commit changes from smap project.
This commit is contained in:
parent
541e125b28
commit
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@ -4,9 +4,9 @@ from pathlib import Path
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import fire
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import numpy as np
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import sys
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sys.path.append("..")
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sys.path.append("../similarities")
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from similarities.similarities_helper import reindex_tfidf
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# sys.path.append("..")
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# sys.path.append("../similarities")
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# from similarities.similarities_helper import pull_tfidf
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# this is the mean of the ratio of the overlap to the focal size.
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# mean shared membership per focal community member
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@ -5,28 +5,28 @@ from similarities_helper import *
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#from similarities_helper import similarities, lsi_column_similarities
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from functools import partial
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inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
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term_colname='term'
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outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
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n_components=[10,50,100]
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included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
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n_iter=5
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random_state=1968
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algorithm='arpack'
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topN = None
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from_date=None
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to_date=None
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min_df=None
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max_df=None
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# inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
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# term_colname='authors'
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# outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_test_compex_LSI'
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# n_components=[10,50,100]
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# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
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# n_iter=5
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# random_state=1968
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# algorithm='randomized'
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# topN = None
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# from_date=None
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# to_date=None
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# min_df=None
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# max_df=None
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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):
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print(n_components,flush=True)
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if lsi_model is None:
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if type(n_components) == list:
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lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl'
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lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}s_LSIMOD.pkl'
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else:
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lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl'
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lsi_model = Path(outfile) / f'{n_components}_{term_colname}s_LSIMOD.pkl'
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simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
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@ -62,7 +62,7 @@ def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/t
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n_components=n_components
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)
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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):
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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):
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return lsi_similarities(inpath,
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'author',
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outfile,
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@ -43,7 +43,7 @@ def reindex_tfidf(*args, **kwargs):
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new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
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new_ids = new_ids.set_index('subreddit_id')
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subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
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subreddit_names = subreddit_names.drop("subreddit_id",1)
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subreddit_names = subreddit_names.drop("subreddit_id",axis=1)
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subreddit_names = subreddit_names.sort_values("subreddit_id_new")
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return(df, subreddit_names)
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@ -51,8 +51,9 @@ def pull_tfidf(*args, **kwargs):
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df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
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return df
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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):
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print(f"loading tfidf {infile}", flush=True)
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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):
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print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
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if week is not None:
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tfidf_ds = ds.dataset(infile, partitioning='hive')
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else:
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@ -94,23 +95,23 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
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projection = {
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'subreddit_id':ds.field('subreddit_id'),
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term_id:ds.field(term_id),
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'relative_tf':ds.field('relative_tf').cast('float32'),
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'tf_idf':ds.field('tf_idf').cast('float32')}
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print(projection)
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print(projection, flush=True)
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print(ds_filter, flush=True)
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df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
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df = df.to_pandas(split_blocks=True,self_destruct=True)
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print("assigning indexes",flush=True)
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if reindex:
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df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
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print("assigning indexes",flush=True)
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df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
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else:
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df['subreddit_id_new'] = df['subreddit_id']
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if reindex:
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grouped = df.groupby(term_id)
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df[term_id_new] = grouped.ngroup()
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df[term_id_new] = grouped.ngroup() + 1
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else:
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df[term_id_new] = df[term_id]
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@ -126,17 +127,17 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
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return (df, tfidf_ds, ds_filter)
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with Pool(cpu_count()) as pool:
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chunks = pool.imap_unordered(pull_names,batches)
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subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
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# with Pool(cpu_count()) as pool:
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# chunks = pool.imap_unordered(pull_names,batches)
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# subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
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subreddit_names = subreddit_names.set_index("subreddit_id")
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new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
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new_ids = new_ids.set_index('subreddit_id')
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subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
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subreddit_names = subreddit_names.drop("subreddit_id",1)
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subreddit_names = subreddit_names.sort_values("subreddit_id_new")
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return(df, subreddit_names)
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# subreddit_names = subreddit_names.set_index("subreddit_id")
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# new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
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# new_ids = new_ids.set_index('subreddit_id')
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# subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
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# subreddit_names = subreddit_names.drop("subreddit_id",1)
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# subreddit_names = subreddit_names.sort_values("subreddit_id_new")
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# return(df, subreddit_names)
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def pull_names(batch):
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return(batch.to_pandas().drop_duplicates())
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@ -170,7 +171,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
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term_id_new = term + '_id_new'
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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)
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mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
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mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
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print("loading matrix")
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@ -238,7 +239,8 @@ def test_lsi_sims():
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# if n_components is a list we'll return a list of similarities with different latent dimensionalities
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# if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
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# this function takes the svd and then the column similarities of it
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def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
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# lsi_model_load = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
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def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model=None):
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# first compute the lsi of the matrix
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# then take the column similarities
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@ -249,28 +251,24 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
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svd_components = n_components[0]
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if lsi_model_load is not None and Path(lsi_model_load).exists():
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print("loading LSI")
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mod = pickle.load(open(lsi_model_load ,'rb'))
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lsi_model_save = lsi_model_load
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else:
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if lsi_model is None:
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print("running LSI",flush=True)
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svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
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mod = svd.fit(tfidfmat.T)
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else:
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mod = lsi_model
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lsimat = mod.transform(tfidfmat.T)
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if lsi_model_save is not None:
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Path(lsi_model_save).parent.mkdir(exist_ok=True,parents=True)
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pickle.dump(mod, open(lsi_model_save,'wb'))
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sims_list = []
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print(n_components)
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for n_dims in n_components:
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print("computing similarities")
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sims = column_similarities(lsimat[:,np.arange(n_dims)])
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if len(n_components) > 1:
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yield (sims, n_dims)
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else:
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return sims
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def column_similarities(mat):
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@ -326,11 +324,11 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
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else: # tf_fam = tf_weight.Norm05
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df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
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df = df.repartition(400,'subreddit','week')
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df = df.repartition('week')
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dfwriter = df.write.partitionBy("week")
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return dfwriter
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def _calc_tfidf(df, term_colname, tf_family):
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def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
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term = term_colname
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term_id = term + '_id'
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@ -348,7 +346,13 @@ def _calc_tfidf(df, term_colname, tf_family):
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idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
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# collect the dictionary to make a pydict of terms to indexes
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terms = idf.select(term).distinct() # terms are distinct
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terms = idf
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if min_df is not None:
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terms = terms.filter(f.col('count')>=min_df)
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if max_df is not None:
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terms = terms.filter(f.col('count')<=max_df)
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terms = terms.select(term).distinct() # terms are distinct
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terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
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# make subreddit ids
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@ -358,12 +362,12 @@ def _calc_tfidf(df, term_colname, tf_family):
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df = df.join(subreddits,on='subreddit')
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# map terms to indexes in the tfs and the idfs
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df = df.join(terms,on=term) # subreddit-term-id is unique
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df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique
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idf = idf.join(terms,on=term)
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idf = idf.join(terms,on=term,how='inner')
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# join on subreddit/term to create tf/dfs indexed by term
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df = df.join(idf, on=[term_id, term])
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df = df.join(idf, on=[term_id, term],how='inner')
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# agg terms by subreddit to make sparse tf/df vectors
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if tf_family == tf_weight.MaxTF:
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@ -374,14 +378,14 @@ def _calc_tfidf(df, term_colname, tf_family):
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return df
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def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
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def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
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term = term_colname
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term_id = term + '_id'
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# aggregate counts by week. now subreddit-term is distinct
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df = df.filter(df.subreddit.isin(include_subs))
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df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
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df = _calc_tfidf(df, term_colname, tf_family)
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df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
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df = df.repartition('subreddit')
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dfwriter = df.write
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return dfwriter
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@ -2,9 +2,12 @@ import fire
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from pyspark.sql import SparkSession
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from pyspark.sql import functions as f
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from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
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from functools import partial
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def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
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spark = SparkSession.builder.getOrCreate()y
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inpath = '/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet'
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# include_terms is a path to a parquet file that contains a column of term_colname + '_id' to include.
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def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=None, min_df=None, max_df=None):
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spark = SparkSession.builder.getOrCreate()
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df = spark.read.parquet(inpath)
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@ -15,50 +18,72 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
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else:
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include_subs = select_topN_subreddits(topN)
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dfwriter = func(df, include_subs, term_colname)
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include_subs = spark.sparkContext.broadcast(include_subs)
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# term_id = term_colname + "_id"
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if included_terms is not None:
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terms_df = spark.read.parquet(included_terms)
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terms_df = terms_df.select(term_colname).distinct()
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df = df.join(terms_df, on=term_colname, how='left_semi')
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dfwriter = func(df, include_subs.value, term_colname)
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dfwriter.parquet(outpath,mode='overwrite',compression='snappy')
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spark.stop()
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def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
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return _tfidf_wrapper(tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
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def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits, min_df, max_df):
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tfidf_func = partial(tfidf_dataset, max_df=max_df, min_df=min_df)
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return _tfidf_wrapper(tfidf_func, inpath, outpath, topN, term_colname, exclude, included_subreddits)
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def tfidf_weekly(inpath, outpath, static_tfidf_path, topN, term_colname, exclude, included_subreddits):
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return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=static_tfidf_path)
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def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
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return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
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def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
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outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
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topN=None,
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included_subreddits=None):
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included_subreddits=None,
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min_df=None,
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max_df=None):
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return tfidf(inpath,
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outpath,
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topN,
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'author',
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['[deleted]','AutoModerator'],
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included_subreddits=included_subreddits
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included_subreddits=included_subreddits,
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min_df=min_df,
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max_df=max_df
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)
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def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
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outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
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topN=None,
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included_subreddits=None):
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included_subreddits=None,
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min_df=None,
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max_df=None):
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return tfidf(inpath,
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outpath,
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topN,
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'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'],
|
||||
@ -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",
|
||||
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',
|
||||
[],
|
||||
|
||||
@ -13,18 +13,23 @@ from similarities_helper import pull_tfidf, column_similarities, write_weekly_si
|
||||
from scipy.sparse import csr_matrix
|
||||
from multiprocessing import Pool, cpu_count
|
||||
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/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
|
||||
# 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;
|
||||
|
||||
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_id = term + '_id'
|
||||
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,
|
||||
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
|
||||
@ -56,18 +60,20 @@ def pull_weeks(batch):
|
||||
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"
|
||||
# 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)
|
||||
|
||||
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
|
||||
@ -84,12 +90,14 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
|
||||
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())
|
||||
for week in weeks:
|
||||
week_similarities_helper(week)
|
||||
# pool = Pool(cpu_count())
|
||||
|
||||
list(pool.imap(week_similarities_helper,weeks))
|
||||
pool.close()
|
||||
# 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?
|
||||
|
||||
|
||||
@ -97,10 +105,11 @@ def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/
|
||||
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,
|
||||
@ -112,32 +121,29 @@ def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/re
|
||||
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
|
||||
})
|
||||
|
||||
|
||||
@ -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",
|
||||
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")
|
||||
@ -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 = ts.repartition('subreddit')
|
||||
spk_clusters = spark.createDataFrame(clusters)
|
||||
|
||||
ts = ts.join(spk_clusters, on='subreddit', how='inner')
|
||||
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.write.parquet(output, mode='overwrite')
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Loading…
Reference in New Issue
Block a user