[ENH] pipeline (#51)

Implements #29 - a simple pipeline compatible with `sklearn` transformers.

skpro/registry/_tags.py

@@ -171,6 +171,12 @@
         "str",
         "name of component list attribute for meta-objects",
     ),
+    (
+        "fitted_named_object_parameters",
+        "estimator",
+        "str",
+        "name of fitted component list attribute for meta-objects",
+    ),
 ]
 
 OBJECT_TAG_TABLE = pd.DataFrame(OBJECT_TAG_REGISTER)

skpro/regression/base/_base.py

@@ -27,6 +27,31 @@ def __init__(self, index=None, columns=None):
         super(BaseProbaRegressor, self).__init__()
         _check_estimator_deps(self)
 
+    def __rmul__(self, other):
+        """Magic * method, return (left) concatenated Pipeline.
+
+        Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
+
+        Parameters
+        ----------
+        other: `sklearn` transformer, must follow `sklearn` API
+            otherwise, `NotImplemented` is returned
+
+        Returns
+        -------
+        Pipeline object,
+            concatenation of `other` (first) with `self` (last).
+            not nested, contains only non-Pipeline `skpro` steps
+        """
+        from skpro.regression.compose._pipeline import Pipeline
+
+        # we wrap self in a pipeline, and concatenate with the other
+        #   the TransformedTargetForecaster does the rest, e.g., dispatch on other
+        if hasattr(other, "transform"):
+            return other * Pipeline([self])
+        else:
+            return NotImplemented
+
     def fit(self, X, y):
         """Fit regressor to training data.
 

skpro/regression/compose/__init__.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+"""Composition and pipelines for probabilistic supervised regression."""
+
+from skpro.regression.compose._pipeline import Pipeline
+
+__all__ = ["Pipeline"]