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


Release history Release notifications | RSS feed

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-0.1.4.tar.gz (12.5 kB view details)

Uploaded Source

File details

Details for the file model-wrapper-0.1.4.tar.gz.

File metadata

  • Download URL: model-wrapper-0.1.4.tar.gz
  • Upload date:
  • Size: 12.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for model-wrapper-0.1.4.tar.gz
Algorithm Hash digest
SHA256 d948f2169bf26de3f34f077b25caaf26f6ffc7054ea652f659f4bdc94bf0e6de
MD5 55f330e6fe51896a08dc6c1ba61827c6
BLAKE2b-256 5b702f1ae24d5e26fac8ffae4d46ea9fb8ced9ecf50b08333720dc151a68c8bd

See more details on using hashes here.

Supported by

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