Note
Click here to download the full example code or to run this example in your browser via Binder
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 annoy and nmslib to
replace KNeighborsTransformer and perform approximate nearest neighbors.
These packages can be installed with pip install annoy nmslib.
Note: Currently TSNE(metric='precomputed') does not modify the precomputed
distances, and thus assumes that precomputed euclidean distances are squared.
In future versions, a parameter in TSNE will control the optional squaring of
precomputed distances (see #12401).
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 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
# Author: Tom Dupre la Tour
#
# License: BSD 3 clause
import time
import sys
try:
import annoy
except ImportError:
print("The package 'annoy' is required to run this example.")
sys.exit()
try:
import nmslib
except ImportError:
print("The package 'nmslib' is required to run this example.")
sys.exit()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
from scipy.sparse import csr_matrix
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.neighbors import KNeighborsTransformer
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.datasets import fetch_openml
from sklearn.pipeline import make_pipeline
from sklearn.manifold import TSNE
from sklearn.utils import shuffle
print(__doc__)
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 = {
'sqeuclidean': 'l2',
'euclidean': 'l2',
'cosine': 'cosinesimil',
'l1': 'l1',
'l2': 'l2',
}[self.metric]
self.nmslib_ = nmslib.init(method=self.method, space=space)
self.nmslib_.addDataPointBatch(X)
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
results = self.nmslib_.knnQueryBatch(X, k=n_neighbors,
num_threads=self.n_jobs)
indices, distances = zip(*results)
indices, distances = np.vstack(indices), np.vstack(distances)
if self.metric == 'sqeuclidean':
distances **= 2
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
class AnnoyTransformer(TransformerMixin, BaseEstimator):
"""Wrapper for using annoy.AnnoyIndex as sklearn's KNeighborsTransformer"""
def __init__(self, n_neighbors=5, metric='euclidean', n_trees=10,
search_k=-1):
self.n_neighbors = n_neighbors
self.n_trees = n_trees
self.search_k = search_k
self.metric = metric
def fit(self, X):
self.n_samples_fit_ = X.shape[0]
metric = self.metric if self.metric != 'sqeuclidean' else 'euclidean'
self.annoy_ = annoy.AnnoyIndex(X.shape[1], metric=metric)
for i, x in enumerate(X):
self.annoy_.add_item(i, x.tolist())
self.annoy_.build(self.n_trees)
return self
def transform(self, X):
return self._transform(X)
def fit_transform(self, X, y=None):
return self.fit(X)._transform(X=None)
def _transform(self, X):
"""As `transform`, but handles X is None for faster `fit_transform`."""
n_samples_transform = self.n_samples_fit_ if X is None else 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
indices = np.empty((n_samples_transform, n_neighbors),
dtype=np.int)
distances = np.empty((n_samples_transform, n_neighbors))
if X is None:
for i in range(self.annoy_.get_n_items()):
ind, dist = self.annoy_.get_nns_by_item(
i, n_neighbors, self.search_k, include_distances=True)
indices[i], distances[i] = ind, dist
else:
for i, x in enumerate(X):
indices[i], distances[i] = self.annoy_.get_nns_by_vector(
x.tolist(), n_neighbors, self.search_k,
include_distances=True)
if self.metric == 'sqeuclidean':
distances **= 2
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 test_transformers():
"""Test that AnnoyTransformer and KNeighborsTransformer give same results
"""
X = np.random.RandomState(42).randn(10, 2)
knn = KNeighborsTransformer()
Xt0 = knn.fit_transform(X)
ann = AnnoyTransformer()
Xt1 = ann.fit_transform(X)
nms = NMSlibTransformer()
Xt2 = nms.fit_transform(X)
assert_array_almost_equal(Xt0.toarray(), Xt1.toarray(), decimal=5)
assert_array_almost_equal(Xt0.toarray(), Xt2.toarray(), decimal=5)
def load_mnist(n_samples):
"""Load MNIST, shuffle the data, and return only n_samples."""
mnist = fetch_openml(data_id=41063)
X, y = shuffle(mnist.data, mnist.target, random_state=42)
return X[:n_samples], y[:n_samples]
def run_benchmark():
datasets = [
('MNIST_2000', load_mnist(n_samples=2000)),
('MNIST_10000', load_mnist(n_samples=10000)),
]
n_iter = 500
perplexity = 30
# 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. * perplexity + 1) + 1
transformers = [
('AnnoyTransformer', AnnoyTransformer(n_neighbors=n_neighbors,
metric='sqeuclidean')),
('NMSlibTransformer', NMSlibTransformer(n_neighbors=n_neighbors,
metric='sqeuclidean')),
('KNeighborsTransformer', KNeighborsTransformer(
n_neighbors=n_neighbors, mode='distance', metric='sqeuclidean')),
('TSNE with AnnoyTransformer', make_pipeline(
AnnoyTransformer(n_neighbors=n_neighbors, metric='sqeuclidean'),
TSNE(metric='precomputed', perplexity=perplexity,
method="barnes_hut", random_state=42, n_iter=n_iter), )),
('TSNE with NMSlibTransformer', make_pipeline(
NMSlibTransformer(n_neighbors=n_neighbors, metric='sqeuclidean'),
TSNE(metric='precomputed', perplexity=perplexity,
method="barnes_hut", random_state=42, n_iter=n_iter), )),
('TSNE with KNeighborsTransformer', make_pipeline(
KNeighborsTransformer(n_neighbors=n_neighbors, mode='distance',
metric='sqeuclidean'),
TSNE(metric='precomputed', perplexity=perplexity,
method="barnes_hut", random_state=42, n_iter=n_iter), )),
('TSNE with internal NearestNeighbors',
TSNE(metric='sqeuclidean', perplexity=perplexity, method="barnes_hut",
random_state=42, n_iter=n_iter)),
]
# 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 = 'Benchmarking on %s:' % dataset_name
print('\n%s\n%s' % (msg, '-' * len(msg)))
for transformer_name, transformer in transformers:
start = time.time()
Xt = transformer.fit_transform(X)
duration = time.time() - start
# print the duration report
longest = np.max([len(name) for name, model in transformers])
whitespaces = ' ' * (longest - len(transformer_name))
print('%s: %s%.3f sec' % (transformer_name, whitespaces, duration))
# plot TSNE embedding which should be very similar across methods
if 'TSNE' in transformer_name:
axes[i_ax].set_title(transformer_name + '\non ' + dataset_name)
axes[i_ax].scatter(Xt[:, 0], Xt[:, 1], c=y, 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()
if __name__ == '__main__':
test_transformers()
run_benchmark()
Total running time of the script: ( 0 minutes 0.000 seconds)
Estimated memory usage: 0 MB
