Feat/encoder improvement #1338
Feat/encoder improvement #1338
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Codecov ReportBase: 93.68% // Head: 93.67% // Decreases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #1338 +/- ##
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- Coverage 93.68% 93.67% -0.01%
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Files 94 94
Lines 9400 9485 +85
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+ Hits 8806 8885 +79
- Misses 594 600 +6
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Looks good, thanks for these changes @dennisbader ! Got a few small comments
| scenarios described below. With user `covariates`, it simply copies and returns the `covariates` time index. | ||
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| It can be used: | ||
| A in combination with :class:`LocalForecastingModel`, or in a model agnostic scenario: |
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[Nitpicking - no need to do it if annoying] Instead of supporting all these different cases based on conventions (e.g "if calling from a RegressionModel, set the parameters in this way"), you could consider using factory methods (a bit like we have in TimeSeries). E.g. CovariatesIndexGenerator.for_regression_model(lags, lags_past_cov, lags_fut_cov, out_len) and CovariatesIndexGenerator.for_torch_model(in_len, out_len).
| future_covariates = future_covariates[ | ||
| start : start + offset * self.training_series.freq | ||
| ] | ||
| future_covariates = future_covariates.slice( |
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Don't forget that slice() is inclusive on the right for DatetimeIndex
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I think it is correct, but let me know if I'm missing something.
start is one step after end of target series, and we end at:
- n - 1 steps ahead of start for DatetimeIndex (as inclusive with slice)
- n steps ahead of start for RangeIndex (as non-inclusive with slice)
darts/tests/models/forecasting/test_local_forecasting_models.py
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darts/timeseries.py
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| @@ -2549,10 +2549,6 @@ def append(self, other: "TimeSeries") -> "TimeSeries": | |||
| attrs=self._xa.attrs, | |||
| ) | |||
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| # new_xa = xr.concat(objs=[self._xa, other_xa], dim=str(self._time_dim)) | |||
| if not self._has_datetime_index: | |||
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This was related to
- fixed TimeSeries.append() that dropped "time" index for integer indexed series
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Also not forget: shift encodings when using one shot. |
Summary
Additional improvements:
predict()accepts a different target than used when fitting the modelAdditional fixes:
TODO (in future PR)