X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/a60747292e91a47d122158659182f82bfd2e922a:/similarities_helper.py..e6294b5b90135a5163441c8dc62252dd6a188412:/similarities/similarities_helper.py diff --git a/similarities_helper.py b/similarities/similarities_helper.py similarity index 74% rename from similarities_helper.py rename to similarities/similarities_helper.py index ef434ac..88c830c 100644 --- a/similarities_helper.py +++ b/similarities/similarities_helper.py @@ -8,11 +8,33 @@ import pyarrow.dataset as ds from scipy.sparse import csr_matrix import pandas as pd import numpy as np +import pathlib class tf_weight(Enum): MaxTF = 1 Norm05 = 2 +def read_tfidf_matrix_weekly(path, term_colname, week): + 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)))) + +def write_weekly_similarities(path, sims, week, names): + sims['week'] = week + p = pathlib.Path(path) + if not p.is_dir(): + p.mkdir() + + # reformat as a pairwise list + 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' @@ -29,6 +51,41 @@ def column_similarities(mat): return(sims) +def prep_tfidf_entries_weekly(tfidf, term_colname, min_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("subreddit").isin(included_subreddits)) + + # we might not have the same terms or subreddits each week, so we need to make unique ids for each week. + sub_ids = tfidf.select(['subreddit_id','week']).distinct() + sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id"))) + tfidf = tfidf.join(sub_ids,['subreddit_id','week']) + + # only use terms in at least min_df included subreddits in a given week + new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count')) + tfidf = tfidf.join(new_count,[term_id,'week'],how='inner') + + # reset the term ids + term_ids = tfidf.select([term_id,'week']).distinct() + term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id))) + tfidf = tfidf.join(term_ids,[term_id,'week']) + + tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") + tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float')) + + tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.') + + tfidf = tfidf.repartition('week') + + tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy') + return(tempdir) + + def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits): term = term_colname term_id = term + '_id' @@ -46,7 +103,6 @@ def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits): # only use terms in at least min_df included subreddits new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) -# new_count = new_count.filter(f.col('new_count') >= min_df) tfidf = tfidf.join(new_count,term_id,how='inner') # reset the term ids @@ -55,8 +111,6 @@ def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits): tfidf = tfidf.join(term_ids,term_id) tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") - # tfidf = tfidf.withColumnRenamed("idf","idf_old") - # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count"))) tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float')) tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.') @@ -64,7 +118,9 @@ def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits): tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy') return tempdir -def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold): + +# try computing cosine similarities using spark +def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold): term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' @@ -82,7 +138,6 @@ def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, simila # only use terms in at least min_df included subreddits new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) -# new_count = new_count.filter(f.col('new_count') >= min_df) tfidf = tfidf.join(new_count,term_id,how='inner') # reset the term ids @@ -91,14 +146,10 @@ def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, simila tfidf = tfidf.join(term_ids,term_id) tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") - # tfidf = tfidf.withColumnRenamed("idf","idf_old") - # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count"))) tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf) # step 1 make an rdd of entires # sorted by (dense) spark subreddit id - # entries = tfidf.filter((f.col('subreddit') == 'asoiaf') | (f.col('subreddit') == 'gameofthrones') | (f.col('subreddit') == 'christianity')).select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd - n_partitions = int(len(included_subreddits)*2 / 5) entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions) @@ -214,7 +265,6 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm df = df.join(idf, on=[term_id, term]) # agg terms by subreddit to make sparse tf/df vectors - if tf_family == tf_weight.MaxTF: df = df.withColumn("tf_idf", df.relative_tf * df.idf) else: # tf_fam = tf_weight.Norm05 @@ -222,4 +272,7 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm return df - +def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv"): + rankdf = pd.read_csv(path) + included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values) + return included_subreddits