A minimalistic machine learning model tracker and reporting tool.
Project description
trackable
A minimalistic machine learning model tracker and reporting tool
- Documentation: https://MillenniumForce.github.io/trackable
- GitHub: https://github.com/MillenniumForce/trackable
- PyPI: https://pypi.org/project/trackable/
- Free software: MIT
trackable
is a package focussed on users already familiar with machine learning in Python and aims to:
- Provide a minimal model tracking tool with no frills
- An intuitive and lightweight api
Installation
The latest released version can be installed from PyPI using:
# pip
pip install trackable
Features
To start using trackable
import the main reporting functionality via:
from trackable import Report
It's simple to start using the package. The example below (although simplistic) shows how easy it is to pick up the api:
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
from trackable import Report
X, y = make_classification()
lr = LogisticRegression().fit(X, y)
rf = RandomForestClassifier().fit(X, y)
# Instantiate the report...
report = Report(X, y, metrics = [accuracy_score, f1_score, roc_auc_score])
# Add models...
report.add_model(lr)
report.add_model(rf)
# Generate the report...
report.generate()
Credits
This package was created with Cookiecutter and the waynerv/cookiecutter-pypackage project template.
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