Replace 'metric' parameter with a better name

Fixes #54.

Author: perimosocordiae

metric_learn/lfda.py

@@ -26,7 +26,7 @@ class LFDA(BaseMetricLearner):
   Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction
   Sugiyama, ICML 2006
   '''
-  def __init__(self, num_dims=None, k=None, metric='weighted'):
+  def __init__(self, num_dims=None, k=None, embedding_type='weighted'):
     '''
     Initialize LFDA.
 
@@ -39,16 +39,16 @@ def __init__(self, num_dims=None, k=None, metric='weighted'):
         Number of nearest neighbors used in local scaling method.
         Defaults to min(7, num_dims - 1).
 
-    metric : str, optional
+    embedding_type : str, optional
         Type of metric in the embedding space (default: 'weighted')
           'weighted'        - weighted eigenvectors
           'orthonormalized' - orthonormalized
           'plain'           - raw eigenvectors
     '''
-    if metric not in ('weighted', 'orthonormalized', 'plain'):
-      raise ValueError('Invalid metric: %r' % metric)
+    if embedding_type not in ('weighted', 'orthonormalized', 'plain'):
+      raise ValueError('Invalid embedding_type: %r' % embedding_type)
     self.num_dims = num_dims
-    self.metric = metric
+    self.embedding_type = embedding_type
     self.k = k
 
   def transformer(self):
@@ -122,9 +122,9 @@ def fit(self, X, y):
     vals = vals[order].real
     vecs = vecs[:,order]
 
-    if self.metric == 'weighted':
+    if self.embedding_type == 'weighted':
        vecs *= np.sqrt(vals)
-    elif self.metric == 'orthonormalized':
+    elif self.embedding_type == 'orthonormalized':
        vecs, _ = np.linalg.qr(vecs)
 
     self.transformer_ = vecs.T

test/metric_learn_test.py

@@ -112,6 +112,10 @@ def test_iris(self):
     csep = class_separation(lfda.transform(), self.iris_labels)
     self.assertLess(csep, 0.15)
 
+    # Sanity checks for learned matrices.
+    self.assertEqual(lfda.metric().shape, (4, 4))
+    self.assertEqual(lfda.transformer().shape, (2, 4))
+
 
 class TestRCA(MetricTestCase):
   def test_iris(self):

test/test_base_metric.py

@@ -20,7 +20,7 @@ def test_nca(self):
 
   def test_lfda(self):
     self.assertEqual(str(metric_learn.LFDA()),
-                     "LFDA(k=None, metric='weighted', num_dims=None)")
+                     "LFDA(embedding_type='weighted', k=None, num_dims=None)")
 
   def test_itml(self):
     self.assertEqual(str(metric_learn.ITML()), """