[ENH] Add preprocessing (scalers and normalizers) to tslib datamodule

[echo-xiao]

Reference Issues/PRs

Fixes #2330.

What does this implement/fix? Explain your changes.

TslibDataModule accepted scalers / target_normalizer but never used them
(stored and ignored). This makes them functional, following #2302 and reusing
ScalerAdapter:

• __init__ wraps them in ScalerAdapter, adds _*_fitted flags and a
  per-series _preprocess_cache.
• _fit_scalers / _fit_target_normalizer fit on the train split only.
• _normalize_features / _normalize_target transform per series (no-op until
  fitted; only configured continuous columns).
• _preprocess_data applies the transforms and caches per series;
  setup("fit") fits on _train_indices.

What should a reviewer concentrate their feedback on?

1. no deepcopy of scalers
2. deferring target_scale** to a follow-up
3. only continuous features are scaled, the models don't consume them yet
4. target_scale, collate_fn and the models are untouched

Did you add any tests for the change?

Yes — 7 tests in pytorch_forecasting/data/tests/test_tslib_data_module.py:

• adapter wrapping + flags;
• fit-on-train (StandardScaler → ~0 mean / ~1 std);
• target-normalizer fit flag;
• feature transform (only configured columns);
• target no-op-until-fitted; _preprocess_data scaling + cache identity;
• setup("fit") produces scaled samples;
• a no-scaler backward-compat guard (byte-identical output, no target_scale).

PR checklist

• The PR title starts with [ENH].
• Added/modified tests.
• Used pre-commit hooks (pre-commit run --files on both changed files, clean).

[echo-xiao]

hi, @phoeenniixx please review these and let me know if these decisions are right:

• no deepcopy of scalers
• deferring target_scale to a follow-up, collate_fn and the models are untouched
• only continuous features are scaled, the models don't consume them yet

TODO:

• lift the shared preprocessing into the base datamodule
• implement target_scale for tslib (sibling of [ENH] Add target_scale support to EncoderDecoderDataModule #2359)

[codecov]

Codecov Report

❌ Patch coverage is 26.15385% with 96 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (main@5a35991). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
...h_forecasting/data/tests/test_tslib_data_module.py	0.00%	77 Missing ⚠️
...forecasting/data/data_module/_tslib_data_module.py	64.15%	19 Missing ⚠️

Additional details and impacted files

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pytest	86.69% <26.15%> (?)

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[phoeenniixx]

please use english docstrings

[phoeenniixx]

Nice!
I have a few questions:

• Did you look at the thuml implementation? I think we should try to keep it as close to this package as possible - when it comes to the preprocessing logic as this dm is based on this package
  • Currently it supports all the scalers and normalizers that EncoderDecoderDataModule supports, but is this true for the tslib implementation as well?

Also, please use english docstrings, to maintain uniformity across the documentation.