rapidoml 0.1.3

RapidoML is a simple Automated Machine Learning (AutoML) library

RapidoML: Automated Machine Learning Made Simple

RapidoML is an easy-to-use Python library that automates the process of building and optimizing machine learning models. It simplifies feature selection, hyperparameter tuning, and model selection, making it perfect for quickly prototyping and testing models. RapidoML relies on popular dependencies like scikit-learn, NumPy, TensorFlow, Keras, XGBoost, LightGBM, and CatBoost.

Installation

You can install RapidoML using pip:

pip install RapidoML

This will install the required dependencies along with the RapidoML library.

Quickstart

First, import the required functions from the library:

import rapidomL
from rapidoml.datasets import sample_datasets

Load a sample dataset, such as Iris, Titanic, or others, and split it into training and testing sets with a 70/30 ratio. The function sample_datasets also performs preprocessing, cleaning, and normalization.

df_train, df_test = sample_datasets('iris')

Create an instance of the AutoML class and train the model using the training dataset. If you want to specify the model to use, you can pass it as an argument, for example: model='XGBClassifier'. Otherwise, RapidoML will automatically pick the best model for the given dataset.

automl = RapidoML.AutoML()
automl.train(df_train)

Evaluate the model using the testing dataset. This will return a dictionary containing evaluation metrics for the trained model, such as accuracy, precision, recall, and F1 score for classification tasks, or R2, MSE, and MAE for regression tasks.

metrics = RapidoML.evaluate(automl, df_test, model_type='classifier')
print(metrics)

Finally, save the trained model to a pickle file for future use.

automl.save()

Custom Datasets

If you want to use your own dataset, follow these steps:

1. Load your dataset using your preferred method (e.g., pandas.read_csv).
2. Preprocess your dataset (cleaning, normalization, etc.).
3. Split the dataset into training and testing sets (e.g., using train_test_split from sklearn.model_selection).
4. Pass the training dataset to the train function of the AutoML instance.
5. Evaluate and save the model as described in the Quickstart example above.

Supported Models

RapidoML supports various models, including:

• DeepLearningClassifier and DeepLearningRegressor
• XGBClassifier and XGBRegressor
• LGBMClassifier and LGBMRegressor
• CatBoostClassifier and CatBoostRegressor

You can easily extend the library by adding more models to the models.py file.

Contributing

If you'd like to contribute to RapidoML, feel free to submit pull requests, report issues, or suggest new features. Your feedback is highly appreciated!

Support

If you have any questions or need help using RapidoML, please post a question or open an issue on GitHub.