script for picking the best clustering given constraints

[cdsc_reddit.git] / clustering / pick_best_clustering.py

diff --git a/clustering/pick_best_clustering.py b/clustering/pick_best_clustering.py
new file mode 100644 (file)
index 0000000..91c443e
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+++ b/clustering/pick_best_clustering.py
@@ -0,0 +1,29 @@
+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"
+
+outpath = 'test_best.feather'
+
+# pick the best clustering according to silhouette score subject to contraints
+def pick_best_clustering(selection_data, output, min_clusters, max_isolates):
+    df = pd.read_csv(selection_data,index_col=0)
+    df = df.sort_values("silhouette_score")
+
+    # not sure I fixed the bug underlying this fully or not.
+    df['n_isolates_str'] = df.n_isolates.str.strip("[]")
+    df['n_isolates_0'] = df['n_isolates_str'].apply(lambda l: len(l) == 0)
+    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]]
+
+    print(best_cluster.to_dict())
+    best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
+    
+    shutil.copy(best_path,output)
+
+if __name__ == "__main__":
+    fire.Fire(pick_best_clustering)