add note to try other tf normalization strategies.

[cdsc_reddit.git] / density / overlap_density.py

diff --git a/density/overlap_density.py b/density/overlap_density.py
index 2bddb8bb97f3d1983a3a9ef2a1db12d82fed0667..5a8e91aee37251ecb37e4978eef5b01968184f34 100644 (file)
--- a/density/overlap_density.py
+++ b/density/overlap_density.py
@@ -2,6 +2,14 @@ import pandas as pd
 from pandas.core.groupby import DataFrameGroupBy as GroupBy
 import fire
 import numpy as np
+import sys
+sys.path.append("..")
+sys.path.append("../similarities")
+from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_interval
+
+# this is the mean of the ratio of the overlap to the focal size.
+# mean shared membership per focal community member
+# the input is the author tf-idf matrix
 
 def overlap_density(inpath, outpath, agg = pd.DataFrame.sum):
     df = pd.read_feather(inpath)
@@ -20,6 +28,16 @@ def overlap_density_weekly(inpath, outpath, agg = GroupBy.sum):
     res.to_feather(outpath)
     return res
 
+
+# inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet";
+# min_df=1;
+# included_subreddits=None;
+# topN=10000;
+# outpath="/gscratch/comdata/output/reddit_density/wang_overlaps_10000.feather"
+
+# to_date=2019-10-28
+
+
 def author_overlap_density(inpath="/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather",
                            outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather", agg=pd.DataFrame.sum):
     if type(agg) == str:
@@ -55,3 +73,4 @@ if __name__ == "__main__":
                'terms':term_overlap_density,
                'author_weekly':author_overlap_density_weekly,
                'term_weekly':term_overlap_density_weekly})
+