# 3. Weakly Supervised Metric Learning¶

Weakly supervised algorithms work on weaker information about the data points than supervised algorithms. Rather than labeled points, they take as input similarity judgments on tuples of data points, for instance pairs of similar and dissimilar points. Refer to the documentation of each algorithm for its particular form of input data.

## 3.1. General API¶

### 3.1.1. Input data¶

In the following paragraph we talk about tuples for sake of generality. These can be pairs, triplets, quadruplets etc, depending on the particular metric learning algorithm we use.

#### 3.1.1.1. Basic form¶

Every weakly supervised algorithm will take as input tuples of points, and if needed labels for theses tuples. The tuples of points can also be called “constraints”. They are a set of points that we consider (ex: two points, three points, etc…). The label is some information we have about this set of points (e.g. “these two points are similar”). Note that some information can be contained in the ordering of these tuples (see for instance the section Learning on quadruplets). For more details about specific forms of tuples, refer to the appropriate sections (Learning on pairs or Learning on quadruplets).

The tuples argument is the first argument of every method (like the X argument for classical algorithms in scikit-learn). The second argument is the label of the tuple: its semantic depends on the algorithm used. For instance for pairs learners y is a label indicating whether the pair is of similar samples or dissimilar samples.

Then one can fit a Weakly Supervised Metric Learner on this tuple, like this:

>>> my_algo.fit(tuples, y)


Like in a classical setting we split the points X between train and test, here we split the tuples between train and test.

>>> from sklearn.model_selection import train_test_split
>>> pairs_train, pairs_test, y_train, y_test = train_test_split(pairs, y)


These are two data structures that can be used to represent tuple in metric learn:

#### 3.1.1.2. 3D array of tuples¶

The most intuitive way to represent tuples is to provide the algorithm with a 3D array-like of tuples of shape (n_tuples, tuple_size, n_features), where n_tuples is the number of tuples, tuple_size is the number of elements in a tuple (2 for pairs, 3 for triplets for instance), and n_features is the number of features of each point.

>>> import numpy as np
>>> tuples = np.array([[[-0.12, -1.21, -0.20],
>>>                     [+0.05, -0.19, -0.05]],
>>>
>>>                    [[-2.16, +0.11, -0.02],
>>>                     [+1.58, +0.16, +0.93]],
>>>
>>>                    [[+1.58, +0.16, +0.93],  # same as tuples[1, 1, :]
>>>                     [+0.89, -0.34, +2.41]],
>>>
>>>                    [[-0.12, -1.21, -0.20],  # same as tuples[0, 0, :]
>>>                     [-2.16, +0.11, -0.02]]])  # same as tuples[1, 0, :]
>>> y = np.array([-1, 1, 1, -1])


Warning

This way of specifying pairs is not recommended for a large number of tuples, as it is redundant (see the comments in the example) and hence takes a lot of memory. Indeed each feature vector of a point will be replicated as many times as a point is involved in a tuple. The second way to specify pairs is more efficient

#### 3.1.1.3. 2D array of indicators + preprocessor¶

Instead of forming each point in each tuple, a more efficient representation would be to keep the dataset of points X aside, and just represent tuples as a collection of tuples of indices from the points in X. Since we loose the feature dimension there, the resulting array is 2D.

>>> X = np.array([[-0.12, -1.21, -0.20],
>>>               [+0.05, -0.19, -0.05],
>>>               [-2.16, +0.11, -0.02],
>>>               [+1.58, +0.16, +0.93],
>>>               [+0.89, -0.34, +2.41]])
>>>
>>> tuples_indices = np.array([[0, 1],
>>>                            [2, 3],
>>>                            [3, 4],
>>>                            [0, 2]])
>>> y = np.array([-1, 1, 1, -1])


In order to fit metric learning algorithms with this type of input, we need to give the original dataset of points X to the estimator so that it knows the points the indices refer to. We do this when initializing the estimator, through the argument Preprocessor (see below Fit, transform, and so on)

Note

Instead of an array-like, you can give a callable in the argument Preprocessor, which will go fetch and form the tuples. This allows to give more general indicators than just indices from an array (for instance paths in the filesystem, name of records in a database etc…) See section Preprocessor for more details on how to use the preprocessor.

### 3.1.2. Fit, transform, and so on¶

The goal of weakly-supervised metric-learning algorithms is to transform points in a new space, in which the tuple-wise constraints between points are respected.

>>> from metric_learn import MMC
>>> mmc = MMC(random_state=42)
>>> mmc.fit(tuples, y)
MMC(A0='deprecated', convergence_threshold=0.001, diagonal=False,
diagonal_c=1.0, init='auto', max_iter=100, max_proj=10000,
preprocessor=None, random_state=42, verbose=False)


Or alternatively (using a preprocessor):

>>> from metric_learn import MMC
>>> mmc = MMC(preprocessor=X, random_state=42)
>>> mmc.fit(pairs_indice, y)


Now that the estimator is fitted, you can use it on new data for several purposes.

First, you can transform the data in the learned space, using transform: Here we transform two points in the new embedding space.

>>> X_new = np.array([[9.4, 4.1, 4.2], [2.1, 4.4, 2.3]])
>>> mmc.transform(X_new)
array([[-3.24667162e+01,  4.62622348e-07,  3.88325421e-08],
[-3.61531114e+01,  4.86778289e-07,  2.12654397e-08]])


Also, as explained before, our metric learner has learned a distance between points. You can use this distance in two main ways:

>>> mmc.score_pairs([[[3.5, 3.6, 5.2], [5.6, 2.4, 6.7]],
...                  [[1.2, 4.2, 7.7], [2.1, 6.4, 0.9]]])
array([7.27607365, 0.88853014])

>>> metric_fun = mmc.get_metric()
>>> metric_fun([3.5, 3.6, 5.2], [5.6, 2.4, 6.7])
7.276073646278203


Note

If the metric learner that you use learns a Mahalanobis distance (like it is the case for all algorithms currently in metric-learn), you can get the plain Mahalanobis matrix using get_mahalanobis_matrix.

>>> mmc.get_mahalanobis_matrix()
array([[ 0.58603894, -5.69883982, -1.66614919],
[-5.69883982, 55.41743549, 16.20219519],
[-1.66614919, 16.20219519,  4.73697721]])


### 3.1.3. Prediction and scoring¶

Since weakly supervised are also able, after being fitted, to predict for a given tuple what is its label (for pairs) or ordering (for quadruplets). See the appropriate section for more details, either this one for pairs, or this one for quadruplets.

They also implement a default scoring method, score, that can be used to evaluate the performance of a metric-learner on a test dataset. See the appropriate section for more details, either this one for pairs, or this one for quadruplets.

### 3.1.4. Scikit-learn compatibility¶

Weakly supervised estimators are compatible with scikit-learn routines for model selection (sklearn.model_selection.cross_val_score, sklearn.model_selection.GridSearchCV, etc).

Example:

>>> from metric_learn import MMC
>>> import numpy as np
>>> from sklearn.model_selection import cross_val_score
>>> rng = np.random.RandomState(42)
>>> # let's sample 30 random pairs and labels of pairs
>>> pairs_indices = rng.randint(X.shape, size=(30, 2))
>>> y = 2 * rng.randint(2, size=30) - 1
>>> mmc = MMC(preprocessor=X)
>>> cross_val_score(mmc, pairs_indices, y)


## 3.2. Learning on pairs¶

Some metric learning algorithms learn on pairs of samples. In this case, one should provide the algorithm with n_samples pairs of points, with a corresponding target containing n_samples values being either +1 or -1. These values indicate whether the given pairs are similar points or dissimilar points.

### 3.2.1. Fitting¶

Here is an example for fitting on pairs (see Fit, transform, and so on for more details on the input data format and how to fit, in the general case of learning on tuples).

>>> from metric_learn import MMC
>>> pairs = np.array([[[1.2, 3.2], [2.3, 5.5]],
>>>                   [[4.5, 2.3], [2.1, 2.3]]])
>>> y_pairs = np.array([1, -1])
>>> mmc = MMC(random_state=42)
>>> mmc.fit(pairs, y_pairs)
MMC(convergence_threshold=0.001, diagonal=False,
diagonal_c=1.0, init='auto', max_iter=100, max_proj=10000, preprocessor=None,
random_state=42, verbose=False)


Here, we learned a metric that puts the two first points closer together in the transformed space, and the two next points further away from each other.

### 3.2.2. Prediction¶

When a pairs learner is fitted, it is also able to predict, for an unseen pair, whether it is a pair of similar or dissimilar points.

>>> mmc.predict([[[0.6, 1.6], [1.15, 2.75]],
...              [[3.2, 1.1], [5.4, 6.1]]])
array([1, -1])


#### 3.2.2.1. Prediction threshold¶

Predicting whether a new pair represents similar or dissimilar samples requires to set a threshold on the learned distance, so that points closer (in the learned space) than this threshold are predicted as similar, and points further away are predicted as dissimilar. Several methods are possible for this thresholding.

• Calibration at fit time: The threshold is set with calibrate_threshold (see below) on the training set. You can specify the calibration parameters directly in the fit method with the threshold_params parameter (see the documentation of the fit method of any metric learner that learns on pairs of points for more information). Note that calibrating on the training set may cause some overfitting. If you want to avoid that, calibrate the threshold after fitting, on a validation set.

>>> mmc.fit(pairs, y) # will fit the threshold automatically after fitting

• Calibration on validation set: calling calibrate_threshold will calibrate the threshold to achieve a particular score on a validation set, the score being among the classical scores for classification (accuracy, f1 score…).

>>> mmc.calibrate_threshold(pairs, y)

• Manual threshold: calling set_threshold will set the threshold to a particular value.

>>> mmc.set_threshold(0.4)


See also: sklearn.calibration.

### 3.2.3. Scoring¶

Pair metric learners can also return a decision_function for a set of pairs. It is basically the “score” that will be thresholded to find the prediction for the pair. This score corresponds to the opposite of the distance in the new space (higher score means points are similar, and lower score dissimilar).

>>> mmc.decision_function([[[0.6, 1.6], [1.15, 2.75]],
...                        [[3.2, 1.1], [5.4, 6.1]]])
array([-0.12811124, -0.74750256])


This allows to use common scoring functions for binary classification, like sklearn.metrics.accuracy_score for instance, which can be used inside cross-validation routines:

>>> from sklearn.model_selection import cross_val_score
>>> pairs_test = np.array([[[0.6, 1.6], [1.15, 2.75]],
...                        [[3.2, 1.1], [5.4, 6.1]],
...                        [[7.7, 5.6], [1.23, 8.4]]])
>>> y_test = np.array([-1., 1., -1.])
>>> cross_val_score(mmc, pairs_test, y_test, scoring='accuracy')
array([1., 0., 1.])


Pairs learners also have a default score, which basically returns the sklearn.metrics.roc_auc_score (which is threshold-independent).

>>> pairs_test = np.array([[[0.6, 1.6], [1.15, 2.75]],
...                        [[3.2, 1.1], [5.4, 6.1]],
...                        [[7.7, 5.6], [1.23, 8.4]]])
>>> y_test = np.array([1., -1., -1.])
>>> mmc.score(pairs_test, y_test)
1.0


Note

See Fit, transform, and so on for more details on metric learners functions that are not specific to learning on pairs, like transform, score_pairs, get_metric and get_mahalanobis_matrix.

### 3.2.4. Algorithms¶

#### 3.2.4.1. ITML¶

Information Theoretic Metric Learning (ITML)

ITML minimizes the (differential) relative entropy, aka Kullback–Leibler divergence, between two multivariate Gaussians subject to constraints on the associated Mahalanobis distance, which can be formulated into a Bregman optimization problem by minimizing the LogDet divergence subject to linear constraints. This algorithm can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Unlike some other methods, ITML does not rely on an eigenvalue computation or semi-definite programming.

Given a Mahalanobis distance parameterized by $$M$$, its corresponding multivariate Gaussian is denoted as:

$p(\mathbf{x}; \mathbf{M}) = \frac{1}{Z}\exp(-\frac{1}{2}d_\mathbf{M} (\mathbf{x}, \mu)) = \frac{1}{Z}\exp(-\frac{1}{2}((\mathbf{x} - \mu)^T\mathbf{M} (\mathbf{x} - \mu))$

where $$Z$$ is the normalization constant, the inverse of Mahalanobis matrix $$\mathbf{M}^{-1}$$ is the covariance of the Gaussian.

Given pairs of similar points $$S$$ and pairs of dissimilar points $$D$$, the distance metric learning problem is to minimize the LogDet divergence, which is equivalent as minimizing $$\textbf{KL}(p(\mathbf{x}; \mathbf{M}_0) || p(\mathbf{x}; \mathbf{M}))$$:

$\begin{split}\min_\mathbf{A} D_{\ell \mathrm{d}}\left(M, M_{0}\right) = \operatorname{tr}\left(M M_{0}^{-1}\right)-\log \operatorname{det} \left(M M_{0}^{-1}\right)-n\\ \text{subject to } \quad d_\mathbf{M}(\mathbf{x}_i, \mathbf{x}_j) \leq u \qquad (\mathbf{x}_i, \mathbf{x}_j)\in S \\ d_\mathbf{M}(\mathbf{x}_i, \mathbf{x}_j) \geq l \qquad (\mathbf{x}_i, \mathbf{x}_j)\in D\end{split}$

where $$u$$ and $$l$$ is the upper and the lower bound of distance for similar and dissimilar pairs respectively, and $$\mathbf{M}_0$$ is the prior distance metric, set to identity matrix by default, $$D_{\ell \mathrm{d}}(\cdot)$$ is the log determinant.

from metric_learn import ITML

pairs = [[[1.2, 7.5], [1.3, 1.5]],
[[6.4, 2.6], [6.2, 9.7]],
[[1.3, 4.5], [3.2, 4.6]],
[[6.2, 5.5], [5.4, 5.4]]]
y = [1, 1, -1, -1]

# in this task we want points where the first feature is close to be closer
# to each other, no matter how close the second feature is

itml = ITML()
itml.fit(pairs, y)


References:

  Jason V. Davis, et al. Information-theoretic Metric Learning. ICML 2007
  Adapted from Matlab code at http://www.cs.utexas.edu/users/pjain/itml/

#### 3.2.4.2. SDML¶

Sparse High-Dimensional Metric Learning (SDML)

SDML is an efficient sparse metric learning in high-dimensional space via double regularization: an L1-penalization on the off-diagonal elements of the Mahalanobis matrix $$\mathbf{M}$$, and a log-determinant divergence between $$\mathbf{M}$$ and $$\mathbf{M_0}$$ (set as either $$\mathbf{I}$$ or $$\mathbf{\Omega}^{-1}$$, where $$\mathbf{\Omega}$$ is the covariance matrix).

The formulated optimization on the semidefinite matrix $$\mathbf{M}$$ is convex:

$\min_{\mathbf{M}} = \text{tr}((\mathbf{M}_0 + \eta \mathbf{XLX}^{T}) \cdot \mathbf{M}) - \log\det \mathbf{M} + \lambda ||\mathbf{M}||_{1, off}$

where $$\mathbf{X}=[\mathbf{x}_1, \mathbf{x}_2, ..., \mathbf{x}_n]$$ is the training data, the incidence matrix $$\mathbf{K}_{ij} = 1$$ if $$(\mathbf{x}_i, \mathbf{x}_j)$$ is a similar pair, otherwise -1. The Laplacian matrix $$\mathbf{L}=\mathbf{D}-\mathbf{K}$$ is calculated from $$\mathbf{K}$$ and $$\mathbf{D}$$, a diagonal matrix whose entries are the sums of the row elements of $$\mathbf{K}$$., $$||\cdot||_{1, off}$$ is the off-diagonal L1 norm.

from metric_learn import SDML

pairs = [[[1.2, 7.5], [1.3, 1.5]],
[[6.4, 2.6], [6.2, 9.7]],
[[1.3, 4.5], [3.2, 4.6]],
[[6.2, 5.5], [5.4, 5.4]]]
y = [1, 1, -1, -1]

# in this task we want points where the first feature is close to be closer
# to each other, no matter how close the second feature is

sdml = SDML()
sdml.fit(pairs, y)


References:

#### 3.2.4.3. RCA¶

Relative Components Analysis (RCA)

RCA learns a full rank Mahalanobis distance metric based on a weighted sum of in-chunklets covariance matrices. It applies a global linear transformation to assign large weights to relevant dimensions and low weights to irrelevant dimensions. Those relevant dimensions are estimated using “chunklets”, subsets of points that are known to belong to the same class.

For a training set with $$n$$ training points in $$k$$ chunklets, the algorithm is efficient since it simply amounts to computing

$\mathbf{C} = \frac{1}{n}\sum_{j=1}^k\sum_{i=1}^{n_j} (\mathbf{x}_{ji}-\hat{\mathbf{m}}_j) (\mathbf{x}_{ji}-\hat{\mathbf{m}}_j)^T$

where chunklet $$j$$ consists of $$\{\mathbf{x}_{ji}\}_{i=1}^{n_j}$$ with a mean $$\hat{m}_j$$. The inverse of $$\mathbf{C}^{-1}$$ is used as the Mahalanobis matrix.

from metric_learn import RCA

X = [[-0.05,  3.0],[0.05, -3.0],
[0.1, -3.55],[-0.1, 3.55],
[-0.95, -0.05],[0.95, 0.05],
[0.4,  0.05],[-0.4, -0.05]]
chunks = [0, 0, 1, 1, 2, 2, 3, 3]

rca = RCA()
rca.fit(X, chunks)


References:

  Shental et al. Adjustment learning and relevant component analysis. ECCV 2002
  Bar-Hillel et al. Learning distance functions using equivalence relations. ICML 2003
  Bar-Hillel et al. Learning a Mahalanobis metric from equivalence constraints. JMLR 2005

#### 3.2.4.4. MMC¶

Metric Learning with Application for Clustering with Side Information (MMC)

MMC minimizes the sum of squared distances between similar points, while enforcing the sum of distances between dissimilar ones to be greater than one. This leads to a convex and, thus, local-minima-free optimization problem that can be solved efficiently. However, the algorithm involves the computation of eigenvalues, which is the main speed-bottleneck. Since it has initially been designed for clustering applications, one of the implicit assumptions of MMC is that all classes form a compact set, i.e., follow a unimodal distribution, which restricts the possible use-cases of this method. However, it is one of the earliest and a still often cited technique.

The algorithm aims at minimizing the sum of distances between all the similar points, while constrains the sum of distances between dissimilar points:

$\min_{\mathbf{M}\in\mathbb{S}_+^d}\sum_{(\mathbf{x}_i, \mathbf{x}_j)\in S} d_{\mathbf{M}}(\mathbf{x}_i, \mathbf{x}_j) \qquad \qquad \text{s.t.} \qquad \sum_{(\mathbf{x}_i, \mathbf{x}_j) \in D} d^2_{\mathbf{M}}(\mathbf{x}_i, \mathbf{x}_j) \geq 1$
from metric_learn import MMC

pairs = [[[1.2, 7.5], [1.3, 1.5]],
[[6.4, 2.6], [6.2, 9.7]],
[[1.3, 4.5], [3.2, 4.6]],
[[6.2, 5.5], [5.4, 5.4]]]
y = [1, 1, -1, -1]

# in this task we want points where the first feature is close to be closer
# to each other, no matter how close the second feature is

mmc = MMC()
mmc.fit(pairs, y)


References:

  Xing et al. Distance metric learning with application to clustering with side-information. NIPS 2002
  Adapted from Matlab code http://www.cs.cmu.edu/%7Eepxing/papers/Old_papers/code_Metric_online.tar.gz

## 3.3. Learning on triplets¶

Some metric learning algorithms learn on triplets of samples. In this case, one should provide the algorithm with n_samples triplets of points. The semantic of each triplet is that the first point should be closer to the second point than to the third one.

### 3.3.1. Fitting¶

Here is an example for fitting on triplets (see Fit, transform, and so on for more details on the input data format and how to fit, in the general case of learning on tuples).

>>> from metric_learn import SCML
>>> triplets = np.array([[[1.2, 3.2], [2.3, 5.5], [2.1, 0.6]],
>>>                      [[4.5, 2.3], [2.1, 2.3], [7.3, 3.4]]])
>>> scml = SCML(random_state=42)
>>> scml.fit(triplets)
SCML(beta=1e-5, B=None, max_iter=100000, verbose=False,
preprocessor=None, random_state=None)


Or alternatively (using a preprocessor):

>>> X = np.array([[[1.2, 3.2],
>>>                [2.3, 5.5],
>>>                [2.1, 0.6],
>>>                [4.5, 2.3],
>>>                [2.1, 2.3],
>>>                [7.3, 3.4]])
>>> triplets_indices = np.array([[0, 1, 2], [3, 4, 5]])
>>> scml = SCML(preprocessor=X, random_state=42)
>>> scml.fit(triplets_indices)
SCML(beta=1e-5, B=None, max_iter=100000, verbose=False,
preprocessor=array([[1.2, 3.2],
[2.3, 5.5],
[2.4, 6.7],
[2.1, 0.6],
[4.5, 2.3],
[2.1, 2.3],
[0.6, 1.2],
[7.3, 3.4]]),
random_state=None)


Here, we want to learn a metric that, for each of the two triplets, will make the first point closer to the second point than to the third one.

### 3.3.2. Prediction¶

When a triplets learner is fitted, it is also able to predict, for an upcoming triplet, whether the first point is closer to the second point than to the third one (+1), or not (-1).

>>> triplets_test = np.array(
... [[[5.6, 5.3], [2.2, 2.1], [1.2, 3.4]],
...  [[6.0, 4.2], [4.3, 1.2], [0.1, 7.8]]])
>>> scml.predict(triplets_test)
array([-1.,  1.])


### 3.3.3. Scoring¶

Triplet metric learners can also return a decision_function for a set of triplets, which corresponds to the distance between the first two points minus the distance between the first and last points of the triplet (the higher the value, the more similar the first point to the second point compared to the last one). This “score” can be interpreted as a measure of likeliness of having a +1 prediction for this triplet.

>>> scml.decision_function(triplets_test)
array([-1.75700306,  4.98982131])


In the above example, for the first triplet in triplets_test, the first point is predicted less similar to the second point than to the last point (they are further away in the transformed space).

Unlike pairs learners, triplets learners do not allow to give a y when fitting: we assume that the ordering of points within triplets is such that the training triplets are all positive. Therefore, it is not possible to use scikit-learn scoring functions (such as ‘f1_score’) for triplets learners.

However, triplets learners do have a default scoring function, which will basically return the accuracy score on a given test set, i.e. the proportion of triplets that have the right predicted ordering.

>>> scml.score(triplets_test)
0.5


Note

See Fit, transform, and so on for more details on metric learners functions that are not specific to learning on pairs, like transform, score_pairs, get_metric and get_mahalanobis_matrix.

### 3.3.4. Algorithms¶

#### 3.3.4.1. SCML¶

Sparse Compositional Metric Learning (SCML)

SCML learns a squared Mahalanobis distance from triplet constraints by optimizing sparse positive weights assigned to a set of $$K$$ rank-one PSD bases. This can be formulated as an optimization problem with only $$K$$ parameters, that can be solved with an efficient stochastic composite scheme.

The Mahalanobis matrix $$M$$ is built from a basis set $$B = \{b_i\}_{i=\{1,...,K\}}$$ weighted by a $$K$$ dimensional vector $$w = \{w_i\}_{i=\{1,...,K\}}$$ as:

$M = \sum_{i=1}^K w_i b_i b_i^T = B \cdot diag(w) \cdot B^T \quad w_i \geq 0$

Learning $$M$$ in this form makes it PSD by design, as it is a nonnegative sum of PSD matrices. The basis set $$B$$ is fixed in advance and it is possible to construct it from the data. The optimization problem over $$w$$ is formulated as a classic margin-based hinge loss function involving the set $$C$$ of triplets. A regularization $$\ell_1$$ is added to yield a sparse combination. The formulation is the following:

$\min_{w\geq 0} \sum_{(x_i,x_j,x_k)\in C} [1 + d_w(x_i,x_j)-d_w(x_i,x_k)]_+ + \beta||w||_1$

where $$[\cdot]_+$$ is the hinge loss.

from metric_learn import SCML

triplets = [[[1.2, 7.5], [1.3, 1.5], [6.2, 9.7]],
[[1.3, 4.5], [3.2, 4.6], [5.4, 5.4]],
[[3.2, 7.5], [3.3, 1.5], [8.2, 9.7]],
[[3.3, 4.5], [5.2, 4.6], [7.4, 5.4]]]

scml = SCML()
scml.fit(triplets)


References:

  Y. Shi, A. Bellet and F. Sha. Sparse Compositional Metric Learning.. (AAAI), 2014.
  Adapted from original Matlab implementation._.

Some metric learning algorithms learn on quadruplets of samples. In this case, one should provide the algorithm with n_samples quadruplets of points. The semantic of each quadruplet is that the first two points should be closer together than the last two points.

### 3.4.1. Fitting¶

Here is an example for fitting on quadruplets (see Fit, transform, and so on for more details on the input data format and how to fit, in the general case of learning on tuples).

>>> from metric_learn import LSML
>>> quadruplets = np.array([[[1.2, 3.2], [2.3, 5.5], [2.4, 6.7], [2.1, 0.6]],
>>>                         [[4.5, 2.3], [2.1, 2.3], [0.6, 1.2], [7.3, 3.4]]])
>>> lsml = LSML(random_state=42)
LSML(max_iter=1000, preprocessor=None, prior=None, random_state=42, tol=0.001,
verbose=False)


Or alternatively (using a preprocessor):

>>> X = np.array([[1.2, 3.2],
>>>               [2.3, 5.5],
>>>               [2.4, 6.7],
>>>               [2.1, 0.6],
>>>               [4.5, 2.3],
>>>               [2.1, 2.3],
>>>               [0.6, 1.2],
>>>               [7.3, 3.4]])
>>> quadruplets_indices = np.array([[0, 1, 2, 3], [4, 5, 6, 7]])
>>> lsml = LSML(preprocessor=X, random_state=42)
LSML(max_iter=1000,
preprocessor=array([[1.2, 3.2],
[2.3, 5.5],
[2.4, 6.7],
[2.1, 0.6],
[4.5, 2.3],
[2.1, 2.3],
[0.6, 1.2],
[7.3, 3.4]]),
prior=None, random_state=42, tol=0.001, verbose=False)


Here, we want to learn a metric that, for each of the two quadruplets, will put the two first points closer together than the two last points.

### 3.4.2. Prediction¶

When a quadruplets learner is fitted, it is also able to predict, for an upcoming quadruplet, whether the two first points are more similar than the two last points (+1), or not (-1).

>>> quadruplets_test = np.array(
... [[[5.6, 5.3], [2.2, 2.1], [0.4, 0.6], [1.2, 3.4]],
...  [[6.0, 4.2], [4.3, 1.2], [4.5, 0.6], [0.1, 7.8]]])
array([-1.,  1.])


### 3.4.3. Scoring¶

Quadruplet metric learners can also return a decision_function for a set of quadruplets, which corresponds to the distance between the first pair of points minus the distance between the second pair of points of the triplet (the higher the value, the more similar the first pair is than the last pair). This “score” can be interpreted as a measure of likeliness of having a +1 prediction for this quadruplet.

>>> lsml.decision_function(quadruplets_test)
array([-1.75700306,  4.98982131])


In the above example, for the first quadruplet in quadruplets_test, the two first points are predicted less similar than the two last points (they are further away in the transformed space).

Like triplet learners, quadruplets learners do not allow to give a y when fitting: we assume that the ordering of points within triplets is such that the training triplets are all positive. Therefore, it is not possible to use scikit-learn scoring functions (such as ‘f1_score’) for triplets learners.

However, quadruplets learners do have a default scoring function, which will basically return the accuracy score on a given test set, i.e. the proportion of quadruplets have the right predicted ordering.

>>> lsml.score(quadruplets_test)
0.5


Note

See Fit, transform, and so on for more details on metric learners functions that are not specific to learning on pairs, like transform, score_pairs, get_metric and get_mahalanobis_matrix.

### 3.4.4. Algorithms¶

#### 3.4.4.1. LSML¶

Metric Learning from Relative Comparisons by Minimizing Squared Residual (LSML)

LSML proposes a simple, yet effective, algorithm that minimizes a convex objective function corresponding to the sum of squared residuals of constraints. This algorithm uses the constraints in the form of the relative distance comparisons, such method is especially useful where pairwise constraints are not natural to obtain, thus pairwise constraints based algorithms become infeasible to be deployed. Furthermore, its sparsity extension leads to more stable estimation when the dimension is high and only a small amount of constraints is given.

The loss function of each constraint $$d(\mathbf{x}_i, \mathbf{x}_j) < d(\mathbf{x}_k, \mathbf{x}_l)$$ is denoted as:

$H(d_\mathbf{M}(\mathbf{x}_i, \mathbf{x}_j) - d_\mathbf{M}(\mathbf{x}_k, \mathbf{x}_l))$

where $$H(\cdot)$$ is the squared Hinge loss function defined as:

\begin{split}H(x) = \left\{\begin{aligned}0 \qquad x\leq 0 \\ \,\,x^2 \qquad x>0\end{aligned}\right.\\\end{split}

The summed loss function $$L(C)$$ is the simple sum over all constraints $$C = \{(\mathbf{x}_i , \mathbf{x}_j , \mathbf{x}_k , \mathbf{x}_l) : d(\mathbf{x}_i , \mathbf{x}_j) < d(\mathbf{x}_k , \mathbf{x}_l)\}$$. The original paper suggested here should be a weighted sum since the confidence or probability of each constraint might differ. However, for the sake of simplicity and assumption of no extra knowledge provided, we just deploy the simple sum here as well as what the authors did in the experiments.

The distance metric learning problem becomes minimizing the summed loss function of all constraints plus a regularization term w.r.t. the prior knowledge:

$\begin{split}\min_\mathbf{M}(D_{ld}(\mathbf{M, M_0}) + \sum_{(\mathbf{x}_i, \mathbf{x}_j, \mathbf{x}_k, \mathbf{x}_l)\in C}H(d_\mathbf{M}( \mathbf{x}_i, \mathbf{x}_j) - d_\mathbf{M}(\mathbf{x}_k, \mathbf{x}_l))\\\end{split}$

where $$\mathbf{M}_0$$ is the prior metric matrix, set as identity by default, $$D_{ld}(\mathbf{\cdot, \cdot})$$ is the LogDet divergence:

$D_{ld}(\mathbf{M, M_0}) = \text{tr}(\mathbf{MM_0}) − \text{logdet} (\mathbf{M})$
from metric_learn import LSML

quadruplets = [[[1.2, 7.5], [1.3, 1.5], [6.4, 2.6], [6.2, 9.7]],
[[1.3, 4.5], [3.2, 4.6], [6.2, 5.5], [5.4, 5.4]],
[[3.2, 7.5], [3.3, 1.5], [8.4, 2.6], [8.2, 9.7]],
[[3.3, 4.5], [5.2, 4.6], [8.2, 5.5], [7.4, 5.4]]]

# we want to make closer points where the first feature is close, and
# further if the second feature is close

lsml = LSML()