Python implementations of metric learning algorithms

## Project description

## metric-learn

Metric Learning algorithms in Python.

**Algorithms**

- Large Margin Nearest Neighbor (LMNN)
- Information Theoretic Metric Learning (ITML)
- Sparse Determinant Metric Learning (SDML)
- Least Squares Metric Learning (LSML)
- Neighborhood Components Analysis (NCA)
- Local Fisher Discriminant Analysis (LFDA)
- Relative Components Analysis (RCA)

**Dependencies**

- Python 2.6+
- numpy, scipy, scikit-learn
- (for running the examples only: matplotlib)

**Installation/Setup**

Run `pip install metric-learn` to download and install from PyPI.

Run `python setup.py install` for default installation.

Run `python setup.py test` to run all tests.

**Usage**

For full usage examples, see the sphinx documentation.

Each metric is a subclass of `BaseMetricLearner`, which provides
default implementations for the methods `metric`, `transformer`, and
`transform`. Subclasses must provide an implementation for either
`metric` or `transformer`.

For an instance of a metric learner named `foo` learning from a set of
`d`-dimensional points, `foo.metric()` returns a `d` by `d`
matrix `M` such that a distance between vectors `x` and `y` is
expressed `(x-y).dot(M).dot(x-y)`.

In the same scenario, `foo.transformer()` returns a `d` by `d`
matrix `L` such that a vector `x` can be represented in the learned
space as the vector `x.dot(L.T)`.

For convenience, the function `foo.transform(X)` is provided for
converting a matrix of points (`X`) into the learned space, in which
standard Euclidean distance can be used.

**Notes**

If a recent version of the Shogun Python modular (`modshogun`) library
is available, the LMNN implementation will use the fast C++ version from
there. The two implementations differ slightly, and the C++ version is
more complete.

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