lazy-learn is a high-level Python interface for automated machine learning (AutoML) for the lazy data scientist. While there are many AutoML libraries available each typically solves a niche area of the overall ML pipeline without providing a covering and approachable end-to-end system. lazy-learn aims at providing the most approachable and fastest access to building baseline models.
Project description
lazy-learn is a high-level Python interface for automated machine learning (AutoML). While there are many AutoML libraries available each typically solves a niche area of the overall ML pipeline without providing a covering and approachable end-to-end system.
The aim of lazy-learn is exactly that. Given a dataset, lazy-learn will analyse types and distributions of attributes, preprocess, feature-engineer and ultimately train models to be used for further evaluation or inference.
Upcoming features
Current stable version is 0.0.3. The upcoming updates will support:
- Abstract construction of model architectures
- XGBoost, LightGBM, Adaboost and Catboost architectures
- Time partitioning of datasets (Added in 0.0.4)
- Automated Hyperparameter Optimisation (HPO) (Added in 0.0.4)
- Text features
- An interface to AutoGluon
- Outlier detection and handling
- Automated suggestions of performance metrics
Usage
Using lazy-learn revolves around the LazyLearner
class. You can think of it as a kind of project, and it is the wrapper for any experiment within lazy-learn. You can consider a simple example with the California Housing dataset:
from lazylearn import LazyLearner
from sklearn.datasets import fetch_california_housing
# get some data
data = fetch_california_housing(as_frame=True)
df = data["data"]
df["MedHouseVal"] = data["target"]
# instantiate and run the LazyLearner
learner = LazyLearner()
learner.create_project(data=df, target="MedHouseVal")
learner.run_autopilot()
# evaluate results
print(learner.leaderboard())
Installation
Dependencies
lazy-learn requires:
- pandas
- scikit-learn
- xgboost
User Installation
pip install lazy-learn
Help and Support
Documentation
Citation
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file lazylearn-0.0.4.tar.gz
.
File metadata
- Download URL: lazylearn-0.0.4.tar.gz
- Upload date:
- Size: 263.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c850ac2ef89e82985969a733bde7b64c42bfc39cff45d82506eb1606656fecaf |
|
MD5 | 5b9ef393aa7185ff4baeb3d2736c3dd8 |
|
BLAKE2b-256 | eca512d1bcef817e0d37d37c2833a276f8efd0d753d67fa7438ab3884ed149cf |
File details
Details for the file lazylearn-0.0.4-py3-none-any.whl
.
File metadata
- Download URL: lazylearn-0.0.4-py3-none-any.whl
- Upload date:
- Size: 20.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4345b1cacf8cece1a2679749106733d91d2c6ea1bc8478e180232e5b7ec917e1 |
|
MD5 | ef89d20c4d1767e64b591b62c536f30e |
|
BLAKE2b-256 | 9d1053a71d2fa73b608ff8447c839103a957ab836df2ffc37cce6ea074986c2b |