# model-wrapper

**Repository Path**: summry/model-wrapper

## Basic Information

- **Project Name**: model-wrapper
- **Description**: Model wrapper for Pytorch
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-11-18
- **Last Updated**: 2025-06-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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))