iter

Author: glemaitre

doc/_static/css/imbalanced-learn.css

@@ -57,3 +57,8 @@
   margin-bottom: 0rem;
   color: #484848;
 }
+
+.navbar-brand img {
+  width: 0%;
+  height: 0%;
+}

doc/_templates/class.rst

@@ -1,4 +1,4 @@
-:mod:`{{module}}`.{{objname}}
+{{objname}}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

doc/_templates/function.rst

@@ -1,4 +1,4 @@
-:mod:`{{module}}`.{{objname}}
+{{objname}}
 {{ underline }}====================
 
 .. currentmodule:: {{ module }}

doc/_templates/sidebar-search-bs.html

@@ -0,0 +1,14 @@
+<div class="navbar-brand-box">
+  <a class="navbar-brand text-wrap" href="{{ pathto('index') }}">
+    {% if logo %}
+    <img
+      src="{{ pathto('_static/' + logo, 1) }}"
+      class="logo"
+      style="width: 60%"
+      alt="logo"
+    />
+    {% endif %} {% if docstitle %}
+    <h4 class="site-logo" id="site-title">{{ docstitle }}</h4>
+    {% endif %}
+  </a>
+</div>

doc/conf.py

@@ -90,14 +90,20 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 html_theme = "pydata_sphinx_theme"
+html_title = f"Version {version}"
 html_favicon = "_static/img/favicon.ico"
 html_logo = "_static/img/logo.png"
 html_style = "css/imbalanced-learn.css"
 html_css_files = [
     "css/imbalanced-learn.css",
 ]
+html_sidebars = {
+    "contributing": ["sidebar-search-bs.html"],
+    "changelog": [],
+}
 
 html_theme_options = {
+    "external_links": [],
     "github_url": "https://github.com/scikit-learn-contrib/imbalanced-learn",
     # "twitter_url": "https://twitter.com/pandas_dev",
     "use_edit_page_button": True,

doc/references/metrics.rst

@@ -1,7 +1,7 @@
 .. _metrics_ref:
 
-:mod:`imblearn.metrics`: Metrics
-================================
+Metrics
+=======
 
 .. automodule:: imblearn.metrics
    :no-members:

imblearn/metrics/pairwise.py

@@ -47,7 +47,7 @@ class ValueDifferenceMetric(BaseEstimator):
         of categories will be computed from `X` at `fit`. Otherwise, you can
         provide an array-like of such counts to avoid computation. You can use
         the fitted attribute `categories_` of the
-        :class:`~sklearn.preprocesssing.OrdinalEncoder`to deduce these counts.
+        :class:`~sklearn.preprocesssing.OrdinalEncoder` to deduce these counts.
 
     k : int, default=1
         Exponent used to compute the distance between feature value.