X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/e6294b5b90135a5163441c8dc62252dd6a188412..01a4c353588ab1a28f36980157daa5e682ea9edc:/similarities/similarities_helper.py?ds=sidebyside diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py index 88c830c..57a36ca 100644 --- a/similarities/similarities_helper.py +++ b/similarities/similarities_helper.py @@ -1,3 +1,4 @@ +from pyspark.sql import SparkSession from pyspark.sql import Window from pyspark.sql import functions as f from enum import Enum @@ -5,23 +6,130 @@ from pyspark.mllib.linalg.distributed import CoordinateMatrix 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 @@ -33,16 +141,14 @@ def write_weekly_similarities(path, sims, week, names): 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)) @@ -51,13 +157,16 @@ def column_similarities(mat): 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)) @@ -86,19 +195,22 @@ def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, 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 @@ -221,15 +333,9 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig 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') @@ -240,9 +346,7 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm # 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) @@ -271,8 +375,20 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm 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