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scripts/evaluations/clustering.py
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'''
This script allows the user to evaluate a classification system on new labels using clustering methods.
The algorithms are applied on the given latent space (embedding).
'''
import argparse
import numpy as np
import pandas as pd
import os
import time
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics import f1_score
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from volia.data_io import read_features,read_lst
if __name__ == "__main__":
# Argparse
parser = argparse.ArgumentParser("Compute clustering on a latent space")
parser.add_argument("features")
parser.add_argument("utt2",
type=str,
help="file with [utt] [value]")
parser.add_argument("--prefix",
type=str,
help="prefix of saved files")
parser.add_argument("--outdir",
default=None,
type=str,
help="Output directory")
args = parser.parse_args()
assert args.outdir
start = time.time()
# Load features and utt2
features = read_features(args.features)
utt2 = read_lst(args.utt2)
ids = list(features.keys())
feats = np.vstack([ features[id_] for id_ in ids ])
classes = [ utt2[id_] for id_ in ids ]
# Encode labels
le = LabelEncoder()
labels = le.fit_transform(classes)
num_classes = len(le.classes_)
# Compute KMEANS clustering on data
estimator = KMeans(
n_clusters=num_classes,
n_init=100,
tol=10-6,
algorithm="elkan"
)
estimator.fit(feats)
print(f"Kmeans: processed {estimator.n_iter_} iterations - intertia={estimator.inertia_}")
# contains distance to each cluster for each sample
dist_space = estimator.transform(feats)
predictions = np.argmin(dist_space, axis=1)
# gives each cluster a name (considering most represented character)
dataframe = pd.DataFrame({
"label": pd.Series(list(map(lambda x: le.classes_[x], labels))),
"prediction": pd.Series(predictions)
})
def find_cluster_name_fn(c):
mask = dataframe["prediction"] == c
return dataframe[mask]["label"].value_counts(sort=False).idxmax()
cluster_names = list(map(find_cluster_name_fn, range(num_classes)))
predicted_labels = le.transform(
[cluster_names[pred] for pred in predictions])
# F-measure
fscores = f1_score(labels, predicted_labels, average=None)
fscores_str = "
".join(map(lambda i: "{0:25s}: {1:.4f}".format(le.classes_[i], fscores[i]), range(len(fscores))))
print(f"F1-scores for each classes:
{fscores_str}")
print(f"Global score : {np.mean(fscores)}")
with open(os.path.join(args.outdir, args.prefix + "eval_clustering.log"), "w") as fd:
print(f"F1-scores for each classes:
{fscores_str}", file=fd)
print(f"Global score : {np.mean(fscores)}", file=fd)
# Process t-SNE and plot
tsne_estimator = TSNE()
embeddings = tsne_estimator.fit_transform(feats)
print("t-SNE: processed {0} iterations - KL_divergence={1:.4f}".format(
tsne_estimator.n_iter_, tsne_estimator.kl_divergence_))
fig, [axe1, axe2] = plt.subplots(1, 2, figsize=(10, 5))
for c, name in enumerate(le.classes_):
c_mask = np.where(labels == c)
axe1.scatter(embeddings[c_mask][:, 0], embeddings[c_mask][:, 1], label=name, alpha=0.2, edgecolors=None)
try:
id_cluster = cluster_names.index(name)
except ValueError:
print("WARNING: no cluster found for {}".format(name))
continue
c_mask = np.where(predictions == id_cluster)
axe2.scatter(embeddings[c_mask][:, 0], embeddings[c_mask][:, 1], label=name, alpha=0.2, edgecolors=None)
axe1.legend(loc="lower center", bbox_to_anchor=(0.5, -0.35))
axe1.set_title("true labels")
axe2.legend(loc="lower center", bbox_to_anchor=(0.5, -0.35))
axe2.set_title("predicted cluster label")
plt.suptitle("Kmeans Clustering")
loc = os.path.join(
args.outdir,
args.prefix + "kmeans.pdf"
)
plt.savefig(loc, bbox_inches="tight")
plt.close()
print("INFO: figure saved at {}".format(loc))
end = time.time()
print("program ended in {0:.2f} seconds".format(end-start))
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