- 24 Apr, 2025 4 commits
- 22 Apr, 2025 1 commit
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fbourgin authored
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- 08 Apr, 2025 1 commit
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fbourgin authored
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- 27 Dec, 2023 1 commit
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Thibault Hallouin authored
so that we use the exact same file as in evalhyd-python/evalhyd-r
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- 26 Dec, 2023 1 commit
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Thibault Hallouin authored
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- 26 Nov, 2023 1 commit
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Thibault Hallouin authored
resolves #10
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- 15 Jun, 2023 1 commit
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Thibault Hallouin authored
resolves #7
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- 12 Apr, 2023 1 commit
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Thibault Hallouin authored
the energy score (ES) is a multi-variate (multi-site) generalisation of the CRPS
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- 24 Mar, 2023 1 commit
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Thibault Hallouin authored
new metric is called CRPS_FROM_ECDF
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- 23 Mar, 2023 1 commit
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Thibault Hallouin authored
new metric is called CRPS_FROM_BS, and existing metric is renamed CRPS_FROM_QS
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- 22 Feb, 2023 1 commit
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Thibault Hallouin authored
where data completeness corresponds to the actual number of time steps available for each subset/sample, once missing data, mask, and bootstrap have been applied
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- 17 Feb, 2023 1 commit
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Thibault Hallouin authored
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- 13 Feb, 2023 1 commit
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Thibault Hallouin authored
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- 07 Feb, 2023 1 commit
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Thibault Hallouin authored
Low flow events were defined as the complement of high flow events, meaning that Brier scores were symmetric. But it is more consistent to include the threshold value in both the definitions of low flow and high flow events since we either study one or the other, and rarely both at the same time, especially not using the same threshold values. Plus, the choice of including the threshold in high flow events rather than low flow ones was really arbitrary. Note, only the unit tests for contingency table-based metrics are impacted because threshold-based metrics are using evants="high".
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- 02 Feb, 2023 1 commit
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Thibault Hallouin authored
in `xt::stddev``, when passing a scalar, it is passed to *ddof* instead of *axes*, so to compute the standard deviation on a given axis, the axis must be passed in curly braces
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- 01 Feb, 2023 1 commit
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Thibault Hallouin authored
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- 30 Jan, 2023 1 commit
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Thibault Hallouin authored
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- 27 Jan, 2023 2 commits
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Thibault Hallouin authored
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Thibault Hallouin authored
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- 25 Jan, 2023 1 commit
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Thibault Hallouin authored
because of the random process in the rank computations, and the different input tensor sizes
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- 23 Jan, 2023 2 commits
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Thibault Hallouin authored
replace clumsy xt::sum ... == xt::scalar, by xt::all
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Thibault Hallouin authored
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- 16 Jan, 2023 2 commits
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Thibault Hallouin authored
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Thibault Hallouin authored
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- 13 Jan, 2023 2 commits
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Thibault Hallouin authored
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Thibault Hallouin authored
while Brier-based scores are symmetric, Contigency Table-based metrics are not, so that a definition how what an "event" means is required. A new optional parameter *events* is added to `evalp` (taking either "high" or "low" as value).
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- 11 Jan, 2023 1 commit
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Thibault Hallouin authored
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- 04 Jan, 2023 1 commit
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Thibault Hallouin authored
so that specifying the template arguments become optional
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- 27 Dec, 2022 2 commits
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Thibault Hallouin authored
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Thibault Hallouin authored
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- 21 Dec, 2022 1 commit
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Thibault Hallouin authored
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- 01 Dec, 2022 3 commits
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Thibault Hallouin authored
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Thibault Hallouin authored
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Thibault Hallouin authored
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- 20 Oct, 2022 1 commit
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Thibault Hallouin authored
Ultimately, the objective is for the user to be able to get the raw sampled metric values, or the mean and standard deviation of the sampled metric values, or a series of quantiles of the sampled metric values. There are still problems with the standard deviation on rtensor, and the computation of the quantiles does not work on n-dim expressions yet. So the second and third options are not possible yet, so only the raw values can be returned. Nonetheless, the machinery and the choice of where to introduce the summary functionality could be implemented, which is the purpose of this commit. A new parameter of the bootstrap experiment called "summary" is added: it can be given a value of 0 (to get the raw values). In the future, it would also take a value of 1 for mean+std, and 2 for quantiles.
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- 06 Oct, 2022 1 commit
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Thibault Hallouin authored
The bootstrapping method is based on a non-overlapping block sampling with replacement, where the blocks are years of data. The number of samples and the sample length (i.e the number of year blocks) are both customisable. The method is accessible both for deterministic and probabilistic evaluation where a new axis is added. For now, the metrics for all the samples are returned, but in the future, some summary statistics would be implemented (e.g. quantiles or mean/standard deviation). /!\ For determinist evaluation, the n-dimensional functionality became untenable such that the number of dimensions was fixed and restricted to 2D tensors. New unit tests are included to test both the bootstrapping generator and the numerical results obtained with the bootstrapping turned on.
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- 30 Sep, 2022 1 commit
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Thibault Hallouin authored
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