EasyMLTool 0.1.0

A lightweight automated ML pipeline for preprocessing, training, and evaluation.

EasyMLTool

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Overview

EasyMLTool is a lightweight, automated machine learning pipeline package designed to simplify the entire ML workflow—from data preprocessing and model training to hyperparameter tuning, evaluation, and deployment. Its modular architecture makes it accessible for beginners while offering enough flexibility and extensibility for advanced users. With EasyMLTool, you can build robust machine learning models quickly and efficiently.

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Features

• Automated Data Preprocessing
  • Handles missing values, feature scaling, encoding, and splitting via scikit-learn pipelines and column transformers.
• Multiple Model Support
  • Offers a variety of built-in traditional ML models (e.g., RandomForest, SVM, Logistic Regression) as well as deep learning models (e.g., LSTM, CNN, and PyTorch-based models).
• Hyperparameter Optimization
  • Integrated methods (e.g., GridSearchCV) to automatically tune model parameters.
• Model Evaluation & Testing
  • Includes utilities for model evaluation using metrics such as accuracy, F1-score, and more.
  • Comes with comprehensive unit and integration tests.
• Easy Deployment
  • Provides deployment utilities (via Flask) to quickly serve models as APIs.
• Lightweight & Modular Design
  • Clean, modular codebase that facilitates easy integration into various ML workflows.
• Extensible
  • Easily extend or customize components to fit your specific use case.

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Installation

You can install EasyMLTool directly from PyPI:

pip install easymltool

Alternatively, if you want to install from source:

git clone https://github.com/FAbdullah17/EasyMLTool.git
cd EasyMLTool
pip install .

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Usage

Below are several examples to get you started.

1. Importing EasyMLTool

from easymltool.data_preprocessing import DataPreprocessor
from easymltool.models import ModelBuilder, DeepLearningModels
from easymltool.training import Trainer, DeepTrainer, PyTorchTrainer

2. Data Preprocessing

import pandas as pd
from sklearn.datasets import make_classification

# Generate a synthetic dataset
X, y = make_classification(n_samples=500, n_features=10, random_state=42)
df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(10)])
df['target'] = y

# Preprocess the data
preprocessor = DataPreprocessor(numerical_strategy="mean", categorical_strategy="most_frequent", scaling_method="standard")
X_processed = preprocessor.fit_transform(df.drop(columns=['target']))

3. Training a Traditional ML Model

trainer = Trainer(model_name="random_forest", hyperparameter_tuning=False)
trained_model = trainer.train(X_processed, df['target'])
predictions = trained_model.predict(X_processed[:5])
print("Predictions:", predictions)

4. Training a Deep Learning Model

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_processed, df['target'], test_size=0.2, random_state=42)

dl_trainer = DeepTrainer(input_shape=X_train.shape[1], model_type="lstm", epochs=50, batch_size=32)
trained_dl_model = dl_trainer.train(X_train, y_train, X_test, y_test)

5. Saving and Loading Models

dl_trainer.save_model("model.h5")
loaded_model = dl_trainer.load_model("model.h5")

6. Deploying a Model

python easymltool/deployment.py

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Project Structure

EasyMLTool/
│── easymltool/
│   ├── __init__.py
│   ├── data_preprocessing.py
│   ├── deployment.py
│   ├── models.py
│   ├── training.py
│   ├── utils.py
│── examples/
│   ├── example_dl.ipynb
│   ├── example_ml.ipynb
│── test/
│   ├── __init__.py
│   ├── test_data_preprocessing.py
│   ├── test_models.py
│   ├── test_training.py
│── .gitignore
│── LICENSE
│── pyproject.toml
│── README.md
│── requirements.txt
│── setup.py

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Contributing

We welcome contributions! If you'd like to contribute:

1. Fork the repository.
2. Create a new feature branch:

   git checkout -b feature-your-feature
   

3. Commit your changes:

   git commit -m "Describe your changes"
   

4. Push to your branch:

   git push origin feature-your-feature
   

5. Open a Pull Request on GitHub.

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Testing

To run the tests, execute:

pytest test/

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Deployment

To deploy a trained model:

python easymltool/deployment.py

Use an API client (e.g., Postman) to send POST requests to the API endpoint and retrieve predictions.

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License

This project is licensed under the MIT License.

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Contact

For questions, suggestions, or support, please contact Fahad Abdullah:

• Email: [e-mail]
• GitHub: FAbdullah17
• LinkedIn: Fahad Abdullah

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Acknowledgments

Special thanks to the open-source community for providing the foundational libraries (such as scikit-learn, TensorFlow, and PyTorch) that made EasyMLTool possible.

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Happy Coding with EasyMLTool! 🚀