Fix tests

Author: mvargas33

bench/benchmarks/iris.py

@@ -7,7 +7,7 @@
     'Covariance': metric_learn.Covariance(),
     'ITML_Supervised': metric_learn.ITML_Supervised(n_constraints=200),
     'LFDA': metric_learn.LFDA(k=2, dim=2),
-    'LMNN': metric_learn.LMNN(k=5, learn_rate=1e-6, verbose=False),
+    'LMNN': metric_learn.LMNN(n_neighbors=5, learn_rate=1e-6, verbose=False),
     'LSML_Supervised': metric_learn.LSML_Supervised(n_constraints=200),
     'MLKR': metric_learn.MLKR(),
     'NCA': metric_learn.NCA(max_iter=700, n_components=2),

doc/supervised.rst

@@ -152,7 +152,7 @@ indicates :math:`\mathbf{x}_{i}, \mathbf{x}_{j}` belong to different classes,
     X = iris_data['data']
     Y = iris_data['target']
 
-    lmnn = LMNN(k=5, learn_rate=1e-6)
+    lmnn = LMNN(n_neighbors=5, learn_rate=1e-6)
     lmnn.fit(X, Y, verbose=False)
 
 .. topic:: References:

examples/plot_metric_learning_examples.py

@@ -139,7 +139,7 @@ def plot_tsne(X, y, colormap=plt.cm.Paired):
 #
 
 # setting up LMNN
-lmnn = metric_learn.LMNN(k=5, learn_rate=1e-6)
+lmnn = metric_learn.LMNN(n_neighbors=5, learn_rate=1e-6)
 
 # fit the data!
 lmnn.fit(X, y)

metric_learn/lmnn.py

@@ -116,7 +116,7 @@ class LMNN(MahalanobisMixin, TransformerMixin):
   >>> iris_data = load_iris()
   >>> X = iris_data['data']
   >>> Y = iris_data['target']
-  >>> lmnn = LMNN(k=5, learn_rate=1e-6)
+  >>> lmnn = LMNN(n_neighbors=5, learn_rate=1e-6)
   >>> lmnn.fit(X, Y, verbose=False)
 
   References

test/metric_learn_test.py

@@ -362,7 +362,7 @@ def test_bounds_parameters_invalid(bounds):
 
 class TestLMNN(MetricTestCase):
   def test_iris(self):
-    lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False)
+    lmnn = LMNN(n_neighbors=5, learn_rate=1e-6, verbose=False)
     lmnn.fit(self.iris_points, self.iris_labels)
 
     csep = class_separation(lmnn.transform(self.iris_points),
@@ -379,7 +379,7 @@ def test_loss_grad_lbfgs(self):
     L = rng.randn(rng.randint(1, X.shape[1] + 1), X.shape[1])
     lmnn = LMNN()
 
-    k = lmnn.k
+    k = lmnn.n_neighbors
     reg = lmnn.regularization
 
     X, y = lmnn._prepare_inputs(X, y, dtype=float,
@@ -555,9 +555,9 @@ def _loss_grad(self, X, L, dfG, k, reg, target_neighbors, label_inds):
 def test_toy_ex_lmnn(X, y, loss):
   """Test that the loss give the right result on a toy example"""
   L = np.array([[1]])
-  lmnn = LMNN(k=1, regularization=0.5)
+  lmnn = LMNN(n_neighbors=1, regularization=0.5)
 
-  k = lmnn.k
+  k = lmnn.n_neighbors
   reg = lmnn.regularization
 
   X, y = lmnn._prepare_inputs(X, y, dtype=float,

test/test_base_metric.py

@@ -44,7 +44,7 @@ def test_lmnn(self):
     nndef_kwargs = {'convergence_tol': 0.01, 'k': 6}
     merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
     self.assertEqual(
-        remove_spaces(str(metric_learn.LMNN(convergence_tol=0.01, k=6))),
+        remove_spaces(str(metric_learn.LMNN(convergence_tol=0.01, n_neighbors=6))),
         remove_spaces(f"LMNN({merged_kwargs})"))
 
   def test_nca(self):

test/test_components_metric_conversion.py

@@ -42,7 +42,7 @@ def test_itml_supervised(self):
     assert_array_almost_equal(L.T.dot(L), itml.get_mahalanobis_matrix())
 
   def test_lmnn(self):
-    lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False)
+    lmnn = LMNN(n_neighbors=5, learn_rate=1e-6, verbose=False)
     lmnn.fit(self.X, self.y)
     L = lmnn.components_
     assert_array_almost_equal(L.T.dot(L), lmnn.get_mahalanobis_matrix())

test/test_fit_transform.py

@@ -52,11 +52,11 @@ def test_itml_supervised(self):
     assert_array_almost_equal(res_1, res_2)
 
   def test_lmnn(self):
-    lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False)
+    lmnn = LMNN(n_neighbors=5, learn_rate=1e-6, verbose=False)
     lmnn.fit(self.X, self.y)
     res_1 = lmnn.transform(self.X)
 
-    lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False)
+    lmnn = LMNN(n_neighbors=5, learn_rate=1e-6, verbose=False)
     res_2 = lmnn.fit_transform(self.X, self.y)
 
     assert_array_almost_equal(res_1, res_2)