Pushing the docs to dev/ for branch: master, commit 3339d802402fd2f2ed5e954434c637bf7a68124d

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Jake Vanderplas -- <[email protected]>\n\nprint(__doc__)\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold, datasets\n\n# Next line to silence pyflakes. This import is needed.\nAxes3D\n\nn_points = 1000\nX, color = datasets.make_s_curve(n_points, random_state=0)\nn_neighbors = 10\nn_components = 2\n\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\"Manifold Learning with %i points, %i neighbors\"\n             % (1000, n_neighbors), fontsize=14)\n\n\nax = fig.add_subplot(251, projection='3d')\nax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)\nax.view_init(4, -72)\n\nmethods = ['standard', 'ltsa', 'hessian', 'modified']\nlabels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components,\n                                        eigen_solver='auto',\n                                        method=method).fit_transform(X)\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis('tight')\n\nt0 = time()\nY = manifold.Isomap(n_neighbors, n_components).fit_transform(X)\nt1 = time()\nprint(\"Isomap: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(257)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"Isomap (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n\nt0 = time()\nmds = manifold.MDS(n_components, max_iter=100, n_init=1)\nY = mds.fit_transform(X)\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(258)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n\nt0 = time()\nse = manifold.SpectralEmbedding(n_components=n_components,\n                                n_neighbors=n_neighbors)\nY = se.fit_transform(X)\nt1 = time()\nprint(\"SpectralEmbedding: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(259)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"SpectralEmbedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nt0 = time()\ntsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0)\nY = tsne.fit_transform(X)\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nplt.show()"
+        "# Author: Jake Vanderplas -- <[email protected]>\n\nprint(__doc__)\n\nfrom collections import OrderedDict\nfrom functools import partial\nfrom time import time\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold, datasets\n\n# Next line to silence pyflakes. This import is needed.\nAxes3D\n\nn_points = 1000\nX, color = datasets.make_s_curve(n_points, random_state=0)\nn_neighbors = 10\nn_components = 2\n\n# Create figure\nfig = plt.figure(figsize=(15, 8))\nfig.suptitle(\"Manifold Learning with %i points, %i neighbors\"\n             % (1000, n_neighbors), fontsize=14)\n\n# Add 3d scatter plot\nax = fig.add_subplot(251, projection='3d')\nax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)\nax.view_init(4, -72)\n\n# Set-up manifold methods\nLLE = partial(manifold.LocallyLinearEmbedding,\n              n_neighbors, n_components, eigen_solver='auto')\n\nmethods = OrderedDict()\nmethods['LLE'] = LLE(method='standard')\nmethods['LTSA'] = LLE(method='ltsa')\nmethods['Hessian LLE'] = LLE(method='hessian')\nmethods['Modified LLE'] = LLE(method='modified')\nmethods['Isomap'] = manifold.Isomap(n_neighbors, n_components)\nmethods['MDS'] = manifold.MDS(n_components, max_iter=100, n_init=1)\nmethods['SE'] = manifold.SpectralEmbedding(n_components=n_components,\n                                           n_neighbors=n_neighbors)\nmethods['t-SNE'] = manifold.TSNE(n_components=n_components, init='pca',\n                                 random_state=0)\n\n# Plot results\nfor i, (label, method) in enumerate(methods.items()):\n    t0 = time()\n    Y = method.fit_transform(X)\n    t1 = time()\n    print(\"%s: %.2g sec\" % (label, t1 - t0))\n    ax = fig.add_subplot(2, 5, 2 + i + (i > 3))\n    ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\n    ax.set_title(\"%s (%.2g sec)\" % (label, t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis('tight')\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/50620cb5a6bfe6c3bcb899f31a3348a3/plot_compare_methods.ipynb

@@ -23,6 +23,8 @@
 
 print(__doc__)
 
+from collections import OrderedDict
+from functools import partial
 from time import time
 
 import matplotlib.pyplot as plt
@@ -39,81 +41,43 @@
 n_neighbors = 10
 n_components = 2
 
+# Create figure
 fig = plt.figure(figsize=(15, 8))
-plt.suptitle("Manifold Learning with %i points, %i neighbors"
+fig.suptitle("Manifold Learning with %i points, %i neighbors"
              % (1000, n_neighbors), fontsize=14)
 
-
+# Add 3d scatter plot
 ax = fig.add_subplot(251, projection='3d')
 ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
 ax.view_init(4, -72)
 
-methods = ['standard', 'ltsa', 'hessian', 'modified']
-labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']
-
-for i, method in enumerate(methods):
+# Set-up manifold methods
+LLE = partial(manifold.LocallyLinearEmbedding,
+              n_neighbors, n_components, eigen_solver='auto')
+
+methods = OrderedDict()
+methods['LLE'] = LLE(method='standard')
+methods['LTSA'] = LLE(method='ltsa')
+methods['Hessian LLE'] = LLE(method='hessian')
+methods['Modified LLE'] = LLE(method='modified')
+methods['Isomap'] = manifold.Isomap(n_neighbors, n_components)
+methods['MDS'] = manifold.MDS(n_components, max_iter=100, n_init=1)
+methods['SE'] = manifold.SpectralEmbedding(n_components=n_components,
+                                           n_neighbors=n_neighbors)
+methods['t-SNE'] = manifold.TSNE(n_components=n_components, init='pca',
+                                 random_state=0)
+
+# Plot results
+for i, (label, method) in enumerate(methods.items()):
     t0 = time()
-    Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components,
-                                        eigen_solver='auto',
-                                        method=method).fit_transform(X)
+    Y = method.fit_transform(X)
     t1 = time()
-    print("%s: %.2g sec" % (methods[i], t1 - t0))
-
-    ax = fig.add_subplot(252 + i)
-    plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
-    plt.title("%s (%.2g sec)" % (labels[i], t1 - t0))
+    print("%s: %.2g sec" % (label, t1 - t0))
+    ax = fig.add_subplot(2, 5, 2 + i + (i > 3))
+    ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
+    ax.set_title("%s (%.2g sec)" % (label, t1 - t0))
     ax.xaxis.set_major_formatter(NullFormatter())
     ax.yaxis.set_major_formatter(NullFormatter())
-    plt.axis('tight')
-
-t0 = time()
-Y = manifold.Isomap(n_neighbors, n_components).fit_transform(X)
-t1 = time()
-print("Isomap: %.2g sec" % (t1 - t0))
-ax = fig.add_subplot(257)
-plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
-plt.title("Isomap (%.2g sec)" % (t1 - t0))
-ax.xaxis.set_major_formatter(NullFormatter())
-ax.yaxis.set_major_formatter(NullFormatter())
-plt.axis('tight')
-
-
-t0 = time()
-mds = manifold.MDS(n_components, max_iter=100, n_init=1)
-Y = mds.fit_transform(X)
-t1 = time()
-print("MDS: %.2g sec" % (t1 - t0))
-ax = fig.add_subplot(258)
-plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
-plt.title("MDS (%.2g sec)" % (t1 - t0))
-ax.xaxis.set_major_formatter(NullFormatter())
-ax.yaxis.set_major_formatter(NullFormatter())
-plt.axis('tight')
-
-
-t0 = time()
-se = manifold.SpectralEmbedding(n_components=n_components,
-                                n_neighbors=n_neighbors)
-Y = se.fit_transform(X)
-t1 = time()
-print("SpectralEmbedding: %.2g sec" % (t1 - t0))
-ax = fig.add_subplot(259)
-plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
-plt.title("SpectralEmbedding (%.2g sec)" % (t1 - t0))
-ax.xaxis.set_major_formatter(NullFormatter())
-ax.yaxis.set_major_formatter(NullFormatter())
-plt.axis('tight')
-
-t0 = time()
-tsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0)
-Y = tsne.fit_transform(X)
-t1 = time()
-print("t-SNE: %.2g sec" % (t1 - t0))
-ax = fig.add_subplot(2, 5, 10)
-plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
-plt.title("t-SNE (%.2g sec)" % (t1 - t0))
-ax.xaxis.set_major_formatter(NullFormatter())
-ax.yaxis.set_major_formatter(NullFormatter())
-plt.axis('tight')
+    ax.axis('tight')
 
 plt.show()