[ENH] EmpiricalFitter distribution fitter

docs/source/api_reference/distfitter.rst

@@ -24,6 +24,17 @@ Parametric fitters
     MOMFitter
     NormalFitter
 
+Non-parametric fitters
+----------------------
+
+.. currentmodule:: skpro.distfitter
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    EmpiricalFitter
+
 Base
 ----
 

skpro/distfitter/__init__.py

@@ -1,10 +1,13 @@
 """Distribution fitter estimators."""
+
 # copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
 
+from skpro.distfitter._empiricalfitter import EmpiricalFitter
 from skpro.distfitter._momfitter import MOMFitter
 from skpro.distfitter._normalfitter import NormalFitter
 
 __all__ = [
+    "EmpiricalFitter",
     "NormalFitter",
     "MOMFitter",
 ]

skpro/distfitter/_empiricalfitter.py

@@ -0,0 +1,78 @@
+"""Empirical distribution fitter."""
+
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+
+import pandas as pd
+
+from skpro.distfitter.base import BaseDistFitter
+
+
+class EmpiricalFitter(BaseDistFitter):
+    """Fit an empirical distribution by wrapping data in an Empirical distribution.
+
+    Converts the full sample into an empirical distribution.
+    For the univariate case (empirical per variable), this simply wraps the
+    data in an ``Empirical`` distribution.
+
+    This is useful as a base component, e.g., for naive distribution fitting
+    in ensemble or reduction strategies.
+
+    Examples
+    --------
+    >>> import pandas as pd
+    >>> from skpro.distfitter import EmpiricalFitter
+    >>> X = pd.DataFrame([1.0, 2.0, 3.0, 4.0, 5.0])
+
+    >>> fitter = EmpiricalFitter()
+    >>> fitter.fit(X)
+    EmpiricalFitter()
+    >>> dist = fitter.proba()
+    """
+
+    _tags = {
+        "authors": ["utsab345"],
+    }
+
+    def _fit(self, X, C=None):
+        """Fit empirical distribution by storing the data.
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            Data to fit the distribution to.
+        C : ignored
+
+        Returns
+        -------
+        self : reference to self
+        """
+        vals = X.values.ravel()
+        self.spl_ = pd.DataFrame(vals, columns=["spl"])
+        return self
+
+    def _proba(self):
+        """Return fitted Empirical distribution.
+
+        Returns
+        -------
+        dist : skpro Empirical distribution (scalar)
+        """
+        from skpro.distributions.empirical import Empirical
+
+        return Empirical(spl=self.spl_)
+
+    @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.
+
+        Returns
+        -------
+        params : dict or list of dict
+            Parameters to create testing instances of the class.
+        """
+        return [{}]