Skip to main content

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

Project description

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

.. code:: python

    from model_wrapper import SplitClassifyModelWrapper

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

    model = ...
    wrapper = SplitClassifyModelWrapper(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-0.5.6.tar.gz (16.3 kB view details)

Uploaded Source

File details

Details for the file model-wrapper-0.5.6.tar.gz.

File metadata

  • Download URL: model-wrapper-0.5.6.tar.gz
  • Upload date:
  • Size: 16.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for model-wrapper-0.5.6.tar.gz
Algorithm Hash digest
SHA256 56dafb6c72a76cb2c13185c462f35c8c1505d7b14b043e136c0bb9fd4b6850b2
MD5 b3dc5194a1d3b2ed6dc6a98e422e5075
BLAKE2b-256 5f1a724be594ac477ba5fe3e2ebb6f3b22d8e5ee7c72e7c821ad8d8d25771c84

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