Pushing the docs to dev/ for branch: master, commit cb16003751e23bf0b1d158a26343f99ebc785571

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"bry\"\nplot_step = 0.02\n\n# Load data\niris = load_iris()\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n                                [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    plt.subplot(2, 3, pairidx + 1)\n\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n                         np.arange(y_min, y_max, plot_step))\n\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n\n    plt.xlabel(iris.feature_names[pair[0]])\n    plt.ylabel(iris.feature_names[pair[1]])\n    plt.axis(\"tight\")\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.where(y == i)\n        plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],\n                    cmap=plt.cm.Paired)\n\n    plt.axis(\"tight\")\n\nplt.suptitle(\"Decision surface of a decision tree using paired features\")\nplt.legend()\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n# Load data\niris = load_iris()\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n                                [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    plt.subplot(2, 3, pairidx + 1)\n\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n                         np.arange(y_min, y_max, plot_step))\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n\n    plt.xlabel(iris.feature_names[pair[0]])\n    plt.ylabel(iris.feature_names[pair[1]])\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.where(y == i)\n        plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],\n                    cmap=plt.cm.RdYlBu, edgecolor='black', s=15)\n\nplt.suptitle(\"Decision surface of a decision tree using paired features\")\nplt.legend(loc='lower right', borderpad=0, handletextpad=0)\nplt.axis(\"tight\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -22,7 +22,7 @@
 
 # Parameters
 n_classes = 3
-plot_colors = "bry"
+plot_colors = "ryb"
 plot_step = 0.02
 
 # Load data
@@ -44,23 +44,22 @@
     y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
     xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                          np.arange(y_min, y_max, plot_step))
+    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
 
     Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
     Z = Z.reshape(xx.shape)
-    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
+    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)
 
     plt.xlabel(iris.feature_names[pair[0]])
     plt.ylabel(iris.feature_names[pair[1]])
-    plt.axis("tight")
 
     # Plot the training points
     for i, color in zip(range(n_classes), plot_colors):
         idx = np.where(y == i)
         plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
-                    cmap=plt.cm.Paired)
-
-    plt.axis("tight")
+                    cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
 
 plt.suptitle("Decision surface of a decision tree using paired features")
-plt.legend()
+plt.legend(loc='lower right', borderpad=0, handletextpad=0)
+plt.axis("tight")
 plt.show()