From 05deb8c276f04065ab01731c1f03eeac240dc87b Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Wed, 13 Nov 2019 22:41:49 +0100
Subject: [PATCH 01/13] chunks return a map of index to chunk

---
 metric_learn/constraints.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index b71c9b96..e7e23444 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -23,6 +23,7 @@ def __init__(self, partial_labels):
     self.num_points, = partial_labels.shape
     self.known_label_idx, = np.where(partial_labels >= 0)
     self.known_labels = partial_labels[self.known_label_idx]
+    self.partial_labels = partial_labels
 
   def adjacency_matrix(self, num_constraints, random_state=None):
     random_state = check_random_state(random_state)
@@ -76,9 +77,11 @@ def chunks(self, num_chunks=100, chunk_size=2, random_state=None):
     the random state object to be passed must be a numpy random seed
     """
     random_state = check_random_state(random_state)
-    chunks = -np.ones_like(self.known_label_idx, dtype=int)
-    uniq, lookup = np.unique(self.known_labels, return_inverse=True)
-    all_inds = [set(np.where(lookup == c)[0]) for c in xrange(len(uniq))]
+    chunks = -np.ones_like(self.partial_labels, dtype=int)
+    uniq, lookup = np.unique(self.partial_labels, return_inverse=True)
+    if self.num_points != len(self.known_labels):
+      uniq = uniq[uniq >= 0]
+    all_inds = [set(np.where(lookup == c)[0]) for c in uniq]
     max_chunks = int(np.sum([len(s) // chunk_size for s in all_inds]))
     if max_chunks < num_chunks:
       raise ValueError(('Not enough possible chunks of %d elements in each'

From 7a6f3027bd3f5e13bf8f76917e41618ff934e925 Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Wed, 13 Nov 2019 23:04:36 +0100
Subject: [PATCH 02/13] maj

---
 metric_learn/constraints.py | 6 ++++--
 test.py                     | 8 ++++++++
 test/test_constraints.py    | 4 ++--
 test_constraints.py         | 7 +++++++
 4 files changed, 21 insertions(+), 4 deletions(-)
 create mode 100644 test.py
 create mode 100644 test_constraints.py

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index e7e23444..523f1ed5 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -80,9 +80,11 @@ def chunks(self, num_chunks=100, chunk_size=2, random_state=None):
     chunks = -np.ones_like(self.partial_labels, dtype=int)
     uniq, lookup = np.unique(self.partial_labels, return_inverse=True)
     if self.num_points != len(self.known_labels):
-      uniq = uniq[uniq >= 0]
-    all_inds = [set(np.where(lookup == c)[0]) for c in uniq]
+      unknown_uniq = np.where(uniq < 0)[0]
+    all_inds = [set(np.where(lookup == c)[0]) for c in xrange(len(uniq))
+                if c not in unknown_uniq]
     max_chunks = int(np.sum([len(s) // chunk_size for s in all_inds]))
+    print(max_chunks)
     if max_chunks < num_chunks:
       raise ValueError(('Not enough possible chunks of %d elements in each'
                         ' class to form expected %d chunks - maximum number'
diff --git a/test.py b/test.py
new file mode 100644
index 00000000..1c9099e8
--- /dev/null
+++ b/test.py
@@ -0,0 +1,8 @@
+from metric_learn import RCA_Supervised
+import numpy as np
+
+X = np.random.rand(5, 2)
+y = [1, 1, -1, 2, 2]
+
+rca = RCA_Supervised(num_chunks=2)
+rca.fit(X, y)
diff --git a/test/test_constraints.py b/test/test_constraints.py
index a135985e..3547349b 100644
--- a/test/test_constraints.py
+++ b/test/test_constraints.py
@@ -34,7 +34,7 @@ def test_exact_num_points_for_chunks(num_chunks, chunk_size):
   chunks = constraints.chunks(num_chunks=num_chunks, chunk_size=chunk_size,
                               random_state=SEED)
 
-  chunk_no, size_each_chunk = np.unique(chunks, return_counts=True)
+  chunk_no, size_each_chunk = np.unique(chunks[chunks > 0], return_counts=True)
 
   np.testing.assert_array_equal(size_each_chunk, chunk_size)
   assert chunk_no.shape[0] == num_chunks
@@ -60,4 +60,4 @@ def test_chunk_case_one_miss_point(num_chunks, chunk_size):
 
 
 if __name__ == '__main__':
-  unittest.main()
+  test_exact_num_points_for_chunks(5, 10)
diff --git a/test_constraints.py b/test_constraints.py
new file mode 100644
index 00000000..feaeb275
--- /dev/null
+++ b/test_constraints.py
@@ -0,0 +1,7 @@
+from metric_learn.constraints import Constraints
+partial_labels = [1, 2, 2, 1, -1, 3, 3]
+cons = Constraints(partial_labels)
+cons.chunks(num_chunks=3, chunk_size=2)
+chunks = cons.chunks(num_chunks=3, chunk_size=2)
+expected_chunk = [0, 1, 1, 0, -1, 2, 2]
+print(chunks, expected_chunk)

From d08c2d85a904e29d1520924c96b4b4cb5b0e8180 Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Thu, 14 Nov 2019 17:29:02 +0100
Subject: [PATCH 03/13] maj

---
 metric_learn/constraints.py | 4 +---
 test.py                     | 8 --------
 test/test_constraints.py    | 8 ++------
 test_constraints.py         | 7 -------
 4 files changed, 3 insertions(+), 24 deletions(-)
 delete mode 100644 test.py
 delete mode 100644 test_constraints.py

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index 523f1ed5..e95923e2 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -79,12 +79,10 @@ def chunks(self, num_chunks=100, chunk_size=2, random_state=None):
     random_state = check_random_state(random_state)
     chunks = -np.ones_like(self.partial_labels, dtype=int)
     uniq, lookup = np.unique(self.partial_labels, return_inverse=True)
-    if self.num_points != len(self.known_labels):
-      unknown_uniq = np.where(uniq < 0)[0]
+    unknown_uniq = np.where(uniq < 0)[0]
     all_inds = [set(np.where(lookup == c)[0]) for c in xrange(len(uniq))
                 if c not in unknown_uniq]
     max_chunks = int(np.sum([len(s) // chunk_size for s in all_inds]))
-    print(max_chunks)
     if max_chunks < num_chunks:
       raise ValueError(('Not enough possible chunks of %d elements in each'
                         ' class to form expected %d chunks - maximum number'
diff --git a/test.py b/test.py
deleted file mode 100644
index 1c9099e8..00000000
--- a/test.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from metric_learn import RCA_Supervised
-import numpy as np
-
-X = np.random.rand(5, 2)
-y = [1, 1, -1, 2, 2]
-
-rca = RCA_Supervised(num_chunks=2)
-rca.fit(X, y)
diff --git a/test/test_constraints.py b/test/test_constraints.py
index 3547349b..7e6ac51c 100644
--- a/test/test_constraints.py
+++ b/test/test_constraints.py
@@ -1,4 +1,3 @@
-import unittest
 import pytest
 import numpy as np
 from sklearn.utils import shuffle
@@ -34,7 +33,8 @@ def test_exact_num_points_for_chunks(num_chunks, chunk_size):
   chunks = constraints.chunks(num_chunks=num_chunks, chunk_size=chunk_size,
                               random_state=SEED)
 
-  chunk_no, size_each_chunk = np.unique(chunks[chunks > 0], return_counts=True)
+  chunk_no, size_each_chunk = np.unique(chunks[chunks >= 0],
+                                        return_counts=True)
 
   np.testing.assert_array_equal(size_each_chunk, chunk_size)
   assert chunk_no.shape[0] == num_chunks
@@ -57,7 +57,3 @@ def test_chunk_case_one_miss_point(num_chunks, chunk_size):
                        ) % (chunk_size, num_chunks, num_chunks - 1))
 
   assert str(e.value) == expected_message
-
-
-if __name__ == '__main__':
-  test_exact_num_points_for_chunks(5, 10)
diff --git a/test_constraints.py b/test_constraints.py
deleted file mode 100644
index feaeb275..00000000
--- a/test_constraints.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from metric_learn.constraints import Constraints
-partial_labels = [1, 2, 2, 1, -1, 3, 3]
-cons = Constraints(partial_labels)
-cons.chunks(num_chunks=3, chunk_size=2)
-chunks = cons.chunks(num_chunks=3, chunk_size=2)
-expected_chunk = [0, 1, 1, 0, -1, 2, 2]
-print(chunks, expected_chunk)

From 6c4ba21d376f1e02909272d3889004afd73123b3 Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Thu, 21 Nov 2019 15:46:33 +0100
Subject: [PATCH 04/13] remove storing of known labels

---
 metric_learn/constraints.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index e95923e2..e8688cb5 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -20,10 +20,8 @@ class Constraints(object):
   def __init__(self, partial_labels):
     '''partial_labels : int arraylike, -1 indicating unknown label'''
     partial_labels = np.asanyarray(partial_labels, dtype=int)
-    self.num_points, = partial_labels.shape
-    self.known_label_idx, = np.where(partial_labels >= 0)
-    self.known_labels = partial_labels[self.known_label_idx]
     self.partial_labels = partial_labels
+    self.num_points, = partial_labels.shape
 
   def adjacency_matrix(self, num_constraints, random_state=None):
     random_state = check_random_state(random_state)
@@ -51,17 +49,19 @@ def positive_negative_pairs(self, num_constraints, same_length=False,
 
   def _pairs(self, num_constraints, same_label=True, max_iter=10,
              random_state=np.random):
-    num_labels = len(self.known_labels)
+    known_label_idx, = np.where(self.partial_labels >= 0)
+    known_labels = self.partial_labels[known_label_idx]
+    num_labels = len(known_labels)
     ab = set()
     it = 0
     while it < max_iter and len(ab) < num_constraints:
       nc = num_constraints - len(ab)
       for aidx in random_state.randint(num_labels, size=nc):
         if same_label:
-          mask = self.known_labels[aidx] == self.known_labels
+          mask = known_labels[aidx] == known_labels
           mask[aidx] = False  # avoid identity pairs
         else:
-          mask = self.known_labels[aidx] != self.known_labels
+          mask = known_labels[aidx] != known_labels
         b_choices, = np.where(mask)
         if len(b_choices) > 0:
           ab.add((aidx, random_state.choice(b_choices)))

From 2b1308d0d91e6208170715d0f0323d68443fa3c6 Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Thu, 21 Nov 2019 16:37:23 +0100
Subject: [PATCH 05/13] typo

---
 metric_learn/constraints.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index e8688cb5..e112fdc8 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -70,7 +70,7 @@ def _pairs(self, num_constraints, same_label=True, max_iter=10,
       warnings.warn("Only generated %d %s constraints (requested %d)" % (
           len(ab), 'positive' if same_label else 'negative', num_constraints))
     ab = np.array(list(ab)[:num_constraints], dtype=int)
-    return self.known_label_idx[ab.T]
+    return known_label_idx[ab.T]
 
   def chunks(self, num_chunks=100, chunk_size=2, random_state=None):
     """

From 8003c0d63de36c0df65cabb9f6473c0fe524e5aa Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Thu, 21 Nov 2019 16:42:34 +0100
Subject: [PATCH 06/13] no self.num_points

---
 metric_learn/constraints.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index e112fdc8..a5c3ad59 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -21,7 +21,6 @@ def __init__(self, partial_labels):
     '''partial_labels : int arraylike, -1 indicating unknown label'''
     partial_labels = np.asanyarray(partial_labels, dtype=int)
     self.partial_labels = partial_labels
-    self.num_points, = partial_labels.shape
 
   def adjacency_matrix(self, num_constraints, random_state=None):
     random_state = check_random_state(random_state)
@@ -31,7 +30,8 @@ def adjacency_matrix(self, num_constraints, random_state=None):
     col = np.concatenate((b, d))
     data = np.ones_like(row, dtype=int)
     data[len(a):] = -1
-    adj = coo_matrix((data, (row, col)), shape=(self.num_points,) * 2)
+    num_points = self.partial_labels.shape[0]
+    adj = coo_matrix((data, (row, col)), shape=(num_points,) * 2)
     # symmetrize
     return adj + adj.T
 

From 57b559cbf05566696fb6d02af8b6a632c8f04d0d Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Thu, 28 Nov 2019 16:06:53 +0100
Subject: [PATCH 07/13] tests for unlabeled, repairs chunk generation

---
 metric_learn/constraints.py | 13 -------------
 test/metric_learn_test.py   | 13 +++++++++++++
 test/test_constraints.py    | 12 ++++++++++++
 3 files changed, 25 insertions(+), 13 deletions(-)

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index a5c3ad59..49eac338 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -22,19 +22,6 @@ def __init__(self, partial_labels):
     partial_labels = np.asanyarray(partial_labels, dtype=int)
     self.partial_labels = partial_labels
 
-  def adjacency_matrix(self, num_constraints, random_state=None):
-    random_state = check_random_state(random_state)
-    a, b, c, d = self.positive_negative_pairs(num_constraints,
-                                              random_state=random_state)
-    row = np.concatenate((a, c))
-    col = np.concatenate((b, d))
-    data = np.ones_like(row, dtype=int)
-    data[len(a):] = -1
-    num_points = self.partial_labels.shape[0]
-    adj = coo_matrix((data, (row, col)), shape=(num_points,) * 2)
-    # symmetrize
-    return adj + adj.T
-
   def positive_negative_pairs(self, num_constraints, same_length=False,
                               random_state=None):
     random_state = check_random_state(random_state)
diff --git a/test/metric_learn_test.py b/test/metric_learn_test.py
index f713a059..4b89fb57 100644
--- a/test/metric_learn_test.py
+++ b/test/metric_learn_test.py
@@ -1136,6 +1136,19 @@ def test_changed_behaviour_warning_random_state(self):
       rca_supervised.fit(X, y)
     assert any(msg == str(wrn.message) for wrn in raised_warning)
 
+  def test_unknown_labels(self):
+    n = 100
+    X, y = make_classification(random_state=42, n_samples=2 * n)
+    y2 = np.concatenate((y[:n], np.ones(n)))
+
+    rca = RCA_Supervised(num_chunks=2)
+    rca.fit(X[:n], y[:n])
+
+    rca2 = RCA_Supervised(num_chunks=2)
+    rca2.fit(X, y2)
+
+    np.testing.assert_array_equal(rca.components_, rca2.components_)
+
 
 @pytest.mark.parametrize('num_dims', [None, 2])
 def test_deprecation_num_dims_rca(num_dims):
diff --git a/test/test_constraints.py b/test/test_constraints.py
index 7e6ac51c..243028f6 100644
--- a/test/test_constraints.py
+++ b/test/test_constraints.py
@@ -57,3 +57,15 @@ def test_chunk_case_one_miss_point(num_chunks, chunk_size):
                        ) % (chunk_size, num_chunks, num_chunks - 1))
 
   assert str(e.value) == expected_message
+
+
+@pytest.mark.parametrize("num_chunks, chunk_size", [(5, 10), (10, 50)])
+def test_unknown_labels_not_in_chunks(num_chunks, chunk_size):
+  """Checks that unknown labels are not assigned to any chunk."""
+  labels = gen_labels_for_chunks(num_chunks, chunk_size)
+
+  constraints = Constraints(labels)
+  chunks = constraints.chunks(num_chunks=num_chunks, chunk_size=chunk_size,
+                              random_state=SEED)
+
+  assert np.all(chunks[labels < 0] < 0)

From 72a9ee53d848d5ae0058d8a281e3531c8e2f0627 Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Thu, 28 Nov 2019 16:20:59 +0100
Subject: [PATCH 08/13] maj

---
 metric_learn/constraints.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index 49eac338..752ca6e0 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -5,7 +5,6 @@
 import numpy as np
 import warnings
 from six.moves import xrange
-from scipy.sparse import coo_matrix
 from sklearn.utils import check_random_state
 
 __all__ = ['Constraints']

From 0c98432ea3b1a5d9b55c71e615df6455d827ca4a Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Tue, 10 Dec 2019 13:08:48 +0100
Subject: [PATCH 09/13] testing diff features

---
 test/metric_learn_test.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/test/metric_learn_test.py b/test/metric_learn_test.py
index 4b89fb57..eff18ca3 100644
--- a/test/metric_learn_test.py
+++ b/test/metric_learn_test.py
@@ -1138,8 +1138,10 @@ def test_changed_behaviour_warning_random_state(self):
 
   def test_unknown_labels(self):
     n = 100
-    X, y = make_classification(random_state=42, n_samples=2 * n)
-    y2 = np.concatenate((y[:n], np.ones(n)))
+    X, y = make_classification(random_state=42, n_samples=2 * n,
+                               n_features=3, n_redundant=0,
+                               n_informative=3)
+    y2 = np.concatenate((y[:n], -np.ones(n)))
 
     rca = RCA_Supervised(num_chunks=2)
     rca.fit(X[:n], y[:n])
@@ -1147,6 +1149,9 @@ def test_unknown_labels(self):
     rca2 = RCA_Supervised(num_chunks=2)
     rca2.fit(X, y2)
 
+    assert not np.any(np.isnan(rca.components_))
+    assert not np.any(np.isnan(rca2.components_))
+
     np.testing.assert_array_equal(rca.components_, rca2.components_)
 
 

From 02168eb034fdce449e2529817faba801257e172d Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Tue, 10 Dec 2019 18:49:12 +0100
Subject: [PATCH 10/13] corrected test

---
 test/metric_learn_test.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/test/metric_learn_test.py b/test/metric_learn_test.py
index eff18ca3..d10acf47 100644
--- a/test/metric_learn_test.py
+++ b/test/metric_learn_test.py
@@ -1137,16 +1137,16 @@ def test_changed_behaviour_warning_random_state(self):
     assert any(msg == str(wrn.message) for wrn in raised_warning)
 
   def test_unknown_labels(self):
-    n = 100
+    n = 200
+    num_chunks = 50
     X, y = make_classification(random_state=42, n_samples=2 * n,
-                               n_features=3, n_redundant=0,
-                               n_informative=3)
+                               n_features=6, n_informative=6, n_redundant=0)
     y2 = np.concatenate((y[:n], -np.ones(n)))
 
-    rca = RCA_Supervised(num_chunks=2)
+    rca = RCA_Supervised(num_chunks=num_chunks, random_state=42)
     rca.fit(X[:n], y[:n])
 
-    rca2 = RCA_Supervised(num_chunks=2)
+    rca2 = RCA_Supervised(num_chunks=num_chunks, random_state=42)
     rca2.fit(X, y2)
 
     assert not np.any(np.isnan(rca.components_))

From 03ad650ebe6c91c1cd08e30bb0cbc89daeb55cfc Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Tue, 10 Dec 2019 19:12:47 +0100
Subject: [PATCH 11/13] diff warning

---
 metric_learn/rca.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/metric_learn/rca.py b/metric_learn/rca.py
index 2a9ab1e8..38373044 100644
--- a/metric_learn/rca.py
+++ b/metric_learn/rca.py
@@ -96,7 +96,8 @@ def _check_dimension(self, rank, X):
     if rank < d:
       warnings.warn('The inner covariance matrix is not invertible, '
                     'so the transformation matrix may contain Nan values. '
-                    'You should reduce the dimensionality of your input,'
+                    'You should remove any linearly dependent features and/or'
+                    'reduce the dimensionality of your input,'
                     'for instance using `sklearn.decomposition.PCA` as a '
                     'preprocessing step.')
 

From b93c1152b18c0d6e394c1ffdc6960841562df463 Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Fri, 20 Dec 2019 11:38:19 +0100
Subject: [PATCH 12/13] maj

---
 metric_learn/rca.py       | 4 ++--
 test/metric_learn_test.py | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/metric_learn/rca.py b/metric_learn/rca.py
index 38373044..591d0384 100644
--- a/metric_learn/rca.py
+++ b/metric_learn/rca.py
@@ -96,8 +96,8 @@ def _check_dimension(self, rank, X):
     if rank < d:
       warnings.warn('The inner covariance matrix is not invertible, '
                     'so the transformation matrix may contain Nan values. '
-                    'You should remove any linearly dependent features and/or'
-                    'reduce the dimensionality of your input,'
+                    'You should remove any linearly dependent features and/or '
+                    'reduce the dimensionality of your input, '
                     'for instance using `sklearn.decomposition.PCA` as a '
                     'preprocessing step.')
 
diff --git a/test/metric_learn_test.py b/test/metric_learn_test.py
index d10acf47..ebd1ec5b 100644
--- a/test/metric_learn_test.py
+++ b/test/metric_learn_test.py
@@ -1100,9 +1100,11 @@ def test_rank_deficient_returns_warning(self):
     rca = RCA()
     msg = ('The inner covariance matrix is not invertible, '
            'so the transformation matrix may contain Nan values. '
-           'You should reduce the dimensionality of your input,'
+           'You should 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(None) as raised_warnings:
       rca.fit(X, y)
     assert any(str(w.message) == msg for w in raised_warnings)

From 5289e6659eb9f86282dfce9f45f3bc0b0cc46da3 Mon Sep 17 00:00:00 2001
From: RobinVogel <robvogel0@gmail.com>
Date: Mon, 23 Dec 2019 10:30:16 +0100
Subject: [PATCH 13/13] added parameter bound test

---
 metric_learn/rca.py       | 10 ++++++++++
 test/metric_learn_test.py | 17 +++++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/metric_learn/rca.py b/metric_learn/rca.py
index 591d0384..204bd360 100644
--- a/metric_learn/rca.py
+++ b/metric_learn/rca.py
@@ -93,6 +93,7 @@ def __init__(self, n_components=None, num_dims='deprecated',
 
   def _check_dimension(self, rank, X):
     d = X.shape[1]
+
     if rank < d:
       warnings.warn('The inner covariance matrix is not invertible, '
                     'so the transformation matrix may contain Nan values. '
@@ -242,4 +243,13 @@ def fit(self, X, y, random_state='deprecated'):
     chunks = Constraints(y).chunks(num_chunks=self.num_chunks,
                                    chunk_size=self.chunk_size,
                                    random_state=self.random_state)
+
+    if self.num_chunks * (self.chunk_size - 1) < X.shape[1]:
+      warnings.warn('Due to the parameters of RCA_Supervised, '
+                    'the inner covariance matrix is not invertible, '
+                    'so the transformation matrix will contain Nan values. '
+                    'Increase the number or size of the chunks to correct '
+                    'this problem.'
+                    )
+
     return RCA.fit(self, X, chunks)
diff --git a/test/metric_learn_test.py b/test/metric_learn_test.py
index ebd1ec5b..5a271890 100644
--- a/test/metric_learn_test.py
+++ b/test/metric_learn_test.py
@@ -1156,6 +1156,23 @@ def test_unknown_labels(self):
 
     np.testing.assert_array_equal(rca.components_, rca2.components_)
 
+  def test_bad_parameters(self):
+    n = 200
+    num_chunks = 3
+    X, y = make_classification(random_state=42, n_samples=n,
+                               n_features=6, n_informative=6, n_redundant=0)
+
+    rca = RCA_Supervised(num_chunks=num_chunks, random_state=42)
+    msg = ('Due to the parameters of RCA_Supervised, '
+           'the inner covariance matrix is not invertible, '
+           'so the transformation matrix will contain Nan values. '
+           'Increase the number or size of the chunks to correct '
+           'this problem.'
+           )
+    with pytest.warns(None) as raised_warning:
+      rca.fit(X, y)
+    assert any(str(w.message) == msg for w in raised_warning)
+
 
 @pytest.mark.parametrize('num_dims', [None, 2])
 def test_deprecation_num_dims_rca(num_dims):