Merge pull request #18 from menpo/version_fixes

Update to Menpo 0.4.0 and fix test failures
menpo · Feb 5, 2015 · e5362aa · e5362aa
2 parents e427b28 + b8bd1e6
commit e5362aa
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Showing 8 changed files with 36 additions and 44 deletions.
diff --git a/.travis.yml b/.travis.yml
@@ -20,7 +20,6 @@ install:
 - wget https://raw.githubusercontent.com/jabooth/condaci/v0.2.0/condaci.py -O condaci.py
 - python condaci.py setup $PYTHON_VERSION --channel $BINSTAR_USER
 - export PATH=$HOME/miniconda/bin:$PATH
-- conda config --add channels $BINSTAR_USER/channel/master
 
 script:
 - python condaci.py auto ./conda --binstaruser $BINSTAR_USER --binstarkey $BINSTAR_KEY

diff --git a/appveyor.yml b/appveyor.yml
@@ -25,7 +25,6 @@ platform:
 init:
 - ps: Start-FileDownload 'https://raw.githubusercontent.com/jabooth/condaci/v0.2.0/condaci.py' C:\\condaci.py; echo "Done"
 - cmd: python C:\\condaci.py setup %PYTHON_VERSION% --channel %BINSTAR_USER%
-- cmd: C:\\Miniconda\\Scripts\\conda config --add channels %BINSTAR_USER%/channel/master
 
 install:
 - cmd: C:\\Miniconda\\python C:\\condaci.py auto ./conda --binstaruser %BINSTAR_USER% --binstarkey %BINSTAR_KEY%

diff --git a/conda/meta.yaml b/conda/meta.yaml
@@ -9,10 +9,8 @@ requirements:
 
   run:
     - python
-    - menpo
-    - numpy 1.9.0
-    - scipy 0.14.0
-    - scikit-learn 0.15.2
+    - menpo 0.4.0|>=0.4.1,<0.5.0  # Make sure we ignore the alpha
+    - scikit-learn 0.15.*
 
 test:
   requires:

diff --git a/menpofit/sdm/fitter.py b/menpofit/sdm/fitter.py
@@ -155,7 +155,8 @@ def __str__(self):
         out = "Supervised Descent Method\n" \
               " - Non-Parametric '{}' Regressor\n" \
               " - {} training images.\n".format(
-            self._fitters[0].regressor.__name__, self._n_training_images)
+            name_of_callable(self._fitters[0].regressor),
+            self._n_training_images)
         # small strings about number of channels, channels string and downscale
         down_str = []
         for j in range(self.n_levels):
@@ -254,7 +255,7 @@ def __str__(self):
         return "{}Supervised Descent Method for AAMs:\n" \
                " - Parametric '{}' Regressor\n" \
                " - {} training images.\n".format(
-            self.aam.__str__(), self._fitters[0].regressor.__name__,
+            self.aam.__str__(), name_of_callable(self._fitters[0].regressor),
             self._n_training_images)
 
 
@@ -299,5 +300,5 @@ def __str__(self):
         return "{}Supervised Descent Method for CLMs:\n" \
                " - Parametric '{}' Regressor\n" \
                " - {} training images.\n".format(
-            self.clm.__str__(), self._fitters[0].regressor.__name__,
+            self.clm.__str__(), name_of_callable(self._fitters[0].regressor),
             self._n_training_images)
diff --git a/menpofit/test/aam_fitter_test.py b/menpofit/test/aam_fitter_test.py
@@ -421,87 +421,87 @@ def aam_helper(aam, algorithm, im_number, max_iters, initial_error,
 
 @attr('fuzzy')
 def test_alternating_ic():
-    aam_helper(aam, AlternatingInverseCompositional, 0, 6, 0.09062, 0.05606,
+    aam_helper(aam, AlternatingInverseCompositional, 0, 6, 0.09062, 0.05607,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_adaptive_ic():
-    aam_helper(aam, AdaptiveInverseCompositional, 1, 5, 0.07697, 0.0255,
+    aam_helper(aam, AdaptiveInverseCompositional, 1, 5, 0.07697, 0.02552,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_simultaneous_ic():
-    aam_helper(aam, SimultaneousInverseCompositional, 2, 7, 0.12616, 0.11566,
+    aam_helper(aam, SimultaneousInverseCompositional, 2, 7, 0.12616, 0.12156,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_projectout_ic():
-    aam_helper(aam, ProjectOutInverseCompositional, 3, 6, 0.10796, 0.07286,
+    aam_helper(aam, ProjectOutInverseCompositional, 3, 6, 0.10796, 0.07346,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_alternating_fa():
-    aam_helper(aam, AlternatingForwardAdditive, 0, 8, 0.09062, 0.07053,
+    aam_helper(aam, AlternatingForwardAdditive, 0, 8, 0.09062, 0.07225,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_adaptive_fa():
-    aam_helper(aam, AdaptiveForwardAdditive, 1, 6, 0.07697, 0.04921, 'me_norm')
+    aam_helper(aam, AdaptiveForwardAdditive, 1, 6, 0.07697, 0.04834, 'me_norm')
 
 
 @attr('fuzzy')
 def test_simultaneous_fa():
-    aam_helper(aam, SimultaneousForwardAdditive, 2, 5, 0.12616, 0.12627,
+    aam_helper(aam, SimultaneousForwardAdditive, 2, 5, 0.12616, 0.11151,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_projectout_fa():
-    aam_helper(aam, ProjectOutForwardAdditive, 3, 6, 0.10796, 0.09725,
+    aam_helper(aam, ProjectOutForwardAdditive, 3, 6, 0.10796, 0.09702,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_alternating_fc():
-    aam_helper(aam, AlternatingForwardCompositional, 0, 6, 0.09062, 0.07109,
+    aam_helper(aam, AlternatingForwardCompositional, 0, 6, 0.09062, 0.07129,
                'me_norm')
 
 @attr('fuzzy')
 def test_adaptive_fc():
-    aam_helper(aam, AdaptiveForwardCompositional, 1, 6, 0.07697, 0.04778,
+    aam_helper(aam, AdaptiveForwardCompositional, 1, 6, 0.07697, 0.04784,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_simultaneous_fc():
-    aam_helper(aam, SimultaneousForwardCompositional, 2, 5, 0.12616, 0.11505,
+    aam_helper(aam, SimultaneousForwardCompositional, 2, 5, 0.12616, 0.11738,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_projectout_fc():
-    aam_helper(aam, ProjectOutForwardCompositional, 3, 6, 0.10796, 0.08451,
+    aam_helper(aam, ProjectOutForwardCompositional, 3, 6, 0.10796, 0.0861,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_probabilistic_ic():
-    aam_helper(aam2, ProbabilisticInverseCompositional, 0, 6, 0.08605, 0.08923,
+    aam_helper(aam2, ProbabilisticInverseCompositional, 0, 6, 0.08605, 0.08924,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_probabilistic_fa():
-    aam_helper(aam2, ProbabilisticForwardAdditive, 1, 7, 0.09051, 0.08708,
+    aam_helper(aam2, ProbabilisticForwardAdditive, 1, 7, 0.09051, 0.08679,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_probabilistic_fc():
     aam_helper(aam2, ProbabilisticForwardCompositional, 2, 6, 0.11714,
-               0.11697, 'me_norm')
+               0.11704, 'me_norm')
diff --git a/menpofit/test/atm_fitter_test.py b/menpofit/test/atm_fitter_test.py
@@ -424,21 +424,21 @@ def atm_helper(atm, algorithm, im_number, max_iters, initial_error,
 
 @attr('fuzzy')
 def test_ic():
-    atm_helper(atm1, ImageInverseCompositional, 0, 6, 0.09062, 0.06783,
+    atm_helper(atm1, ImageInverseCompositional, 0, 6, 0.09062, 0.06788,
                'me_norm')
 
 
 @attr('fuzzy')
 def test_fa():
-    atm_helper(atm2, ImageForwardAdditive, 1, 8, 0.09051, 0.08237, 'me_norm')
+    atm_helper(atm2, ImageForwardAdditive, 1, 8, 0.09051, 0.08188, 'me_norm')
 
 
 @attr('fuzzy')
 def test_fc():
-    atm_helper(atm3, ImageForwardCompositional, 2, 6, 0.12615, 0.07522,
+    atm_helper(atm3, ImageForwardCompositional, 2, 6, 0.12615, 0.08255,
                'me_norm')
 
 @attr('fuzzy')
 def test_ic_2():
-    atm_helper(atm4, ImageInverseCompositional, 3, 7, 0.09748, 0.09509,
+    atm_helper(atm4, ImageInverseCompositional, 3, 7, 0.09748, 0.09511,
                'me_norm')
diff --git a/menpofit/test/clm_fitter_test.py b/menpofit/test/clm_fitter_test.py
@@ -306,37 +306,34 @@
 clm = CLMBuilder(classifier_trainers=linear_svm_lr,
                  patch_shape=(8, 8),
                  features=sparse_hog,
-                 normalization_diagonal=150,
-                 n_levels=3,
+                 normalization_diagonal=100,
+                 n_levels=2,
                  downscale=1.1,
                  scaled_shape_models=True,
-                 max_shape_components=[1, 2, 3],
+                 max_shape_components=[2, 2],
                  boundary=3).build(training_images)
 
 
 def test_clm():
     assert (clm.n_training_images == 4)
-    assert (clm.n_levels == 3)
+    assert (clm.n_levels == 2)
     assert (clm.downscale == 1.1)
     #assert (clm.features[0] == sparse_hog and len(clm.features) == 1)
-    assert_allclose(np.around(clm.reference_shape.range()), (109., 103.))
+    assert_allclose(np.around(clm.reference_shape.range()), (72.,  69.))
     assert clm.scaled_shape_models
     assert clm.pyramid_on_features
     assert_allclose(clm.patch_shape, (8, 8))
     assert_allclose([clm.shape_models[j].n_components
-                     for j in range(clm.n_levels)], (1, 2, 3))
-    assert_allclose(clm.n_classifiers_per_level, [68, 68, 68])
+                     for j in range(clm.n_levels)], (2, 2))
+    assert_allclose(clm.n_classifiers_per_level, [68, 68])
 
     ran_0 = np.random.randint(0, clm.n_classifiers_per_level[0])
     ran_1 = np.random.randint(0, clm.n_classifiers_per_level[1])
-    ran_2 = np.random.randint(0, clm.n_classifiers_per_level[2])
 
     assert (name_of_callable(clm.classifiers[0][ran_0])
             == 'linear_svm_lr')
     assert (name_of_callable(clm.classifiers[1][ran_1])
             == 'linear_svm_lr')
-    assert (name_of_callable(clm.classifiers[2][ran_2])
-            == 'linear_svm_lr')
 
 
 @raises(ValueError)
@@ -346,7 +343,7 @@ def test_n_shape_1_exception():
 
 @raises(ValueError)
 def test_n_shape_2_exception():
-    fitter = GradientDescentCLMFitter(clm, n_shape=[10, 20])
+    fitter = GradientDescentCLMFitter(clm, n_shape=[10, 20, 3])
 
 
 def test_perturb_shape():

diff --git a/setup.py b/setup.py
@@ -20,9 +20,7 @@
       author='The Menpo Development Team',
       author_email='[email protected]',
       packages=find_packages(),
-      install_requires=['numpy==1.9.0',
-                        'scipy==0.14.0',
-                        'menpo==0.4.0a3'
-                        ],
-      tests_require=['nose==1.3.4', 'mock==1.0.1']
+      install_requires=['menpo>=0.4.0,<0.5',
+                        'scikit-learn>=0.15.2,<0.16.0'],
+      tests_require=['nose', 'mock']
       )