FEA add link for HTML representation

skrub/_gap_encoder.py

@@ -19,10 +19,18 @@
 
 from . import _dataframe as sbd
 from ._on_each_column import RejectColumn, SingleColumnTransformer
+from ._repr import (
+    _HTMLDocumentationLinkMixin,
+    doc_link_module,
+    doc_link_template,
+    doc_link_url_param_generator,
+)
 from ._utils import unique_strings
 
 
-class GapEncoder(TransformerMixin, SingleColumnTransformer):
+class GapEncoder(
+    _HTMLDocumentationLinkMixin, TransformerMixin, SingleColumnTransformer
+):
     """Constructs latent topics with continuous encoding.
 
     This encoder can be understood as a continuous encoding on a set of latent
@@ -177,6 +185,10 @@ class GapEncoder(TransformerMixin, SingleColumnTransformer):
     The higher the value, the bigger the correspondence with the topic.
     """
 
+    _doc_link_module = doc_link_module
+    _doc_link_template = doc_link_template
+    _doc_link_url_param_generator = doc_link_url_param_generator
+
     def __init__(
         self,
         n_components=10,

skrub/_repr.py

@@ -0,0 +1,103 @@
+import itertools
+
+import sklearn
+from sklearn.utils.fixes import parse_version
+
+sklearn_version = parse_version(sklearn.__version__)
+
+# TODO: remove when scikit-learn 1.6 is the minimum supported version
+# TODO: subsequently, we should remove the inheritance from _HTMLDocumentationLinkMixin
+# for each estimator then.
+if sklearn_version > parse_version("1.6"):
+    from sklearn.utils._estimator_html_repr import _HTMLDocumentationLinkMixin
+else:
+
+    class _HTMLDocumentationLinkMixin:
+        """Mixin class allowing to generate a link to the API documentation."""
+
+        _doc_link_module = "sklearn"
+        _doc_link_url_param_generator = None
+
+        @property
+        def _doc_link_template(self):
+            sklearn_version = parse_version(sklearn.__version__)
+            if sklearn_version.dev is None:
+                version_url = f"{sklearn_version.major}.{sklearn_version.minor}"
+            else:
+                version_url = "dev"
+            return getattr(
+                self,
+                "__doc_link_template",
+                (
+                    f"https://scikit-learn.org/{version_url}/modules/generated/"
+                    "{estimator_module}.{estimator_name}.html"
+                ),
+            )
+
+        @_doc_link_template.setter
+        def _doc_link_template(self, value):
+            setattr(self, "__doc_link_template", value)
+
+        def _get_doc_link(self):
+            """Generates a link to the API documentation for a given estimator.
+
+            This method generates the link to the estimator's documentation page
+            by using the template defined by the attribute `_doc_link_template`.
+
+            Returns
+            -------
+            url : str
+                The URL to the API documentation for this estimator. If the estimator
+                does not belong to module `_doc_link_module`, the empty string (i.e.
+                `""`) is returned.
+            """
+            if self.__class__.__module__.split(".")[0] != self._doc_link_module:
+                return ""
+
+            if self._doc_link_url_param_generator is None:
+                estimator_name = self.__class__.__name__
+                # Construct the estimator's module name, up to the first private
+                # submodule. This works because in scikit-learn all public estimators
+                # are exposed at that level, even if they actually live in a private
+                # sub-module.
+                estimator_module = ".".join(
+                    itertools.takewhile(
+                        lambda part: not part.startswith("_"),
+                        self.__class__.__module__.split("."),
+                    )
+                )
+                return self._doc_link_template.format(
+                    estimator_module=estimator_module, estimator_name=estimator_name
+                )
+            return self._doc_link_template.format(
+                **self._doc_link_url_param_generator()
+            )
+
+
+doc_link_template = (
+    "https://skrub-data.org/{version}/reference/generated/"
+    "{estimator_module}.{estimator_name}.html"
+)
+doc_link_module = "skrub"
+
+
+def doc_link_url_param_generator(estimator):
+    from skrub import __version__
+
+    skrub_version = parse_version(__version__)
+    if skrub_version.dev is None:
+        version_url = f"{skrub_version.major}.{skrub_version.minor}"
+    else:
+        version_url = "dev"
+    estimator_name = estimator.__class__.__name__
+    estimator_module = ".".join(
+        itertools.takewhile(
+            lambda part: not part.startswith("_"),
+            estimator.__class__.__module__.split("."),
+        )
+    )
+    return {
+        "version": version_url,
+        "estimator_module": estimator_module,
+        "estimator_name": estimator_name,
+    }

skrub/_table_vectorizer.py

@@ -17,6 +17,12 @@
 from ._datetime_encoder import DatetimeEncoder
 from ._gap_encoder import GapEncoder
 from ._on_each_column import SingleColumnTransformer
+from ._repr import (
+    _HTMLDocumentationLinkMixin,
+    doc_link_module,
+    doc_link_template,
+    doc_link_url_param_generator,
+)
 from ._select_cols import Drop
 from ._to_datetime import ToDatetime
 from ._to_float32 import ToFloat32
@@ -110,7 +116,7 @@ def _check_transformer(transformer):
     return clone(transformer)
 
 
-class TableVectorizer(TransformerMixin, BaseEstimator):
+class TableVectorizer(_HTMLDocumentationLinkMixin, TransformerMixin, BaseEstimator):
     """Transform a dataframe to a numerical (vectorized) representation.
 
     Applies a different transformation to each of several kinds of columns:
@@ -405,6 +411,10 @@ class TableVectorizer(TransformerMixin, BaseEstimator):
     ValueError: Column 'A' used twice in 'specific_transformers', at indices 0 and 1.
     """  # noqa: E501
 
+    _doc_link_module = doc_link_module
+    _doc_link_template = doc_link_template
+    _doc_link_url_param_generator = doc_link_url_param_generator
+
     def __init__(
         self,
         *,