StadisticsML 1.0.0

Librería para obtener valores estadísticos para pruebas de distribución de signos, determinación de outliers y pruebas Durbin-Watson

StadisticsML

StadisticsML is a Python library designed to facilitate the use of machine learning models, specifically neural networks and Support Vector Regression (SVR), with a focus on data prediction and analysis. This library allows users to create customizable models, adjust hyperparameters, and obtain predictions and performance evaluations.

Features

• Customizable Neural Network: Allows the creation of neural networks with multiple layers and activations, specifying the number of epochs and optimization function.
• SVR: Implementation of Support Vector Regression, with easy adjustment of parameters like C, gamma, and epsilon.
• Model Evaluation: Generates performance metrics such as MSE and RMSE to assess the accuracy of trained models.
• User-Friendly Interface: Easy-to-use functions to train models and make predictions on new data.

Installation

To install the library, use the following pip command:

pip install StadisticsML

Requirements

• numpy
• scipy
• scikit-learn
• tensorflow

These packages will be installed automatically as dependencies when you install StadisticsML.

Functions

1. Customizable Neural Network (train_neural_network)

This function allows the user to create and train a neural network for regression. The adjustable parameters include:

• X: Training input data.
• y: Labels or expected outcomes.
• hidden_layers_config: Configuration of the hidden layers with the number of neurons and activation functions.
• output_neurons: Number of neurons in the output layer.
• output_activation: Activation function for the output layer.
• optimizer: Optimizer to use (e.g., adam, sgd).
• loss: Loss function to use (e.g., mean_squared_error).
• metrics: Additional metrics for model evaluation (e.g., mae, mse).
• epochs: Number of training epochs.
• test_size: Percentage of data allocated for testing.
• random_state: Seed for randomization.

Example:

from StadisticsML import train_neural_network

# Example data
X = [[1], [2], [3], [4], [5]]
y = [1.1, 2.0, 2.9, 4.0, 5.1]

# Hidden layers configuration: [(neurons, activation function)]
hidden_layers_config = [(12, 'relu'), (8, 'relu')]

# Train the neural network
model, history, test_loss, predictions = train_neural_network(
    X=X, y=y, 
    hidden_layers_config=hidden_layers_config,
    epochs=100,
    test_size=0.2,
    random_state=42
)

print("Predictions:", predictions)
print("Test Loss:", test_loss)

2. Support Vector Regression (SVR) (train_svr)

This function trains a support vector regression model using customizable parameters.

• X: Training input data.
• y: Labels or expected outcomes.
• C: Penalty parameter.
• gamma: Kernel function coefficient.
• epsilon: Tolerance margin.
• test_size: Percentage of data allocated for testing.

Example:

from StadisticsML import train_svr

# Example data
X = [[1], [2], [3], [4], [5]]
y = [1.1, 2.0, 2.9, 4.0, 5.1]

# Train the SVR model
mse, rmse, predictions = train_svr(X=X, y=y, C=100, gamma=0.001, epsilon=0.001, test_size=0.2)

print("SVR Predictions:", predictions)
print("RMSE:", rmse)

3. Cross-Validation for Model Optimization (cross_validate_model)

This function allows the user to perform cross-validation for optimizing the hyperparameters of a neural network or SVR model.

• model_type: Choose between 'nn' (neural network) or 'svr' (Support Vector Regression).
• X: Input data for training.
• y: Expected outcomes.
• hyperparameters: A dictionary of hyperparameters to tune, such as C, gamma, epochs, etc.
• cv_folds: Number of cross-validation folds.
• random_state: Seed for reproducibility.

Example:

from StadisticsML import cross_validate_model

# Example data
X = [[1], [2], [3], [4], [5]]
y = [1.1, 2.0, 2.9, 4.0, 5.1]

# Hyperparameters to tune
hyperparameters = {'C': [0.1, 1, 10], 'gamma': [0.001, 0.01, 0.1]}

# Perform cross-validation for SVR
best_params, mean_score = cross_validate_model(
    model_type='svr', X=X, y=y, hyperparameters=hyperparameters, cv_folds=5, random_state=42
)

print("Best parameters:", best_params)
print("Mean score from cross-validation:", mean_score)

Contribution

If you want to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/new-feature).
3. Make your changes and commit them (git commit -am 'Add new feature').
4. Push the branch (git push origin feature/new-feature).
5. Open a pull request.

Expected Contribution: Implementation of additional machine learning models.

License

This project is licensed under the MIT License. For more details, please refer to the LICENSE file.

Contact

• Author: Jorge Eduardo Londoño Arango
• Email: [e-mail] - [e-mail]