Pushing the docs to dev/ for branch: master, commit 871b25162339c60557e5bf1754ea553ec33adf52

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -18,29 +18,28 @@
 compatibility reasons, one extra neighbor is computed when
 `mode == 'distance'`. Please note that we do the same in the proposed wrappers.
 
-Sample output:
-
-```
-Benchmarking on MNIST_2000:
----------------------------
-AnnoyTransformer:                    0.583 sec
-NMSlibTransformer:                   0.321 sec
-KNeighborsTransformer:               1.225 sec
-TSNE with AnnoyTransformer:          4.903 sec
-TSNE with NMSlibTransformer:         5.009 sec
-TSNE with KNeighborsTransformer:     6.210 sec
-TSNE with internal NearestNeighbors: 6.365 sec
-
-Benchmarking on MNIST_10000:
-----------------------------
-AnnoyTransformer:                    4.457 sec
-NMSlibTransformer:                   2.080 sec
-KNeighborsTransformer:               30.680 sec
-TSNE with AnnoyTransformer:          30.225 sec
-TSNE with NMSlibTransformer:         43.295 sec
-TSNE with KNeighborsTransformer:     64.845 sec
-TSNE with internal NearestNeighbors: 64.984 sec
-```
+Sample output::
+
+    Benchmarking on MNIST_2000:
+    ---------------------------
+    AnnoyTransformer:                    0.583 sec
+    NMSlibTransformer:                   0.321 sec
+    KNeighborsTransformer:               1.225 sec
+    TSNE with AnnoyTransformer:          4.903 sec
+    TSNE with NMSlibTransformer:         5.009 sec
+    TSNE with KNeighborsTransformer:     6.210 sec
+    TSNE with internal NearestNeighbors: 6.365 sec
+
+    Benchmarking on MNIST_10000:
+    ----------------------------
+    AnnoyTransformer:                    4.457 sec
+    NMSlibTransformer:                   2.080 sec
+    KNeighborsTransformer:               30.680 sec
+    TSNE with AnnoyTransformer:          30.225 sec
+    TSNE with NMSlibTransformer:         43.295 sec
+    TSNE with KNeighborsTransformer:     64.845 sec
+    TSNE with internal NearestNeighbors: 64.984 sec
+
 """
 # Author: Tom Dupre la Tour
 #

dev/_downloads/76064e174f651530bf4f9c3043b45bad/approximate_nearest_neighbors.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Approximate nearest neighbors in TSNE\n\n\nThis example presents how to chain KNeighborsTransformer and TSNE in a\npipeline. It also shows how to wrap the packages `annoy` and `nmslib` to\nreplace KNeighborsTransformer and perform approximate nearest neighbors.\nThese packages can be installed with `pip install annoy nmslib`.\n\nNote: Currently `TSNE(metric='precomputed')` does not modify the precomputed\ndistances, and thus assumes that precomputed euclidean distances are squared.\nIn future versions, a parameter in TSNE will control the optional squaring of\nprecomputed distances (see #12401).\n\nNote: In KNeighborsTransformer we use the definition which includes each\ntraining point as its own neighbor in the count of `n_neighbors`, and for\ncompatibility reasons, one extra neighbor is computed when\n`mode == 'distance'`. Please note that we do the same in the proposed wrappers.\n\nSample output:\n\n```\nBenchmarking on MNIST_2000:\n---------------------------\nAnnoyTransformer:                    0.583 sec\nNMSlibTransformer:                   0.321 sec\nKNeighborsTransformer:               1.225 sec\nTSNE with AnnoyTransformer:          4.903 sec\nTSNE with NMSlibTransformer:         5.009 sec\nTSNE with KNeighborsTransformer:     6.210 sec\nTSNE with internal NearestNeighbors: 6.365 sec\n\nBenchmarking on MNIST_10000:\n----------------------------\nAnnoyTransformer:                    4.457 sec\nNMSlibTransformer:                   2.080 sec\nKNeighborsTransformer:               30.680 sec\nTSNE with AnnoyTransformer:          30.225 sec\nTSNE with NMSlibTransformer:         43.295 sec\nTSNE with KNeighborsTransformer:     64.845 sec\nTSNE with internal NearestNeighbors: 64.984 sec\n```\n"
+        "\n# Approximate nearest neighbors in TSNE\n\n\nThis example presents how to chain KNeighborsTransformer and TSNE in a\npipeline. It also shows how to wrap the packages `annoy` and `nmslib` to\nreplace KNeighborsTransformer and perform approximate nearest neighbors.\nThese packages can be installed with `pip install annoy nmslib`.\n\nNote: Currently `TSNE(metric='precomputed')` does not modify the precomputed\ndistances, and thus assumes that precomputed euclidean distances are squared.\nIn future versions, a parameter in TSNE will control the optional squaring of\nprecomputed distances (see #12401).\n\nNote: In KNeighborsTransformer we use the definition which includes each\ntraining point as its own neighbor in the count of `n_neighbors`, and for\ncompatibility reasons, one extra neighbor is computed when\n`mode == 'distance'`. Please note that we do the same in the proposed wrappers.\n\nSample output::\n\n    Benchmarking on MNIST_2000:\n    ---------------------------\n    AnnoyTransformer:                    0.583 sec\n    NMSlibTransformer:                   0.321 sec\n    KNeighborsTransformer:               1.225 sec\n    TSNE with AnnoyTransformer:          4.903 sec\n    TSNE with NMSlibTransformer:         5.009 sec\n    TSNE with KNeighborsTransformer:     6.210 sec\n    TSNE with internal NearestNeighbors: 6.365 sec\n\n    Benchmarking on MNIST_10000:\n    ----------------------------\n    AnnoyTransformer:                    4.457 sec\n    NMSlibTransformer:                   2.080 sec\n    KNeighborsTransformer:               30.680 sec\n    TSNE with AnnoyTransformer:          30.225 sec\n    TSNE with NMSlibTransformer:         43.295 sec\n    TSNE with KNeighborsTransformer:     64.845 sec\n    TSNE with internal NearestNeighbors: 64.984 sec\n"
       ]
     },
     {