Pushing the docs to 1.0/ for branch: 1.0.X, commit 0d378913be6d7e485b792ea36e9268be31ed52d0

Author: sklearn-ci

1.0/.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: 61eee5c1734c1de5af47281cdb7e94fd
+config: 2eb390e0f69446f70670632283d7ce9d
 tags: 645f666f9bcd5a90fca523b33c5a78b7

1.0/_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\",\n                         **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"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "for assign_labels in ('kmeans', 'discretize'):\n    t0 = time.time()\n    labels = spectral_clustering(graph, n_clusters=N_REGIONS,\n                                 assign_labels=assign_labels, random_state=42)\n    t1 = time.time()\n    labels = labels.reshape(rescaled_coins.shape)\n\n    plt.figure(figsize=(5, 5))\n    plt.imshow(rescaled_coins, cmap=plt.cm.gray)\n    for l in range(N_REGIONS):\n        plt.contour(labels == l,\n                    colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])\n    plt.xticks(())\n    plt.yticks(())\n    title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))\n    print(title)\n    plt.title(title)\nplt.show()"
+        "for assign_labels in (\"kmeans\", \"discretize\"):\n    t0 = time.time()\n    labels = spectral_clustering(\n        graph, n_clusters=N_REGIONS, assign_labels=assign_labels, random_state=42\n    )\n    t1 = time.time()\n    labels = labels.reshape(rescaled_coins.shape)\n\n    plt.figure(figsize=(5, 5))\n    plt.imshow(rescaled_coins, cmap=plt.cm.gray)\n    for l in range(N_REGIONS):\n        plt.contour(labels == l, colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])\n    plt.xticks(())\n    plt.yticks(())\n    title = \"Spectral clustering: %s, %.2fs\" % (assign_labels, (t1 - t0))\n    print(title)\n    plt.title(title)\nplt.show()"
       ]
     }
   ],

1.0/_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
@@ -30,8 +29,7 @@
 # Load data
 iris = load_iris()
 
-for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
-                                [1, 2], [1, 3], [2, 3]]):
+for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):
     # We only take the two corresponding features
     X = iris.data[:, pair]
     y = iris.target
@@ -44,8 +42,9 @@
 
     x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
     y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
-    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
-                         np.arange(y_min, y_max, plot_step))
+    xx, yy = np.meshgrid(
+        np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)
+    )
     plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
 
     Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
@@ -58,11 +57,18 @@
     # Plot the training points
     for i, color in zip(range(n_classes), plot_colors):
         idx = np.where(y == i)
-        plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
-                    cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
+        plt.scatter(
+            X[idx, 0],
+            X[idx, 1],
+            c=color,
+            label=iris.target_names[i],
+            cmap=plt.cm.RdYlBu,
+            edgecolor="black",
+            s=15,
+        )
 
 plt.suptitle("Decision surface of a decision tree using paired features")
-plt.legend(loc='lower right', borderpad=0, handletextpad=0)
+plt.legend(loc="lower right", borderpad=0, handletextpad=0)
 plt.axis("tight")
 
 plt.figure()

1.0/_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

1.0/_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]>
@@ -50,9 +51,11 @@
 
 pipeline = Pipeline([("features", combined_features), ("svm", svm)])
 
-param_grid = dict(features__pca__n_components=[1, 2, 3],
-                  features__univ_select__k=[1, 2],
-                  svm__C=[0.1, 1, 10])
+param_grid = dict(
+    features__pca__n_components=[1, 2, 3],
+    features__univ_select__k=[1, 2],
+    svm__C=[0.1, 1, 10],
+)
 
 grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)
 grid_search.fit(X, y)

1.0/_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
@@ -20,7 +20,7 @@
 
 np.random.seed(0)
 
-X, y = make_circles(n_samples=400, factor=.3, noise=.05)
+X, y = make_circles(n_samples=400, factor=0.3, noise=0.05)
 
 kpca = KernelPCA(kernel="rbf", fit_inverse_transform=True, gamma=10)
 X_kpca = kpca.fit_transform(X)
@@ -31,47 +31,39 @@
 # Plot results
 
 plt.figure()
-plt.subplot(2, 2, 1, aspect='equal')
+plt.subplot(2, 2, 1, aspect="equal")
 plt.title("Original space")
 reds = y == 0
 blues = y == 1
 
-plt.scatter(X[reds, 0], X[reds, 1], c="red",
-            s=20, edgecolor='k')
-plt.scatter(X[blues, 0], X[blues, 1], c="blue",
-            s=20, edgecolor='k')
+plt.scatter(X[reds, 0], X[reds, 1], c="red", s=20, edgecolor="k")
+plt.scatter(X[blues, 0], X[blues, 1], c="blue", s=20, edgecolor="k")
 plt.xlabel("$x_1$")
 plt.ylabel("$x_2$")
 
 X1, X2 = np.meshgrid(np.linspace(-1.5, 1.5, 50), np.linspace(-1.5, 1.5, 50))
 X_grid = np.array([np.ravel(X1), np.ravel(X2)]).T
 # projection on the first principal component (in the phi space)
 Z_grid = kpca.transform(X_grid)[:, 0].reshape(X1.shape)
-plt.contour(X1, X2, Z_grid, colors='grey', linewidths=1, origin='lower')
+plt.contour(X1, X2, Z_grid, colors="grey", linewidths=1, origin="lower")
 
-plt.subplot(2, 2, 2, aspect='equal')
-plt.scatter(X_pca[reds, 0], X_pca[reds, 1], c="red",
-            s=20, edgecolor='k')
-plt.scatter(X_pca[blues, 0], X_pca[blues, 1], c="blue",
-            s=20, edgecolor='k')
+plt.subplot(2, 2, 2, aspect="equal")
+plt.scatter(X_pca[reds, 0], X_pca[reds, 1], c="red", s=20, edgecolor="k")
+plt.scatter(X_pca[blues, 0], X_pca[blues, 1], c="blue", s=20, edgecolor="k")
 plt.title("Projection by PCA")
 plt.xlabel("1st principal component")
 plt.ylabel("2nd component")
 
-plt.subplot(2, 2, 3, aspect='equal')
-plt.scatter(X_kpca[reds, 0], X_kpca[reds, 1], c="red",
-            s=20, edgecolor='k')
-plt.scatter(X_kpca[blues, 0], X_kpca[blues, 1], c="blue",
-            s=20, edgecolor='k')
+plt.subplot(2, 2, 3, aspect="equal")
+plt.scatter(X_kpca[reds, 0], X_kpca[reds, 1], c="red", s=20, edgecolor="k")
+plt.scatter(X_kpca[blues, 0], X_kpca[blues, 1], c="blue", s=20, edgecolor="k")
 plt.title("Projection by KPCA")
 plt.xlabel(r"1st principal component in space induced by $\phi$")
 plt.ylabel("2nd component")
 
-plt.subplot(2, 2, 4, aspect='equal')
-plt.scatter(X_back[reds, 0], X_back[reds, 1], c="red",
-            s=20, edgecolor='k')
-plt.scatter(X_back[blues, 0], X_back[blues, 1], c="blue",
-            s=20, edgecolor='k')
+plt.subplot(2, 2, 4, aspect="equal")
+plt.scatter(X_back[reds, 0], X_back[reds, 1], c="red", s=20, edgecolor="k")
+plt.scatter(X_back[blues, 0], X_back[blues, 1], c="blue", s=20, edgecolor="k")
 plt.title("Original space after inverse transform")
 plt.xlabel("$x_1$")
 plt.ylabel("$x_2$")

1.0/_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
@@ -48,6 +49,7 @@
 # otherwise, we'll improvise later...
 try:
     from mpl_toolkits.basemap import Basemap
+
     basemap = True
 except ImportError:
     basemap = False
@@ -82,13 +84,14 @@ def construct_grids(batch):
 
 # Get matrices/arrays of species IDs and locations
 data = fetch_species_distributions()
-species_names = ['Bradypus Variegatus', 'Microryzomys Minutus']
+species_names = ["Bradypus Variegatus", "Microryzomys Minutus"]
 
-Xtrain = np.vstack([data['train']['dd lat'],
-                    data['train']['dd long']]).T
-ytrain = np.array([d.decode('ascii').startswith('micro')
-                  for d in data['train']['species']], dtype='int')
-Xtrain *= np.pi / 180.  # Convert lat/long to radians
+Xtrain = np.vstack([data["train"]["dd lat"], data["train"]["dd long"]]).T
+ytrain = np.array(
+    [d.decode("ascii").startswith("micro") for d in data["train"]["species"]],
+    dtype="int",
+)
+Xtrain *= np.pi / 180.0  # Convert lat/long to radians
 
 # Set up the data grid for the contour plot
 xgrid, ygrid = construct_grids(data)
@@ -98,7 +101,7 @@ def construct_grids(batch):
 
 xy = np.vstack([Y.ravel(), X.ravel()]).T
 xy = xy[land_mask]
-xy *= np.pi / 180.
+xy *= np.pi / 180.0
 
 # Plot map of South America with distributions of each species
 fig = plt.figure()
@@ -109,12 +112,13 @@ def construct_grids(batch):
 
     # construct a kernel density estimate of the distribution
     print(" - computing KDE in spherical coordinates")
-    kde = KernelDensity(bandwidth=0.04, metric='haversine',
-                        kernel='gaussian', algorithm='ball_tree')
+    kde = KernelDensity(
+        bandwidth=0.04, metric="haversine", kernel="gaussian", algorithm="ball_tree"
+    )
     kde.fit(Xtrain[ytrain == i])
 
     # evaluate only on the land: -9999 indicates ocean
-    Z = np.full(land_mask.shape[0], -9999, dtype='int')
+    Z = np.full(land_mask.shape[0], -9999, dtype="int")
     Z[land_mask] = np.exp(kde.score_samples(xy))
     Z = Z.reshape(X.shape)
 
@@ -124,16 +128,21 @@ def construct_grids(batch):
 
     if basemap:
         print(" - plot coastlines using basemap")
-        m = Basemap(projection='cyl', llcrnrlat=Y.min(),
-                    urcrnrlat=Y.max(), llcrnrlon=X.min(),
-                    urcrnrlon=X.max(), resolution='c')
+        m = Basemap(
+            projection="cyl",
+            llcrnrlat=Y.min(),
+            urcrnrlat=Y.max(),
+            llcrnrlon=X.min(),
+            urcrnrlon=X.max(),
+            resolution="c",
+        )
         m.drawcoastlines()
         m.drawcountries()
     else:
         print(" - plot coastlines from coverage")
-        plt.contour(X, Y, land_reference,
-                    levels=[-9998], colors="k",
-                    linestyles="solid")
+        plt.contour(
+            X, Y, land_reference, levels=[-9998], colors="k", linestyles="solid"
+        )
         plt.xticks([])
         plt.yticks([])
 

1.0/_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
@@ -26,7 +27,7 @@
 data = pca.fit_transform(digits.data)
 
 # use grid search cross-validation to optimize the bandwidth
-params = {'bandwidth': np.logspace(-1, 1, 20)}
+params = {"bandwidth": np.logspace(-1, 1, 20)}
 grid = GridSearchCV(KernelDensity(), params)
 grid.fit(data)
 
@@ -48,14 +49,16 @@
 for j in range(11):
     ax[4, j].set_visible(False)
     for i in range(4):
-        im = ax[i, j].imshow(real_data[i, j].reshape((8, 8)),
-                             cmap=plt.cm.binary, interpolation='nearest')
+        im = ax[i, j].imshow(
+            real_data[i, j].reshape((8, 8)), cmap=plt.cm.binary, interpolation="nearest"
+        )
         im.set_clim(0, 16)
-        im = ax[i + 5, j].imshow(new_data[i, j].reshape((8, 8)),
-                                 cmap=plt.cm.binary, interpolation='nearest')
+        im = ax[i + 5, j].imshow(
+            new_data[i, j].reshape((8, 8)), cmap=plt.cm.binary, interpolation="nearest"
+        )
         im.set_clim(0, 16)
 
-ax[0, 5].set_title('Selection from the input data')
+ax[0, 5].set_title("Selection from the input data")
 ax[5, 5].set_title('"New" digits drawn from the kernel density model')
 
 plt.show()