Pushing the docs to dev/ for branch: main, commit 0ff613e8dd1e340784d49e2a2308b26eec6c7ff4

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Jaques Grobler <[email protected]>\n# License: BSD 3 clause\n\nfrom time import time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Next line to silence pyflakes.\nAxes3D\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\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\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(n_components=2, n_neighbors=n_neighbors)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, init=\"pca\", random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\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: Jaques Grobler <[email protected]>\n# License: BSD 3 clause\n\nfrom time import time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import NullFormatter\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\nimport warnings\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\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\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(n_components=2, n_neighbors=n_neighbors)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\n# TODO(1.2) Remove warning handling.\nwith warnings.catch_warnings():\n    warnings.filterwarnings(\n        \"ignore\", message=\"The PCA initialization\", category=FutureWarning\n    )\n    t0 = time()\n    tsne = manifold.TSNE(\n        n_components=2, init=\"pca\", random_state=0, learning_rate=\"auto\"\n    )\n    trans_data = tsne.fit_transform(sphere_data).T\n    t1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\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()"
       ]
     }
   ],

dev/_downloads/604c0a9de0e1b80dae9e6754fdb27014/plot_manifold_sphere.ipynb

@@ -30,17 +30,15 @@
 # License: BSD 3 clause
 
 from time import time
-
 import numpy as np
 import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
 from matplotlib.ticker import NullFormatter
-
 from sklearn import manifold
 from sklearn.utils import check_random_state
 
-# Next line to silence pyflakes.
-Axes3D
+# Unused but required import for doing 3d projections with matplotlib < 3.2
+import mpl_toolkits.mplot3d  # noqa: F401
+import warnings
 
 # Variables for manifold learning.
 n_neighbors = 10
@@ -141,10 +139,17 @@
 plt.axis("tight")
 
 # Perform t-distributed stochastic neighbor embedding.
-t0 = time()
-tsne = manifold.TSNE(n_components=2, init="pca", random_state=0)
-trans_data = tsne.fit_transform(sphere_data).T
-t1 = time()
+# TODO(1.2) Remove warning handling.
+with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore", message="The PCA initialization", category=FutureWarning
+    )
+    t0 = time()
+    tsne = manifold.TSNE(
+        n_components=2, init="pca", random_state=0, learning_rate="auto"
+    )
+    trans_data = tsne.fit_transform(sphere_data).T
+    t1 = time()
 print("t-SNE: %.2g sec" % (t1 - t0))
 
 ax = fig.add_subplot(2, 5, 10)

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -23,7 +23,7 @@
 
 # %%
 # Define a data preprocessing function
-# ----------------------------------
+# ------------------------------------
 #
 # The example uses real-world datasets available in
 # :class:`sklearn.datasets` and the sample size of some datasets is reduced