docs

docs/api/sklearn.md

@@ -0,0 +1,6 @@
+# Scikit-learn Compatible Time Based Cross Validation
+
+::: timebasedcv.sklearn.TimeBasedCVSplitter
+    options:
+        show_root_full_path: false
+        show_root_heading: true

docs/api/timebasedsplit.md

@@ -1,14 +1,11 @@
 # TimeBased Cross Validation
 
-::: timebasedcv.timebasedsplit.TimeBasedCVSplitter
-    options:
-        show_root_full_path: false
-        show_root_heading: true
-
 ::: timebasedcv.timebasedsplit.TimeBasedSplit
     options:
         show_root_full_path: false
         show_root_heading: true
+        members:
+            - split
 
 ::: timebasedcv.timebasedsplit.ExpandingTimeSplit
     options:
@@ -25,6 +22,6 @@
         show_root_full_path: false
         show_root_heading: true
         members:
+            - split
             - _splits_from_period
             - n_splits_of
-            - split

mkdocs.yml

@@ -15,9 +15,7 @@ watch:
 use_directory_urls: true
 theme:
   name: material
-  font:
-    text: Ubuntu
-    code: Ubuntu Mono
+  font: false
   palette:
   - media: '(prefers-color-scheme: light)'
     scheme: default
@@ -49,11 +47,11 @@ theme:
     - content.code.copy
     - content.tooltips
     - content.tabs.link
-    
+
     - search.suggest
     - search.highlight
     - search.share
-  
+
     - toc.follow
 
   logo: img/timebasedcv-logo.png
@@ -114,6 +112,7 @@ nav:
   - Getting Started: getting-started.md
   - API:
     - Time based splits: api/timebasedsplit.md
+    - Scikit-learn Compatible CV: api/sklearn.md
     - Split state: api/splitstate.md
     - Types and protocols: api/types.md
   - Contributing: contribute.md

timebasedcv/sklearn.py

@@ -91,17 +91,15 @@ class TimeBasedCVSplitter(BaseCrossValidator):
     from sklearn.linear_model import Ridge
     from sklearn.model_selection import RandomizedSearchCV
 
-    from timebasedcv import TimeBasedCVSplitter
+    from timebasedcv.sklearn import TimeBasedCVSplitter
 
     start_dt = pd.Timestamp(2023, 1, 1)
     end_dt = pd.Timestamp(2023, 1, 31)
 
     time_series = pd.Series(pd.date_range(start_dt, end_dt, freq="D"))
     size = len(time_series)
 
-    df = (
-        pd.DataFrame(data=np.random.randn(size, 2), columns=["a", "b"]).assign(y=lambda t: t[["a", "b"]].sum(axis=1)),
-    )
+    df = pd.DataFrame(data=np.random.randn(size, 2), columns=["a", "b"]).assign(y=lambda t: t[["a", "b"]].sum(axis=1))
 
     X, y = df[["a", "b"]], df["y"]