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(map(str.strip,map(str.lower,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(f'computing similarities on mat. mat.shape:{mat.shape}')
  93     print(f"size of mat is:{mat.data.nbytes}")
  94     sims = simfunc(mat)
  95     del mat
  96
  97     if issparse(sims):
  98         sims = sims.todense()
  99
 100     print(f"shape of sims:{sims.shape}")
 101     print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
 102     sims = pd.DataFrame(sims)
 103     sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
 104     sims['subreddit'] = subreddit_names.subreddit.values
 105
 106     p = Path(outfile)
 107
 108     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
 109     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
 110     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
 111
 112     sims.to_feather(outfile)
 113     tempdir.cleanup()
 114
 115 def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
 116     term = term_colname
 117     term_id = term + '_id'
 118     term_id_new = term + '_id_new'
 119
 120     dataset = ds.dataset(path,format='parquet')
 121     entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
 122     return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
 123
 124 def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
 125     term = term_colname
 126     term_id = term + '_id'
 127     term_id_new = term + '_id_new'
 128     dataset = ds.dataset(path,format='parquet')
 129     print(f"tfidf_colname:{tfidf_colname}")
 130     entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
 131     return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
 132
 133
 134 def write_weekly_similarities(path, sims, week, names):
 135     sims['week'] = week
 136     p = pathlib.Path(path)
 137     if not p.is_dir():
 138         p.mkdir()
 139
 140     # reformat as a pairwise list
 141     sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
 142     sims.to_parquet(p / week.isoformat())
 143
 144 def column_overlaps(mat):
 145     non_zeros = (mat != 0).astype('double')
 146
 147     intersection = non_zeros.T @ non_zeros
 148     card1 = non_zeros.sum(axis=0)
 149     den = np.add.outer(card1,card1) - intersection
 150
 151     return intersection / den
 152
 153 def column_similarities(mat):
 154     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
 155     mat = mat.multiply(1/norm)
 156     sims = mat.T @ mat
 157     return(sims)
 158
 159
 160 def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits):
 161     term = term_colname
 162     term_id = term + '_id'
 163     term_id_new = term + '_id_new'
 164
 165     if min_df is None:
 166         min_df = 0.1 * len(included_subreddits)
 167         tfidf = tfidf.filter(f.col('count') >= min_df)
 168     if max_df is not None:
 169         tfidf = tfidf.filter(f.col('count') <= max_df)
 170
 171     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 172
 173     # we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
 174     sub_ids = tfidf.select(['subreddit_id','week']).distinct()
 175     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
 176     tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
 177
 178     # only use terms in at least min_df included subreddits in a given week
 179     new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
 180     tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
 181
 182     # reset the term ids
 183     term_ids = tfidf.select([term_id,'week']).distinct()
 184     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
 185     tfidf = tfidf.join(term_ids,[term_id,'week'])
 186
 187     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 188     tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
 189
 190     tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
 191
 192     tfidf = tfidf.repartition('week')
 193
 194     tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
 195     return(tempdir)
 196
 197
 198 def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits):
 199     term = term_colname
 200     term_id = term + '_id'
 201     term_id_new = term + '_id_new'
 202
 203     if min_df is None:
 204         min_df = 0.1 * len(included_subreddits)
 205         tfidf = tfidf.filter(f.col('count') >= min_df)
 206     if max_df is not None:
 207         tfidf = tfidf.filter(f.col('count') <= max_df)
 208
 209     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 210
 211     # reset the subreddit ids
 212     sub_ids = tfidf.select('subreddit_id').distinct()
 213     sub_ids = sub_ids.withColumn("subreddit_id_new", f.row_number().over(Window.orderBy("subreddit_id")))
 214     tfidf = tfidf.join(sub_ids,'subreddit_id')
 215
 216     # only use terms in at least min_df included subreddits
 217     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
 218     tfidf = tfidf.join(new_count,term_id,how='inner')
 219
 220     # reset the term ids
 221     term_ids = tfidf.select([term_id]).distinct()
 222     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
 223     tfidf = tfidf.join(term_ids,term_id)
 224
 225     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 226     tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
 227
 228     tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
 229
 230     tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
 231     return tempdir
 232
 233
 234 # try computing cosine similarities using spark
 235 def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
 236     term = term_colname
 237     term_id = term + '_id'
 238     term_id_new = term + '_id_new'
 239
 240     if min_df is None:
 241         min_df = 0.1 * len(included_subreddits)
 242
 243     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 244     tfidf = tfidf.cache()
 245
 246     # reset the subreddit ids
 247     sub_ids = tfidf.select('subreddit_id').distinct()
 248     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
 249     tfidf = tfidf.join(sub_ids,'subreddit_id')
 250
 251     # only use terms in at least min_df included subreddits
 252     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
 253     tfidf = tfidf.join(new_count,term_id,how='inner')
 254
 255     # reset the term ids
 256     term_ids = tfidf.select([term_id]).distinct()
 257     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
 258     tfidf = tfidf.join(term_ids,term_id)
 259
 260     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 261     tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
 262
 263     # step 1 make an rdd of entires
 264     # sorted by (dense) spark subreddit id
 265     n_partitions = int(len(included_subreddits)*2 / 5)
 266
 267     entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
 268
 269     # put like 10 subredis in each partition
 270
 271     # step 2 make it into a distributed.RowMatrix
 272     coordMat = CoordinateMatrix(entries)
 273
 274     coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
 275
 276     # this needs to be an IndexedRowMatrix()
 277     mat = coordMat.toRowMatrix()
 278
 279     #goal: build a matrix of subreddit columns and tf-idfs rows
 280     sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
 281
 282     return (sim_dist, tfidf)
 283
 284
 285 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 286     term = term_colname
 287     term_id = term + '_id'
 288
 289     # aggregate counts by week. now subreddit-term is distinct
 290     df = df.filter(df.subreddit.isin(include_subs))
 291     df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf'))
 292
 293     max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
 294     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 295     df = df.join(max_subreddit_terms, on=['subreddit','week'])
 296     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 297
 298     # group by term. term is unique
 299     idf = df.groupby([term,'week']).count()
 300
 301     N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
 302
 303     idf = idf.join(N_docs, on=['week'])
 304
 305     # add a little smoothing to the idf
 306     idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
 307
 308     # collect the dictionary to make a pydict of terms to indexes
 309     terms = idf.select([term,'week']).distinct() # terms are distinct
 310
 311     terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
 312
 313     # make subreddit ids
 314     subreddits = df.select(['subreddit','week']).distinct()
 315     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
 316
 317     df = df.join(subreddits,on=['subreddit','week'])
 318
 319     # map terms to indexes in the tfs and the idfs
 320     df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
 321
 322     idf = idf.join(terms,on=[term,'week'])
 323
 324     # join on subreddit/term to create tf/dfs indexed by term
 325     df = df.join(idf, on=[term_id, term,'week'])
 326
 327     # agg terms by subreddit to make sparse tf/df vectors
 328
 329     if tf_family == tf_weight.MaxTF:
 330         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 331     else: # tf_fam = tf_weight.Norm05
 332         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 333
 334     return df
 335
 336 def _calc_tfidf(df, term_colname, tf_family):
 337     term = term_colname
 338     term_id = term + '_id'
 339
 340     max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
 341     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 342
 343     df = df.join(max_subreddit_terms, on='subreddit')
 344
 345     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 346
 347     # group by term. term is unique
 348     idf = df.groupby([term]).count()
 349     N_docs = df.select('subreddit').distinct().count()
 350     # add a little smoothing to the idf
 351     idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
 352
 353     # collect the dictionary to make a pydict of terms to indexes
 354     terms = idf.select(term).distinct() # terms are distinct
 355     terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 356
 357     # make subreddit ids
 358     subreddits = df.select(['subreddit']).distinct()
 359     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 360
 361     df = df.join(subreddits,on='subreddit')
 362
 363     # map terms to indexes in the tfs and the idfs
 364     df = df.join(terms,on=term) # subreddit-term-id is unique
 365
 366     idf = idf.join(terms,on=term)
 367
 368     # join on subreddit/term to create tf/dfs indexed by term
 369     df = df.join(idf, on=[term_id, term])
 370
 371     # agg terms by subreddit to make sparse tf/df vectors
 372     if tf_family == tf_weight.MaxTF:
 373         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 374     else: # tf_fam = tf_weight.Norm05
 375         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 376
 377     return df
 378
 379
 380 def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 381     term = term_colname
 382     term_id = term + '_id'
 383     # aggregate counts by week. now subreddit-term is distinct
 384     df = df.filter(df.subreddit.isin(include_subs))
 385     df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
 386
 387     df = _calc_tfidf(df, term_colname, tf_family)
 388
 389     return df
 390
 391 def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
 392     rankdf = pd.read_csv(path)
 393     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
 394     return included_subreddits