# Experimentation within Jupyter Lab.

Using the launched Jupyter Lab environment, you can explore ideas for AI evaluation programs and conduct experiments related to AI evaluation.

``` important:: When considering AI evaluation, it is recommended to define specifications with future AIT implementation in mind.
```

Specifications to Consider for Converting AI Evaluation Programs into AITs:
* What to evaluate?
  * For example, measure the accuracy of a classification model, use Accuracy to measure that accuracy, and use algorithm XX to calculate Accuracy.
  * At this time, referring to quality guidelines to determine which quality aspect this measurement relates to can help define the direction of quality assessment.
  * It is desirable for an AIT to measure a single quality metric.
  * The quality metric should be a specific numerical value.
* What to input?
  * AIT can accept multiple assets (data, models, etc., that are the subjects of evaluation) and parameters (runtime settings).
* What to output?
  * AIT can output multiple reports (metrics of evaluation results or images). Additionally, intermediate results and preliminary evaluation information generated during the evaluation can also be included in the output.

``` tip:: You'll be moving the AI evaluation program you experimented with on Jupyter Lab to the "my_ait.ipynb" template starting from section 3.3 and onwards.
```

##  Environmental information
Development environment information:
* Python 3.9.13
* jupyterlab 2.2.9
* matplotlib 3.7.3

``` important:: The environment information may change with updates to the AIT template. Before use, please ensure to check the latest AIT template Dockerfile.
```

## Jupyter Lab Usage
Access "http://localhost:9888/" from a browser like Chrome.

``` important:: For the location of input/output data for Jupyter Lab notebooks and Jupyter Lab notebooks, please use the location directly under the root directory.

    - Path to the template seen from the host: `qunomon/ait_repository/ait/{YOUR_AIT_ROOT}/`
    - Path to the template seen from the Jupyter Lab: `/root/`
```

* You can create and run excutable notebook like normal jupyter notebook or jupyter lab.

Create a new Python3 notebook by selecting `Python3` in the `Notebook` category located on the launcher tab.

![create notebook](02/01_create.png)
<br>

Then the new notebook is opening at the right pane, and now you are able to do some experiments and implementation freely.
![write notebook](02/02_write_notebook.png)

* Environment & Version Information:
  * JupyterLab 2.2.9 running on Python 3.9.13 Docker image.
  * For more details, refer to `{YourAITname}/tool/docker/jupyter/dockerfile`.