Pushing the docs to 0.23/ for branch: 0.23.X, commit 483cd3eaa3c636a57ebb0dc4765531183b274df0

Author: sklearn-ci

0.23/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 3d4ccbdbd4613be0a63487194504dc50
+config: d7433eaf5230c5568f23548ac6841c3b
 tags: 645f666f9bcd5a90fca523b33c5a78b7

0.23/_downloads/03d92e4804175ff27d91620c6dcbe283/plot_random_dataset.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot randomly generated classification dataset\n\n\nPlot several randomly generated 2D classification datasets.\nThis example illustrates the :func:`datasets.make_classification`\n:func:`datasets.make_blobs` and :func:`datasets.make_gaussian_quantiles`\nfunctions.\n\nFor ``make_classification``, three binary and two multi-class classification\ndatasets are generated, with different numbers of informative features and\nclusters per class.  \n"
+        "\n# Plot randomly generated classification dataset\n\n\nThis example plots several randomly generated classification datasets.\nFor easy visualization, all datasets have 2 features, plotted on the x and y\naxis. The color of each point represents its class label.\n\nThe first 4 plots use the :func:`~sklearn.datasets.make_classification` with\ndifferent numbers of informative features, clusters per class and classes.\nThe final 2 plots use :func:`~sklearn.datasets.make_blobs` and\n:func:`~sklearn.datasets.make_gaussian_quantiles`.\n"
       ]
     },
     {

0.23/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import ShuffleSplit\n\n\ndef plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,\n                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n    \"\"\"\n    Generate 3 plots: the test and training learning curve, the training\n    samples vs fit times curve, the fit times vs score curve.\n\n    Parameters\n    ----------\n    estimator : object type that implements the \"fit\" and \"predict\" methods\n        An object of that type which is cloned for each validation.\n\n    title : string\n        Title for the chart.\n\n    X : array-like, shape (n_samples, n_features)\n        Training vector, where n_samples is the number of samples and\n        n_features is the number of features.\n\n    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n        Target relative to X for classification or regression;\n        None for unsupervised learning.\n\n    axes : array of 3 axes, optional (default=None)\n        Axes to use for plotting the curves.\n\n    ylim : tuple, shape (ymin, ymax), optional\n        Defines minimum and maximum yvalues plotted.\n\n    cv : int, cross-validation generator or an iterable, optional\n        Determines the cross-validation splitting strategy.\n        Possible inputs for cv are:\n          - None, to use the default 5-fold cross-validation,\n          - integer, to specify the number of folds.\n          - :term:`CV splitter`,\n          - An iterable yielding (train, test) splits as arrays of indices.\n\n        For integer/None inputs, if ``y`` is binary or multiclass,\n        :class:`StratifiedKFold` used. If the estimator is not a classifier\n        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n\n        Refer :ref:`User Guide <cross_validation>` for the various\n        cross-validators that can be used here.\n\n    n_jobs : int or None, optional (default=None)\n        Number of jobs to run in parallel.\n        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n        for more details.\n\n    train_sizes : array-like, shape (n_ticks,), dtype float or int\n        Relative or absolute numbers of training examples that will be used to\n        generate the learning curve. If the dtype is float, it is regarded as a\n        fraction of the maximum size of the training set (that is determined\n        by the selected validation method), i.e. it has to be within (0, 1].\n        Otherwise it is interpreted as absolute sizes of the training sets.\n        Note that for classification the number of samples usually have to\n        be big enough to contain at least one sample from each class.\n        (default: np.linspace(0.1, 1.0, 5))\n    \"\"\"\n    if axes is None:\n        _, axes = plt.subplots(1, 3, figsize=(20, 5))\n\n    axes[0].set_title(title)\n    if ylim is not None:\n        axes[0].set_ylim(*ylim)\n    axes[0].set_xlabel(\"Training examples\")\n    axes[0].set_ylabel(\"Score\")\n\n    train_sizes, train_scores, test_scores, fit_times, _ = \\\n        learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,\n                       train_sizes=train_sizes,\n                       return_times=True)\n    train_scores_mean = np.mean(train_scores, axis=1)\n    train_scores_std = np.std(train_scores, axis=1)\n    test_scores_mean = np.mean(test_scores, axis=1)\n    test_scores_std = np.std(test_scores, axis=1)\n    fit_times_mean = np.mean(fit_times, axis=1)\n    fit_times_std = np.std(fit_times, axis=1)\n\n    # Plot learning curve\n    axes[0].grid()\n    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n                         train_scores_mean + train_scores_std, alpha=0.1,\n                         color=\"r\")\n    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n                         test_scores_mean + test_scores_std, alpha=0.1,\n                         color=\"g\")\n    axes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n                 label=\"Training score\")\n    axes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n                 label=\"Cross-validation score\")\n    axes[0].legend(loc=\"best\")\n\n    # Plot n_samples vs fit_times\n    axes[1].grid()\n    axes[1].plot(train_sizes, fit_times_mean, 'o-')\n    axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,\n                         fit_times_mean + fit_times_std, alpha=0.1)\n    axes[1].set_xlabel(\"Training examples\")\n    axes[1].set_ylabel(\"fit_times\")\n    axes[1].set_title(\"Scalability of the model\")\n\n    # Plot fit_time vs score\n    axes[2].grid()\n    axes[2].plot(fit_times_mean, test_scores_mean, 'o-')\n    axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,\n                         test_scores_mean + test_scores_std, alpha=0.1)\n    axes[2].set_xlabel(\"fit_times\")\n    axes[2].set_ylabel(\"Score\")\n    axes[2].set_title(\"Performance of the model\")\n\n    return plt\n\n\nfig, axes = plt.subplots(3, 2, figsize=(10, 15))\n\nX, y = load_digits(return_X_y=True)\n\ntitle = \"Learning Curves (Naive Bayes)\"\n# Cross validation with 100 iterations to get smoother mean test and train\n# score curves, each time with 20% data randomly selected as a validation set.\ncv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n\nestimator = GaussianNB()\nplot_learning_curve(estimator, title, X, y, axes=axes[:, 0], ylim=(0.7, 1.01),\n                    cv=cv, n_jobs=4)\n\ntitle = r\"Learning Curves (SVM, RBF kernel, $\\gamma=0.001$)\"\n# SVC is more expensive so we do a lower number of CV iterations:\ncv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\nestimator = SVC(gamma=0.001)\nplot_learning_curve(estimator, title, X, y, axes=axes[:, 1], ylim=(0.7, 1.01),\n                    cv=cv, n_jobs=4)\n\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import ShuffleSplit\n\n\ndef plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,\n                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n    \"\"\"\n    Generate 3 plots: the test and training learning curve, the training\n    samples vs fit times curve, the fit times vs score curve.\n\n    Parameters\n    ----------\n    estimator : object type that implements the \"fit\" and \"predict\" methods\n        An object of that type which is cloned for each validation.\n\n    title : string\n        Title for the chart.\n\n    X : array-like, shape (n_samples, n_features)\n        Training vector, where n_samples is the number of samples and\n        n_features is the number of features.\n\n    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n        Target relative to X for classification or regression;\n        None for unsupervised learning.\n\n    axes : array of 3 axes, optional (default=None)\n        Axes to use for plotting the curves.\n\n    ylim : tuple, shape (ymin, ymax), optional\n        Defines minimum and maximum yvalues plotted.\n\n    cv : int, cross-validation generator or an iterable, optional\n        Determines the cross-validation splitting strategy.\n        Possible inputs for cv are:\n\n          - None, to use the default 5-fold cross-validation,\n          - integer, to specify the number of folds.\n          - :term:`CV splitter`,\n          - An iterable yielding (train, test) splits as arrays of indices.\n\n        For integer/None inputs, if ``y`` is binary or multiclass,\n        :class:`StratifiedKFold` used. If the estimator is not a classifier\n        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n\n        Refer :ref:`User Guide <cross_validation>` for the various\n        cross-validators that can be used here.\n\n    n_jobs : int or None, optional (default=None)\n        Number of jobs to run in parallel.\n        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n        for more details.\n\n    train_sizes : array-like, shape (n_ticks,), dtype float or int\n        Relative or absolute numbers of training examples that will be used to\n        generate the learning curve. If the dtype is float, it is regarded as a\n        fraction of the maximum size of the training set (that is determined\n        by the selected validation method), i.e. it has to be within (0, 1].\n        Otherwise it is interpreted as absolute sizes of the training sets.\n        Note that for classification the number of samples usually have to\n        be big enough to contain at least one sample from each class.\n        (default: np.linspace(0.1, 1.0, 5))\n    \"\"\"\n    if axes is None:\n        _, axes = plt.subplots(1, 3, figsize=(20, 5))\n\n    axes[0].set_title(title)\n    if ylim is not None:\n        axes[0].set_ylim(*ylim)\n    axes[0].set_xlabel(\"Training examples\")\n    axes[0].set_ylabel(\"Score\")\n\n    train_sizes, train_scores, test_scores, fit_times, _ = \\\n        learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,\n                       train_sizes=train_sizes,\n                       return_times=True)\n    train_scores_mean = np.mean(train_scores, axis=1)\n    train_scores_std = np.std(train_scores, axis=1)\n    test_scores_mean = np.mean(test_scores, axis=1)\n    test_scores_std = np.std(test_scores, axis=1)\n    fit_times_mean = np.mean(fit_times, axis=1)\n    fit_times_std = np.std(fit_times, axis=1)\n\n    # Plot learning curve\n    axes[0].grid()\n    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n                         train_scores_mean + train_scores_std, alpha=0.1,\n                         color=\"r\")\n    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n                         test_scores_mean + test_scores_std, alpha=0.1,\n                         color=\"g\")\n    axes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n                 label=\"Training score\")\n    axes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n                 label=\"Cross-validation score\")\n    axes[0].legend(loc=\"best\")\n\n    # Plot n_samples vs fit_times\n    axes[1].grid()\n    axes[1].plot(train_sizes, fit_times_mean, 'o-')\n    axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,\n                         fit_times_mean + fit_times_std, alpha=0.1)\n    axes[1].set_xlabel(\"Training examples\")\n    axes[1].set_ylabel(\"fit_times\")\n    axes[1].set_title(\"Scalability of the model\")\n\n    # Plot fit_time vs score\n    axes[2].grid()\n    axes[2].plot(fit_times_mean, test_scores_mean, 'o-')\n    axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,\n                         test_scores_mean + test_scores_std, alpha=0.1)\n    axes[2].set_xlabel(\"fit_times\")\n    axes[2].set_ylabel(\"Score\")\n    axes[2].set_title(\"Performance of the model\")\n\n    return plt\n\n\nfig, axes = plt.subplots(3, 2, figsize=(10, 15))\n\nX, y = load_digits(return_X_y=True)\n\ntitle = \"Learning Curves (Naive Bayes)\"\n# Cross validation with 100 iterations to get smoother mean test and train\n# score curves, each time with 20% data randomly selected as a validation set.\ncv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n\nestimator = GaussianNB()\nplot_learning_curve(estimator, title, X, y, axes=axes[:, 0], ylim=(0.7, 1.01),\n                    cv=cv, n_jobs=4)\n\ntitle = r\"Learning Curves (SVM, RBF kernel, $\\gamma=0.001$)\"\n# SVC is more expensive so we do a lower number of CV iterations:\ncv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\nestimator = SVC(gamma=0.001)\nplot_learning_curve(estimator, title, X, y, axes=axes[:, 1], ylim=(0.7, 1.01),\n                    cv=cv, n_jobs=4)\n\nplt.show()"
       ]
     }
   ],

0.23/_downloads/95048801088377025c1575a6a6bf598c/plot_learning_curve.ipynb

@@ -3,14 +3,15 @@
 Plot randomly generated classification dataset
 ==============================================
 
-Plot several randomly generated 2D classification datasets.
-This example illustrates the :func:`datasets.make_classification`
-:func:`datasets.make_blobs` and :func:`datasets.make_gaussian_quantiles`
-functions.
+This example plots several randomly generated classification datasets.
+For easy visualization, all datasets have 2 features, plotted on the x and y
+axis. The color of each point represents its class label.
 
-For ``make_classification``, three binary and two multi-class classification
-datasets are generated, with different numbers of informative features and
-clusters per class.  """
+The first 4 plots use the :func:`~sklearn.datasets.make_classification` with
+different numbers of informative features, clusters per class and classes.
+The final 2 plots use :func:`~sklearn.datasets.make_blobs` and
+:func:`~sklearn.datasets.make_gaussian_quantiles`.
+"""
 
 print(__doc__)
 

0.23/_downloads/9534d593e925347a4e0eee78c7d5b226/plot_random_dataset.py

@@ -53,12 +53,15 @@
 print(gbdt.score(X_test, y_test))
 
 ##############################################################################
-# Rich HTML representation for estimators
-# ---------------------------------------
-# Estimators can now be rendered in html in notebooks by enabling the
-# `display='diagram'` option. This is particularly useful to visualize
-# pipelines and composite estimators. Click on the entries to expand and see
-# details.
+# Rich visual representation of estimators
+# -----------------------------------------
+# Estimators can now be visualized in notebooks by enabling the
+# `display='diagram'` option. This is particularly useful to summarise the
+# structure of pipelines and other composite estimators, with interactivity to
+# provide detail.  Click on the example image below to expand Pipeline
+# elements.  See :ref:`visualizing_composite_estimators` for how you can use
+# this feature.
+
 from sklearn import set_config
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import OneHotEncoder, StandardScaler

0.23/_downloads/a4ab16c5452534a66bb87e6404a1d2a1/plot_release_highlights_0_23_0.py

@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Rich HTML representation for estimators\n---------------------------------------\nEstimators can now be rendered in html in notebooks by enabling the\n`display='diagram'` option. This is particularly useful to visualize\npipelines and composite estimators. Click on the entries to expand and see\ndetails.\n\n"
+        "Rich visual representation of estimators\n-----------------------------------------\nEstimators can now be visualized in notebooks by enabling the\n`display='diagram'` option. This is particularly useful to summarise the\nstructure of pipelines and other composite estimators, with interactivity to\nprovide detail.  Click on the example image below to expand Pipeline\nelements.  See `visualizing_composite_estimators` for how you can use\nthis feature.\n\n"
       ]
     },
     {