metric_learn.LSML_Supervised

class metric_learn.LSML_Supervised(tol=0.001, max_iter=1000, prior='identity', n_constraints=None, weights=None, verbose=False, preprocessor=None, random_state=None, num_constraints='deprecated')

Supervised version of Least Squared-residual Metric Learning (LSML)

LSML_Supervised creates quadruplets from labeled samples by taking two samples from the same class, and two samples from different classes. This way it builds quadruplets where the two first points must be more similar than the two last points.

Parameters:

tolfloat, optional (default=1e-3)

Convergence tolerance of the optimization procedure.

max_iterint, optional (default=1000)

Number of maximum iterations of the optimization procedure.

priorstring or numpy array, optional (default=’identity’)

Prior to set for the metric. Possible options are ‘identity’, ‘covariance’, ‘random’, and a numpy array of shape (n_features, n_features). For LSML, the prior should be strictly positive definite (PD).

‘identity’

An identity matrix of shape (n_features, n_features).

‘covariance’

The inverse covariance matrix.

‘random’

The initial Mahalanobis matrix will be a random positive definite (PD) matrix of shape (n_features, n_features), generated using sklearn.datasets.make_spd_matrix.

numpy array

A positive definite (PD) matrix of shape (n_features, n_features), that will be used as such to set the prior.

n_constraints: int, optional (default=None)

Number of constraints to generate. If None, default to 20 * num_classes**2.

weights(n_constraints,) array of floats, optional (default=None)

Relative weight given to each constraint. If None, defaults to uniform weights.

verbosebool, optional (default=False)

If True, prints information while learning

preprocessorarray-like, shape=(n_samples, n_features) or callable

The preprocessor to call to get tuples from indices. If array-like, tuples will be formed like this: X[indices].

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

A pseudo random number generator object or a seed for it if int. If init='random', random_state is used to set the random prior. In any case, random_state is also used to randomly sample constraints from labels.

num_constraintsRenamed to n_constraints. Will be deprecated in 0.7.0

Examples

>>> from metric_learn import LSML_Supervised
>>> from sklearn.datasets import load_iris
>>> iris_data = load_iris()
>>> X = iris_data['data']
>>> Y = iris_data['target']
>>> lsml = LSML_Supervised(n_constraints=200)
>>> lsml.fit(X, Y)

Attributes:

n_iter_int

The number of iterations the solver has run.

components_numpy.ndarray, shape=(n_features, n_features)

The linear transformation L deduced from the learned Mahalanobis metric (See function components_from_metric.)

Methods

fit(X, y)	Create constraints from labels and learn the LSML model.
fit_transform(X[, y])	Fit to data, then transform it.
get_mahalanobis_matrix()	Returns a copy of the Mahalanobis matrix learned by the metric learner.
get_metadata_routing()	Get metadata routing of this object.
get_metric()	Returns a function that takes as input two 1D arrays and outputs the value of the learned metric on these two points.
get_params([deep])	Get parameters for this estimator.
pair_distance(pairs)	Returns the learned Mahalanobis distance between pairs.
pair_score(pairs)	Returns the opposite of the learned Mahalanobis distance between pairs.
score_pairs(pairs)	Returns the learned Mahalanobis distance between pairs.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Embeds data points in the learned linear embedding space.

__init__(tol=0.001, max_iter=1000, prior='identity', n_constraints=None, weights=None, verbose=False, preprocessor=None, random_state=None, num_constraints='deprecated')

fit(X, y)

Create constraints from labels and learn the LSML model.

Parameters:

X(n x d) matrix

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

y(n) array-like

Data labels.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_mahalanobis_matrix()

Returns a copy of the Mahalanobis matrix learned by the metric learner.

Returns:

Mnumpy.ndarray, shape=(n_features, n_features)

The copy of the learned Mahalanobis matrix.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_metric()

Returns a function that takes as input two 1D arrays and outputs the value of the learned metric on these two points. Depending on the algorithm, it can return a distance or a similarity function between pairs.

This function will be independent from the metric learner that learned it (it will not be modified if the initial metric learner is modified), and it can be directly plugged into the metric argument of scikit-learn’s estimators.

Returns:

metric_funfunction

The function described above.

See also

pair_distance

a method that returns the distance between several pairs of points. Unlike get_metric, this is a method of the metric learner and therefore can change if the metric learner changes. Besides, it can use the metric learner’s preprocessor, and works on concatenated arrays.

pair_score

a method that returns the similarity score between several pairs of points. Unlike get_metric, this is a method of the metric learner and therefore can change if the metric learner changes. Besides, it can use the metric learner’s preprocessor, and works on concatenated arrays.

Examples

>>> from metric_learn import NCA
>>> from sklearn.datasets import make_classification
>>> from sklearn.neighbors import KNeighborsClassifier
>>> nca = NCA()
>>> X, y = make_classification()
>>> nca.fit(X, y)
>>> knn = KNeighborsClassifier(metric=nca.get_metric())
>>> knn.fit(X, y) 
KNeighborsClassifier(algorithm='auto', leaf_size=30,
  metric=<function MahalanobisMixin.get_metric.<locals>.metric_fun
          at 0x...>,
  metric_params=None, n_jobs=None, n_neighbors=5, p=2,
  weights='uniform')

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

pair_distance(pairs)

Returns the learned Mahalanobis distance between pairs.

This distance is defined as: \(d_M(x, x') = \sqrt{(x-x')^T M (x-x')}\) where M is the learned Mahalanobis matrix, for every pair of points x and x'. This corresponds to the euclidean distance between embeddings of the points in a new space, obtained through a linear transformation. Indeed, we have also: \(d_M(x, x') = \sqrt{(x_e - x_e')^T (x_e- x_e')}\), with \(x_e = L x\) (See MahalanobisMixin).

Parameters:

pairsarray-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2)

3D Array of pairs to score, with each row corresponding to two points, for 2D array of indices of pairs if the metric learner uses a preprocessor.

Returns:

scoresnumpy.ndarray of shape=(n_pairs,)

The learned Mahalanobis distance for every pair.

See also

get_metric

a method that returns a function to compute the metric between two points. The difference with pair_distance is that it works on two 1D arrays and cannot use a preprocessor. Besides, the returned function is independent of the metric learner and hence is not modified if the metric learner is.

Mahalanobis Distances

The section of the project documentation that describes Mahalanobis Distances.

pair_score(pairs)

Returns the opposite of the learned Mahalanobis distance between pairs.

Parameters:

pairsarray-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2)

3D Array of pairs to score, with each row corresponding to two points, for 2D array of indices of pairs if the metric learner uses a preprocessor.

Returns:

scoresnumpy.ndarray of shape=(n_pairs,)

The opposite of the learned Mahalanobis distance for every pair.

See also

get_metric

a method that returns a function to compute the metric between two points. The difference with pair_score is that it works on two 1D arrays and cannot use a preprocessor. Besides, the returned function is independent of the metric learner and hence is not modified if the metric learner is.

Mahalanobis Distances

The section of the project documentation that describes Mahalanobis Distances.

score_pairs(pairs)

Returns the learned Mahalanobis distance between pairs.

This distance is defined as: \(d_M(x, x') = \\sqrt{(x-x')^T M (x-x')}\) where M is the learned Mahalanobis matrix, for every pair of points x and x'. This corresponds to the euclidean distance between embeddings of the points in a new space, obtained through a linear transformation. Indeed, we have also: \(d_M(x, x') = \\sqrt{(x_e - x_e')^T (x_e- x_e')}\), with \(x_e = L x\) (See MahalanobisMixin).

Deprecated since version 0.7.0: Please use pair_distance instead.

Warning

This method will be removed in 0.8.0. Please refer to pair_distance or pair_score. This change will occur in order to add learners that don’t necessarily learn a Mahalanobis distance.

Parameters:

pairsarray-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2)

3D Array of pairs to score, with each row corresponding to two points, for 2D array of indices of pairs if the metric learner uses a preprocessor.

Returns:

scoresnumpy.ndarray of shape=(n_pairs,)

The learned Mahalanobis distance for every pair.

See also

get_metric

a method that returns a function to compute the metric between two points. The difference with score_pairs is that it works on two 1D arrays and cannot use a preprocessor. Besides, the returned function is independent of the metric learner and hence is not modified if the metric learner is.

Mahalanobis Distances

The section of the project documentation that describes Mahalanobis Distances.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• None: Transform configuration is unchanged

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(X)

Embeds data points in the learned linear embedding space.

Transforms samples in X into X_embedded, samples inside a new embedding space such that: X_embedded = X.dot(L.T), where L is the learned linear transformation (See MahalanobisMixin).

Parameters:

Xnumpy.ndarray, shape=(n_samples, n_features)

The data points to embed.

Returns:

X_embeddednumpy.ndarray, shape=(n_samples, n_components)

The embedded data points.

Examples using metric_learn.LSML_Supervised

Sandwich demo

Sandwich demo

Algorithms walkthrough

Algorithms walkthrough