import pathlib
from datetime import datetime
from pathlib import Path
+import pickle
class tf_weight(Enum):
MaxTF = 1
Norm05 = 2
-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
-cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
-
-def termauthor_tfidf(term_tfidf_callable, author_tfidf_callable):
-
+# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
+# cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
# subreddits missing after this step don't have any terms that have a high enough idf
# try rewriting without merges
-def 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):
- print("loading tfidf", flush=True)
- tfidf_ds = ds.dataset(infile)
+
+# does reindex_tfidf, but without reindexing.
+def reindex_tfidf(*args, **kwargs):
+ df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
+
+ print("assigning names")
+ subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
+ batches = subreddit_names.to_batches()
+
+ 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_tfidf(*args, **kwargs):
+ 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)
+ if week is not None:
+ tfidf_ds = ds.dataset(infile, partitioning='hive')
+ else:
+ tfidf_ds = ds.dataset(infile)
if included_subreddits is None:
included_subreddits = select_topN_subreddits(topN)
else:
- included_subreddits = set(open(included_subreddits))
+ included_subreddits = set(map(str.strip,open(included_subreddits)))
ds_filter = ds.field("subreddit").isin(included_subreddits)
'relative_tf':ds.field('relative_tf').cast('float32'),
'tf_idf':ds.field('tf_idf').cast('float32')}
- tfidf_ds = ds.dataset(infile)
-
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)
- df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
- grouped = df.groupby(term_id)
- df[term_id_new] = grouped.ngroup()
+ if reindex:
+ df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+ else:
+ df['subreddit_id_new'] = df['subreddit_id']
+
+ if reindex:
+ grouped = df.groupby(term_id)
+ df[term_id_new] = grouped.ngroup()
+ else:
+ df[term_id_new] = df[term_id]
if rescale_idf:
print("computing idf", flush=True)
else: # tf_fam = tf_weight.Norm05
df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
- print("assigning names")
- subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
- batches = subreddit_names.to_batches()
+ 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())
-def similarities(infile, 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'):
+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'):
'''
tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
'''
output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
- outfile.parent.mkdir(exist_ok=True, parents=True)
+ p.parent.mkdir(exist_ok=True, parents=True)
sims.to_feather(outfile)
term_id = term + '_id'
term_id_new = term + '_id_new'
- entries, subreddit_names = reindex_tfidf(infile, 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)
+ 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)))
print("loading matrix")
print(f'computing similarities on mat. mat.shape:{mat.shape}')
print(f"size of mat is:{mat.data.nbytes}",flush=True)
+ # transform this to debug term tfidf
sims = simfunc(mat)
del mat
for simmat, name in sims:
proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
else:
- proc_sims(simmat, outfile)
+ proc_sims(sims, outfile)
def write_weekly_similarities(path, sims, week, names):
sims['week'] = week
# if n_components is a list we'll return a list of similarities with different latent dimensionalities
# if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
# this function takes the svd and then the column similarities of it
-def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
+def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
# first compute the lsi of the matrix
# then take the column similarities
print("running LSI",flush=True)
n_components = sorted(n_components,reverse=True)
svd_components = n_components[0]
- svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
- mod = svd.fit(tfidfmat.T)
+
+ if lsi_model_load is not None:
+ mod = pickle.load(open(lsi_model_load ,'rb'))
+
+ else:
+ 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:
+ pickle.dump(mod, open(lsi_model_save,'wb'))
+
+ sims_list = []
for n_dims in n_components:
sims = column_similarities(lsimat[:,np.arange(n_dims)])
if len(n_components) > 1:
yield (sims, n_dims)
else:
return sims
-
def column_similarities(mat):
return 1 - pairwise_distances(mat,metric='cosine')
-
+# need to rewrite this so that subreddit ids and term ids are fixed over the whole thing.
+# this affords taking the LSI similarities.
+# fill all 0s if we don't have it.
def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
term = term_colname
term_id = term + '_id'
idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
# collect the dictionary to make a pydict of terms to indexes
- terms = idf.select([term,'week']).distinct() # terms are distinct
+ terms = idf.select([term]).distinct() # terms are distinct
- terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
+ terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
# make subreddit ids
- subreddits = df.select(['subreddit','week']).distinct()
- subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
+ subreddits = df.select(['subreddit']).distinct()
+ subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
- df = df.join(subreddits,on=['subreddit','week'])
+ # df = df.cache()
+ df = df.join(subreddits,on=['subreddit'])
# map terms to indexes in the tfs and the idfs
- df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
+ df = df.join(terms,on=[term]) # subreddit-term-id is unique
- idf = idf.join(terms,on=[term,'week'])
+ idf = idf.join(terms,on=[term])
# join on subreddit/term to create tf/dfs indexed by term
df = df.join(idf, on=[term_id, term,'week'])
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
df = df.repartition(400,'subreddit','week')
- dfwriter = df.write.partitionBy("week").sortBy("subreddit")
+ dfwriter = df.write.partitionBy("week")
return dfwriter
def _calc_tfidf(df, term_colname, tf_family):
return df
-def build_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):
term = term_colname
term_id = term + '_id'
# aggregate counts by week. now subreddit-term is distinct
df = _calc_tfidf(df, term_colname, tf_family)
df = df.repartition('subreddit')
- dfwriter = df.write.sortBy("subreddit","tf")
+ dfwriter = df.write
return dfwriter
def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
projects / cdsc_reddit.git / blobdiff