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))
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-0.4.2.tar.gz
(15.6 kB
view details)
File details
Details for the file model-wrapper-0.4.2.tar.gz.
File metadata
- Download URL: model-wrapper-0.4.2.tar.gz
- Upload date:
- Size: 15.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
85cf7c73812ff652e295c87e990b0302774bd02bec94b4397349b35239db748d
|
|
| MD5 |
f05faed3b604855c2ede28d4487f20c9
|
|
| BLAKE2b-256 |
08d1e73e89d3de924d8dbcf28d4859e1cc5ec1b6f79892914a2ce43c71ff0e73
|
