Pushing the docs to dev/ for branch: main, commit f57a5f9bc0d37a33dba94b0934a00d69c9c4c60d

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom matplotlib import offsetbox\nfrom sklearn.preprocessing import MinMaxScaler\n\n\ndef plot_embedding(X, title, ax):\n    X = MinMaxScaler().fit_transform(X)\n\n    shown_images = np.array([[1.0, 1.0]])  # just something big\n    for i in range(X.shape[0]):\n        # plot every digit on the embedding\n        ax.text(\n            X[i, 0],\n            X[i, 1],\n            str(y[i]),\n            color=plt.cm.Dark2(y[i]),\n            fontdict={\"weight\": \"bold\", \"size\": 9},\n        )\n\n        # show an annotation box for a group of digits\n        dist = np.sum((X[i] - shown_images) ** 2, 1)\n        if np.min(dist) < 4e-3:\n            # don't show points that are too close\n            continue\n        shown_images = np.concatenate([shown_images, [X[i]]], axis=0)\n        imagebox = offsetbox.AnnotationBbox(\n            offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r), X[i]\n        )\n        ax.add_artist(imagebox)\n\n    ax.set_title(title)\n    ax.axis(\"off\")"
+        "import numpy as np\nfrom matplotlib import offsetbox\nfrom sklearn.preprocessing import MinMaxScaler\n\n\ndef plot_embedding(X, title, ax):\n    X = MinMaxScaler().fit_transform(X)\n    for digit in digits.target_names:\n        ax.scatter(\n            *X[y == digit].T,\n            marker=f\"${digit}$\",\n            s=60,\n            color=plt.cm.Dark2(digit),\n            alpha=0.425,\n            zorder=2,\n        )\n    shown_images = np.array([[1.0, 1.0]])  # just something big\n    for i in range(X.shape[0]):\n        # plot every digit on the embedding\n        # show an annotation box for a group of digits\n        dist = np.sum((X[i] - shown_images) ** 2, 1)\n        if np.min(dist) < 4e-3:\n            # don't show points that are too close\n            continue\n        shown_images = np.concatenate([shown_images, [X[i]]], axis=0)\n        imagebox = offsetbox.AnnotationBbox(\n            offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r), X[i]\n        )\n        imagebox.set(zorder=1)\n        ax.add_artist(imagebox)\n\n    ax.set_title(title)\n    ax.axis(\"off\")"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    Isomap,\n    LocallyLinearEmbedding,\n    MDS,\n    SpectralEmbedding,\n    TSNE,\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=100),\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 embeedding\": TSNE(\n        n_components=2, init=\"pca\", learning_rate=\"auto\", random_state=0\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"random\", 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    Isomap,\n    LocallyLinearEmbedding,\n    MDS,\n    SpectralEmbedding,\n    TSNE,\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 embeedding\": TSNE(\n        n_components=2,\n        init=\"pca\",\n        learning_rate=\"auto\",\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}"
       ]
     },
     {