grid sweep selection for clustering hyperparameters

clustering/selection.py

@@ -0,0 +1,87 @@
+from sklearn.metrics import silhouette_score
+from sklearn.cluster import AffinityPropagation
+from functools import partial
+from clustering import _affinity_clustering, read_similarity_mat
+from dataclasses import dataclass
+from multiprocessing  import Pool, cpu_count, Array, Process
+from pathlib import Path
+from itertools import product, starmap
+import pandas as pd
+import fire
+import sys
+
+# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
+
+@dataclass
+class clustering_result:
+    outpath:Path
+    damping:float
+    max_iter:int
+    convergence_iter:int
+    preference_quantile:float
+    silhouette_score:float
+    alt_silhouette_score:float
+    name:str
+
+def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat):
+    if name is None:
+        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{convergence_iter}"
+    print(name)
+    sys.stdout.flush()
+    outpath = outdir / (str(name) + ".feather")
+    print(outpath)
+    clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
+    score = silhouette_score(clustering.affinity_matrix_, clustering.labels_, metric='precomputed')
+    alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
+    
+    res = clustering_result(outpath=outpath,
+                            damping=damping,
+                            max_iter=max_iter,
+                            convergence_iter=convergence_iter,
+                            preference_quantile=preference_quantile,
+                            silhouette_score=score,
+                            alt_silhouette_score=score,
+                            name=str(name))
+
+    return res
+
+# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
+
+def select_affinity_clustering(similarities, outdir, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
+
+    damping = list(map(float,damping))
+    convergence_iter = convergence_iter = list(map(int,convergence_iter))
+    preference_quantile = list(map(float,preference_quantile))
+
+    if type(outdir) is str:
+        outdir = Path(outdir)
+
+    outdir.mkdir(parents=True,exist_ok=True)
+
+    subreddits, mat = read_similarity_mat(similarities,use_threads=True)
+
+    if alt_similarities is not None:
+        alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
+    else:
+        alt_mat = None
+
+    if J is None:
+        J = cpu_count()
+    pool = Pool(J)
+
+    # get list of tuples: the combinations of hyperparameters
+    hyper_grid = product(damping, convergence_iter, preference_quantile)
+    hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
+
+    _do_clustering = partial(do_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
+
+    #    similarities = Array('d', mat)
+    # call pool.starmap
+    print("running clustering selection")
+    clustering_data = pool.starmap(_do_clustering, hyper_grid)
+    clustering_data = pd.DataFrame(list(clustering_data))
+    return clustering_data
+
+
+if __name__ == "__main__":
+    fire.Fire(select_affinity_clustering)