[Feat] Add pipeline logic #27
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enhancement
New feature or request
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DrShushen
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Mar 3, 2023
* Rename model dir to plugins
* Set up plugins/core dir
* Simplify estimator, transformer
* Add predictor
* Fix circular import
* Add core plugin methods like name()
* Implement a Plugin interface
* Factorize out test utility "patch_module"
* Add test for plugin infrastructure
* Add test for loaded plugins
* add fit_{predict,transform} methods
* Add hyperparameter methods
* Add Base* to indicate base models
* Keep only estimator init with params as kwargs
* Rename test file
* Add parent constructor calls for clarity
DrShushen
added a commit
that referenced
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Mar 3, 2023
* Update all configurations * Set up data format and validation (#2) * Implement library config * Isort docs/conf.py * Improve configuration * Add logging * Add .print to logger * Set up way of updating things on config changes * Simplify and use get_config() * Tidy multi-line logger messages * Add logger.raise_ convenience method * Move logging into a package * Add diagnose, backtrace as configurable options * Make default config be defined in yaml file only * Add validator design * Add extra config test * Define global data settings * Clean up validator * Use builtin types in DataSettings * Minor refactor validator * Set up DataRequirements * Set up validation implementation * Decouple tests, tidy imports * Imports to Google style where possible * Implement some data validators (root_validate) * Use decorator in val. impl. method registry * Improve exception logging * Update some validation_implementation methods * Reorganize dir structure * Clean up tests * Allow for inheritance in val. impl. * Add validator > df tests * Separate out SupportsContainer interface * Shorten package names * Factor out SupportsImplementations * Factor out RegisterMethodDecorator * Add dispatch_to_implementation() * Separate out interface * Set up framework for *Samples objects * Add data utils * Implement as_array for TimeSeriesSamples * Introduce data container def. * Update data container defs, separate out tests * Set up default container flavor mechanism * Add check_untyped_defs = True in mypy.ini * Implement EventSamples * Implement as_array for EventSamples * Update setup requirements * Set up docs (#4) * Remove unneeded BAK file * Small additions to README * Update image display * Major update README * Table width * Change image align * Prepare README for docs * Add logo * Update docs * Fix issue in README * Dr shushen/model setup (#23) * Remove unneeded BAK file * Change abc import to be Google code format style * Introduce TemporBaseModel * Introduce fit() * Bugfix, add typing overloads for fit() * Add core requirements * Introduce RequirementCategory * Set up requirement validator concept * Reorganize data/ * Introduce DataBundle * Add DataBundle requirements * Set up from_data_containers static method * Update install_requires * Introduce RequirementsConfig * Reorganize packages to improve naming * Develop RequirementsConfig further * Improve RequirementsConfig repr * Deal with _validate_method_config * Minor bugfixes * Add some int. tests for base model fit config * Add _fit_called flag * Introduce transform method * Tidy imports * Update LICENSE (#24) * [Feat] Basic plugin interface and loader (#27) * Rename model dir to plugins * Set up plugins/core dir * Simplify estimator, transformer * Add predictor * Fix circular import * Add core plugin methods like name() * Implement a Plugin interface * Factorize out test utility "patch_module" * Add test for plugin infrastructure * Add test for loaded plugins * add fit_{predict,transform} methods * Add hyperparameter methods * Add Base* to indicate base models * Keep only estimator init with params as kwargs * Rename test file * Add parent constructor calls for clarity * Set up Dataset (#36) * Remove old data format * Initial interface for Dataset * Remove unnecessary decorator * Add StaticSamples validation and tests * Add TimeSeriesSamples validation * Add EventSamples validation * Add tests for samples basics * Test EventSamples.split method * Add time/sample_index helper methods * Add @validate_arguments and some note comments * Add repr's * Implement from_numpy for {Static,Event}Samples * Implement .numpy for {Static,Event}Samples * Add utils for array/df manipulation * Implement TimeSeriesSamples .numpy() * Fix some typing definitions * Write utils for array -> TS df conversion * Check in register_plugin if re-imported (no exc.) * Add docstrings in utils * Add pydantic.validate_arguments in data utils * Implement TimeSeriesSamples from_numpy() * Update docstring in TimeSeriesSamples.__init__ * Add unit tests for Dataset * Set default debug level to INFO * Update tests * Add data format tutorial notebook * Add reprs * Simplify _check_same_class * Add docstring to Dataset classes
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Feature Description
The library should offer the possibility to execute multiple plugins in sequence, and sample hyperparameters for all of them.
Sampling hyperparameters require a class, so that you don't instantiate an useless object.
To that end, you can create meta classes in python by inheriting the
typeclass directly.The pipeline wrapper should offer the following interface:
fit- train the pipelinepredict- transform(for preprocessing plugins in the pipeline) and predicthyperarameters_space/sample_hyperparameterswhich should call the sampling logic from each plugin.Reference implementation https://github.com/vanderschaarlab/autoprognosis/blob/main/src/autoprognosis/plugins/pipeline/__init__.py
The feature should be covered by tests, covering:
depends on https://github.com/vanderschaarlab/temporai-priv/issues/5
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