Add doctest to travis

.travis.yml

@@ -124,7 +124,13 @@ jobs:
         - sudo apt-get -y install python3-enchant
         - sudo apt-get -y install hunspell-en-us
         - pip install pyenchant
-      script: make -k all_check SPHINXOPTS=-W
+      script:
+        - make spell
+        - make style
+        - make lint
+        - make copyright
+        - make html SPHINXOPTS=-W
+        - make doctest
     - name: "Test Aqua 1 Python 3.7"
       <<: *stage_test_aqua
       if: tag IS blank

Makefile

@@ -35,9 +35,9 @@ endif
 # You can set this variable from the command line.
 SPHINXOPTS    =
 
-.PHONY: lint style test test_ci spell copyright html coverage coverage_erase
+.PHONY: lint style test test_ci spell copyright html doctest coverage coverage_erase
 
-all_check: spell style lint copyright html
+all_check: spell style lint copyright html doctest
 
 lint:
 	pylint -rn --ignore=gauopen qiskit/aqua qiskit/chemistry qiskit/finance qiskit/ml qiskit/optimization test tools
@@ -60,6 +60,9 @@ copyright:
 
 html:
 	make -C docs html SPHINXOPTS=$(SPHINXOPTS)
+
+doctest:
+	make -C docs doctest SPHINXOPTS=$(SPHINXOPTS)
 
 coverage:
 	coverage3 run --source qiskit/aqua,qiskit/chemistry,qiskit/finance,qiskit/ml,qiskit/optimization --omit */gauopen/* -m unittest discover -s test -q

docs/conf.py

@@ -68,6 +68,7 @@
     'jupyter_sphinx.execute',
     'sphinx_autodoc_typehints',
     'reno.sphinxext',
+    'sphinx.ext.doctest',
 ]
 html_static_path = ['_static']
 templates_path = ['_templates']

qiskit/ml/datasets/ad_hoc.py

@@ -19,11 +19,6 @@
 import numpy as np
 import scipy
 from qiskit.aqua import aqua_globals
-try:
-    import matplotlib.pyplot as plt
-    HAS_MATPLOTLIB = True
-except ImportError:
-    HAS_MATPLOTLIB = False
 
 
 def ad_hoc_data(training_size, test_size, n, gap, plot_data=False):
@@ -151,8 +146,10 @@ def ad_hoc_data(training_size, test_size, n, gap, plot_data=False):
             training_size+test_size)] for k, key in enumerate(class_labels)}
 
         if plot_data:
-            if not HAS_MATPLOTLIB:
-                raise NameError('Matplotlib not installed. Plase install it before plotting')
+            try:
+                import matplotlib.pyplot as plt
+            except ImportError:
+                raise NameError('Matplotlib not installed. Please install it before plotting')
 
             plt.show()
             fig2 = plt.figure()
@@ -222,8 +219,10 @@ def ad_hoc_data(training_size, test_size, n, gap, plot_data=False):
             training_size+test_size)] for k, key in enumerate(class_labels)}
 
         if plot_data:
-            if not HAS_MATPLOTLIB:
-                raise NameError('Matplotlib not installed. Plase install it before plotting')
+            try:
+                import matplotlib.pyplot as plt
+            except ImportError:
+                raise NameError('Matplotlib not installed. Please install it before plotting')
             sample_total_a = np.asarray(sample_total_a)
             sample_total_b = np.asarray(sample_total_b)
             x_1 = sample_total_a[:, 0]

qiskit/ml/datasets/breast_cancer.py

@@ -2,7 +2,7 @@
 
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2019.
+# (C) Copyright IBM 2018, 2020.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,11 +21,6 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, MinMaxScaler
 from sklearn.decomposition import PCA
-try:
-    import matplotlib.pyplot as plt
-    HAS_MATPLOTLIB = True
-except ImportError:
-    HAS_MATPLOTLIB = False
 
 
 def breast_cancer(training_size, test_size, n, plot_data=False):
@@ -58,8 +53,10 @@ def breast_cancer(training_size, test_size, n, plot_data=False):
                   for k, key in enumerate(class_labels)}
 
     if plot_data:
-        if not HAS_MATPLOTLIB:
-            raise NameError('Matplotlib not installed. Plase install it before plotting')
+        try:
+            import matplotlib.pyplot as plt
+        except ImportError:
+            raise NameError('Matplotlib not installed. Please install it before plotting')
         for k in range(0, 2):
             plt.scatter(sample_train[label_train == k, 0][:training_size],
                         sample_train[label_train == k, 1][:training_size])

qiskit/ml/datasets/digits.py

@@ -2,7 +2,7 @@
 
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2019.
+# (C) Copyright IBM 2018, 2020.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,11 +21,6 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, MinMaxScaler
 from sklearn.decomposition import PCA
-try:
-    import matplotlib.pyplot as plt
-    HAS_MATPLOTLIB = True
-except ImportError:
-    HAS_MATPLOTLIB = False
 
 
 def digits(training_size, test_size, n, plot_data=False):
@@ -59,8 +54,10 @@ def digits(training_size, test_size, n, plot_data=False):
                   for k, key in enumerate(class_labels)}
 
     if plot_data:
-        if not HAS_MATPLOTLIB:
-            raise NameError('Matplotlib not installed. Plase install it before plotting')
+        try:
+            import matplotlib.pyplot as plt
+        except ImportError:
+            raise NameError('Matplotlib not installed. Please install it before plotting')
         for k in range(0, 9):
             plt.scatter(sample_train[label_train == k, 0][:training_size],
                         sample_train[label_train == k, 1][:training_size])

qiskit/ml/datasets/gaussian.py

@@ -18,11 +18,6 @@
 
 import numpy as np
 from qiskit.aqua import aqua_globals
-try:
-    import matplotlib.pyplot as plt
-    HAS_MATPLOTLIB = True
-except ImportError:
-    HAS_MATPLOTLIB = False
 
 
 def gaussian(training_size, test_size, n, plot_data=False):
@@ -61,8 +56,10 @@ def gaussian(training_size, test_size, n, plot_data=False):
             training_size+test_size)] for k, key in enumerate(class_labels)}
 
         if plot_data:
-            if not HAS_MATPLOTLIB:
-                raise NameError('Matplotlib not installed. Plase install it before plotting')
+            try:
+                import matplotlib.pyplot as plt
+            except ImportError:
+                raise NameError('Matplotlib not installed. Please install it before plotting')
 
             for k in range(0, 2):
                 plt.scatter(sample_train[label_train == k, 0][:training_size],
@@ -120,8 +117,10 @@ def gaussian(training_size, test_size, n, plot_data=False):
             training_size+test_size)] for k, key in enumerate(class_labels)}
 
         if plot_data:
-            if not HAS_MATPLOTLIB:
-                raise NameError('Matplotlib not installed. Plase install it before plotting')
+            try:
+                import matplotlib.pyplot as plt
+            except ImportError:
+                raise NameError('Matplotlib not installed. Please install it before plotting')
 
             for k in range(0, 3):
                 plt.scatter(sample_train[label_train == k, 0][:training_size],

qiskit/ml/datasets/iris.py

@@ -2,7 +2,7 @@
 
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2019.
+# (C) Copyright IBM 2018, 2020.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,11 +21,6 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, MinMaxScaler
 from sklearn.decomposition import PCA
-try:
-    import matplotlib.pyplot as plt
-    HAS_MATPLOTLIB = True
-except ImportError:
-    HAS_MATPLOTLIB = False
 
 
 def iris(training_size, test_size, n, plot_data=False):
@@ -58,8 +53,10 @@ def iris(training_size, test_size, n, plot_data=False):
                   for k, key in enumerate(class_labels)}
 
     if plot_data:
-        if not HAS_MATPLOTLIB:
-            raise NameError('Matplotlib not installed. Plase install it before plotting')
+        try:
+            import matplotlib.pyplot as plt
+        except ImportError:
+            raise NameError('Matplotlib not installed. Please install it before plotting')
         for k in range(0, 3):
             plt.scatter(sample_train[label_train == k, 0][:training_size],
                         sample_train[label_train == k, 1][:training_size])

qiskit/ml/datasets/wine.py

@@ -2,7 +2,7 @@
 
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2019.
+# (C) Copyright IBM 2018, 2020.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,11 +21,6 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, MinMaxScaler
 from sklearn.decomposition import PCA
-try:
-    import matplotlib.pyplot as plt
-    HAS_MATPLOTLIB = True
-except ImportError:
-    HAS_MATPLOTLIB = False
 
 
 def wine(training_size, test_size, n, plot_data=False):
@@ -58,8 +53,10 @@ def wine(training_size, test_size, n, plot_data=False):
                   for k, key in enumerate(class_labels)}
 
     if plot_data:
-        if not HAS_MATPLOTLIB:
-            raise NameError('Matplotlib not installed. Plase install it before plotting')
+        try:
+            import matplotlib.pyplot as plt
+        except ImportError:
+            raise NameError('Matplotlib not installed. Please install it before plotting')
         for k in range(0, 3):
             plt.scatter(sample_train[label_train == k, 0][:training_size],
                         sample_train[label_train == k, 1][:training_size])