collective/cdsc_reddit
17
0
Fork 0
Code

commit changes from smap project.

Browse Source
This commit is contained in:
Nathan TeBlunthuis 2022-01-19 13:57:02 -08:00
parent 541e125b28
commit 7b130a30af
6 changed files with 160 additions and 122 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
density/overlap_density.py
View File

@ -4,9 +4,9 @@ from pathlib import Path
import fire import fire
import numpy as np import numpy as np
import sys import sys
sys.path.append("..") # sys.path.append("..")
sys.path.append("../similarities") # sys.path.append("../similarities")
from similarities.similarities_helper import reindex_tfidf # from similarities.similarities_helper import pull_tfidf
# this is the mean of the ratio of the overlap to the focal size. # this is the mean of the ratio of the overlap to the focal size.
# mean shared membership per focal community member # mean shared membership per focal community member

34
similarities/lsi_similarities.py
View File

@ -5,28 +5,28 @@ from similarities_helper import *
#from similarities_helper import similarities, lsi_column_similarities #from similarities_helper import similarities, lsi_column_similarities
from functools import partial from functools import partial
inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/" # inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
term_colname='term' # term_colname='authors'
outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI' # outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_test_compex_LSI'
n_components=[10,50,100] # n_components=[10,50,100]
included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt" # included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
n_iter=5 # n_iter=5
random_state=1968 # random_state=1968
algorithm='arpack' # algorithm='randomized'
topN = None # topN = None
from_date=None # from_date=None
to_date=None # to_date=None
min_df=None # min_df=None
max_df=None # max_df=None
def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None): def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
print(n_components,flush=True) print(n_components,flush=True)
if lsi_model is None: if lsi_model is None:
if type(n_components) == list: if type(n_components) == list:
lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl' lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}s_LSIMOD.pkl'
else: else:
lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl' lsi_model = Path(outfile) / f'{n_components}_{term_colname}s_LSIMOD.pkl'
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model) simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
@ -62,7 +62,7 @@ def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/t
n_components=n_components n_components=n_components
) )
def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968): def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
return lsi_similarities(inpath, return lsi_similarities(inpath,
'author', 'author',
outfile, outfile,

88
similarities/similarities_helper.py
View File

@ -43,7 +43,7 @@ def reindex_tfidf(*args, **kwargs):
new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates() new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
new_ids = new_ids.set_index('subreddit_id') new_ids = new_ids.set_index('subreddit_id')
subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index() subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
subreddit_names = subreddit_names.drop("subreddit_id",1) subreddit_names = subreddit_names.drop("subreddit_id",axis=1)
subreddit_names = subreddit_names.sort_values("subreddit_id_new") subreddit_names = subreddit_names.sort_values("subreddit_id_new")
return(df, subreddit_names) return(df, subreddit_names)
@ -51,8 +51,9 @@ def pull_tfidf(*args, **kwargs):
df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False) df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
return df return df
def _pull_or_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, reindex=True): 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}", flush=True) print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
if week is not None: if week is not None:
tfidf_ds = ds.dataset(infile, partitioning='hive') tfidf_ds = ds.dataset(infile, partitioning='hive')
else: else:
@ -94,23 +95,23 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
projection = { projection = {
'subreddit_id':ds.field('subreddit_id'), 'subreddit_id':ds.field('subreddit_id'),
term_id:ds.field(term_id), term_id:ds.field(term_id),
'relative_tf':ds.field('relative_tf').cast('float32'),
'tf_idf':ds.field('tf_idf').cast('float32')} 'tf_idf':ds.field('tf_idf').cast('float32')}
print(projection) print(projection, flush=True)
print(ds_filter, flush=True)
df = tfidf_ds.to_table(filter=ds_filter,columns=projection) df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
df = df.to_pandas(split_blocks=True,self_destruct=True) df = df.to_pandas(split_blocks=True,self_destruct=True)
print("assigning indexes",flush=True)
if reindex: if reindex:
df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() print("assigning indexes",flush=True)
df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
else: else:
df['subreddit_id_new'] = df['subreddit_id'] df['subreddit_id_new'] = df['subreddit_id']
if reindex: if reindex:
grouped = df.groupby(term_id) grouped = df.groupby(term_id)
df[term_id_new] = grouped.ngroup() df[term_id_new] = grouped.ngroup() + 1
else: else:
df[term_id_new] = df[term_id] df[term_id_new] = df[term_id]
@ -126,17 +127,17 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
return (df, tfidf_ds, ds_filter) return (df, tfidf_ds, ds_filter)
with Pool(cpu_count()) as pool: # with Pool(cpu_count()) as pool:
chunks = pool.imap_unordered(pull_names,batches) # chunks = pool.imap_unordered(pull_names,batches)
subreddit_names = pd.concat(chunks,copy=False).drop_duplicates() # subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
subreddit_names = subreddit_names.set_index("subreddit_id") # subreddit_names = subreddit_names.set_index("subreddit_id")
new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates() # new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
new_ids = new_ids.set_index('subreddit_id') # new_ids = new_ids.set_index('subreddit_id')
subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index() # subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
subreddit_names = subreddit_names.drop("subreddit_id",1) # subreddit_names = subreddit_names.drop("subreddit_id",1)
subreddit_names = subreddit_names.sort_values("subreddit_id_new") # subreddit_names = subreddit_names.sort_values("subreddit_id_new")
return(df, subreddit_names) # return(df, subreddit_names)
def pull_names(batch): def pull_names(batch):
return(batch.to_pandas().drop_duplicates()) return(batch.to_pandas().drop_duplicates())
@ -170,7 +171,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
term_id_new = term + '_id_new' 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) 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], entries.subreddit_id_new))) mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
print("loading matrix") print("loading matrix")
@ -238,7 +239,8 @@ def test_lsi_sims():
# if n_components is a list we'll return a list of similarities with different latent dimensionalities # 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. # 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 # 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',lsi_model_save=None,lsi_model_load=None): # 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 # first compute the lsi of the matrix
# then take the column similarities # then take the column similarities
@ -249,28 +251,24 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
svd_components = n_components[0] svd_components = n_components[0]
if lsi_model_load is not None and Path(lsi_model_load).exists(): if lsi_model is None:
print("loading LSI")
mod = pickle.load(open(lsi_model_load ,'rb'))
lsi_model_save = lsi_model_load
else:
print("running LSI",flush=True) print("running LSI",flush=True)
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter) svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
mod = svd.fit(tfidfmat.T) mod = svd.fit(tfidfmat.T)
else:
mod = lsi_model
lsimat = mod.transform(tfidfmat.T) lsimat = mod.transform(tfidfmat.T)
if lsi_model_save is not None: 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')) pickle.dump(mod, open(lsi_model_save,'wb'))
sims_list = [] print(n_components)
for n_dims in n_components: for n_dims in n_components:
print("computing similarities")
sims = column_similarities(lsimat[:,np.arange(n_dims)]) sims = column_similarities(lsimat[:,np.arange(n_dims)])
if len(n_components) > 1: yield (sims, n_dims)
yield (sims, n_dims)
else:
return sims
def column_similarities(mat): def column_similarities(mat):
@ -326,11 +324,11 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
else: # tf_fam = tf_weight.Norm05 else: # tf_fam = tf_weight.Norm05
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf) df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
df = df.repartition(400,'subreddit','week') df = df.repartition('week')
dfwriter = df.write.partitionBy("week") dfwriter = df.write.partitionBy("week")
return dfwriter return dfwriter
def _calc_tfidf(df, term_colname, tf_family): def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
term = term_colname term = term_colname
term_id = term + '_id' term_id = term + '_id'
@ -348,7 +346,13 @@ def _calc_tfidf(df, term_colname, tf_family):
idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1) idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
# collect the dictionary to make a pydict of terms to indexes # collect the dictionary to make a pydict of terms to indexes
terms = idf.select(term).distinct() # terms are distinct 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 terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
# make subreddit ids # make subreddit ids
@ -358,12 +362,12 @@ def _calc_tfidf(df, term_colname, tf_family):
df = df.join(subreddits,on='subreddit') df = df.join(subreddits,on='subreddit')
# map terms to indexes in the tfs and the idfs # map terms to indexes in the tfs and the idfs
df = df.join(terms,on=term) # subreddit-term-id is unique df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique
idf = idf.join(terms,on=term) idf = idf.join(terms,on=term,how='inner')
# join on subreddit/term to create tf/dfs indexed by term # join on subreddit/term to create tf/dfs indexed by term
df = df.join(idf, on=[term_id, term]) df = df.join(idf, on=[term_id, term],how='inner')
# agg terms by subreddit to make sparse tf/df vectors # agg terms by subreddit to make sparse tf/df vectors
if tf_family == tf_weight.MaxTF: if tf_family == tf_weight.MaxTF:
@ -374,14 +378,14 @@ def _calc_tfidf(df, term_colname, tf_family):
return df return df
def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05): def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
term = term_colname term = term_colname
term_id = term + '_id' term_id = term + '_id'
# aggregate counts by week. now subreddit-term is distinct
df = df.filter(df.subreddit.isin(include_subs)) df = df.filter(df.subreddit.isin(include_subs))
df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf')) df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
df = _calc_tfidf(df, term_colname, tf_family) df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
df = df.repartition('subreddit') df = df.repartition('subreddit')
dfwriter = df.write dfwriter = df.write
return dfwriter return dfwriter

54
similarities/tfidf.py
View File

@ -2,9 +2,12 @@ import fire
from pyspark.sql import SparkSession from pyspark.sql import SparkSession
from pyspark.sql import functions as f from pyspark.sql import functions as f
from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
from functools import partial
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits): inpath = '/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet'
spark = SparkSession.builder.getOrCreate()y # include_terms is a path to a parquet file that contains a column of term_colname + '_id' to include.
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=None, min_df=None, max_df=None):
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(inpath) df = spark.read.parquet(inpath)
@ -15,50 +18,72 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
else: else:
include_subs = select_topN_subreddits(topN) include_subs = select_topN_subreddits(topN)
dfwriter = func(df, include_subs, term_colname) include_subs = spark.sparkContext.broadcast(include_subs)
# term_id = term_colname + "_id"
if included_terms is not None:
terms_df = spark.read.parquet(included_terms)
terms_df = terms_df.select(term_colname).distinct()
df = df.join(terms_df, on=term_colname, how='left_semi')
dfwriter = func(df, include_subs.value, term_colname)
dfwriter.parquet(outpath,mode='overwrite',compression='snappy') dfwriter.parquet(outpath,mode='overwrite',compression='snappy')
spark.stop() spark.stop()
def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits): def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits, min_df, max_df):
return _tfidf_wrapper(tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits) tfidf_func = partial(tfidf_dataset, max_df=max_df, min_df=min_df)
return _tfidf_wrapper(tfidf_func, inpath, outpath, topN, term_colname, exclude, included_subreddits)
def tfidf_weekly(inpath, outpath, static_tfidf_path, topN, term_colname, exclude, included_subreddits):
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=static_tfidf_path)
def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet", def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet', outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
topN=None, topN=None,
included_subreddits=None): included_subreddits=None,
min_df=None,
max_df=None):
return tfidf(inpath, return tfidf(inpath,
outpath, outpath,
topN, topN,
'author', 'author',
['[deleted]','AutoModerator'], ['[deleted]','AutoModerator'],
included_subreddits=included_subreddits included_subreddits=included_subreddits,
min_df=min_df,
max_df=max_df
) )
def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet", def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet', outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
topN=None, topN=None,
included_subreddits=None): included_subreddits=None,
min_df=None,
max_df=None):
return tfidf(inpath, return tfidf(inpath,
outpath, outpath,
topN, topN,
'term', 'term',
[], [],
included_subreddits=included_subreddits included_subreddits=included_subreddits,
min_df=min_df,
max_df=max_df
) )
def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet", def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet', outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
topN=None, topN=None,
included_subreddits=None): included_subreddits=None
):
return tfidf_weekly(inpath, return tfidf_weekly(inpath,
outpath, outpath,
static_tfidf_path,
topN, topN,
'author', 'author',
['[deleted]','AutoModerator'], ['[deleted]','AutoModerator'],
@ -66,13 +91,16 @@ def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_
) )
def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet", def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
topN=None, topN=None,
included_subreddits=None): included_subreddits=None
):
return tfidf_weekly(inpath, return tfidf_weekly(inpath,
outpath, outpath,
static_tfidf_path,
topN, topN,
'term', 'term',
[], [],

84
similarities/weekly_cosine_similarities.py
View File

@ -13,18 +13,23 @@ from similarities_helper import pull_tfidf, column_similarities, write_weekly_si
from scipy.sparse import csr_matrix from scipy.sparse import csr_matrix
from multiprocessing import Pool, cpu_count from multiprocessing import Pool, cpu_count
from functools import partial from functools import partial
import pickle
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet" # tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet" # #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
min_df=None # min_df=2
included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt" # included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
max_df = None # max_df = None
topN=100 # topN=100
term_colname='author' # term_colname='author'
# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet' # # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
# included_subreddits=None # # included_subreddits=None
outfile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors.parquet"; infile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_authors_tfidf.parquet"; included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"; lsi_model="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/2000_authors_LSIMOD.pkl"; n_components=1500; algorithm="randomized"; term_colname='author'; tfidf_path=infile; random_state=1968;
def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms): # static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
# dftest = spark.read.parquet(static_tfidf)
def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
term = term_colname term = term_colname
term_id = term + '_id' term_id = term + '_id'
term_id_new = term + '_id_new' term_id_new = term + '_id_new'
@ -32,20 +37,19 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
entries = pull_tfidf(infile = tfidf_path, entries = pull_tfidf(infile = tfidf_path,
term_colname=term_colname, term_colname=term_colname,
min_df=min_df,
max_df=max_df,
included_subreddits=included_subreddits, included_subreddits=included_subreddits,
topN=topN, topN=topN,
week=week, week=week.isoformat(),
rescale_idf=False) rescale_idf=False)
tfidf_colname='tf_idf' tfidf_colname='tf_idf'
# if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0])) mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
print('computing similarities') print('computing similarities')
print(simfunc)
sims = simfunc(mat) sims = simfunc(mat)
del mat del mat
sims = next(sims)[0]
sims = pd.DataFrame(sims) sims = pd.DataFrame(sims)
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1) sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
sims['_subreddit'] = subreddit_names.subreddit.values sims['_subreddit'] = subreddit_names.subreddit.values
@ -56,18 +60,20 @@ def pull_weeks(batch):
return set(batch.to_pandas()['week']) return set(batch.to_pandas()['week'])
# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week. # This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs): def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
print(args)
print(kwargs)
term_colname= kwargs.get('term_colname') term_colname= kwargs.get('term_colname')
#lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl" # lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
# simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model) lsi_model = pickle.load(open(lsi_model,'rb'))
#simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model) simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs) return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet') #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500, simfunc=column_similarities): def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
print(outfile) print(outfile)
# do this step in parallel if we have the memory for it. # do this step in parallel if we have the memory for it.
# should be doable with pool.map # should be doable with pool.map
@ -84,12 +90,14 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
spark.stop() spark.stop()
print(f"computing weekly similarities") print(f"computing weekly similarities")
week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms) week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=None, subreddit_names=subreddit_names,nterms=nterms)
pool = Pool(cpu_count()) for week in weeks:
week_similarities_helper(week)
# pool = Pool(cpu_count())
list(pool.imap(week_similarities_helper,weeks)) # list(pool.imap(week_similarities_helper, weeks))
pool.close() # pool.close()
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine? # with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
@ -97,10 +105,11 @@ def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/
return cosine_similarities_weekly(infile, return cosine_similarities_weekly(infile,
outfile, outfile,
'author', 'author',
min_df,
max_df, max_df,
included_subreddits, included_subreddits,
topN) topN,
min_df=2
)
def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None): def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
return cosine_similarities_weekly(infile, return cosine_similarities_weekly(infile,
@ -112,32 +121,29 @@ def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/re
topN) topN)
def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None): def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', included_subreddits=None, n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile, return cosine_similarities_weekly_lsi(infile,
outfile, outfile,
'author', 'author',
min_df, included_subreddits=included_subreddits,
max_df,
included_subreddits,
topN,
n_components=n_components, n_components=n_components,
lsi_model=lsi_model) lsi_model=lsi_model
)
def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None): def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', included_subreddits=None, n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile, return cosine_similarities_weekly_lsi(infile,
outfile, outfile,
'term', 'term',
min_df, included_subreddits=included_subreddits,
max_df,
included_subreddits,
topN,
n_components=n_components, n_components=n_components,
lsi_model=lsi_model) lsi_model=lsi_model,
)
if __name__ == "__main__": if __name__ == "__main__":
fire.Fire({'authors':author_cosine_similarities_weekly, fire.Fire({'authors':author_cosine_similarities_weekly,
'terms':term_cosine_similarities_weekly, 'terms':term_cosine_similarities_weekly,
'authors-lsi':author_cosine_similarities_weekly_lsi, 'authors-lsi':author_cosine_similarities_weekly_lsi,
'terms-lsi':term_cosine_similarities_weekly 'terms-lsi':term_cosine_similarities_weekly_lsi
}) })

14
timeseries/cluster_timeseries.py
View File

@ -12,10 +12,6 @@ def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit
author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather", author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
output="data/subreddit_timeseries.parquet"): output="data/subreddit_timeseries.parquet"):
clusters = load_clusters(term_clusters_path, author_clusters_path)
densities = load_densities(term_densities_path, author_densities_path)
spark = SparkSession.builder.getOrCreate() spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet") df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
@ -26,11 +22,15 @@ def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit
ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count() ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count()
ts = ts.repartition('subreddit') ts = ts.repartition('subreddit')
spk_clusters = spark.createDataFrame(clusters)
if term_densities_path is not None and author_densities_path is not None:
densities = load_densities(term_densities_path, author_densities_path)
spk_densities = spark.createDataFrame(densities)
ts = ts.join(spk_densities, on='subreddit', how='inner')
clusters = load_clusters(term_clusters_path, author_clusters_path)
spk_clusters = spark.createDataFrame(clusters)
ts = ts.join(spk_clusters, on='subreddit', how='inner') ts = ts.join(spk_clusters, on='subreddit', how='inner')
spk_densities = spark.createDataFrame(densities)
ts = ts.join(spk_densities, on='subreddit', how='inner')
ts.write.parquet(output, mode='overwrite') ts.write.parquet(output, mode='overwrite')
if __name__ == "__main__": if __name__ == "__main__":