Pushing the docs to dev/ for branch: master, commit e0c60fe6078f88ecd8b8b38179af51160c234018

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,43 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model, decomposition, datasets\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\n\nlogistic = linear_model.LogisticRegression()\n\npca = decomposition.PCA()\npipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n\ndigits = datasets.load_digits()\nX_digits = digits.data\ny_digits = digits.target"
-      ], 
-      "outputs": [], 
-      "metadata": {
-        "collapsed": false
-      }
-    }, 
-    {
-      "source": [
-        "Plot the PCA spectrum\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
-    {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "pca.fit(X_digits)\n\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.axes([.2, .2, .7, .7])\nplt.plot(pca.explained_variance_, linewidth=2)\nplt.axis('tight')\nplt.xlabel('n_components')\nplt.ylabel('explained_variance_')"
-      ], 
-      "outputs": [], 
-      "metadata": {
-        "collapsed": false
-      }
-    }, 
-    {
-      "source": [
-        "Prediction\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
-    {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "n_components = [20, 40, 64]\nCs = np.logspace(-4, 4, 3)\n\n#Parameters of pipelines can be set using \u2018__\u2019 separated parameter names:\n\nestimator = GridSearchCV(pipe,\n                         dict(pca__n_components=n_components,\n                              logistic__C=Cs))\nestimator.fit(X_digits, y_digits)\n\nplt.axvline(estimator.best_estimator_.named_steps['pca'].n_components,\n            linestyle=':', label='n_components chosen')\nplt.legend(prop=dict(size=12))\nplt.show()"
+        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model, decomposition, datasets\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\n\nlogistic = linear_model.LogisticRegression()\n\npca = decomposition.PCA()\npipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n\ndigits = datasets.load_digits()\nX_digits = digits.data\ny_digits = digits.target\n\n# Plot the PCA spectrum\npca.fit(X_digits)\n\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.axes([.2, .2, .7, .7])\nplt.plot(pca.explained_variance_, linewidth=2)\nplt.axis('tight')\nplt.xlabel('n_components')\nplt.ylabel('explained_variance_')\n\n# Prediction\nn_components = [20, 40, 64]\nCs = np.logspace(-4, 4, 3)\n\n# Parameters of pipelines can be set using \u2018__\u2019 separated parameter names:\nestimator = GridSearchCV(pipe,\n                         dict(pca__n_components=n_components,\n                              logistic__C=Cs))\nestimator.fit(X_digits, y_digits)\n\nplt.axvline(estimator.best_estimator_.named_steps['pca'].n_components,\n            linestyle=':', label='n_components chosen')\nplt.legend(prop=dict(size=12))\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -36,7 +36,6 @@
 X_digits = digits.data
 y_digits = digits.target
 
-###############################################################################
 # Plot the PCA spectrum
 pca.fit(X_digits)
 
@@ -48,14 +47,11 @@
 plt.xlabel('n_components')
 plt.ylabel('explained_variance_')
 
-###############################################################################
 # Prediction
-
 n_components = [20, 40, 64]
 Cs = np.logspace(-4, 4, 3)
 
-#Parameters of pipelines can be set using ‘__’ separated parameter names:
-
+# Parameters of pipelines can be set using ‘__’ separated parameter names:
 estimator = GridSearchCV(pipe,
                          dict(pca__n_components=n_components,
                               logistic__C=Cs))