173 lines
6.3 KiB
Python
173 lines
6.3 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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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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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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# 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)
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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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# entries = tfidf.filter((f.col('subreddit') == 'asoiaf') | (f.col('subreddit') == 'gameofthrones') | (f.col('subreddit') == 'christianity')).select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
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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_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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