X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/87ffaa6858919bd830694d60dd4fc7b1857b462a..65deba5e4e4ad9e3f23e82573491f7d6b190e644:/clustering/pick_best_clustering.py?ds=sidebyside

diff --git a/clustering/pick_best_clustering.py b/clustering/pick_best_clustering.py
old mode 100644
new mode 100755
index 91c443e..e05e3ac
--- a/clustering/pick_best_clustering.py
+++ b/clustering/pick_best_clustering.py
@@ -1,16 +1,17 @@
+#!/usr/bin/env python3
 import fire
 import pandas as pd
 from pathlib import Path
 import shutil
-
-selection_data="/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/affinity/selection_data.csv"
+selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv"
 
 outpath = 'test_best.feather'
+min_clusters=50; max_isolates=7500; min_cluster_size=2
 
 # pick the best clustering according to silhouette score subject to contraints
-def pick_best_clustering(selection_data, output, min_clusters, max_isolates):
+def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size):
     df = pd.read_csv(selection_data,index_col=0)
-    df = df.sort_values("silhouette_score")
+    df = df.sort_values("silhouette_score",ascending=False)
 
     # not sure I fixed the bug underlying this fully or not.
     df['n_isolates_str'] = df.n_isolates.str.strip("[]")
@@ -18,12 +19,15 @@ def pick_best_clustering(selection_data, output, min_clusters, max_isolates):
     df.loc[df.n_isolates_0,'n_isolates'] = 0
     df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
     
-    best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)].iloc[df.shape[1]]
+    best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)]
+
+    best_cluster = best_cluster.iloc[0]
 
+    best_lsi_dimensions = best_cluster.lsi_dimensions
     print(best_cluster.to_dict())
     best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
-    
     shutil.copy(best_path,output)
-
+    print(f"lsi dimensions:{best_lsi_dimensions}")
+    
 if __name__ == "__main__":
     fire.Fire(pick_best_clustering)