Getting started¶
Installation and Setup¶
Installation
metric-learn can be installed in either of the following ways:
- If you use Anaconda:
conda install -c conda-forge metric-learn
. See more options here. - To install from PyPI:
pip install metric-learn
. - For a manual install of the latest code, download the source repository and run
python setup.py install
. You may then runpytest test
to run all tests (you will need to have thepytest
package installed).
Dependencies
- Python 3.6+ (the last version supporting Python 2 and Python 3.5 was v0.5.0)
- numpy, scipy, scikit-learn>=0.20.3
Optional dependencies
- For SDML, using skggm will allow the algorithm to solve problematic cases
(install from commit a0ed406).
pip install 'git+https://github.com/skggm/skggm.git@a0ed406586c4364ea3297a658f415e13b5cbdaf8'
to install the required version of skggm from GitHub. - For running the examples only: matplotlib
Quick start¶
This example loads the iris dataset, and evaluates a k-nearest neighbors algorithm on an embedding space learned with NCA.
from metric_learn import NCA
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.neighbors import KNeighborsClassifier
X, y = load_iris(return_X_y=True)
clf = make_pipeline(NCA(), KNeighborsClassifier())
cross_val_score(clf, X, y)