-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
import pandas as pd
import fire
from pathlib import Path
-from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits, column_similarities
+from similarities_helper import similarities
+def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False,from_date=None, to_date=None):
+ return similiarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date)
-def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
- spark = SparkSession.builder.getOrCreate()
- conf = spark.sparkContext.getConf()
- print(outfile)
- 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, 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()
-
- print("loading matrix")
- mat = read_tfidf_matrix(tempdir.name, term_colname)
- print('computing similarities')
- sims = column_similarities(mat)
- del mat
-
- sims = pd.DataFrame(sims.todense())
- 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 term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
+def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
'term',
outfile,
min_df,
included_subreddits,
topN,
- exclude_phrases)
+ exclude_phrasesby.)
-def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
+def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000, from_date=None, to_date=None):
return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
'author',
outfile,
+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
+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):
+
+ if from_date is not None or to_date is not None:
+ tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname='author', 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='author', 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)
+ print('computing similarities')
+ 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):
term = term_colname
term_id = term + '_id'
sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
sims.to_parquet(p / week.isoformat())
-
-
def read_tfidf_matrix(path,term_colname):
term = term_colname
term_id = term + '_id'
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))))
+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
+
+ return intersection / den
+
def column_similarities(mat):
norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
mat = mat.multiply(1/norm)
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"):
rankdf = pd.read_csv(path)
projects / cdsc_reddit.git / commitdiff