Pushing the docs to dev/ for branch: main, commit 0852d7bc9cff705d50d76c93c9280d94f4cd647a

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import Pipeline\n\n\nn_neighbors = 1\n\ndataset = datasets.load_iris()\nX, y = dataset.data, dataset.target\n\n# we only take two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = X[:, [0, 2]]\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, stratify=y, test_size=0.7, random_state=42\n)\n\nh = 0.05  # step size in the mesh\n\n# Create color maps\ncmap_light = ListedColormap([\"#FFAAAA\", \"#AAFFAA\", \"#AAAAFF\"])\ncmap_bold = ListedColormap([\"#FF0000\", \"#00FF00\", \"#0000FF\"])\n\nnames = [\"KNN\", \"NCA, KNN\"]\n\nclassifiers = [\n    Pipeline(\n        [\n            (\"scaler\", StandardScaler()),\n            (\"knn\", KNeighborsClassifier(n_neighbors=n_neighbors)),\n        ]\n    ),\n    Pipeline(\n        [\n            (\"scaler\", StandardScaler()),\n            (\"nca\", NeighborhoodComponentsAnalysis()),\n            (\"knn\", KNeighborsClassifier(n_neighbors=n_neighbors)),\n        ]\n    ),\n]\n\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n\nfor name, clf in zip(names, classifiers):\n\n    clf.fit(X_train, y_train)\n    score = clf.score(X_test, y_test)\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.figure()\n    plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.8)\n\n    # Plot also the training and testing points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor=\"k\", s=20)\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.title(\"{} (k = {})\".format(name, n_neighbors))\n    plt.text(\n        0.9,\n        0.1,\n        \"{:.2f}\".format(score),\n        size=15,\n        ha=\"center\",\n        va=\"center\",\n        transform=plt.gca().transAxes,\n    )\n\nplt.show()"
+        "# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import Pipeline\n\n\nn_neighbors = 1\n\ndataset = datasets.load_iris()\nX, y = dataset.data, dataset.target\n\n# we only take two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = X[:, [0, 2]]\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, stratify=y, test_size=0.7, random_state=42\n)\n\nh = 0.01  # step size in the mesh\n\n# Create color maps\ncmap_light = ListedColormap([\"#FFAAAA\", \"#AAFFAA\", \"#AAAAFF\"])\ncmap_bold = ListedColormap([\"#FF0000\", \"#00FF00\", \"#0000FF\"])\n\nnames = [\"KNN\", \"NCA, KNN\"]\n\nclassifiers = [\n    Pipeline(\n        [\n            (\"scaler\", StandardScaler()),\n            (\"knn\", KNeighborsClassifier(n_neighbors=n_neighbors)),\n        ]\n    ),\n    Pipeline(\n        [\n            (\"scaler\", StandardScaler()),\n            (\"nca\", NeighborhoodComponentsAnalysis()),\n            (\"knn\", KNeighborsClassifier(n_neighbors=n_neighbors)),\n        ]\n    ),\n]\n\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n\nfor name, clf in zip(names, classifiers):\n\n    clf.fit(X_train, y_train)\n    score = clf.score(X_test, y_test)\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.figure()\n    plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.8)\n\n    # Plot also the training and testing points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor=\"k\", s=20)\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.title(\"{} (k = {})\".format(name, n_neighbors))\n    plt.text(\n        0.9,\n        0.1,\n        \"{:.2f}\".format(score),\n        size=15,\n        ha=\"center\",\n        va=\"center\",\n        transform=plt.gca().transAxes,\n    )\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/54c7ea3b3671861fbfb2161a6f0ab6d0/plot_nca_classification.ipynb

@@ -40,7 +40,7 @@
     X, y, stratify=y, test_size=0.7, random_state=42
 )
 
-h = 0.05  # step size in the mesh
+h = 0.01  # step size in the mesh
 
 # Create color maps
 cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])