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Writing scikit-learn compatible estimators might be harder than expected. | ||
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While everyone knows about the `fit` and `predict`, there are other behaviours, methods and attributes that scikit-learn might be expecting from your estimator depending on: | ||
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- The type of estimator you're writing. | ||
- The signature of the estimator. | ||
- The signature of the `.fit(...)` method. | ||
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Scikit-learn Smithy to the rescue: this tool aims to help you crafting your own estimator by asking a few questions about it, and then generating the boilerplate code. | ||
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In this way you will be able to fully focus on the core implementation logic, and not on nitty-gritty details of the | ||
scikit-learn API. |
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from sksmithy.tui._tui import ForgeTUI | ||
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__all__ = ("ForgeTUI",) |
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from sksmithy.tui._tui import ForgeTUI | ||
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def forge_tui() -> None: | ||
"""Entrypoint function.""" | ||
tui = ForgeTUI() | ||
tui.run() | ||
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if __name__ == "__main__": | ||
forge_tui() |
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