Pushing the docs to dev/ for branch: main, commit 38173e9c4e4ec422611d6c4e50af54d76ebe5863

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom matplotlib import offsetbox\nfrom sklearn.preprocessing import MinMaxScaler\n\n\ndef plot_embedding(X, title, ax):\n    X = MinMaxScaler().fit_transform(X)\n    for digit in digits.target_names:\n        ax.scatter(\n            *X[y == digit].T,\n            marker=f\"${digit}$\",\n            s=60,\n            color=plt.cm.Dark2(digit),\n            alpha=0.425,\n            zorder=2,\n        )\n    shown_images = np.array([[1.0, 1.0]])  # just something big\n    for i in range(X.shape[0]):\n        # plot every digit on the embedding\n        # show an annotation box for a group of digits\n        dist = np.sum((X[i] - shown_images) ** 2, 1)\n        if np.min(dist) < 4e-3:\n            # don't show points that are too close\n            continue\n        shown_images = np.concatenate([shown_images, [X[i]]], axis=0)\n        imagebox = offsetbox.AnnotationBbox(\n            offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r), X[i]\n        )\n        imagebox.set(zorder=1)\n        ax.add_artist(imagebox)\n\n    ax.set_title(title)\n    ax.axis(\"off\")"
+        "import numpy as np\nfrom matplotlib import offsetbox\nfrom sklearn.preprocessing import MinMaxScaler\n\n\ndef plot_embedding(X, title):\n    _, ax = plt.subplots()\n    X = MinMaxScaler().fit_transform(X)\n\n    for digit in digits.target_names:\n        ax.scatter(\n            *X[y == digit].T,\n            marker=f\"${digit}$\",\n            s=60,\n            color=plt.cm.Dark2(digit),\n            alpha=0.425,\n            zorder=2,\n        )\n    shown_images = np.array([[1.0, 1.0]])  # just something big\n    for i in range(X.shape[0]):\n        # plot every digit on the embedding\n        # show an annotation box for a group of digits\n        dist = np.sum((X[i] - shown_images) ** 2, 1)\n        if np.min(dist) < 4e-3:\n            # don't show points that are too close\n            continue\n        shown_images = np.concatenate([shown_images, [X[i]]], axis=0)\n        imagebox = offsetbox.AnnotationBbox(\n            offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r), X[i]\n        )\n        imagebox.set(zorder=1)\n        ax.add_artist(imagebox)\n\n    ax.set_title(title)\n    ax.axis(\"off\")"
       ]
     },
     {
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "from itertools import zip_longest\n\nfig, axs = plt.subplots(nrows=7, ncols=2, figsize=(17, 24))\n\nfor name, ax in zip_longest(timing, axs.ravel()):\n    if name is None:\n        ax.axis(\"off\")\n        continue\n    title = f\"{name} (time {timing[name]:.3f}s)\"\n    plot_embedding(projections[name], title, ax)\n\nplt.show()"
+        "for name in timing:\n    title = f\"{name} (time {timing[name]:.3f}s)\"\n    plot_embedding(projections[name], title)\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb

@@ -48,8 +48,10 @@
 from sklearn.preprocessing import MinMaxScaler
 
 
-def plot_embedding(X, title, ax):
+def plot_embedding(X, title):
+    _, ax = plt.subplots()
     X = MinMaxScaler().fit_transform(X)
+
     for digit in digits.target_names:
         ax.scatter(
             *X[y == digit].T,
@@ -175,15 +177,8 @@ def plot_embedding(X, title, ax):
 
 # %%
 # Finally, we can plot the resulting projection given by each method.
-from itertools import zip_longest
-
-fig, axs = plt.subplots(nrows=7, ncols=2, figsize=(17, 24))
-
-for name, ax in zip_longest(timing, axs.ravel()):
-    if name is None:
-        ax.axis("off")
-        continue
+for name in timing:
     title = f"{name} (time {timing[name]:.3f}s)"
-    plot_embedding(projections[name], title, ax)
+    plot_embedding(projections[name], title)
 
 plt.show()