X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/e6294b5b90135a5163441c8dc62252dd6a188412..36b24ee933b95424686cfeaa2b2bd9776f23f853:/similarities/similarities_helper.py diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py index 88c830c..fd532a9 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 @@ -5,59 +6,217 @@ 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 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 -def read_tfidf_matrix_weekly(path, term_colname, week): +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' - 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)))) + spark = SparkSession.builder.getOrCreate() + conf = spark.sparkContext.getConf() + print(exclude_phrases) + tfidf_weekly = spark.read.parquet(infile) -def write_weekly_similarities(path, sims, week, names): - sims['week'] = week - p = pathlib.Path(path) - if not p.is_dir(): - p.mkdir() + # 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) - # reformat as a pairwise list - sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values) - sims.to_parquet(p / week.isoformat()) + 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 -def read_tfidf_matrix(path,term_colname): 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') - 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)))) + 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 column_similarities(mat): - norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32)) - mat = mat.multiply(1/norm) - sims = mat.T @ mat - return(sims) +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 prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits): +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)) @@ -86,7 +245,7 @@ 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' @@ -94,11 +253,15 @@ def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits): 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 +384,9 @@ 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 +397,7 @@ 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 +426,22 @@ 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 + +