204 lines
11 KiB
Python
Executable File
204 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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from pyspark.sql import functions as f
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from pyspark.sql import SparkSession
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from pyspark.sql import Window
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import numpy as np
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import duckdb
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import pyarrow
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import pyarrow.dataset as ds
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import pandas as pd
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import fire
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from itertools import islice, chain
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from pathlib import Path
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from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
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from scipy.sparse import csr_matrix
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from multiprocessing import Pool, cpu_count
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from functools import partial
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import pickle
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import pytz
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# tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
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# #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
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# min_df=2
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# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
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# max_df = None
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# topN=100
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# term_colname='author'
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# # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
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# # included_subreddits=None
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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;
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# static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
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# dftest = spark.read.parquet(static_tfidf)
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def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None, clusters=None, term_ids=None):
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term = term_colname
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term_id = term + '_id'
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term_id_new = term + '_id_new'
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print(f"loading matrix: {week}")
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entries = pull_tfidf(infile = tfidf_path,
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term_colname=term_colname,
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included_subreddits=included_subreddits,
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topN=topN,
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week=week,
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rescale_idf=False)
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tfidf_colname='tf_idf'
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if term_ids is not None:
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entries = duckdb.sql(f"SELECT A.{tfidf_colname}, B.{term_id} AS {term_id_new}, A.subreddit_id_new FROM entries AS A JOIN read_parquet('{term_ids}') AS B ON A.{term_id_new} == B.{'old_'+term_id}").df()
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nterms = duckdb.sql(f"SELECT MAX({term_colname}_id AS nterms FROM read_parquet('{term_ids}')").df()
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nterms = list(nterms.nterms.values)[0]
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# if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
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shape = (nterms,subreddit_names.shape[0])
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print(shape)
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mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=shape)
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print('computing similarities')
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print(simfunc)
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sims = simfunc(mat)
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del mat
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sims = next(sims)[0]
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sims = pd.DataFrame(sims)
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sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
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sims['_subreddit'] = subreddit_names.subreddit.values
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outfile = str(Path(outdir) / str(week))
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write_weekly_similarities(outfile, sims, week, subreddit_names, clusters=clusters)
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def pull_weeks(batch):
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return set(batch.to_pandas()['week'])
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# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
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def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
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print(args)
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print(kwargs)
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term_colname= kwargs.get('term_colname')
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# lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
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#simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
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if isinstance(lsi_model,str):
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lsi_model = pickle.load(open(lsi_model,'rb'))
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simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
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return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
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#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_submission_terms_tfidf.parquet')
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def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=0, max_df=None, static_tfidf_path=None, clusters=None, min_date=None, max_date=None, cores=1, term_ids=None):
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print(outfile)
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# do this step in parallel if we have the memory for it.
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# should be doable with pool.map
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conn = duckdb.connect()
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subreddit_names = conn.execute(f"SELECT DISTINCT subreddit, subreddit_id from read_parquet('{tfidf_path}/*/*.parquet') ORDER BY subreddit_id;").df()
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if static_tfidf_path is not None:
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q = f"SELECT COUNT(DISTINCT({term_colname + '_id'})) as nterms FROM read_parquet('{static_tfidf_path}/*.parquet')"
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if min_df is not None and max_df is not None:
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q = q + f" WHERE count >= {min_df} AND count <= {max_df}"
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else:
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if min_df is not None:
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q = q + f" WHERE count >= {min_df}"
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if max_df is not None:
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q = q + f" WHERE count <= {max_df}"
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nterms = conn.execute(q).df()
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else:
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nterms = conn.execute(f"SELECT MAX({term_colname + '_id'}) as nterms FROM read_parquet('{tfidf_path}/*/*.parquet')").df()
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nterms = nterms.nterms.values
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nterms = int(nterms[0])
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q = f"SELECT DISTINCT CAST(CAST(week AS DATE) AS STRING) AS week FROM read_parquet('{tfidf_path}/*/*.parquet')"
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if min_date is not None and max_date is not None:
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q = q + f" WHERE CAST(week AS DATE) >= CAST('{min_date}' AS DATE) AND CAST(week AS DATE) <= CAST('{max_date}' AS DATE)"
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elif max_date is not None:
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q = q + f" WHERE CAST(week AS DATE) <= CAST('{max_date}' AS DATE)"
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elif min_date is not None:
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q = q + f" WHERE CAST(week AS DATE) >= CAST('{min_date}' AS DATE)"
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weeks = conn.execute(q).df()
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weeks = weeks.week.values
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conn.close()
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if clusters is not None:
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clusters_raw = pd.read_feather(clusters)
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clusters = duckdb.sql("SELECT A.subreddit AS sr_i, B.subreddit AS sr_j FROM clusters_raw AS A JOIN clusters_raw AS B ON A.cluster == B.cluster WHERE A.cluster != -1 AND B.cluster != -1").df()
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print(f"computing weekly similarities")
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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, clusters = clusters, term_ids=term_ids)
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if cores > 1:
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with Pool(cores) as pool: # maybe it can be done with 128 cores on the huge machine?
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list(pool.imap_unordered(week_similarities_helper, weeks, 5))
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pool.close()
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else:
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for week in weeks:
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week_similarities_helper(week)
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def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500, static_tfidf_path=None, clusters=None):
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return cosine_similarities_weekly(infile,
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outfile,
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'author',
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max_df,
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included_subreddits,
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topN,
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min_df=min_df,
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max_df=max_df,
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static_tfidf_path=static_tfidf_path,
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clusters=clusters
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)
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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, static_tfidf_path=None, clusters=None,term_ids=None):
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return cosine_similarities_weekly(infile,
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outfile,
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'term',
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min_df,
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max_df,
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included_subreddits,
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topN,
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static_tfidf_path=static_tfidf_path,
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clusters=clusters,
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term_ids=term_ids)
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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,static_tfidf_path=None, min_df=None, clusters=None, min_date=None, max_date=None,cores=1,term_ids=None):
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return cosine_similarities_weekly_lsi(infile,
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outfile,
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'author',
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min_df=min_df,
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included_subreddits=included_subreddits,
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n_components=n_components,
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lsi_model=lsi_model,
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static_tfidf_path=static_tfidf_path,
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clusters=clusters,
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min_date=min_date,
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max_date=max_date,
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cores=cores,
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term_ids=term_ids
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)
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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,static_tfidf_path=None,clusters=None,cores=1,term_ids=None):
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return cosine_similarities_weekly_lsi(infile,
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outfile,
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'term',
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included_subreddits=included_subreddits,
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n_components=n_components,
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lsi_model=lsi_model,
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static_tfidf_path=static_tfidf_path,
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clusters=clusters,
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min_date=min_date,
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max_date=max_date,
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cores=cores,
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term_ids=term_ids
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)
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if __name__ == "__main__":
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fire.Fire({'authors':author_cosine_similarities_weekly,
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'terms':term_cosine_similarities_weekly,
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'authors-lsi':author_cosine_similarities_weekly_lsi,
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'terms-lsi':term_cosine_similarities_weekly_lsi
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})
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