Pushing the docs to dev/ for branch: main, commit 2335a8e831b44b28f30cedb355bab6ac4803ef8b

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Though the following import is not directly being used, it is required\n# for 3D projection to work with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\n\nfrom sklearn.cluster import KMeans\nfrom sklearn import datasets\n\nnp.random.seed(5)\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\nestimators = [\n    (\"k_means_iris_8\", KMeans(n_clusters=8)),\n    (\"k_means_iris_3\", KMeans(n_clusters=3)),\n    (\"k_means_iris_bad_init\", KMeans(n_clusters=3, n_init=1, init=\"random\")),\n]\n\nfignum = 1\ntitles = [\"8 clusters\", \"3 clusters\", \"3 clusters, bad initialization\"]\nfor name, est in estimators:\n    fig = plt.figure(fignum, figsize=(4, 3))\n    ax = fig.add_subplot(111, projection=\"3d\", elev=48, azim=134)\n    ax.set_position([0, 0, 0.95, 1])\n    est.fit(X)\n    labels = est.labels_\n\n    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(float), edgecolor=\"k\")\n\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n    ax.set_xlabel(\"Petal width\")\n    ax.set_ylabel(\"Sepal length\")\n    ax.set_zlabel(\"Petal length\")\n    ax.set_title(titles[fignum - 1])\n    ax.dist = 12\n    fignum = fignum + 1\n\n# Plot the ground truth\nfig = plt.figure(fignum, figsize=(4, 3))\nax = fig.add_subplot(111, projection=\"3d\", elev=48, azim=134)\nax.set_position([0, 0, 0.95, 1])\n\nfor name, label in [(\"Setosa\", 0), (\"Versicolour\", 1), (\"Virginica\", 2)]:\n    ax.text3D(\n        X[y == label, 3].mean(),\n        X[y == label, 0].mean(),\n        X[y == label, 2].mean() + 2,\n        name,\n        horizontalalignment=\"center\",\n        bbox=dict(alpha=0.2, edgecolor=\"w\", facecolor=\"w\"),\n    )\n# Reorder the labels to have colors matching the cluster results\ny = np.choose(y, [1, 2, 0]).astype(float)\nax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor=\"k\")\n\nax.w_xaxis.set_ticklabels([])\nax.w_yaxis.set_ticklabels([])\nax.w_zaxis.set_ticklabels([])\nax.set_xlabel(\"Petal width\")\nax.set_ylabel(\"Sepal length\")\nax.set_zlabel(\"Petal length\")\nax.set_title(\"Ground Truth\")\nax.dist = 12\n\nfig.show()"
+        "# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Though the following import is not directly being used, it is required\n# for 3D projection to work with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\n\nfrom sklearn.cluster import KMeans\nfrom sklearn import datasets\n\nnp.random.seed(5)\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\nestimators = [\n    (\"k_means_iris_8\", KMeans(n_clusters=8, n_init=\"auto\")),\n    (\"k_means_iris_3\", KMeans(n_clusters=3, n_init=\"auto\")),\n    (\"k_means_iris_bad_init\", KMeans(n_clusters=3, n_init=1, init=\"random\")),\n]\n\nfig = plt.figure(figsize=(10, 8))\ntitles = [\"8 clusters\", \"3 clusters\", \"3 clusters, bad initialization\"]\nfor idx, (name, est) in enumerate(estimators):\n    ax = fig.add_subplot(2, 2, idx + 1, projection=\"3d\", elev=48, azim=134)\n    est.fit(X)\n    labels = est.labels_\n\n    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(float), edgecolor=\"k\")\n\n    ax.xaxis.set_ticklabels([])\n    ax.yaxis.set_ticklabels([])\n    ax.zaxis.set_ticklabels([])\n    ax.set_xlabel(\"Petal width\")\n    ax.set_ylabel(\"Sepal length\")\n    ax.set_zlabel(\"Petal length\")\n    ax.set_title(titles[idx - 1])\n\n# Plot the ground truth\nax = fig.add_subplot(2, 2, 4, projection=\"3d\", elev=48, azim=134)\n\nfor name, label in [(\"Setosa\", 0), (\"Versicolour\", 1), (\"Virginica\", 2)]:\n    ax.text3D(\n        X[y == label, 3].mean(),\n        X[y == label, 0].mean(),\n        X[y == label, 2].mean() + 2,\n        name,\n        horizontalalignment=\"center\",\n        bbox=dict(alpha=0.2, edgecolor=\"w\", facecolor=\"w\"),\n    )\n# Reorder the labels to have colors matching the cluster results\ny = np.choose(y, [1, 2, 0]).astype(float)\nax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor=\"k\")\n\nax.xaxis.set_ticklabels([])\nax.yaxis.set_ticklabels([])\nax.zaxis.set_ticklabels([])\nax.set_xlabel(\"Petal width\")\nax.set_ylabel(\"Sepal length\")\nax.set_zlabel(\"Petal length\")\nax.set_title(\"Ground Truth\")\n\nplt.subplots_adjust(wspace=0.25, hspace=0.25)\nplt.show()"
       ]
     }
   ],

dev/_downloads/18eb95af29bd5554020a8428b3ceac54/plot_cluster_iris.ipynb

@@ -36,36 +36,30 @@
 y = iris.target
 
 estimators = [
-    ("k_means_iris_8", KMeans(n_clusters=8)),
-    ("k_means_iris_3", KMeans(n_clusters=3)),
+    ("k_means_iris_8", KMeans(n_clusters=8, n_init="auto")),
+    ("k_means_iris_3", KMeans(n_clusters=3, n_init="auto")),
     ("k_means_iris_bad_init", KMeans(n_clusters=3, n_init=1, init="random")),
 ]
 
-fignum = 1
+fig = plt.figure(figsize=(10, 8))
 titles = ["8 clusters", "3 clusters", "3 clusters, bad initialization"]
-for name, est in estimators:
-    fig = plt.figure(fignum, figsize=(4, 3))
-    ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
-    ax.set_position([0, 0, 0.95, 1])
+for idx, (name, est) in enumerate(estimators):
+    ax = fig.add_subplot(2, 2, idx + 1, projection="3d", elev=48, azim=134)
     est.fit(X)
     labels = est.labels_
 
     ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(float), edgecolor="k")
 
-    ax.w_xaxis.set_ticklabels([])
-    ax.w_yaxis.set_ticklabels([])
-    ax.w_zaxis.set_ticklabels([])
+    ax.xaxis.set_ticklabels([])
+    ax.yaxis.set_ticklabels([])
+    ax.zaxis.set_ticklabels([])
     ax.set_xlabel("Petal width")
     ax.set_ylabel("Sepal length")
     ax.set_zlabel("Petal length")
-    ax.set_title(titles[fignum - 1])
-    ax.dist = 12
-    fignum = fignum + 1
+    ax.set_title(titles[idx - 1])
 
 # Plot the ground truth
-fig = plt.figure(fignum, figsize=(4, 3))
-ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
-ax.set_position([0, 0, 0.95, 1])
+ax = fig.add_subplot(2, 2, 4, projection="3d", elev=48, azim=134)
 
 for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
     ax.text3D(
@@ -80,13 +74,13 @@
 y = np.choose(y, [1, 2, 0]).astype(float)
 ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor="k")
 
-ax.w_xaxis.set_ticklabels([])
-ax.w_yaxis.set_ticklabels([])
-ax.w_zaxis.set_ticklabels([])
+ax.xaxis.set_ticklabels([])
+ax.yaxis.set_ticklabels([])
+ax.zaxis.set_ticklabels([])
 ax.set_xlabel("Petal width")
 ax.set_ylabel("Sepal length")
 ax.set_zlabel("Petal length")
 ax.set_title("Ground Truth")
-ax.dist = 12
 
-fig.show()
+plt.subplots_adjust(wspace=0.25, hspace=0.25)
+plt.show()