Fix/linear quantile interpolation #899
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… produce sample paths. Quantile sampling changed, now uses linear interpolations between values
gnwhr
requested review from
hrzn,
tomasvanpottelbergh,
dennisbader and
brunnedu
as code owners
Codecov Report
@@ Coverage Diff @@
## master #899 +/- ##
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+ Coverage 91.67% 91.70% +0.03%
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Files 74 74
Lines 7375 7402 +27
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+ Hits 6761 6788 +27
Misses 614 614
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hrzn
approved these changes
Apr 12, 2022
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| # linear interpolation | ||
| weights = (probs - left_q) / (right_q - left_q) | ||
| inter = left_value + weights * (right_value - left_value) |
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Nice one, definitely an improvement 👍
| model_output is of shape (n_series * n_samples, output_chunk_length, n_components, n_quantiles) | ||
| """ | ||
| num_samples, n_timesteps, n_components, n_quantiles = model_output.shape | ||
| model_output = model_output.reshape( |
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I don't think so, will remove
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| def _quantile_sampling(self, model_output: np.ndarray) -> np.ndarray: | ||
| """ | ||
| Select a quantile (for each batch example) and return this quantile (over time and components). |
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Is this docstring correct? Don't you rather return an interpolation between the quantiles?
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No, I forgot to update the docstrings
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Fixes #869 .
Summary
Now quantile sampling samples uniformly between fitted quantiles. RegressionModels now correctly construct paths when generating samples.