scikit-learn-contrib · bellet · Feb 4, 2020 · Jan 24, 2020 · Jan 24, 2020 · Jan 27, 2020
diff --git a/metric_learn/_util.py b/metric_learn/_util.py
@@ -1,5 +1,4 @@
 import numpy as np
-import scipy
 import six
 from numpy.linalg import LinAlgError
 from sklearn.datasets import make_spd_matrix
@@ -8,9 +7,10 @@
 from sklearn.utils.validation import check_X_y, check_random_state
 from .exceptions import PreprocessorError, NonPSDError
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-from scipy.linalg import pinvh
+from scipy.linalg import pinvh, eigh
 import sys
 import time
+import warnings
 
 # hack around lack of axis kwarg in older numpy versions
 try:
@@ -678,17 +678,20 @@ def _initialize_metric_mahalanobis(input, init='identity', random_state=None,
 
   random_state = check_random_state(random_state)
   M = init
-  if isinstance(init, np.ndarray):
-    s, u = scipy.linalg.eigh(init)
-    init_is_definite = _check_sdp_from_eigen(s)
+  if isinstance(M, np.ndarray):
+    w, V = eigh(M, check_finite=False)
+    init_is_definite = _check_sdp_from_eigen(w)
     if strict_pd and not init_is_definite:
       raise LinAlgError("You should provide a strictly positive definite "
                         "matrix as `{}`. This one is not definite. Try another"
                         " {}, or an algorithm that does not "
                         "require the {} to be strictly positive definite."
                         .format(*((matrix_name,) * 3)))
+    elif return_inverse and not init_is_definite:
+      warnings.warn('The initialization matrix is not invertible: '
+                    'using the pseudo-inverse instead.')
     if return_inverse:
-      M_inv = np.dot(u / s, u.T)
+      M_inv = _pseudo_inverse_from_eig(w, V)
       return M, M_inv
     else:
       return M
@@ -707,15 +710,23 @@ def _initialize_metric_mahalanobis(input, init='identity', random_state=None,
       X = input
     # atleast2d is necessary to deal with scalar covariance matrices
     M_inv = np.atleast_2d(np.cov(X, rowvar=False))
-    s, u = scipy.linalg.eigh(M_inv)
-    cov_is_definite = _check_sdp_from_eigen(s)
+    w, V = eigh(M_inv, check_finite=False)
+    cov_is_definite = _check_sdp_from_eigen(w)
     if strict_pd and not cov_is_definite:
       raise LinAlgError("Unable to get a true inverse of the covariance "
                         "matrix since it is not definite. Try another "
                         "`{}`, or an algorithm that does not "
                         "require the `{}` to be strictly positive definite."
                         .format(*((matrix_name,) * 2)))
-    M = np.dot(u / s, u.T)
+    elif not cov_is_definite:
+      warnings.warn('The covariance matrix is not invertible: '
+                    'using the pseudo-inverse instead.'
+                    'To make the covariance matrix invertible'
+                    ' you can remove any linearly dependent features and/or '
+                    'reduce the dimensionality of your input, '
+                    'for instance using `sklearn.decomposition.PCA` as a '
+                    'preprocessing step.')
+    M = _pseudo_inverse_from_eig(w, V)
     if return_inverse:
       return M, M_inv
     else:
@@ -742,3 +753,36 @@ def _check_n_components(n_features, n_components):
   if 0 < n_components <= n_features:
     return n_components
   raise ValueError('Invalid n_components, must be in [1, %d]' % n_features)
+
+
+def _pseudo_inverse_from_eig(w, V, tol=None):
+  """Compute the (Moore-Penrose) pseudo-inverse of the EVD of a symetric
+  matrix.
+
+  Parameters
+  ----------
+  w : (..., M) ndarray
+    The eigenvalues in ascending order, each repeated according to
+    its multiplicity.
+
+  v : {(..., M, M) ndarray, (..., M, M) matrix}
+    The column ``v[:, i]`` is the normalized eigenvector corresponding
+    to the eigenvalue ``w[i]``.  Will return a matrix object if `a` is
+    a matrix object.
+
+  tol : positive `float`, optional
+    Absolute eigenvalues below tol are considered zero.
+
+  Returns
+  -------
+  output : (..., M, N) array_like
+    The pseudo-inverse given by the EVD.
+  """
+  if tol is None:
+    tol = np.amax(w) * np.max(w.shape) * np.finfo(w.dtype).eps
+  # discard small eigenvalues and invert the rest
+  large = np.abs(w) > tol
+  w = np.divide(1, w, where=large, out=w)
+  w[~large] = 0
+
+  return np.dot(V * w, np.conjugate(V).T)
diff --git a/test/test_mahalanobis_mixin.py b/test/test_mahalanobis_mixin.py
@@ -8,12 +8,12 @@
 from scipy.stats import ortho_group
 from sklearn import clone
 from sklearn.cluster import DBSCAN
-from sklearn.datasets import make_spd_matrix
-from sklearn.utils import check_random_state
+from sklearn.datasets import make_spd_matrix, make_blobs
+from sklearn.utils import check_random_state, shuffle
 from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.testing import set_random_state
 
-from metric_learn._util import make_context
+from metric_learn._util import make_context, _initialize_metric_mahalanobis
 from metric_learn.base_metric import (_QuadrupletsClassifierMixin,
                                       _PairsClassifierMixin)
 from metric_learn.exceptions import NonPSDError
@@ -569,7 +569,7 @@ def test_init_mahalanobis(estimator, build_dataset):
                               in zip(ids_metric_learners,
                                      metric_learners)
                               if idml[:4] in ['ITML', 'SDML', 'LSML']])
-def test_singular_covariance_init_or_prior(estimator, build_dataset):
+def test_singular_covariance_init_or_prior_strictpd(estimator, build_dataset):
     """Tests that when using the 'covariance' init or prior, it returns the
     appropriate error if the covariance matrix is singular, for algorithms
     that need a strictly PD prior or init (see
@@ -603,6 +603,48 @@ def test_singular_covariance_init_or_prior(estimator, build_dataset):
       assert str(raised_err.value) == msg
 
 
+@pytest.mark.integration
+@pytest.mark.parametrize('estimator, build_dataset',
+                         [(ml, bd) for idml, (ml, bd)
+                          in zip(ids_metric_learners,
+                                 metric_learners)
+                          if idml[:3] in ['MMC']],
+                         ids=[idml for idml, (ml, _)
+                              in zip(ids_metric_learners,
+                                     metric_learners)
+                              if idml[:3] in ['MMC']])
+def test_singular_covariance_init_of_non_strict_pd(estimator, build_dataset):
+    """Tests that when using the 'covariance' init or prior, it returns the
+    appropriate warning if the covariance matrix is singular, for algorithms
+    that don't need a strictly PD init. Also checks that the returned
+    inverse matrix has finite values
+    """
+    input_data, labels, _, X = build_dataset()
+    model = clone(estimator)
+    set_random_state(model)
+    # We create a feature that is a linear combination of the first two
+    # features:
+    input_data = np.concatenate([input_data, input_data[:, ..., :2].dot([[2],
+                                                                        [3]])],
+                                axis=-1)
+    model.set_params(init='covariance')
+    msg = ('The covariance matrix is not invertible: '
+           'using the pseudo-inverse instead.'
+           'To make the covariance matrix invertible'
+           ' you can remove any linearly dependent features and/or '
+           'reduce the dimensionality of your input, '
+           'for instance using `sklearn.decomposition.PCA` as a '
+           'preprocessing step.')
+    with pytest.warns(UserWarning) as raised_warning:
+      model.fit(input_data, labels)
+    assert np.any([str(warning.message) == msg for warning in raised_warning])
+    M, _ = _initialize_metric_mahalanobis(X, init='covariance',
+                                          random_state=RNG,
+                                          return_inverse=True,
+                                          strict_pd=False)
+    assert np.isfinite(M).all()
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize('estimator, build_dataset',
                          [(ml, bd) for idml, (ml, bd)
@@ -614,7 +656,7 @@ def test_singular_covariance_init_or_prior(estimator, build_dataset):
                                      metric_learners)
                               if idml[:4] in ['ITML', 'SDML', 'LSML']])
 @pytest.mark.parametrize('w0', [1e-20, 0., -1e-20])
-def test_singular_array_init_or_prior(estimator, build_dataset, w0):
+def test_singular_array_init_or_prior_strictpd(estimator, build_dataset, w0):
     """Tests that when using a custom array init (or prior), it returns the
     appropriate error if it is singular, for algorithms
     that need a strictly PD prior or init (see
@@ -654,6 +696,31 @@ def test_singular_array_init_or_prior(estimator, build_dataset, w0):
       assert str(raised_err.value) == msg
 
 
+@pytest.mark.parametrize('w0', [1e-20, 0., -1e-20])
+def test_singular_array_init_of_non_strict_pd(w0):
+    """Tests that when using a custom array init, it returns the
+    appropriate warning if it is singular. Also checks if the returned
+    inverse matrix is finite. This isn't checked for model fitting as no
+    model curently uses this setting.
+    """
+    rng = np.random.RandomState(42)
+    X, y = shuffle(*make_blobs(random_state=rng),
+                   random_state=rng)
+    P = ortho_group.rvs(X.shape[1], random_state=rng)
+    w = np.abs(rng.randn(X.shape[1]))
+    w[0] = w0
+    M = P.dot(np.diag(w)).dot(P.T)
+    msg = ('The initialization matrix is not invertible: '
+           'using the pseudo-inverse instead.')
+    with pytest.warns(UserWarning) as raised_warning:
+      _, M_inv = _initialize_metric_mahalanobis(X, init=M,
+                                                random_state=rng,
+                                                return_inverse=True,
+                                                strict_pd=False)
+    assert str(raised_warning[0].message) == msg
+    assert np.isfinite(M_inv).all()
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize('estimator, build_dataset', metric_learners,
                          ids=ids_metric_learners)

diff --git a/test/test_utils.py b/test/test_utils.py
@@ -1,4 +1,5 @@
 import pytest
+from scipy.linalg import eigh, pinvh
 from collections import namedtuple
 import numpy as np
 from numpy.testing import assert_array_equal, assert_equal
@@ -11,7 +12,7 @@
                                 check_collapsed_pairs, validate_vector,
                                 _check_sdp_from_eigen, _check_n_components,
                                 check_y_valid_values_for_pairs,
-                                _auto_select_init)
+                                _auto_select_init, _pseudo_inverse_from_eig)
 from metric_learn import (ITML, LSML, MMC, RCA, SDML, Covariance, LFDA,
                           LMNN, MLKR, NCA, ITML_Supervised, LSML_Supervised,
                           MMC_Supervised, RCA_Supervised, SDML_Supervised,
@@ -1146,3 +1147,27 @@ def test__auto_select_init(has_classes, n_features, n_samples, n_components,
   """Checks that the auto selection of the init works as expected"""
   assert (_auto_select_init(has_classes, n_features,
                             n_samples, n_components, n_classes) == result)
+
+
+@pytest.mark.parametrize('w0', [1e-20, 0., -1e-20])
+def test_pseudo_inverse_from_eig_and_pinvh_singular(w0):
+  """Checks that _pseudo_inverse_from_eig returns the same result as
+  scipy.linalg.pinvh for a singular matrix"""
+  rng = np.random.RandomState(SEED)
+  A = rng.rand(100, 100)
+  A = A + A.T
+  w, V = eigh(A)
+  w[0] = w0
+  A = V.dot(np.diag(w)).dot(V.T)
+  np.testing.assert_allclose(_pseudo_inverse_from_eig(w, V), pinvh(A),
+                             rtol=1e-05)
+
+
+def test_pseudo_inverse_from_eig_and_pinvh_nonsingular():
+  """Checks that _pseudo_inverse_from_eig returns the same result as
+  scipy.linalg.pinvh for a non singular matrix"""
+  rng = np.random.RandomState(SEED)
+  A = rng.rand(100, 100)
+  A = A + A.T
+  w, V = eigh(A, check_finite=False)
+  np.testing.assert_allclose(_pseudo_inverse_from_eig(w, V), pinvh(A))