Repository: sktime/sktime
Pull request #7366 (2024-11-05 18:10:21)

 [DOC] Small typo on explanation of regression
The description seemed copy-pasted from that of classification. Updated a couple of words

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#### What does this implement/fix? Explain your changes.
The description seemed copy-pasted from that of classification. Updated a couple of words

#### Does your contribution introduce a new dependency? If yes, which one?
No

#### What should a reviewer concentrate their feedback on?
The 3 words I changed in the text

#### Did you add any tests for the change?
Not needed, only docs.

#### Any other comments?
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##### For all contributions
- [x] I've added myself to the [list of contributors](https://github.com/sktime/sktime/blob/main/CONTRIBUTORS.md) with any new badges I've earned :-)
  How to: add yourself to the [all-contributors file](https://github.com/sktime/sktime/blob/main/.all-contributorsrc) in the `sktime` root directory (not the `CONTRIBUTORS.md`). Common badges: `code` - fixing a bug, or adding code logic. `doc` - writing or improving documentation or docstrings. `bug` - reporting or diagnosing a bug (get this plus `code` if you also fixed the bug in the PR).`maintenance` - CI, test framework, release.
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Patch:
diff --git a/.all-contributorsrc b/.all-contributorsrc
index c2b7043be..325456df5 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -3164,6 +3164,16 @@
       "contributions": [
         "code"
       ]
-    }
+    },
+    {
+      "login": "manolotis",
+      "name": "Manuel Muñoz Sánchez",
+      "avatar_url": "https://avatars.githubusercontent.com/manolotis",
+      "profile": "https://github.com/manolotis",
+      "contributions": [
+        "doc"
+      ]
+     }
+
   ]
 }
diff --git a/examples/02_classification.ipynb b/examples/02_classification.ipynb
index 00b2e0df0..0a5f05201 100644
--- a/examples/02_classification.ipynb
+++ b/examples/02_classification.ipynb
@@ -32,7 +32,7 @@
     "\n",
     "- Example: Daily energy consumption profile over time - Predict season, e.g., winter/summer, or type of consumer\n",
     "\n",
-    "Regression = try to assign one *category* per time series, after training on time series/category examples\n",
+    "Regression = try to assign one continuous *numerical value* per time series, after training on time series/category examples\n",
     "\n",
     "- Example: Temperature/pressure/time profile of chemical reactor - Predict total purity (fraction of 1)\n",
     "\n",
diff --git a/sktime/performance_metrics/forecasting/_classes.py b/sktime/performance_metrics/forecasting/_classes.py
index d739d8057..5265e54bd 100644
--- a/sktime/performance_metrics/forecasting/_classes.py
+++ b/sktime/performance_metrics/forecasting/_classes.py
@@ -44,9 +44,12 @@ from sktime.performance_metrics.forecasting._functions import (
     median_squared_scaled_error,
     relative_loss,
 )
+from sktime.performance_metrics.forecasting.sample_weight._types import (
+    check_sample_weight_generator,
+)
 from sktime.utils.warnings import warn
 
-__author__ = ["mloning", "tch", "RNKuhns", "fkiraly"]
+__author__ = ["mloning", "tch", "RNKuhns", "fkiraly", "markussagen"]
 __all__ = [
     "make_forecasting_scorer",
     "MeanAbsoluteScaledError",
@@ -133,7 +136,11 @@ class BaseForecastingErrorMetric(BaseMetric):
         "reserved_params": ["multioutput", "multilevel"],
     }
 
-    def __init__(self, multioutput="uniform_average", multilevel="uniform_average"):
+    def __init__(
+        self,
+        multioutput="uniform_average",
+        multilevel="uniform_average",
+    ):
         self.multioutput = multioutput
         self.multilevel = multilevel
 
@@ -187,7 +194,7 @@ class BaseForecastingErrorMetric(BaseMetric):
             Must be of same format as ``y_true``, same columns if indexed,
             but not necessarily same indices.
 
-        sample_weight : optional, 1D array-like, default=None
+        sample_weight : optional, 1D array-like, or callable, default=None
             Sample weights for each time point.
 
             * If ``None``, the time indices are considered equally weighted.
@@ -198,6 +205,10 @@ class BaseForecastingErrorMetric(BaseMetric):
               individual time
               series must be the same, and equal to the length of ``sample_weight``,
               for all instances of time series passed.
+            * If a callable, it must follow ``SampleWeightGenerator`` interface,
+              or have one of the following signatures:
+              ``y_true: pd.DataFrame -> 1D array-like``,
+              or ``y_true: pd.DataFrame x y_pred: pd.DataFrame -> 1D array-like``.
 
         Returns
         -------
@@ -219,6 +230,18 @@ class BaseForecastingErrorMetric(BaseMetric):
         """  # noqa: E501
         return self.evaluate(y_true, y_pred, **kwargs)
 
+    def _apply_sample_weight_to_kwargs(self, y_true, y_pred, **kwargs):
+        """Apply sample weight to kwargs.
+
+        Sample weight is updated to kwargs if it is a callable and follows the
+        SampleWeightGenerator interface.
+        """
+        sample_weight = kwargs.get("sample_weight", None)
+        if callable(sample_weight) and check_sample_weight_generator(sample_weight):
+            kwargs["sample_weight"] = sample_weight(y_true, y_pred, **kwargs)
+
+        return kwargs
+
     def evaluate(self, y_true, y_pred, **kwargs):
         """Evaluate the desired metric on given inputs.
 
@@ -264,8 +287,8 @@ class BaseForecastingErrorMetric(BaseMetric):
             Must be of same format as ``y_true``, same columns if indexed,
             but not necessarily same indices.
 
-        sample_weight : optional, 1D array-like, default=None
-            Sample weights for each time point.
+        sample_weight : optional, 1D array-like, or callable, default=None
+            Sample weights or callable for each time point.
 
             * If ``None``, the time indices are considered equally weighted.
             * If an array, must be 1D.
@@ -275,6 +298,10 @@ class BaseForecastingErrorMetric(BaseMetric):
               individual time
               series must be the same, and equal to the length of ``sample_weight``,
               for all instances of time series passed.
+            * If a callable, it must follow ``SampleWeightGenerator`` interface,
+              or have one of the following signatures:
+              ``y_true: pd.DataFrame -> 1D array-like``,
+              or ``y_true: pd.DataFrame x y_pred: pd.DataFrame -> 1D array-like``.
 
         Returns
         -------
@@ -296,11 +323,16 @@ class BaseForecastingErrorMetric(BaseMetric):
         """  # noqa: E501
         multioutput = self.multioutput
         multilevel = self.multilevel
+
         # Input checks and conversions
         y_true_inner, y_pred_inner, multioutput, multilevel, kwargs = self._check_ys(
             y_true, y_pred, multioutput, multilevel, **kwargs
         )
 
+        kwargs = self._apply_sample_weight_to_kwargs(
+            y_true=y_true_inner, y_pred=y_pred_inner, **kwargs
+        )
+
         requires_vectorization = isinstance(y_true_inner, VectorizedDF)
         if not requires_vectorization:
             # pass to inner function
@@ -467,8 +499,8 @@ class BaseForecastingErrorMetric(BaseMetric):
             Must be of same format as ``y_true``, same columns if indexed,
             but not necessarily same indices.
 
-        sample_weight : optional, 1D array-like, default=None
-            Sample weights for each time point.
+        sample_weight : optional, 1D array-like, or callable, default=None
+            Sample weights or callable for each time point.
 
             * If ``None``, the time indices are considered equally weighted.
             * If an array, must be 1D.
@@ -478,6 +510,10 @@ class BaseForecastingErrorMetric(BaseMetric):
               individual time
               series must be the same, and equal to the length of ``sample_weight``,
               for all instances of time series passed.
+            * If a callable, it must follow ``SampleWeightGenerator`` interface,
+              or have one of the following signatures:
+              ``y_true: pd.DataFrame -> 1D array-like``,
+              or ``y_true: pd.DataFrame x y_pred: pd.DataFrame -> 1D array-like``.
 
         Returns
         -------
@@ -494,10 +530,16 @@ class BaseForecastingErrorMetric(BaseMetric):
         """  # noqa: E501
         multioutput = self.multioutput
         multilevel = self.multilevel
+
         # Input checks and conversions
         y_true_inner, y_pred_inner, multioutput, multilevel, kwargs = self._check_ys(
             y_true, y_pred, multioutput, multilevel, **kwargs
         )
+
+        kwargs = self._apply_sample_weight_to_kwargs(
+            y_true=y_true_inner, y_pred=y_pred_inner, **kwargs
+        )
+
         requires_vectorization = isinstance(y_true_inner, VectorizedDF)
         if not requires_vectorization:
             # pass to inner function
@@ -695,7 +737,7 @@ class BaseForecastingErrorMetric(BaseMetric):
 
         return y_true, y_pred, multioutput, multilevel, kwargs
 
-    def _get_sample_weight(self, **kwargs):
+    def _set_sample_weight_on_kwargs(self, **kwargs):
         """Get sample weights from kwargs.
 
         Assumes that either ``sample_weight`` is passed, or not.
@@ -736,7 +778,7 @@ class BaseForecastingErrorMetric(BaseMetric):
         df : pd.DataFrame
             Weighted DataFrame.
         """
-        sample_weight = self._get_sample_weight(**kwargs)
+        sample_weight = self._set_sample_weight_on_kwargs(**kwargs)
         if sample_weight is not None:
             df = df.mul(sample_weight, axis=0)
         return df
@@ -846,7 +888,6 @@ class _DynamicForecastingErrorMetric(BaseForecastingErrorMetricFunc):
         self.func = func
         self.name = name
         self.lower_is_better = lower_is_better
-
         super().__init__(multioutput=multioutput, multilevel=multilevel)
 
         self.set_tags(**{"lower_is_better": lower_is_better})
@@ -1581,13 +1622,6 @@ class MedianAbsoluteError(BaseForecastingErrorMetricFunc):
 
     func = median_absolute_error
 
-    def __init__(
-        self,
-        multioutput="uniform_average",
-        multilevel="uniform_average",
-    ):
-        super().__init__(multioutput=multioutput, multilevel=multilevel)
-
     def _evaluate_by_index(self, y_true, y_pred, **kwargs):
         """Return the metric evaluated at each time point.
 
diff --git a/sktime/performance_metrics/forecasting/probabilistic/_classes.py b/sktime/performance_metrics/forecasting/probabilistic/_classes.py
index 961a3b274..7aff5d314 100644
--- a/sktime/performance_metrics/forecasting/probabilistic/_classes.py
+++ b/sktime/performance_metrics/forecasting/probabilistic/_classes.py
@@ -55,7 +55,7 @@ class _BaseProbaForecastingErrorMetric(BaseForecastingErrorMetric):
                 (fh, n_outputs) where fh is the forecasting horizon
             Ground truth (correct) target values.
 
-        y_pred : return object of probabilistic predictition method scitype:y_pred
+        y_pred : return object of probabilistic prediction method scitype:y_pred
             must be at fh and for variables equal to those in y_true.
 
         Returns
@@ -82,7 +82,7 @@ class _BaseProbaForecastingErrorMetric(BaseForecastingErrorMetric):
                 (fh, n_outputs) where fh is the forecasting horizon
             Ground truth (correct) target values.
 
-        y_pred : return object of probabilistic predictition method scitype:y_pred
+        y_pred : return object of probabilistic prediction method scitype:y_pred
             must be at fh and for variables equal to those in y_true
 
         multioutput : string "uniform_average" or "raw_values" determines how\
@@ -176,7 +176,7 @@ class _BaseProbaForecastingErrorMetric(BaseForecastingErrorMetric):
                 (fh, n_outputs) where fh is the forecasting horizon
             Ground truth (correct) target values.
 
-        y_pred : return object of probabilistic predictition method scitype:y_pred
+        y_pred : return object of probabilistic prediction method scitype:y_pred
             must be at fh and for variables equal to those in y_true
 
         multioutput : string "uniform_average" or "raw_values" determines how\
@@ -855,7 +855,7 @@ class _BaseDistrForecastingMetric(_BaseProbaForecastingErrorMetric):
                 (fh, n_outputs) where fh is the forecasting horizon
             Ground truth (correct) target values.
 
-        y_pred : return object of probabilistic predictition method scitype:y_pred
+        y_pred : return object of probabilistic prediction method scitype:y_pred
             must be at fh and for variables equal to those in y_true
 
         multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
diff --git a/sktime/performance_metrics/forecasting/sample_weight/__init__.py b/sktime/performance_metrics/forecasting/sample_weight/__init__.py
new file mode 100644
index 000000000..e7f69d293
--- /dev/null
+++ b/sktime/performance_metrics/forecasting/sample_weight/__init__.py
@@ -0,0 +1,16 @@
+"""Sample weight generators for sktime performance metrics."""
+
+__author__ = ["markussagen"]
+__all__ = [
+    "BaseSampleWeightGenerator",
+    "check_sample_weight_generator",
+    "SampleWeightGenerator",
+]
+
+from sktime.performance_metrics.forecasting.sample_weight._base import (
+    BaseSampleWeightGenerator,
+)
+from sktime.performance_metrics.forecasting.sample_weight._types import (
+    SampleWeightGenerator,
+    check_sample_weight_generator,
+)
diff --git a/sktime/performance_metrics/forecasting/sample_weight/_base.py b/sktime/performance_metrics/forecasting/sample_weight/_base.py
new file mode 100644
index 000000000..a2530baf0
--- /dev/null
+++ b/sktime/performance_metrics/forecasting/sample_weight/_base.py
@@ -0,0 +1,77 @@
+"""Implements base class for customer sample weights of performance metric in sktime."""
+
+__author__ = ["markussagen"]
+__all__ = ["BaseSampleWeightGenerator", "check_sample_weight_generator"]
+
+from sktime.base import BaseObject
+from sktime.performance_metrics.forecasting.sample_weight._types import (
+    check_sample_weight_generator,
+)
+
+
+class BaseSampleWeightGenerator(BaseObject):
+    """Base class for defining sample weight generators in sktime.
+
+    Extends sktime BaseObject.
+    """
+
+    _tags = {
+        "object_type": "sample_weight",
+        "authors": "markussagen",
+        "maintainers": "markussagen",
+    }
+
+    def __init__(self):
+        super().__init__()
+
+    def __call__(self, y_true, y_pred=None, **kwargs):
+        """Generate sample weights for a given set of true and predicted values.
+
+        Parameters
+        ----------
+        y_true : time series in sktime compatible data container format
+            Ground truth (correct) target values.
+
+        y_pred : time series in sktime compatible data container format, optional
+            Predicted values to evaluate against ground truth.
+            Must be of same format as y_true, same indices and columns if indexed.
+            If None, only y_true is used for weight generation.
+
+        **kwargs : dict
+            Additional keyword arguments specific to the weight generation method.
+
+        Returns
+        -------
+        sample_weight : 1D np.ndarray or None
+            Sample weights for each time point.
+
+            * If an array, must be 1D.
+              If y_true and y_pred are a single time series,
+              sample_weight must be of the same length as y_true.
+              If the time series are panel or hierarchical, the length of all
+              individual time series must be the same, and equal to the length
+              of sample_weight, for all instances of time series passed.
+            * If None, the time indices are considered equally weighted.
+        """
+        raise NotImplementedError("abstract method")
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return ``"default"`` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance.
+            ``create_test_instance`` uses the first (or only) dictionary in ``params``
+        """
+        return [{}]
diff --git a/sktime/performance_metrics/forecasting/sample_weight/_types.py b/sktime/performance_metrics/forecasting/sample_weight/_types.py
new file mode 100644
index 000000000..9e5aeb93a
--- /dev/null
+++ b/sktime/performance_metrics/forecasting/sample_weight/_types.py
@@ -0,0 +1,113 @@
+"""Implements base class for customer sample weights of performance metric in sktime."""
+
+__author__ = ["markussagen"]
+__all__ = ["SampleWeightGenerator", "check_sample_weight_generator"]
+
+from inspect import signature
+from typing import Protocol, runtime_checkable
+
+
+@runtime_checkable
+class SampleWeightGenerator(Protocol):
+    """Protocol for sample weight generators.
+
+    This protocol defines the interface for sample weight generator functions used in
+    performance metrics calculations. Sample weight generators are used to assign
+    weights to individual time points in time series data.
+
+    The generator must have at least one parameter (y_true) and accept **kwargs.
+    It must return a 1D numpy array of sample weights.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
+        Ground truth (correct) target values.
+    y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs), optional
+        Estimated target values.
+    **kwargs : dict
+        Additional keyword arguments that may be used in weight calculation.
+
+    Returns
+    -------
+    sample_weights : np.ndarray of shape (n_samples,)
+        1D array of sample weights.
+
+    Raises
+    ------
+    ValueError
+        If the sample weight generator does not have at least one parameter (y_true).
+        If the first parameter is not y_true.
+        If the sample weight generator does not accept **kwargs.
+
+    Notes
+    -----
+    Implementations of this protocol should ensure that the returned weights are
+    non-negative and sum to a value greater than zero.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> class ExampleWeightGenerator:
+    ...     def __call__(self, y_true, y_pred=None, **kwargs):
+    ...         return np.ones_like(y_true)
+    >>> weight_gen = ExampleWeightGenerator()
+    >>> y_true = np.array([1, 2, 3, 4, 5])
+    >>> weights = weight_gen(y_true)
+    >>> print(weights)
+    [1. 1. 1. 1. 1.]
+    """
+
+    def __call__(self, y_true, y_pred=None, **kwargs):
+        """Generate sample weights for time series data.
+
+        This method should be implemented by concrete sample weight generators.
+
+        Parameters
+        ----------
+        y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Ground truth (correct) target values.
+        y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs), optional
+            Estimated target values. Default is None.
+        **kwargs : dict
+            Additional keyword arguments that may be used in weight calculation.
+
+        Returns
+        -------
+        sample_weights : np.ndarray of shape (n_samples,)
+            1D array of sample weights.
+
+        Notes
+        -----
+        - The method should always return a 1D numpy array of weights.
+        - The length of the returned array should match the number of samples in y_true.
+        - If y_pred is not used in the weight calculation, it should be ignored.
+        """
+        ...
+
+
+def check_sample_weight_generator(obj):
+    """Check if obj is a valid SampleWeightGenerator."""
+    if obj is None or not callable(obj):
+        return False
+
+    sig = signature(obj)
+    params = sig.parameters
+
+    # Check if the function has at least one parameter (y_true)
+    if len(params) < 1:
+        msg = "Sample weight generator must have at least one parameter (y_true)"
+        raise ValueError(msg)
+
+    param_names = list(params.keys())
+
+    # Check if the first parameter is y_true
+    if param_names[0] != "y_true":
+        raise ValueError("First parameter of sample weight generator must be 'y_true'")
+
+    # Check if the function accepts **kwargs
+    if not any(param.kind == param.VAR_KEYWORD for param in params.values()):
+        raise ValueError("Sample weight generator must accept **kwargs")
+
+    # NOTE: this does not check the return type!
+
+    return True


Test patch:
diff --git a/sktime/performance_metrics/forecasting/sample_weight/tests/__init__.py b/sktime/performance_metrics/forecasting/sample_weight/tests/__init__.py
new file mode 100644
index 000000000..1af936133
--- /dev/null
+++ b/sktime/performance_metrics/forecasting/sample_weight/tests/__init__.py
@@ -0,0 +1,1 @@
+"""Tests for sktime sample weight generators."""
diff --git a/sktime/performance_metrics/forecasting/sample_weight/tests/test_sample_weight_generator.py b/sktime/performance_metrics/forecasting/sample_weight/tests/test_sample_weight_generator.py
new file mode 100644
index 000000000..4cd7e1ca0
--- /dev/null
+++ b/sktime/performance_metrics/forecasting/sample_weight/tests/test_sample_weight_generator.py
@@ -0,0 +1,260 @@
+"""Tests for sample weight generators."""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from sktime.performance_metrics.forecasting.sample_weight._base import (
+    BaseSampleWeightGenerator,
+)
+from sktime.performance_metrics.forecasting.sample_weight._types import (
+    check_sample_weight_generator,
+)
+from sktime.tests.test_switch import run_test_module_changed
+
+
+class DummyWeightGenerator(BaseSampleWeightGenerator):
+    def __call__(self, y_true, y_pred=None, **kwargs) -> None:
+        return None
+
+
+class UniformWeightGenerator(BaseSampleWeightGenerator):
+    def __call__(self, y_true, y_pred=None, **kwargs) -> np.ndarray:
+        return np.ones_like(y_true)
+
+
+class RecencyWeightGenerator(BaseSampleWeightGenerator):
+    def __init__(self, decay_rate=0.1):
+        self.decay_rate = decay_rate
+
+    def __call__(self, y_true, y_pred=None, **kwargs):
+        weights = np.exp(-self.decay_rate * np.arange(len(y_true))[::-1])
+        return weights / np.sum(weights)
+
+
+class DateRangeWeightGenerator(BaseSampleWeightGenerator):
+    def __init__(self, date_ranges, default_weight=1.0):
+        self.date_ranges = date_ranges
+        self.default_weight = default_weight
+
+    def _get_dt(self, date_series):
+        """Get the datetime object from the series."""
+        dt = date_series
+        if isinstance(date_series, pd.Timestamp):
+            dt = date_series
+        elif isinstance(date_series, pd.DatetimeIndex):
+            dt = date_series
+        elif isinstance(date_series, pd.Series):
+            dt = date_series.dt
+        elif isinstance(date_series, pd.DataFrame):
+            dt = date_series.dt
+        elif isinstance(date_series, np.datetime64):
+            dt = pd.to_datetime(date_series)
+        else:
+            msg = f"Invalid type for conversion to datetime: {type(date_series)}"
+            raise ValueError(msg)
+        return dt
+
+    def __call__(self, y_true, y_pred=None, **kwargs):
+        if "dates" not in kwargs:
+            msg = "'dates' must be provided in kwargs for DateRangeWeightGenerator"
+            raise ValueError(msg)
+
+        if not (
+            isinstance(self.date_ranges, list)
+            and (
+                all(isinstance(range_config, dict) for range_config in self.date_ranges)
+            )
+        ):
+            raise ValueError("'date_ranges' must be a list of dictionaries")
+
+        if not all(
+            "start" in range_config
+            and "end" in range_config
+            and "weight" in range_config
+            for range_config in self.date_ranges
+        ):
+            msg = "'date_ranges' must be a list of dicts with the keys: "
+            msg += "'start', 'end', and 'weight'"
+            raise ValueError(msg)
+
+        dates = pd.to_datetime(kwargs["dates"])
+        weights = np.full(len(y_true), self.default_weight)
+
+        for range_config in self.date_ranges:
+            start_date = pd.to_datetime(range_config["start"], format="%m-%d")
+            end_date = pd.to_datetime(range_config["end"], format="%m-%d")
+
+            dt = self._get_dt(dates)
+            mask = (
+                (dt.month >= start_date.month)
+                & (dt.month <= end_date.month)
+                & (dt.day >= start_date.day)
+                & (dt.day <= end_date.day)
+            )
+            weights[mask] = range_config["weight"]
+
+        return weights / np.sum(weights)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_base_sample_weight_generator():
+    """Test BaseSampleWeightGenerator."""
+
+    class TestGenerator(BaseSampleWeightGenerator):
+        def __call__(self, y_true, y_pred=None, **kwargs):
+            return np.ones_like(y_true)
+
+    generator = TestGenerator()
+    y_true = np.array([1, 2, 3])
+    weights = generator(y_true)
+
+    assert isinstance(weights, np.ndarray)
+    assert np.array_equal(weights, np.ones_like(y_true))
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_check_sample_weight_generator():
+    """Test check_sample_weight_generator function."""
+
+    def valid_generator(y_true, y_pred=None, **kwargs):
+        return np.ones_like(y_true)
+
+    def valid_generator_no_params(y_true, y_pred=None, **kwargs):
+        return None
+
+    def valid_generator_no_y_pred(y_true, **kwargs):
+        return np.ones_like(y_true)
+
+    assert check_sample_weight_generator(valid_generator)
+    assert check_sample_weight_generator(valid_generator_no_params)
+    assert check_sample_weight_generator(valid_generator_no_y_pred)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_check_sample_weight_generator_are_invalid():
+    """Test check_sample_weight_generator function."""
+
+    def valid_generator_no_kwargs(y_true, y_pred=None):
+        return np.ones_like(y_true)
+
+    def invalid_generator():
+        return "invalid"
+
+    def invalid_generator_no_param_y_true(invalid):
+        return np.ones((1, 1))
+
+    # Validate
+    with pytest.raises(ValueError):
+        check_sample_weight_generator(valid_generator_no_kwargs)
+
+    with pytest.raises(ValueError):
+        check_sample_weight_generator(invalid_generator)
+
+    with pytest.raises(ValueError):
+        check_sample_weight_generator(invalid_generator_no_param_y_true)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_dummy_weight_generator():
+    generator = DummyWeightGenerator()
+    y_true = np.array([1, 2, 3])
+    weights = generator(y_true)
+
+    assert weights is None
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_uniform_weight_generator():
+    generator = UniformWeightGenerator()
+    y_true = np.array([1, 2, 3])
+    weights = generator(y_true)
+
+    assert isinstance(weights, np.ndarray)
+    assert np.array_equal(weights, np.ones_like(y_true))
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_recency_weight_generator():
+    generator = RecencyWeightGenerator(decay_rate=0.1)
+    y_true = np.array([1, 2, 3])
+    weights = generator(y_true)
+
+    assert isinstance(weights, np.ndarray)
+    assert len(weights) == len(y_true)
+    assert np.all(weights[:-1] < weights[1:])  # Weights should be increasing
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_date_range_weight_generator():
+    date_ranges = [
+        {"start": "01-01", "end": "03-31", "weight": 2},
+        {"start": "06-01", "end": "08-31", "weight": 3},
+    ]
+    generator = DateRangeWeightGenerator(date_ranges=date_ranges)
+    y_true = np.array([1, 2, 3, 4, 5])
+    dates = pd.date_range(start="2023-01-01", periods=5, freq="MS")
+    weights = generator(y_true, dates=dates)
+
+    assert isinstance(weights, np.ndarray)
+    assert len(weights) == len(y_true)
+    assert weights[0] > weights[3]  # January should have higher weight than April
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_sample_weight_generator_with_y_pred():
+    class TestGenerator(BaseSampleWeightGenerator):
+        def __call__(self, y_true, y_pred=None, **kwargs):
+            if y_pred is None:
+                return np.ones_like(y_true)
+            return np.abs(y_true - y_pred)
+
+    generator = TestGenerator()
+    y_true = np.array([1, 2, 3])
+    y_pred = np.array([1.1, 2.2, 2.8])
+    weights = generator(y_true, y_pred)
+
+    assert isinstance(weights, np.ndarray)
+    assert np.allclose(weights, np.abs(y_true - y_pred))
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+def test_sample_weight_generator_with_kwargs():
+    class TestGenerator(BaseSampleWeightGenerator):
+        def __call__(self, y_true, y_pred=None, **kwargs):
+            factor = kwargs.get("factor", 1)
+            return np.ones_like(y_true) * factor
+
+    generator = TestGenerator()
+    y_true = np.array([1, 2, 3])
+    weights = generator(y_true, factor=2)
+
+    assert isinstance(weights, np.ndarray)
+    assert np.array_equal(weights, np.ones_like(y_true) * 2)
diff --git a/sktime/performance_metrics/tests/test_performance_metrics_forecasting.py b/sktime/performance_metrics/tests/test_performance_metrics_forecasting.py
index 5789c8121..63ff1d140 100644
--- a/sktime/performance_metrics/tests/test_performance_metrics_forecasting.py
+++ b/sktime/performance_metrics/tests/test_performance_metrics_forecasting.py
@@ -47,6 +47,9 @@ from sktime.performance_metrics.forecasting import (
     median_squared_scaled_error,
     relative_loss,
 )
+from sktime.performance_metrics.forecasting.sample_weight._base import (
+    BaseSampleWeightGenerator,
+)
 from sktime.performance_metrics.tests._config import RANDOM_SEED
 from sktime.tests.test_switch import run_test_module_changed
 from sktime.utils._testing.series import _make_series
@@ -588,3 +591,99 @@ def test_y_true_y_pred_inconsistent_n_variables_raises_error(metric_func_name):
         ValueError, match="y_true and y_pred have different number of output"
     ):
         metric_func(y_true, y_pred, y_train=y_train, y_pred_benchmark=y_pred_benchmark)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+@pytest.mark.parametrize("random_state", RANDOM_STATES)
+@pytest.mark.parametrize("metric_func_name", LOSS_RESULTS.keys())
+def test_sample_weight_generator_has_no_effect_on_metric_function(
+    metric_func_name, random_state
+):
+    """Tests that loss function with univariate input should return scalar number."""
+    y = _make_series(n_timepoints=75, random_state=random_state)
+    y_train, y_true = y.iloc[:50], y.iloc[50:]
+    y_pred = y.shift(1).iloc[50:]
+    y_pred_benchmark = y.rolling(2).mean().iloc[50:]
+
+    class TestWeightGenerator(BaseSampleWeightGenerator):
+        def __call__(self, y_true, y_pred=None, **kwargs):
+            return np.arange(1, len(y_true) + 1)
+
+    weight_generator = TestWeightGenerator()
+
+    metric_func = LOSS_RESULTS[metric_func_name]["func"]
+
+    function_loss = metric_func(
+        y_true, y_pred, y_train=y_train, y_pred_benchmark=y_pred_benchmark
+    )
+
+    function_loss_with_weights = metric_func(
+        y_true,
+        y_pred,
+        y_train=y_train,
+        y_pred_benchmark=y_pred_benchmark,
+        sample_weight=weight_generator,
+    )
+
+    is_num = is_numeric_dtype(function_loss)
+    is_scalar = np.isscalar(function_loss)
+    assert is_num and is_scalar, " ".join(
+        ["Loss function with univariate input should return scalar number"]
+    )
+
+    is_num = is_numeric_dtype(function_loss_with_weights)
+    is_scalar = np.isscalar(function_loss_with_weights)
+    assert is_num and is_scalar, " ".join(
+        ["Loss function with sample weight generator should return scalar number"]
+    )
+
+    assert np.isclose(function_loss, function_loss_with_weights), " ".join(
+        ["Loss function with sample weight generator should return different value"]
+    )
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.performance_metrics"]),
+    reason="Run if performance_metrics module has changed.",
+)
+@pytest.mark.parametrize("random_state", RANDOM_STATES)
+@pytest.mark.parametrize(
+    "metric_class",
+    [
+        MeanSquaredError,
+        MeanAbsoluteError,
+        MeanAbsolutePercentageError,
+    ],
+)
+def test_sample_weight_generator_is_passed_to_func(metric_class, random_state):
+    """Tests that loss function with univariate input should return scalar number."""
+    y = _make_series(n_timepoints=75, random_state=random_state)
+    y_train, y_true = y.iloc[:50], y.iloc[50:]
+    y_pred = y.shift(1).iloc[50:]
+    y_pred_benchmark = y.rolling(2).mean().iloc[50:]
+
+    class TestWeightGenerator(BaseSampleWeightGenerator):
+        def __call__(self, y_true, y_pred=None, **kwargs):
+            return np.arange(1, len(y_true) + 1)
+
+    weight_generator = TestWeightGenerator()
+
+    metric = metric_class()
+
+    function_loss = metric(
+        y_true, y_pred, y_train=y_train, y_pred_benchmark=y_pred_benchmark
+    )
+    function_loss_with_weights = metric(
+        y_true,
+        y_pred,
+        y_train=y_train,
+        y_pred_benchmark=y_pred_benchmark,
+        sample_weight=weight_generator,
+    )
+
+    assert not np.isclose(function_loss, function_loss_with_weights), " ".join(
+        ["Loss function with sample weight generator should return different value"]
+    )