# Unit test
In this chapter, we will verify whether the AIT created up to Section 3.3 operates without using Qunomon. 
When this verification is complete, AIT execution program that operates on Qunomon will be created.

## Prepare for unit test
### Preparation of input data for unit test
``` important:: This task is a necessary preparation for the unit test of AI evaluation program, and it is not involved in the packaged AIT.
```
Create a directory in `{YOUR_AIT_ROOT}/local_qai/inventory` to store your test data for unit test (as test input), and store the test input.
The directory name is obtained from the `name` argument of the `add_ait_inventories` function used in #5 Manifest Generation area.

* Here is a sample structure of test input:
    ```
    {YOUR_AIT_ROOT}
    │
    └─local_qai
        │
        └─inventory
            │
            └─iris_data
                   tableconvert_csv_4nryby.csv
    ```

### Register test input
Set up the `value` argument for the input data and parameters defined in the Prepare for the Input area with data for unit testing.

* Example
```py
    input_generator = AITInputGenerator(manifest_path)
    input_generator.add_ait_inventories(name='iris_data', value='iris_data/tableconvert_csv_4nryby.csv')
    input_generator.set_ait_params(name='mean_column_name', value='petal.width')
```

``` important:: The value defined in this section is used only for unit testing and is not used when running on Qunomon.
```

## Running unit test
### Generation of the AIT Execution Program
Run all cells in `my_ait.ipynb` and confirm that the execution is successful.

``` tip:: When ALL RUN is completed, an AIT execution program that runs on Qunomon will be automatically generated.
```

### Copying your own python modules (optional)
If you have imported your own python module with Jupyter Lab, copy the module to `{YOUR_AIT_ROOT}/deploy/container/repository/`.

``` note:: If you have not created any files other than 
    the ``my_ait.ipynb`` Jupyter file, this operation is not necessary.
```

### Preparation for unit test execution

Open Terminal from Jupyter and invoke the script for initialization.

``` tip:: It is recommended running the bash command.
```

``` bash
bash
cd /workdir/root/tool/local_test/
./init.sh
```

``` important:: If the following message is seen, please change the line feed code of the init.sh from CRLF to the LF.

  bash: ./init.sh: No such file or directory
``` 

### Execute unit test
Open Terminal from Jupyter and invoke the script which execute a unit test.

``` bash
cd /workdir/root/tool/local_test/
./run.sh
```

``` important:: If the following message is seen, please change the line feed code of the run.sh from CRLF to the LF.

  bash: ./run.sh: No such file or directory
``` 

## Evaluate unit test
Check the files below and make sure the unit tests are successful.
* {YOUR_AIT_ROOT}/tool/local_test/ait.log
* {YOUR_AIT_ROOT}/local_qai/mnt/ip/job_args/1/1/ait.input.json
* {YOUR_AIT_ROOT}/local_qai/mnt/ip/job_result/1/1/ait.output.json
* {YOUR_AIT_ROOT}/local_qai/mnt/ip/job_result/1/1/downloads/Log/ait.log

``` tip:: {YOUR_AIT_ROOT}/tool/local_test/ait.log is output only if the AIT execution fails.

    The destination of ait.log can be changed. See the Function definitions area for details.
```

``` tip:: If an E901 error occurs, define an object (dictionary etc.) that can be expanded to a String as a method argument.
```

If your execution was succeeded, the results of execution will be saved to `{YOUR_AIT_ROOT}/local_qai/mnt/ip/job_result/1/1`.