similarities/similarities_helper.py

   1 from pyspark.sql import Window
   2 from pyspark.sql import functions as f
   3 from enum import Enum
   4 from pyspark.mllib.linalg.distributed import CoordinateMatrix
   5 from tempfile import TemporaryDirectory
   6 import pyarrow
   7 import pyarrow.dataset as ds
   8 from scipy.sparse import csr_matrix
   9 import pandas as pd
  10 import numpy as np
  11 import pathlib
  12
  13 class tf_weight(Enum):
  14     MaxTF = 1
  15     Norm05 = 2
  16
  17 def read_tfidf_matrix_weekly(path, term_colname, week):
  18     term = term_colname
  19     term_id = term + '_id'
  20     term_id_new = term + '_id_new'
  21
  22     dataset = ds.dataset(path,format='parquet')
  23     entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new],filter=ds.field('week')==week).to_pandas()
  24     return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
  25
  26 def write_weekly_similarities(path, sims, week, names):
  27     sims['week'] = week
  28     p = pathlib.Path(path)
  29     if not p.is_dir():
  30         p.mkdir()
  31
  32     # reformat as a pairwise list
  33     sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
  34     sims.to_parquet(p / week.isoformat())
  35
  36
  37
  38 def read_tfidf_matrix(path,term_colname):
  39     term = term_colname
  40     term_id = term + '_id'
  41     term_id_new = term + '_id_new'
  42
  43     dataset = ds.dataset(path,format='parquet')
  44     entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new]).to_pandas()
  45     return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
  46
  47 def column_similarities(mat):
  48     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
  49     mat = mat.multiply(1/norm)
  50     sims = mat.T @ mat
  51     return(sims)
  52
  53
  54 def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits):
  55     term = term_colname
  56     term_id = term + '_id'
  57     term_id_new = term + '_id_new'
  58
  59     if min_df is None:
  60         min_df = 0.1 * len(included_subreddits)
  61
  62     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
  63
  64     # we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
  65     sub_ids = tfidf.select(['subreddit_id','week']).distinct()
  66     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
  67     tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
  68
  69     # only use terms in at least min_df included subreddits in a given week
  70     new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
  71     tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
  72
  73     # reset the term ids
  74     term_ids = tfidf.select([term_id,'week']).distinct()
  75     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
  76     tfidf = tfidf.join(term_ids,[term_id,'week'])
  77
  78     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
  79     tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
  80
  81     tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
  82
  83     tfidf = tfidf.repartition('week')
  84
  85     tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
  86     return(tempdir)
  87
  88
  89 def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits):
  90     term = term_colname
  91     term_id = term + '_id'
  92     term_id_new = term + '_id_new'
  93
  94     if min_df is None:
  95         min_df = 0.1 * len(included_subreddits)
  96
  97     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
  98
  99     # reset the subreddit ids
 100     sub_ids = tfidf.select('subreddit_id').distinct()
 101     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
 102     tfidf = tfidf.join(sub_ids,'subreddit_id')
 103
 104     # only use terms in at least min_df included subreddits
 105     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
 106     tfidf = tfidf.join(new_count,term_id,how='inner')
 107
 108     # reset the term ids
 109     term_ids = tfidf.select([term_id]).distinct()
 110     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
 111     tfidf = tfidf.join(term_ids,term_id)
 112
 113     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 114     tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
 115
 116     tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
 117
 118     tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
 119     return tempdir
 120
 121
 122 # try computing cosine similarities using spark
 123 def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
 124     term = term_colname
 125     term_id = term + '_id'
 126     term_id_new = term + '_id_new'
 127
 128     if min_df is None:
 129         min_df = 0.1 * len(included_subreddits)
 130
 131     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 132     tfidf = tfidf.cache()
 133
 134     # reset the subreddit ids
 135     sub_ids = tfidf.select('subreddit_id').distinct()
 136     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
 137     tfidf = tfidf.join(sub_ids,'subreddit_id')
 138
 139     # only use terms in at least min_df included subreddits
 140     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
 141     tfidf = tfidf.join(new_count,term_id,how='inner')
 142
 143     # reset the term ids
 144     term_ids = tfidf.select([term_id]).distinct()
 145     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
 146     tfidf = tfidf.join(term_ids,term_id)
 147
 148     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
 149     tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
 150
 151     # step 1 make an rdd of entires
 152     # sorted by (dense) spark subreddit id
 153     n_partitions = int(len(included_subreddits)*2 / 5)
 154
 155     entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
 156
 157     # put like 10 subredis in each partition
 158
 159     # step 2 make it into a distributed.RowMatrix
 160     coordMat = CoordinateMatrix(entries)
 161
 162     coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
 163
 164     # this needs to be an IndexedRowMatrix()
 165     mat = coordMat.toRowMatrix()
 166
 167     #goal: build a matrix of subreddit columns and tf-idfs rows
 168     sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
 169
 170     return (sim_dist, tfidf)
 171
 172
 173 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 174     term = term_colname
 175     term_id = term + '_id'
 176
 177     # aggregate counts by week. now subreddit-term is distinct
 178     df = df.filter(df.subreddit.isin(include_subs))
 179     df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf'))
 180
 181     max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
 182     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 183     df = df.join(max_subreddit_terms, on=['subreddit','week'])
 184     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 185
 186     # group by term. term is unique
 187     idf = df.groupby([term,'week']).count()
 188
 189     N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
 190
 191     idf = idf.join(N_docs, on=['week'])
 192
 193     # add a little smoothing to the idf
 194     idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
 195
 196     # collect the dictionary to make a pydict of terms to indexes
 197     terms = idf.select([term,'week']).distinct() # terms are distinct
 198
 199     terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
 200
 201     # make subreddit ids
 202     subreddits = df.select(['subreddit','week']).distinct()
 203     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
 204
 205     df = df.join(subreddits,on=['subreddit','week'])
 206
 207     # map terms to indexes in the tfs and the idfs
 208     df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
 209
 210     idf = idf.join(terms,on=[term,'week'])
 211
 212     # join on subreddit/term to create tf/dfs indexed by term
 213     df = df.join(idf, on=[term_id, term,'week'])
 214
 215     # agg terms by subreddit to make sparse tf/df vectors
 216
 217     if tf_family == tf_weight.MaxTF:
 218         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 219     else: # tf_fam = tf_weight.Norm05
 220         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 221
 222     return df
 223
 224
 225
 226 def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 227
 228     term = term_colname
 229     term_id = term + '_id'
 230     # aggregate counts by week. now subreddit-term is distinct
 231     df = df.filter(df.subreddit.isin(include_subs))
 232     df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
 233
 234     max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
 235     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 236
 237     df = df.join(max_subreddit_terms, on='subreddit')
 238
 239     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 240
 241     # group by term. term is unique
 242     idf = df.groupby([term]).count()
 243
 244     N_docs = df.select('subreddit').distinct().count()
 245
 246     # add a little smoothing to the idf
 247     idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
 248
 249     # collect the dictionary to make a pydict of terms to indexes
 250     terms = idf.select(term).distinct() # terms are distinct
 251     terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 252
 253     # make subreddit ids
 254     subreddits = df.select(['subreddit']).distinct()
 255     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 256
 257     df = df.join(subreddits,on='subreddit')
 258
 259     # map terms to indexes in the tfs and the idfs
 260     df = df.join(terms,on=term) # subreddit-term-id is unique
 261
 262     idf = idf.join(terms,on=term)
 263
 264     # join on subreddit/term to create tf/dfs indexed by term
 265     df = df.join(idf, on=[term_id, term])
 266
 267     # agg terms by subreddit to make sparse tf/df vectors
 268     if tf_family == tf_weight.MaxTF:
 269         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 270     else: # tf_fam = tf_weight.Norm05
 271         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 272
 273     return df
 274
 275 def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv"):
 276     rankdf = pd.read_csv(path)
 277     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
 278     return included_subreddits