Pushing the docs to dev/ for branch: main, commit 5900e2660f4293b52ea57bf830e7bff312779036

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Jake Vanderplas -- <[email protected]>\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(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\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(\n    manifold.LocallyLinearEmbedding,\n    n_neighbors=n_neighbors,\n    n_components=n_components,\n    eigen_solver=\"auto\",\n)\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_neighbors, n_components=n_components)\nmethods[\"MDS\"] = manifold.MDS(n_components, max_iter=100, n_init=1)\nmethods[\"SE\"] = manifold.SpectralEmbedding(\n    n_components=n_components, n_neighbors=n_neighbors\n)\nmethods[\"t-SNE\"] = manifold.TSNE(n_components=n_components, init=\"pca\", 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()"
+        "# Author: Jake Vanderplas -- <[email protected]>\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(\n    \"Manifold Learning with %i points, %i neighbors\" % (n_points, n_neighbors),\n    fontsize=14,\n)\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(\n    manifold.LocallyLinearEmbedding,\n    n_neighbors=n_neighbors,\n    n_components=n_components,\n    eigen_solver=\"auto\",\n    random_state=0,\n)\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_neighbors, n_components=n_components)\nmethods[\"MDS\"] = manifold.MDS(n_components, max_iter=50, n_init=1, random_state=0)\nmethods[\"SE\"] = manifold.SpectralEmbedding(\n    n_components=n_components, n_neighbors=n_neighbors, random_state=0\n)\nmethods[\"t-SNE\"] = manifold.TSNE(\n    n_components=n_components, perplexity=30, n_iter=250, init=\"pca\", random_state=0\n)\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/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -43,7 +43,8 @@
 # Create figure
 fig = plt.figure(figsize=(15, 8))
 fig.suptitle(
-    "Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14
+    "Manifold Learning with %i points, %i neighbors" % (n_points, n_neighbors),
+    fontsize=14,
 )
 
 # Add 3d scatter plot
@@ -57,6 +58,7 @@
     n_neighbors=n_neighbors,
     n_components=n_components,
     eigen_solver="auto",
+    random_state=0,
 )
 
 methods = OrderedDict()
@@ -65,11 +67,13 @@
 methods["Hessian LLE"] = LLE(method="hessian")
 methods["Modified LLE"] = LLE(method="modified")
 methods["Isomap"] = manifold.Isomap(n_neighbors=n_neighbors, n_components=n_components)
-methods["MDS"] = manifold.MDS(n_components, max_iter=100, n_init=1)
+methods["MDS"] = manifold.MDS(n_components, max_iter=50, n_init=1, random_state=0)
 methods["SE"] = manifold.SpectralEmbedding(
-    n_components=n_components, n_neighbors=n_neighbors
+    n_components=n_components, n_neighbors=n_neighbors, random_state=0
+)
+methods["t-SNE"] = manifold.TSNE(
+    n_components=n_components, perplexity=30, n_iter=250, init="pca", random_state=0
 )
-methods["t-SNE"] = manifold.TSNE(n_components=n_components, init="pca", random_state=0)
 
 # Plot results
 for i, (label, method) in enumerate(methods.items()):