Installation and Setup¶
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 run
pytest testto run all tests (you will need to have the
- Python 3.6+ (the last version supporting Python 2 and Python 3.5 was v0.5.0)
- numpy, scipy, scikit-learn>=0.20.3
- 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
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)