-
-def affinity_clustering(similarities, output, *args, **kwargs):
- subreddits, mat = read_similarity_mat(similarities)
- clustering = _affinity_clustering(mat, *args, **kwargs)
- cluster_data = process_clustering_result(clustering, subreddits)
- cluster_data['algorithm'] = 'affinity'
- return(cluster_data)
-
-def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
- '''
- similarities: matrix 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.
- damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author.
- '''
- print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
-
- preference = np.quantile(mat,preference_quantile)
-
- print(f"preference is {preference}")
- print("data loaded")
- sys.stdout.flush()
- clustering = AffinityPropagation(damping=damping,
- max_iter=max_iter,
- convergence_iter=convergence_iter,
- copy=False,
- preference=preference,
- affinity='precomputed',
- verbose=verbose,
- random_state=random_state).fit(mat)
-
- cluster_data = process_clustering_result(clustering, subreddits)
- output = Path(output)
- output.parent.mkdir(parents=True,exist_ok=True)
- cluster_data.to_feather(output)
- print(f"saved {output}")
- return clustering
-
-
-def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
- if name is None:
- name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
- print(name)
- sys.stdout.flush()
- outpath = outdir / (str(name) + ".feather")
- outpath.parent.mkdir(parents=True,exist_ok=True)
- print(outpath)
- clustering = _affinity_clustering(mat, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
- cluster_data = process_clustering_result(clustering, subreddits)
- mat = sim_to_dist(clustering.affinity_matrix_)
-
- try:
- score = silhouette_score(mat, clustering.labels_, metric='precomputed')
- except ValueError:
- score = None
-
- if alt_mat is not None:
- alt_distances = sim_to_dist(alt_mat)
- try:
- alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
- except ValueError:
- alt_score = None
+ preference:float
+ max_iter:int
+
+class affinity_job(clustering_job):
+ def __init__(self, infile, outpath, name, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
+ super().__init__(infile,
+ outpath,
+ name,
+ call=self._affinity_clustering,
+ preference_quantile=preference_quantile,
+ damping=damping,
+ max_iter=max_iter,
+ convergence_iter=convergence_iter,
+ random_state=1968,
+ verbose=verbose)
+ self.damping=damping
+ self.max_iter=max_iter
+ self.convergence_iter=convergence_iter
+ self.preference_quantile=preference_quantile
+
+ def _affinity_clustering(self, mat, preference_quantile, *args, **kwargs):
+ mat = 1-mat
+ preference = np.quantile(mat, preference_quantile)
+ self.preference = preference
+ print(f"preference is {preference}")
+ print("data loaded")
+ sys.stdout.flush()
+ clustering = AffinityPropagation(*args,
+ preference=preference,
+ affinity='precomputed',
+ copy=False,
+ **kwargs).fit(mat)
+ return clustering
+
+ def get_info(self):
+ result = super().get_info()
+ self.result=affinity_clustering_result(**result.__dict__,
+ damping=self.damping,
+ max_iter=self.max_iter,
+ convergence_iter=self.convergence_iter,
+ preference_quantile=self.preference_quantile,
+ preference=self.preference)
+
+ return self.result
+
+class affinity_grid_sweep(grid_sweep):
+ def __init__(self,
+ inpath,
+ outpath,
+ *args,
+ **kwargs):
+
+ super().__init__(affinity_job,
+ _afffinity_grid_sweep,
+ inpath,
+ outpath,
+ self.namer,
+ *args,
+ **kwargs)
+ def namer(self,
+ damping,
+ max_iter,
+ convergence_iter,
+ preference_quantile):
+
+ return f"damp-{damping}_maxit-{max_iter}_convit-{convergence_iter}_prefq-{preference_quantile}"
+
+def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5],n_cores=10):
+ """Run affinity clustering once or more with different parameters.