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Adaptative / Conditional Conformal Prediction #300

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thibaultcordier opened this issue Apr 20, 2023 · 3 comments
Open

Adaptative / Conditional Conformal Prediction #300

thibaultcordier opened this issue Apr 20, 2023 · 3 comments
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Enhancement Type: enhancement (new feature or request) Regression Related to regression (excluding time series) Source: developers Proposed by developers. Work in progress The MAPIE team is working on this.

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@thibaultcordier
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thibaultcordier commented Apr 20, 2023

Is your feature request related to a problem? Please describe.

Despite attractive theoretical guarantees and practical successes, Predictive Interval (PI) given by Conformal Prediction (CP) may not reflect the uncertainty of a given model. This limitation arises from CP methods using a constant correction for all test points, disregarding their individual uncertainties, to ensure coverage properties. (From Salim I. Amoukou, N. Brunel (2023)).

Different methods are proposed to deal with this problem, which we have grouped under the name "adaptive conformal prediction" (see additional context).

Describe the solution you'd like

The objective of this enhancement is to propose conditional / adaptative conformal prediction for MapieRegressor and MapieClassifier (in two different issues/PRs ?).

Describe alternatives you've considered

One way to achieve this is to adapt the fit and predict methods to the different classes of ConformityScore.

Additional context

In "adaptive conformal prediction", we distinguish different approaches (non exhaustive list):

  • Reweighting of the nonconformity score
    • Localized Conformal Prediction (LCP): Guan, L. (2021). Localized Conformal Prediction: A Generalized Inference Framework for Conformal Prediction. Biometrika.
    • SLCP: Han, X., Tang, Z., Ghosh, J., & Liu, Q. (2022). Split Localized Conformal Prediction. ArXiv, abs/2206.13092.
    • LCP-RF: Amoukou, S.I., & Brunel, N.J. (2023). Adaptive Conformal Prediction by Reweighting Nonconformity Score. ArXiv, abs/2303.12695.
  • Renormalization of the nonconformity score
    • Locally adaptive split conformal prediction (also known as conformalized residual fitting - CRF): Papadopoulos, H., Gammerman, A., & Vovk, V. (2008). Normalized nonconformity measures for regression Conformal Prediction.
    • Self-supervised Conformal Prediction (SSCP): Seedat, N., Jeffares, A., Imrie, F., & Schaar, M.V. (2023). Improving Adaptive Conformal Prediction Using Self-Supervised Learning. ArXiv, abs/2302.12238.
  • Estimation of the nonconformity score
    • Conformalized Quantile Regression (CQR): Romano, Y., Patterson, E., & Candès, E.J. (2019). Conformalized Quantile Regression. Neural Information Processing Systems.
@thibaultcordier thibaultcordier added the Enhancement Type: enhancement (new feature or request) label Apr 20, 2023
@thibaultcordier thibaultcordier linked a pull request May 4, 2023 that will close this issue
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@thibaultcordier thibaultcordier linked a pull request May 5, 2023 that will close this issue
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@jesuinovieira
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It seems that this issue was closed together with #305 🤔

@thibaultcordier
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Good point, thank you for this comment :)

@thibaultcordier thibaultcordier added the Source: developers Proposed by developers. label Jul 4, 2023
@thibaultcordier thibaultcordier removed a link to a pull request Jul 5, 2023
7 tasks
@thibaultcordier thibaultcordier added this to the Backlog H2 milestone Aug 3, 2023
@thibaultcordier thibaultcordier removed a link to a pull request Aug 3, 2023
7 tasks
@thibaultcordier thibaultcordier changed the title [ENHANCEMENT] Adaptative / Conditional Conformal Prediction Adaptative / Conditional Conformal Prediction Aug 3, 2023
@LacombeLouis LacombeLouis removed this from the Backlog H2 2023 milestone Aug 3, 2023
@joshdunnlime
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What about Self-supervised Conformal Prediction (SSCP)? Is that likely to get added? +1 from me :)

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Enhancement Type: enhancement (new feature or request) Regression Related to regression (excluding time series) Source: developers Proposed by developers. Work in progress The MAPIE team is working on this.
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