415 lines
16 KiB
Python
415 lines
16 KiB
Python
from pyspark.sql import SparkSession
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from pyspark.sql import Window
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from pyspark.sql import functions as f
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from enum import Enum
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from multiprocessing import cpu_count, Pool
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from pyspark.mllib.linalg.distributed import CoordinateMatrix
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from tempfile import TemporaryDirectory
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import pyarrow
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import pyarrow.dataset as ds
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from sklearn.metrics import pairwise_distances
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from scipy.sparse import csr_matrix, issparse
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from sklearn.decomposition import TruncatedSVD
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import pandas as pd
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import numpy as np
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import pathlib
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from datetime import datetime
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from pathlib import Path
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import pickle
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import pytz
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import duckdb
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class tf_weight(Enum):
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MaxTF = 1
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Norm05 = 2
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# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
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# cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
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# subreddits missing after this step don't have any terms that have a high enough idf
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# try rewriting without merges
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# does reindex_tfidf, but without reindexing.
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def reindex_tfidf(*args, **kwargs):
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df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
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print("assigning names")
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subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
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batches = subreddit_names.to_batches()
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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",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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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=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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tfidf_ds = ds.dataset(infile)
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if included_subreddits is None:
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included_subreddits = select_topN_subreddits(topN)
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else:
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included_subreddits = set(map(str.strip,open(included_subreddits)))
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ds_filter = ds.field("subreddit").isin(included_subreddits)
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if min_df is not None:
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ds_filter &= ds.field("count") >= min_df
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if max_df is not None:
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ds_filter &= ds.field("count") <= max_df
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if week is not None:
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ds_filter &= ds.field("week") == week
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if from_date is not None:
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ds_filter &= ds.field("week") >= from_date
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if to_date is not None:
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ds_filter &= ds.field("week") <= to_date
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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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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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}
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if not rescale_idf:
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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, 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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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() + 1
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else:
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df[term_id_new] = df[term_id]
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if rescale_idf:
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print("computing idf", flush=True)
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df['new_count'] = grouped[term_id].transform('count')
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N_docs = df.subreddit_id_new.max() + 1
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df['idf'] = np.log(N_docs/(1+df.new_count),dtype='float32') + 1
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if tf_family == tf_weight.MaxTF:
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df["tf_idf"] = df.relative_tf * df.idf
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else: # tf_fam = tf_weight.Norm05
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df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
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return (df, tfidf_ds, ds_filter)
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def pull_names(batch):
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return(batch.to_pandas().drop_duplicates())
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def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
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'''
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tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
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'''
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def proc_sims(sims, outfile):
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if issparse(sims):
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sims = sims.todense()
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print(f"shape of sims:{sims.shape}")
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print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}",flush=True)
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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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p = Path(outfile)
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output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
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output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
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output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
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p.parent.mkdir(exist_ok=True, parents=True)
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sims.to_feather(outfile)
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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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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]-1, entries.subreddit_id_new-1)))
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print("loading matrix")
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# mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
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print(f'computing similarities on mat. mat.shape:{mat.shape}')
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print(f"size of mat is:{mat.data.nbytes}",flush=True)
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sims = simfunc(mat)
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del mat
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if hasattr(sims,'__next__'):
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for simmat, name in sims:
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proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
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else:
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proc_sims(sims, outfile)
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def write_weekly_similarities(path, sims, week, names, clusters=None):
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sims['week'] = week
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p = pathlib.Path(path)
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if not p.is_dir():
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p.mkdir(exist_ok=True,parents=True)
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# reformat as a pairwise list
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sims = sims.melt(id_vars=['_subreddit','week'],value_vars=names.subreddit.values)
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if clusters is not None:
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cluster_sims = duckdb.sql("SELECT sims.* FROM sims SEMI JOIN clusters ON _subreddit == sr_i AND variable == sr_j").df()
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cluster_sims.to_parquet(p / week)
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def column_overlaps(mat):
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non_zeros = (mat != 0).astype('double')
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intersection = non_zeros.T @ non_zeros
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card1 = non_zeros.sum(axis=0)
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den = np.add.outer(card1,card1) - intersection
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return intersection / den
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def test_lsi_sims():
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term = "term"
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term_id = term + '_id'
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term_id_new = term + '_id_new'
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t1 = time.perf_counter()
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entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet",
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term_colname='term',
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min_df=2000,
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topN=10000
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)
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t2 = time.perf_counter()
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print(f"first load took:{t2 - t1}s")
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entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
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term_colname='term',
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min_df=2000,
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topN=10000
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)
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t3=time.perf_counter()
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print(f"second load took:{t3 - t2}s")
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mat = csr_matrix((entries['tf_idf'],(entries[term_id_new], entries.subreddit_id_new)))
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sims = list(lsi_column_similarities(mat, [10,50]))
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sims_og = sims
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sims_test = list(lsi_column_similarities(mat,[10,50],algorithm='randomized',n_iter=10))
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# n_components is the latent dimensionality. sklearn recommends 100. More might be better
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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=None,lsi_model_save=None,lsi_model_load=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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if type(n_components) is int:
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n_components = [n_components]
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n_components = sorted(n_components,reverse=True)
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svd_components = n_components[0]
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if lsi_model is not None:
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mod = lsi_model
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elif 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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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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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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print(n_components, flush=True)
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lsimat = mod.transform(tfidfmat.T)
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for n_dims in n_components:
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print("computing similarities", flush=True)
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sims = column_similarities(lsimat[:,np.arange(n_dims)])
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yield (sims, n_dims)
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def column_similarities(mat):
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return 1 - pairwise_distances(mat,metric='cosine')
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def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
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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,'week']).agg(f.sum('tf').alias('tf'))
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max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
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max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
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df = df.join(max_subreddit_terms, on=['subreddit','week'])
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df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
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# group by term. term is unique
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idf = df.groupby([term,'week']).count()
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N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
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idf = idf.join(N_docs, on=['week'])
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# add a little smoothing to the idf
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idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (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 = 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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subreddits = df.select(['subreddit']).distinct()
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subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
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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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idf = idf.join(terms,on=[term])
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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,'week'])
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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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df = df.withColumn("tf_idf", df.relative_tf * df.idf)
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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('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, 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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max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
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max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
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df = df.join(max_subreddit_terms, on='subreddit')
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df = df.withColumn("relative_tf", (df.tf / df.sr_max_tf))
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# group by term. term is unique
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idf = df.groupby([term]).count()
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N_docs = df.select('subreddit').distinct().count()
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# add a little smoothing to the idf
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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
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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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subreddits = df.select(['subreddit']).distinct()
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subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
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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,how='inner') # subreddit-term-id is unique
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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],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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df = df.withColumn("tf_idf", df.relative_tf * df.idf)
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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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return df
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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, 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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def select_topN_subreddits(topN, path="../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
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rankdf = pd.read_csv(path)
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included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
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return included_subreddits
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def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
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outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"):
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spark = SparkSession.builder.getOrCreate()
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df = spark.read.parquet(inpath)
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df = df.repartition(400,'subreddit')
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df.write.parquet(outpath,mode='overwrite')
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def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet",
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outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"):
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spark = SparkSession.builder.getOrCreate()
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df = spark.read.parquet(inpath)
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df = df.repartition(400,'subreddit','week')
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dfwriter = df.write.partitionBy("week")
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dfwriter.parquet(outpath,mode='overwrite')
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