133 lines
5.7 KiB
Python
133 lines
5.7 KiB
Python
from sklearn.metrics import silhouette_score
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from sklearn.cluster import AffinityPropagation
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from functools import partial
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from dataclasses import dataclass
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from clustering import _affinity_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
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from multiprocessing import Pool, cpu_count, Array, Process
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from pathlib import Path
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from itertools import product, starmap
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import numpy as np
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import pandas as pd
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import fire
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import sys
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# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying.
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@dataclass
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class affinity_clustering_result(clustering_result):
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damping:float
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convergence_iter:int
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preference_quantile:float
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def affinity_clustering(similarities, output, *args, **kwargs):
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subreddits, mat = read_similarity_mat(similarities)
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clustering = _affinity_clustering(mat, *args, **kwargs)
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cluster_data = process_clustering_result(clustering, subreddits)
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cluster_data['algorithm'] = 'affinity'
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return(cluster_data)
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def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
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'''
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similarities: matrix of similarity scores
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preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
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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.
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'''
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print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
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preference = np.quantile(mat,preference_quantile)
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print(f"preference is {preference}")
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print("data loaded")
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sys.stdout.flush()
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clustering = AffinityPropagation(damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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copy=False,
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preference=preference,
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affinity='precomputed',
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verbose=verbose,
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random_state=random_state).fit(mat)
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cluster_data = process_clustering_result(clustering, subreddits)
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output = Path(output)
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output.parent.mkdir(parents=True,exist_ok=True)
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cluster_data.to_feather(output)
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print(f"saved {output}")
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return clustering
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def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
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if name is None:
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name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
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print(name)
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sys.stdout.flush()
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outpath = outdir / (str(name) + ".feather")
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outpath.parent.mkdir(parents=True,exist_ok=True)
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print(outpath)
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clustering = _affinity_clustering(mat, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
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cluster_data = process_clustering_result(clustering, subreddits)
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mat = sim_to_dist(clustering.affinity_matrix_)
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try:
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score = silhouette_score(mat, clustering.labels_, metric='precomputed')
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except ValueError:
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score = None
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if alt_mat is not None:
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alt_distances = sim_to_dist(alt_mat)
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try:
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alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
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except ValueError:
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alt_score = None
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res = affinity_clustering_result(outpath=outpath,
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damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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preference_quantile=preference_quantile,
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silhouette_score=score,
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alt_silhouette_score=score,
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name=str(name))
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return res
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# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
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def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
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damping = list(map(float,damping))
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convergence_iter = convergence_iter = list(map(int,convergence_iter))
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preference_quantile = list(map(float,preference_quantile))
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if type(outdir) is str:
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outdir = Path(outdir)
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outdir.mkdir(parents=True,exist_ok=True)
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subreddits, mat = read_similarity_mat(similarities,use_threads=True)
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if alt_similarities is not None:
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alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
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else:
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alt_mat = None
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if J is None:
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J = cpu_count()
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pool = Pool(J)
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# get list of tuples: the combinations of hyperparameters
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hyper_grid = product(damping, convergence_iter, preference_quantile)
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hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
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_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)
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# similarities = Array('d', mat)
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# call pool.starmap
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print("running clustering selection")
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clustering_data = pool.starmap(_do_clustering, hyper_grid)
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clustering_data = pd.DataFrame(list(clustering_data))
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clustering_data.to_csv(outinfo)
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return clustering_data
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if __name__ == "__main__":
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x = fire.Fire(select_affinity_clustering)
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