Pushing the docs to dev/ for branch: main, commit 78f0c0000a293ee4d01e3aa95e507f980088db4e

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):\n    \"\"\"Create a sample plot for indices of a cross-validation object.\"\"\"\n\n    # Generate the training/testing visualizations for each CV split\n    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):\n        # Fill in indices with the training/test groups\n        indices = np.array([np.nan] * len(X))\n        indices[tt] = 1\n        indices[tr] = 0\n\n        # Visualize the results\n        ax.scatter(\n            range(len(indices)),\n            [ii + 0.5] * len(indices),\n            c=indices,\n            marker=\"_\",\n            lw=lw,\n            cmap=cmap_cv,\n            vmin=-0.2,\n            vmax=1.2,\n        )\n\n    # Plot the data classes and groups at the end\n    ax.scatter(\n        range(len(X)), [ii + 1.5] * len(X), c=y, marker=\"_\", lw=lw, cmap=cmap_data\n    )\n\n    ax.scatter(\n        range(len(X)), [ii + 2.5] * len(X), c=group, marker=\"_\", lw=lw, cmap=cmap_data\n    )\n\n    # Formatting\n    yticklabels = list(range(n_splits)) + [\"class\", \"group\"]\n    ax.set(\n        yticks=np.arange(n_splits + 2) + 0.5,\n        yticklabels=yticklabels,\n        xlabel=\"Sample index\",\n        ylabel=\"CV iteration\",\n        ylim=[n_splits + 2.2, -0.2],\n        xlim=[0, 100],\n    )\n    ax.set_title(\"{}\".format(type(cv).__name__), fontsize=15)\n    return ax"
+        "def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):\n    \"\"\"Create a sample plot for indices of a cross-validation object.\"\"\"\n    use_groups = \"Group\" in type(cv).__name__\n    groups = group if use_groups else None\n    # Generate the training/testing visualizations for each CV split\n    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=groups)):\n        # Fill in indices with the training/test groups\n        indices = np.array([np.nan] * len(X))\n        indices[tt] = 1\n        indices[tr] = 0\n\n        # Visualize the results\n        ax.scatter(\n            range(len(indices)),\n            [ii + 0.5] * len(indices),\n            c=indices,\n            marker=\"_\",\n            lw=lw,\n            cmap=cmap_cv,\n            vmin=-0.2,\n            vmax=1.2,\n        )\n\n    # Plot the data classes and groups at the end\n    ax.scatter(\n        range(len(X)), [ii + 1.5] * len(X), c=y, marker=\"_\", lw=lw, cmap=cmap_data\n    )\n\n    ax.scatter(\n        range(len(X)), [ii + 2.5] * len(X), c=group, marker=\"_\", lw=lw, cmap=cmap_data\n    )\n\n    # Formatting\n    yticklabels = list(range(n_splits)) + [\"class\", \"group\"]\n    ax.set(\n        yticks=np.arange(n_splits + 2) + 0.5,\n        yticklabels=yticklabels,\n        xlabel=\"Sample index\",\n        ylabel=\"CV iteration\",\n        ylim=[n_splits + 2.2, -0.2],\n        xlim=[0, 100],\n    )\n    ax.set_title(\"{}\".format(type(cv).__name__), fontsize=15)\n    return ax"
       ]
     },
     {

dev/_downloads/49cd91d05440a1c88b074430761aeb76/plot_cv_indices.ipynb

@@ -99,9 +99,10 @@ def visualize_groups(classes, groups, name):
 
 def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):
     """Create a sample plot for indices of a cross-validation object."""
-
+    use_groups = "Group" in type(cv).__name__
+    groups = group if use_groups else None
     # Generate the training/testing visualizations for each CV split
-    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):
+    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=groups)):
         # Fill in indices with the training/test groups
         indices = np.array([np.nan] * len(X))
         indices[tt] = 1