unit8co/darts

mc_dropout with predict_likelihood_parameters

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#2,105 建立於 2023年12月4日

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描述

Hi, thanks for the prompt response regarding issue #2097 . I'm now able to utilize mc_dropout in historical_forecasts.

My current objective is to incorporate mc_dropout for obtaining epistemic uncertainty and predict_likelihood_parameters for acquiring aleatoric uncertainty. However, when setting predict_likelihood_parameters to True, I encounter an issue where I'm unable to use the sample method. Consequently, I'm limited to obtaining only one result with each call to historical_forecasts with predict_likelihood_parameters=True and mc_dropout=True.

I suspect that the solution might involve distinguishing the sample generation process by using mc_dropout and sample generation process based on the fitted distribution. Now they are using the same num_samples parameter.

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技術棧
python
領域
machine learningdata
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
1-2 days
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
fresh
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
mostly clear
前置要求
Familiarity with darts libraryUnderstanding of Monte Carlo DropoutUnderstanding of aleatoric vs epistemic uncertainty
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
35
研究方向
Investigate the implementation of `historical forecasts` in darts, specifically how `num samples` is used for both `mc dropout` and `predict likelihood parameters`. Examine the sampling logic to separate the two processes. Check the related issue #2097 for context. Consider modifying the function to allow different sample counts for each uncertainty source.
mc_dropout with predict_likelihood_parameters · unit8co/darts#2105 | Good First Issue