Pushing the docs to dev/ for branch: main, commit f6951474459793ea4f7d1b4d325360924f2e6521

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Narine Kokhlikyan <[email protected]>\n# License: BSD\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom matplotlib.ticker import NullFormatter\nfrom sklearn import manifold, datasets\nfrom time import time\n\nn_samples = 300\nn_components = 2\n(fig, subplots) = plt.subplots(3, 5, figsize=(15, 8))\nperplexities = [5, 30, 50, 100]\n\nX, y = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)\n\nred = y == 0\ngreen = y == 1\n\nax = subplots[0][0]\nax.scatter(X[red, 0], X[red, 1], c=\"r\")\nax.scatter(X[green, 0], X[green, 1], c=\"g\")\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[0][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(\n        n_components=n_components, init=\"random\", random_state=0, perplexity=perplexity\n    )\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"circles, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[red, 0], Y[red, 1], c=\"r\")\n    ax.scatter(Y[green, 0], Y[green, 1], c=\"g\")\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis(\"tight\")\n\n# Another example using s-curve\nX, color = datasets.make_s_curve(n_samples, random_state=0)\n\nax = subplots[1][0]\nax.scatter(X[:, 0], X[:, 2], c=color)\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[1][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(\n        n_components=n_components, init=\"random\", random_state=0, perplexity=perplexity\n    )\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"S-curve, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[:, 0], Y[:, 1], c=color)\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis(\"tight\")\n\n\n# Another example using a 2D uniform grid\nx = np.linspace(0, 1, int(np.sqrt(n_samples)))\nxx, yy = np.meshgrid(x, x)\nX = np.hstack(\n    [\n        xx.ravel().reshape(-1, 1),\n        yy.ravel().reshape(-1, 1),\n    ]\n)\ncolor = xx.ravel()\nax = subplots[2][0]\nax.scatter(X[:, 0], X[:, 1], c=color)\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[2][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(\n        n_components=n_components, init=\"random\", random_state=0, perplexity=perplexity\n    )\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"uniform grid, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[:, 0], Y[:, 1], c=color)\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis(\"tight\")\n\n\nplt.show()"
+        "# Author: Narine Kokhlikyan <[email protected]>\n# License: BSD\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom matplotlib.ticker import NullFormatter\nfrom sklearn import manifold, datasets\nfrom time import time\n\nn_samples = 150\nn_components = 2\n(fig, subplots) = plt.subplots(3, 5, figsize=(15, 8))\nperplexities = [5, 30, 50, 100]\n\nX, y = datasets.make_circles(\n    n_samples=n_samples, factor=0.5, noise=0.05, random_state=0\n)\n\nred = y == 0\ngreen = y == 1\n\nax = subplots[0][0]\nax.scatter(X[red, 0], X[red, 1], c=\"r\")\nax.scatter(X[green, 0], X[green, 1], c=\"g\")\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[0][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(\n        n_components=n_components,\n        init=\"random\",\n        random_state=0,\n        perplexity=perplexity,\n        learning_rate=\"auto\",\n        n_iter=300,\n    )\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"circles, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[red, 0], Y[red, 1], c=\"r\")\n    ax.scatter(Y[green, 0], Y[green, 1], c=\"g\")\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis(\"tight\")\n\n# Another example using s-curve\nX, color = datasets.make_s_curve(n_samples, random_state=0)\n\nax = subplots[1][0]\nax.scatter(X[:, 0], X[:, 2], c=color)\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[1][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(\n        n_components=n_components,\n        init=\"random\",\n        random_state=0,\n        perplexity=perplexity,\n        learning_rate=\"auto\",\n        n_iter=300,\n    )\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"S-curve, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[:, 0], Y[:, 1], c=color)\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis(\"tight\")\n\n\n# Another example using a 2D uniform grid\nx = np.linspace(0, 1, int(np.sqrt(n_samples)))\nxx, yy = np.meshgrid(x, x)\nX = np.hstack(\n    [\n        xx.ravel().reshape(-1, 1),\n        yy.ravel().reshape(-1, 1),\n    ]\n)\ncolor = xx.ravel()\nax = subplots[2][0]\nax.scatter(X[:, 0], X[:, 1], c=color)\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[2][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(\n        n_components=n_components,\n        init=\"random\",\n        random_state=0,\n        perplexity=perplexity,\n        learning_rate=\"auto\",\n        n_iter=400,\n    )\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"uniform grid, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[:, 0], Y[:, 1], c=color)\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis(\"tight\")\n\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -34,12 +34,14 @@
 from sklearn import manifold, datasets
 from time import time
 
-n_samples = 300
+n_samples = 150
 n_components = 2
 (fig, subplots) = plt.subplots(3, 5, figsize=(15, 8))
 perplexities = [5, 30, 50, 100]
 
-X, y = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
+X, y = datasets.make_circles(
+    n_samples=n_samples, factor=0.5, noise=0.05, random_state=0
+)
 
 red = y == 0
 green = y == 1
@@ -56,7 +58,12 @@
 
     t0 = time()
     tsne = manifold.TSNE(
-        n_components=n_components, init="random", random_state=0, perplexity=perplexity
+        n_components=n_components,
+        init="random",
+        random_state=0,
+        perplexity=perplexity,
+        learning_rate="auto",
+        n_iter=300,
     )
     Y = tsne.fit_transform(X)
     t1 = time()
@@ -81,7 +88,12 @@
 
     t0 = time()
     tsne = manifold.TSNE(
-        n_components=n_components, init="random", random_state=0, perplexity=perplexity
+        n_components=n_components,
+        init="random",
+        random_state=0,
+        perplexity=perplexity,
+        learning_rate="auto",
+        n_iter=300,
     )
     Y = tsne.fit_transform(X)
     t1 = time()
@@ -114,7 +126,12 @@
 
     t0 = time()
     tsne = manifold.TSNE(
-        n_components=n_components, init="random", random_state=0, perplexity=perplexity
+        n_components=n_components,
+        init="random",
+        random_state=0,
+        perplexity=perplexity,
+        learning_rate="auto",
+        n_iter=400,
     )
     Y = tsne.fit_transform(X)
     t1 = time()