on='subreddit')
 343
 344     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 345
 346     # group by term. term is unique
 347     idf = df.groupby([term]).count()
 348     N_docs = df.select('subreddit').distinct().count()
 349     # add a little smoothing to the idf
 350     idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
 351
 352     # collect the dictionary to make a pydict of terms to indexes
 353     terms = idf.select(term).distinct() # terms are distinct
 354     terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 355
 356     # make subreddit ids
 357     subreddits = df.select(['subreddit']).distinct()
 358     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 359
 360     df = df.join(subreddits,on='subreddit')
 361
 362     # map terms to indexes in the tfs and the idfs
 363     df = df.join(terms,on=term) # subreddit-term-id is unique
 364
 365     idf = idf.join(terms,on=term)
 366
 367     # join on subreddit/term to create tf/dfs indexed by term
 368     df = df.join(idf, on=[term_id, term])
 369
 370     # agg terms by subreddit to make sparse tf/df vectors
 371     if tf_family == tf_weight.MaxTF:
 372         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 373     else: # tf_fam = tf_weight.Norm05
 374         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 375
 376     return df
 377
 378
 379 def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 380     term = term_colname
 381     term_id = term + '_id'
 382     # aggregate counts by week. now subreddit-term is distinct
 383     df = df.filter(df.subreddit.isin(include_subs))
 384     df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
 385
 386     df = _calc_tfidf(df, term_colname, tf_family)
 387
 388     return df
 389
 390 def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonswf.csv"):
 391     rankdf = pd.read_csv(path)
 392     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
 393     return included_subreddits