metric_learn.Constraints

class metric_learn.Constraints(partial_labels)

Class to build constraints from labeled data.

See more in the User Guide.

Parameters:

partial_labelsnumpy.ndarray of ints, shape=(n_samples,)

Array of labels, with -1 indicating unknown label.

Attributes:

partial_labelsnumpy.ndarray of ints, shape=(n_samples,)

Array of labels, with -1 indicating unknown label.

Methods

chunks([n_chunks, chunk_size, random_state, ...])	Generates chunks from labeled data.
generate_knntriplets(X, k_genuine, k_impostor)	Generates triplets from labeled data.
positive_negative_pairs(n_constraints[, ...])	Generates positive pairs and negative pairs from labeled data.

__init__(partial_labels)

chunks(n_chunks=100, chunk_size=2, random_state=None, num_chunks='deprecated')

Generates chunks from labeled data.

Each of n_chunks chunks is composed of chunk_size points from the same class drawn at random. Each point can belong to at most 1 chunk.

In the case where there is not enough points to generate n_chunks chunks of size chunk_size, a ValueError will be raised.

Parameters:

n_chunksint, optional (default=100)

Number of chunks to generate.

chunk_sizeint, optional (default=2)

Number of points in each chunk.

random_stateint or numpy.RandomState or None, optional (default=None)

A pseudo random number generator object or a seed for it if int.

num_chunksRenamed to n_chunks. Will be deprecated in 0.7.0

Returns:

chunksarray-like, shape=(n_samples,)

1D array of chunk indicators, where -1 indicates that the point does not belong to any chunk.

generate_knntriplets(X, k_genuine, k_impostor)

Generates triplets from labeled data.

For every point (X_a) the triplets (X_a, X_b, X_c) are constructed from all the combinations of taking one of its k_genuine-nearest neighbors of the same class (X_b) and taking one of its k_impostor-nearest neighbors of other classes (X_c).

In the case a class doesn’t have enough points in the same class (other classes) to yield k_genuine (k_impostor) neighbors a warning will be raised and the maximum value of genuine (impostor) neighbors will be used for that class.

Parameters:

X(n x d) matrix

Input data, where each row corresponds to a single instance.

k_genuineint

Number of neighbors of the same class to be taken into account.

k_impostorint

Number of neighbors of different classes to be taken into account.

Returns:

tripletsarray-like, shape=(n_constraints, 3)

2D array of triplets of indicators.

positive_negative_pairs(n_constraints, same_length=False, random_state=None, num_constraints='deprecated')

Generates positive pairs and negative pairs from labeled data.

Positive pairs are formed by randomly drawing n_constraints pairs of points with the same label. Negative pairs are formed by randomly drawing n_constraints pairs of points with different label.

In the case where it is not possible to generate enough positive or negative pairs, a smaller number of pairs will be returned with a warning.

Parameters:

n_constraintsint

Number of positive and negative constraints to generate.

same_lengthbool, optional (default=False)

If True, forces the number of positive and negative pairs to be equal by ignoring some pairs from the larger set.

random_stateint or numpy.RandomState or None, optional (default=None)

A pseudo random number generator object or a seed for it if int.

num_constraintsRenamed to n_constraints. Will be deprecated in 0.7.0

Returns:

aarray-like, shape=(n_constraints,)

1D array of indicators for the left elements of positive pairs.

barray-like, shape=(n_constraints,)

1D array of indicators for the right elements of positive pairs.

carray-like, shape=(n_constraints,)

1D array of indicators for the left elements of negative pairs.

darray-like, shape=(n_constraints,)

1D array of indicators for the right elements of negative pairs.