Merge branch 'devel' into deploy

CHANGELOG.md

@@ -1,5 +1,38 @@
 # Changelog
 
+## v1.7.6 (2017-06-19)
+
+### Fix
+
+* Appveyor not uploading to testpypi for now. [mzwiessele]
+
+### Other
+
+* Bump version: 1.7.5 → 1.7.6. [mzwiessele]
+
+
+## v1.7.5 (2017-06-19)
+
+### Fix
+
+* Splitting forecast tests into 3 to circumvent 10 minute stop of travis. [mzwiessele]
+
+### Other
+
+* Bump version: 1.7.4 → 1.7.5. [mzwiessele]
+
+
+## v1.7.4 (2017-06-19)
+
+### Fix
+
+* Paramz version for parallel optimization fix. [mzwiessele]
+
+### Other
+
+* Bump version: 1.7.3 → 1.7.4. [mzwiessele]
+
+
 ## v1.7.3 (2017-06-19)
 
 ### Fix

GPy/__version__.py

@@ -1 +1 @@
-__version__ = "1.7.3"
+__version__ = "1.7.6"

GPy/testing/gpy_kernels_state_space_tests.py

@@ -306,11 +306,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
                             gp_kernel=gp_kernel,
                             mean_compare_decimal=2, var_compare_decimal=2)
 
-    def test_forecast(self,):
-        """
-        Test time-series forecasting.
-        """
-
+    def test_forecast_regular(self,):
         # Generate data ->
         np.random.seed(339) # seed the random number generator
         #import pdb; pdb.set_trace()
@@ -334,37 +330,102 @@ class StateSpaceKernelsTests(np.testing.TestCase):
 
         #import pdb; pdb.set_trace()
 
-        def get_new_kernels():
-            periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
-            gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
-            gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
-            gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
+        periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
+        gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
+        gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
+        gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
 
-            periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
-            ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
-                GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
+        periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
+        ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
+            GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
 
-            ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
-            ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
+        ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
+        ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
 
-            return ss_kernel, gp_kernel
-
-        ss_kernel, gp_kernel = get_new_kernels()
         self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'regular',
                            use_cython=False, optimize_max_iters=30, check_gradients=True,
                            predict_X=X_test,
                            gp_kernel=gp_kernel,
                            mean_compare_decimal=2, var_compare_decimal=2)
 
+    def test_forecast_svd(self,):
+        # Generate data ->
+        np.random.seed(339) # seed the random number generator
+        #import pdb; pdb.set_trace()
+        (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
+                        plot = False, points_num=100, x_interval = (0, 40), random=True)
+
+        (X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
+                    plot = False, points_num=100, x_interval = (0, 40), random=True)
+
+        Y = Y + Y1
+
+        X_train = X[X <= 20]
+        Y_train = Y[X <= 20]
+        X_test = X[X > 20]
+        Y_test = Y[X > 20]
+
+        X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
+        X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1)
+        X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1)
+        # Generate data <-
+
+        #import pdb; pdb.set_trace()
+
+        periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
+        gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
+        gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
+        gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
+
+        periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
+        ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
+            GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
+
+        ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
+        ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
 
-        ss_kernel, gp_kernel = get_new_kernels()
         self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
                            use_cython=False, optimize_max_iters=30, check_gradients=False,
                            predict_X=X_test,
                            gp_kernel=gp_kernel,
                            mean_compare_decimal=2, var_compare_decimal=2)
 
-        ss_kernel, gp_kernel = get_new_kernels()
+    def test_forecast_svd_cython(self,):
+        # Generate data ->
+        np.random.seed(339) # seed the random number generator
+        #import pdb; pdb.set_trace()
+        (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
+                        plot = False, points_num=100, x_interval = (0, 40), random=True)
+
+        (X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
+                    plot = False, points_num=100, x_interval = (0, 40), random=True)
+
+        Y = Y + Y1
+
+        X_train = X[X <= 20]
+        Y_train = Y[X <= 20]
+        X_test = X[X > 20]
+        Y_test = Y[X > 20]
+
+        X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
+        X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1)
+        X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1)
+        # Generate data <-
+
+        #import pdb; pdb.set_trace()
+
+        periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
+        gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
+        gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
+        gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
+
+        periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
+        ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
+            GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
+
+        ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
+        ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
+
         self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
                            use_cython=True, optimize_max_iters=30, check_gradients=False,
                            predict_X=X_test,

appveyor.yml

@@ -3,7 +3,7 @@ environment:
     secure: 8/ZjXFwtd1S7ixd7PJOpptupKKEDhm2da/q3unabJ00=
   COVERALLS_REPO_TOKEN:
     secure: d3Luic/ESkGaWnZrvWZTKrzO+xaVwJWaRCEP0F+K/9DQGPSRZsJ/Du5g3s4XF+tS
-  gpy_version: 1.7.3
+  gpy_version: 1.7.6
   matrix:
     - PYTHON_VERSION: 2.7
       MINICONDA: C:\Miniconda-x64
@@ -72,16 +72,17 @@ deploy_script:
 - echo username = maxz >> %USERPROFILE%\\.pypirc
 - echo password = %pip_access% >> %USERPROFILE%\\.pypirc
 - ps: >-
-    if ($env:APPVEYOR_REPO_BRANCH -eq 'devel') { 
-        twine upload --skip-existing -r test dist/*
-    }
-    elseif ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {  
+    if ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {  
         twine upload --skip-existing dist/*
     }
     else {
         echo not deploying on other branches
     }
  
+# if ($env:APPVEYOR_REPO_BRANCH -eq 'devel') { 
+#     twine upload --skip-existing -r test dist/*
+# } # This is for testing the upload to testpypi, it causes a fail, so we will undo it here
+
 # deploy:
 #   - provider: GitHub
 #     release: GPy-v$(gpy_version)

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.7.3
+current_version = 1.7.6
 tag = True
 commit = True
 

setup.py

@@ -150,7 +150,7 @@ setup(name = 'GPy',
       py_modules = ['GPy.__init__'],
       test_suite = 'GPy.testing',
       setup_requires = ['numpy>=1.7'],
-      install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.6.9'],
+      install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.7.4'],
       extras_require = {'docs':['sphinx'],
                         'optional':['mpi4py',
                                     'ipython>=4.0.0',