Replaced all 'convergence_threshold' with 'tol'

Author: mvargas33

doc/weakly_supervised.rst

@@ -134,7 +134,7 @@ are respected.
 >>> from metric_learn import MMC
 >>> mmc = MMC(random_state=42)
 >>> mmc.fit(tuples, y)
-MMC(A0='deprecated', convergence_threshold=0.001, diagonal=False,
+MMC(A0='deprecated', tol=0.001, diagonal=False,
   diagonal_c=1.0, init='auto', max_iter=100, max_proj=10000,
   preprocessor=None, random_state=42, verbose=False)
 
@@ -250,7 +250,7 @@ tuples).
 >>> y_pairs = np.array([1, -1])
 >>> mmc = MMC(random_state=42)
 >>> mmc.fit(pairs, y_pairs)
-MMC(convergence_threshold=0.001, diagonal=False,
+MMC(tol=0.001, diagonal=False,
     diagonal_c=1.0, init='auto', max_iter=100, max_proj=10000, preprocessor=None,
     random_state=42, verbose=False)
 

metric_learn/itml.py

@@ -16,12 +16,12 @@ class _BaseITML(MahalanobisMixin):
 
   _tuple_size = 2  # constraints are pairs
 
-  def __init__(self, gamma=1., max_iter=1000, convergence_threshold=1e-3,
+  def __init__(self, gamma=1., max_iter=1000, tol=1e-3,
                prior='identity', verbose=False,
                preprocessor=None, random_state=None):
     self.gamma = gamma
     self.max_iter = max_iter
-    self.convergence_threshold = convergence_threshold
+    self.tol = tol
     self.prior = prior
     self.verbose = verbose
     self.random_state = random_state
@@ -86,7 +86,7 @@ def _fit(self, pairs, y, bounds=None):
         conv = np.inf
         break
       conv = np.abs(lambdaold - _lambda).sum() / normsum
-      if conv < self.convergence_threshold:
+      if conv < self.tol:
         break
       lambdaold = _lambda.copy()
       if self.verbose:
@@ -122,7 +122,7 @@ class ITML(_BaseITML, _PairsClassifierMixin):
   max_iter : int, optional (default=1000)
     Maximum number of iteration of the optimization procedure.
 
-  convergence_threshold : float, optional (default=1e-3)
+  tol : float, optional (default=1e-3)
     Convergence tolerance.
 
   prior : string or numpy array, optional (default='identity')
@@ -260,7 +260,7 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
   max_iter : int, optional (default=1000)
     Maximum number of iterations of the optimization procedure.
 
-  convergence_threshold : float, optional (default=1e-3)
+  tol : float, optional (default=1e-3)
     Tolerance of the optimization procedure.
 
   n_constraints : int, optional (default=None)
@@ -338,11 +338,11 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
     that describes the supervised version of weakly supervised estimators.
   """
 
-  def __init__(self, gamma=1.0, max_iter=1000, convergence_threshold=1e-3,
+  def __init__(self, gamma=1.0, max_iter=1000, tol=1e-3,
                n_constraints=None, prior='identity',
                verbose=False, preprocessor=None, random_state=None):
     _BaseITML.__init__(self, gamma=gamma, max_iter=max_iter,
-                       convergence_threshold=convergence_threshold,
+                       tol=tol,
                        prior=prior, verbose=verbose,
                        preprocessor=preprocessor, random_state=random_state)
     self.n_constraints = n_constraints

metric_learn/mmc.py

@@ -12,13 +12,13 @@ class _BaseMMC(MahalanobisMixin):
 
   _tuple_size = 2  # constraints are pairs
 
-  def __init__(self, max_iter=100, max_proj=10000, convergence_threshold=1e-3,
+  def __init__(self, max_iter=100, max_proj=10000, tol=1e-3,
                init='identity', diagonal=False,
                diagonal_c=1.0, verbose=False, preprocessor=None,
                random_state=None):
     self.max_iter = max_iter
     self.max_proj = max_proj
-    self.convergence_threshold = convergence_threshold
+    self.tol = tol
     self.init = init
     self.diagonal = diagonal
     self.diagonal_c = diagonal_c
@@ -145,13 +145,13 @@ def _fit_full(self, pairs, y):
         A[:] = A_old + alpha * M
 
       delta = np.linalg.norm(alpha * M) / np.linalg.norm(A_old)
-      if delta < self.convergence_threshold:
+      if delta < self.tol:
         break
       if self.verbose:
         print('mmc iter: %d, conv = %f, projections = %d' %
               (cycle, delta, it + 1))
 
-    if delta > self.convergence_threshold:
+    if delta > self.tol:
       self.converged_ = False
       if self.verbose:
         print('mmc did not converge, conv = %f' % (delta,))
@@ -185,7 +185,7 @@ def _fit_diag(self, pairs, y):
     reduction = 2.0
     w = np.diag(self.A_).copy()
 
-    while error > self.convergence_threshold and it < self.max_iter:
+    while error > self.tol and it < self.max_iter:
 
       fD0, fD_1st_d, fD_2nd_d = self._D_constraint(neg_pairs, w)
       obj_initial = np.dot(s_sum, w) + self.diagonal_c * fD0
@@ -332,7 +332,7 @@ class MMC(_BaseMMC, _PairsClassifierMixin):
   max_proj : int, optional (default=10000)
     Maximum number of projection steps.
 
-  convergence_threshold : float, optional (default=1e-3)
+  tol : float, optional (default=1e-3)
     Convergence threshold for the optimization procedure.
 
   init : string or numpy array, optional (default='identity')
@@ -469,7 +469,7 @@ class MMC_Supervised(_BaseMMC, TransformerMixin):
   max_proj : int, optional (default=10000)
     Maximum number of projection steps.
 
-  convergence_threshold : float, optional (default=1e-3)
+  tol : float, optional (default=1e-3)
     Convergence threshold for the optimization procedure.
 
   n_constraints: int, optional (default=None)
@@ -538,12 +538,12 @@ class MMC_Supervised(_BaseMMC, TransformerMixin):
     metric (See function `components_from_metric`.)
   """
 
-  def __init__(self, max_iter=100, max_proj=10000, convergence_threshold=1e-6,
+  def __init__(self, max_iter=100, max_proj=10000, tol=1e-6,
                n_constraints=None, init='identity',
                diagonal=False, diagonal_c=1.0, verbose=False,
                preprocessor=None, random_state=None):
     _BaseMMC.__init__(self, max_iter=max_iter, max_proj=max_proj,
-                      convergence_threshold=convergence_threshold,
+                      tol=tol,
                       init=init, diagonal=diagonal,
                       diagonal_c=diagonal_c, verbose=verbose,
                       preprocessor=preprocessor, random_state=random_state)

test/metric_learn_test.py

@@ -1067,7 +1067,7 @@ def test_iris(self):
 
     # Full metric
     n_features = self.iris_points.shape[1]
-    mmc = MMC(convergence_threshold=0.01, init=np.eye(n_features) / 10)
+    mmc = MMC(tol=0.01, init=np.eye(n_features) / 10)
     mmc.fit(*wrap_pairs(self.iris_points, [a, b, c, d]))
     expected = [[+0.000514, +0.000868, -0.001195, -0.001703],
                 [+0.000868, +0.001468, -0.002021, -0.002879],

test/test_base_metric.py

@@ -65,14 +65,14 @@ def test_lfda(self):
                      remove_spaces(f"LFDA({merged_kwargs})"))
 
   def test_itml(self):
-    def_kwargs = {'convergence_threshold': 0.001, 'gamma': 1.0,
+    def_kwargs = {'tol': 0.001, 'gamma': 1.0,
                   'max_iter': 1000, 'preprocessor': None,
                   'prior': 'identity', 'random_state': None, 'verbose': False}
     nndef_kwargs = {'gamma': 0.5}
     merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
     self.assertEqual(remove_spaces(str(metric_learn.ITML(gamma=0.5))),
                      remove_spaces(f"ITML({merged_kwargs})"))
-    def_kwargs = {'convergence_threshold': 0.001, 'gamma': 1.0,
+    def_kwargs = {'tol': 0.001, 'gamma': 1.0,
                   'max_iter': 1000, 'n_constraints': None,
                   'preprocessor': None, 'prior': 'identity',
                   'random_state': None, 'verbose': False}
@@ -141,15 +141,15 @@ def test_mlkr(self):
                      remove_spaces(f"MLKR({merged_kwargs})"))
 
   def test_mmc(self):
-    def_kwargs = {'convergence_threshold': 0.001, 'diagonal': False,
+    def_kwargs = {'tol': 0.001, 'diagonal': False,
                   'diagonal_c': 1.0, 'init': 'identity', 'max_iter': 100,
                   'max_proj': 10000, 'preprocessor': None,
                   'random_state': None, 'verbose': False}
     nndef_kwargs = {'diagonal': True}
     merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
     self.assertEqual(remove_spaces(str(metric_learn.MMC(diagonal=True))),
                      remove_spaces(f"MMC({merged_kwargs})"))
-    def_kwargs = {'convergence_threshold': 1e-06, 'diagonal': False,
+    def_kwargs = {'tol': 1e-06, 'diagonal': False,
                   'diagonal_c': 1.0, 'init': 'identity', 'max_iter': 100,
                   'max_proj': 10000, 'n_constraints': None,
                   'preprocessor': None, 'random_state': None,