.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neighbors/approximate_nearest_neighbors.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py: ===================================== Approximate nearest neighbors in TSNE ===================================== This example presents how to chain KNeighborsTransformer and TSNE in a pipeline. It also shows how to wrap the packages `nmslib` and `pynndescent` to replace KNeighborsTransformer and perform approximate nearest neighbors. These packages can be installed with `pip install nmslib pynndescent`. Note: In KNeighborsTransformer we use the definition which includes each training point as its own neighbor in the count of `n_neighbors`, and for compatibility reasons, one extra neighbor is computed when `mode == 'distance'`. Please note that we do the same in the proposed `nmslib` wrapper. .. GENERATED FROM PYTHON SOURCE LINES 16-20 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 21-23 First we try to import the packages and warn the user in case they are missing. .. GENERATED FROM PYTHON SOURCE LINES 23-37 .. code-block:: Python import sys try: import nmslib except ImportError: print("The package 'nmslib' is required to run this example.") sys.exit() try: from pynndescent import PyNNDescentTransformer except ImportError: print("The package 'pynndescent' is required to run this example.") sys.exit() .. GENERATED FROM PYTHON SOURCE LINES 38-40 We define a wrapper class for implementing the scikit-learn API to the `nmslib`, as well as a loading function. .. GENERATED FROM PYTHON SOURCE LINES 40-111 .. code-block:: Python import joblib import numpy as np from scipy.sparse import csr_matrix from sklearn.base import BaseEstimator, TransformerMixin from sklearn.datasets import fetch_openml from sklearn.utils import shuffle class NMSlibTransformer(TransformerMixin, BaseEstimator): """Wrapper for using nmslib as sklearn's KNeighborsTransformer""" def __init__(self, n_neighbors=5, metric="euclidean", method="sw-graph", n_jobs=-1): self.n_neighbors = n_neighbors self.method = method self.metric = metric self.n_jobs = n_jobs def fit(self, X): self.n_samples_fit_ = X.shape[0] # see more metric in the manual # https://github.com/nmslib/nmslib/tree/master/manual space = { "euclidean": "l2", "cosine": "cosinesimil", "l1": "l1", "l2": "l2", }[self.metric] self.nmslib_ = nmslib.init(method=self.method, space=space) self.nmslib_.addDataPointBatch(X.copy()) self.nmslib_.createIndex() return self def transform(self, X): n_samples_transform = X.shape[0] # For compatibility reasons, as each sample is considered as its own # neighbor, one extra neighbor will be computed. n_neighbors = self.n_neighbors + 1 if self.n_jobs < 0: # Same handling as done in joblib for negative values of n_jobs: # in particular, `n_jobs == -1` means "as many threads as CPUs". num_threads = joblib.cpu_count() + self.n_jobs + 1 else: num_threads = self.n_jobs results = self.nmslib_.knnQueryBatch( X.copy(), k=n_neighbors, num_threads=num_threads ) indices, distances = zip(*results) indices, distances = np.vstack(indices), np.vstack(distances) indptr = np.arange(0, n_samples_transform * n_neighbors + 1, n_neighbors) kneighbors_graph = csr_matrix( (distances.ravel(), indices.ravel(), indptr), shape=(n_samples_transform, self.n_samples_fit_), ) return kneighbors_graph def load_mnist(n_samples): """Load MNIST, shuffle the data, and return only n_samples.""" mnist = fetch_openml("mnist_784", as_frame=False) X, y = shuffle(mnist.data, mnist.target, random_state=2) return X[:n_samples] / 255, y[:n_samples] .. GENERATED FROM PYTHON SOURCE LINES 112-113 We benchmark the different exact/approximate nearest neighbors transformers. .. GENERATED FROM PYTHON SOURCE LINES 113-181 .. code-block:: Python import time from sklearn.manifold import TSNE from sklearn.neighbors import KNeighborsTransformer from sklearn.pipeline import make_pipeline datasets = [ ("MNIST_10000", load_mnist(n_samples=10_000)), ("MNIST_20000", load_mnist(n_samples=20_000)), ] n_iter = 500 perplexity = 30 metric = "euclidean" # TSNE requires a certain number of neighbors which depends on the # perplexity parameter. # Add one since we include each sample as its own neighbor. n_neighbors = int(3.0 * perplexity + 1) + 1 tsne_params = dict( init="random", # pca not supported for sparse matrices perplexity=perplexity, method="barnes_hut", random_state=42, n_iter=n_iter, learning_rate="auto", ) transformers = [ ( "KNeighborsTransformer", KNeighborsTransformer(n_neighbors=n_neighbors, mode="distance", metric=metric), ), ( "NMSlibTransformer", NMSlibTransformer(n_neighbors=n_neighbors, metric=metric), ), ( "PyNNDescentTransformer", PyNNDescentTransformer( n_neighbors=n_neighbors, metric=metric, parallel_batch_queries=True ), ), ] for dataset_name, (X, y) in datasets: msg = f"Benchmarking on {dataset_name}:" print(f"\n{msg}\n" + str("-" * len(msg))) for transformer_name, transformer in transformers: longest = np.max([len(name) for name, model in transformers]) start = time.time() transformer.fit(X) fit_duration = time.time() - start print(f"{transformer_name:<{longest}} {fit_duration:.3f} sec (fit)") start = time.time() Xt = transformer.transform(X) transform_duration = time.time() - start print(f"{transformer_name:<{longest}} {transform_duration:.3f} sec (transform)") if transformer_name == "PyNNDescentTransformer": start = time.time() Xt = transformer.transform(X) transform_duration = time.time() - start print( f"{transformer_name:<{longest}} {transform_duration:.3f} sec" " (transform)" ) .. GENERATED FROM PYTHON SOURCE LINES 182-211 Sample output:: Benchmarking on MNIST_10000: ---------------------------- KNeighborsTransformer 0.007 sec (fit) KNeighborsTransformer 1.139 sec (transform) NMSlibTransformer 0.208 sec (fit) NMSlibTransformer 0.315 sec (transform) PyNNDescentTransformer 4.823 sec (fit) PyNNDescentTransformer 4.884 sec (transform) PyNNDescentTransformer 0.744 sec (transform) Benchmarking on MNIST_20000: ---------------------------- KNeighborsTransformer 0.011 sec (fit) KNeighborsTransformer 5.769 sec (transform) NMSlibTransformer 0.733 sec (fit) NMSlibTransformer 1.077 sec (transform) PyNNDescentTransformer 14.448 sec (fit) PyNNDescentTransformer 7.103 sec (transform) PyNNDescentTransformer 1.759 sec (transform) Notice that the `PyNNDescentTransformer` takes more time during the first `fit` and the first `transform` due to the overhead of the numba just in time compiler. But after the first call, the compiled Python code is kept in a cache by numba and subsequent calls do not suffer from this initial overhead. Both :class:`~sklearn.neighbors.KNeighborsTransformer` and `NMSlibTransformer` are only run once here as they would show more stable `fit` and `transform` times (they don't have the cold start problem of PyNNDescentTransformer). .. GENERATED FROM PYTHON SOURCE LINES 213-276 .. code-block:: Python import matplotlib.pyplot as plt from matplotlib.ticker import NullFormatter transformers = [ ("TSNE with internal NearestNeighbors", TSNE(metric=metric, **tsne_params)), ( "TSNE with KNeighborsTransformer", make_pipeline( KNeighborsTransformer( n_neighbors=n_neighbors, mode="distance", metric=metric ), TSNE(metric="precomputed", **tsne_params), ), ), ( "TSNE with NMSlibTransformer", make_pipeline( NMSlibTransformer(n_neighbors=n_neighbors, metric=metric), TSNE(metric="precomputed", **tsne_params), ), ), ] # init the plot nrows = len(datasets) ncols = np.sum([1 for name, model in transformers if "TSNE" in name]) fig, axes = plt.subplots( nrows=nrows, ncols=ncols, squeeze=False, figsize=(5 * ncols, 4 * nrows) ) axes = axes.ravel() i_ax = 0 for dataset_name, (X, y) in datasets: msg = f"Benchmarking on {dataset_name}:" print(f"\n{msg}\n" + str("-" * len(msg))) for transformer_name, transformer in transformers: longest = np.max([len(name) for name, model in transformers]) start = time.time() Xt = transformer.fit_transform(X) transform_duration = time.time() - start print( f"{transformer_name:<{longest}} {transform_duration:.3f} sec" " (fit_transform)" ) # plot TSNE embedding which should be very similar across methods axes[i_ax].set_title(transformer_name + "\non " + dataset_name) axes[i_ax].scatter( Xt[:, 0], Xt[:, 1], c=y.astype(np.int32), alpha=0.2, cmap=plt.cm.viridis, ) axes[i_ax].xaxis.set_major_formatter(NullFormatter()) axes[i_ax].yaxis.set_major_formatter(NullFormatter()) axes[i_ax].axis("tight") i_ax += 1 fig.tight_layout() plt.show() .. GENERATED FROM PYTHON SOURCE LINES 277-315 Sample output:: Benchmarking on MNIST_10000: ---------------------------- TSNE with internal NearestNeighbors 24.828 sec (fit_transform) TSNE with KNeighborsTransformer 20.111 sec (fit_transform) TSNE with NMSlibTransformer 21.757 sec (fit_transform) Benchmarking on MNIST_20000: ---------------------------- TSNE with internal NearestNeighbors 51.955 sec (fit_transform) TSNE with KNeighborsTransformer 50.994 sec (fit_transform) TSNE with NMSlibTransformer 43.536 sec (fit_transform) We can observe that the default :class:`~sklearn.manifold.TSNE` estimator with its internal :class:`~sklearn.neighbors.NearestNeighbors` implementation is roughly equivalent to the pipeline with :class:`~sklearn.manifold.TSNE` and :class:`~sklearn.neighbors.KNeighborsTransformer` in terms of performance. This is expected because both pipelines rely internally on the same :class:`~sklearn.neighbors.NearestNeighbors` implementation that performs exacts neighbors search. The approximate `NMSlibTransformer` is already slightly faster than the exact search on the smallest dataset but this speed difference is expected to become more significant on datasets with a larger number of samples. Notice however that not all approximate search methods are guaranteed to improve the speed of the default exact search method: indeed the exact search implementation significantly improved since scikit-learn 1.1. Furthermore, the brute-force exact search method does not require building an index at `fit` time. So, to get an overall performance improvement in the context of the :class:`~sklearn.manifold.TSNE` pipeline, the gains of the approximate search at `transform` need to be larger than the extra time spent to build the approximate search index at `fit` time. Finally, the TSNE algorithm itself is also computationally intensive, irrespective of the nearest neighbors search. So speeding-up the nearest neighbors search step by a factor of 5 would not result in a speed up by a factor of 5 for the overall pipeline. .. _sphx_glr_download_auto_examples_neighbors_approximate_nearest_neighbors.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/approximate_nearest_neighbors.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: approximate_nearest_neighbors.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: approximate_nearest_neighbors.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: approximate_nearest_neighbors.zip ` .. include:: approximate_nearest_neighbors.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_