from pyspark.sql import SparkSession from pyspark.sql import Window from pyspark.sql import functions as f from enum import Enum from multiprocessing import cpu_count, Pool 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_weekly/comment_terms.parquet" cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet" # subreddits missing after this step don't have any terms that have a high enough idf # try rewriting without merges def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF): print("loading tfidf", flush=True) 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 if week is not None: ds_filter &= ds.field("week") == week if from_date is not None: ds_filter &= ds.field("week") >= from_date if to_date is not None: ds_filter &= ds.field("week") <= to_date term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' projection = { 'subreddit_id':ds.field('subreddit_id'), term_id:ds.field(term_id), 'relative_tf':ds.field("relative_tf").cast('float32') } if not rescale_idf: projection = { 'subreddit_id':ds.field('subreddit_id'), term_id:ds.field(term_id), 'relative_tf':ds.field('relative_tf').cast('float32'), 'tf_idf':ds.field('tf_idf').cast('float32')} tfidf_ds = ds.dataset(infile) df = tfidf_ds.to_table(filter=ds_filter,columns=projection) df = df.to_pandas(split_blocks=True,self_destruct=True) print("assigning indexes",flush=True) df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() grouped = df.groupby(term_id) df[term_id_new] = grouped.ngroup() if rescale_idf: print("computing idf", flush=True) df['new_count'] = grouped[term_id].transform('count') N_docs = df.subreddit_id_new.max() + 1 df['idf'] = np.log(N_docs/(1+df.new_count),dtype='float32') + 1 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 print("assigning names") subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id']) batches = subreddit_names.to_batches() with Pool(cpu_count()) as pool: chunks = pool.imap_unordered(pull_names,batches) subreddit_names = pd.concat(chunks,copy=False).drop_duplicates() subreddit_names = subreddit_names.set_index("subreddit_id") new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates() new_ids = new_ids.set_index('subreddit_id') subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index() subreddit_names = subreddit_names.drop("subreddit_id",1) subreddit_names = subreddit_names.sort_values("subreddit_id_new") return(df, subreddit_names) def pull_names(batch): return(batch.to_pandas().drop_duplicates()) def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, 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. ''' def proc_sims(sims, outfile): 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)}",flush=True) 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")) outfile.parent.mkdir(exist_ok=True, parents=True) sims.to_feather(outfile) term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date) mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new))) 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}",flush=True) sims = simfunc(mat) del mat if hasattr(sims,'__next__'): for simmat, name in sims: proc_sims(simmat, Path(outfile)/(str(name) + ".feather")) else: proc_sims(simmat, outfile) 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 def test_lsi_sims(): term = "term" term_id = term + '_id' term_id_new = term + '_id_new' t1 = time.perf_counter() entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet", term_colname='term', min_df=2000, topN=10000 ) t2 = time.perf_counter() print(f"first load took:{t2 - t1}s") entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet", term_colname='term', min_df=2000, topN=10000 ) t3=time.perf_counter() print(f"second load took:{t3 - t2}s") mat = csr_matrix((entries['tf_idf'],(entries[term_id_new], entries.subreddit_id_new))) sims = list(lsi_column_similarities(mat, [10,50])) sims_og = sims sims_test = list(lsi_column_similarities(mat,[10,50],algorithm='randomized',n_iter=10)) # n_components is the latent dimensionality. sklearn recommends 100. More might be better # if n_components is a list we'll return a list of similarities with different latent dimensionalities # if algorithm is 'randomized' 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=10,random_state=1968,algorithm='randomized'): # first compute the lsi of the matrix # then take the column similarities print("running LSI",flush=True) if type(n_components) is int: n_components = [n_components] n_components = sorted(n_components,reverse=True) svd_components = n_components[0] svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter) mod = svd.fit(tfidfmat.T) lsimat = mod.transform(tfidfmat.T) for n_dims in n_components: sims = column_similarities(lsimat[:,np.arange(n_dims)]) if len(n_components) > 1: yield (sims, n_dims) else: return sims def column_similarities(mat): return 1 - pairwise_distances(mat,metric='cosine') 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) df = df.repartition(400,'subreddit','week') dfwriter = df.write.partitionBy("week") return dfwriter 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) df = df.repartition('subreddit') dfwriter = df.write return dfwriter 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 def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet", outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"): spark = SparkSession.builder.getOrCreate() df = spark.read.parquet(inpath) df = df.repartition(400,'subreddit') df.write.parquet(outpath,mode='overwrite') def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet", outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"): spark = SparkSession.builder.getOrCreate() df = spark.read.parquet(inpath) df = df.repartition(400,'subreddit','week') dfwriter = df.write.partitionBy("week") dfwriter.parquet(outpath,mode='overwrite')