log-loss-tool 0.1.1
A Python library to visualize the Log-Loss function.
Requires Python: >=3.6

# **Log Loss Tool**

A Python library designed to visualize and simplify the understanding of the Log-Loss function, gradients, and probabilities in binary logistic regression models.

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## **Purpose**

This library was created to assist beginners and professionals in:
- Abstracting complex calculations behind the Log-Loss function.
- Visualizing gradients and probabilities to better understand their influence on the loss function.
- Building intuition about binary logistic regression through clear, customizable plots.

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## **Features**
- Visualization of the Log-Loss function for both classes (y=1 and y=0).
- Gradient Behavior Analysis: Explore how gradients change with probability.- Customizable Plots: Configure colors, labels, gridlines, and more.
- Educational Support: Perfect for students, educators, and professionals who want to strengthen their understanding of logistic regression.

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## **Installation**

To install the tool, use the following command:

```bash
pip install log_loss_tool
```

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## **Usage Example**

Below is an example of how to use the tool to visualize the Log-Loss function:

```python
from log_loss_tool import plot_log_loss_with_custom_input

# Visualize the Log-Loss function with a predicted probability of 0.7 and a threshold of 0.5
plot_log_loss_with_custom_input(probability=0.7, threshold=0.5)

# Alternatively, use log-loss and gradient values to calculate threshold automatically
log_loss = 0.5  # log-loss calculated from training/testing
gradient = -3.0  # gradient calculated from the log-loss
plot_log_loss_with_custom_input(log_loss=log_loss, gradient=gradient)

```

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## **Example Visualization**

Below is an example of the Log-Loss function visualization:

```python

# Plot with calculated log-loss and optimal threshold
plot_log_loss_with_custom_input(log_loss=log_loss_value, threshold=threshold_otimo)

```

![Log-Loss Graph](log_loss_example.png)


This image illustrates how the Log-Loss function behaves with the given probability and threshold. The image above is for illustrative purposes, showing the impact of different thresholds on the loss function.

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## **Input Parameters**
- **probability:** Predicted probability (h(x)). Must be between 0 and 1.
- **log_loss:** Log-loss value for y=1. If provided, it calculates the corresponding probability.
- **gradient:** Gradient value for y=1. If provided, it calculates the corresponding probability.
- **optimal_threshold:** The `optimal_threshold` is used to classify the predicted value into class 0 or 1:

- If not provided, the function will automatically use the default value of **0.5**.
- This threshold determines the cutoff for classification, where values above it will be classified as class 1 and below it as class 0.

If you would like to calculate an optimal threshold based on the data, you can provide a value. Otherwise, the default of 0.5 will be used.
- **title, figsize, font_size, etc.:** Options to customize the plot style.

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## **Why Use Log Loss Tool?**

Understanding machine learning models often requires digging into mathematical details. This tool provides a visual and intuitive way to:
- Grasp how probabilities influence the Log-Loss function.
- See how the gradient behaves during optimization.
- Communicate model behavior to others through visuals.

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## **Contributing**  

This project is under **continuous development**, and all contributions are welcome! Our goal is to improve the **Log Loss Tool** so that it becomes even more useful for the community. If you have ideas, improvements, or found an issue, we would love to have your contribution!  

To contribute, please follow the steps below:  

1. **Fork** this repository. This will create a copy of the project where you can make your changes.  
2. **Create a new branch** for your contribution:    
   ```bash
   git checkout -b my-new-feature  

3.	Make your changes and don’t forget to add tests to ensure the new code is working correctly. If you made improvements or fixes, explain what you changed in the commit.   

4.	Submit a pull request with a clear description of your changes. Please explain the feature you added or the issue you fixed so we can review your contribution effectively.  

Examples of contribution:  
- Improvements: New features or changes that make the library more efficient or useful.
- Bug fixes: If you find any bugs or unexpected behavior, please open an issue or submit a pull request with the fix.
- Documentation: If you see areas where the documentation can be clearer or more helpful, improvements to documentation are also highly appreciated.

This project is open source and continuously evolving. By contributing, you help make the Log Loss Tool more robust and applicable to different needs.

We greatly appreciate any contribution, big or small!

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## **License**

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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## **Author**

This project was created by Rodrigo Campos.  
**Email:** rodrigocamposag90@gmail.com  
**GitHub:** RodrigoCamposDS  
**Date:** 2025-01-01