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…different classes. Moved the test to project the seasonal signal up to the parent Decomposer class. Moved the testing for the seasonal projection to the decomposer test module.
… to move that up to the base Decomposer class.
* Updated get_trend_df() to work out of sample. * Fixed transform() to work with in sample, but not spanning the sample. * Fixed inverse_transform to work with smaller than sample, in sample data.
…ple. Also updated test for transform to return same if y is None and moved that to parent class.
…etter reflect what's going on. Docstring changes.
…e seasonal sample to match the STLDecomposer.
…s and regression pipelines.
| self.gap, | ||
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| # Properly fill in the dates in the gap |
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This was a pre-existing bug where we fill in the gap between the training and testing data with the last row of training data, but that means creating an irregularly spaced time index.
| X_schema = X.ww.schema | ||
| y_schema = y.ww.schema | ||
| X = X.set_index(X[self.time_index]) | ||
| y = y.set_axis(X[self.time_index]) | ||
| X.ww.init(schema=X_schema) | ||
| y.ww.init(schema=y_schema) |
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Feels weird to be saving the time index in the data's indices when we're trying to drop it, but the decomposer needs the time index data to decompose successfully. If anyone has suggestions on a better or cleaner way to get around this I am very open to it.
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Can we drop after fit or transform in the decomposer then? The problem is non-time-series native estimators will fail with a datetime column. Maybe we can amend the logic here to save the time index only when having the decomposer in the pipeline and then dropping the time index later?
Codecov Report
@@ Coverage Diff @@
## main #3781 +/- ##
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- Coverage 99.7% 99.7% -0.0%
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Files 343 343
Lines 35680 35754 +74
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+ Hits 35544 35617 +73
- Misses 136 137 +1
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eccabay
requested review from
ParthivNaresh,
christopherbunn,
chukarsten,
fjlanasa and
jeremyliweishih
| X_schema = X.ww.schema | ||
| y_schema = y.ww.schema | ||
| X = X.set_index(X[self.time_index]) | ||
| y = y.set_axis(X[self.time_index]) | ||
| X.ww.init(schema=X_schema) | ||
| y.ww.init(schema=y_schema) |
There was a problem hiding this comment.
Can we drop after fit or transform in the decomposer then? The problem is non-time-series native estimators will fail with a datetime column. Maybe we can amend the logic here to save the time index only when having the decomposer in the pipeline and then dropping the time index later?
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can we also add |
jeremyliweishih
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jeremyliweishih
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nvm - we're good but think we should add to test_automl_supports_time_series_regression still!
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Closing in favor of #3785 |
4 participants
Includes some other small code changes to allow integration to be successful!