Add code for running tf-idf at the weekly level.

[cdsc_reddit.git] / similarities_helper.py

diff --git a/similarities_helper.py b/similarities_helper.py
index c69983f7f9b027d0e08903731052673fe23489d5..ef434ac4ac52d297533d1282a13e3ad1714f3b2e 100644 (file)
--- a/similarities_helper.py
+++ b/similarities_helper.py
@@ -2,11 +2,67 @@ 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
@@ -63,6 +119,59 @@ def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, simila
     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