Evaluate your biometric verification models literally in seconds.
BSD-3-CLAUSE License
Evaluate Biometric Authentication Models Literally in Seconds.
pip install evalify
pip install git+https://github.com/ma7555/evalify.git
Evaluating all biometric authentication models, where the model output is a high-level embeddings known as feature vectors for visual or behaviour biometrics or d-vectors for auditory biometrics.
import numpy as np
from evalify import Experiment
rng = np.random.default_rng()
nphotos = 500
emb_size = 32
nclasses = 10
X = rng.random((self.nphotos, self.emb_size))
y = rng.integers(self.nclasses, size=self.nphotos)
experiment = Experiment()
experiment.run(X, y)
experiment.get_roc_auc()
print(experiment.roc_auc)
print(experiment.find_threshold_at_fpr(0.01))
X
and y
parameters and returns the results including FPR, TPR, FNR, TNR and ROC AUC. X
is an array of embeddings and y
is an array of corresponding targets.cosine_similarity
pearson_similarity
cosine_distance
euclidean_distance
euclidean_distance_l2
minkowski_distance
manhattan_distance
chebyshev_distance
scipy.spatial.distance
implemntations.batch_size
argument.