grid sweep selection for clustering hyperparameters

[cdsc_reddit.git] / similarities / similarities_helper.py

diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index 88c830cacf7d5971e6e885882b1771d0c864183b..57a36ca924ea5d0d991e976347cac0362b97ac20 100644 (file)
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -1,3 +1,4 @@
+from pyspark.sql import SparkSession

 from pyspark.sql import Window
 from pyspark.sql import functions as f
 from enum import Enum
 from pyspark.sql import Window
 from pyspark.sql import functions as f
 from enum import Enum


@@ -5,23 +6,130 @@ from pyspark.mllib.linalg.distributed import CoordinateMatrix
 from tempfile import TemporaryDirectory
 import pyarrow
 import pyarrow.dataset as ds
 from tempfile import TemporaryDirectory
 import pyarrow
 import pyarrow.dataset as ds

-from scipy.sparse import csr_matrix
+from scipy.sparse import csr_matrix, issparse

 import pandas as pd
 import numpy as np
 import pathlib
 import pandas as pd
 import numpy as np
 import pathlib

+from datetime import datetime
+from pathlib import Path

 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 
 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 

-def read_tfidf_matrix_weekly(path, term_colname, week):
+infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet"
+
+def reindex_tfidf_time_interval(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):

     term = term_colname
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 
     term = term_colname
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 

+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
+    print(exclude_phrases)
+    tfidf_weekly = spark.read.parquet(infile)
+
+    # create the time interval
+    if from_date is not None:
+        if type(from_date) is str:
+            from_date = datetime.fromisoformat(from_date)
+
+        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
+        
+    if to_date is not None:
+        if type(to_date) is str:
+            to_date = datetime.fromisoformat(to_date)
+        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)
+
+    tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
+    tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
+    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
+    tfidf = spark.read_parquet(tempdir.name)
+    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
+    return(tempdir, subreddit_names)
+
+def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
+    print(exclude_phrases)
+
+    tfidf = spark.read.parquet(infile)
+
+    if included_subreddits is None:
+        included_subreddits = select_topN_subreddits(topN)
+    else:
+        included_subreddits = set(open(included_subreddits))
+
+    if exclude_phrases == True:
+        tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
+
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
+
+    tfidf = spark.read.parquet(tempdir.name)
+    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
+    spark.stop()
+    return (tempdir, subreddit_names)
+
+
+def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
+    '''
+    tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
+    '''
+    if from_date is not None or to_date is not None:
+        tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False, from_date=from_date, to_date=to_date)
+        
+    else:
+        tempdir, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False)
+
+    print("loading matrix")
+    #    mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
+    mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)
+    print(f'computing similarities on mat. mat.shape:{mat.shape}')
+    print(f"size of mat is:{mat.data.nbytes}")
+    sims = simfunc(mat)
+    del mat
+
+    if issparse(sims):
+        sims = sims.todense()
+
+    print(f"shape of sims:{sims.shape}")
+    print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
+    sims = pd.DataFrame(sims)
+    sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
+    sims['subreddit'] = subreddit_names.subreddit.values
+
+    p = Path(outfile)
+
+    output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
+    output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
+    output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
+
+    sims.to_feather(outfile)
+    tempdir.cleanup()
+
+def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    dataset = ds.dataset(path,format='parquet')
+    entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
+    return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
+
+def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'

     dataset = ds.dataset(path,format='parquet')
     dataset = ds.dataset(path,format='parquet')

-    entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new],filter=ds.field('week')==week).to_pandas()
-    return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+    print(f"tfidf_colname:{tfidf_colname}")
+    entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
+    return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+    

 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week
 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week


@@ -33,16 +141,14 @@ def write_weekly_similarities(path, sims, week, names):
     sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
     sims.to_parquet(p / week.isoformat())
 
     sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
     sims.to_parquet(p / week.isoformat())
 

+def column_overlaps(mat):
+    non_zeros = (mat != 0).astype('double')
+    
+    intersection = non_zeros.T @ non_zeros
+    card1 = non_zeros.sum(axis=0)
+    den = np.add.outer(card1,card1) - intersection

 
 

-
-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))))
+    return intersection / den

     
 def column_similarities(mat):
     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
     
 def column_similarities(mat):
     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))


@@ -51,13 +157,16 @@ def column_similarities(mat):
     return(sims)
 
 
     return(sims)
 
 

-def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits):
+def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_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)
     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('count') >= min_df)
+    if max_df is not None:
+        tfidf = tfidf.filter(f.col('count') <= max_df)

 
     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 
 
     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 


@@ -86,19 +195,22 @@ def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits):
     return(tempdir)
     
 
     return(tempdir)
     
 

-def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits):
+def prep_tfidf_entries(tfidf, term_colname, min_df, max_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)
     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('count') >= min_df)
+    if max_df is not None:
+        tfidf = tfidf.filter(f.col('count') <= max_df)

 
     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
 
     # reset the subreddit ids
     sub_ids = tfidf.select('subreddit_id').distinct()
 
     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")))
+    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
     tfidf = tfidf.join(sub_ids,'subreddit_id')
 
     # only use terms in at least min_df included subreddits


@@ -221,15 +333,9 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
 
     return df
 
 
     return df
 

-
-
-def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
-
+def _calc_tfidf(df, term_colname, tf_family):

     term = term_colname
     term_id = term + '_id'
     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')
 
     max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
     max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')


@@ -240,9 +346,7 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm
 
     # group by term. term is unique
     idf = df.groupby([term]).count()
 
     # group by term. term is unique
     idf = df.groupby([term]).count()

-

     N_docs = df.select('subreddit').distinct().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)
 
     # add a little smoothing to the idf
     idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
 


@@ -271,8 +375,20 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm
         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 
     return df
         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'))
+
+    df = _calc_tfidf(df, term_colname, tf_family)
+
+    return df

 
 

-def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv"):
+def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):

     rankdf = pd.read_csv(path)
     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
     return included_subreddits
     rankdf = pd.read_csv(path)
     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
     return included_subreddits