GPALexp

Your task is to develop a user-friendly mini-application in Python that leverages the GPALexp package to conduct psychological experiments involving active learning. This application will simulate a simple cognitive task where participants must classify stimuli as either belonging to category A or B based on certain characteristics. The goal is to use Gaussian Process Active Learning (GPAL) to optimize the selection of stimuli for each participant to maximize learning efficiency and accuracy.

### Application Requirements:
1. **User Interface**: Create a basic GUI using Tkinter or any other lightweight Python library for simplicity. The interface should allow users to input their responses and view feedback after each trial.
2. **Experiment Setup**: Allow the user to configure the experiment parameters such as the number of trials, categories of stimuli, and initial set of training data.
3. **GPAL Integration**: Use GPALexp to model the probability of correct classification for each stimulus. Based on the model, select the next stimulus to present to the participant that maximizes information gain about the underlying decision boundary between the two categories.
4. **Feedback Mechanism**: After each trial, provide immediate feedback to the participant regarding the correctness of their response.
5. **Data Visualization**: Implement a feature to visualize the evolution of the decision boundary over time as more data is collected from the participant. This could be done using matplotlib or seaborn.
6. **Performance Metrics**: Calculate and display performance metrics such as accuracy, precision, recall, and F1 score at the end of the experiment.

### Suggested Features:
- Option to save and load experiment configurations and results.
- Ability to run multiple sessions for different participants and compare their learning curves.
- Incorporate real-time adjustment of model hyperparameters based on user interaction.
- Provide a summary report at the end of the session detailing the participant's performance and the effectiveness of the active learning strategy.

### How to Utilize GPALexp:
- Initialize a GPAL model with GPALexp using the initial set of training data provided by the user.
- For each trial, use the GPAL model to predict the uncertainty of classifying new stimuli. Select the stimulus with the highest uncertainty for presentation to the participant.
- Update the GPAL model with the participant's response after each trial.
- Use the updated model to make predictions and provide feedback to the participant.

By following these guidelines, you'll create a powerful tool for conducting psychological experiments that not only engages participants but also optimizes the learning process through advanced machine learning techniques.