Pushing the docs to dev/ for branch: main, commit d4d5f8c7e02cfaced76757fbf38e21c5b28b67b0

Author: sklearn-ci

dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Gael Varoquaux <[email protected]>, Brian Cheung\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nfrom scipy.ndimage.filters import gaussian_filter\nimport matplotlib.pyplot as plt\nimport skimage\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.feature_extraction import image\nfrom sklearn.cluster import spectral_clustering\nfrom sklearn.utils.fixes import parse_version\n\n# these were introduced in skimage-0.14\nif parse_version(skimage.__version__) >= parse_version(\"0.14\"):\n    rescale_params = {\"anti_aliasing\": False, \"multichannel\": False}\nelse:\n    rescale_params = {}\n\n# load the coins as a numpy array\norig_coins = coins()\n\n# Resize it to 20% of the original size to speed up the processing\n# Applying a Gaussian filter for smoothing prior to down-scaling\n# reduces aliasing artifacts.\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(smoothened_coins, 0.2, mode=\"reflect\", **rescale_params)\n\n# Convert the image into a graph with the value of the gradient on the\n# edges.\ngraph = image.img_to_graph(rescaled_coins)\n\n# Take a decreasing function of the gradient: an exponential\n# The smaller beta is, the more independent the segmentation is of the\n# actual image. For beta=1, the segmentation is close to a voronoi\nbeta = 10\neps = 1e-6\ngraph.data = np.exp(-beta * graph.data / graph.data.std()) + eps\n\n# Apply spectral clustering (this step goes much faster if you have pyamg\n# installed)\nN_REGIONS = 25"
+        "# Author: Gael Varoquaux <[email protected]>, Brian Cheung\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nfrom scipy.ndimage.filters import gaussian_filter\nimport matplotlib.pyplot as plt\nimport skimage\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.feature_extraction import image\nfrom sklearn.cluster import spectral_clustering\nfrom sklearn.utils.fixes import parse_version\n\n# these were introduced in skimage-0.14\nif parse_version(skimage.__version__) >= parse_version(\"0.14\"):\n    rescale_params = {\"anti_aliasing\": False, \"multichannel\": False}\nelse:\n    rescale_params = {}\n\n# load the coins as a numpy array\norig_coins = coins()\n\n# Resize it to 20% of the original size to speed up the processing\n# Applying a Gaussian filter for smoothing prior to down-scaling\n# reduces aliasing artifacts.\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(smoothened_coins, 0.2, mode=\"reflect\", **rescale_params)\n\n# Convert the image into a graph with the value of the gradient on the\n# edges.\ngraph = image.img_to_graph(rescaled_coins)\n\n# Take a decreasing function of the gradient: an exponential\n# The smaller beta is, the more independent the segmentation is of the\n# actual image. For beta=1, the segmentation is close to a voronoi\nbeta = 10\neps = 1e-6\ngraph.data = np.exp(-beta * graph.data / graph.data.std()) + eps\n\n# Apply spectral clustering (this step goes much faster if you have pyamg\n# installed)\nN_REGIONS = 25"
       ]
     },
     {

dev/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py

@@ -14,7 +14,6 @@
 
 We also show the tree structure of a model built on all of the features.
 """
-print(__doc__)
 
 import numpy as np
 import matplotlib.pyplot as plt

dev/_downloads/010337852815f8103ac6cca38a812b3c/plot_roc_crossval.py

@@ -29,7 +29,6 @@
              :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py`,
 
 """
-print(__doc__)
 
 import numpy as np
 import matplotlib.pyplot as plt

dev/_downloads/01fdc7c95204e4a420de7cd297711693/plot_feature_union.py

@@ -13,6 +13,7 @@
 
 The combination used in this example is not particularly helpful on this
 dataset and is only used to illustrate the usage of FeatureUnion.
+
 """
 
 # Author: Andreas Mueller <[email protected]>

dev/_downloads/023324c27491610e7c0ccff87c59abf9/plot_kernel_pca.py

@@ -5,8 +5,8 @@
 
 This example shows that Kernel PCA is able to find a projection of the data
 that makes data linearly separable.
+
 """
-print(__doc__)
 
 # Authors: Mathieu Blondel
 #          Andreas Mueller

dev/_downloads/02a1306a494b46cc56c930ceec6e8c4a/plot_species_kde.py

@@ -35,6 +35,7 @@
    S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
    190:231-259, 2006.
 """  # noqa: E501
+
 # Author: Jake Vanderplas <[email protected]>
 #
 # License: BSD 3 clause

dev/_downloads/02a7bbce3c39c70d62d80e875968e5c6/plot_digits_kde_sampling.py

@@ -8,6 +8,7 @@
 a generative model for a dataset.  With this generative model in place,
 new samples can be drawn.  These new samples reflect the underlying model
 of the data.
+
 """
 
 import numpy as np

dev/_downloads/02d88d76c60b7397c8c6e221b31568dd/plot_grid_search_refit_callable.py

@@ -15,10 +15,10 @@
 [1] Hastie, T., Tibshirani, R.,, Friedman, J. (2001). Model Assessment and
 Selection. The Elements of Statistical Learning (pp. 219-260). New York,
 NY, USA: Springer New York Inc..
+
 """
-# Author: Wenhao Zhang <[email protected]>
 
-print(__doc__)
+# Author: Wenhao Zhang <[email protected]>
 
 import numpy as np
 import matplotlib.pyplot as plt

dev/_downloads/0486bf9e537e44cedd2a236d034bcd90/plot_pcr_vs_pls.ipynb

@@ -18,17 +18,6 @@
         "\n# Principal Component Regression vs Partial Least Squares Regression\n\nThis example compares `Principal Component Regression\n<https://en.wikipedia.org/wiki/Principal_component_regression>`_ (PCR) and\n`Partial Least Squares Regression\n<https://en.wikipedia.org/wiki/Partial_least_squares_regression>`_ (PLS) on a\ntoy dataset. Our goal is to illustrate how PLS can outperform PCR when the\ntarget is strongly correlated with some directions in the data that have a\nlow variance.\n\nPCR is a regressor composed of two steps: first,\n:class:`~sklearn.decomposition.PCA` is applied to the training data, possibly\nperforming dimensionality reduction; then, a regressor (e.g. a linear\nregressor) is trained on the transformed samples. In\n:class:`~sklearn.decomposition.PCA`, the transformation is purely\nunsupervised, meaning that no information about the targets is used. As a\nresult, PCR may perform poorly in some datasets where the target is strongly\ncorrelated with *directions* that have low variance. Indeed, the\ndimensionality reduction of PCA projects the data into a lower dimensional\nspace where the variance of the projected data is greedily maximized along\neach axis. Despite them having the most predictive power on the target, the\ndirections with a lower variance will be dropped, and the final regressor\nwill not be able to leverage them.\n\nPLS is both a transformer and a regressor, and it is quite similar to PCR: it\nalso applies a dimensionality reduction to the samples before applying a\nlinear regressor to the transformed data. The main difference with PCR is\nthat the PLS transformation is supervised. Therefore, as we will see in this\nexample, it does not suffer from the issue we just mentioned.\n"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "print(__doc__)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

dev/_downloads/055e50ba9fa8c7dcf45dc8f1f32a0243/plot_nnls.py

@@ -6,8 +6,9 @@
 In this example, we fit a linear model with positive constraints on the
 regression coefficients and compare the estimated coefficients to a classic
 linear regression.
+
 """
-print(__doc__)
+
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.metrics import r2_score

dev/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import RocCurveDisplay\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, random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\nfig, ax = plt.subplots()\nfor i, (train, test) in enumerate(cv.split(X, y)):\n    classifier.fit(X[train], y[train])\n    viz = RocCurveDisplay.from_estimator(\n        classifier,\n        X[test],\n        y[test],\n        name=\"ROC fold {}\".format(i),\n        alpha=0.3,\n        lw=1,\n        ax=ax,\n    )\n    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n    interp_tpr[0] = 0.0\n    tprs.append(interp_tpr)\n    aucs.append(viz.roc_auc)\n\nax.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"r\", label=\"Chance\", alpha=0.8)\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)\nax.plot(\n    mean_fpr,\n    mean_tpr,\n    color=\"b\",\n    label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n    lw=2,\n    alpha=0.8,\n)\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)\nax.fill_between(\n    mean_fpr,\n    tprs_lower,\n    tprs_upper,\n    color=\"grey\",\n    alpha=0.2,\n    label=r\"$\\pm$ 1 std. dev.\",\n)\n\nax.set(\n    xlim=[-0.05, 1.05],\n    ylim=[-0.05, 1.05],\n    title=\"Receiver operating characteristic example\",\n)\nax.legend(loc=\"lower right\")\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import RocCurveDisplay\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, random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\nfig, ax = plt.subplots()\nfor i, (train, test) in enumerate(cv.split(X, y)):\n    classifier.fit(X[train], y[train])\n    viz = RocCurveDisplay.from_estimator(\n        classifier,\n        X[test],\n        y[test],\n        name=\"ROC fold {}\".format(i),\n        alpha=0.3,\n        lw=1,\n        ax=ax,\n    )\n    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n    interp_tpr[0] = 0.0\n    tprs.append(interp_tpr)\n    aucs.append(viz.roc_auc)\n\nax.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"r\", label=\"Chance\", alpha=0.8)\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)\nax.plot(\n    mean_fpr,\n    mean_tpr,\n    color=\"b\",\n    label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n    lw=2,\n    alpha=0.8,\n)\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)\nax.fill_between(\n    mean_fpr,\n    tprs_lower,\n    tprs_upper,\n    color=\"grey\",\n    alpha=0.2,\n    label=r\"$\\pm$ 1 std. dev.\",\n)\n\nax.set(\n    xlim=[-0.05, 1.05],\n    ylim=[-0.05, 1.05],\n    title=\"Receiver operating characteristic example\",\n)\nax.legend(loc=\"lower right\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/05ca8a4e90b4cc2acd69f9e24b4a1f3a/plot_classifier_chain_yeast.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Adam Kleczewski\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.multioutput import ClassifierChain\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.metrics import jaccard_score\nfrom sklearn.linear_model import LogisticRegression\n\nprint(__doc__)\n\n# Load a multi-label dataset from https://www.openml.org/d/40597\nX, Y = fetch_openml(\"yeast\", version=4, return_X_y=True)\nY = Y == \"TRUE\"\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n\n# Fit an independent logistic regression model for each class using the\n# OneVsRestClassifier wrapper.\nbase_lr = LogisticRegression()\novr = OneVsRestClassifier(base_lr)\novr.fit(X_train, Y_train)\nY_pred_ovr = ovr.predict(X_test)\novr_jaccard_score = jaccard_score(Y_test, Y_pred_ovr, average=\"samples\")\n\n# Fit an ensemble of logistic regression classifier chains and take the\n# take the average prediction of all the chains.\nchains = [ClassifierChain(base_lr, order=\"random\", random_state=i) for i in range(10)]\nfor chain in chains:\n    chain.fit(X_train, Y_train)\n\nY_pred_chains = np.array([chain.predict(X_test) for chain in chains])\nchain_jaccard_scores = [\n    jaccard_score(Y_test, Y_pred_chain >= 0.5, average=\"samples\")\n    for Y_pred_chain in Y_pred_chains\n]\n\nY_pred_ensemble = Y_pred_chains.mean(axis=0)\nensemble_jaccard_score = jaccard_score(\n    Y_test, Y_pred_ensemble >= 0.5, average=\"samples\"\n)\n\nmodel_scores = [ovr_jaccard_score] + chain_jaccard_scores\nmodel_scores.append(ensemble_jaccard_score)\n\nmodel_names = (\n    \"Independent\",\n    \"Chain 1\",\n    \"Chain 2\",\n    \"Chain 3\",\n    \"Chain 4\",\n    \"Chain 5\",\n    \"Chain 6\",\n    \"Chain 7\",\n    \"Chain 8\",\n    \"Chain 9\",\n    \"Chain 10\",\n    \"Ensemble\",\n)\n\nx_pos = np.arange(len(model_names))\n\n# Plot the Jaccard similarity scores for the independent model, each of the\n# chains, and the ensemble (note that the vertical axis on this plot does\n# not begin at 0).\n\nfig, ax = plt.subplots(figsize=(7, 4))\nax.grid(True)\nax.set_title(\"Classifier Chain Ensemble Performance Comparison\")\nax.set_xticks(x_pos)\nax.set_xticklabels(model_names, rotation=\"vertical\")\nax.set_ylabel(\"Jaccard Similarity Score\")\nax.set_ylim([min(model_scores) * 0.9, max(model_scores) * 1.1])\ncolors = [\"r\"] + [\"b\"] * len(chain_jaccard_scores) + [\"g\"]\nax.bar(x_pos, model_scores, alpha=0.5, color=colors)\nplt.tight_layout()\nplt.show()"
+        "# Author: Adam Kleczewski\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.multioutput import ClassifierChain\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.metrics import jaccard_score\nfrom sklearn.linear_model import LogisticRegression\n\n# Load a multi-label dataset from https://www.openml.org/d/40597\nX, Y = fetch_openml(\"yeast\", version=4, return_X_y=True)\nY = Y == \"TRUE\"\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n\n# Fit an independent logistic regression model for each class using the\n# OneVsRestClassifier wrapper.\nbase_lr = LogisticRegression()\novr = OneVsRestClassifier(base_lr)\novr.fit(X_train, Y_train)\nY_pred_ovr = ovr.predict(X_test)\novr_jaccard_score = jaccard_score(Y_test, Y_pred_ovr, average=\"samples\")\n\n# Fit an ensemble of logistic regression classifier chains and take the\n# take the average prediction of all the chains.\nchains = [ClassifierChain(base_lr, order=\"random\", random_state=i) for i in range(10)]\nfor chain in chains:\n    chain.fit(X_train, Y_train)\n\nY_pred_chains = np.array([chain.predict(X_test) for chain in chains])\nchain_jaccard_scores = [\n    jaccard_score(Y_test, Y_pred_chain >= 0.5, average=\"samples\")\n    for Y_pred_chain in Y_pred_chains\n]\n\nY_pred_ensemble = Y_pred_chains.mean(axis=0)\nensemble_jaccard_score = jaccard_score(\n    Y_test, Y_pred_ensemble >= 0.5, average=\"samples\"\n)\n\nmodel_scores = [ovr_jaccard_score] + chain_jaccard_scores\nmodel_scores.append(ensemble_jaccard_score)\n\nmodel_names = (\n    \"Independent\",\n    \"Chain 1\",\n    \"Chain 2\",\n    \"Chain 3\",\n    \"Chain 4\",\n    \"Chain 5\",\n    \"Chain 6\",\n    \"Chain 7\",\n    \"Chain 8\",\n    \"Chain 9\",\n    \"Chain 10\",\n    \"Ensemble\",\n)\n\nx_pos = np.arange(len(model_names))\n\n# Plot the Jaccard similarity scores for the independent model, each of the\n# chains, and the ensemble (note that the vertical axis on this plot does\n# not begin at 0).\n\nfig, ax = plt.subplots(figsize=(7, 4))\nax.grid(True)\nax.set_title(\"Classifier Chain Ensemble Performance Comparison\")\nax.set_xticks(x_pos)\nax.set_xticklabels(model_names, rotation=\"vertical\")\nax.set_ylabel(\"Jaccard Similarity Score\")\nax.set_ylim([min(model_scores) * 0.9, max(model_scores) * 1.1])\ncolors = [\"r\"] + [\"b\"] * len(chain_jaccard_scores) + [\"g\"]\nax.bar(x_pos, model_scores, alpha=0.5, color=colors)\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],

dev/_downloads/061854726c268bcdae5cd1c330cf8c75/plot_sgd_penalties.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nl1_color = \"navy\"\nl2_color = \"c\"\nelastic_net_color = \"darkorange\"\n\nline = np.linspace(-1.5, 1.5, 1001)\nxx, yy = np.meshgrid(line, line)\n\nl2 = xx ** 2 + yy ** 2\nl1 = np.abs(xx) + np.abs(yy)\nrho = 0.5\nelastic_net = rho * l1 + (1 - rho) * l2\n\nplt.figure(figsize=(10, 10), dpi=100)\nax = plt.gca()\n\nelastic_net_contour = plt.contour(\n    xx, yy, elastic_net, levels=[1], colors=elastic_net_color\n)\nl2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)\nl1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)\nax.set_aspect(\"equal\")\nax.spines[\"left\"].set_position(\"center\")\nax.spines[\"right\"].set_color(\"none\")\nax.spines[\"bottom\"].set_position(\"center\")\nax.spines[\"top\"].set_color(\"none\")\n\nplt.clabel(\n    elastic_net_contour,\n    inline=1,\n    fontsize=18,\n    fmt={1.0: \"elastic-net\"},\n    manual=[(-1, -1)],\n)\nplt.clabel(l2_contour, inline=1, fontsize=18, fmt={1.0: \"L2\"}, manual=[(-1, -1)])\nplt.clabel(l1_contour, inline=1, fontsize=18, fmt={1.0: \"L1\"}, manual=[(-1, -1)])\n\nplt.tight_layout()\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\n\nl1_color = \"navy\"\nl2_color = \"c\"\nelastic_net_color = \"darkorange\"\n\nline = np.linspace(-1.5, 1.5, 1001)\nxx, yy = np.meshgrid(line, line)\n\nl2 = xx ** 2 + yy ** 2\nl1 = np.abs(xx) + np.abs(yy)\nrho = 0.5\nelastic_net = rho * l1 + (1 - rho) * l2\n\nplt.figure(figsize=(10, 10), dpi=100)\nax = plt.gca()\n\nelastic_net_contour = plt.contour(\n    xx, yy, elastic_net, levels=[1], colors=elastic_net_color\n)\nl2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)\nl1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)\nax.set_aspect(\"equal\")\nax.spines[\"left\"].set_position(\"center\")\nax.spines[\"right\"].set_color(\"none\")\nax.spines[\"bottom\"].set_position(\"center\")\nax.spines[\"top\"].set_color(\"none\")\n\nplt.clabel(\n    elastic_net_contour,\n    inline=1,\n    fontsize=18,\n    fmt={1.0: \"elastic-net\"},\n    manual=[(-1, -1)],\n)\nplt.clabel(l2_contour, inline=1, fontsize=18, fmt={1.0: \"L2\"}, manual=[(-1, -1)])\nplt.clabel(l1_contour, inline=1, fontsize=18, fmt={1.0: \"L1\"}, manual=[(-1, -1)])\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],