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getting_started.rst

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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 run pytest test to run all tests (you will need to have the pytest package installed).

Dependencies

  • Python 3.6+ (the last version supporting Python 2 and Python 3.5 was v0.5.0)
  • numpy>= 1.11.0, scipy>= 0.17.0, scikit-learn>=0.21.3

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)
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