]> code.communitydata.science - cdsc_reddit.git/blob - similarities/similarities_helper.py
add note to try other tf normalization strategies.
[cdsc_reddit.git] / 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

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