call corresponding endpoint in cross_validation #300
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This reverts commit f99d4e0.
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AzulGarza
approved these changes
May 1, 2024
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| from .client import Nixtla | ||
| from .core import ApiError | ||
| from .types.multi_series_anomaly import MultiSeriesAnomaly | ||
| from .types.multi_series_cross_validation import MultiSeriesCrossValidation |
Comment on lines
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+549
| def _restrict_input_samples(self) -> int: | ||
| main_logger.info("Restricting input...") | ||
| if self.level is not None: | ||
| # add sufficient info to compute | ||
| # conformal interval | ||
| # @AzulGarza | ||
| # this is an old opinionated decision | ||
| # about reducing the data sent to the api | ||
| # to reduce latency when | ||
| # a user passes level. since currently the model | ||
| # uses conformal prediction, we can change a minimum | ||
| # amount of data if the series are too large | ||
| new_input_size = 3 * self.input_size + max(self.model_horizon, self.h) | ||
| else: | ||
| # we only want to forecast | ||
| new_input_size = self.input_size | ||
| return new_input_size | ||
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Calls the cross_validation endpoint in the API when using the cross_validation method.
This required a couple of refactors: