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# mkdocs_gallery_thumbnail_path = 'images/example_thumnail.png'
import numpy as np
from krisi import score
from krisi.utils.data import create_probabilities
num_labels = 3
num_samples = 1000
probabilities = create_probabilities(num_labels=num_labels, num_samples=num_samples)
sc = score(
y=np.random.randint(0, num_labels, num_samples),
predictions=np.random.randint(0, num_labels, num_samples),
probabilities=probabilities,
calculation="both"
# dataset_type="classification_multilabel", # if automatic inference of dataset type fails
)
sc.print()
sc.generate_report()
Total running time of the script: ( 0 minutes 0.000 seconds)
Download Python source code: evaluate_probabilities.py