279 lines
10 KiB
Python
279 lines
10 KiB
Python
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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import pathlib
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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_weekly(path, term_colname, week):
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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],filter=ds.field('week')==week).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 write_weekly_similarities(path, sims, week, names):
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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()
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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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sims.to_parquet(p / week.isoformat())
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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_weekly(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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# we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
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sub_ids = tfidf.select(['subreddit_id','week']).distinct()
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sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
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tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
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# only use terms in at least min_df included subreddits in a given week
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new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
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tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
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# reset the term ids
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term_ids = tfidf.select([term_id,'week']).distinct()
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term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
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tfidf = tfidf.join(term_ids,[term_id,'week'])
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tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
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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 = tfidf.repartition('week')
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tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
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return(tempdir)
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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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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.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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# try computing cosine similarities using spark
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def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
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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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tfidf = tfidf.cache()
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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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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.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
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# step 1 make an rdd of entires
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# sorted by (dense) spark subreddit id
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n_partitions = int(len(included_subreddits)*2 / 5)
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entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
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# put like 10 subredis in each partition
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# step 2 make it into a distributed.RowMatrix
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coordMat = CoordinateMatrix(entries)
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coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
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# this needs to be an IndexedRowMatrix()
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mat = coordMat.toRowMatrix()
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#goal: build a matrix of subreddit columns and tf-idfs rows
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sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
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return (sim_dist, tfidf)
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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,'week']).distinct() # terms are distinct
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terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
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# make subreddit ids
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subreddits = df.select(['subreddit','week']).distinct()
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subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
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df = df.join(subreddits,on=['subreddit','week'])
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# map terms to indexes in the tfs and the idfs
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df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
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idf = idf.join(terms,on=[term,'week'])
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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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return df
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def build_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]).agg(f.sum('tf').alias('tf'))
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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.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])
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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 select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.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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