Pymdma

Documentation and API updates.

Added
- Objective field in the class docstrings for specifying the objective of the metric
- Added .readthedocs.yml file for readthedocs configuration
- General description of the package in the Getting Started section of the documentation

Fixed
- Updated unit tests for the API to reflect the new changes in the metric categorization
- Updated developer guidelines for pre-commit hooks
- Updated Makefile hook for documentation generation with output directory
- Reorganized the documentation structure to the new `metric_group` categorization
- Removed any reference to the previous categorizations from the API models

Changed
- mkdocs heading from 3 to 4 for better organization
- `FrechetDistance` metric now uses `InceptionFID` from piq as a default feature extractor

Added
- CLI device specification
- CLI compute arguments in reports dir
- Preprocessing transform in image datasets when using the API

Fixed
- Moved `sklearn` and `gudhi` dependencies to the main dependency tree

Changed
- Default image feature extractor is now `vit_b_32`
- Confusing synthetic and input metric goals where aggregated to `quality`, `privacy`, `annotation` and `utility` categories
- Moved metrics to sepecific folders based on `metric_group` (feature-based, data-based)

Fixed
- Fixed project configuration conflict between setup.py and pyproject.toml by reverting to poetry as main build engine
- Updated dependencies in `pyproject.toml` and ignoring versions in requirements folder
- Fixed error in validation type for the cli
- Fixed example `tabular` and `time-series` scripts to use cli application

Removed
- Removed `setup.py` file as it is deprecated in favor of `pyproject.toml`
- Removed `cookiecutter` template files as they are not needed for the project

Changed
- Changed default image extractor model from `dino_vits8` to `vit_b_32`

- Bumped version to 0.1.0 to indicate the first stable release of the package

Added
- Documentation for image, tabular and time-series class metrics
- Tabular and time-series unit tests for direct import from the package
- Dummy notebook with examples of quality evaluation on the imagenette dataset
- Mkdocs documentation for the package (only available locally and for metric classes)
- Each modality has a seciton in the documentation site
- Metrics are organized as input_val and synthesis_val groups
- Groups may be divided into other subgroups depending on metrics' specificities
- Added distance metric parameter for the PRDC metrics


Fixed
- Entire code structure moved to package level under src/
- Allows for easire import of metrics and models and correct package builds
- Package submodules defined in upper __init__.py files to allow for direct import of metrics
- Updated `mkdocs` from `1.5.3` to `1.6.1` to fix a bug in the previous version
- Updated `mkdocs-material` from `9.4.14` to `9.5.34` to fix a bug in the previous version
- Updated `mkdocstrings` from `0.24.0` to `0.26.0` to fix a bug in the previous version

Added
- Standard version of `main.py` and documentation on how to run locally
- Support for specific feature extractors in the `main.py` script
- Added key -> value/array dtypes for metric results
- API data access is now controlled by the `API_DATA_ROOT` environment variable in .env
- Modality batch sizes are now controlled by the `<MODALITY>_BATCH_SIZE` environment variables in .env
- Reorganized metrics into metric group categories to account for shared requirements. A diagram explaining the new metric structure can be found [here](docs/hierarchy_diagram.png)
- input metrics are grouped under `quality`, `privacy` or `annotation` categories
- Synthesized metrics are grouped under `feature` or `data` categories
- Older and confusing `metric_subgoals` were moved to the `metric_goal` field
- `metric_goal` field is an optional subcategory of the `metric_group` field
- Integrated image COCO annotation metrics into the API
- Annotation files must be present under the input_val/annotations/ directory
- Annotation files must be in COCO format and the filename given as a parameter in the API
- API unit testing for every modality with FastAPI testing utilities
- Documentation of shared metrics and image metrics
- Added annotation_file field to the API for image datasets


Fixed
- Using resize of 224 in DINO-ViT for image datasets
- Updated `pycaret` from `3.1.0` to `3.3.1` due to a bug in the previous version
- Removed redundant hooks in the Makefile
- Text datasets are now kept under text/input_val/dataset/ for consistency with the other modalities
- Input layers now extend the InputLayer abastract class for consistency
- Renamed input variables in input layers from `data_src1`, `data_src2` to `reference` and `target`
- Refactored code struture from properties-based structure to a more modular class-based structure
- Metrics are now classes that inherit from a base `Metric` class
- Each metric has a `compute` method that returns the metric value
- Older property attributes are now class attributes of the specific metric class
- Introduced the `MetricResult` class to enforce and validate return types of the `compute` method
- Input layers provide batched samples to the metrics via the `batched_samples` property
- Embedder models are now passed to the `get_embeddings` method of the input layers (allows for more flexibility)
- Moved common API and CLI code to the `common`package to avoid code repetition
- Added the `ComputationManager` class to manage the computation of metrics. Allows for the batch computation and merger of metric results and the computation of metrics in parallel. Also keeps track of a shared state of the metrics and their results.
- Removed FhP git dependencies from the pyproject.toml file (not needed)
- Dockerfile now uses only the `requirements-prod.txt` file for production builds
- Dockerfile pip install with torch-cpu source for lighter builds
- Removed unused environment variables from the .env file
- Standard error messages being returned in the API for invalid requests


Changed
- `evaluation_level` field in the API is now optional
- `metric_goal` field in the API is now optional
- `metric_group` field in the API is mandatory and indicates the group of the metric
- `annotation_file` optional field for the API to specify the annotation file for image datasets
- `metric_subgoals` field in the API was removed and replaced by the `metric_goal` field
- `metric_plots` field in the returned JSON of the API was removed
- Each metric now has a `plot_params` field that contains the plot parameters
- Each metric now has an optional `stats` field that contains additional statistical information of the metric results
- Previous iterations returned these statistics in separate metric results
- The response body now contains an `errors` field to indicate any errors caught during the metric computation
- This is only used in known errors/warnings that are known to occur in a specific metric (e.g. a metric that is supposed to be between 0 and 1 but is not due to numerical instability). Other API errors are still returned as HTTP status codes.
- Renamed metric result `type` field to `dtype` to avoid conflicts with Python's `type` function
- Renamed the `dtype` in the metric result to `subtype`