Pushing the docs to dev/ for branch: main, commit 39c341ad91b545c895ede9c6240a04659b82defb

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -22,8 +22,8 @@
 
 # plot the weighted data points
 xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
-plt.figure()
-plt.scatter(
+fig, ax = plt.subplots()
+ax.scatter(
     X[:, 0],
     X[:, 1],
     c=y,
@@ -38,21 +38,22 @@
 clf.fit(X, y)
 Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
 Z = Z.reshape(xx.shape)
-no_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=["solid"])
+no_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["solid"])
 
 # fit the weighted model
 clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)
 clf.fit(X, y, sample_weight=sample_weight)
 Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
 Z = Z.reshape(xx.shape)
-samples_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=["dashed"])
+samples_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["dashed"])
 
-plt.legend(
-    [no_weights.collections[0], samples_weights.collections[0]],
+no_weights_handles, _ = no_weights.legend_elements()
+weights_handles, _ = samples_weights.legend_elements()
+ax.legend(
+    [no_weights_handles[0], weights_handles[0]],
     ["no weights", "with weights"],
     loc="lower left",
 )
 
-plt.xticks(())
-plt.yticks(())
+ax.set(xticks=(), yticks=())
 plt.show()

dev/_downloads/1fbe96ffa301da6ec5d1c4be786df135/plot_sgd_weighted_samples.py

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import linear_model\n\n# we create 20 points\nnp.random.seed(0)\nX = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]\ny = [1] * 10 + [-1] * 10\nsample_weight = 100 * np.abs(np.random.randn(20))\n# and assign a bigger weight to the last 10 samples\nsample_weight[:10] *= 10\n\n# plot the weighted data points\nxx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))\nplt.figure()\nplt.scatter(\n    X[:, 0],\n    X[:, 1],\n    c=y,\n    s=sample_weight,\n    alpha=0.9,\n    cmap=plt.cm.bone,\n    edgecolor=\"black\",\n)\n\n# fit the unweighted model\nclf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)\nclf.fit(X, y)\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\nno_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=[\"solid\"])\n\n# fit the weighted model\nclf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)\nclf.fit(X, y, sample_weight=sample_weight)\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\nsamples_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=[\"dashed\"])\n\nplt.legend(\n    [no_weights.collections[0], samples_weights.collections[0]],\n    [\"no weights\", \"with weights\"],\n    loc=\"lower left\",\n)\n\nplt.xticks(())\nplt.yticks(())\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import linear_model\n\n# we create 20 points\nnp.random.seed(0)\nX = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]\ny = [1] * 10 + [-1] * 10\nsample_weight = 100 * np.abs(np.random.randn(20))\n# and assign a bigger weight to the last 10 samples\nsample_weight[:10] *= 10\n\n# plot the weighted data points\nxx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))\nfig, ax = plt.subplots()\nax.scatter(\n    X[:, 0],\n    X[:, 1],\n    c=y,\n    s=sample_weight,\n    alpha=0.9,\n    cmap=plt.cm.bone,\n    edgecolor=\"black\",\n)\n\n# fit the unweighted model\nclf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)\nclf.fit(X, y)\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\nno_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=[\"solid\"])\n\n# fit the weighted model\nclf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)\nclf.fit(X, y, sample_weight=sample_weight)\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\nsamples_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=[\"dashed\"])\n\nno_weights_handles, _ = no_weights.legend_elements()\nweights_handles, _ = samples_weights.legend_elements()\nax.legend(\n    [no_weights_handles[0], weights_handles[0]],\n    [\"no weights\", \"with weights\"],\n    loc=\"lower left\",\n)\n\nax.set(xticks=(), yticks=())\nplt.show()"
       ]
     }
   ],