automl-alex 1.2.23

State-of-the art Automated Machine Learning python library for Tabular Data

AutoML Alex

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State-of-the art Automated Machine Learning python library for Tabular Data

From AutoML-Benchmark

Scheme

Features

• Automated Data Clean (Auto Clean)
• Automated Feature Engineering (Auto FE)
• Smart Hyperparameter Optimization (HPO)
• Feature Generation
• Feature Selection
• Models Selection
• Cross Validation
• Timelimit and EarlyStoping

Installation

pip install automl-alex

🚀 Examples

Classifier:

from automl_alex import AutoMLClassifier

model = AutoMLClassifier(X_train, y_train, X_test,)
predict_test, predict_train = model.fit_predict(timeout=2000,)

Regression:

from automl_alex import AutoMLRegressor

model = AutoMLRegressor(X_train, y_train, X_test,)
predict_test, predict_train = model.fit_predict(timeout=2000,)

More examples in the folder ./examples:

• 01_Quick_Start.ipynb
• 02_Models.ipynb
• 03_Data_Cleaning_and_Encoding_(DataBunch).ipynb
• 04_ModelsReview.ipynb
• 05_BestSingleModel.ipynb

What's inside

It integrates many popular frameworks:

• scikit-learn
• XGBoost
• LightGBM
• CatBoost
• Optuna
• ...

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.
• Do not work with timeseries and texts yet

Road Map

• Feature Generation

• Advanced Logging

• New Features Generators

• DL Encoders

• Save/Load and Predict on New Samples

• Add More libs (NNs)

• Add opt Pruners

• Build pipelines

• Docs Site

Contact

Telegram Group