from pyspark.sql import SparkSession from pyspark.sql import Window from pyspark.sql import functions as f from enum import Enum from pyspark.mllib.linalg.distributed import CoordinateMatrix from tempfile import TemporaryDirectory import pyarrow import pyarrow.dataset as ds from sklearn.metrics import pairwise_distances from scipy.sparse import csr_matrix, issparse from sklearn.decomposition import TruncatedSVD 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/comment_authors_100k.parquet" cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.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) # subreddits missing after this step don't have any terms that have a high enough idf def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, tf_family=tf_weight.MaxTF): spark = SparkSession.builder.getOrCreate() conf = spark.sparkContext.getConf() print(exclude_phrases) tfidf_ds = ds.dataset(infile) if included_subreddits is None: included_subreddits = select_topN_subreddits(topN) else: included_subreddits = set(open(included_subreddits)) ds_filter = ds.field("subreddit").isin(included_subreddits) if min_df is not None: ds_filter &= ds.field("count") >= min_df if max_df is not None: ds_filter &= ds.field("count") <= max_df term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' df = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id',term_id,'relative_tf']).to_pandas() sub_ids = df.subreddit_id.drop_duplicates() new_sub_ids = pd.DataFrame({'subreddit_id':old,'subreddit_id_new':new} for new, old in enumerate(sorted(sub_ids))) df = df.merge(new_sub_ids,on='subreddit_id',how='inner',validate='many_to_one') new_count = df.groupby(term_id)[term_id].aggregate(new_count='count').reset_index() df = df.merge(new_count,on=term_id,how='inner',validate='many_to_one') term_ids = df[term_id].drop_duplicates() new_term_ids = pd.DataFrame({term_id:old,term_id_new:new} for new, old in enumerate(sorted(term_ids))) df = df.merge(new_term_ids, on=term_id, validate='many_to_one') N_docs = sub_ids.shape[0] df['idf'] = np.log(N_docs/(1+df.new_count)) + 1 # agg terms by subreddit to make sparse tf/df vectors if tf_family == tf_weight.MaxTF: df["tf_idf"] = df.relative_tf * df.idf else: # tf_fam = tf_weight.Norm05 df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf subreddit_names = df.loc[:,['subreddit','subreddit_id_new']].drop_duplicates() subreddit_names = subreddit_names.sort_values("subreddit_id_new") return(df, 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) mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname) else: entries, 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) mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))) print("loading matrix") # mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_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') 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 p = pathlib.Path(path) if not p.is_dir(): p.mkdir(exist_ok=True,parents=True) # reformat as a pairwise list 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 return intersection / den # n_components is the latent dimensionality. sklearn recommends 100. More might be better # if algorithm is 'random' 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=5,random_state=1968,algorithm='arpack'): # first compute the lsi of the matrix # then take the column similarities svd = TruncatedSVD(n_components=n_components,random_state=random_state,algorithm='arpack') mod = svd.fit(tfidfmat.T) lsimat = mod.transform(tfidfmat.T) sims = column_similarities(lsimat) return sims def column_similarities(mat): return 1 - pairwise_distances(mat,metric='cosine') # if issparse(mat): # norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32)) # mat = mat.multiply(1/norm) # else: # norm = np.matrix(np.power(np.power(mat,2).sum(axis=0),0.5,dtype=np.float32)) # mat = np.multiply(mat,1/norm) # sims = mat.T @ mat # return(sims) 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)) # 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, 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"))) tfidf = tfidf.join(sub_ids,'subreddit_id') # only use terms in at least min_df included subreddits new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) tfidf = tfidf.join(new_count,term_id,how='inner') # reset the term ids term_ids = tfidf.select([term_id]).distinct() term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id))) tfidf = tfidf.join(term_ids,term_id) 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.write.parquet(tempdir.name,mode='overwrite',compression='snappy') return tempdir # 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' if min_df is None: min_df = 0.1 * len(included_subreddits) tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits)) tfidf = tfidf.cache() # 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"))) tfidf = tfidf.join(sub_ids,'subreddit_id') # only use terms in at least min_df included subreddits new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) tfidf = tfidf.join(new_count,term_id,how='inner') # reset the term ids term_ids = tfidf.select([term_id]).distinct() term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id))) tfidf = tfidf.join(term_ids,term_id) tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf) # step 1 make an rdd of entires # sorted by (dense) spark subreddit id 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) # put like 10 subredis in each partition # step 2 make it into a distributed.RowMatrix coordMat = CoordinateMatrix(entries) coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions)) # this needs to be an IndexedRowMatrix() mat = coordMat.toRowMatrix() #goal: build a matrix of subreddit columns and tf-idfs rows sim_dist = mat.columnSimilarities(threshold=similarity_threshold) return (sim_dist, tfidf) def build_weekly_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,'week']).agg(f.sum('tf').alias('tf')) max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf') df = df.join(max_subreddit_terms, on=['subreddit','week']) df = df.withColumn("relative_tf", df.tf / df.sr_max_tf) # group by term. term is unique idf = df.groupby([term,'week']).count() N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week")) idf = idf.join(N_docs, on=['week']) # add a little smoothing to the idf 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 = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').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"))) df = df.join(subreddits,on=['subreddit','week']) # map terms to indexes in the tfs and the idfs df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique idf = idf.join(terms,on=[term,'week']) # join on subreddit/term to create tf/dfs indexed by term df = df.join(idf, on=[term_id, term,'week']) # 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 df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf) return df def _calc_tfidf(df, term_colname, tf_family): term = term_colname term_id = term + '_id' max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf') df = df.join(max_subreddit_terms, on='subreddit') df = df.withColumn("relative_tf", df.tf / df.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) # collect the dictionary to make a pydict of terms to indexes terms = idf.select(term).distinct() # terms 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']).distinct() subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit"))) df = df.join(subreddits,on='subreddit') # map terms to indexes in the tfs and the idfs df = df.join(terms,on=term) # subreddit-term-id is unique 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]) # 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 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_nonsfw.csv"): rankdf = pd.read_csv(path) included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values) return included_subreddits