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
  12 class tf_weight(Enum):
  13     MaxTF = 1
  14     Norm05 = 2
  15
  16 def read_tfidf_matrix(path,term_colname):
  17     term = term_colname
  18     term_id = term + '_id'
  19     term_id_new = term + '_id_new'
  20
  21     dataset = ds.dataset(path,format='parquet')
  22     entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new]).to_pandas()
  23     return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
  24
  25 def column_similarities(mat):
  26     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
  27     mat = mat.multiply(1/norm)
  28     sims = mat.T @ mat
  29     return(sims)
  30
  31
  32 def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits):
  33     term = term_colname
  34     term_id = term + '_id'
  35     term_id_new = term + '_id_new'
  36
  37     if min_df is None:
  38         min_df = 0.1 * len(included_subreddits)
  39
  40     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
  41
  42     # reset the subreddit ids
  43     sub_ids = tfidf.select('subreddit_id').distinct()
  44     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
  45     tfidf = tfidf.join(sub_ids,'subreddit_id')
  46
  47     # only use terms in at least min_df included subreddits
  48     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
  49 #    new_count = new_count.filter(f.col('new_count') >= min_df)
  50     tfidf = tfidf.join(new_count,term_id,how='inner')
  51
  52     # reset the term ids
  53     term_ids = tfidf.select([term_id]).distinct()
  54     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
  55     tfidf = tfidf.join(term_ids,term_id)
  56
  57     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
  58     # tfidf = tfidf.withColumnRenamed("idf","idf_old")
  59     # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
  60     tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
  61
  62     tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
  63
  64     tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
  65     return tempdir
  66
  67 def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
  68     term = term_colname
  69     term_id = term + '_id'
  70     term_id_new = term + '_id_new'
  71
  72     if min_df is None:
  73         min_df = 0.1 * len(included_subreddits)
  74
  75     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
  76     tfidf = tfidf.cache()
  77
  78     # reset the subreddit ids
  79     sub_ids = tfidf.select('subreddit_id').distinct()
  80     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
  81     tfidf = tfidf.join(sub_ids,'subreddit_id')
  82
  83     # only use terms in at least min_df included subreddits
  84     new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
  85 #    new_count = new_count.filter(f.col('new_count') >= min_df)
  86     tfidf = tfidf.join(new_count,term_id,how='inner')
  87
  88     # reset the term ids
  89     term_ids = tfidf.select([term_id]).distinct()
  90     term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
  91     tfidf = tfidf.join(term_ids,term_id)
  92
  93     tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
  94     # tfidf = tfidf.withColumnRenamed("idf","idf_old")
  95     # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
  96     tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
  97
  98     # step 1 make an rdd of entires
  99     # sorted by (dense) spark subreddit id
 100     #    entries = tfidf.filter((f.col('subreddit') == 'asoiaf') | (f.col('subreddit') == 'gameofthrones') | (f.col('subreddit') == 'christianity')).select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
 101
 102     n_partitions = int(len(included_subreddits)*2 / 5)
 103
 104     entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
 105
 106     # put like 10 subredis in each partition
 107
 108     # step 2 make it into a distributed.RowMatrix
 109     coordMat = CoordinateMatrix(entries)
 110
 111     coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
 112
 113     # this needs to be an IndexedRowMatrix()
 114     mat = coordMat.toRowMatrix()
 115
 116     #goal: build a matrix of subreddit columns and tf-idfs rows
 117     sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
 118
 119     return (sim_dist, tfidf)
 120
 121
 122 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 123     term = term_colname
 124     term_id = term + '_id'
 125
 126     # aggregate counts by week. now subreddit-term is distinct
 127     df = df.filter(df.subreddit.isin(include_subs))
 128     df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf'))
 129
 130     max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
 131     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 132     df = df.join(max_subreddit_terms, on=['subreddit','week'])
 133     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 134
 135     # group by term. term is unique
 136     idf = df.groupby([term,'week']).count()
 137
 138     N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
 139
 140     idf = idf.join(N_docs, on=['week'])
 141
 142     # add a little smoothing to the idf
 143     idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
 144
 145     # collect the dictionary to make a pydict of terms to indexes
 146     terms = idf.select([term,'week']).distinct() # terms are distinct
 147
 148     terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
 149
 150     # make subreddit ids
 151     subreddits = df.select(['subreddit','week']).distinct()
 152     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
 153
 154     df = df.join(subreddits,on=['subreddit','week'])
 155
 156     # map terms to indexes in the tfs and the idfs
 157     df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
 158
 159     idf = idf.join(terms,on=[term,'week'])
 160
 161     # join on subreddit/term to create tf/dfs indexed by term
 162     df = df.join(idf, on=[term_id, term,'week'])
 163
 164     # agg terms by subreddit to make sparse tf/df vectors
 165
 166     if tf_family == tf_weight.MaxTF:
 167         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 168     else: # tf_fam = tf_weight.Norm05
 169         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 170
 171     return df
 172
 173
 174
 175 def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
 176
 177     term = term_colname
 178     term_id = term + '_id'
 179     # aggregate counts by week. now subreddit-term is distinct
 180     df = df.filter(df.subreddit.isin(include_subs))
 181     df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
 182
 183     max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
 184     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
 185
 186     df = df.join(max_subreddit_terms, on='subreddit')
 187
 188     df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
 189
 190     # group by term. term is unique
 191     idf = df.groupby([term]).count()
 192
 193     N_docs = df.select('subreddit').distinct().count()
 194
 195     # add a little smoothing to the idf
 196     idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
 197
 198     # collect the dictionary to make a pydict of terms to indexes
 199     terms = idf.select(term).distinct() # terms are distinct
 200     terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 201
 202     # make subreddit ids
 203     subreddits = df.select(['subreddit']).distinct()
 204     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 205
 206     df = df.join(subreddits,on='subreddit')
 207
 208     # map terms to indexes in the tfs and the idfs
 209     df = df.join(terms,on=term) # subreddit-term-id is unique
 210
 211     idf = idf.join(terms,on=term)
 212
 213     # join on subreddit/term to create tf/dfs indexed by term
 214     df = df.join(idf, on=[term_id, term])
 215
 216     # agg terms by subreddit to make sparse tf/df vectors
 217
 218     if tf_family == tf_weight.MaxTF:
 219         df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
 220     else: # tf_fam = tf_weight.Norm05
 221         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 222
 223     return df
 224
 225