Merge branch 'master' into master

Author: madtoinou

.dockerignore

@@ -1,6 +1,8 @@
-.github
-static
-scripts
-docs
-conda_recipe
-LICENSE
+**/.DS_Store
+**/.ipynb_checkpoints
+**/__pycache__
+
+# Test files
+darts/tests/
+darts/examples/static
+**/darts_logs

.gitignore

@@ -9,6 +9,7 @@ docs/source/README.rst
 docs/source/release_notes/
 docs/source/generated_api
 darts.egg-info/
+u8darts.egg-info/
 build/
 dist/
 examples/.ipynb_checkpoints/

CHANGELOG.md

@@ -11,12 +11,28 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Improved**
 
+- Added support for classification forecasting with SKLearn-like forecasting models. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc) and [Dennis Bader](https://github.com/dennisbader).
+ - Added `SklearnClassifierModel` which can take any sklearn-like classifier model.
+ - Added `LightGBMClassifierModel`, `XGBClassifierModel` and `CatBoostClassifierModel` which use the classifier models of the respective libraries.
+ - Added `ClassProbabilityLikelihood` and set it as the default likelihood for classifiers to predict class probabilities with `predict_likelihood_parameters=True` when calling `predict()`.
+- Added classification metrics `accuracy()`, `f1()`, `precision()`, and `recall()`, `confusion_matrix()` to the `metrics` module. Use these metrics to evaluate the performance of classification models. [#2767](https://github.com/unit8co/darts/pull/2767) by [Jonas Blanc](https://github.com/jonasblanc) and [Dennis Bader](https://github.com/dennisbader).
+
 **Fixed**
 
+- Fixed a bug in `SKLearnModel.get_estimator()` for univariate quantile models that use `multi_models=False` , where using `quantile` did not return the correct fitted quantile model / estimator. [#2838](https://github.com/unit8co/darts/pull/2838) by [Dennis Bader](https://github.com/dennisbader).
+- Fixed a bug in `LightGBMModel` and `CatBoostModel` when using component-specific lags and categorical features, where certain lag scenarios could result in incorrect categorical feature declaration. [#2852](https://github.com/unit8co/darts/pull/2852) by [Dennis Bader](https://github.com/dennisbader).
+- Removed `darts/tests` and `examples` from the Darts package distribution. These are only required for internal testing. [#2854](https://github.com/unit8co/darts/pull/2854) by [Dennis Bader](https://github.com/dennisbader).
+
 **Dependencies**
 
+- Removed the upper version cap on `scipy<1.16.0` since `statsmodels` added support in version `0.14.5`. [#2853](https://github.com/unit8co/darts/pull/2853) by [Dennis Bader](https://github.com/dennisbader).
+
 ### For developers of the library:
 
+- Renamed `RegressionModelWithCategoricalCovariates` to `RegressionModelWithCategoricalFeatures` which now also supports categorical target features. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc)
+- Added `MultiOutputClassifier` for handling multi-output classification tasks. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc)
+- Cleaned up `Dockerfile` to only include the necessary files for building the Darts package. [#2854](https://github.com/unit8co/darts/pull/2854) by [Dennis Bader](https://github.com/dennisbader).
+
 ## [0.36.0](https://github.com/unit8co/darts/tree/0.36.0) (2025-06-29)
 
 ### For users of the library:

Dockerfile

@@ -1,18 +1,26 @@
+# Use a specific Python version for better reproducibility
 FROM python:3.10
 
-# install python requirements before copying the rest of the files
-# this way we can cache the requirements and not have to reinstall them
+# Set work directory
+WORKDIR /app
+
+# Copy requirements first for better layer caching
 COPY requirements/ /app/requirements/
-RUN pip install -r /app/requirements/dev-all.txt
 
-# copy local files
-COPY . /app
+# Install Python dependencies
+RUN pip install --no-cache-dir -r /app/requirements/dev-all.txt
 
-# set work directory
-WORKDIR /app
+# Copy only the files explicitly needed for installation
+COPY setup.py pyproject.toml MANIFEST.in README.md /app/
+COPY darts/ /app/darts/
+
+# Install darts in development mode
+RUN pip install --no-cache-dir -e . && \
+    # Clean up pip cache
+    rm -rf ~/.cache/pip
 
-# install darts
-RUN pip install -e .
+# Copy examples
+COPY examples/ /app/examples/
 
 # assuming you are working from inside your darts directory:
 # docker build . -t darts-test:latest

darts/ad/scorers/kmeans_scorer.py

@@ -15,7 +15,7 @@
 from darts import metrics
 from darts.ad.scorers.scorers import WindowedAnomalyScorer
 from darts.logging import get_logger
-from darts.metrics.metrics import METRIC_TYPE
+from darts.metrics.utils import METRIC_TYPE
 
 logger = get_logger(__name__)
 

darts/ad/scorers/pyod_scorer.py

@@ -12,7 +12,7 @@
 from darts import metrics
 from darts.ad.scorers.scorers import WindowedAnomalyScorer
 from darts.logging import get_logger, raise_if_not
-from darts.metrics.metrics import METRIC_TYPE
+from darts.metrics.utils import METRIC_TYPE
 
 logger = get_logger(__name__)
 

darts/ad/scorers/scorers.py

@@ -32,7 +32,7 @@
     show_anomalies_from_scores,
 )
 from darts.logging import get_logger, raise_log
-from darts.metrics.metrics import METRIC_TYPE
+from darts.metrics.utils import METRIC_TYPE
 from darts.utils.data.tabularization import create_lagged_data
 from darts.utils.ts_utils import series2seq
 from darts.utils.utils import _build_tqdm_iterator, _parallel_apply

darts/ad/scorers/wasserstein_scorer.py

@@ -19,7 +19,7 @@
 from darts import TimeSeries, metrics
 from darts.ad.scorers.scorers import WindowedAnomalyScorer
 from darts.logging import get_logger
-from darts.metrics.metrics import METRIC_TYPE
+from darts.metrics.utils import METRIC_TYPE
 
 logger = get_logger(__name__)
 

darts/metrics/__init__.py

@@ -1,6 +1,9 @@
 """
 Metrics
--------
+=======
+
+Regression Metrics
+------------------
 
 For deterministic forecasts (point predictions with `num_samples == 1`), probabilistic forecasts (`num_samples > 1`),
 and quantile forecasts. For probabilistic and quantile forecasts, use parameter `q` to define the quantile(s) to
@@ -65,19 +68,42 @@
         - :func:`IC <darts.metrics.metrics.ic>`: Interval Coverage
         - :func:`INCS_QR <darts.metrics.metrics.incs_qr>`: Interval Non-Conformity Score for Quantile Regression
 
+Classification Metrics
+----------------------
+
+For deterministic forecasts (point predictions with `num_samples == 1`), probabilistic forecasts (`num_samples > 1`),
+and class label probability forecasts. Most metrics allow to extract the scores for specific labels with parameter
+`labels`.
+
+- Aggregated over time:
+    - :func:`Accuracy <darts.metrics.metrics.accuracy>`: Accuracy Score
+    - :func:`Precision <darts.metrics.metrics.precision>`: Precision Score
+    - :func:`Recall <darts.metrics.metrics.recall>`: Recall Score
+    - :func:`F1 <darts.metrics.metrics.f1>`: F1 Score
+    - :func:`CM <darts.metrics.metrics.confusion_matrix>`: Confusion Matrix
+
+Dynamic Time Warping Metrics
+----------------------------
+
 For Dynamic Time Warping (DTW) (aggregated over time):
 
 - :func:`DTW <darts.metrics.metrics.dtw_metric>`: Dynamic Time Warping Metric
+
+
+
 """
 
 from darts.metrics.metrics import (
+    accuracy,
     ae,
     ape,
     arre,
     ase,
     coefficient_of_variation,
+    confusion_matrix,
     dtw_metric,
     err,
+    f1,
     ic,
     incs_qr,
     iw,
@@ -95,9 +121,11 @@
     mse,
     msse,
     ope,
+    precision,
     ql,
     qr,
     r2_score,
+    recall,
     rmse,
     rmsle,
     rmsse,
@@ -109,45 +137,6 @@
     wmape,
 )
 
-ALL_METRICS = {
-    ae,
-    ape,
-    arre,
-    ase,
-    coefficient_of_variation,
-    dtw_metric,
-    err,
-    iw,
-    iws,
-    mae,
-    mape,
-    wmape,
-    marre,
-    mase,
-    merr,
-    miw,
-    miws,
-    mql,
-    mse,
-    msse,
-    ope,
-    ql,
-    qr,
-    r2_score,
-    rmse,
-    rmsle,
-    rmsse,
-    sape,
-    se,
-    sle,
-    smape,
-    sse,
-    ic,
-    mic,
-    incs_qr,
-    mincs_qr,
-}
-
 TIME_DEPENDENT_METRICS = {
     ae,
     ape,
@@ -165,18 +154,14 @@
     incs_qr,
 }
 
-Q_INTERVAL_METRICS = {
-    iw,
-    iws,
-    miw,
-    miws,
-    ic,
-    mic,
-    incs_qr,
+CLASSIFICATION_METRICS = {
+    accuracy,
+    precision,
+    recall,
+    f1,
+    confusion_matrix,
 }
 
-NON_Q_METRICS = {dtw_metric}
-
 __all__ = [
     "ae",
     "ape",
@@ -214,4 +199,9 @@
     "mic",
     "incs_qr",
     "mincs_qr",
+    "accuracy",
+    "precision",
+    "recall",
+    "f1",
+    "confusion_matrix",
 ]