Add Support for monotonic_cst Parameter

docs/conf.py

@@ -40,6 +40,7 @@
     "sphinx_design",
     "sphinxcontrib.bibtex",
     "sphinxext_altair.altairplot",
+    "sphinxext.directives",
     "sphinxext.gallery",
 ]
 

docs/sphinxext/directives.py

@@ -0,0 +1,107 @@
+from docutils import nodes
+from docutils.parsers.rst import Directive
+from sphinx.locale import _
+
+
+def process_text(text):
+    """Process the text to identify and format literals."""
+    parts = []
+    start = 0
+    while True:
+        start_literal = text.find("`", start)
+        if start_literal == -1:
+            parts.append(nodes.Text(text[start:]))
+            break
+        parts.append(nodes.Text(text[start:start_literal]))
+        end_literal = text.find("`", start_literal + 1)
+        if end_literal == -1:
+            break  # unmatched backticks
+        literal_text = text[start_literal + 1 : end_literal]
+        parts.append(nodes.literal(literal_text, literal_text))
+        start = end_literal + 1
+    return parts
+
+
+class div(nodes.General, nodes.Element):
+    @staticmethod
+    def visit_div(self, node):
+        self.body.append(self.starttag(node, "div"))
+
+    @staticmethod
+    def depart_div(self, node=None):
+        self.body.append("</div>\n")
+
+
+class span(nodes.Inline, nodes.TextElement):
+    @staticmethod
+    def visit_span(self, node):
+        self.body.append(self.starttag(node, "span", ""))
+
+    @staticmethod
+    def depart_span(self, node=None):
+        self.body.append("</span>")
+
+
+class SklearnVersionAddedDirective(Directive):
+    """Custom directive to denote the version additions to scikit-learn."""
+
+    required_arguments = 1
+    optional_arguments = 0
+    final_argument_whitespace = True
+    has_content = True
+
+    def run(self):
+        text = None
+        if len(self.arguments[0].split("\n", 1)) > 1:
+            version, text = self.arguments[0].split("\n", 1)
+        else:
+            version = self.arguments[0]
+        container = div(classes=["versionadded"])
+        paragraph = nodes.paragraph()
+        span_node = span(
+            "",
+            _(f"New in scikit-learn version {version}{'.' if text is None else ': '} "),
+            classes=["versionmodified", "added"],
+        )
+        paragraph += span_node
+        if text is not None:
+            paragraph += process_text(text)
+        container += paragraph
+        self.state.nested_parse(self.content, self.content_offset, container)
+        return [container]
+
+
+class SklearnVersionChangedDirective(Directive):
+    """Custom directive to denote the version changes to scikit-learn."""
+
+    required_arguments = 1
+    optional_arguments = 0
+    final_argument_whitespace = True
+    has_content = True
+
+    def run(self):
+        text = None
+        if len(self.arguments[0].split("\n")) > 1:
+            version, text = self.arguments[0].split("\n", 1)
+        else:
+            version = self.arguments[0]
+        container = div(classes=["versionchanged"])
+        paragraph = nodes.paragraph()
+        span_node = span(
+            "",
+            _(f"Changed in scikit-learn version {version}{'.' if text is None else ': '} "),
+            classes=["versionmodified", "changed"],
+        )
+        paragraph += span_node
+        if text is not None:
+            paragraph += process_text(text)
+        container += paragraph
+        self.state.nested_parse(self.content, self.content_offset, container)
+        return [container]
+
+
+def setup(app):
+    app.add_node(div, html=(div.visit_div, div.depart_div))
+    app.add_node(span, html=(span.visit_span, span.depart_span))
+    app.add_directive("sklearn-versionadded", SklearnVersionAddedDirective)
+    app.add_directive("sklearn-versionchanged", SklearnVersionChangedDirective)

examples/plot_quantile_extrapolation.py

@@ -319,7 +319,7 @@ def train_test_split(train_indices, **kwargs):
 
 
 def prob_randomized_pi(qmat, y, coverage):
-    """Calculate calibration probability"""
+    """Calculate calibration probability."""
     alpha_included = np.mean((qmat[:, 0] <= y) & (y <= qmat[:, 1]))
     alpha_excluded = np.mean((qmat[:, 0] < y) & (y < qmat[:, 1]))
     if coverage <= alpha_excluded: