+from pyspark.sql import SparkSession
from pyspark.sql import Window
from pyspark.sql import functions as f
from enum import Enum
from tempfile import TemporaryDirectory
import pyarrow
import pyarrow.dataset as ds
-from scipy.sparse import csr_matrix
+from scipy.sparse import csr_matrix, issparse
import pandas as pd
import numpy as np
import pathlib
+from datetime import datetime
+from pathlib import Path
class tf_weight(Enum):
MaxTF = 1
Norm05 = 2
-def read_tfidf_matrix_weekly(path, term_colname, week):
+infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet"
+
+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):
term = term_colname
term_id = term + '_id'
term_id_new = term + '_id_new'
+ spark = SparkSession.builder.getOrCreate()
+ conf = spark.sparkContext.getConf()
+ print(exclude_phrases)
+ tfidf_weekly = spark.read.parquet(infile)
+
+ # create the time interval
+ if from_date is not None:
+ if type(from_date) is str:
+ from_date = datetime.fromisoformat(from_date)
+
+ tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
+
+ if to_date is not None:
+ if type(to_date) is str:
+ to_date = datetime.fromisoformat(to_date)
+ tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)
+
+ tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
+ tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
+ tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
+ tfidf = spark.read_parquet(tempdir.name)
+ subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
+ subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+ subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
+ return(tempdir, subreddit_names)
+
+def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
+ spark = SparkSession.builder.getOrCreate()
+ conf = spark.sparkContext.getConf()
+ print(exclude_phrases)
+
+ tfidf = spark.read.parquet(infile)
+
+ if included_subreddits is None:
+ included_subreddits = select_topN_subreddits(topN)
+ else:
+ included_subreddits = set(open(included_subreddits))
+
+ if exclude_phrases == True:
+ tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
+
+ print("creating temporary parquet with matrix indicies")
+ tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
+
+ tfidf = spark.read.parquet(tempdir.name)
+ subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
+ subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+ subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
+ spark.stop()
+ return (tempdir, subreddit_names)
+
+
+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'):
+ '''
+ tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
+ '''
+ if from_date is not None or to_date is not None:
+ 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)
+
+ else:
+ 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)
+
+ print("loading matrix")
+ # mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
+ mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)
+ print(f'computing similarities on mat. mat.shape:{mat.shape}')
+ print(f"size of mat is:{mat.data.nbytes}")
+ sims = simfunc(mat)
+ del mat
+
+ if issparse(sims):
+ sims = sims.todense()
+
+ print(f"shape of sims:{sims.shape}")
+ print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
+ sims = pd.DataFrame(sims)
+ sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
+ sims['subreddit'] = subreddit_names.subreddit.values
+
+ p = Path(outfile)
+
+ 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"))
+
+ sims.to_feather(outfile)
+ tempdir.cleanup()
+
+def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
+ term = term_colname
+ term_id = term + '_id'
+ term_id_new = term + '_id_new'
+
+ dataset = ds.dataset(path,format='parquet')
+ entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
+ return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
+
+def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
+ term = term_colname
+ term_id = term + '_id'
+ term_id_new = term + '_id_new'
dataset = ds.dataset(path,format='parquet')
- entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new],filter=ds.field('week')==week).to_pandas()
- return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+ print(f"tfidf_colname:{tfidf_colname}")
+ entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
+ return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+
def write_weekly_similarities(path, sims, week, names):
sims['week'] = week
sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
sims.to_parquet(p / week.isoformat())
+def column_overlaps(mat):
+ non_zeros = (mat != 0).astype('double')
+
+ intersection = non_zeros.T @ non_zeros
+ card1 = non_zeros.sum(axis=0)
+ den = np.add.outer(card1,card1) - intersection
-
-def read_tfidf_matrix(path,term_colname):
- term = term_colname
- term_id = term + '_id'
- term_id_new = term + '_id_new'
-
- dataset = ds.dataset(path,format='parquet')
- entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new]).to_pandas()
- return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+ return intersection / den
def column_similarities(mat):
norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
return(sims)
-def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits):
+def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits):
term = term_colname
term_id = term + '_id'
term_id_new = term + '_id_new'
if min_df is None:
min_df = 0.1 * len(included_subreddits)
+ tfidf = tfidf.filter(f.col('count') >= min_df)
+ if max_df is not None:
+ tfidf = tfidf.filter(f.col('count') <= max_df)
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
return(tempdir)
-def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits):
+def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits):
term = term_colname
term_id = term + '_id'
term_id_new = term + '_id_new'
if min_df is None:
min_df = 0.1 * len(included_subreddits)
+ tfidf = tfidf.filter(f.col('count') >= min_df)
+ if max_df is not None:
+ tfidf = tfidf.filter(f.col('count') <= max_df)
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
# reset the subreddit ids
sub_ids = tfidf.select('subreddit_id').distinct()
- sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
+ sub_ids = sub_ids.withColumn("subreddit_id_new", f.row_number().over(Window.orderBy("subreddit_id")))
tfidf = tfidf.join(sub_ids,'subreddit_id')
# only use terms in at least min_df included subreddits
return df
-
-
-def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
-
+def _calc_tfidf(df, term_colname, tf_family):
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'))
max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
# group by term. term is unique
idf = df.groupby([term]).count()
-
N_docs = df.select('subreddit').distinct().count()
-
# add a little smoothing to the idf
idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
return df
+
+
+def build_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 = 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)
+
+ return df
-def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv"):
+def select_topN_subreddits(topN, path="/gscratch/comdata/output/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