Commit 3b960e0f1923a5a9427417aa75b5fb2eef90657c
1 parent
d97ef08feb
Exists in
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Clustering command allows you to compute kmeans specifying k, kmin and kmax or a list of k-values.
Showing 1 changed file with 90 additions and 0 deletions Inline Diff
volia/clustering.py
| File was created | 1 | import argparse | |
| 2 | from os import path, mkdir | ||
| 3 | from utils import SubCommandRunner | ||
| 4 | from core.data import read_features, read_lst | ||
| 5 | |||
| 6 | import numpy as np | ||
| 7 | from sklearn.cluster import KMeans | ||
| 8 | import pickle | ||
| 9 | |||
| 10 | |||
| 11 | def kmeans_run(features: str, lst: str, k:int, kmax: int, klist, output: str): | ||
| 12 | """ | ||
| 13 | |||
| 14 | @param features: output features | ||
| 15 | @param lst: list file | ||
| 16 | @param k: k (kmin if kmax specified) | ||
| 17 | @param kmax: maximum k to compute | ||
| 18 | @param klist: list of k values to compute, ignore k value | ||
| 19 | @param output: output file if kmax not specified, else, output directory | ||
| 20 | """ | ||
| 21 | # -- READE FILES -- | ||
| 22 | features_dict = read_features(features) | ||
| 23 | lst_dict = read_lst(lst) | ||
| 24 | X = np.asarray([features_dict[x] for x in lst_dict]) | ||
| 25 | |||
| 26 | # Exception cases | ||
| 27 | if kmax is None and klist is None and path.isdir(output): | ||
| 28 | raise Exception("The \"output\" is an existing directory while the system is waiting the path of a file.") | ||
| 29 | |||
| 30 | if (kmax is not None or klist is not None) and path.isfile(output): | ||
| 31 | raise Exception("The \"output\" is an existing file while the system is waiting the path of a directory.") | ||
| 32 | |||
| 33 | # Mono value case | ||
| 34 | if kmax is None and klist is None: | ||
| 35 | print(f"Computing clustering with k={k}") | ||
| 36 | kmeans = KMeans(n_clusters=k, n_init=10, random_state=0).fit(X) | ||
| 37 | preds = kmeans.predict(X) | ||
| 38 | pickle.dump(kmeans, open(output, "wb")) | ||
| 39 | |||
| 40 | # Multi values case with kmax | ||
| 41 | if kmax is not None: | ||
| 42 | if not path.isdir(output): | ||
| 43 | mkdir(output) | ||
| 44 | Ks = range(k, kmax + 1) | ||
| 45 | for i in Ks: | ||
| 46 | print(f"Computing clustering with k={i}") | ||
| 47 | kmeans = KMeans(n_clusters=i, n_init=10, random_state=0).fit(X) | ||
| 48 | preds = kmeans.predict(X) | ||
| 49 | pickle.dump(kmeans, open(path.join(output, "clustering_" + str(i) + ".pkl"), "wb")) | ||
| 50 | |||
| 51 | # Second multi values case with klist | ||
| 52 | if klist is not None: | ||
| 53 | if not path.isdir(output): | ||
| 54 | mkdir(output) | ||
| 55 | for k in klist: | ||
| 56 | k = int(k) | ||
| 57 | print(f"Computing clustering with k={k}") | ||
| 58 | kmeans = KMeans(n_clusters=k, n_init=10, random_state=0).fit(X) | ||
| 59 | preds = kmeans.predict(X) | ||
| 60 | pickle.dump(kmeans, open(path.join(output, "clustering_" + str(k) + ".pkl"), "wb")) | ||
| 61 | |||
| 62 | |||
| 63 | if __name__ == "__main__": | ||
| 64 | # Main parser | ||
| 65 | parser = argparse.ArgumentParser(description="Clustering methods to apply") | ||
| 66 | subparsers = parser.add_subparsers(title="action") | ||
| 67 | |||
| 68 | # kmeans | ||
| 69 | parser_kmeans = subparsers.add_parser( | ||
| 70 | "kmeans", help="Compute clustering using k-means algorithm") | ||
| 71 | |||
| 72 | parser_kmeans.add_argument("--features", required=True, type=str, help="Features file (works with list)") | ||
| 73 | parser_kmeans.add_argument("--lst", required=True, type=str, help="List file (.lst)") | ||
| 74 | parser_kmeans.add_argument("-k", default=2, type=int, | ||
| 75 | help="number of clusters to compute. It is kmin if kmax is specified.") | ||
| 76 | parser_kmeans.add_argument("--kmax", default=None, type=int, help="if specified, k is kmin.") | ||
| 77 | parser_kmeans.add_argument("--klist", nargs="+", | ||
| 78 | help="List of k values to test. As kmax, activate the multi values mod.") | ||
| 79 | parser_kmeans.add_argument("--output", default=".kmeans", help="output file if only k. Output directory if multiple kmax specified.") | ||
| 80 | parser_kmeans.set_defaults(which="kmeans") | ||
| 81 | |||
| 82 | # Parse | ||
| 83 | args = parser.parse_args() | ||
| 84 | |||
| 85 | # Run commands | ||
| 86 | runner = SubCommandRunner({ | ||
| 87 | "kmeans": kmeans_run | ||
| 88 | }) | ||
| 89 | |||
| 90 | runner.run(args.which, args.__dict__, remove="which") |