Add dataset.schema.Schema + types.
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jaheba
changed the title
dataset.schema.Schema + types.
added 4 commits
October 24, 2022 16:13
lostella
reviewed
Oct 25, 2022
src/gluonts/dataset/schema/types.py
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| `time_dim` is just a marker, indicating which axis notes the time-axis, | ||
| useful for splitting. If `time_dim` is none, the array is time invariant. | ||
| """ | ||
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| ndim: int | ||
| dtype: typing.Optional[typing.Type[T]] = None | ||
| time_dim: typing.Optional[int] = None | ||
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| def apply(self, data): | ||
| arr = np.asarray(data, dtype=self.dtype) | ||
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| if arr.ndim < self.ndim: | ||
| to_expand = self.ndim - arr.ndim | ||
| new_shape = (1,) * to_expand + arr.shape | ||
| arr = arr.reshape(new_shape) |
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It looks like time_dim ends up referring to the reshaped array. Then if to_expand > 0 (and reshaping kicks in) one needs to track what time_dim will refer to. Not sure how to fix this though
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Maybe reshaping should not happen at all? And simply, a badly-shaped list not be convertible to the desired array type?
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Maybe that's easiest. I don't think we allow 1d arrays for dynamic_feat, or do we?
The other thing one can do is to use translate, to add dimensions on the fly.
added 2 commits
October 25, 2022 11:50
lostella
approved these changes
Oct 25, 2022
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