Commit e403ed5fb6202dae56d47815d5961cced00f1c85
1 parent
11ee97e2cc
Exists in
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Add a script that allow user to evaluate a representation using classification labels.
Showing 1 changed file with 126 additions and 0 deletions Inline Diff
scripts/evaluations/clustering.py
| File was created | 1 | ''' | |
| 2 | This script allows the user to evaluate a classification system on new labels using clustering methods. | ||
| 3 | The algorithms are applied on the given latent space (embedding). | ||
| 4 | ''' | ||
| 5 | import argparse | ||
| 6 | import numpy as np | ||
| 7 | import pandas as pd | ||
| 8 | import os | ||
| 9 | import time | ||
| 10 | from sklearn.preprocessing import LabelEncoder | ||
| 11 | from sklearn.metrics.pairwise import pairwise_distances | ||
| 12 | from sklearn.metrics import f1_score | ||
| 13 | from sklearn.cluster import KMeans | ||
| 14 | from sklearn.manifold import TSNE | ||
| 15 | import matplotlib.pyplot as plt | ||
| 16 | |||
| 17 | from volia.data_io import read_features,read_lst | ||
| 18 | |||
| 19 | if __name__ == "__main__": | ||
| 20 | # Argparse | ||
| 21 | parser = argparse.ArgumentParser("Compute clustering on a latent space") | ||
| 22 | parser.add_argument("features") | ||
| 23 | parser.add_argument("utt2", | ||
| 24 | type=str, | ||
| 25 | help="file with [utt] [value]") | ||
| 26 | parser.add_argument("--prefix", | ||
| 27 | type=str, | ||
| 28 | help="prefix of saved files") | ||
| 29 | parser.add_argument("--outdir", | ||
| 30 | default=None, | ||
| 31 | type=str, | ||
| 32 | help="Output directory") | ||
| 33 | |||
| 34 | args = parser.parse_args() | ||
| 35 | |||
| 36 | assert args.outdir | ||
| 37 | |||
| 38 | start = time.time() | ||
| 39 | |||
| 40 | # Load features and utt2 | ||
| 41 | features = read_features(args.features) | ||
| 42 | utt2 = read_lst(args.utt2) | ||
| 43 | |||
| 44 | ids = list(features.keys()) | ||
| 45 | feats = np.vstack([ features[id_] for id_ in ids ]) | ||
| 46 | classes = [ utt2[id_] for id_ in ids ] | ||
| 47 | |||
| 48 | # Encode labels | ||
| 49 | le = LabelEncoder() | ||
| 50 | labels = le.fit_transform(classes) | ||
| 51 | num_classes = len(le.classes_) | ||
| 52 | |||
| 53 | # Compute KMEANS clustering on data | ||
| 54 | estimator = KMeans( | ||
| 55 | n_clusters=num_classes, | ||
| 56 | n_init=100, | ||
| 57 | tol=10-6, | ||
| 58 | algorithm="elkan" | ||
| 59 | ) | ||
| 60 | estimator.fit(feats) | ||
| 61 | print(f"Kmeans: processed {estimator.n_iter_} iterations - intertia={estimator.inertia_}") | ||
| 62 | |||
| 63 | # contains distance to each cluster for each sample | ||
| 64 | dist_space = estimator.transform(feats) | ||
| 65 | predictions = np.argmin(dist_space, axis=1) | ||
| 66 | |||
| 67 | # gives each cluster a name (considering most represented character) | ||
| 68 | dataframe = pd.DataFrame({ | ||
| 69 | "label": pd.Series(list(map(lambda x: le.classes_[x], labels))), | ||
| 70 | "prediction": pd.Series(predictions) | ||
| 71 | }) | ||
| 72 | |||
| 73 | def find_cluster_name_fn(c): | ||
| 74 | mask = dataframe["prediction"] == c | ||
| 75 | return dataframe[mask]["label"].value_counts(sort=False).idxmax() | ||
| 76 | |||
| 77 | cluster_names = list(map(find_cluster_name_fn, range(num_classes))) | ||
| 78 | predicted_labels = le.transform( | ||
| 79 | [cluster_names[pred] for pred in predictions]) | ||
| 80 | |||
| 81 | # F-measure | ||
| 82 | fscores = f1_score(labels, predicted_labels, average=None) | ||
| 83 | fscores_str = "\n".join(map(lambda i: "{0:25s}: {1:.4f}".format(le.classes_[i], fscores[i]), range(len(fscores)))) | ||
| 84 | print(f"F1-scores for each classes:\n{fscores_str}") | ||
| 85 | print(f"Global score : {np.mean(fscores)}") | ||
| 86 | with open(os.path.join(args.outdir, args.prefix + "eval_clustering.log"), "w") as fd: | ||
| 87 | print(f"F1-scores for each classes:\n{fscores_str}", file=fd) | ||
| 88 | print(f"Global score : {np.mean(fscores)}", file=fd) | ||
| 89 | |||
| 90 | # Process t-SNE and plot | ||
| 91 | tsne_estimator = TSNE() | ||
| 92 | embeddings = tsne_estimator.fit_transform(feats) | ||
| 93 | print("t-SNE: processed {0} iterations - KL_divergence={1:.4f}".format( | ||
| 94 | tsne_estimator.n_iter_, tsne_estimator.kl_divergence_)) | ||
| 95 | |||
| 96 | fig, [axe1, axe2] = plt.subplots(1, 2, figsize=(10, 5)) | ||
| 97 | for c, name in enumerate(le.classes_): | ||
| 98 | c_mask = np.where(labels == c) | ||
| 99 | axe1.scatter(embeddings[c_mask][:, 0], embeddings[c_mask][:, 1], label=name, alpha=0.2, edgecolors=None) | ||
| 100 | |||
| 101 | try: | ||
| 102 | id_cluster = cluster_names.index(name) | ||
| 103 | except ValueError: | ||
| 104 | print("WARNING: no cluster found for {}".format(name)) | ||
| 105 | continue | ||
| 106 | c_mask = np.where(predictions == id_cluster) | ||
| 107 | axe2.scatter(embeddings[c_mask][:, 0], embeddings[c_mask][:, 1], label=name, alpha=0.2, edgecolors=None) | ||
| 108 | |||
| 109 | axe1.legend(loc="lower center", bbox_to_anchor=(0.5, -0.35)) | ||
| 110 | axe1.set_title("true labels") | ||
| 111 | axe2.legend(loc="lower center", bbox_to_anchor=(0.5, -0.35)) | ||
| 112 | axe2.set_title("predicted cluster label") | ||
| 113 | |||
| 114 | plt.suptitle("Kmeans Clustering") | ||
| 115 | |||
| 116 | loc = os.path.join( | ||
| 117 | args.outdir, | ||
| 118 | args.prefix + "kmeans.pdf" | ||
| 119 | ) | ||
| 120 | plt.savefig(loc, bbox_inches="tight") | ||
| 121 | plt.close() | ||
| 122 | |||
| 123 | print("INFO: figure saved at {}".format(loc)) | ||
| 124 | |||
| 125 | end = time.time() | ||
| 126 | print("program ended in {0:.2f} seconds".format(end-start)) |