Pushing the docs to dev/ for branch: master, commit 6500d3cbd7ccf4831deacbb9d296c01fd05fea8b

Author: MachineAtKrypton

dev/_downloads/002ebccb35a2de5ac6d32e3f54d8fa4f/plot_iris_exercise.py

@@ -27,7 +27,7 @@
 np.random.seed(0)
 order = np.random.permutation(n_sample)
 X = X[order]
-y = y[order].astype(float)
+y = y[order].astype(np.float)
 
 X_train = X[:int(.9 * n_sample)]
 y_train = y[:int(.9 * n_sample)]

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\",\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"
+        "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 distutils.version import LooseVersion\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\n\n# these were introduced in skimage-0.14\nif LooseVersion(skimage.__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"
       ]
     },
     {

dev/_downloads/036b9372e2e7802453cbb994da7a6786/plot_linearsvc_support_vectors.py

@@ -24,10 +24,7 @@
     decision_function = clf.decision_function(X)
     # we can also calculate the decision function manually
     # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
-    # The support vectors are the samples that lie within the margin
-    # boundaries, whose size is conventionally constrained to 1
-    support_vector_indices = np.where(
-        np.abs(decision_function) <= 1 + 1e-15)[0]
+    support_vector_indices = np.where((2 * y - 1) * decision_function <= 1)[0]
     support_vectors = X[support_vector_indices]
 
     plt.subplot(1, 2, i + 1)

dev/_downloads/1168f82083b3e70f31672e7c33738f8d/plot_pca_iris.py

@@ -49,7 +49,7 @@
               horizontalalignment='center',
               bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
 # Reorder the labels to have colors matching the cluster results
-y = np.choose(y, [1, 2, 0]).astype(float)
+y = np.choose(y, [1, 2, 0]).astype(np.float)
 ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral,
            edgecolor='k')
 

dev/_downloads/12a392e818ac5fa47dd91461855f3f77/plot_linearsvc_support_vectors.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_blobs\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = np.where(\n        np.abs(decision_function) <= 1 + 1e-15)[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    xlim = ax.get_xlim()\n    ylim = ax.get_ylim()\n    xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),\n                         np.linspace(ylim[0], ylim[1], 50))\n    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n    plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n                linestyles=['--', '-', '--'])\n    plt.scatter(support_vectors[:, 0], support_vectors[:, 1], s=100,\n                linewidth=1, facecolors='none', edgecolors='k')\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_blobs\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    support_vector_indices = np.where((2 * y - 1) * decision_function <= 1)[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    xlim = ax.get_xlim()\n    ylim = ax.get_ylim()\n    xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),\n                         np.linspace(ylim[0], ylim[1], 50))\n    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n    plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n                linestyles=['--', '-', '--'])\n    plt.scatter(support_vectors[:, 0], support_vectors[:, 1], s=100,\n                linewidth=1, facecolors='none', edgecolors='k')\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],

dev/_downloads/18eb95af29bd5554020a8428b3ceac54/plot_cluster_iris.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n# Though the following import is not directly being used, it is required\n# for 3D projection to work\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom sklearn.cluster import KMeans\nfrom sklearn import datasets\n\nnp.random.seed(5)\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\nestimators = [('k_means_iris_8', KMeans(n_clusters=8)),\n              ('k_means_iris_3', KMeans(n_clusters=3)),\n              ('k_means_iris_bad_init', KMeans(n_clusters=3, n_init=1,\n                                               init='random'))]\n\nfignum = 1\ntitles = ['8 clusters', '3 clusters', '3 clusters, bad initialization']\nfor name, est in estimators:\n    fig = plt.figure(fignum, figsize=(4, 3))\n    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n    est.fit(X)\n    labels = est.labels_\n\n    ax.scatter(X[:, 3], X[:, 0], X[:, 2],\n               c=labels.astype(float), edgecolor='k')\n\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n    ax.set_xlabel('Petal width')\n    ax.set_ylabel('Sepal length')\n    ax.set_zlabel('Petal length')\n    ax.set_title(titles[fignum - 1])\n    ax.dist = 12\n    fignum = fignum + 1\n\n# Plot the ground truth\nfig = plt.figure(fignum, figsize=(4, 3))\nax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n\nfor name, label in [('Setosa', 0),\n                    ('Versicolour', 1),\n                    ('Virginica', 2)]:\n    ax.text3D(X[y == label, 3].mean(),\n              X[y == label, 0].mean(),\n              X[y == label, 2].mean() + 2, name,\n              horizontalalignment='center',\n              bbox=dict(alpha=.2, edgecolor='w', facecolor='w'))\n# Reorder the labels to have colors matching the cluster results\ny = np.choose(y, [1, 2, 0]).astype(float)\nax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor='k')\n\nax.w_xaxis.set_ticklabels([])\nax.w_yaxis.set_ticklabels([])\nax.w_zaxis.set_ticklabels([])\nax.set_xlabel('Petal width')\nax.set_ylabel('Sepal length')\nax.set_zlabel('Petal length')\nax.set_title('Ground Truth')\nax.dist = 12\n\nfig.show()"
+        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n# Though the following import is not directly being used, it is required\n# for 3D projection to work\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom sklearn.cluster import KMeans\nfrom sklearn import datasets\n\nnp.random.seed(5)\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\nestimators = [('k_means_iris_8', KMeans(n_clusters=8)),\n              ('k_means_iris_3', KMeans(n_clusters=3)),\n              ('k_means_iris_bad_init', KMeans(n_clusters=3, n_init=1,\n                                               init='random'))]\n\nfignum = 1\ntitles = ['8 clusters', '3 clusters', '3 clusters, bad initialization']\nfor name, est in estimators:\n    fig = plt.figure(fignum, figsize=(4, 3))\n    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n    est.fit(X)\n    labels = est.labels_\n\n    ax.scatter(X[:, 3], X[:, 0], X[:, 2],\n               c=labels.astype(np.float), edgecolor='k')\n\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n    ax.set_xlabel('Petal width')\n    ax.set_ylabel('Sepal length')\n    ax.set_zlabel('Petal length')\n    ax.set_title(titles[fignum - 1])\n    ax.dist = 12\n    fignum = fignum + 1\n\n# Plot the ground truth\nfig = plt.figure(fignum, figsize=(4, 3))\nax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n\nfor name, label in [('Setosa', 0),\n                    ('Versicolour', 1),\n                    ('Virginica', 2)]:\n    ax.text3D(X[y == label, 3].mean(),\n              X[y == label, 0].mean(),\n              X[y == label, 2].mean() + 2, name,\n              horizontalalignment='center',\n              bbox=dict(alpha=.2, edgecolor='w', facecolor='w'))\n# Reorder the labels to have colors matching the cluster results\ny = np.choose(y, [1, 2, 0]).astype(np.float)\nax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor='k')\n\nax.w_xaxis.set_ticklabels([])\nax.w_yaxis.set_ticklabels([])\nax.w_zaxis.set_ticklabels([])\nax.set_xlabel('Petal width')\nax.set_ylabel('Sepal length')\nax.set_zlabel('Petal length')\nax.set_title('Ground Truth')\nax.dist = 12\n\nfig.show()"
       ]
     }
   ],

dev/_downloads/1dcd684ce26b8c407ec2c2d2101c5c73/plot_kernel_ridge_regression.py

@@ -119,7 +119,7 @@
 X = 5 * rng.rand(10000, 1)
 y = np.sin(X).ravel()
 y[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))
-sizes = np.logspace(1, 4, 7).astype(int)
+sizes = np.logspace(1, 4, 7).astype(np.int)
 for name, estimator in {"KRR": KernelRidge(kernel='rbf', alpha=0.1,
                                            gamma=10),
                         "SVR": SVR(kernel='rbf', C=1e1, gamma=10)}.items():

dev/_downloads/2338f6e7d44c2931a41926d4f9726d9b/plot_linkage_comparison.py

@@ -123,7 +123,7 @@
 
         t1 = time.time()
         if hasattr(algorithm, 'labels_'):
-            y_pred = algorithm.labels_.astype(int)
+            y_pred = algorithm.labels_.astype(np.int)
         else:
             y_pred = algorithm.predict(X)
 

dev/_downloads/285b194a4740110cb23e241031123972/plot_johnson_lindenstrauss_bound.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport sys\nfrom time import time\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom sklearn.random_projection import johnson_lindenstrauss_min_dim\nfrom sklearn.random_projection import SparseRandomProjection\nfrom sklearn.datasets import fetch_20newsgroups_vectorized\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics.pairwise import euclidean_distances\nfrom sklearn.utils.fixes import parse_version\n\n# `normed` is being deprecated in favor of `density` in histograms\nif parse_version(matplotlib.__version__) >= parse_version('2.1'):\n    density_param = {'density': True}\nelse:\n    density_param = {'normed': True}"
+        "print(__doc__)\n\nimport sys\nfrom time import time\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom distutils.version import LooseVersion\nfrom sklearn.random_projection import johnson_lindenstrauss_min_dim\nfrom sklearn.random_projection import SparseRandomProjection\nfrom sklearn.datasets import fetch_20newsgroups_vectorized\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics.pairwise import euclidean_distances\n\n# `normed` is being deprecated in favor of `density` in histograms\nif LooseVersion(matplotlib.__version__) >= '2.1':\n    density_param = {'density': True}\nelse:\n    density_param = {'normed': True}"
       ]
     },
     {

dev/_downloads/2a14e362a70d246e83fa6a89ca069cee/plot_sparse_coding.py

@@ -16,11 +16,12 @@
 """
 print(__doc__)
 
+from distutils.version import LooseVersion
+
 import numpy as np
 import matplotlib.pyplot as plt
 
 from sklearn.decomposition import SparseCoder
-from sklearn.utils.fixes import np_version, parse_version
 
 
 def ricker_function(resolution, center, width):
@@ -67,7 +68,7 @@ def ricker_matrix(width, resolution, n_components):
               ('Lasso', 'lasso_lars', 2, None, 'turquoise'), ]
 lw = 2
 # Avoid FutureWarning about default value change when numpy >= 1.14
-lstsq_rcond = None if np_version >= parse_version('1.14') else -1
+lstsq_rcond = None if LooseVersion(np.__version__) >= '1.14' else -1
 
 plt.figure(figsize=(13, 6))
 for subplot, (D, title) in enumerate(zip((D_fixed, D_multi),