new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
new_ids = new_ids.set_index('subreddit_id')
subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
- subreddit_names = subreddit_names.drop("subreddit_id",1)
+ subreddit_names = subreddit_names.drop("subreddit_id",axis=1)
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
return(df, subreddit_names)
df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
return df
-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):
- print(f"loading tfidf {infile}", flush=True)
+def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=None, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
+ print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
+
if week is not None:
tfidf_ds = ds.dataset(infile, partitioning='hive')
else:
'relative_tf':ds.field('relative_tf').cast('float32'),
'tf_idf':ds.field('tf_idf').cast('float32')}
- print(projection)
-
+ print(projection, flush=True)
+ print(ds_filter, flush=True)
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
df = df.to_pandas(split_blocks=True,self_destruct=True)
print("assigning indexes",flush=True)
if reindex:
- df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+ print("assigning indexes",flush=True)
+ df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
else:
df['subreddit_id_new'] = df['subreddit_id']
if reindex:
grouped = df.groupby(term_id)
- df[term_id_new] = grouped.ngroup()
+ df[term_id_new] = grouped.ngroup() + 1
else:
df[term_id_new] = df[term_id]
return (df, tfidf_ds, ds_filter)
- with Pool(cpu_count()) as pool:
- chunks = pool.imap_unordered(pull_names,batches)
- subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
-
- subreddit_names = subreddit_names.set_index("subreddit_id")
- new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
- new_ids = new_ids.set_index('subreddit_id')
- subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
- subreddit_names = subreddit_names.drop("subreddit_id",1)
- subreddit_names = subreddit_names.sort_values("subreddit_id_new")
- return(df, subreddit_names)
def pull_names(batch):
return(batch.to_pandas().drop_duplicates())
term_id_new = term + '_id_new'
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)
- mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
+ mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
print("loading matrix")
else:
print("running LSI",flush=True)
-
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
mod = svd.fit(tfidfmat.T)
- lsimat = mod.transform(tfidfmat.T)
if lsi_model_save is not None:
Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
pickle.dump(mod, open(lsi_model_save,'wb'))
- sims_list = []
+ print(n_components, flush=True)
+ lsimat = mod.transform(tfidfmat.T)
for n_dims in n_components:
+ print("computing similarities", flush=True)
sims = column_similarities(lsimat[:,np.arange(n_dims)])
- if len(n_components) > 1:
- yield (sims, n_dims)
- else:
- return sims
+ yield (sims, n_dims)
+
def column_similarities(mat):
else: # tf_fam = tf_weight.Norm05
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
- df = df.repartition(400,'subreddit','week')
+ df = df.repartition('week')
dfwriter = df.write.partitionBy("week")
return dfwriter
-def _calc_tfidf(df, term_colname, tf_family):
+def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
term = term_colname
term_id = term + '_id'
idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
# collect the dictionary to make a pydict of terms to indexes
- terms = idf.select(term).distinct() # terms are distinct
+ terms = idf
+ if min_df is not None:
+ terms = terms.filter(f.col('count')>=min_df)
+ if max_df is not None:
+ terms = terms.filter(f.col('count')<=max_df)
+
+ terms = terms.select(term).distinct() # terms are distinct
terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
# make subreddit ids
df = df.join(subreddits,on='subreddit')
# map terms to indexes in the tfs and the idfs
- df = df.join(terms,on=term) # subreddit-term-id is unique
+ df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique
- idf = idf.join(terms,on=term)
+ idf = idf.join(terms,on=term,how='inner')
# join on subreddit/term to create tf/dfs indexed by term
- df = df.join(idf, on=[term_id, term])
+ df = df.join(idf, on=[term_id, term],how='inner')
# agg terms by subreddit to make sparse tf/df vectors
if tf_family == tf_weight.MaxTF:
return df
-def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
term = term_colname
term_id = term + '_id'
# aggregate counts by week. now subreddit-term is distinct
df = df.filter(df.subreddit.isin(include_subs))
df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
- df = _calc_tfidf(df, term_colname, tf_family)
+ df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
df = df.repartition('subreddit')
dfwriter = df.write
return dfwriter
-def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
+def select_topN_subreddits(topN, path="../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
rankdf = pd.read_csv(path)
included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
return included_subreddits
projects / cdsc_reddit.git / blobdiff