mlektic 0.0.7

A python machine learning library to use and visualize gradient descent for linear regression and logistic regression optimization.

Mlektic: A Simple and Efficient Machine Learning Library

Mlektic is a Python library built on top of TensorFlow, designed to simplify the implementation and experimentation with univariate/multivariate linear and logistic regression models. By providing a variety of gradient descent algorithms and regularization techniques, Mlektic enables both beginners and experienced practitioners to efficiently build, train, and evaluate regression models with minimal code.

Key Features

• Linear and Logistic Regression: Easily implement and experiment with these fundamental machine learning models.
• Gradient Descent Variants: Choose from various gradient descent methods, including batch, stochastic, and mini-batch, to optimize your models.
• Regularization Techniques: Apply L1, L2, or elastic net regularization to improve model generalization and prevent overfitting.
• DataFrame Compatibility: Seamlessly integrate with Pandas and Polars DataFrames, allowing you to preprocess and manage your data effortlessly.
• Cost Visualization: Visualize the evolution of the cost function over time using dynamic or static plots, helping you better understand the training process.
• Evaluation Metrics: Access a range of evaluation metrics to assess the performance of both linear and logistic regression models, ensuring that your models meet your performance criteria.
• User-Friendly API: Designed with simplicity in mind, Mlektic's API is intuitive and easy to use, making it accessible for users with varying levels of expertise.

When to Use Mlektic?

• Educational Purposes: Ideal for students and educators to demonstrate the principles of regression and gradient descent in a practical setting.
• Prototyping and Experimentation: Quickly prototype regression models and experiment with different optimization techniques without the overhead of more complex machine learning frameworks.
• Small to Medium Scale Projects: Perfect for small to medium-sized projects where ease of use and quick iteration are more important than handling large-scale data.

Installation

You can install Mlektic using pip:

pip install mlektic

Getting Started

To train a model using linear regression with standard gradient descent and L1 regularization:

    from mlektic.linear_reg import LinearRegressionArcht
    from mlektic import preprocessing
    from mlektic import methods
    import pandas as pd
    import numpy as np

    # Generate random data.
    np.random.seed(42)
    n_samples = 100
    feature1 = np.random.rand(n_samples)
    feature2 = np.random.rand(n_samples)
    target = 3 * feature1 + 5 * feature2 + np.random.randn(n_samples) * 0.5

    # Create pandas dataframe from the data.
    df = pd.DataFrame({
        'feature1': feature1,
        'feature2': feature2,
        'target': target
    })

    # Create train and test sets.
    train_set, test_set = preprocessing.pd_dataset(df, ['feature1', 'feature2'], 'target', 0.8)

    # Define regulizer and optimizer.
    regularizer = methods.regularizer_archt('l1', lambda_value=0.01)
    optimizer = methods.optimizer_archt('sgd-standard')

    # Configure the model.
    lin_reg = LinearRegressionArcht(iterations=50, optimizer=optimizer, regularizer=regularizer)

    # Train the model.
    lin_reg.train(train_set)

    Epoch 5, Loss: 15.191523551940918
    Epoch 10, Loss: 11.642797470092773
    Epoch 15, Loss: 9.021803855895996
    Epoch 20, Loss: 7.08500862121582
    Epoch 25, Loss: 5.652813911437988
    Epoch 30, Loss: 4.592779636383057
    Epoch 35, Loss: 3.807236909866333
    Epoch 40, Loss: 3.2241621017456055
    Epoch 45, Loss: 2.790440320968628
    Epoch 50, Loss: 2.4669017791748047

The cost evolution can be plotted with:

    from mlektic.plot_utils import plot_cost

    cost_history = lin_reg.get_cost_history()
    plot_cost(cost_history, dim = (7, 5))

You can replace LinearRegressionArcht with LogisticRegressionArcht, and try different types of optimizers and regularizers.

Documentation

For more detailed information, including API references and advanced usage, please refer to the full documentation.

Contributing

Contributions are welcome! If you have suggestions for improvements, feel free to open an issue or send me an email to [e-mail].

License

Mlektic is licensed under the Apache 2.0 License. See the LICENSE file for more details.