import fire
import numpy as np
import sys
-sys.path.append("..")
-sys.path.append("../similarities")
-from similarities.similarities_helper import reindex_tfidf
+# sys.path.append("..")
+# sys.path.append("../similarities")
+# from similarities.similarities_helper import pull_tfidf
# this is the mean of the ratio of the overlap to the focal size.
# mean shared membership per focal community member
#from similarities_helper import similarities, lsi_column_similarities
from functools import partial
-inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
-term_colname='term'
-outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
-n_components=[10,50,100]
-included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
-n_iter=5
-random_state=1968
-algorithm='arpack'
-topN = None
-from_date=None
-to_date=None
-min_df=None
-max_df=None
+# inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
+# term_colname='authors'
+# outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_test_compex_LSI'
+# n_components=[10,50,100]
+# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+# n_iter=5
+# random_state=1968
+# algorithm='randomized'
+# topN = None
+# from_date=None
+# to_date=None
+# min_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):
print(n_components,flush=True)
-
if lsi_model is None:
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:
- 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)
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,
'author',
outfile,
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.drop("subreddit_id",axis=1)
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
return(df, subreddit_names)
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=500, 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)
+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:
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')}
- print(projection)
-
+ 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)
- print("assigning indexes",flush=True)
+
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:
df['subreddit_id_new'] = df['subreddit_id']
if reindex:
grouped = df.groupby(term_id)
- df[term_id_new] = grouped.ngroup()
+ df[term_id_new] = grouped.ngroup() + 1
else:
df[term_id_new] = df[term_id]
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()
+ # 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)
+ # 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())
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], entries.subreddit_id_new)))
+ mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
print("loading matrix")
# 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',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
# then take the column similarities
svd_components = n_components[0]
- if lsi_model_load is not None and Path(lsi_model_load).exists():
- print("loading LSI")
- mod = pickle.load(open(lsi_model_load ,'rb'))
- lsi_model_save = lsi_model_load
-
- else:
+ 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'))
- sims_list = []
+ print(n_components)
for n_dims in n_components:
+ print("computing similarities")
sims = column_similarities(lsimat[:,np.arange(n_dims)])
- if len(n_components) > 1:
- yield (sims, n_dims)
- else:
- return sims
+ yield (sims, n_dims)
+
def column_similarities(mat):
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')
+ df = df.repartition('week')
dfwriter = df.write.partitionBy("week")
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_id = term + '_id'
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 = 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
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
+ 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
- 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
if tf_family == tf_weight.MaxTF:
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_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 = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
df = df.repartition('subreddit')
dfwriter = df.write
return dfwriter
from pyspark.sql import SparkSession
from pyspark.sql import functions as f
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):
- spark = SparkSession.builder.getOrCreate()y
+inpath = '/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet'
+# 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)
else:
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')
spark.stop()
-def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
- return _tfidf_wrapper(tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
+def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits, min_df, max_df):
+ 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",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
topN=None,
- included_subreddits=None):
+ included_subreddits=None,
+ min_df=None,
+ max_df=None):
return tfidf(inpath,
outpath,
topN,
'author',
['[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",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
topN=None,
- included_subreddits=None):
+ included_subreddits=None,
+ min_df=None,
+ max_df=None):
return tfidf(inpath,
outpath,
topN,
'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",
+ static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
topN=None,
- included_subreddits=None):
+ included_subreddits=None
+ ):
return tfidf_weekly(inpath,
outpath,
+ static_tfidf_path,
topN,
'author',
['[deleted]','AutoModerator'],
)
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',
topN=None,
- included_subreddits=None):
+ included_subreddits=None
+ ):
return tfidf_weekly(inpath,
outpath,
+ static_tfidf_path,
topN,
'term',
[],
from scipy.sparse import csr_matrix
from multiprocessing import Pool, cpu_count
from functools import partial
-
-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
-tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
-min_df=None
-included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
-max_df = None
-topN=100
-term_colname='author'
-# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
-# included_subreddits=None
-
-def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
+import pickle
+
+# 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//comment_authors_compex.parquet"
+# min_df=2
+# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+# max_df = None
+# topN=100
+# term_colname='author'
+# # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
+# # 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;
+
+# 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_id = term + '_id'
term_id_new = term + '_id_new'
entries = pull_tfidf(infile = tfidf_path,
term_colname=term_colname,
- min_df=min_df,
- max_df=max_df,
included_subreddits=included_subreddits,
topN=topN,
- week=week,
+ week=week.isoformat(),
rescale_idf=False)
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
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(simfunc)
sims = simfunc(mat)
del mat
+ sims = next(sims)[0]
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
return set(batch.to_pandas()['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')
- #lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_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 = "/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=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),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,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
#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)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
spark.stop()
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())
-
- list(pool.imap(week_similarities_helper,weeks))
- pool.close()
+ for week in weeks:
+ week_similarities_helper(week)
+ # pool = Pool(cpu_count())
+
+ # list(pool.imap(week_similarities_helper, weeks))
+ # pool.close()
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
return cosine_similarities_weekly(infile,
outfile,
'author',
- min_df,
max_df,
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):
return cosine_similarities_weekly(infile,
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,
outfile,
'author',
- min_df,
- max_df,
- included_subreddits,
- topN,
+ included_subreddits=included_subreddits,
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,
outfile,
'term',
- min_df,
- max_df,
- included_subreddits,
- topN,
+ included_subreddits=included_subreddits,
n_components=n_components,
- lsi_model=lsi_model)
+ lsi_model=lsi_model,
+ )
if __name__ == "__main__":
fire.Fire({'authors':author_cosine_similarities_weekly,
'terms':term_cosine_similarities_weekly,
'authors-lsi':author_cosine_similarities_weekly_lsi,
- 'terms-lsi':term_cosine_similarities_weekly
+ 'terms-lsi':term_cosine_similarities_weekly_lsi
})
+
author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
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()
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count()
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')
- spk_densities = spark.createDataFrame(densities)
- ts = ts.join(spk_densities, on='subreddit', how='inner')
ts.write.parquet(output, mode='overwrite')
if __name__ == "__main__":
projects / cdsc_reddit.git / commitdiff