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updating release notes
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docs/core_classes/transforms/features_to_model.md

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For custom models we recommend the following steps:
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!!! warning inline end "Experimental"
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The SageWorks Custom Models are currently in experimental mode so have fun but expect issues. Requires `sageworks >= 0.8.59`. Feel free to submit issues to [SageWorks Github](https://github.com/SuperCowPowers/sageworks)
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The SageWorks Custom Models are currently in experimental mode so have fun but expect issues. Requires `sageworks >= 0.8.60`. Feel free to submit issues to [SageWorks Github](https://github.com/SuperCowPowers/sageworks)
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- Copy the example custom model script into your own directory
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- See: [Custom Model Script](https://github.com/SuperCowPowers/sageworks/tree/main/src/sageworks/model_scripts/custom_script_example)

docs/release_notes/0_8_58.md

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## General
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This release is an incremental release as part of the road map for `v.0.9.0`. Please see the full details of the planned changes here: [v0.9.0 Roadmap](../road_maps/0_9_0.md).
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### Caching Refatoring
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### Caching Refactoring
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We've created a new set of Cached Classes:
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- CachedDataSource

docs/release_notes/0_8_60.md

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# Release 0.8.60
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!!!tip inline end "Need Help?"
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The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at [[email protected]](mailto:[email protected]) or on chat us up on [Discord](https://discord.gg/WHAJuz8sw8)
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The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.
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**Note:** These release notes cover the changes from `0.8.58` to `0.8.60`
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## General
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This release is an incremental release as part of the road map for `v.0.9.0`. Please see the full details of the planned changes here: [v0.9.0 Roadmap](../road_maps/0_9_0.md).
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### Custom Models
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We've now exposed additional functionality and API around adding your own custom models. The new custom model support is documented on the [Features to Models](../core_classes/transforms/features_to_model.md) page.
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## API Changes
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None
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## Notes
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Custom models introduce models that don't have model metrics or inference runs, so you'll see a lot of log messages complaining about not finding metrics or inference results, please just ignore those, we'll put in additional logic to address those cases.
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## Specific Code Changes
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<a href="https://github.com/supercowpowers/sageworks/compare/v0.8.58...v0.8.60" target="_blank">Code Diff v0.8.58 --> v0.8.60</a>
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Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:
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> ***What do you call a nonsense meeting?
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.... Moo-larkey***
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## Questions?
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<img align="right" src="../../images/scp.png" width="180">
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The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at [[email protected]](mailto:[email protected]) or on chat us up on [Discord](https://discord.gg/WHAJuz8sw8)
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mkdocs.yml

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- 0.9.0: road_maps/0_9_0.md
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- 0.9.5: road_maps/0_9_5.md
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- Release Notes:
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- 0.8.60: release_notes/0_8_60.md
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- 0.8.58: release_notes/0_8_58.md
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- 0.8.55: release_notes/0_8_55.md
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- 0.8.50: release_notes/0_8_50.md

src/sageworks/core/transforms/features_to_model/features_to_model.py

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Common Usage:
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```python
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from sageworks.core.transforms.features_to_model.features_to_model import FeaturesToModel
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to_model = FeaturesToModel(feature_uuid, model_uuid, model_type=ModelType)
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to_model.set_output_tags(["abalone", "public", "whatever"])
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to_model.transform(target_column="class_number_of_rings",
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if __name__ == "__main__":
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"""Exercise the FeaturesToModel Class"""
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"""
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# Regression Model
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input_uuid = "abalone_features"
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output_uuid = "abalone-regression"
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to_model = FeaturesToModel(input_uuid, output_uuid, ModelType.CLASSIFIER)
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to_model.set_output_tags(["wine", "public"])
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to_model.transform(target_column="wine_class", description="Wine Classification")
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"""
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# Quantile Regression Model (Abalone)
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"""
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input_uuid = "abalone_features"
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output_uuid = "abalone-quantile-reg"
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to_model = FeaturesToModel(input_uuid, output_uuid, ModelType.QUANTILE_REGRESSOR)
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to_model.set_output_tags(["abalone", "quantiles"])
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to_model.transform(target_column="class_number_of_rings", description="Abalone Quantile Regression")
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"""
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"""
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# Scikit-Learn Kmeans Clustering Model
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input_uuid = "wine_features"
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output_uuid = "wine-clusters"
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)
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to_model.set_output_tags(["wine", "2d-projection"])
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to_model.transform(target_column=None, description="Wine 2D Projection", train_all_data=True)
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"""
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# Custom Script Models
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scripts_root = Path(__file__).resolve().parents[3] / "model_scripts"
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"""
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my_custom_script = scripts_root / "custom_script_example" / "custom_model_script.py"
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input_uuid = "wine_features"
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output_uuid = "wine-custom"
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to_model = FeaturesToModel(input_uuid, output_uuid, model_type=ModelType.CLASSIFIER, custom_script=my_custom_script)
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to_model.set_output_tags(["wine", "custom"])
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to_model.transform(target_column="wine_class", description="Wine Custom Classification")
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"""
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# Temp Molecular Descriptors Model
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my_script = scripts_root / "custom_models" / "chem_info" / "rdkit_mordred_features.py"

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