Pushing the docs to dev/ for branch: master, commit 19e2772cac4a58de36e44a3edd89a9010e18765b

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nfrom scipy import interp\nimport matplotlib.pyplot as plt\nfrom itertools import cycle\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.model_selection import StratifiedKFold\n\n# #############################################################################\n# Data IO and generation\n\n# Import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nX, y = X[y != 2], y[y != 2]\nn_samples, n_features = X.shape\n\n# Add noisy features\nrandom_state = np.random.RandomState(0)\nX = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n\n# #############################################################################\n# Classification and ROC analysis\n\n# Run classifier with cross-validation and plot ROC curves\ncv = StratifiedKFold(n_splits=6)\nclassifier = svm.SVC(kernel='linear', probability=True,\n                     random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\ni = 0\nfor train, test in cv.split(X, y):\n    probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])\n    # Compute ROC curve and area the curve\n    fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])\n    tprs.append(interp(mean_fpr, fpr, tpr))\n    tprs[-1][0] = 0.0\n    roc_auc = auc(fpr, tpr)\n    aucs.append(roc_auc)\n    plt.plot(fpr, tpr, lw=1, alpha=0.3,\n             label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n\n    i += 1\nplt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n         label='Luck', alpha=.8)\n\nmean_tpr = np.mean(tprs, axis=0)\nmean_tpr[-1] = 1.0\nmean_auc = auc(mean_fpr, mean_tpr)\nstd_auc = np.std(aucs)\nplt.plot(mean_fpr, mean_tpr, color='b',\n         label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n         lw=2, alpha=.8)\n\nstd_tpr = np.std(tprs, axis=0)\ntprs_upper = np.minimum(mean_tpr + std_tpr, 1)\ntprs_lower = np.maximum(mean_tpr - std_tpr, 0)\nplt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n                 label=r'$\\pm$ 1 std. dev.')\n\nplt.xlim([-0.05, 1.05])\nplt.ylim([-0.05, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic example')\nplt.legend(loc=\"lower right\")\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nfrom scipy import interp\nimport matplotlib.pyplot as plt\nfrom itertools import cycle\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.model_selection import StratifiedKFold\n\n# #############################################################################\n# Data IO and generation\n\n# Import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nX, y = X[y != 2], y[y != 2]\nn_samples, n_features = X.shape\n\n# Add noisy features\nrandom_state = np.random.RandomState(0)\nX = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n\n# #############################################################################\n# Classification and ROC analysis\n\n# Run classifier with cross-validation and plot ROC curves\ncv = StratifiedKFold(n_splits=6)\nclassifier = svm.SVC(kernel='linear', probability=True,\n                     random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\ni = 0\nfor train, test in cv.split(X, y):\n    probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])\n    # Compute ROC curve and area the curve\n    fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])\n    tprs.append(interp(mean_fpr, fpr, tpr))\n    tprs[-1][0] = 0.0\n    roc_auc = auc(fpr, tpr)\n    aucs.append(roc_auc)\n    plt.plot(fpr, tpr, lw=1, alpha=0.3,\n             label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n\n    i += 1\nplt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n         label='Chance', alpha=.8)\n\nmean_tpr = np.mean(tprs, axis=0)\nmean_tpr[-1] = 1.0\nmean_auc = auc(mean_fpr, mean_tpr)\nstd_auc = np.std(aucs)\nplt.plot(mean_fpr, mean_tpr, color='b',\n         label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n         lw=2, alpha=.8)\n\nstd_tpr = np.std(tprs, axis=0)\ntprs_upper = np.minimum(mean_tpr + std_tpr, 1)\ntprs_lower = np.maximum(mean_tpr - std_tpr, 0)\nplt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n                 label=r'$\\pm$ 1 std. dev.')\n\nplt.xlim([-0.05, 1.05])\nplt.ylim([-0.05, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic example')\nplt.legend(loc=\"lower right\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -80,7 +80,7 @@
 
     i += 1
 plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
-         label='Luck', alpha=.8)
+         label='Chance', alpha=.8)
 
 mean_tpr = np.mean(tprs, axis=0)
 mean_tpr[-1] = 1.0

dev/_downloads/plot_roc_crossval.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Effect of transforming the targets in regression model\n\n\nIn this example, we give an overview of the\n:class:`sklearn.preprocessing.TransformedTargetRegressor`. Two examples\nillustrate the benefit of transforming the targets before learning a linear\nregression model. The first example uses synthetic data while the second\nexample is based on the Boston housing data set.\n\n\n"
+        "\n# Effect of transforming the targets in regression model\n\n\nIn this example, we give an overview of the\n:class:`sklearn.compose.TransformedTargetRegressor`. Two examples\nillustrate the benefit of transforming the targets before learning a linear\nregression model. The first example uses synthetic data while the second\nexample is based on the Boston housing data set.\n\n\n"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_regression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import RidgeCV\nfrom sklearn.preprocessing import TransformedTargetRegressor\nfrom sklearn.metrics import median_absolute_error, r2_score"
+        "from sklearn.datasets import make_regression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import RidgeCV\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.metrics import median_absolute_error, r2_score"
       ]
     },
     {

dev/_downloads/plot_roc_crossval.py

@@ -7,7 +7,7 @@
 ======================================================
 
 In this example, we give an overview of the
-:class:`sklearn.preprocessing.TransformedTargetRegressor`. Two examples
+:class:`sklearn.compose.TransformedTargetRegressor`. Two examples
 illustrate the benefit of transforming the targets before learning a linear
 regression model. The first example uses synthetic data while the second
 example is based on the Boston housing data set.
@@ -31,7 +31,7 @@
 from sklearn.datasets import make_regression
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import RidgeCV
-from sklearn.preprocessing import TransformedTargetRegressor
+from sklearn.compose import TransformedTargetRegressor
 from sklearn.metrics import median_absolute_error, r2_score
 
 ###############################################################################