[MRG] update impostors, closer to original implem

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, init='random')
+lmnn = metric_learn.LMNN(k=5, learn_rate=1e-6)
 
 # fit the data!
 lmnn.fit(X, y)

metric_learn/lmnn.py

@@ -1,8 +1,6 @@
 """
 Large Margin Nearest Neighbor Metric learning (LMNN)
 """
-# TODO: periodic recalculation of impostors, PCA initialization
-
 from __future__ import print_function, absolute_import
 import numpy as np
 import warnings
@@ -208,31 +206,19 @@ def fit(self, X, y):
                        ' (smallest class has %d)' % required_k)
 
     target_neighbors = self._select_targets(X, label_inds)
-    impostors = self._find_impostors(target_neighbors[:, -1], X, label_inds)
-    if len(impostors) == 0:
-        # L has already been initialized to an identity matrix
-        return
 
     # sum outer products
     dfG = _sum_outer_products(X, target_neighbors.flatten(),
                               np.repeat(np.arange(X.shape[0]), k))
-    df = np.zeros_like(dfG)
-
-    # storage
-    a1 = [None]*k
-    a2 = [None]*k
-    for nn_idx in xrange(k):
-      a1[nn_idx] = np.array([])
-      a2[nn_idx] = np.array([])
 
     # initialize L
     L = self.transformer_
 
     # first iteration: we compute variables (including objective and gradient)
     #  at initialization point
-    G, objective, total_active, df, a1, a2 = (
-        self._loss_grad(X, L, dfG, impostors, 1, k, reg, target_neighbors, df,
-                        a1, a2))
+    G, objective, total_active = self._loss_grad(X, L, dfG, k,
+                                                 reg, target_neighbors,
+                                                 label_inds)
 
     it = 1  # we already made one iteration
 
@@ -246,10 +232,9 @@ def fit(self, X, y):
         # we compute the objective at next point
         # we copy variables that can be modified by _loss_grad, because if we
         # retry we don t want to modify them several times
-        (G_next, objective_next, total_active_next, df_next, a1_next,
-         a2_next) = (
-            self._loss_grad(X, L_next, dfG, impostors, it, k, reg,
-                            target_neighbors, df.copy(), list(a1), list(a2)))
+        (G_next, objective_next, total_active_next) = (
+          self._loss_grad(X, L_next, dfG, k, reg, target_neighbors,
+                          label_inds))
         assert not np.isnan(objective)
         delta_obj = objective_next - objective
         if delta_obj > 0:
@@ -264,8 +249,7 @@ def fit(self, X, y):
       # old variables to these new ones before next iteration and we
       # slightly increase the learning rate
       L = L_next
-      G, df, objective, total_active, a1, a2 = (
-          G_next, df_next, objective_next, total_active_next, a1_next, a2_next)
+      G, objective, total_active = G_next, objective_next, total_active_next
       learn_rate *= 1.01
 
       if self.verbose:
@@ -285,54 +269,45 @@ def fit(self, X, y):
     self.n_iter_ = it
     return self
 
-  def _loss_grad(self, X, L, dfG, impostors, it, k, reg, target_neighbors, df,
-                 a1, a2):
+  def _loss_grad(self, X, L, dfG, k, reg, target_neighbors, label_inds):
     # Compute pairwise distances under current metric
     Lx = L.dot(X.T).T
-    g0 = _inplace_paired_L2(*Lx[impostors])
+
+    # we need to find the furthest neighbor:
     Ni = 1 + _inplace_paired_L2(Lx[target_neighbors], Lx[:, None, :])
+    furthest_neighbors = np.take_along_axis(target_neighbors,
+                                            Ni.argmax(axis=1)[:, None], 1)
+    impostors = self._find_impostors(furthest_neighbors.ravel(), X,
+                                     label_inds, L)
+
+    g0 = _inplace_paired_L2(*Lx[impostors])
+
+    # we reorder the target neighbors
     g1, g2 = Ni[impostors]
     # compute the gradient
     total_active = 0
-    for nn_idx in reversed(xrange(k)):
+    df = np.zeros((X.shape[1], X.shape[1]))
+    for nn_idx in reversed(xrange(k)):  # note: reverse not useful here
       act1 = g0 < g1[:, nn_idx]
       act2 = g0 < g2[:, nn_idx]
       total_active += act1.sum() + act2.sum()
 
-      if it > 1:
-        plus1 = act1 & ~a1[nn_idx]
-        minus1 = a1[nn_idx] & ~act1
-        plus2 = act2 & ~a2[nn_idx]
-        minus2 = a2[nn_idx] & ~act2
-      else:
-        plus1 = act1
-        plus2 = act2
-        minus1 = np.zeros(0, dtype=int)
-        minus2 = np.zeros(0, dtype=int)
-
       targets = target_neighbors[:, nn_idx]
-      PLUS, pweight = _count_edges(plus1, plus2, impostors, targets)
+      PLUS, pweight = _count_edges(act1, act2, impostors, targets)
       df += _sum_outer_products(X, PLUS[:, 0], PLUS[:, 1], pweight)
-      MINUS, mweight = _count_edges(minus1, minus2, impostors, targets)
-      df -= _sum_outer_products(X, MINUS[:, 0], MINUS[:, 1], mweight)
 
       in_imp, out_imp = impostors
-      df += _sum_outer_products(X, in_imp[minus1], out_imp[minus1])
-      df += _sum_outer_products(X, in_imp[minus2], out_imp[minus2])
-
-      df -= _sum_outer_products(X, in_imp[plus1], out_imp[plus1])
-      df -= _sum_outer_products(X, in_imp[plus2], out_imp[plus2])
+      df -= _sum_outer_products(X, in_imp[act1], out_imp[act1])
+      df -= _sum_outer_products(X, in_imp[act2], out_imp[act2])
 
-      a1[nn_idx] = act1
-      a2[nn_idx] = act2
     # do the gradient update
     assert not np.isnan(df).any()
     G = dfG * reg + df * (1 - reg)
     G = L.dot(G)
     # compute the objective function
     objective = total_active * (1 - reg)
     objective += G.flatten().dot(L.flatten())
-    return 2 * G, objective, total_active, df, a1, a2
+    return 2 * G, objective, total_active
 
   def _select_targets(self, X, label_inds):
     target_neighbors = np.empty((X.shape[0], self.k), dtype=int)
@@ -344,8 +319,8 @@ def _select_targets(self, X, label_inds):
       target_neighbors[inds] = inds[nn]
     return target_neighbors
 
-  def _find_impostors(self, furthest_neighbors, X, label_inds):
-    Lx = self.transform(X)
+  def _find_impostors(self, furthest_neighbors, X, label_inds, L):
+    Lx = X.dot(L.T)
     margin_radii = 1 + _inplace_paired_L2(Lx[furthest_neighbors], Lx)
     impostors = []
     for label in self.labels_[:-1]:

test/metric_learn_test.py

@@ -2,9 +2,10 @@
 import re
 import pytest
 import numpy as np
+import scipy
 from scipy.optimize import check_grad, approx_fprime
 from six.moves import xrange
-from sklearn.metrics import pairwise_distances
+from sklearn.metrics import pairwise_distances, euclidean_distances
 from sklearn.datasets import (load_iris, make_classification, make_regression,
                               make_spd_matrix)
 from numpy.testing import (assert_array_almost_equal, assert_array_equal,
@@ -242,25 +243,15 @@ def test_loss_grad_lbfgs(self):
     lmnn.transformer_ = np.eye(n_components)
 
     target_neighbors = lmnn._select_targets(X, label_inds)
-    impostors = lmnn._find_impostors(target_neighbors[:, -1], X, label_inds)
 
     # sum outer products
     dfG = _sum_outer_products(X, target_neighbors.flatten(),
                               np.repeat(np.arange(X.shape[0]), k))
-    df = np.zeros_like(dfG)
-
-    # storage
-    a1 = [None]*k
-    a2 = [None]*k
-    for nn_idx in xrange(k):
-      a1[nn_idx] = np.array([])
-      a2[nn_idx] = np.array([])
 
     # initialize L
     def loss_grad(flat_L):
-      return lmnn._loss_grad(X, flat_L.reshape(-1, X.shape[1]), dfG, impostors,
-                             1, k, reg, target_neighbors, df.copy(),
-                             list(a1), list(a2))
+      return lmnn._loss_grad(X, flat_L.reshape(-1, X.shape[1]), dfG,
+                             k, reg, target_neighbors, label_inds)
 
     def fun(x):
       return loss_grad(x)[1]
@@ -292,6 +283,141 @@ def test_changed_behaviour_warning(self):
     assert any(msg == str(wrn.message) for wrn in raised_warning)
 
 
+def test_loss_func(capsys):
+  """Test the loss function (and its gradient) on a simple example,
+  by comparing the results with the actual implementation of metric-learn,
+  with a very simple (but nonperformant) implementation"""
+
+  # toy dataset to use
+  X, y = make_classification(n_samples=10, n_classes=2,
+                             n_features=6,
+                             n_redundant=0, shuffle=True,
+                             scale=[1, 1, 20, 20, 20, 20], random_state=42)
+
+  def hinge(a):
+    if a > 0:
+      return a, 1
+    else:
+      return 0, 0
+
+  def loss_fn(L, X, y, target_neighbors, reg):
+     L = L.reshape(-1, X.shape[1])
+     Lx = np.dot(X, L.T)
+     loss = 0
+     total_active = 0
+     grad = np.zeros_like(L)
+     for i in range(X.shape[0]):
+       for j in target_neighbors[i]:
+         loss += (1 - reg) * np.sum((Lx[i] - Lx[j]) ** 2)
+         grad += (1 - reg) * np.outer(Lx[i] - Lx[j], X[i] - X[j])
+         for l in range(X.shape[0]):
+           if y[i] != y[l]:
+             hin, active = hinge(1 + np.sum((Lx[i] - Lx[j])**2) -
+                                 np.sum((Lx[i] - Lx[l])**2))
+             total_active += active
+             if active:
+               loss += reg * hin
+               grad += (reg * (np.outer(Lx[i] - Lx[j], X[i] - X[j]) -
+                               np.outer(Lx[i] - Lx[l], X[i] - X[l])))
+     grad = 2 * grad
+     return grad, loss, total_active
+
+  # we check that the gradient we have computed in the non-performant implem
+  # is indeed the true gradient on a toy example:
+
+  def _select_targets(X, y, k):
+    target_neighbors = np.empty((X.shape[0], k), dtype=int)
+    for label in np.unique(y):
+      inds, = np.nonzero(y == label)
+      dd = euclidean_distances(X[inds], squared=True)
+      np.fill_diagonal(dd, np.inf)
+      nn = np.argsort(dd)[..., :k]
+      target_neighbors[inds] = inds[nn]
+    return target_neighbors
+
+  target_neighbors = _select_targets(X, y, 2)
+  regularization = 0.5
+  n_features = X.shape[1]
+  x0 = np.random.randn(1, n_features)
+
+  def loss(x0):
+    return loss_fn(x0.reshape(-1, X.shape[1]), X, y, target_neighbors,
+                   regularization)[1]
+
+  def grad(x0):
+    return loss_fn(x0.reshape(-1, X.shape[1]), X, y, target_neighbors,
+                   regularization)[0].ravel()
+
+  scipy.optimize.check_grad(loss, grad, x0.ravel())
+
+  class LMNN_with_callback(LMNN):
+    """ We will use a callback to get the gradient (see later)
+    """
+
+    def __init__(self, callback, *args, **kwargs):
+      self.callback = callback
+      super(LMNN_with_callback, self).__init__(*args, **kwargs)
+
+    def _loss_grad(self, *args, **kwargs):
+      grad, objective, total_active = (
+        super(LMNN_with_callback, self)._loss_grad(*args, **kwargs))
+      self.callback.append(grad)
+      return grad, objective, total_active
+
+  class LMNN_nonperformant(LMNN_with_callback):
+
+    def fit(self, X, y):
+      self.y = y
+      return super(LMNN_nonperformant, self).fit(X, y)
+
+    def _loss_grad(self, X, L, dfG, k, reg, target_neighbors, label_inds):
+      grad, loss, total_active = loss_fn(L.ravel(), X, self.y,
+                                         target_neighbors, self.regularization)
+      self.callback.append(grad)
+      return grad, loss, total_active
+
+  mem1, mem2 = [], []
+  lmnn_perf = LMNN_with_callback(verbose=True, random_state=42,
+                                 init='identity', max_iter=30, callback=mem1)
+  lmnn_nonperf = LMNN_nonperformant(verbose=True, random_state=42,
+                                    init='identity', max_iter=30,
+                                    callback=mem2)
+  objectives, obj_diffs, learn_rate, total_active = (dict(), dict(), dict(),
+                                                     dict())
+  for algo, name in zip([lmnn_perf, lmnn_nonperf], ['perf', 'nonperf']):
+    algo.fit(X, y)
+    out, _ = capsys.readouterr()
+    lines = re.split("\n+", out)
+    # we get every variable that is printed from the algorithm in verbose
+    num = '(-?\d+.?\d*(e[+|-]\d+)?)'
+    strings = [re.search("\d+ (?:{}) (?:{}) (?:(\d+)) (?:{})"
+                         .format(num, num, num), s) for s in lines]
+    objectives[name] = [float(match.group(1)) for match in strings if match is
+                        not None]
+    obj_diffs[name] = [float(match.group(3)) for match in strings if match is
+                             not None]
+    total_active[name] = [float(match.group(5)) for match in strings if
+                          match is not
+                          None]
+    learn_rate[name] = [float(match.group(6)) for match in strings if match is
+                      not None]
+    assert len(strings) >= 10  # we ensure that we actually did more than 10
+    # iterations
+    assert total_active[name][0] >= 2  # we ensure that we have some active
+    # constraints (that's the case we want to test)
+    # we remove the last element because it can be equal to the penultimate
+    # if the last gradient update is null
+  for i in range(len(mem1)):
+    np.testing.assert_allclose(lmnn_perf.callback[i],
+                               lmnn_nonperf.callback[i],
+                               err_msg='Gradient different at position '
+                                       '{}'.format(i))
+  np.testing.assert_allclose(objectives['perf'], objectives['nonperf'])
+  np.testing.assert_allclose(obj_diffs['perf'], obj_diffs['nonperf'])
+  np.testing.assert_allclose(total_active['perf'], total_active['nonperf'])
+  np.testing.assert_allclose(learn_rate['perf'], learn_rate['nonperf'])
+
+
 @pytest.mark.parametrize('X, y, loss', [(np.array([[0], [1], [2], [3]]),
                                          [1, 1, 0, 0], 3.0),
                                         (np.array([[0], [1], [2], [3]]),
@@ -312,7 +438,7 @@ def test_toy_ex_lmnn(X, y, loss):
   lmnn.transformer_ = np.eye(n_components)
 
   target_neighbors = lmnn._select_targets(X, label_inds)
-  impostors = lmnn._find_impostors(target_neighbors[:, -1], X, label_inds)
+  impostors = lmnn._find_impostors(target_neighbors[:, -1], X, label_inds, L)
 
   # sum outer products
   dfG = _sum_outer_products(X, target_neighbors.flatten(),
@@ -327,9 +453,8 @@ def test_toy_ex_lmnn(X, y, loss):
     a2[nn_idx] = np.array([])
 
   #  assert that the loss equals the one computed by hand
-  assert lmnn._loss_grad(X, L.reshape(-1, X.shape[1]), dfG, impostors, 1, k,
-                         reg, target_neighbors, df, a1, a2)[1] == loss
-
+  assert lmnn._loss_grad(X, L.reshape(-1, X.shape[1]), dfG, k,
+                         reg, target_neighbors, label_inds)[1] == loss
 
 def test_convergence_simple_example(capsys):
   # LMNN should converge on this simple example, which it did not with