Pushing the docs to dev/ for branch: main, commit 391baef660b08db50fc2545dd5ad381eeb6bf905

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Sparsity Example: Fitting only features 1  and 2\n\nFeatures 1 and 2 of the diabetes-dataset are fitted and\nplotted below. It illustrates that although feature 2\nhas a strong coefficient on the full model, it does not\ngive us much regarding `y` when compared to just feature 1\n"
+        "\n# Sparsity Example: Fitting only features 1  and 2\n\nFeatures 1 and 2 of the diabetes-dataset are fitted and\nplotted below. It illustrates that although feature 2\nhas a strong coefficient on the full model, it does not\ngive us much regarding `y` when compared to just feature 1.\n"
       ]
     },
     {
@@ -26,7 +26,61 @@
       },
       "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\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom sklearn import datasets, linear_model\n\nX, y = datasets.load_diabetes(return_X_y=True)\nindices = (0, 1)\n\nX_train = X[:-20, indices]\nX_test = X[-20:, indices]\ny_train = y[:-20]\ny_test = y[-20:]\n\nols = linear_model.LinearRegression()\nols.fit(X_train, y_train)\n\n\n# #############################################################################\n# Plot the figure\ndef plot_figs(fig_num, elev, azim, X_train, clf):\n    fig = plt.figure(fig_num, figsize=(4, 3))\n    plt.clf()\n    ax = Axes3D(fig, elev=elev, azim=azim)\n\n    ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c=\"k\", marker=\"+\")\n    ax.plot_surface(\n        np.array([[-0.1, -0.1], [0.15, 0.15]]),\n        np.array([[-0.1, 0.15], [-0.1, 0.15]]),\n        clf.predict(\n            np.array([[-0.1, -0.1, 0.15, 0.15], [-0.1, 0.15, -0.1, 0.15]]).T\n        ).reshape((2, 2)),\n        alpha=0.5,\n    )\n    ax.set_xlabel(\"X_1\")\n    ax.set_ylabel(\"X_2\")\n    ax.set_zlabel(\"Y\")\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n\n\n# Generate the three different figures from different views\nelev = 43.5\nazim = -110\nplot_figs(1, elev, azim, X_train, ols)\n\nelev = -0.5\nazim = 0\nplot_figs(2, elev, azim, X_train, ols)\n\nelev = -0.5\nazim = 90\nplot_figs(3, elev, azim, X_train, ols)\n\nplt.show()"
+        "# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First we load the diabetes dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn import datasets\nimport numpy as np\n\nX, y = datasets.load_diabetes(return_X_y=True)\nindices = (0, 1)\n\nX_train = X[:-20, indices]\nX_test = X[-20:, indices]\ny_train = y[:-20]\ny_test = y[-20:]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next we fit a linear regression model.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn import linear_model\n\nols = linear_model.LinearRegression()\n_ = ols.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally we plot the figure from three different views.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\n\ndef plot_figs(fig_num, elev, azim, X_train, clf):\n    fig = plt.figure(fig_num, figsize=(4, 3))\n    plt.clf()\n    ax = fig.add_subplot(111, projection=\"3d\", elev=elev, azim=azim)\n\n    ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c=\"k\", marker=\"+\")\n    ax.plot_surface(\n        np.array([[-0.1, -0.1], [0.15, 0.15]]),\n        np.array([[-0.1, 0.15], [-0.1, 0.15]]),\n        clf.predict(\n            np.array([[-0.1, -0.1, 0.15, 0.15], [-0.1, 0.15, -0.1, 0.15]]).T\n        ).reshape((2, 2)),\n        alpha=0.5,\n    )\n    ax.set_xlabel(\"X_1\")\n    ax.set_ylabel(\"X_2\")\n    ax.set_zlabel(\"Y\")\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n\n\n# Generate the three different figures from different views\nelev = 43.5\nazim = -110\nplot_figs(1, elev, azim, X_train, ols)\n\nelev = -0.5\nazim = 0\nplot_figs(2, elev, azim, X_train, ols)\n\nelev = -0.5\nazim = 90\nplot_figs(3, elev, azim, X_train, ols)\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/113d936063a64e05f36c8cd5e870300a/plot_ols_3d.ipynb

@@ -7,19 +7,18 @@
 Features 1 and 2 of the diabetes-dataset are fitted and
 plotted below. It illustrates that although feature 2
 has a strong coefficient on the full model, it does not
-give us much regarding `y` when compared to just feature 1
-
+give us much regarding `y` when compared to just feature 1.
 """
 
 # Code source: Gaël Varoquaux
 # Modified for documentation by Jaques Grobler
 # License: BSD 3 clause
 
-import matplotlib.pyplot as plt
-import numpy as np
-from mpl_toolkits.mplot3d import Axes3D
+# %%
+# First we load the diabetes dataset.
 
-from sklearn import datasets, linear_model
+from sklearn import datasets
+import numpy as np
 
 X, y = datasets.load_diabetes(return_X_y=True)
 indices = (0, 1)
@@ -29,16 +28,25 @@
 y_train = y[:-20]
 y_test = y[-20:]
 
+# %%
+# Next we fit a linear regression model.
+
+from sklearn import linear_model
+
 ols = linear_model.LinearRegression()
-ols.fit(X_train, y_train)
+_ = ols.fit(X_train, y_train)
+
+
+# %%
+# Finally we plot the figure from three different views.
+
+import matplotlib.pyplot as plt
 
 
-# #############################################################################
-# Plot the figure
 def plot_figs(fig_num, elev, azim, X_train, clf):
     fig = plt.figure(fig_num, figsize=(4, 3))
     plt.clf()
-    ax = Axes3D(fig, elev=elev, azim=azim)
+    ax = fig.add_subplot(111, projection="3d", elev=elev, azim=azim)
 
     ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c="k", marker="+")
     ax.plot_surface(