dont refit

docs/source/api_reference/regression.rst

@@ -24,14 +24,6 @@ Composition
 
     Pipeline
 
-.. currentmodule:: skpro.regression.online
-
-.. autosummary::
-    :toctree: auto_generated/
-    :template: class.rst
-
-    OnlineRefit
-
 Model selection and tuning
 --------------------------
 
@@ -52,6 +44,18 @@ Model selection and tuning
 
     evaluate
 
+Online learning
+---------------
+
+.. currentmodule:: skpro.regression.online
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    OnlineRefit
+    OnlineDontRefit
+
 Reduction - adding ``predict_proba``
 ------------------------------------
 

skpro/regression/online/__init__.py

@@ -1,6 +1,7 @@
 """Meta-algorithms to build online regression models."""
 # copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
 
+from skpro.regression.online._dont_refit import OnlineDontRefit
 from skpro.regression.online._refit import OnlineRefit
 
-__all__ = ["OnlineRefit"]
+__all__ = ["OnlineDontRefit", "OnlineRefit"]

skpro/regression/online/_dont_refit.py

@@ -0,0 +1,104 @@
+"""Meta-strategy for online learning: turn off online update."""
+
+__author__ = ["fkiraly"]
+__all__ = ["OnlineDontRefit"]
+
+from skpro.regression.base import _DelegatedProbaRegressor
+
+
+class OnlineDontRefit(_DelegatedProbaRegressor):
+    """Simple online regression strategy, turns off any refitting.
+
+    In ``fit``, behaves like the wrapped regressor.
+    In ``update``, does nothing, overriding any other logic.
+
+    This strategy is useful when the wrapped regressor is already an online regressor,
+    to create a "no-op" online regressor for comparison.
+
+    Parameters
+    ----------
+    estimator : skpro regressor, descendant of BaseProbaRegressor
+        regressor to be update-refitted on all data, blueprint
+
+    Attributes
+    ----------
+    estimator_ : skpro regressor, descendant of BaseProbaRegressor
+        clone of the regressor passed in the constructor, fitted on all data
+    """
+
+    _tags = {"capability:online": False}
+
+    def __init__(self, estimator):
+        self.estimator = estimator
+
+        super().__init__()
+
+        tags_to_clone = [
+            "capability:missing",
+            "capability:survival",
+        ]
+        self.clone_tags(estimator, tags_to_clone)
+
+    def _update(self, X, y, C=None):
+        """Update regressor with new batch of training data.
+
+        State required:
+            Requires state to be "fitted".
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pandas DataFrame, must be same length as X
+            labels to fit regressor to
+        C : pd.DataFrame, optional (default=None)
+            censoring information for survival analysis,
+            should have same column name as y, same length as X and y
+            should have entries 0 and 1 (float or int)
+            0 = uncensored, 1 = (right) censored
+            if None, all observations are assumed to be uncensored
+            Can be passed to any probabilistic regressor,
+            but is ignored if capability:survival tag is False.
+
+        Returns
+        -------
+        self : reference to self
+        """
+        return self
+
+    @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`
+        """
+        from sklearn.linear_model import LinearRegression
+
+        from skpro.regression.residual import ResidualDouble
+        from skpro.survival.coxph import CoxPH
+        from skpro.utils.validation._dependencies import _check_estimator_deps
+
+        regressor = ResidualDouble(LinearRegression())
+
+        params = [{"estimator": regressor}]
+
+        if _check_estimator_deps(CoxPH, severity="none"):
+            coxph = CoxPH()
+            params.append({"estimator": coxph})
+
+        return params

skpro/regression/online/_refit.py

@@ -1,4 +1,4 @@
-"""Bagging probabilistic regressors."""
+"""Meta-strategy for online learning: refit on full data."""
 
 __author__ = ["fkiraly"]
 __all__ = ["OnlineRefit"]