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https://github.com/eva-kaushik/automl
AutoML
https://github.com/eva-kaushik/automl
automl automl-algorithms automl-pipeline
Last synced: about 2 months ago
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AutoML
- Host: GitHub
- URL: https://github.com/eva-kaushik/automl
- Owner: Eva-Kaushik
- License: mit
- Created: 2024-08-03T06:39:58.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-03T06:46:30.000Z (6 months ago)
- Last Synced: 2024-11-05T12:20:04.106Z (3 months ago)
- Topics: automl, automl-algorithms, automl-pipeline
- Language: Python
- Homepage:
- Size: 13.1 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
AutoML
[](https://pepy.tech/project/automl-alex)
[](https://www.codefactor.io/repository/github/alex-lekov/automl_alex)
[](https://t.me/automlalex)
[](/LICENSE)
---
State-of-the art Automated Machine Learning python library for Tabular Data
## Works with Tasks:
- [x] Binary Classification
- [x] Regression
- [ ] Multiclass Classification (in progress...)
### Benchmark Results
### Scheme
# Features
- Automated Data Clean (Auto Clean)
- Automated **Feature Engineering** (Auto FE)
- Smart Hyperparameter Optimization (HPO)
- Feature Generation
- Feature Selection
- Models Selection
- Cross Validation
- Optimization Timelimit and EarlyStoping
- Save and Load (Predict new data)
# Installation
```python
pip install automl
```
# 🚀 Examples
Classifier:
```python
from automl import AutoMLClassifier
model = AutoMLClassifier()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
Regression:
```python
from automl import AutoMLRegressor
model = AutoMLRegressor()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
DataPrepare:
```python
from automl import DataPrepare
de = DataPrepare()
X_train = de.fit_transform(X_train)
X_test = de.transform(X_test)
```
Simple Models Wrapper:
```python
from automl import LightGBMClassifier
model = LightGBMClassifier()
model.fit(X_train, y_train)
predicts = model.predict_proba(X_test)
model.opt(X_train, y_train,
timeout=600, # optimization time in seconds,
)
predicts = model.predict_proba(X_test)
```
# What's inside
It integrates many popular frameworks:
- scikit-learn
- XGBoost
- LightGBM
- CatBoost
- Optuna
- ...
# Works with Features
- [x] Categorical Features
- [x] Numerical Features
- [x] Binary Features
- [ ] Text
- [ ] Datetime
- [ ] Timeseries
- [ ] Image
# Note
- **With a large dataset, a lot of memory is required!**
Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.
Run
```console
$ optuna-dashboard sqlite:///db.sqlite3
```
# Road Map
- [x] Feature Generation
- [x] Save/Load and Predict on New Samples
- [x] Advanced Logging
- [x] Add opt Pruners
- [ ] Docs Site
- [ ] DL Encoders
- [ ] Add More libs (NNs)
- [ ] Multiclass Classification
- [ ] Build pipelines
Contact: [email protected]