93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
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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 clustering import _kmeans_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
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from dataclasses import dataclass
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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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@dataclass
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class kmeans_clustering_result(clustering_result):
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n_clusters:int
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n_init:int
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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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def do_clustering(n_clusters, n_init, 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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print(outpath)
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mat = sim_to_dist(mat)
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clustering = _kmeans_clustering(mat, outpath, n_clusters, n_init, max_iter, random_state, verbose)
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outpath.parent.mkdir(parents=True,exist_ok=True)
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cluster_data.to_feather(outpath)
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cluster_data = process_clustering_result(clustering, subreddits)
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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 = kmeans_clustering_result(outpath=outpath,
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max_iter=max_iter,
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n_clusters=n_clusters,
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n_init = n_init,
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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_kmeans_clustering(similarities, outdir, outinfo, n_clusters=[1000], max_iter=100000, n_init=10, random_state=1968, verbose=True, alt_similarities=None):
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n_clusters = list(map(int,n_clusters))
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n_init = list(map(int,n_init))
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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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# get list of tuples: the combinations of hyperparameters
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hyper_grid = product(n_clusters, n_init)
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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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# call starmap
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print("running clustering selection")
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clustering_data = 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_kmeans_clustering)
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