Pushing the docs to dev/ for branch: main, commit 5273e6eed5f56a1ef85b09a1652c69f450b4159c

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: e4973f4ec0b2eee118f5608cb49f1d67
+config: 6cf84a4398407de34ef4dc1f83c797ff
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        n_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
+        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
       ]
     },
     {

dev/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Narine Kokhlikyan <[email protected]>\n# License: BSD\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import datasets, manifold\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        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        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()"
+        "# Author: Narine Kokhlikyan <[email protected]>\n# License: BSD\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import datasets, manifold\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        max_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        max_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        max_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

@@ -145,7 +145,7 @@ def plot_embedding(X, title):
     ),
     "t-SNE embedding": TSNE(
         n_components=2,
-        n_iter=500,
+        max_iter=500,
         n_iter_without_progress=150,
         n_jobs=2,
         random_state=0,

dev/_downloads/8b98cea0e0ec1ca3cc503c13ddac0537/plot_t_sne_perplexity.ipynb

@@ -170,7 +170,7 @@
       },
       "outputs": [],
       "source": [
-        "t_sne = manifold.TSNE(\n    n_components=n_components,\n    perplexity=30,\n    init=\"random\",\n    n_iter=250,\n    random_state=0,\n)\nS_t_sne = t_sne.fit_transform(S_points)\n\nplot_2d(S_t_sne, S_color, \"T-distributed Stochastic  \\n Neighbor Embedding\")"
+        "t_sne = manifold.TSNE(\n    n_components=n_components,\n    perplexity=30,\n    init=\"random\",\n    max_iter=250,\n    random_state=0,\n)\nS_t_sne = t_sne.fit_transform(S_points)\n\nplot_2d(S_t_sne, S_color, \"T-distributed Stochastic  \\n Neighbor Embedding\")"
       ]
     }
   ],

dev/_downloads/9d97cc4ed755b7f2c7f9311bccc89a00/plot_lle_digits.py

@@ -202,7 +202,7 @@ def add_2d_scatter(ax, points, points_color, title=None):
     n_components=n_components,
     perplexity=30,
     init="random",
-    n_iter=250,
+    max_iter=250,
     random_state=0,
 )
 S_t_sne = t_sne.fit_transform(S_points)

dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb

@@ -63,7 +63,7 @@
         init="random",
         random_state=0,
         perplexity=perplexity,
-        n_iter=300,
+        max_iter=300,
     )
     Y = tsne.fit_transform(X)
     t1 = time()
@@ -93,7 +93,7 @@
         random_state=0,
         perplexity=perplexity,
         learning_rate="auto",
-        n_iter=300,
+        max_iter=300,
     )
     Y = tsne.fit_transform(X)
     t1 = time()
@@ -130,7 +130,7 @@
         init="random",
         random_state=0,
         perplexity=perplexity,
-        n_iter=400,
+        max_iter=400,
     )
     Y = tsne.fit_transform(X)
     t1 = time()