lsi support for weekly similarities

[cdsc_reddit.git] / clustering / selection.py

diff --git a/clustering/selection.py b/clustering/selection.py
index d2fa6de60ccd58ebc41d68985bd1708e5339ca54..81641db00155389739634075dcb413946da8672c 100644 (file)
--- a/clustering/selection.py
+++ b/clustering/selection.py
@@ -1,7 +1,38 @@
-import fire
-from select_affinity import select_affinity_clustering
-from select_kmeans import select_kmeans_clustering
+import pandas as pd
+import plotnine as pn
+from pathlib import Path
+from clustering.fit_tsne import fit_tsne
+from visualization.tsne_vis import build_visualization
+
+df = pd.read_csv("/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv",index_col=0)
+
+# plot silhouette_score as a function of isolates
+df = df.sort_values("silhouette_score")
+
+df['n_isolates'] = df.n_isolates.str.split("\n0").apply(lambda rg: int(rg[1]))
+p = pn.ggplot(df,pn.aes(x='n_isolates',y='silhouette_score')) + pn.geom_point()
+p.save("isolates_x_score.png")
+
+p = pn.ggplot(df,pn.aes(y='n_clusters',x='n_isolates',color='silhouette_score')) + pn.geom_point()
+p.save("clusters_x_isolates.png")
+
+# the best result for hdbscan seems like this one: it has a decent number of 
+# i think I prefer the 'eom' clustering style because larger clusters are less likely to suffer from ommitted variables
+best_eom = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='eom')&(df.min_cluster_size==2)].iloc[df.shape[1]]
+
+best_lsi = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='leaf')&(df.min_cluster_size==2)].iloc[df.shape[1]]
+
+tsne_data = Path("./clustering/authors-tf_lsi850_tsne.feather")
+
+if not tnse_data.exists():
+    fit_tsne("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather",
+             tnse_data)
+
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_eom.outpath)/(best_eom['name']+'.feather'),
+                    "./authors-tf_lsi850_best_eom.html")
+
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_leaf.outpath)/(best_leaf['name']+'.feather'),
+                    "./authors-tf_lsi850_best_leaf.html")

 
 

-if __name__ == "__main__":
-    fire.Fire({"kmeans":select_kmeans_clustering,
-               "affinity":select_affinity_clustering})