Merge pull request #49 from GPflow/notebook_tests

javdrher · web-flow · commit cb0164988dbb · 2017-08-14T23:35:42.000+02:00
Notebook tests
diff --git a/.travis.yml b/.travis.yml
@@ -1,4 +1,4 @@
-sudo: false
+sudo: required
 dist: trusty
 language: python
 python:
@@ -9,9 +9,9 @@ cache: pip
 install:
   - pip install -U pip wheel
   - pip install tensorflow==1.0.1
-  - pip install --process-dependency-links .
-  - pip install .[test]
-  - pip install codecov
+  - pip install -U --process-dependency-links .
+  - pip install -U .[test]
+  - pip install -U codecov
 script:
   - nosetests testing
 after_success:
diff --git a/GPflowOpt/optim.py b/GPflowOpt/optim.py
@@ -142,6 +142,8 @@ def __init__(self, domain, nsamples):
         """
         super(MCOptimizer, self).__init__(domain, exclude_gradient=True)
         self._nsamples = nsamples
+        # Clear the initial data points
+        self.set_initial(np.empty((0, self.domain.size)))
 
     @Optimizer.domain.setter
     def domain(self, dom):
@@ -163,9 +165,11 @@ def _optimize(self, objective):
 
     def set_initial(self, initial):
         initial = np.atleast_2d(initial)
-        super(MCOptimizer, self).set_initial(initial)
         if initial.size > 0:
             warnings.warn("Initial points set in {0} are ignored.".format(self.__class__.__name__), UserWarning)
+            return
+
+        super(MCOptimizer, self).set_initial(initial)
 
 
 class CandidateOptimizer(MCOptimizer):
@@ -183,8 +187,6 @@ def __init__(self, domain, candidates):
         super(CandidateOptimizer, self).__init__(domain, candidates.shape[0])
         assert (candidates in domain)
         self.candidates = candidates
-        # Clear the initial data points
-        self.set_initial(np.empty((0, self.domain.size)))
 
     def _get_eval_points(self):
         return self.candidates
@@ -235,11 +237,11 @@ def __init__(self, optimizers):
         no_gradient = any(map(lambda opt: not opt.gradient_enabled(), optimizers))
         super(StagedOptimizer, self).__init__(optimizers[0].domain, exclude_gradient=no_gradient)
         self.optimizers = optimizers
-        self.set_initial(np.empty((0, self.domain.size)))
+        del self._initial
 
     @Optimizer.domain.setter
     def domain(self, domain):
-        super(StagedOptimizer, self.__class__).domain.fset(self, domain)
+        self._domain = domain
         for optimizer in self.optimizers:
             optimizer.domain = domain
 
@@ -254,7 +256,6 @@ def optimize(self, objectivefx):
         is returned.
         """
 
-        self.optimizers[0].set_initial(self.get_initial())
         results = []
         for current, following in zip(self.optimizers[:-1], self.optimizers[1:]):
             result = current.optimize(objectivefx)
@@ -273,3 +274,9 @@ def optimize(self, objectivefx):
         if any(r.success for r in results):
             result.x, result.fun = self._best_x(results)
         return result
+
+    def get_initial(self):
+        return self.optimizers[0].get_initial()
+
+    def set_initial(self, initial):
+        self.optimizers[0].set_initial(initial)
diff --git a/doc/source/notebooks/constrained_bo.ipynb b/doc/source/notebooks/constrained_bo.ipynb
diff --git a/doc/source/notebooks/hyperopt.ipynb b/doc/source/notebooks/hyperopt.ipynb
diff --git a/doc/source/notebooks/multiobjective.ipynb b/doc/source/notebooks/multiobjective.ipynb
diff --git a/doc/source/notebooks/new_acquisition.ipynb b/doc/source/notebooks/new_acquisition.ipynb
diff --git a/doc/source/notebooks/structure.ipynb b/doc/source/notebooks/structure.ipynb
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -46,7 +46,7 @@
     "def fx(X):\n",
     "    X = np.atleast_2d(X)\n",
     "    # Return objective & gradient\n",
-    "    return np.sum(np.square(X), axis=1), 2*X"
+    "    return np.sum(np.square(X), axis=1)[:,None], 2*X"
    ]
   },
   {
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -68,10 +68,10 @@
        "<table id='domain' width=100%><tr><td>Name</td><td>Type</td><td>Values</td></tr><tr><td>x1</td><td>Continuous</td><td>[-2.  2.]</td></tr><tr><td>x2</td><td>Continuous</td><td>[-1.  2.]</td></tr></table>"
       ],
       "text/plain": [
-       "<GPflowOpt.domain.Domain at 0x7fcc640c42b0>"
+       "<GPflowOpt.domain.Domain at 0x7f60e22b2dd8>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +110,7 @@
        "       x: array([[ 0.,  0.]])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -132,20 +132,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "     fun: array([[ 0.0330519]])\n",
+       "     fun: array([[ 0.02115951]])\n",
        " message: 'OK'\n",
-       "    nfev: 201\n",
+       "    nfev: 200\n",
        " success: True\n",
-       "       x: array([[-0.04434619, -0.17631027]])"
+       "       x: array([[ 0.05731395, -0.13369599]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,18 +191,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "     fun: array([ 0.0008643])\n",
+      "     fun: array([ 0.17684308])\n",
       " message: 'OK'\n",
       "    nfev: 15\n",
       " success: True\n",
-      "       x: array([[ 0.        ,  0.02939904]])\n"
+      "       x: array([[ 0.        ,  0.42052714]])\n"
      ]
     }
    ],
@@ -293,7 +293,7 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3.0
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
@@ -304,5 +304,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
+ "nbformat_minor": 1
+}
diff --git a/setup.py b/setup.py
@@ -46,7 +46,9 @@
       extras_require={'tensorflow': ['tensorflow>=1.0.0'],
                       'tensorflow with gpu': ['tensorflow-gpu>=1.0.0'],
                       'docs': ['sphinx', 'sphinx_rtd_theme', 'numpydoc', 'nbsphinx', 'jupyter'],
-                      'test': ['nose', 'coverage', 'six', 'parameterized']},
+                      'test': ['nose', 'coverage', 'six', 'parameterized', 'nbconvert', 'nbformat','jupyter',
+                               'jupyter_client', 'matplotlib']
+                      },
       dependency_links=['https://github.com/GPflow/GPflow/tarball/master#egg=GPflow-0.3.5'],
       classifiers=['License :: OSI Approved :: Apache Software License',
                    'Natural Language :: English',
diff --git a/testing/test_acquisition.py b/testing/test_acquisition.py
@@ -9,7 +9,6 @@
 def parabola2d(X):
     return np.atleast_2d(np.sum(X ** 2, axis=1)).T
 
-
 def plane(X):
     return X[:, [0]] - 0.5
 
@@ -125,7 +124,6 @@ def test_enable_scaling(self):
         self.assertFalse(
             any(m.wrapped.X.value in GPflowOpt.domain.UnitCube(self.domain.size) for m in self.acquisition.models))
         self.acquisition.enable_scaling(self.domain)
-        print(self.acquisition.models[0].wrapped.X.value)
         self.assertTrue(
             all(m.wrapped.X.value in GPflowOpt.domain.UnitCube(self.domain.size) for m in self.acquisition.models))
 
diff --git a/testing/test_datascaler.py b/testing/test_datascaler.py
@@ -103,6 +103,4 @@ def test_predict_scaling(self):
         Yt = parabola2d(Xt) #+ np.random.rand(20, 1) * 0.05
         fr = m.predict_density(Xt, Yt)
         fs = n.predict_density(Xt, Yt)
-        print(fr)
-        print(fs)
         np.testing.assert_allclose(fr, fs, rtol=1e-3)
diff --git a/testing/test_notebooks.py b/testing/test_notebooks.py
@@ -0,0 +1,46 @@
+from __future__ import print_function
+import nbformat
+from nbconvert.preprocessors import ExecutePreprocessor
+from nbconvert.preprocessors.execute import CellExecutionError
+import glob
+import traceback
+import unittest
+import sys
+import time
+import os
+from nose.plugins.attrib import attr
+from parameterized import parameterized
+
+this_dir = os.path.dirname(__file__)
+nbpath = os.path.join(this_dir, '../doc/source/notebooks/')
+blacklist = [nbpath+'hyperopt.ipynb']
+lfiles = [(f,) for f in glob.glob(nbpath+"*.ipynb") if f not in blacklist]
+
+
+@attr('notebooks')
+class TestNotebooks(unittest.TestCase):
+
+    _multiprocess_can_split_ = True
+
+    def _execNotebook(self, notebook_filename, nbpath):
+        with open(notebook_filename) as f:
+            nb = nbformat.read(f, as_version=nbformat.current_nbformat)
+            try:
+                self.ep.preprocess(nb, {'metadata': {'path': nbpath}})
+            except CellExecutionError:
+                print('-' * 60)
+                traceback.print_exc(file=sys.stdout)
+                print('-' * 60)
+                self.assertTrue(False, 'Error executing the notebook %s.\
+                                        See above for error.' % notebook_filename)
+
+    def setUp(self):
+        pythonkernel = 'python' + str(sys.version_info[0])
+        # see http://nbconvert.readthedocs.io/en/stable/execute_api.html
+        self.ep = ExecutePreprocessor(timeout=600, kernel_name=pythonkernel, interrupt_on_timeout=True)
+
+    @parameterized.expand(lfiles)
+    def test_notebook(self, notebook_filename):
+        t = time.time()
+        self._execNotebook(notebook_filename, nbpath)
+        print(notebook_filename, 'took %g seconds.' % (time.time()-t))
diff --git a/testing/test_optimizers.py b/testing/test_optimizers.py
@@ -81,7 +81,7 @@ def test_set_initial(self):
         # When run separately this test works, however when calling nose to run all tests on python 2.7 this records
         # no warnings
         with warnings.catch_warnings(record=True) as w:
-            super(TestCandidateOptimizer, self).test_set_initial()
+            self.optimizer.set_initial([1, 1])
             assert len(w) == 1
             assert issubclass(w[-1].category, UserWarning)
 
@@ -150,7 +150,14 @@ def setUp(self):
                                                           GPflowOpt.optim.SciPyOptimizer(self.domain, maxiter=10)])
 
     def test_default_initial(self):
-        self.assertTupleEqual(self.optimizer._initial.shape, (0,2))
+        self.assertTupleEqual(self.optimizer.optimizers[0]._initial.shape, (0,2))
+
+    def test_set_initial(self):
+        self.optimizer.set_initial([1, 1])
+        self.assertTupleEqual(self.optimizer.optimizers[0]._initial.shape, (0, 2))
+        self.assertTupleEqual(self.optimizer.optimizers[1]._initial.shape, (0, 2))
+        self.assertTupleEqual(self.optimizer.optimizers[2]._initial.shape, (1, 2))
+        self.assertTupleEqual(self.optimizer.get_initial().shape, (0, 2))
 
     def test_object_integrity(self):
         self.assertEqual(len(self.optimizer.optimizers), 3, msg="Two optimizers expected in optimizerlist")
@@ -181,7 +188,6 @@ def test_optimizer_interrupt(self):
             self.assertEqual(result.nfev, 5)
 
             result = self.optimizer.optimize(KeyboardRaiser(12, parabola2d))
-            print(result)
             self.assertFalse(result.success, msg="non-succesfull result expected.")
             self.assertEqual(result.nfev, 12)
             self.assertFalse(np.allclose(result.x[0, :], 0.0), msg="The optimum should not be found yet")
