Support Tools for Machine Learning VIVIDLY
Project description
Vivid
Support Tools for Machine Learning Vividly 🚀
Usage
The concept of vivid is easy to use. Only make instance and run fit, vivid save model metrics and weights (like feature_imporance, pr/auc curve, training time, ...) .
import pandas as pd
from sklearn.datasets import load_boston
from vivid.backends.experiments import LocalExperimentBackend
from vivid.estimators.boosting import XGBRegressorBlock
X, y = load_boston(return_X_y=True)
train_df = pd.DataFrame(X)
# create model and experiment
xgb = XGBRegressorBlock('xgb')
experiment = LocalExperimentBackend('./outputs/simple')
# run models
from vivid.runner import create_runner
runner = create_runner(blocks=xgb, experiment=experiment)
runner.fit(train_df, y)
runner.predict(train_df)
VIVID makes it easy to describe model/feature relationships. For example, you can easily describe stacking, which can be quite complicated if you create it normally.
Install
pip install python-vivid
Sample Code
In /vivid/samples
, Some sample script codes exist.
Developer
Requirements
- docker
- docker-compose
create docker-image from docker-compose file
docker-compose build
docker-compose up -d
docker exec -it vivid-test bash
Test
use pytest
for test tool (see gitlab-ci.yml).
pytest tests
Project details
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