Add LogScaler transformer #932
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Task linked: CU-86b3gbr2k RDT - Add |
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## main #932 +/- ##
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Files 18 18
Lines 2233 2274 +41
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+ Hits 2233 2274 +41
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rwedge
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| value was missing. Then use it to recreate missing values. | ||
| * ``None``: Do nothing with the missing values on the reverse transform. Simply | ||
| pass whatever data we get through. | ||
| constant (float): |
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"Default to 0" -> "Defaults to 0".
Also, either add the `` quotation marks around the 0, False, True values here, or remove them from the other values in the docstring, so it's conistent.
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| class TestLogScaler: | ||
| def test___init__super_attrs(self): | ||
| """super() arguments are properly passed and set as attributes.""" |
| data = super()._transform(data) | ||
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| if data.ndim > 1: | ||
| self._validate_data(data[:, 0]) |
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I would create a helper function for lines 711-715 like you did for _fit, so we don't repeat the code.
| data = data.to_numpy() | ||
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| if data.ndim > 1: | ||
| if self.invert: |
| invert: bool = False, | ||
| learn_rounding_scheme: bool = False, | ||
| ): | ||
| self.constant = constant |
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Could we add validation to the constant/invert args?
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| class TestLogScaler: | ||
| def test_learn_rounding(self): | ||
| # Setup |
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Add short docstrings to these tests.
Implements #930
CU-86b3gbr2k