1 from pyspark.sql import SparkSession
2 from pyspark.sql import Window
3 from pyspark.sql import functions as f
5 from multiprocessing import cpu_count, Pool
6 from pyspark.mllib.linalg.distributed import CoordinateMatrix
7 from tempfile import TemporaryDirectory
9 import pyarrow.dataset as ds
10 from sklearn.metrics import pairwise_distances
11 from scipy.sparse import csr_matrix, issparse
12 from sklearn.decomposition import TruncatedSVD
16 from datetime import datetime
17 from pathlib import Path
20 class tf_weight(Enum):
24 # infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
25 # cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
27 # subreddits missing after this step don't have any terms that have a high enough idf
28 # try rewriting without merges
30 # does reindex_tfidf, but without reindexing.
31 def reindex_tfidf(*args, **kwargs):
32 df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
34 print("assigning names")
35 subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
36 batches = subreddit_names.to_batches()
38 with Pool(cpu_count()) as pool:
39 chunks = pool.imap_unordered(pull_names,batches)
40 subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
41 subreddit_names = subreddit_names.set_index("subreddit_id")
43 new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
44 new_ids = new_ids.set_index('subreddit_id')
45 subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
46 subreddit_names = subreddit_names.drop("subreddit_id",1)
47 subreddit_names = subreddit_names.sort_values("subreddit_id_new")
48 return(df, subreddit_names)
50 def pull_tfidf(*args, **kwargs):
51 df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
54 def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
55 print(f"loading tfidf {infile}", flush=True)
57 tfidf_ds = ds.dataset(infile, partitioning='hive')
59 tfidf_ds = ds.dataset(infile)
61 if included_subreddits is None:
62 included_subreddits = select_topN_subreddits(topN)
64 included_subreddits = set(map(str.strip,open(included_subreddits)))
66 ds_filter = ds.field("subreddit").isin(included_subreddits)
68 if min_df is not None:
69 ds_filter &= ds.field("count") >= min_df
71 if max_df is not None:
72 ds_filter &= ds.field("count") <= max_df
75 ds_filter &= ds.field("week") == week
77 if from_date is not None:
78 ds_filter &= ds.field("week") >= from_date
80 if to_date is not None:
81 ds_filter &= ds.field("week") <= to_date
84 term_id = term + '_id'
85 term_id_new = term + '_id_new'
88 'subreddit_id':ds.field('subreddit_id'),
89 term_id:ds.field(term_id),
90 'relative_tf':ds.field("relative_tf").cast('float32')
95 'subreddit_id':ds.field('subreddit_id'),
96 term_id:ds.field(term_id),
97 'relative_tf':ds.field('relative_tf').cast('float32'),
98 'tf_idf':ds.field('tf_idf').cast('float32')}
101 df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
103 df = df.to_pandas(split_blocks=True,self_destruct=True)
104 print("assigning indexes",flush=True)
106 df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
108 df['subreddit_id_new'] = df['subreddit_id']
111 grouped = df.groupby(term_id)
112 df[term_id_new] = grouped.ngroup()
114 df[term_id_new] = df[term_id]
117 print("computing idf", flush=True)
118 df['new_count'] = grouped[term_id].transform('count')
119 N_docs = df.subreddit_id_new.max() + 1
120 df['idf'] = np.log(N_docs/(1+df.new_count),dtype='float32') + 1
121 if tf_family == tf_weight.MaxTF:
122 df["tf_idf"] = df.relative_tf * df.idf
123 else: # tf_fam = tf_weight.Norm05
124 df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
126 return (df, tfidf_ds, ds_filter)
128 with Pool(cpu_count()) as pool:
129 chunks = pool.imap_unordered(pull_names,batches)
130 subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
132 subreddit_names = subreddit_names.set_index("subreddit_id")
133 new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
134 new_ids = new_ids.set_index('subreddit_id')
135 subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
136 subreddit_names = subreddit_names.drop("subreddit_id",1)
137 subreddit_names = subreddit_names.sort_values("subreddit_id_new")
138 return(df, subreddit_names)
140 def pull_names(batch):
141 return(batch.to_pandas().drop_duplicates())
143 def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
145 tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
148 def proc_sims(sims, outfile):
150 sims = sims.todense()
152 print(f"shape of sims:{sims.shape}")
153 print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}",flush=True)
154 sims = pd.DataFrame(sims)
155 sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
156 sims['_subreddit'] = subreddit_names.subreddit.values
160 output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
161 output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
162 output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
163 p.parent.mkdir(exist_ok=True, parents=True)
165 sims.to_feather(outfile)
168 term_id = term + '_id'
169 term_id_new = term + '_id_new'
171 entries, subreddit_names = reindex_tfidf(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
172 mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
174 print("loading matrix")
176 # mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
178 print(f'computing similarities on mat. mat.shape:{mat.shape}')
179 print(f"size of mat is:{mat.data.nbytes}",flush=True)
183 if hasattr(sims,'__next__'):
184 for simmat, name in sims:
185 proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
187 proc_sims(sims, outfile)
189 def write_weekly_similarities(path, sims, week, names):
191 p = pathlib.Path(path)
193 p.mkdir(exist_ok=True,parents=True)
195 # reformat as a pairwise list
196 sims = sims.melt(id_vars=['_subreddit','week'],value_vars=names.subreddit.values)
197 sims.to_parquet(p / week.isoformat())
199 def column_overlaps(mat):
200 non_zeros = (mat != 0).astype('double')
202 intersection = non_zeros.T @ non_zeros
203 card1 = non_zeros.sum(axis=0)
204 den = np.add.outer(card1,card1) - intersection
206 return intersection / den
210 term_id = term + '_id'
211 term_id_new = term + '_id_new'
213 t1 = time.perf_counter()
214 entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet",
219 t2 = time.perf_counter()
220 print(f"first load took:{t2 - t1}s")
222 entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
227 t3=time.perf_counter()
229 print(f"second load took:{t3 - t2}s")
231 mat = csr_matrix((entries['tf_idf'],(entries[term_id_new], entries.subreddit_id_new)))
232 sims = list(lsi_column_similarities(mat, [10,50]))
234 sims_test = list(lsi_column_similarities(mat,[10,50],algorithm='randomized',n_iter=10))
236 # n_components is the latent dimensionality. sklearn recommends 100. More might be better
237 # if n_components is a list we'll return a list of similarities with different latent dimensionalities
238 # if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
239 # this function takes the svd and then the column similarities of it
240 def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
241 # first compute the lsi of the matrix
242 # then take the column similarities
243 print("running LSI",flush=True)
245 if type(n_components) is int:
246 n_components = [n_components]
248 n_components = sorted(n_components,reverse=True)
250 svd_components = n_components[0]
252 if lsi_model_load is not None:
253 mod = pickle.load(open(lsi_model_load ,'rb'))
256 svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
257 mod = svd.fit(tfidfmat.T)
259 lsimat = mod.transform(tfidfmat.T)
260 if lsi_model_save is not None:
261 pickle.dump(mod, open(lsi_model_save,'wb'))
264 for n_dims in n_components:
265 sims = column_similarities(lsimat[:,np.arange(n_dims)])
266 if len(n_components) > 1:
272 def column_similarities(mat):
273 return 1 - pairwise_distances(mat,metric='cosine')
276 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
278 term_id = term + '_id'
280 # aggregate counts by week. now subreddit-term is distinct
281 df = df.filter(df.subreddit.isin(include_subs))
282 df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf'))
284 max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
285 max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
286 df = df.join(max_subreddit_terms, on=['subreddit','week'])
287 df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
289 # group by term. term is unique
290 idf = df.groupby([term,'week']).count()
292 N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
294 idf = idf.join(N_docs, on=['week'])
296 # add a little smoothing to the idf
297 idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
299 # collect the dictionary to make a pydict of terms to indexes
300 terms = idf.select([term]).distinct() # terms are distinct
302 terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
305 subreddits = df.select(['subreddit']).distinct()
306 subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
308 df = df.join(subreddits,on=['subreddit'])
310 # map terms to indexes in the tfs and the idfs
311 df = df.join(terms,on=[term]) # subreddit-term-id is unique
313 idf = idf.join(terms,on=[term])
315 # join on subreddit/term to create tf/dfs indexed by term
316 df = df.join(idf, on=[term_id, term,'week'])
318 # agg terms by subreddit to make sparse tf/df vectors
320 if tf_family == tf_weight.MaxTF:
321 df = df.withColumn("tf_idf", df.relative_tf * df.idf)
322 else: # tf_fam = tf_weight.Norm05
323 df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
325 df = df.repartition(400,'subreddit','week')
326 dfwriter = df.write.partitionBy("week")
329 def _calc_tfidf(df, term_colname, tf_family):
331 term_id = term + '_id'
333 max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
334 max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
336 df = df.join(max_subreddit_terms, on='subreddit')
338 df = df.withColumn("relative_tf", (df.tf / df.sr_max_tf))
340 # group by term. term is unique
341 idf = df.groupby([term]).count()
342 N_docs = df.select('subreddit').distinct().count()
343 # add a little smoothing to the idf
344 idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
346 # collect the dictionary to make a pydict of terms to indexes
347 terms = idf.select(term).distinct() # terms are distinct
348 terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
351 subreddits = df.select(['subreddit']).distinct()
352 subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
354 df = df.join(subreddits,on='subreddit')
356 # map terms to indexes in the tfs and the idfs
357 df = df.join(terms,on=term) # subreddit-term-id is unique
359 idf = idf.join(terms,on=term)
361 # join on subreddit/term to create tf/dfs indexed by term
362 df = df.join(idf, on=[term_id, term])
364 # agg terms by subreddit to make sparse tf/df vectors
365 if tf_family == tf_weight.MaxTF:
366 df = df.withColumn("tf_idf", df.relative_tf * df.idf)
367 else: # tf_fam = tf_weight.Norm05
368 df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
373 def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
375 term_id = term + '_id'
376 # aggregate counts by week. now subreddit-term is distinct
377 df = df.filter(df.subreddit.isin(include_subs))
378 df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
380 df = _calc_tfidf(df, term_colname, tf_family)
381 df = df.repartition('subreddit')
385 def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
386 rankdf = pd.read_csv(path)
387 included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
388 return included_subreddits
391 def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
392 outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"):
393 spark = SparkSession.builder.getOrCreate()
394 df = spark.read.parquet(inpath)
395 df = df.repartition(400,'subreddit')
396 df.write.parquet(outpath,mode='overwrite')
399 def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet",
400 outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"):
401 spark = SparkSession.builder.getOrCreate()
402 df = spark.read.parquet(inpath)
403 df = df.repartition(400,'subreddit','week')
404 dfwriter = df.write.partitionBy("week")
405 dfwriter.parquet(outpath,mode='overwrite')