Eva-Kaushik/AutoML

AutoML

AutoML

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

Works with Tasks:

• Binary Classification

• 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

pip install automl

🚀 Examples

Classifier:

from automl import AutoMLClassifier

model = AutoMLClassifier()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)

Regression:

from automl import AutoMLRegressor

model = AutoMLRegressor()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)

DataPrepare:

from automl import DataPrepare

de = DataPrepare()
X_train = de.fit_transform(X_train)
X_test = de.transform(X_test)

Simple Models Wrapper:

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

• Categorical Features

• Numerical Features

• 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

$ optuna-dashboard sqlite:///db.sqlite3

Road Map

• Feature Generation

• Save/Load and Predict on New Samples

• Advanced Logging

• Add opt Pruners

• Docs Site

• DL Encoders

• Add More libs (NNs)

• Multiclass Classification

• Build pipelines

Contact: kaushikeva0026@gmail.com