mlon 1.0.3

A comprehensive utility package for machine learning development

MLON

A comprehensive utility package for machine learning development that works seamlessly with popular ML libraries like TensorFlow, scikit-learn, Keras, and PyTorch.

Features

• Data Preprocessing

  • Missing value handling
  • Feature scaling
  • Categorical encoding

• Model Evaluation

  • Classification metrics
  • Regression metrics
  • Confusion matrix analysis
  • Cross-validation utilities

• Visualization

  • Confusion matrix plots
  • Learning curves
  • Feature importance plots
  • Distribution plots
  • Correlation matrices

• Model Utilities

  • Model saving/loading
  • Hyperparameter tuning
  • Grid search and random search
  • Model size estimation

• Cross Validation

  • K-fold cross-validation
  • Stratified k-fold
  • Custom scoring support

Installation

pip install mlon

Quick Start

from mlon import DataPreprocessor, ModelEvaluator, Visualizer, ModelUtils, CrossValidator

# Data Preprocessing
preprocessor = DataPreprocessor()
scaled_data = preprocessor.scale_features(data, method='standard')
encoded_data = preprocessor.encode_categorical(data, method='onehot')

# Model Evaluation
evaluator = ModelEvaluator()
metrics = evaluator.classification_metrics(y_true, y_pred)
conf_matrix = evaluator.get_confusion_matrix(y_true, y_pred)

# Visualization
viz = Visualizer()
viz.plot_confusion_matrix(conf_matrix)
viz.plot_learning_curve(train_scores, val_scores)

# Model Management
model_utils = ModelUtils()
model_utils.save_model(model, 'model.pkl')
best_model = model_utils.grid_search(model, param_grid, X, y)

# Cross Validation
cv = CrossValidator(n_splits=5)
scores = cv.cross_validate(model, X, y)

Requirements

• Python 3.7+
• NumPy
• Pandas
• scikit-learn
• Matplotlib
• Seaborn
• Joblib

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.