LMNN k parameter renamed to n_neighbors

Author: mvargas33

metric_learn/lmnn.py

@@ -63,7 +63,7 @@ class LMNN(MahalanobisMixin, TransformerMixin):
       :meth:`fit` and n_features_a must be less than or equal to that.
       If ``n_components`` is not None, n_features_a must match it.
 
-  k : int, optional (default=3)
+  n_neighbors : int, optional (default=3)
     Number of neighbors to consider, not including self-edges.
 
   min_iter : int, optional (default=50)
@@ -128,12 +128,12 @@ class LMNN(MahalanobisMixin, TransformerMixin):
          2005.
   """
 
-  def __init__(self, init='auto', k=3, min_iter=50, max_iter=1000,
+  def __init__(self, init='auto', n_neighbors=3, min_iter=50, max_iter=1000,
                learn_rate=1e-7, regularization=0.5, convergence_tol=0.001,
                verbose=False, preprocessor=None,
                n_components=None, random_state=None):
     self.init = init
-    self.k = k
+    self.n_neighbors = n_neighbors
     self.min_iter = min_iter
     self.max_iter = max_iter
     self.learn_rate = learn_rate
@@ -145,7 +145,7 @@ def __init__(self, init='auto', k=3, min_iter=50, max_iter=1000,
     super(LMNN, self).__init__(preprocessor)
 
   def fit(self, X, y):
-    k = self.k
+    k = self.n_neighbors
     reg = self.regularization
     learn_rate = self.learn_rate
 
@@ -162,7 +162,7 @@ def fit(self, X, y):
                                               self.verbose,
                                               random_state=self.random_state)
     required_k = np.bincount(label_inds).min()
-    if self.k > required_k:
+    if self.n_neighbors > required_k:
       raise ValueError('not enough class labels for specified k'
                        ' (smallest class has %d)' % required_k)
 
@@ -275,12 +275,12 @@ def _loss_grad(self, X, L, dfG, k, reg, target_neighbors, label_inds):
     return 2 * G, objective, total_active
 
   def _select_targets(self, X, label_inds):
-    target_neighbors = np.empty((X.shape[0], self.k), dtype=int)
+    target_neighbors = np.empty((X.shape[0], self.n_neighbors), dtype=int)
     for label in self.labels_:
       inds, = np.nonzero(label_inds == label)
       dd = euclidean_distances(X[inds], squared=True)
       np.fill_diagonal(dd, np.inf)
-      nn = np.argsort(dd)[..., :self.k]
+      nn = np.argsort(dd)[..., :self.n_neighbors]
       target_neighbors[inds] = inds[nn]
     return target_neighbors