feat(tasks)!: promote develop to main — remove unused task modules (9.0.0)

CHANGELOG.md

@@ -1,3 +1,19 @@
+## [9.0.0-rc.1](https://github.com/sequential-parameter-optimization/spotforecast2/compare/v8.1.1...v9.0.0-rc.1) (2026-06-12)
+
+### ⚠ BREAKING CHANGES
+
+* **tasks:** the spotforecast-demo, spotforecast-n2o1,
+spotforecast-n2o1-df, spotforecast-n2o1-cov, and spotforecast-n2o1-cov-df
+console scripts and the spotforecast2.tasks.task_demo /
+spotforecast2.tasks.task_n_to_1* modules are removed.
+spotforecast2-entsoe and spotforecast2.tasks.task_entsoe are unaffected.
+
+Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
+
+### Features
+
+* **tasks:** remove unused demo and n-to-1 task modules and console scripts ([98dc393](https://github.com/sequential-parameter-optimization/spotforecast2/commit/98dc393a06f1e34b675275144f8a37c88178e755))
+
 ## [8.1.1](https://github.com/sequential-parameter-optimization/spotforecast2/compare/v8.1.0...v8.1.1) (2026-06-12)
 
 ### Code Refactoring

MODEL_CARD.md

@@ -35,7 +35,7 @@ The current release is 3.3.0, tracking the stable public interface re-exported a
 
 ## 2. Intended Use and Scope
 
-spotforecast2 supports exploratory and applied forecasting of hourly electricity load and spot prices on ENTSO-E data, end to end, through the `spotforecast2-entsoe` and `spotforecast-demo` console scripts. Its distinctive capabilities are hyperparameter search — Bayesian search over lags, window features, and regressor parameters with Optuna (`bayesian_search_forecaster`), or surrogate-model search with SpotOptim (`spotoptim_search_forecaster`) — interactive inspection through Plotly figures, and global SHAP feature importances via `shap.TreeExplainer`. The `MultiTask` dispatcher and the `run()` entry point orchestrate multi-target pipelines: per-target data preparation, outlier handling, imputation, feature engineering, tuning, and prediction.
+spotforecast2 supports exploratory and applied forecasting of hourly electricity load and spot prices on ENTSO-E data, end to end, through the `spotforecast2-entsoe` console script. Its distinctive capabilities are hyperparameter search — Bayesian search over lags, window features, and regressor parameters with Optuna (`bayesian_search_forecaster`), or surrogate-model search with SpotOptim (`spotoptim_search_forecaster`) — interactive inspection through Plotly figures, and global SHAP feature importances via `shap.TreeExplainer`. The `MultiTask` dispatcher and the `run()` entry point orchestrate multi-target pipelines: per-target data preparation, outlier handling, imputation, feature engineering, tuning, and prediction.
 
 The intended downstream use is development and model selection: choosing lag windows and regressor hyperparameters here, then promoting the validated configuration into a spotforecast2-safe deployment for the deterministic inference path. Tuned `ForecasterRecursiveLGBMFull` and `ForecasterRecursiveXGBFull` models, or just their best parameters, also feed research notebooks and reporting.
 
@@ -60,15 +60,12 @@ importances = model.get_global_shap_feature_importance(frac=0.1)
 print(importances.head())
 ```
 
-Run a complete multi-target pipeline programmatically via `run()`, or use the bundled console scripts:
+Run a complete multi-target pipeline programmatically via `run()`, or use the bundled console script:
 
 ```bash
 spotforecast2-entsoe      # ENTSO-E download / train / predict (needs ENTSOE_API_KEY)
-spotforecast-demo         # baseline vs. covariate vs. custom-LightGBM comparison
 ```
 
-Additional N-to-1 pipeline variants are registered as `spotforecast-n2o1`, `spotforecast-n2o1-df`, `spotforecast-n2o1-cov`, and `spotforecast-n2o1-cov-df`.
-
 ## 4. Technical Specification
 
 ### Task and model family

_quarto.yml

@@ -132,18 +132,8 @@ website:
                 file: docs/reference/stats.autocorrelation.qmd
           - section: "Tasks"
             contents:
-              - text: "task_demo"
-                file: docs/reference/tasks.task_demo.qmd
               - text: "task_entsoe"
                 file: docs/reference/tasks.task_entsoe.qmd
-              - text: "task_n_to_1"
-                file: docs/reference/tasks.task_n_to_1.qmd
-              - text: "task_n_to_1_dataframe"
-                file: docs/reference/tasks.task_n_to_1_dataframe.qmd
-              - text: "task_n_to_1_with_covariates"
-                file: docs/reference/tasks.task_n_to_1_with_covariates.qmd
-              - text: "task_n_to_1_with_covariates_and_dataframe"
-                file: docs/reference/tasks.task_n_to_1_with_covariates_and_dataframe.qmd
 
       - section: "Processing Guides"
         contents:
@@ -166,8 +156,6 @@ website:
 
       - section: "Tasks Guide"
         contents:
-          - text: "Overview"
-            file: docs/tasks/tasks.qmd
           - text: "Multitask Tutorial"
             file: docs/tasks/multitask.qmd
           - text: "ENTSO-E CLI"
@@ -264,14 +252,9 @@ quartodoc:
         - stats.autocorrelation
 
     - title: "Tasks"
-      desc: "Demonstration and predefined forecasting tasks."
+      desc: "ENTSO-E forecasting task and CLI."
       contents:
-        - tasks.task_demo
         - tasks.task_entsoe
-        - tasks.task_n_to_1
-        - tasks.task_n_to_1_dataframe
-        - tasks.task_n_to_1_with_covariates
-        - tasks.task_n_to_1_with_covariates_and_dataframe
 
     - title: "Warnings"
       desc: "Warning-style configuration for spotforecast2."

docs/tasks/entsoe.qmd

@@ -470,6 +470,5 @@ uv run pytest tests/test_tasks_entsoe.py -v
 
 ## See Also
 
-- [Tasks Overview](../tasks/tasks.qmd) - All available CLI commands
 - [API Reference](../reference/index.qmd#preprocessing) - Detailed API documentation
 - [Model Persistence](../processing/model_persistence.qmd) - Saving and loading models

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "spotforecast2"
-version = "8.1.1"
+version = "9.0.0-rc.1"
 description = "Forecasting with spot"
 readme = "README.md"
 license = { text = "AGPL-3.0-or-later" }
@@ -75,11 +75,6 @@ dev = [
 
 [project.scripts]
 spotforecast2-entsoe = "spotforecast2.tasks.task_entsoe:main"
-spotforecast-demo = "spotforecast2.tasks.task_demo:main"
-spotforecast-n2o1 = "spotforecast2.tasks.task_n_to_1:main"
-spotforecast-n2o1-df = "spotforecast2.tasks.task_n_to_1_dataframe:main"
-spotforecast-n2o1-cov = "spotforecast2.tasks.task_n_to_1_with_covariates:main"
-spotforecast-n2o1-cov-df = "spotforecast2.tasks.task_n_to_1_with_covariates_and_dataframe:main"
 
 [tool.uv]
 # Accept pre-releases ONLY for dependencies whose specifier carries an explicit

src/spotforecast2/multitask/base.py

@@ -21,6 +21,8 @@
 
 from spotforecast2_safe.multitask.base import (  # noqa: F401  (re-exported)
     BaseTask as SafeBaseTask,
+)
+from spotforecast2_safe.multitask.base import (
     PipelineConfig,
     agg_predictor,
 )

src/spotforecast2/plots/diagnostics.py

@@ -185,23 +185,19 @@ def plot_feature_importance_by_family(
         print(type(fig).__name__)
         ```
     """
-    ranking = sorted(
-        zip(names, importances), key=lambda kv: kv[1], reverse=True
-    )[:top_n]
+    ranking = sorted(zip(names, importances), key=lambda kv: kv[1], reverse=True)[
+        :top_n
+    ]
     labels = [n for n, _ in ranking][::-1]
     values = [v for _, v in ranking][::-1]
     colors = [_FAMILY_COLOR.get(feature_family(n), "#888888") for n in labels]
 
     fig, ax = plt.subplots(figsize=(8.5, 5.5))
     ax.barh(labels, values, color=colors)
     ax.set_xlabel("split count (feature importance)")
-    ax.set_title(
-        f"Top-{top_n} feature importances (coloured by family; lags in red)"
-    )
+    ax.set_title(f"Top-{top_n} feature importances (coloured by family; lags in red)")
     ax.grid(True, axis="x", color="#E5E5E5", linewidth=0.5)
-    handles = [
-        plt.Rectangle((0, 0), 1, 1, color=c) for c in _FAMILY_COLOR.values()
-    ]
+    handles = [plt.Rectangle((0, 0), 1, 1, color=c) for c in _FAMILY_COLOR.values()]
     ax.legend(handles, _FAMILY_COLOR.keys(), fontsize=7, loc="lower right")
     for spine in ("top", "right"):
         ax.spines[spine].set_visible(False)