Nitpick for concatenation and refactor HAS_SKGGM

Author: William de Vazelhes

metric_learn/_util.py

@@ -15,14 +15,6 @@ def vector_norm(X):
     return np.linalg.norm(X, axis=1)
 
 
-def has_installed_skggm():
-  try:
-    import inverse_covariance
-    return True
-  except ImportError:
-    return False
-
-
 def check_input(input_data, y=None, preprocessor=None,
                 type_of_inputs='classic', tuple_size=None, accept_sparse=False,
                 dtype='numeric', order=None,

metric_learn/constraints.py

@@ -96,6 +96,6 @@ def wrap_pairs(X, constraints):
   c = np.array(constraints[2])
   d = np.array(constraints[3])
   constraints = np.vstack((np.column_stack((a, b)), np.column_stack((c, d))))
-  y = np.hstack([np.ones((len(a),)), - np.ones((len(c),))])
+  y = np.concatenate([np.ones_like(a), -np.ones_like(c)])
   pairs = X[constraints]
   return pairs, y

metric_learn/sdml.py

@@ -18,9 +18,13 @@
 
 from .base_metric import MahalanobisMixin, _PairsClassifierMixin
 from .constraints import Constraints, wrap_pairs
-from ._util import transformer_from_metric, has_installed_skggm
-if has_installed_skggm():
+from ._util import transformer_from_metric
+try:
   from inverse_covariance import quic
+except ImportError:
+  HAS_SKGGM = False
+else:
+  HAS_SKGGM = True
 
 
 class _BaseSDML(MahalanobisMixin):
@@ -55,7 +59,7 @@ def __init__(self, balance_param=0.5, sparsity_param=0.01, use_cov=True,
     super(_BaseSDML, self).__init__(preprocessor)
 
   def _fit(self, pairs, y):
-    if not has_installed_skggm():
+    if not HAS_SKGGM:
       msg = ("Warning, skggm is not installed, so SDML will use "
              "scikit-learn's graphical_lasso method. It can fail to converge"
              "on some non SPD matrices where skggm would converge. If so, "
@@ -89,7 +93,7 @@ def _fit(self, pairs, y):
                     "`balance_param` and/or to set use_covariance=False.",
                     ConvergenceWarning)
     sigma0 = (V * (w - min(0, np.min(w)) + 1e-10)).dot(V.T)
-    if has_installed_skggm():
+    if HAS_SKGGM:
       theta0 = pinvh(sigma0)
       M, _, _, _, _, _ = quic(emp_cov, lam=self.sparsity_param,
                               msg=self.verbose,

test/metric_learn_test.py

@@ -10,12 +10,16 @@
 from sklearn.utils.testing import assert_warns_message
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.utils.validation import check_X_y
-
+try:
+  from inverse_covariance import quic
+except ImportError:
+  HAS_SKGGM = False
+else:
+  HAS_SKGGM = True
 from metric_learn import (LMNN, NCA, LFDA, Covariance, MLKR, MMC,
                           LSML_Supervised, ITML_Supervised, SDML_Supervised,
                           RCA_Supervised, MMC_Supervised, SDML)
 # Import this specially for testing.
-from metric_learn._util import has_installed_skggm
 from metric_learn.constraints import wrap_pairs
 from metric_learn.lmnn import python_LMNN
 
@@ -150,7 +154,7 @@ def test_no_twice_same_objective(capsys):
 
 class TestSDML(MetricTestCase):
 
-  @pytest.mark.skipif(has_installed_skggm(),
+  @pytest.mark.skipif(HAS_SKGGM,
                       reason="The warning will be thrown only if skggm is "
                              "not installed.")
   def test_raises_warning_msg_not_installed_skggm(self):
@@ -174,7 +178,7 @@ def test_raises_warning_msg_not_installed_skggm(self):
       sdml_supervised.fit(X, y)
     assert str(record[0].message) == msg
 
-  @pytest.mark.skipif(not has_installed_skggm(),
+  @pytest.mark.skipif(not HAS_SKGGM,
                       reason="It's only in the case where skggm is installed"
                              "that no warning should be thrown.")
   def test_raises_no_warning_installed_skggm(self):
@@ -245,7 +249,7 @@ def test_sdml_converges_if_psd(self):
     sdml.fit(pairs, y)
     assert np.isfinite(sdml.get_mahalanobis_matrix()).all()
 
-  @pytest.mark.skipif(not has_installed_skggm(),
+  @pytest.mark.skipif(not HAS_SKGGM,
                       reason="sklearn's graphical_lasso can sometimes not "
                              "work on some non SPD problems. We test that "
                              "is works only if skggm is installed.")
@@ -258,7 +262,7 @@ def test_sdml_works_on_non_spd_pb_with_skggm(self):
     sdml.fit(X, y)
 
 
-@pytest.mark.skipif(not has_installed_skggm(),
+@pytest.mark.skipif(not HAS_SKGGM,
                     reason='The message should be printed only if skggm is '
                            'installed.')
 def test_verbose_has_installed_skggm_sdml(capsys):
@@ -273,7 +277,7 @@ def test_verbose_has_installed_skggm_sdml(capsys):
   assert "SDML will use skggm's solver." in out
 
 
-@pytest.mark.skipif(not has_installed_skggm(),
+@pytest.mark.skipif(not HAS_SKGGM,
                     reason='The message should be printed only if skggm is '
                            'installed.')
 def test_verbose_has_installed_skggm_sdml_supervised(capsys):