Skip to main content

Model wrapper for Pytorch, which can training, predict, evaluate, etc.

Project description

Usage Sample ''''''''''''

.. code:: python

    from model_wrapper import SplitClassModelWrapper

    classes = ['class1', 'class2', 'class3'...]
    X = [[...], [...],]
    y = [0, 0, 1, 2, 1...]

    model = ...
    wrapper = SplitClassModelWrapper(model, classes=classes)
    wrapper.train(X, y, val_size=0.2)

    X_test = [[...], [...],]
    y_test = [0, 1, 1, 2, 1...]
    result = wrapper.evaluate(X_test, y_test)
    # 0.953125

    result = wrapper.predict(X_test)
    # [0, 1]

    result = wrapper.predict_classes(X_test)
    # ['class1', 'class2']

    result = wrapper.predict_proba(X_test)
    # ([0, 1], array([0.99439645, 0.99190724], dtype=float32))

    result = wrapper.predict_classes_proba(X_test)
    # (['class1', 'class2'], array([0.99439645, 0.99190724], dtype=float32))

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

model-wrapper-0.0.9.tar.gz (11.7 kB view details)

Uploaded Source

File details

Details for the file model-wrapper-0.0.9.tar.gz.

File metadata

  • Download URL: model-wrapper-0.0.9.tar.gz
  • Upload date:
  • Size: 11.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for model-wrapper-0.0.9.tar.gz
Algorithm Hash digest
SHA256 d0f9e17e4c96aa478fb6c60a1d5611f65fdfde427cb747b833df5c2bce3a8cd8
MD5 67238a76780b8fc25d842f9e57e7ad53
BLAKE2b-256 5b78b13d4ecafa4462c6127f5b2d916f58c8f1024d3c4147119f8deffb07142f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page