Merge remote-tracking branch 'origin/master' into fix/remove_num_labeled_parameter

# Conflicts:
#	metric_learn/itml.py
#	metric_learn/lsml.py
#	metric_learn/mmc.py

Author: William de Vazelhes

doc/conf.py

@@ -15,7 +15,7 @@
 
 # General information about the project.
 project = u'metric-learn'
-copyright = u'2015-2017, CJ Carey and Yuan Tang'
+copyright = u'2015-2018, CJ Carey and Yuan Tang'
 author = u'CJ Carey and Yuan Tang'
 version = '0.4.0'
 release = '0.4.0'

metric_learn/itml.py

@@ -197,7 +197,7 @@ def fit(self, X, y, random_state=np.random):
       warnings.warn('"num_labeled" parameter is not used.'
                     ' It has been deprecated in version 0.4 and will be'
                     'removed in 0.5', DeprecationWarning)
-    X, y = check_X_y(X, y)
+    X, y = check_X_y(X, y, ensure_min_samples=2)
     num_constraints = self.num_constraints
     if num_constraints is None:
       num_classes = len(np.unique(y))

metric_learn/lmnn.py

@@ -52,7 +52,7 @@ def transformer(self):
 class python_LMNN(_base_LMNN):
 
   def _process_inputs(self, X, labels):
-    self.X_ = check_array(X, dtype=float)
+    self.X_ = check_array(X, dtype=float, ensure_min_samples=2)
     num_pts, num_dims = self.X_.shape
     unique_labels, self.label_inds_ = np.unique(labels, return_inverse=True)
     if len(self.label_inds_) != num_pts:

metric_learn/lsml.py

@@ -184,7 +184,7 @@ def fit(self, X, y, random_state=np.random):
       warnings.warn('"num_labeled" parameter is not used.'
                     ' It has been deprecated in version 0.4 and will be'
                     'removed in 0.5', DeprecationWarning)
-    X, y = check_X_y(X, y)
+    X, y = check_X_y(X, y, ensure_min_samples=2)
     num_constraints = self.num_constraints
     if num_constraints is None:
       num_classes = len(np.unique(y))

metric_learn/mmc.py

@@ -439,7 +439,7 @@ def fit(self, X, y, random_state=np.random):
       warnings.warn('"num_labeled" parameter is not used.'
                     ' It has been deprecated in version 0.4 and will be'
                     'removed in 0.5', DeprecationWarning)
-    X, y = check_X_y(X, y)
+    X, y = check_X_y(X, y, ensure_min_samples=2)
     num_constraints = self.num_constraints
     if num_constraints is None:
       num_classes = len(np.unique(y))

setup.py

@@ -2,17 +2,23 @@
 # -*- coding: utf-8 -*-
 from setuptools import setup
 import os
+import io
 
 version = {}
-with open(os.path.join('metric_learn', '_version.py')) as fp:
+with io.open(os.path.join('metric_learn', '_version.py')) as fp:
   exec(fp.read(), version)
 
+# Get the long description from README.md
+with io.open('README.rst', encoding='utf-8') as f:
+  long_description = f.read()
+
 setup(name='metric-learn',
       version=version['__version__'],
       description='Python implementations of metric learning algorithms',
+      long_description=long_description,
       author=['CJ Carey', 'Yuan Tang'],
       author_email='ccarey@cs.umass.edu',
-      url='http://github.com/all-umass/metric-learn',
+      url='http://github.com/metric-learn/metric-learn',
       license='MIT',
       classifiers=[
           'Development Status :: 4 - Beta',
@@ -40,5 +46,9 @@
           'Information Theoretic Metric Learning',
           'Sparse Determinant Metric Learning',
           'Least Squares Metric Learning',
-          'Neighborhood Components Analysis'
+          'Neighborhood Components Analysis',
+          'Local Fisher Discriminant Analysis',
+          'Relative Components Analysis',
+          'Mahalanobis Metric for Clustering',
+          'Metric Learning for Kernel Regression'
       ])

test/metric_learn_test.py

@@ -44,7 +44,7 @@ def test_iris(self):
 
     csep = class_separation(cov.transform(), self.iris_labels)
     # deterministic result
-    self.assertAlmostEqual(csep, 0.73068122)
+    self.assertAlmostEqual(csep, 0.72981476)
 
 
 class TestLSML(MetricTestCase):
@@ -166,7 +166,7 @@ def test_iris(self):
     nca = NCA(max_iter=(100000//n), num_dims=2, tol=1e-9)
     nca.fit(self.iris_points, self.iris_labels)
     csep = class_separation(nca.transform(), self.iris_labels)
-    self.assertLess(csep, 0.15)
+    self.assertLess(csep, 0.20)
 
   def test_finite_differences(self):
     """Test gradient of loss function
@@ -352,16 +352,17 @@ def test_iris(self):
     # Full metric
     mmc = MMC(convergence_threshold=0.01)
     mmc.fit(self.iris_points, [a,b,c,d])
-    expected = [[+0.00046504, +0.00083371, -0.00111959, -0.00165265],
-                [+0.00083371, +0.00149466, -0.00200719, -0.00296284],
-                [-0.00111959, -0.00200719, +0.00269546, +0.00397881],
-                [-0.00165265, -0.00296284, +0.00397881, +0.00587320]]
+    expected = [[ 0.000514,  0.000868, -0.001195, -0.001703],
+                [ 0.000868,  0.001468, -0.002021, -0.002879],
+                [-0.001195, -0.002021,  0.002782,  0.003964],
+                [-0.001703, -0.002879,  0.003964,  0.005648]]
     assert_array_almost_equal(expected, mmc.metric(), decimal=6)
 
     # Diagonal metric
     mmc = MMC(diagonal=True)
     mmc.fit(self.iris_points, [a,b,c,d])
-    expected = [0, 0, 1.21045968, 1.22552608]
+    expected = [0, 0, 1.210220, 1.228596]
+
     assert_array_almost_equal(np.diag(expected), mmc.metric(), decimal=6)
 
     # Supervised Full