Pushing the docs to dev/ for branch: master, commit 2277068c8832cd6f1ffbf4a6e3b2f8dc7a146671

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Jan Hendrik Metzen <[email protected]>\n# License: BSD 3 clause\n\n\nfrom __future__ import division\nimport time\n\nimport numpy as np\n\nfrom sklearn.svm import SVR\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.kernel_ridge import KernelRidge\nimport matplotlib.pyplot as plt\n\nrng = np.random.RandomState(0)\n\n# #############################################################################\n# Generate sample data\nX = 5 * rng.rand(10000, 1)\ny = np.sin(X).ravel()\n\n# Add noise to targets\ny[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))\n\nX_plot = np.linspace(0, 5, 100000)[:, None]\n\n# #############################################################################\n# Fit regression model\ntrain_size = 100\nsvr = GridSearchCV(SVR(kernel='rbf', gamma=0.1), cv=5,\n                   param_grid={\"C\": [1e0, 1e1, 1e2, 1e3],\n                               \"gamma\": np.logspace(-2, 2, 5)})\n\nkr = GridSearchCV(KernelRidge(kernel='rbf', gamma=0.1), cv=5,\n                  param_grid={\"alpha\": [1e0, 0.1, 1e-2, 1e-3],\n                              \"gamma\": np.logspace(-2, 2, 5)})\n\nt0 = time.time()\nsvr.fit(X[:train_size], y[:train_size])\nsvr_fit = time.time() - t0\nprint(\"SVR complexity and bandwidth selected and model fitted in %.3f s\"\n      % svr_fit)\n\nt0 = time.time()\nkr.fit(X[:train_size], y[:train_size])\nkr_fit = time.time() - t0\nprint(\"KRR complexity and bandwidth selected and model fitted in %.3f s\"\n      % kr_fit)\n\nsv_ratio = svr.best_estimator_.support_.shape[0] / train_size\nprint(\"Support vector ratio: %.3f\" % sv_ratio)\n\nt0 = time.time()\ny_svr = svr.predict(X_plot)\nsvr_predict = time.time() - t0\nprint(\"SVR prediction for %d inputs in %.3f s\"\n      % (X_plot.shape[0], svr_predict))\n\nt0 = time.time()\ny_kr = kr.predict(X_plot)\nkr_predict = time.time() - t0\nprint(\"KRR prediction for %d inputs in %.3f s\"\n      % (X_plot.shape[0], kr_predict))\n\n\n# #############################################################################\n# Look at the results\nsv_ind = svr.best_estimator_.support_\nplt.scatter(X[sv_ind], y[sv_ind], c='r', s=50, label='SVR support vectors',\n            zorder=2, edgecolors=(0, 0, 0))\nplt.scatter(X[:100], y[:100], c='k', label='data', zorder=1,\n            edgecolors=(0, 0, 0))\nplt.hold('on')\nplt.plot(X_plot, y_svr, c='r',\n         label='SVR (fit: %.3fs, predict: %.3fs)' % (svr_fit, svr_predict))\nplt.plot(X_plot, y_kr, c='g',\n         label='KRR (fit: %.3fs, predict: %.3fs)' % (kr_fit, kr_predict))\nplt.xlabel('data')\nplt.ylabel('target')\nplt.title('SVR versus Kernel Ridge')\nplt.legend()\n\n# Visualize training and prediction time\nplt.figure()\n\n# Generate sample data\nX = 5 * rng.rand(10000, 1)\ny = np.sin(X).ravel()\ny[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))\nsizes = np.logspace(1, 4, 7, dtype=np.int)\nfor name, estimator in {\"KRR\": KernelRidge(kernel='rbf', alpha=0.1,\n                                           gamma=10),\n                        \"SVR\": SVR(kernel='rbf', C=1e1, gamma=10)}.items():\n    train_time = []\n    test_time = []\n    for train_test_size in sizes:\n        t0 = time.time()\n        estimator.fit(X[:train_test_size], y[:train_test_size])\n        train_time.append(time.time() - t0)\n\n        t0 = time.time()\n        estimator.predict(X_plot[:1000])\n        test_time.append(time.time() - t0)\n\n    plt.plot(sizes, train_time, 'o-', color=\"r\" if name == \"SVR\" else \"g\",\n             label=\"%s (train)\" % name)\n    plt.plot(sizes, test_time, 'o--', color=\"r\" if name == \"SVR\" else \"g\",\n             label=\"%s (test)\" % name)\n\nplt.xscale(\"log\")\nplt.yscale(\"log\")\nplt.xlabel(\"Train size\")\nplt.ylabel(\"Time (seconds)\")\nplt.title('Execution Time')\nplt.legend(loc=\"best\")\n\n# Visualize learning curves\nplt.figure()\n\nsvr = SVR(kernel='rbf', C=1e1, gamma=0.1)\nkr = KernelRidge(kernel='rbf', alpha=0.1, gamma=0.1)\ntrain_sizes, train_scores_svr, test_scores_svr = \\\n    learning_curve(svr, X[:100], y[:100], train_sizes=np.linspace(0.1, 1, 10),\n                   scoring=\"neg_mean_squared_error\", cv=10)\ntrain_sizes_abs, train_scores_kr, test_scores_kr = \\\n    learning_curve(kr, X[:100], y[:100], train_sizes=np.linspace(0.1, 1, 10),\n                   scoring=\"neg_mean_squared_error\", cv=10)\n\nplt.plot(train_sizes, -test_scores_svr.mean(1), 'o-', color=\"r\",\n         label=\"SVR\")\nplt.plot(train_sizes, -test_scores_kr.mean(1), 'o-', color=\"g\",\n         label=\"KRR\")\nplt.xlabel(\"Train size\")\nplt.ylabel(\"Mean Squared Error\")\nplt.title('Learning curves')\nplt.legend(loc=\"best\")\n\nplt.show()"
+        "# Authors: Jan Hendrik Metzen <[email protected]>\n# License: BSD 3 clause\n\n\nfrom __future__ import division\nimport time\n\nimport numpy as np\n\nfrom sklearn.svm import SVR\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.kernel_ridge import KernelRidge\nimport matplotlib.pyplot as plt\n\nrng = np.random.RandomState(0)\n\n# #############################################################################\n# Generate sample data\nX = 5 * rng.rand(10000, 1)\ny = np.sin(X).ravel()\n\n# Add noise to targets\ny[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))\n\nX_plot = np.linspace(0, 5, 100000)[:, None]\n\n# #############################################################################\n# Fit regression model\ntrain_size = 100\nsvr = GridSearchCV(SVR(kernel='rbf', gamma=0.1), cv=5,\n                   param_grid={\"C\": [1e0, 1e1, 1e2, 1e3],\n                               \"gamma\": np.logspace(-2, 2, 5)})\n\nkr = GridSearchCV(KernelRidge(kernel='rbf', gamma=0.1), cv=5,\n                  param_grid={\"alpha\": [1e0, 0.1, 1e-2, 1e-3],\n                              \"gamma\": np.logspace(-2, 2, 5)})\n\nt0 = time.time()\nsvr.fit(X[:train_size], y[:train_size])\nsvr_fit = time.time() - t0\nprint(\"SVR complexity and bandwidth selected and model fitted in %.3f s\"\n      % svr_fit)\n\nt0 = time.time()\nkr.fit(X[:train_size], y[:train_size])\nkr_fit = time.time() - t0\nprint(\"KRR complexity and bandwidth selected and model fitted in %.3f s\"\n      % kr_fit)\n\nsv_ratio = svr.best_estimator_.support_.shape[0] / train_size\nprint(\"Support vector ratio: %.3f\" % sv_ratio)\n\nt0 = time.time()\ny_svr = svr.predict(X_plot)\nsvr_predict = time.time() - t0\nprint(\"SVR prediction for %d inputs in %.3f s\"\n      % (X_plot.shape[0], svr_predict))\n\nt0 = time.time()\ny_kr = kr.predict(X_plot)\nkr_predict = time.time() - t0\nprint(\"KRR prediction for %d inputs in %.3f s\"\n      % (X_plot.shape[0], kr_predict))\n\n\n# #############################################################################\n# Look at the results\nsv_ind = svr.best_estimator_.support_\nplt.scatter(X[sv_ind], y[sv_ind], c='r', s=50, label='SVR support vectors',\n            zorder=2, edgecolors=(0, 0, 0))\nplt.scatter(X[:100], y[:100], c='k', label='data', zorder=1,\n            edgecolors=(0, 0, 0))\nplt.plot(X_plot, y_svr, c='r',\n         label='SVR (fit: %.3fs, predict: %.3fs)' % (svr_fit, svr_predict))\nplt.plot(X_plot, y_kr, c='g',\n         label='KRR (fit: %.3fs, predict: %.3fs)' % (kr_fit, kr_predict))\nplt.xlabel('data')\nplt.ylabel('target')\nplt.title('SVR versus Kernel Ridge')\nplt.legend()\n\n# Visualize training and prediction time\nplt.figure()\n\n# Generate sample data\nX = 5 * rng.rand(10000, 1)\ny = np.sin(X).ravel()\ny[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))\nsizes = np.logspace(1, 4, 7, dtype=np.int)\nfor name, estimator in {\"KRR\": KernelRidge(kernel='rbf', alpha=0.1,\n                                           gamma=10),\n                        \"SVR\": SVR(kernel='rbf', C=1e1, gamma=10)}.items():\n    train_time = []\n    test_time = []\n    for train_test_size in sizes:\n        t0 = time.time()\n        estimator.fit(X[:train_test_size], y[:train_test_size])\n        train_time.append(time.time() - t0)\n\n        t0 = time.time()\n        estimator.predict(X_plot[:1000])\n        test_time.append(time.time() - t0)\n\n    plt.plot(sizes, train_time, 'o-', color=\"r\" if name == \"SVR\" else \"g\",\n             label=\"%s (train)\" % name)\n    plt.plot(sizes, test_time, 'o--', color=\"r\" if name == \"SVR\" else \"g\",\n             label=\"%s (test)\" % name)\n\nplt.xscale(\"log\")\nplt.yscale(\"log\")\nplt.xlabel(\"Train size\")\nplt.ylabel(\"Time (seconds)\")\nplt.title('Execution Time')\nplt.legend(loc=\"best\")\n\n# Visualize learning curves\nplt.figure()\n\nsvr = SVR(kernel='rbf', C=1e1, gamma=0.1)\nkr = KernelRidge(kernel='rbf', alpha=0.1, gamma=0.1)\ntrain_sizes, train_scores_svr, test_scores_svr = \\\n    learning_curve(svr, X[:100], y[:100], train_sizes=np.linspace(0.1, 1, 10),\n                   scoring=\"neg_mean_squared_error\", cv=10)\ntrain_sizes_abs, train_scores_kr, test_scores_kr = \\\n    learning_curve(kr, X[:100], y[:100], train_sizes=np.linspace(0.1, 1, 10),\n                   scoring=\"neg_mean_squared_error\", cv=10)\n\nplt.plot(train_sizes, -test_scores_svr.mean(1), 'o-', color=\"r\",\n         label=\"SVR\")\nplt.plot(train_sizes, -test_scores_kr.mean(1), 'o-', color=\"g\",\n         label=\"KRR\")\nplt.xlabel(\"Train size\")\nplt.ylabel(\"Mean Squared Error\")\nplt.title('Learning curves')\nplt.legend(loc=\"best\")\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -104,7 +104,6 @@
             zorder=2, edgecolors=(0, 0, 0))
 plt.scatter(X[:100], y[:100], c='k', label='data', zorder=1,
             edgecolors=(0, 0, 0))
-plt.hold('on')
 plt.plot(X_plot, y_svr, c='r',
          label='SVR (fit: %.3fs, predict: %.3fs)' % (svr_fit, svr_predict))
 plt.plot(X_plot, y_kr, c='g',

dev/_downloads/plot_kernel_ridge_regression.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nfrom sklearn.svm import SVR\nimport matplotlib.pyplot as plt\n\n# #############################################################################\n# Generate sample data\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\ny = np.sin(X).ravel()\n\n# #############################################################################\n# Add noise to targets\ny[::5] += 3 * (0.5 - np.random.rand(8))\n\n# #############################################################################\n# Fit regression model\nsvr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\nsvr_lin = SVR(kernel='linear', C=1e3)\nsvr_poly = SVR(kernel='poly', C=1e3, degree=2)\ny_rbf = svr_rbf.fit(X, y).predict(X)\ny_lin = svr_lin.fit(X, y).predict(X)\ny_poly = svr_poly.fit(X, y).predict(X)\n\n# #############################################################################\n# Look at the results\nlw = 2\nplt.scatter(X, y, color='darkorange', label='data')\nplt.hold('on')\nplt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')\nplt.plot(X, y_lin, color='c', lw=lw, label='Linear model')\nplt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')\nplt.xlabel('data')\nplt.ylabel('target')\nplt.title('Support Vector Regression')\nplt.legend()\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nfrom sklearn.svm import SVR\nimport matplotlib.pyplot as plt\n\n# #############################################################################\n# Generate sample data\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\ny = np.sin(X).ravel()\n\n# #############################################################################\n# Add noise to targets\ny[::5] += 3 * (0.5 - np.random.rand(8))\n\n# #############################################################################\n# Fit regression model\nsvr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\nsvr_lin = SVR(kernel='linear', C=1e3)\nsvr_poly = SVR(kernel='poly', C=1e3, degree=2)\ny_rbf = svr_rbf.fit(X, y).predict(X)\ny_lin = svr_lin.fit(X, y).predict(X)\ny_poly = svr_poly.fit(X, y).predict(X)\n\n# #############################################################################\n# Look at the results\nlw = 2\nplt.scatter(X, y, color='darkorange', label='data')\nplt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')\nplt.plot(X, y_lin, color='c', lw=lw, label='Linear model')\nplt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')\nplt.xlabel('data')\nplt.ylabel('target')\nplt.title('Support Vector Regression')\nplt.legend()\nplt.show()"
       ]
     }
   ],

dev/_downloads/plot_kernel_ridge_regression.py

@@ -34,7 +34,6 @@
 # Look at the results
 lw = 2
 plt.scatter(X, y, color='darkorange', label='data')
-plt.hold('on')
 plt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')
 plt.plot(X, y_lin, color='c', lw=lw, label='Linear model')
 plt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')