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))
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-0.3.4.tar.gz
(20.9 kB
view details)
File details
Details for the file model-wrapper-0.3.4.tar.gz.
File metadata
- Download URL: model-wrapper-0.3.4.tar.gz
- Upload date:
- Size: 20.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f3e0bfa396d67827589872ccfe1c25788402af9c483e348abe2f53e439245967
|
|
| MD5 |
7e1ff321960044734ded01930edc4fea
|
|
| BLAKE2b-256 |
902902a55e12f1b24829cdf1c395044514119f2f8cfa392e1460ca90864be400
|
