Update code for clustering + tsne.

diff --git a/clustering.py b/clustering.py
new file mode 100644 (file)
index 0000000..552d8ae
--- /dev/null
+++ b/clustering.py
@@ -0,0 +1,45 @@
+import pandas as pd
+import numpy as np
+from sklearn.cluster import AffinityPropagation
+import fire
+
+def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968):
+    '''
+    similarities: feather file with a dataframe of similarity scores
+    preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
+    '''
+
+    df = pd.read_feather(similarities)
+    n = df.shape[0]
+    mat = np.array(df.drop('subreddit',1))
+    mat[range(n),range(n)] = 1
+
+    preference = np.quantile(mat,preference_quantile)
+
+    clustering = AffinityPropagation(damping=damping,
+                                     max_iter=max_iter,
+                                     convergence_iter=convergence_iter,
+                                     copy=False,
+                                     preference=preference,
+                                     affinity='precomputed',
+                                     random_state=random_state).fit(mat)
+
+
+    print(f"clustering took {clustering.n_iter_} iterations")
+    clusters = clustering.labels_
+
+    print(f"found {len(set(clusters))} clusters")
+
+    cluster_data = pd.DataFrame({'subreddit': df.subreddit,'cluster':clustering.labels_})
+
+    cluster_sizes = cluster_data.groupby("cluster").count()
+    print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
+
+    print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+
+    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
+
+    cluster_data.to_feather(output)
+
+if __name__ == "__main__":
+    fire.Fire(affinity_clustering)