.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` 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_plot_nca_classification.py:
=============================================================================
Comparing Nearest Neighbors with and without Neighborhood Components Analysis
=============================================================================
An example comparing nearest neighbors classification with and without
Neighborhood Components Analysis.
It will plot the class decision boundaries given by a Nearest Neighbors
classifier when using the Euclidean distance on the original features, versus
using the Euclidean distance after the transformation learned by Neighborhood
Components Analysis. The latter aims to find a linear transformation that
maximises the (stochastic) nearest neighbor classification accuracy on the
training set.
.. rst-class:: sphx-glr-horizontal
*
.. image:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_001.png
:alt: KNN (k = 1)
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_002.png
:alt: NCA, KNN (k = 1)
:class: sphx-glr-multi-img
.. code-block:: default
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import (KNeighborsClassifier,
NeighborhoodComponentsAnalysis)
from sklearn.pipeline import Pipeline
print(__doc__)
n_neighbors = 1
dataset = datasets.load_iris()
X, y = dataset.data, dataset.target
# we only take two features. We could avoid this ugly
# slicing by using a two-dim dataset
X = X[:, [0, 2]]
X_train, X_test, y_train, y_test = \
train_test_split(X, y, stratify=y, test_size=0.7, random_state=42)
h = .01 # step size in the mesh
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
names = ['KNN', 'NCA, KNN']
classifiers = [Pipeline([('scaler', StandardScaler()),
('knn', KNeighborsClassifier(n_neighbors=n_neighbors))
]),
Pipeline([('scaler', StandardScaler()),
('nca', NeighborhoodComponentsAnalysis()),
('knn', KNeighborsClassifier(n_neighbors=n_neighbors))
])
]
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
for name, clf in zip(names, classifiers):
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8)
# Plot also the training and testing points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("{} (k = {})".format(name, n_neighbors))
plt.text(0.9, 0.1, '{:.2f}'.format(score), size=15,
ha='center', va='center', transform=plt.gca().transAxes)
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 18.443 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_nca_classification.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_nca_classification.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_nca_classification.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_