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# Setup
## Install required software
Install Docker:
* (Mac, Linux) Follow the [installation instructions](https://docs.docker.com/get-docker/)
* (Windows): Follow the manual installation steps for Windows Subsystem for Linux [here](https://docs.microsoft.com/en-us/windows/wsl/install). On
step 1, follow the recommendation of updating to WSL 2. You do not necessarily need to install Windows Terminal. Now
go [here](https://docs.docker.com/desktop/windows/install/) and follow the "Install Docker Desktop on Windows" instructions. You can then start
Docker Desktop and follow the quick start quide.
Install VS Code [using the instructions online](https://code.visualstudio.com/download).
## Open the folder in VS Code using "Dev Container"
Select File -> Open and select *the entire folder*.
VS Code will propose to install "Dev Container". Click "install".
VS Code will give you a message similar to:
> Folder contains a Dev Container configuration file. Reopen folder to develop in a container.
Select "Reopen in container".
Now you should have the folder open while VS Code is in "container development mode".
You can create a new terminal using Terminal -> New Terminal.
## Install dependencies
Install this package and dependencies using:
pip install -e .
The main dependency installed is `ACT4E-mcdp` which is available [on this repo](https://github.com/ACT4E/ACT4E-mcdp).
That library takes care of parsing the models and queries.
Please refer to the [online documentation](https://act4e-mcdp.readthedocs.io/en/latest/) for more information.
If we tell you to update the library, use this:
pip install -U ACT4E-mcdp
## Make sure everything is OK
### Downloading test cases
Use this command to download the test cases:
act4e-mcdp-download-tests --out downloaded
Then you have available a few test cases in the directory `downloaded/`.
### Running the DP solver for a specific model and query
`act4e-mcdp-solve-dp` is the command you use to run the DP solver:
act4e-mcdp-solve-dp \
--solver act4e_mcdp_solution.DPSolver \
--query FixFunMinRes \
--model downloaded/lib1-parts.e03_splitter1.primitivedps.mcdpr1.yaml \
--data '42'
In brief:
* `--solver act4e_mcdp_solution.DPSolver`: this selects the class for your solver;
* `--query FixFunMinRes`: this selects `FixFunMinRes` (other choice: `FixResMaxFun`);
* `--model downloaded/lib1-parts.e03_splitter1.primitivedps.mcdpr1.yaml`: this selects the model to use for optimization;
* `--data '10'`: this selects the query to give.
It is a YAML dictionary with a key for each functionality name.
You will see the result in the logs:
```
INFO query: 10
INFO solution: Interval(pessimistic=UpperSet(minima=[]), optimistic=UpperSet(minima=[]))
```
The template `act4e_mcdp_solution.MySolution` always returns an empty `UpperSet` (= infeasible).
### Running the DP solver on a set of test cases
`act4e-mcdp-solve-dp-queries` is the command you use to run the DP solver on a set of test cases:
act4e-mcdp-solve-dp-queries \
-d downloaded \
--solver act4e_mcdp_solution.DPSolver
In brief:
* `-d downloaded`: this selects the directory with the test cases to use;
* `--solver act4e_mcdp_solution.DPSolver`: this selects the class for your solver.
At the end of the processing, you will see an output similar to this:
```
Summary:
comparison_not_implemented:
- downloaded/lib1-parts.dp-queries.FixFunMinRes.e10_conversions2-0006.mcdpr1.yaml
- downloaded/lib1-parts.dp-queries.FixResMaxFun.e05_sumf-0002.mcdpr1.yaml
...
failed:
- downloaded/lib1-parts.dp-queries.FixResMaxFun.e03_splitter1-0006.mcdpr1.yaml
- downloaded/lib1-parts.dp-queries.FixFunMinRes.e12_catalogue_true-0010.mcdpr1.yaml
...
succeeded:
- downloaded/lib1-parts.dp-queries.FixResMaxFun.e12_catalogue_empty-0001.mcdpr1.yaml
- downloaded/lib1-parts.dp-queries.FixFunMinRes.e12_catalogue-0001.mcdpr1.yaml
...
INFO Find the summary at 'output_summary.yaml'
```
For each query, the result is either `succeeded` or `failed`. (The status `comparison_not_implemented` means that we didn't implement yet
the comparison between the result and the expected result.)
In the file `output_summary.yaml` you will find details of the results.
For example, one of the results could be:
```yaml
downloaded/lib1-simple.dp-queries.FixResMaxFun.all_together-0011.mcdpr1.yaml:
query: FixResMaxFun
value: Decimal('Infinity')
result_expected: Interval(pessimistic=LowerSet(maximals=[Decimal('Infinity')]),
optimistic=LowerSet(maximals=[Decimal('Infinity')]))
result_obtained: Interval(pessimistic=LowerSet(maximals=[]), optimistic=LowerSet(maximals=[]))
status: failed
```
This indicated the type of query (`FixResMaxFun`), the value of the query (`Decimal('Infinity')`), the expected result (`result_expected`) and the
obtained result (`result_obtained`).
This particular test case failed because the obtained result is empty ("infeasible"), while the expected result is not empty.
### Running the MCDP solver
This is the command you use to run the MCDP solver:
act4e-mcdp-solve-mcdp \
--solver act4e_mcdp_solution.MCDPSolver \
--query FixFunMinRes \
--model downloaded/lib1-parts.e03_splitter1.models.mcdpr1.yaml \
--data '{f: 42}'
Note that for the MCDP solver we give a file of type `models.mcdpr1.yaml` instead of `primitivedps.mcdpr1.yaml`.
For the data, we use a key-value pair with the functionality name and the value.
You should see the output:
query: {'f': Decimal('42')}
solution: Interval(pessimistic=UpperSet(minima=[]), optimistic=UpperSet(minima=[]))
## Running the MCDP solver on a set of test cases
TODO: finish this part
|
