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bartzbeielstein merged 2 commits into from
Jun 7, 2026
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feat(multitask)!: 19.0.0 — remove RunState mirror shim, config.task write, get_target_data config-window fallback #340

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feat(multitask)!: drop config.task write and get_target_data config-w…
bartzbeielstein Jun 7, 2026
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feat(multitask)!: remove the RunState mirror shim
bartzbeielstein Jun 7, 2026
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4 changes: 2 additions & 2 deletions _freeze/docs/reference/manager.features.get_target_data/execute-results/html.json
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4 changes: 2 additions & 2 deletions _freeze/docs/reference/multitask.base.BaseTask/execute-results/html.json
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4 changes: 2 additions & 2 deletions _freeze/docs/reference/multitask.defaults.DefaultsTask/execute-results/html.json
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@@ -1,8 +1,8 @@
{
"hash": "30931594ac51bdd2e08ac48f0fdeb89e",
"hash": "59a92755f2d22d8d2f65aff5974d179e",
"result": {
"engine": "jupyter",
"markdown": "---\ntitle: multitask.defaults.DefaultsTask\n---\n\n\n\n```python\nmultitask.defaults.DefaultsTask(\n config=None,\n *,\n dataframe=None,\n data_test=None,\n cache_home=None,\n log_level=logging.INFO,\n **overrides,\n)\n```\n\nTask 2 — Defaults fitting (no tuning, no cached params).\n\nCreates an unfitted forecaster per target via ``config.forecaster_factory``\n(or the package default) and fits with whatever parameters that factory\nchooses. Unlike ``LazyTask``, never reads the tuning-result cache.\n\n## Examples {.doc-section .doc-section-examples}\n\n\n::: {#00891dc1 .cell execution_count=1}\n``` {.python .cell-code}\nimport tempfile\nfrom pathlib import Path\nfrom spotforecast2_safe.multitask import DefaultsTask\nfrom spotforecast2_safe.configurator.config_multi import ConfigMulti\n\nwith tempfile.TemporaryDirectory() as tmp:\n cfg = ConfigMulti(data_frame_name=\"demo10\", predict_size=24, cache_home=Path(tmp))\n task = DefaultsTask(cfg)\n print(f\"Task: {task.TASK}\")\n print(f\"Predict size: {task.config.predict_size}\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nTask: defaults\nPredict size: 24\n```\n:::\n:::\n\n\n## Methods\n\n| Name | Description |\n| --- | --- |\n| [run](#spotforecast2_safe.multitask.defaults.DefaultsTask.run) | Run defaults fitting for all targets. |\n\n### run { #spotforecast2_safe.multitask.defaults.DefaultsTask.run }\n\n```python\nmultitask.defaults.DefaultsTask.run(show=False, **kwargs)\n```\n\nRun defaults fitting for all targets.\n\n#### Parameters {.doc-section .doc-section-parameters}\n\n| Name | Type | Description | Default |\n|----------|---------------------|------------------------------------------------------------------------------------------------------------|-----------|\n| show | [bool](`bool`) | If ``True``, invoke the visualisation hooks. | `False` |\n| **kwargs | [Any](`typing.Any`) | Forwarded for compatibility with ``BaseTask.run``; ``DefaultsTask`` does not consume any extra parameters. | `{}` |\n\n#### Returns {.doc-section .doc-section-returns}\n\n| Name | Type | Description |\n|--------|------------------------------------------------------------|----------------------------------------------------------------|\n| | [Dict](`typing.Dict`)\\[[str](`str`), [Any](`typing.Any`)\\] | Aggregated prediction package. Per-target packages are stored |\n| | [Dict](`typing.Dict`)\\[[str](`str`), [Any](`typing.Any`)\\] | on ``self.results[\"defaults\"]``. |\n\n#### Examples {.doc-section .doc-section-examples}\n\n::: {#c2229c1d .cell execution_count=2}\n``` {.python .cell-code}\nimport tempfile\nimport warnings\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom spotforecast2_safe.multitask.defaults import DefaultsTask\nfrom spotforecast2_safe.configurator.config_multi import ConfigMulti\n\nrng = np.random.default_rng(0)\nidx = pd.date_range(\"2023-01-01\", periods=24 * 14, freq=\"h\", tz=\"UTC\")\ndf = pd.DataFrame({\"load\": rng.normal(100, 10, len(idx))}, index=idx)\ndf.index.name = \"DateTime\"\n\nwith tempfile.TemporaryDirectory() as tmp:\n cfg = ConfigMulti(\n predict_size=6,\n use_exogenous_features=False,\n use_outlier_detection=False,\n auto_save_models=False,\n number_folds=2,\n cache_home=Path(tmp),\n verbose=False,\n )\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", DeprecationWarning)\n task = DefaultsTask(cfg, dataframe=df)\n task.prepare_data().detect_outliers().impute().build_exogenous_features()\n result = task.run()\n\nprint(f\"Future predictions: {len(result['future_pred'])} steps\")\nassert \"defaults\" in task.results\nassert isinstance(result[\"future_pred\"], pd.Series)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nFuture predictions: 6 steps\n```\n:::\n:::\n\n\n",
"markdown": "---\ntitle: multitask.defaults.DefaultsTask\n---\n\n\n\n```python\nmultitask.defaults.DefaultsTask(\n config=None,\n *,\n dataframe=None,\n data_test=None,\n cache_home=None,\n log_level=logging.INFO,\n **overrides,\n)\n```\n\nTask 2 — Defaults fitting (no tuning, no cached params).\n\nCreates an unfitted forecaster per target via ``config.forecaster_factory``\n(or the package default) and fits with whatever parameters that factory\nchooses. Unlike ``LazyTask``, never reads the tuning-result cache.\n\n## Examples {.doc-section .doc-section-examples}\n\n\n::: {#c3da1d24 .cell execution_count=1}\n``` {.python .cell-code}\nimport tempfile\nfrom pathlib import Path\nfrom spotforecast2_safe.multitask import DefaultsTask\nfrom spotforecast2_safe.configurator.config_multi import ConfigMulti\n\nwith tempfile.TemporaryDirectory() as tmp:\n cfg = ConfigMulti(data_frame_name=\"demo10\", predict_size=24, cache_home=Path(tmp))\n task = DefaultsTask(cfg)\n print(f\"Task: {task.TASK}\")\n print(f\"Predict size: {task.config.predict_size}\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nTask: defaults\nPredict size: 24\n```\n:::\n:::\n\n\n## Methods\n\n| Name | Description |\n| --- | --- |\n| [run](#spotforecast2_safe.multitask.defaults.DefaultsTask.run) | Run defaults fitting for all targets. |\n\n### run { #spotforecast2_safe.multitask.defaults.DefaultsTask.run }\n\n```python\nmultitask.defaults.DefaultsTask.run(show=False, **kwargs)\n```\n\nRun defaults fitting for all targets.\n\n#### Parameters {.doc-section .doc-section-parameters}\n\n| Name | Type | Description | Default |\n|----------|---------------------|------------------------------------------------------------------------------------------------------------|-----------|\n| show | [bool](`bool`) | If ``True``, invoke the visualisation hooks. | `False` |\n| **kwargs | [Any](`typing.Any`) | Forwarded for compatibility with ``BaseTask.run``; ``DefaultsTask`` does not consume any extra parameters. | `{}` |\n\n#### Returns {.doc-section .doc-section-returns}\n\n| Name | Type | Description |\n|--------|------------------------------------------------------------|----------------------------------------------------------------|\n| | [Dict](`typing.Dict`)\\[[str](`str`), [Any](`typing.Any`)\\] | Aggregated prediction package. Per-target packages are stored |\n| | [Dict](`typing.Dict`)\\[[str](`str`), [Any](`typing.Any`)\\] | on ``self.results[\"defaults\"]``. |\n\n#### Examples {.doc-section .doc-section-examples}\n\n::: {#f4d150b3 .cell execution_count=2}\n``` {.python .cell-code}\nimport tempfile\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom spotforecast2_safe.multitask.defaults import DefaultsTask\nfrom spotforecast2_safe.configurator.config_multi import ConfigMulti\n\nrng = np.random.default_rng(0)\nidx = pd.date_range(\"2023-01-01\", periods=24 * 14, freq=\"h\", tz=\"UTC\")\ndf = pd.DataFrame({\"load\": rng.normal(100, 10, len(idx))}, index=idx)\ndf.index.name = \"DateTime\"\n\nwith tempfile.TemporaryDirectory() as tmp:\n cfg = ConfigMulti(\n predict_size=6,\n use_exogenous_features=False,\n use_outlier_detection=False,\n auto_save_models=False,\n number_folds=2,\n cache_home=Path(tmp),\n verbose=False,\n )\n task = DefaultsTask(cfg, dataframe=df)\n task.prepare_data().detect_outliers().impute().build_exogenous_features()\n result = task.run()\n\nprint(f\"Future predictions: {len(result['future_pred'])} steps\")\nassert \"defaults\" in task.results\nassert isinstance(result[\"future_pred\"], pd.Series)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nFuture predictions: 6 steps\n```\n:::\n:::\n\n\n",
"supporting": [
"multitask.defaults.DefaultsTask_files/figure-html"
],
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30 changes: 15 additions & 15 deletions docs/reference/manager.features.get_target_data.qmd
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,12 @@ manager.features.get_target_data(
target,
df_pipeline,
config,
*,
data_with_exog=None,
exog_feature_names=None,
exo_pred=None,
start_train_ts=None,
end_train_ts=None,
start_train_ts,
end_train_ts,
)
```

Expand All @@ -27,22 +28,21 @@ engineering are applied consistently across all forecasting tasks.

The training-window timestamps are supplied as explicit parameters so that
this helper stays decoupled from ``RunState`` (ADR
``adr-multitask-configmulti-merge``, step 5). When either is ``None``
the function falls back to the corresponding attribute on *config* for
backward compatibility with existing direct callers.
``adr-multitask-configmulti-merge``, step 5). Both parameters are
required; passing ``None`` raises ``ValueError``.

## Parameters {.doc-section .doc-section-parameters}

| Name | Type | Description | Default |
|--------------------|---------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|
| target | [str](`str`) | Name of the target column to extract from *df_pipeline*. | _required_ |
| df_pipeline | [pd](`pandas`).[DataFrame](`pandas.DataFrame`) | DataFrame with a tz-aware `DatetimeIndex` containing all target columns produced by the preprocessing pipeline. | _required_ |
| config | \'ConfigMulti\' | Pipeline configuration object. ``use_exogenous_features`` must be set. ``start_train_ts`` / ``end_train_ts`` are only read from *config* when the explicit parameters are not supplied. | _required_ |
| data_with_exog | [Optional](`typing.Optional`)\[[pd](`pandas`).[DataFrame](`pandas.DataFrame`)\] | Merged DataFrame of target and exogenous columns covering at least the training window. Required when ``config.use_exogenous_features`` is ``True``. Pass ``None`` (default) to skip exogenous slicing. | `None` |
| exog_feature_names | [Optional](`typing.Optional`)\[[List](`typing.List`)\[[str](`str`)\]\] | Column names to select from *data_with_exog* and *exo_pred*. Required when *data_with_exog* is not ``None``. Pass ``None`` (default) when exogenous features are disabled. | `None` |
| exo_pred | [Optional](`typing.Optional`)\[[pd](`pandas`).[DataFrame](`pandas.DataFrame`)\] | Exogenous feature DataFrame covering the forecast horizon. Required when *data_with_exog* is not ``None``. Pass ``None`` (default) when exogenous features are disabled. | `None` |
| start_train_ts | [Optional](`typing.Optional`)\[[pd](`pandas`).[Timestamp](`pandas.Timestamp`)\] | Inclusive start of the training window (tz-aware ``pd.Timestamp``). **Required** — pass ``task.run_state.start_train_ts`` after the pipeline has been prepared. Omitting this argument raises ``ValueError``; reading the value from ``config.start_train_ts`` is deprecated and emits a ``DeprecationWarning``. | `None` |
| end_train_ts | [Optional](`typing.Optional`)\[[pd](`pandas`).[Timestamp](`pandas.Timestamp`)\] | Inclusive end of the training window (tz-aware ``pd.Timestamp``). **Required** — pass ``task.run_state.end_train_ts`` after the pipeline has been prepared. Omitting this argument raises ``ValueError``; reading the value from ``config.end_train_ts`` is deprecated and emits a ``DeprecationWarning``. | `None` |
| Name | Type | Description | Default |
|--------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|
| target | [str](`str`) | Name of the target column to extract from *df_pipeline*. | _required_ |
| df_pipeline | [pd](`pandas`).[DataFrame](`pandas.DataFrame`) | DataFrame with a tz-aware `DatetimeIndex` containing all target columns produced by the preprocessing pipeline. | _required_ |
| config | \'ConfigMulti\' | Pipeline configuration object. ``use_exogenous_features`` must be set. | _required_ |
| data_with_exog | [Optional](`typing.Optional`)\[[pd](`pandas`).[DataFrame](`pandas.DataFrame`)\] | Merged DataFrame of target and exogenous columns covering at least the training window. Required when ``config.use_exogenous_features`` is ``True``. Pass ``None`` (default) to skip exogenous slicing. | `None` |
| exog_feature_names | [Optional](`typing.Optional`)\[[List](`typing.List`)\[[str](`str`)\]\] | Column names to select from *data_with_exog* and *exo_pred*. Required when *data_with_exog* is not ``None``. Pass ``None`` (default) when exogenous features are disabled. | `None` |
| exo_pred | [Optional](`typing.Optional`)\[[pd](`pandas`).[DataFrame](`pandas.DataFrame`)\] | Exogenous feature DataFrame covering the forecast horizon. Required when *data_with_exog* is not ``None``. Pass ``None`` (default) when exogenous features are disabled. | `None` |
| start_train_ts | [pd](`pandas`).[Timestamp](`pandas.Timestamp`) | Inclusive start of the training window (tz-aware ``pd.Timestamp``). **Keyword-only, required** — pass ``task.run_state.start_train_ts`` after the pipeline has been prepared. Passing ``None`` raises ``ValueError``. | _required_ |
| end_train_ts | [pd](`pandas`).[Timestamp](`pandas.Timestamp`) | Inclusive end of the training window (tz-aware ``pd.Timestamp``). **Keyword-only, required** — pass ``task.run_state.end_train_ts`` after the pipeline has been prepared. Passing ``None`` raises ``ValueError``. | _required_ |

## Returns {.doc-section .doc-section-returns}

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