Metadata-Version: 2.1
Name: autoresevaluator
Version: 0.1.8
Summary: 
Author: toma tanaka
Author-email: ulti4929@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: anthropic (>=0.18.1,<0.19.0)
Requires-Dist: google-generativeai (>=0.4.0,<0.5.0)
Requires-Dist: openai (>=1.11.1,<2.0.0)
Requires-Dist: optuna (>=3.5.0,<4.0.0)
Requires-Dist: torch (>=2.2.0,<3.0.0)
Requires-Dist: torchvision (>=0.17.0,<0.18.0)
Requires-Dist: tqdm (>=4.66.1,<5.0.0)
Description-Content-Type: text/markdown

# AutoRes Evaluator

※ 追加で実装して欲しい機能や質問などがあれば以下のissuesから投稿をお願いします

https://github.com/auto-res/autores-evaluator/issues/new


## Examples
- Prediction by logistic regression on the Titanic dataset
<a href="https://colab.research.google.com/github/fuyu-quant/autores-evaluator/blob/main/examples/logistic_regression.ipynb" target="_blank" rel="noopener noreferrer"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

- Prediction by CNN on CIFAR10
<a href="https://colab.research.google.com/github/fuyu-quant/autores-evaluator/blob/main/examples/cnn.ipynb" target="_blank" rel="noopener noreferrer"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>


## Architecture

![ロゴ1](images/image.png)


## How to use
- Install
    - https://pypi.org/project/autoresevaluator/

```Python
pip install autoresevaluator

from autoresevaluator import AutoResEvaluator
```

- Setting
```python
# Hyperparameter setting
# Specify "type" and "args" for items to be searched in optuna.
params = {
    'lambda_l1': {'type': 'log_float', 'args': [1e-8, 10.0]},
    'lambda_l2': {'type': 'log_float', 'args': [1e-8, 10.0]},
    'num_leaves': {'type': 'int', 'args': [2, 256]},
    'feature_fraction': {'type': 'float', 'args': [0.4, 1.0]},
    'bagging_fraction': {'type': 'float', 'args': [0.4, 1.0]},
    'verbosity': -1
}

are = AutoResEvaluator(
    # dataset name
    dataset_name='titanic',
    # model file path
    model_path='/content/example.py',
    # parameter
    params=params,
    # Metrics you want to maximize/minimize
    valuation_index='roc_auc',
    # Where to store data
    datasave_path=None
    )
```

- Execution
```python
are.exec()
```

## Output
- result.log
    - File to output the results

- model_error.log
    - File to write errors in model files

