fix: calculation of nb_dimensions for approximated inverse #137
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jpetot
requested changes
Sep 24, 2024
saiph/inverse_transform.py
Outdated
| @@ -37,7 +38,7 @@ def inverse_transform( | |||
| random_gen = np.random.default_rng(model.seed) | |||
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| # Check dimension size regarding N | |||
| n_dimensions = len(model.dummy_categorical) + len(model.original_continuous) | |||
| n_dimensions = len(model.dummy_categorical) + len(model.original_continuous) - 1 | |||
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Suggested change
| n_dimensions = len(model.dummy_categorical) + len(model.original_continuous) - 1 | |
| n_dimensions = len(model.dummy_categorical) + len(model.original_continuous) - nb_variable_categorical |
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could you create unit test of this fix?
tas17
commented
Sep 24, 2024
Comment on lines
+41
to
+44
| n_dimensions = get_number_of_dimensions_from_categorical_and_continuous( | ||
| dummy_categorical=model.dummy_categorical, | ||
| original_continuous=model.original_continuous, | ||
| ) |
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Note: I was first using model: Model as the only input, but as it's a @DataClass, I could not generate a factory for it.
So I went to use that workaround instead
jpetot
requested changes
Sep 25, 2024
| def get_number_of_dimensions_from_categorical_and_continuous( | ||
| *, dummy_categorical: List[Any], original_continuous: List[Any] | ||
| ) -> int: | ||
| return len(dummy_categorical) + len(original_continuous) - 1 |
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Suggested change
| return len(dummy_categorical) + len(original_continuous) - 1 | |
| return len(dummy_categorical) + len(original_continuous) - nb_categorical_variable |
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