Pushing the docs to dev/ for branch: master, commit 6463406490888f0695eaa58573423fae9a397d14

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Jan Hendrik Metzen <[email protected]>\n#\n# License: BSD 3 clause\n\nfrom __future__ import division, print_function\n\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels \\\n    import RBF, WhiteKernel, RationalQuadratic, ExpSineSquared\ntry:\n    from urllib.request import urlopen\nexcept ImportError:\n    # Python 2\n    from urllib2 import urlopen\n\nprint(__doc__)\n\n\ndef load_mauna_loa_atmospheric_co2():\n    ml_data = fetch_openml(data_id=41187)\n    months = []\n    ppmv_sums = []\n    counts = []\n\n    y = ml_data.data[:, 0]\n    m = ml_data.data[:, 1]\n    month_float = y + (m - 1) / 12\n    ppmvs = ml_data.target\n\n    for month, ppmv in zip(month_float, ppmvs):\n        if not months or month != months[-1]:\n            months.append(month)\n            ppmv_sums.append(ppmv)\n            counts.append(1)\n        else:\n            # aggregate monthly sum to produce average\n            ppmv_sums[-1] += ppmv\n            counts[-1] += 1\n\n    months = np.asarray(months).reshape(-1, 1)\n    avg_ppmvs = np.asarray(ppmv_sums) / counts\n    return months, avg_ppmvs\n\n\nX, y = load_mauna_loa_atmospheric_co2()\n\n# Kernel with parameters given in GPML book\nk1 = 66.0**2 * RBF(length_scale=67.0)  # long term smooth rising trend\nk2 = 2.4**2 * RBF(length_scale=90.0) \\\n    * ExpSineSquared(length_scale=1.3, periodicity=1.0)  # seasonal component\n# medium term irregularity\nk3 = 0.66**2 \\\n    * RationalQuadratic(length_scale=1.2, alpha=0.78)\nk4 = 0.18**2 * RBF(length_scale=0.134) \\\n    + WhiteKernel(noise_level=0.19**2)  # noise terms\nkernel_gpml = k1 + k2 + k3 + k4\n\ngp = GaussianProcessRegressor(kernel=kernel_gpml, alpha=0,\n                              optimizer=None, normalize_y=True)\ngp.fit(X, y)\n\nprint(\"GPML kernel: %s\" % gp.kernel_)\nprint(\"Log-marginal-likelihood: %.3f\"\n      % gp.log_marginal_likelihood(gp.kernel_.theta))\n\n# Kernel with optimized parameters\nk1 = 50.0**2 * RBF(length_scale=50.0)  # long term smooth rising trend\nk2 = 2.0**2 * RBF(length_scale=100.0) \\\n    * ExpSineSquared(length_scale=1.0, periodicity=1.0,\n                     periodicity_bounds=\"fixed\")  # seasonal component\n# medium term irregularities\nk3 = 0.5**2 * RationalQuadratic(length_scale=1.0, alpha=1.0)\nk4 = 0.1**2 * RBF(length_scale=0.1) \\\n    + WhiteKernel(noise_level=0.1**2,\n                  noise_level_bounds=(1e-3, np.inf))  # noise terms\nkernel = k1 + k2 + k3 + k4\n\ngp = GaussianProcessRegressor(kernel=kernel, alpha=0,\n                              normalize_y=True)\ngp.fit(X, y)\n\nprint(\"\\nLearned kernel: %s\" % gp.kernel_)\nprint(\"Log-marginal-likelihood: %.3f\"\n      % gp.log_marginal_likelihood(gp.kernel_.theta))\n\nX_ = np.linspace(X.min(), X.max() + 30, 1000)[:, np.newaxis]\ny_pred, y_std = gp.predict(X_, return_std=True)\n\n# Illustration\nplt.scatter(X, y, c='k')\nplt.plot(X_, y_pred)\nplt.fill_between(X_[:, 0], y_pred - y_std, y_pred + y_std,\n                 alpha=0.5, color='k')\nplt.xlim(X_.min(), X_.max())\nplt.xlabel(\"Year\")\nplt.ylabel(r\"CO$_2$ in ppm\")\nplt.title(r\"Atmospheric CO$_2$ concentration at Mauna Loa\")\nplt.tight_layout()\nplt.show()"
+        "# Authors: Jan Hendrik Metzen <[email protected]>\n#\n# License: BSD 3 clause\n\nfrom __future__ import division, print_function\n\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels \\\n    import RBF, WhiteKernel, RationalQuadratic, ExpSineSquared\n\nprint(__doc__)\n\n\ndef load_mauna_loa_atmospheric_co2():\n    ml_data = fetch_openml(data_id=41187)\n    months = []\n    ppmv_sums = []\n    counts = []\n\n    y = ml_data.data[:, 0]\n    m = ml_data.data[:, 1]\n    month_float = y + (m - 1) / 12\n    ppmvs = ml_data.target\n\n    for month, ppmv in zip(month_float, ppmvs):\n        if not months or month != months[-1]:\n            months.append(month)\n            ppmv_sums.append(ppmv)\n            counts.append(1)\n        else:\n            # aggregate monthly sum to produce average\n            ppmv_sums[-1] += ppmv\n            counts[-1] += 1\n\n    months = np.asarray(months).reshape(-1, 1)\n    avg_ppmvs = np.asarray(ppmv_sums) / counts\n    return months, avg_ppmvs\n\n\nX, y = load_mauna_loa_atmospheric_co2()\n\n# Kernel with parameters given in GPML book\nk1 = 66.0**2 * RBF(length_scale=67.0)  # long term smooth rising trend\nk2 = 2.4**2 * RBF(length_scale=90.0) \\\n    * ExpSineSquared(length_scale=1.3, periodicity=1.0)  # seasonal component\n# medium term irregularity\nk3 = 0.66**2 \\\n    * RationalQuadratic(length_scale=1.2, alpha=0.78)\nk4 = 0.18**2 * RBF(length_scale=0.134) \\\n    + WhiteKernel(noise_level=0.19**2)  # noise terms\nkernel_gpml = k1 + k2 + k3 + k4\n\ngp = GaussianProcessRegressor(kernel=kernel_gpml, alpha=0,\n                              optimizer=None, normalize_y=True)\ngp.fit(X, y)\n\nprint(\"GPML kernel: %s\" % gp.kernel_)\nprint(\"Log-marginal-likelihood: %.3f\"\n      % gp.log_marginal_likelihood(gp.kernel_.theta))\n\n# Kernel with optimized parameters\nk1 = 50.0**2 * RBF(length_scale=50.0)  # long term smooth rising trend\nk2 = 2.0**2 * RBF(length_scale=100.0) \\\n    * ExpSineSquared(length_scale=1.0, periodicity=1.0,\n                     periodicity_bounds=\"fixed\")  # seasonal component\n# medium term irregularities\nk3 = 0.5**2 * RationalQuadratic(length_scale=1.0, alpha=1.0)\nk4 = 0.1**2 * RBF(length_scale=0.1) \\\n    + WhiteKernel(noise_level=0.1**2,\n                  noise_level_bounds=(1e-3, np.inf))  # noise terms\nkernel = k1 + k2 + k3 + k4\n\ngp = GaussianProcessRegressor(kernel=kernel, alpha=0,\n                              normalize_y=True)\ngp.fit(X, y)\n\nprint(\"\\nLearned kernel: %s\" % gp.kernel_)\nprint(\"Log-marginal-likelihood: %.3f\"\n      % gp.log_marginal_likelihood(gp.kernel_.theta))\n\nX_ = np.linspace(X.min(), X.max() + 30, 1000)[:, np.newaxis]\ny_pred, y_std = gp.predict(X_, return_std=True)\n\n# Illustration\nplt.scatter(X, y, c='k')\nplt.plot(X_, y_pred)\nplt.fill_between(X_[:, 0], y_pred - y_std, y_pred + y_std,\n                 alpha=0.5, color='k')\nplt.xlim(X_.min(), X_.max())\nplt.xlabel(\"Year\")\nplt.ylabel(r\"CO$_2$ in ppm\")\nplt.title(r\"Atmospheric CO$_2$ concentration at Mauna Loa\")\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -70,11 +70,6 @@
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels \
     import RBF, WhiteKernel, RationalQuadratic, ExpSineSquared
-try:
-    from urllib.request import urlopen
-except ImportError:
-    # Python 2
-    from urllib2 import urlopen
 
 print(__doc__)