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For this repository to work, downloading CSA benchmark from IMPROVE README is not required
Make sure to setup environmental variable for this project:
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Start working with the Molecule Property Prediction Benchmark
## Suggestions for a good README
### Abstraction Levels
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
Benchmark class provides the following levels of abstraction that are suitable for the most evaluation tasks.
Note: available datasets and dataframes are implemented as Enum, so the actual class is located under `DATASET_NAME.value`
## Name
Choose a self-explaining name for your project.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
* <b>Benchmark.</b> Top-level entity that contains all relevant information on available datasets. Can be invoked by `benchmark = MPPBenchmark()`
* <b>Dataset.</b> This is a description of each specific dataset with corresponding data columns and metrics
* <b>Dataframe.</b> Specific file under the given dataset. For now, all datasets in this repository contains a single dataframe NAME. However, in more complex situations (e.g., drug response prediction) it may be necessary to distinguish between specific components.
* <b>Split ID.</b> Data split under the K-Fold Cross Validation. Necessary part for evaluating Machine Learning models.
* <b>Split type.</b> Type of the performed K-Fold CV. Currently, available options are `RANDOM` (common shuffle split) and `SCAFFOLD` (Scaffold-blind split of the molecules, more challenging task for prediction)
* <b>Split stage.</b> Specifies whether requested data corresponds to `TRAIN`, `TEST`, or `VALIDATION` stages.
* <b>Metric.</b> Specific metric to run prediction task on.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
## Example
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
Initialize benchmark and point it to the directory containing data
```
benchmark_dir = 'benchmark_data'
benchmark = MPPBenchmark()
```
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
Get all available benchmark options
```
splits_ids = benchmark.get_splits_ids()
split_types = benchmark.get_split_types()
datasets = benchmark.get_datasets()
stages = benchmark.get_stages()
metrics = benchmark.get_metrics()
```
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
Set up a benchmark object state to fit your specific needs
```
benchmark.set_benchmark_dir(benchmark_dir)
benchmark.set_dataset(datasets.NUlrich)
benchmark.set_split_id(splits_ids[0])
benchmark.set_split_type(split_types.SCAFFOLD)
benchmark.set_stage(stages.TEST)
benchmark.set_metric(metrics.A_LOG_P)
```
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
Get data for your model from specific dataframe using unified benchmark interface
```
data = benchmark.get_dataframe(datasets.NUlrich.value.Dataframes.MAIN)
```
## License
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
File "/Users/onarykov/git/improve-lib/IMPROVE/improvelib/applications/molecule_property_prediction/generate_splits.py", line 4, in <module>
from improvelib.applications.molecule_property_prediction.molecule_property_prediction_benchmark import MPPDatasets
File "/Users/onarykov/git/improve-lib/IMPROVE/improvelib/applications/molecule_property_prediction/molecule_property_prediction_benchmark.py", line 64, in <module>
class MPPDatasets(Enum):
File "/Users/onarykov/git/improve-lib/IMPROVE/improvelib/applications/molecule_property_prediction/molecule_property_prediction_benchmark.py", line 68, in MPPDatasets
QM9 = QM9Dataset()
^^^^^^^^^^^^
File "/Users/onarykov/git/improve-lib/IMPROVE/improvelib/applications/molecule_property_prediction/molecule_property_prediction_benchmark.py", line 34, in __init__
self.metrics2cols_map
AttributeError: 'QM9Dataset' object has no attribute 'metrics2cols_map'
