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Update `Recipe` contract to allow for more generic transformations

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まだ誰も着手していません。

評価

難易度
5/5
見積もり時間
1週間以上
初心者へのやさしさ
35/100
issue の種類
機能追加
明瞭さ
おおむね明確
活発さ
活発
技術スタック
python, pytorch
領域
machine-learning

調査の方向性

Start by reading the Recipe, processor lifecycle, and RecipeExecution APIs, then trace how model forward, fit, and predict calls are wired. The issue identifies no files or tests, so locate existing processor and recipe tests before proposing an acceptance test for forecasts at different query positions and clear failure when required coordinates are absent. The coordinate shapes, compatibility contract, and related-table behavior still need design decisions.

索引モデルが issue の本文から書いたものです。

説明

My initial goal was to extend Recipe such that a transformation such as conditional detrending (but not only) could fit there. I'd like to find a way to make it as general and clean as possible.

Proposal for discussion

Recipe can fit a transformation on context rows and invert it on model predictions, but processor calls cannot currently receive the position of each row. A fitted trend therefore cannot be evaluated reliably at future rows, especially when positions have gaps or forecasts are requested out of order.

Here is an illustrative call site. ConditionalLinearDetrend does not exist yet; the goal is a contract that would also support other transformations needing row coordinates.

import torch
import sdm

context_positions = torch.tensor([0., 1., 3., 4.])
query_positions = torch.tensor([6., 9.])
targets = sdm.TableTensor.from_tensor(
    torch.tensor([
        [10., 4.],
        [12., 7.],
        [16., 4.],
        [18., 7.],
    ])
    columns=["load", "alternating"],
)

recipe = sdm.Recipe(
    target=ConditionalLinearDetrend(residual_std_ratio=0.5)
)
recipe.target.fit_transform(
    targets, row_positions=context_positions
)

raw_forecast = sdm.TableTensor.from_tensor(
    torch.tensor([[0., 5.], [0., 6.]]),
    columns=["load", "alternating"],
)
forecast = recipe.target.inverse_transform(
    raw_forecast, row_positions=query_positions
)
assert forecast.numerical.tolist() == [[22., 5.], [28., 6.]]

The load series follows 10 + 2 × position and meets the detrending condition. The alternating series does not, so its predictions remain unchanged. Fitting uses context values only; inversion evaluates the fitted trend at the supplied query positions.

Proposed direction

  • Accept optional row coordinates in the processor lifecycle and pass them through RecipeExecution and model forward, fit, and predict calls.
  • Let processors that need coordinates require them explicitly. Existing processors and recipes should continue to work without them.

Questions to settle

  • What shapes and broadcasting rules should coordinates support for batched tables and ensemble members? Should they represent ordinal steps, arbitrary numeric coordinates, or both?
  • How should a processor declare that coordinates are required while preserving existing processor subclasses?
  • Related tables have their own row axes. How should their coordinates be supplied?
  • Forecasts with several quantiles per target also need an explicit target-to-output mapping for inversion. Should that be a separate issue?

A useful acceptance test would fit once on context, then request forecasts at different query positions in either order. Each result should depend on its supplied positions, while a processor called without required coordinates should fail clearly.

主要言語
Python
スター
316
フォーク
27
平均マージ
1日 3時間
マージ済み PR（30日）
157

環境構築

はじめの一歩

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  2. 着手することを issue にコメントします — 二人が同じ作業をするのを防げます。
  3. リポジトリをフォークし、ブランチを切って変更します。
  4. issue 番号を参照したプルリクエストを送ります。

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