[ENH] online update capability for probabilistic regressors

docs/source/api_reference/regression.rst

@@ -44,6 +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/registry/_tags.py

@@ -122,6 +122,12 @@
         "bool",
         "whether estimator supports missing values",
     ),
+    (
+        "capability:online",
+        "regressor_proba",
+        "bool",
+        "whether estimator supports online updates via update",
+    ),
     (
         "X_inner_mtype",
         "regressor_proba",

skpro/regression/base/_base.py

@@ -33,6 +33,7 @@ class BaseProbaRegressor(BaseEstimator):
         "capability:survival": False,
         "capability:multioutput": False,
         "capability:missing": True,
+        "capability:online": False,
         "X_inner_mtype": "pd_DataFrame_Table",
         "y_inner_mtype": "pd_DataFrame_Table",
         "C_inner_mtype": "pd_DataFrame_Table",
@@ -136,6 +137,74 @@ def _fit(self, X, y, C=None):
         """
         raise NotImplementedError
 
+    def update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        Only estimators with the ``capability:online`` tag (value ``True``)
+        provide this method, otherwise the method ignores the call and
+        discards the data passed.
+
+        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 : pd.DataFrame, must be same length as X
+            labels to fit regressor to
+        C : ignored, optional (default=None)
+            censoring information for survival analysis
+            All probabilistic regressors assume data to be uncensored
+
+        Returns
+        -------
+        self : reference to self
+        """
+        capa_online = self.get_tag("capability:online")
+        capa_surv = self.get_tag("capability:survival")
+
+        if not capa_online:
+            return self
+
+        check_ret = self._check_X_y(X, y, C, return_metadata=True)
+
+        # get inner X, y, C
+        X_inner = check_ret["X_inner"]
+        y_inner = check_ret["y_inner"]
+        if capa_surv:
+            C_inner = check_ret["C_inner"]
+
+        if not capa_surv:
+            return self._update(X_inner, y_inner)
+        else:
+            return self._update(X_inner, y_inner, C=C_inner)
+
+    def _update(self, X, y, C=None):
+        """Update regressor with a 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
+
+        Returns
+        -------
+        self : reference to self
+        """
+        raise NotImplementedError
+
     def predict(self, X):
         """Predict labels for data from features.
 

skpro/regression/base/_delegate.py

@@ -67,6 +67,38 @@ def _fit(self, X, y, C=None):
         estimator.fit(X=X, y=y, C=C)
         return self
 
+    def _update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        State required:
+            Requires state to be "fitted" = self.is_fitted=True
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pd.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
+        """
+        estimator = self._get_delegate()
+        estimator.update(X=X, y=y, C=C)
+        return self
+
     def _predict(self, X):
         """Predict labels for data from features.
 

skpro/regression/compose/_pipeline.py

@@ -336,7 +336,11 @@ def __init__(self, steps):
 
         super().__init__()
 
-        tags_to_clone = ["capability:multioutput", "capability:survival"]
+        tags_to_clone = [
+            "capability:multioutput",
+            "capability:survival",
+            "capability:online",
+        ]
         self.clone_tags(self.regressor_, tags_to_clone)
 
     @property
@@ -427,6 +431,38 @@ def _fit(self, X, y, C=None):
 
         return self
 
+    def _update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        State required:
+            Requires state to be "fitted" = self.is_fitted=True
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pd.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
+        """
+        X = self._transform(X)
+        self.regressor_.update(X=X, y=y, C=C)
+        return self
+
     def _predict(self, X):
         """Predict labels for data from features.
 

skpro/regression/online/__init__.py

@@ -0,0 +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__ = ["OnlineDontRefit", "OnlineRefit"]

skpro/regression/online/_dont_refit.py

@@ -0,0 +1,106 @@
+"""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)
+
+        self.estimator_ = self.estimator.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