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Commitfc06ad3

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Pushing the docs to dev/ for branch: main, commit 9258e329e05b75f4b531b6d0660c0b1bd4d20a16
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‎dev/_downloads/73ec8f08ae3ece02509539ee03cc3cdd/approximate_nearest_neighbors.ipynb

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‎dev/_downloads/dcd99fee3ee8ac76b69a1d2d6f5c7e78/approximate_nearest_neighbors.py

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Approximate nearest neighbors in TSNE
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=====================================
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This example presents how to chain KNeighborsTransformer and TSNE in a
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pipeline.It also shows how to wrap the packages `annoy` and `nmslib` to
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replaceKNeighborsTransformer and perform approximate nearest neighbors.
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These packagescan be installed with `pip install annoy nmslib`.
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This example presents how to chain KNeighborsTransformer and TSNE in a pipeline.
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It also shows how to wrap the packages `annoy` and `nmslib` to replace
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KNeighborsTransformer and perform approximate nearest neighbors. These packages
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can be installed with `pip install annoy nmslib`.
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Note: In KNeighborsTransformer we use the definition which includes each
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training point as its own neighbor in the count of `n_neighbors`, and for
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compatibility reasons, one extra neighbor is computed when
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`mode == 'distance'`.Please note that we do the same in the proposed wrappers.
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compatibility reasons, one extra neighbor is computed when `mode == 'distance'`.
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Please note that we do the same in the proposed wrappers.
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Sample output::
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Benchmarking on MNIST_2000:
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---------------------------
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AnnoyTransformer: 0.583 sec
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NMSlibTransformer: 0.321 sec
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KNeighborsTransformer:1.225 sec
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TSNE with AnnoyTransformer:4.903 sec
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TSNE with NMSlibTransformer:5.009 sec
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TSNE with KNeighborsTransformer:6.210 sec
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TSNE with internal NearestNeighbors:6.365 sec
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AnnoyTransformer: 0.305 sec
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NMSlibTransformer: 0.144 sec
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KNeighborsTransformer:0.090 sec
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TSNE with AnnoyTransformer:2.818 sec
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TSNE with NMSlibTransformer:2.592 sec
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TSNE with KNeighborsTransformer:2.338 sec
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TSNE with internal NearestNeighbors:2.364 sec
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Benchmarking on MNIST_10000:
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----------------------------
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AnnoyTransformer: 4.457 sec
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NMSlibTransformer: 2.080 sec
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KNeighborsTransformer: 30.680 sec
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TSNE with AnnoyTransformer: 30.225 sec
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TSNE with NMSlibTransformer: 43.295 sec
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TSNE with KNeighborsTransformer: 64.845 sec
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TSNE with internal NearestNeighbors: 64.984 sec
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AnnoyTransformer: 2.874 sec
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NMSlibTransformer: 1.098 sec
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KNeighborsTransformer: 1.264 sec
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TSNE with AnnoyTransformer: 16.118 sec
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TSNE with NMSlibTransformer: 15.281 sec
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TSNE with KNeighborsTransformer: 15.400 sec
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TSNE with internal NearestNeighbors: 15.573 sec
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Note that the prediction speed KNeighborsTransformer was optimized in
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scikit-learn 1.1 and therefore approximate methods are not necessarily faster
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because computing the index takes time and can nullify the gains obtained at
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prediction time.
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"""
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n_neighbors=int(3.0*perplexity+1)+1
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tsne_params=dict(
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init="random",# pca not supported for sparse matrices
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perplexity=perplexity,
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method="barnes_hut",
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random_state=42,
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n_iter=n_iter,
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square_distances=True,
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learning_rate="auto",
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)
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transformers= [

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‎dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt

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‎dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt

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‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt

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‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt

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‎dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt

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‎dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt

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‎dev/_sources/auto_examples/applications/plot_prediction_latency.rst.txt

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‎dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt

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‎dev/_sources/auto_examples/applications/plot_stock_market.rst.txt

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‎dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt

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