refactor clustering.py into method-specific files.
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Nate E TeBlunthuis
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@ -18,7 +18,44 @@ class affinity_clustering_result(clustering_result):
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convergence_iter:int
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preference_quantile:float
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def do_affinity_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
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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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@ -53,41 +90,6 @@ def do_affinity_clustering(damping, convergence_iter, preference_quantile, name,
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return res
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def do_affinity_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, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
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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 = 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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@ -116,7 +118,7 @@ def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max
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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_affinity_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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_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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@ -124,8 +126,6 @@ def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max
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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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@ -3,7 +3,7 @@
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import sys
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import pandas as pd
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import numpy as np
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from sklearn.cluster import AffinityPropagation, KMeans
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from sklearn.cluster import AffinityPropagation
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import fire
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from pathlib import Path
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from multiprocessing import cpu_count
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@ -46,24 +46,6 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
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print(f"saved {output}")
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return clustering
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def kmeans_clustering(similarities, *args, **kwargs):
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subreddits, mat = read_similarity_mat(similarities)
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mat = sim_to_dist(mat)
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clustering = _kmeans_clustering(mat, *args, **kwargs)
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cluster_data = process_clustering_result(clustering, subreddits)
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return(cluster_data)
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def _kmeans_clustering(mat, output, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
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clustering = KMeans(n_clusters=n_clusters,
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n_init=n_init,
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max_iter=max_iter,
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random_state=random_state,
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verbose=verbose
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).fit(mat)
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return clustering
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if __name__ == "__main__":
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@ -28,6 +28,13 @@ def test_select_hdbscan_clustering():
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cluster_selection_methods=['eom'];
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lsi_dimensions='all'
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df = pd.read_csv("test_hdbscan/selection_data.csv")
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test_select_hdbscan_clustering()
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check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
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silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
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c = check_clusters.merge(silscores,on='subreddit')# fire.Fire(select_hdbscan_clustering)
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@dataclass
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class hdbscan_clustering_result(clustering_result):
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min_cluster_size:int
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@ -165,8 +172,4 @@ def make_KNN_plots():
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KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
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if __name__ == "__main__":
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df = pd.read_csv("test_hdbscan/selection_data.csv")
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test_select_hdbscan_clustering()
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check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
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silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
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c = check_clusters.merge(silscores,on='subreddit')# fire.Fire(select_hdbscan_clustering)
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fire.Fire(select_hdbscan_clustering)
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@ -1,23 +1,32 @@
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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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from sklearn.cluster import KMeans
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import fire
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import sys
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from pathlib import Path
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from multiprocessing import cpu_count
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from dataclasses import dataclass
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from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
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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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def kmeans_clustering(similarities, *args, **kwargs):
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subreddits, mat = read_similarity_mat(similarities)
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mat = sim_to_dist(mat)
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clustering = _kmeans_clustering(mat, *args, **kwargs)
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cluster_data = process_clustering_result(clustering, subreddits)
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return(cluster_data)
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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 _kmeans_clustering(mat, output, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
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clustering = KMeans(n_clusters=n_clusters,
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n_init=n_init,
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max_iter=max_iter,
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random_state=random_state,
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verbose=verbose
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).fit(mat)
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return clustering
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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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