Update code for building simlarity matrices.
This commit is contained in:
Nate E TeBlunthuis
parent
e794214653
commit
82d184d9c6
@ -2,11 +2,67 @@ 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 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 scipy.sparse import csr_matrix
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import pandas as pd
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import numpy as np
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class tf_weight(Enum):
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MaxTF = 1
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Norm05 = 2
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def read_tfidf_matrix(path,term_colname):
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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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dataset = ds.dataset(path,format='parquet')
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entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new]).to_pandas()
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return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
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def column_similarities(mat):
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norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
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mat = mat.multiply(1/norm)
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sims = mat.T @ mat
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return(sims)
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def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits):
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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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if min_df is None:
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min_df = 0.1 * len(included_subreddits)
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tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
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# reset the subreddit ids
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sub_ids = tfidf.select('subreddit_id').distinct()
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sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
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tfidf = tfidf.join(sub_ids,'subreddit_id')
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# only use terms in at least min_df included subreddits
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new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
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# new_count = new_count.filter(f.col('new_count') >= min_df)
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tfidf = tfidf.join(new_count,term_id,how='inner')
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# reset the term ids
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term_ids = tfidf.select([term_id]).distinct()
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term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
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tfidf = tfidf.join(term_ids,term_id)
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tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
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# tfidf = tfidf.withColumnRenamed("idf","idf_old")
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# tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
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tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
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tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
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tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
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return tempdir
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def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
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term = term_colname
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@ -8,38 +8,23 @@ import pandas as pd
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import fire
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from itertools import islice
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from pathlib import Path
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from similarities_helper import cosine_similarities
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spark = SparkSession.builder.getOrCreate()
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conf = spark.sparkContext.getConf()
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# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
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def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
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'''
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Compute similarities between subreddits based on tfi-idf vectors of comment texts
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included_subreddits : string
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Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
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similarity_threshold : double (default = 0)
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set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
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https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
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min_df : int (default = 0.1 * (number of included_subreddits)
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exclude terms that appear in fewer than this number of documents.
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outfile: string
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where to output csv and feather outputs
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'''
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from similarities_helper import cosine_similarities, prep_tfidf_entries, read_tfidf_matrix, column_similarities
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import scipy
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# outfile='test_similarities_500.feather';
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# min_df = None;
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# included_subreddits=None; topN=100; exclude_phrases=True;
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def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, topN=500, exclude_phrases=False):
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spark = SparkSession.builder.getOrCreate()
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conf = spark.sparkContext.getConf()
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print(outfile)
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print(exclude_phrases)
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tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
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if included_subreddits is None:
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included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
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included_subreddits = {s.strip('\n') for s in included_subreddits}
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rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
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included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
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else:
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included_subreddits = set(open(included_subreddits))
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@ -47,7 +32,23 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
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if exclude_phrases == True:
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tfidf = tfidf.filter(~f.col(term).contains("_"))
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sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)
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print("creating temporary parquet with matrix indicies")
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tempdir = prep_tfidf_entries(tfidf, 'term', min_df, included_subreddits)
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tfidf = spark.read.parquet(tempdir.name)
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subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
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subreddit_names = subreddit_names.sort_values("subreddit_id_new")
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subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
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spark.stop()
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print("loading matrix")
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mat = read_tfidf_matrix(tempdir.name,'term')
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print('computing similarities')
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sims = column_similarities(mat)
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del mat
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sims = pd.DataFrame(sims.todense())
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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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@ -55,25 +56,72 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
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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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sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
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sims.to_feather(outfile)
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tempdir.cleanup()
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path = "term_tfidf_entriesaukjy5gv.parquet"
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#instead of toLocalMatrix() why not read as entries and put strait into numpy
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sim_entries = pd.read_parquet(output_parquet)
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df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
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spark.stop()
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df['subreddit_id_new'] = df['subreddit_id_new'] - 1
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df = df.sort_values('subreddit_id_new').reset_index(drop=True)
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df = df.set_index('subreddit_id_new')
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# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
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# def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
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# '''
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# Compute similarities between subreddits based on tfi-idf vectors of comment texts
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# included_subreddits : string
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# Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
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similarities = sim_entries.join(df, on='i')
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similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
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similarities = similarities.join(df, on='j')
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similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
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# similarity_threshold : double (default = 0)
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# set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
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# https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
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similarities.to_feather(output_feather)
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similarities.to_csv(output_csv)
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return similarities
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# min_df : int (default = 0.1 * (number of included_subreddits)
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# exclude terms that appear in fewer than this number of documents.
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# outfile: string
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# where to output csv and feather outputs
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# '''
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# print(outfile)
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# print(exclude_phrases)
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# tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
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# if included_subreddits is None:
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# included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
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# included_subreddits = {s.strip('\n') for s in included_subreddits}
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# else:
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# included_subreddits = set(open(included_subreddits))
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# if exclude_phrases == True:
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# tfidf = tfidf.filter(~f.col(term).contains("_"))
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# sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)
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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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# sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
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# #instead of toLocalMatrix() why not read as entries and put strait into numpy
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# sim_entries = pd.read_parquet(output_parquet)
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# df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
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# spark.stop()
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# df['subreddit_id_new'] = df['subreddit_id_new'] - 1
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# df = df.sort_values('subreddit_id_new').reset_index(drop=True)
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# df = df.set_index('subreddit_id_new')
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# similarities = sim_entries.join(df, on='i')
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# similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
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# similarities = similarities.join(df, on='j')
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# similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
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# similarities.to_feather(output_feather)
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# similarities.to_csv(output_csv)
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# return similarities
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if __name__ == '__main__':
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fire.Fire(term_cosine_similarities)
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30
top_subreddits_by_comments.py
Normal file
30
top_subreddits_by_comments.py
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@ -0,0 +1,30 @@
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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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from pyspark.mllib.linalg.distributed import RowMatrix, CoordinateMatrix
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import numpy as np
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import pyarrow
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import pandas as pd
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import fire
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from itertools import islice
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from pathlib import Path
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from similarities_helper import cosine_similarities
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spark = SparkSession.builder.getOrCreate()
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conf = spark.sparkContext.getConf()
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df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
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# remove /u/ pages
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df = df.filter(~df.subreddit.like("u_%"))
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df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))
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win = Window.orderBy(f.col('n_comments').desc())
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df = df.withColumn('comments_rank',f.rank().over(win))
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df = df.toPandas()
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df = df.sort_values("n_comments")
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df.to_csv('/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv',index=False)
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