Pushing the docs to dev/ for branch: main, commit 98df647ab1656bb778b0d4484a3c0972f2934d93

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 matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom sklearn.decomposition import PCA\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features.\ny = iris.target\n\nx_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\ny_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n\nplt.figure(2, figsize=(8, 6))\nplt.clf()\n\n# Plot the training points\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor=\"k\")\nplt.xlabel(\"Sepal length\")\nplt.ylabel(\"Sepal width\")\n\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.xticks(())\nplt.yticks(())\n\n# To getter a better understanding of interaction of the dimensions\n# plot the first three PCA dimensions\nfig = plt.figure(1, figsize=(8, 6))\nax = fig.add_subplot(111, projection=\"3d\", elev=-150, azim=110)\n\nX_reduced = PCA(n_components=3).fit_transform(iris.data)\nax.scatter(\n    X_reduced[:, 0],\n    X_reduced[:, 1],\n    X_reduced[:, 2],\n    c=y,\n    cmap=plt.cm.Set1,\n    edgecolor=\"k\",\n    s=40,\n)\n\nax.set_title(\"First three PCA directions\")\nax.set_xlabel(\"1st eigenvector\")\nax.w_xaxis.set_ticklabels([])\nax.set_ylabel(\"2nd eigenvector\")\nax.w_yaxis.set_ticklabels([])\nax.set_zlabel(\"3rd eigenvector\")\nax.w_zaxis.set_ticklabels([])\n\nplt.show()"
+        "# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\n\n# unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\n\nfrom sklearn import datasets\nfrom sklearn.decomposition import PCA\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features.\ny = iris.target\n\nx_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\ny_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n\nplt.figure(2, figsize=(8, 6))\nplt.clf()\n\n# Plot the training points\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor=\"k\")\nplt.xlabel(\"Sepal length\")\nplt.ylabel(\"Sepal width\")\n\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.xticks(())\nplt.yticks(())\n\n# To getter a better understanding of interaction of the dimensions\n# plot the first three PCA dimensions\nfig = plt.figure(1, figsize=(8, 6))\nax = fig.add_subplot(111, projection=\"3d\", elev=-150, azim=110)\n\nX_reduced = PCA(n_components=3).fit_transform(iris.data)\nax.scatter(\n    X_reduced[:, 0],\n    X_reduced[:, 1],\n    X_reduced[:, 2],\n    c=y,\n    cmap=plt.cm.Set1,\n    edgecolor=\"k\",\n    s=40,\n)\n\nax.set_title(\"First three PCA directions\")\nax.set_xlabel(\"1st eigenvector\")\nax.w_xaxis.set_ticklabels([])\nax.set_ylabel(\"2nd eigenvector\")\nax.w_yaxis.set_ticklabels([])\nax.set_zlabel(\"3rd eigenvector\")\nax.w_zaxis.set_ticklabels([])\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/26998096b90db15754e891c733ae032c/plot_iris_dataset.ipynb

@@ -21,6 +21,10 @@
 # License: BSD 3 clause
 
 import matplotlib.pyplot as plt
+
+# unused but required import for doing 3d projections with matplotlib < 3.2
+import mpl_toolkits.mplot3d  # noqa: F401
+
 from sklearn import datasets
 from sklearn.decomposition import PCA
 

dev/_downloads/5b08a262d5845e4674288edb801adf93/plot_iris_dataset.py

@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "from numpy.random import RandomState\nimport matplotlib.pyplot as plt\nfrom matplotlib import ticker\n\nfrom sklearn import manifold, datasets\n\nrng = RandomState(0)\n\nn_samples = 1500\nS_points, S_color = datasets.make_s_curve(n_samples, random_state=rng)"
+        "from numpy.random import RandomState\nimport matplotlib.pyplot as plt\nfrom matplotlib import ticker\n\n# unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\n\nfrom sklearn import manifold, datasets\n\nrng = RandomState(0)\n\nn_samples = 1500\nS_points, S_color = datasets.make_s_curve(n_samples, random_state=rng)"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -32,6 +32,9 @@
 import matplotlib.pyplot as plt
 from matplotlib import ticker
 
+# unused but required import for doing 3d projections with matplotlib < 3.2
+import mpl_toolkits.mplot3d  # noqa: F401
+
 from sklearn import manifold, datasets
 
 rng = RandomState(0)