from pyspark.sql import Window from pyspark.sql import functions as f from enum import Enum from pyspark.mllib.linalg.distributed import CoordinateMatrix from tempfile import TemporaryDirectory import pyarrow import pyarrow.dataset as ds from scipy.sparse import csr_matrix import pandas as pd import numpy as np class tf_weight(Enum): MaxTF = 1 Norm05 = 2 def read_tfidf_matrix(path,term_colname): term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' dataset = ds.dataset(path,format='parquet') entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new]).to_pandas() return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1)))) def column_similarities(mat): norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32)) mat = mat.multiply(1/norm) sims = mat.T @ mat return(sims) def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits): term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' if min_df is None: min_df = 0.1 * len(included_subreddits) tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits)) # reset the subreddit ids sub_ids = tfidf.select('subreddit_id').distinct() sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id"))) tfidf = tfidf.join(sub_ids,'subreddit_id') # only use terms in at least min_df included subreddits new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) # new_count = new_count.filter(f.col('new_count') >= min_df) tfidf = tfidf.join(new_count,term_id,how='inner') # reset the term ids term_ids = tfidf.select([term_id]).distinct() term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id))) tfidf = tfidf.join(term_ids,term_id) tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") # tfidf = tfidf.withColumnRenamed("idf","idf_old") # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count"))) tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float')) tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.') tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy') return tempdir def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold): term = term_colname term_id = term + '_id' term_id_new = term + '_id_new' if min_df is None: min_df = 0.1 * len(included_subreddits) tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits)) tfidf = tfidf.cache() # reset the subreddit ids sub_ids = tfidf.select('subreddit_id').distinct() sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id"))) tfidf = tfidf.join(sub_ids,'subreddit_id') # only use terms in at least min_df included subreddits new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count')) # new_count = new_count.filter(f.col('new_count') >= min_df) tfidf = tfidf.join(new_count,term_id,how='inner') # reset the term ids term_ids = tfidf.select([term_id]).distinct() term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id))) tfidf = tfidf.join(term_ids,term_id) tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old") # tfidf = tfidf.withColumnRenamed("idf","idf_old") # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count"))) tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf) # step 1 make an rdd of entires # sorted by (dense) spark subreddit id # 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 n_partitions = int(len(included_subreddits)*2 / 5) entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions) # put like 10 subredis in each partition # step 2 make it into a distributed.RowMatrix coordMat = CoordinateMatrix(entries) coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions)) # this needs to be an IndexedRowMatrix() mat = coordMat.toRowMatrix() #goal: build a matrix of subreddit columns and tf-idfs rows sim_dist = mat.columnSimilarities(threshold=similarity_threshold) return (sim_dist, tfidf) def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05): term = term_colname term_id = term + '_id' # aggregate counts by week. now subreddit-term is distinct df = df.filter(df.subreddit.isin(include_subs)) df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf')) max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf') df = df.join(max_subreddit_terms, on=['subreddit','week']) df = df.withColumn("relative_tf", df.tf / df.sr_max_tf) # group by term. term is unique idf = df.groupby([term,'week']).count() N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week")) idf = idf.join(N_docs, on=['week']) # add a little smoothing to the idf idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1) # collect the dictionary to make a pydict of terms to indexes terms = idf.select([term,'week']).distinct() # terms are distinct terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct # make subreddit ids subreddits = df.select(['subreddit','week']).distinct() subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit"))) df = df.join(subreddits,on=['subreddit','week']) # map terms to indexes in the tfs and the idfs df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique idf = idf.join(terms,on=[term,'week']) # join on subreddit/term to create tf/dfs indexed by term df = df.join(idf, on=[term_id, term,'week']) # agg terms by subreddit to make sparse tf/df vectors if tf_family == tf_weight.MaxTF: df = df.withColumn("tf_idf", df.relative_tf * df.idf) else: # tf_fam = tf_weight.Norm05 df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf) return df def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05): term = term_colname term_id = term + '_id' # aggregate counts by week. now subreddit-term is distinct df = df.filter(df.subreddit.isin(include_subs)) df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf')) max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf') df = df.join(max_subreddit_terms, on='subreddit') df = df.withColumn("relative_tf", df.tf / df.sr_max_tf) # group by term. term is unique idf = df.groupby([term]).count() N_docs = df.select('subreddit').distinct().count() # add a little smoothing to the idf idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1) # collect the dictionary to make a pydict of terms to indexes terms = idf.select(term).distinct() # terms are distinct terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct # make subreddit ids subreddits = df.select(['subreddit']).distinct() subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit"))) df = df.join(subreddits,on='subreddit') # map terms to indexes in the tfs and the idfs df = df.join(terms,on=term) # subreddit-term-id is unique idf = idf.join(terms,on=term) # join on subreddit/term to create tf/dfs indexed by term df = df.join(idf, on=[term_id, term]) # agg terms by subreddit to make sparse tf/df vectors if tf_family == tf_weight.MaxTF: df = df.withColumn("tf_idf", df.relative_tf * df.idf) else: # tf_fam = tf_weight.Norm05 df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf) return df