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