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- import time
- from warnings import simplefilter
- import numpy as np
- from joblib import Parallel, delayed
- from sklearn.base import clone
- import wandb
- # ignore all future warnings
- simplefilter(action="ignore", category=FutureWarning)
- def elbow_curve(clusterer, X, cluster_ranges, n_jobs, show_cluster_time): # noqa: N803
- if cluster_ranges is None:
- cluster_ranges = range(1, 10, 2)
- else:
- cluster_ranges = sorted(cluster_ranges)
- clfs, times = _compute_results_parallel(n_jobs, clusterer, X, cluster_ranges)
- clfs = np.absolute(clfs)
- table = make_table(cluster_ranges, clfs, times)
- chart = wandb.visualize("wandb/elbow/v1", table)
- return chart
- def make_table(cluster_ranges, clfs, times):
- columns = ["cluster_ranges", "errors", "clustering_time"]
- data = list(zip(cluster_ranges, clfs, times))
- table = wandb.Table(columns=columns, data=data)
- return table
- def _compute_results_parallel(n_jobs, clusterer, x, cluster_ranges):
- parallel_runner = Parallel(n_jobs=n_jobs)
- _cluster_scorer = delayed(_clone_and_score_clusterer)
- results = parallel_runner(_cluster_scorer(clusterer, x, i) for i in cluster_ranges)
- clfs, times = zip(*results)
- return clfs, times
- def _clone_and_score_clusterer(clusterer, x, n_clusters):
- start = time.time()
- clusterer = clone(clusterer)
- clusterer.n_clusters = n_clusters
- return clusterer.fit(x).score(x), time.time() - start
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