scikit-learn
diff --git a/‎doc/conf.py
Lines changed: 10 additions & 14 deletions b/‎doc/conf.py
Lines changed: 10 additions & 14 deletions
diff --git a/‎doc/visualizations.rst
Lines changed: 9 additions & 13 deletions b/‎doc/visualizations.rst
Lines changed: 9 additions & 13 deletions
@@ -29,7 +29,6 @@
 
 from github_link import make_linkcode_resolve
 import sphinx_gallery
-import matplotlib as mpl
 from sphinx_gallery.sorting import ExampleTitleSortKey
 
 # -- General configuration ---------------------------------------------------
@@ -50,22 +49,19 @@
     "sphinx-prompt",
     "sphinxext.opengraph",
     "doi_role",
+    "matplotlib.sphinxext.plot_directive",
 ]
 
-# Support for `plot::` directives in sphinx 3.2 requires matplotlib 3.1.0 or newer
-if parse(mpl.__version__) >= parse("3.1.0"):
-    extensions.append("matplotlib.sphinxext.plot_directive")
+# Produce `plot::` directives for examples that contain `import matplotlib` or
+# `from matplotlib import`.
+numpydoc_use_plots = True
 
-    # Produce `plot::` directives for examples that contain `import matplotlib` or
-    # `from matplotlib import`.
-    numpydoc_use_plots = True
-
-    # Options for the `::plot` directive:
-    # https://matplotlib.org/stable/api/sphinxext_plot_directive_api.html
-    plot_formats = ["png"]
-    plot_include_source = True
-    plot_html_show_formats = False
-    plot_html_show_source_link = False
+# Options for the `::plot` directive:
+# https://matplotlib.org/stable/api/sphinxext_plot_directive_api.html
+plot_formats = ["png"]
+plot_include_source = True
+plot_html_show_formats = False
+plot_html_show_source_link = False
 
 # this is needed for some reason...
 # see https://github.com/numpy/numpydoc/issues/69
 
@@ -20,24 +20,23 @@ like to only compute the predictions once and one should use `from_predictions`
 vector machine:
 
-.. code-block:: python
+.. plot::
+   :context:
+   :align: center
 
     from sklearn.model_selection import train_test_split
     from sklearn.svm import SVC
     from sklearn.metrics import RocCurveDisplay
     from sklearn.datasets import load_wine
 
+    X, y = load_wine(return_X_y=True)
+    y = y == 2  # make binary
     X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
     svc = SVC(random_state=42)
     svc.fit(X_train, y_train)
 
     svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
 
-.. figure:: auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_001.png
-    :target: auto_examples/miscellaneous/plot_roc_curve_visualization_api.html
-    :align: center
-    :scale: 75%
-
 The returned `svc_disp` object allows us to continue using the already computed
 ROC curve for SVC in future plots. In this case, the `svc_disp` is a
 :class:`~sklearn.metrics.RocCurveDisplay` that stores the computed values as
@@ -47,23 +46,20 @@ instead of `from_estimator` Next, we train a random forest classifier and plot
 the previously computed roc curve again by using the `plot` method of the
 `Display` object.
 
-.. code-block:: python
+.. plot::
+   :context: close-figs
+   :align: center
 
     import matplotlib.pyplot as plt
     from sklearn.ensemble import RandomForestClassifier
 
-    rfc = RandomForestClassifier(random_state=42)
+    rfc = RandomForestClassifier(n_estimators=10, random_state=42)
     rfc.fit(X_train, y_train)
 
     ax = plt.gca()
     rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
     svc_disp.plot(ax=ax, alpha=0.8)
 
-.. figure:: auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_002.png
-    :target: auto_examples/miscellaneous/plot_roc_curve_visualization_api.html
-    :align: center
-    :scale: 75%
-
 Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha
 values of the curves.