414 lines
16 KiB
Python
414 lines
16 KiB
Python
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
|
|
import pickle
|
|
|
|
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
|
|
|
|
# does reindex_tfidf, but without reindexing.
|
|
def reindex_tfidf(*args, **kwargs):
|
|
df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
|
|
|
|
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",axis=1)
|
|
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
|
return(df, subreddit_names)
|
|
|
|
def pull_tfidf(*args, **kwargs):
|
|
df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
|
|
return df
|
|
|
|
def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=None, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
|
|
print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
|
|
|
|
if week is not None:
|
|
tfidf_ds = ds.dataset(infile, partitioning='hive')
|
|
else:
|
|
tfidf_ds = ds.dataset(infile)
|
|
|
|
if included_subreddits is None:
|
|
included_subreddits = select_topN_subreddits(topN)
|
|
else:
|
|
included_subreddits = set(map(str.strip,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),
|
|
'tf_idf':ds.field('tf_idf').cast('float32')}
|
|
|
|
print(projection, flush=True)
|
|
print(ds_filter, flush=True)
|
|
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
|
|
|
|
df = df.to_pandas(split_blocks=True,self_destruct=True)
|
|
|
|
if reindex:
|
|
print("assigning indexes",flush=True)
|
|
df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
|
|
else:
|
|
df['subreddit_id_new'] = df['subreddit_id']
|
|
|
|
if reindex:
|
|
grouped = df.groupby(term_id)
|
|
df[term_id_new] = grouped.ngroup() + 1
|
|
else:
|
|
df[term_id_new] = df[term_id]
|
|
|
|
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
|
|
|
|
return (df, tfidf_ds, ds_filter)
|
|
|
|
# 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(inpath, 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"))
|
|
p.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(inpath, 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]-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}",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(sims, 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
|
|
# lsi_model_load = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
|
|
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model=None):
|
|
# first compute the lsi of the matrix
|
|
# then take the column similarities
|
|
|
|
if type(n_components) is int:
|
|
n_components = [n_components]
|
|
|
|
n_components = sorted(n_components,reverse=True)
|
|
|
|
svd_components = n_components[0]
|
|
|
|
if lsi_model is None:
|
|
print("running LSI",flush=True)
|
|
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
|
|
mod = svd.fit(tfidfmat.T)
|
|
else:
|
|
mod = lsi_model
|
|
|
|
lsimat = mod.transform(tfidfmat.T)
|
|
if lsi_model_save is not None:
|
|
Path(lsi_model_save).parent.mkdir(exist_ok=True,parents=True)
|
|
pickle.dump(mod, open(lsi_model_save,'wb'))
|
|
|
|
print(n_components)
|
|
for n_dims in n_components:
|
|
print("computing similarities")
|
|
sims = column_similarities(lsimat[:,np.arange(n_dims)])
|
|
yield (sims, n_dims)
|
|
|
|
|
|
|
|
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]).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,'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('week')
|
|
dfwriter = df.write.partitionBy("week")
|
|
return dfwriter
|
|
|
|
def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
|
|
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
|
|
if min_df is not None:
|
|
terms = terms.filter(f.col('count')>=min_df)
|
|
if max_df is not None:
|
|
terms = terms.filter(f.col('count')<=max_df)
|
|
|
|
terms = terms.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,how='inner') # subreddit-term-id is unique
|
|
|
|
idf = idf.join(terms,on=term,how='inner')
|
|
|
|
# join on subreddit/term to create tf/dfs indexed by term
|
|
df = df.join(idf, on=[term_id, term],how='inner')
|
|
|
|
# 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 tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
|
|
term = term_colname
|
|
term_id = term + '_id'
|
|
|
|
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, min_df, max_df)
|
|
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')
|