Add all warnings regarding n_constrains

Author: mvargas33

metric_learn/constraints.py

@@ -6,7 +6,7 @@
 import warnings
 from sklearn.utils import check_random_state
 from sklearn.neighbors import NearestNeighbors
-import warnings
+
 
 __all__ = ['Constraints']
 
@@ -56,7 +56,7 @@ def positive_negative_pairs(self, n_constraints, same_length=False,
     random_state : int or numpy.RandomState or None, optional (default=None)
       A pseudo random number generator object or a seed for it if int.
 
-    num_constraints : Renamed to n_constrains. Will be deprecated 0.7.0
+    num_constraints : Renamed to n_constraints. Will be deprecated in 0.7.0
 
     Returns
     -------
@@ -72,12 +72,14 @@ def positive_negative_pairs(self, n_constraints, same_length=False,
     d : array-like, shape=(n_constraints,)
       1D array of indicators for the right elements of negative pairs.
     """
-    if self.num_constraints != 'deprecated':
-      warnings.warn('"num_constraints" parameter has been renamed to '
-                    '"n_constraints". It has been deprecated in'
+    if num_constraints != 'deprecated':
+      warnings.warn('"num_constraints" parameter has been renamed to'
+                    ' "n_constraints". It has been deprecated in'
                     ' version 0.6.3 and will be removed in 0.7.0'
                     '', FutureWarning)
       self.n_constraints = num_constraints
+    else:
+      self.n_constraints = n_constraints
     random_state = check_random_state(random_state)
     a, b = self._pairs(n_constraints, same_label=True,
                        random_state=random_state)

metric_learn/itml.py

@@ -9,6 +9,7 @@
 from .base_metric import _PairsClassifierMixin, MahalanobisMixin
 from .constraints import Constraints, wrap_pairs
 from ._util import components_from_metric, _initialize_metric_mahalanobis
+import warnings
 
 
 class _BaseITML(MahalanobisMixin):
@@ -302,6 +303,7 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
     case, `random_state` is also used to randomly sample constraints from
     labels.
 
+  num_constraints : Renamed to n_constraints. Will be deprecated in 0.7.0
 
   Attributes
   ----------
@@ -340,12 +342,22 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
 
   def __init__(self, gamma=1.0, max_iter=1000, tol=1e-3,
                n_constraints=None, prior='identity',
-               verbose=False, preprocessor=None, random_state=None):
+               verbose=False, preprocessor=None, random_state=None,
+               num_constraints='deprecated'):
     _BaseITML.__init__(self, gamma=gamma, max_iter=max_iter,
                        tol=tol,
                        prior=prior, verbose=verbose,
                        preprocessor=preprocessor, random_state=random_state)
-    self.n_constraints = n_constraints
+    if num_constraints != 'deprecated':
+      warnings.warn('"num_constraints" parameter has been renamed to'
+                    ' "n_constraints". It has been deprecated in'
+                    ' version 0.6.3 and will be removed in 0.7.0'
+                    '', FutureWarning)
+      self.n_constraints = num_constraints
+    else:
+      self.n_constraints = n_constraints
+    # Avoid test get_params from failing (all params passed sholud be set)
+    self.num_constraints = 'deprecated'
 
   def fit(self, X, y, bounds=None):
     """Create constraints from labels and learn the ITML model.

metric_learn/lsml.py

@@ -9,6 +9,7 @@
 from .base_metric import _QuadrupletsClassifierMixin, MahalanobisMixin
 from .constraints import Constraints
 from ._util import components_from_metric, _initialize_metric_mahalanobis
+import warnings
 
 
 class _BaseLSML(MahalanobisMixin):
@@ -282,6 +283,8 @@ class LSML_Supervised(_BaseLSML, TransformerMixin):
     prior. In any case, `random_state` is also used to randomly sample
     constraints from labels.
 
+  num_constraints : Renamed to n_constraints. Will be deprecated in 0.7.0
+
   Examples
   --------
   >>> from metric_learn import LSML_Supervised
@@ -304,11 +307,21 @@ class LSML_Supervised(_BaseLSML, TransformerMixin):
 
   def __init__(self, tol=1e-3, max_iter=1000, prior='identity',
                n_constraints=None, weights=None,
-               verbose=False, preprocessor=None, random_state=None):
+               verbose=False, preprocessor=None, random_state=None,
+               num_constraints='deprecated'):
     _BaseLSML.__init__(self, tol=tol, max_iter=max_iter, prior=prior,
                        verbose=verbose, preprocessor=preprocessor,
                        random_state=random_state)
-    self.n_constraints = n_constraints
+    if num_constraints != 'deprecated':
+      warnings.warn('"num_constraints" parameter has been renamed to'
+                    ' "n_constraints". It has been deprecated in'
+                    ' version 0.6.3 and will be removed in 0.7.0'
+                    '', FutureWarning)
+      self.n_constraints = num_constraints
+    else:
+      self.n_constraints = n_constraints
+    # Avoid test get_params from failing (all params passed sholud be set)
+    self.num_constraints = 'deprecated'
     self.weights = weights
 
   def fit(self, X, y):

metric_learn/mmc.py

@@ -6,6 +6,7 @@
 from .base_metric import _PairsClassifierMixin, MahalanobisMixin
 from .constraints import Constraints, wrap_pairs
 from ._util import components_from_metric, _initialize_metric_mahalanobis
+import warnings
 
 
 class _BaseMMC(MahalanobisMixin):
@@ -518,6 +519,8 @@ class MMC_Supervised(_BaseMMC, TransformerMixin):
     Mahalanobis matrix.  In any case, `random_state` is also used to
     randomly sample constraints from labels.
 
+  num_constraints : Renamed to n_constraints. Will be deprecated in 0.7.0
+
   Examples
   --------
   >>> from metric_learn import MMC_Supervised
@@ -541,13 +544,23 @@ class MMC_Supervised(_BaseMMC, TransformerMixin):
   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):
+               preprocessor=None, random_state=None,
+               num_constraints='deprecated'):
     _BaseMMC.__init__(self, max_iter=max_iter, max_proj=max_proj,
                       tol=tol,
                       init=init, diagonal=diagonal,
                       diagonal_c=diagonal_c, verbose=verbose,
                       preprocessor=preprocessor, random_state=random_state)
-    self.n_constraints = n_constraints
+    if num_constraints != 'deprecated':
+      warnings.warn('"num_constraints" parameter has been renamed to'
+                    ' "n_constraints". It has been deprecated in'
+                    ' version 0.6.3 and will be removed in 0.7.0'
+                    '', FutureWarning)
+      self.n_constraints = num_constraints
+    else:
+      self.n_constraints = n_constraints
+    # Avoid test get_params from failing (all params passed sholud be set)
+    self.num_constraints = 'deprecated'
 
   def fit(self, X, y):
     """Create constraints from labels and learn the MMC model.

metric_learn/rca.py

@@ -182,7 +182,7 @@ def __init__(self, n_components=None, n_chunks=100, chunk_size=2,
 
   def fit(self, X, y):
     """Create constraints from labels and learn the RCA model.
-    Needs n_constraints specified in constructor.
+    Needs n_constraints specified in constructor. (Not true?)
 
     Parameters
     ----------

metric_learn/sdml.py

@@ -279,6 +279,8 @@ class SDML_Supervised(_BaseSDML, TransformerMixin):
     prior. In any case, `random_state` is also used to randomly sample
     constraints from labels.
 
+  num_constraints : Renamed to n_constraints. Will be deprecated in 0.7.0
+
   Attributes
   ----------
   components_ : `numpy.ndarray`, shape=(n_features, n_features)
@@ -294,12 +296,21 @@ class SDML_Supervised(_BaseSDML, TransformerMixin):
 
   def __init__(self, balance_param=0.5, sparsity_param=0.01, prior='identity',
                n_constraints=None, verbose=False, preprocessor=None,
-               random_state=None):
+               random_state=None, num_constraints='deprecated'):
     _BaseSDML.__init__(self, balance_param=balance_param,
                        sparsity_param=sparsity_param, prior=prior,
                        verbose=verbose,
                        preprocessor=preprocessor, random_state=random_state)
-    self.n_constraints = n_constraints
+    if num_constraints != 'deprecated':
+      warnings.warn('"num_constraints" parameter has been renamed to'
+                    ' "n_constraints". It has been deprecated in'
+                    ' version 0.6.3 and will be removed in 0.7.0'
+                    '', FutureWarning)
+      self.n_constraints = num_constraints
+    else:
+      self.n_constraints = n_constraints
+    # Avoid test get_params from failing (all params passed sholud be set)
+    self.num_constraints = 'deprecated'
 
   def fit(self, X, y):
     """Create constraints from labels and learn the SDML model.

test/test_base_metric.py

@@ -44,7 +44,8 @@ def test_lmnn(self):
     nndef_kwargs = {'convergence_tol': 0.01, 'n_neighbors': 6}
     merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
     self.assertEqual(
-        remove_spaces(str(metric_learn.LMNN(convergence_tol=0.01, n_neighbors=6))),
+        remove_spaces(str(metric_learn.LMNN(convergence_tol=0.01,
+                                            n_neighbors=6))),
         remove_spaces(f"LMNN({merged_kwargs})"))
 
   def test_nca(self):