test(NLC): Add integration tests to NLC

Author: ehdsouza

examples/natural_language_classifier_v1.py

@@ -1,10 +1,9 @@
 from __future__ import print_function
 import json
 import os
-import time
+
 # from os.path import join, dirname
 from watson_developer_cloud import NaturalLanguageClassifierV1
-FIVE_SECONDS = 5
 
 natural_language_classifier = NaturalLanguageClassifierV1(
     username='YOUR SERVICE USERNAME',
@@ -23,7 +22,6 @@
     classifier_id = classifier['classifier_id']
     print(json.dumps(classifier, indent=2))
 
-time.sleep(FIVE_SECONDS)
 status = natural_language_classifier.get_classifier(classifier_id)
 print(json.dumps(status, indent=2))
 

test/integration/test_natural_language_classifier_v1.py

@@ -0,0 +1,56 @@
+# coding: utf-8
+from unittest import TestCase
+import os
+import watson_developer_cloud
+import pytest
+import json
+import time
+
+FIVE_SECONDS = 5
+
+@pytest.mark.skipif(os.getenv('VCAP_SERVICES') is None, reason='requires VCAP_SERVICES')
+class TestNaturalLanguageClassifierV1(TestCase):
+    def setUp(self):
+        self.natural_language_classifier = watson_developer_cloud.NaturalLanguageClassifierV1(
+            username='YOUR SERVICE USERNAME',
+            password='YOUR SERVICE PASSWORD')
+        self.natural_language_classifier.set_default_headers({
+            'X-Watson-Learning-Opt-Out': '1',
+            'X-Watson-Test': '1'
+        })
+
+        # Create a classifier
+        with open(os.path.join(os.path.dirname(__file__), '../../resources/weather_data_train.csv'), 'rb') as training_data:
+            metadata = json.dumps({'name': 'my-classifier', 'language': 'en'})
+            classifier = self.natural_language_classifier.create_classifier(
+                metadata=metadata,
+                training_data=training_data
+            )
+            self.classifier_id = classifier['classifier_id']
+
+    def tearDown(self):
+        self.natural_language_classifier.delete_classifier(self.classifier_id)
+
+    def test_list_classifier(self):
+        list_classifiers = self.natural_language_classifier.list_classifiers()
+        assert list_classifiers is not None
+
+    @pytest.mark.skip(reason="The classifier takes more than a minute")
+    def test_classify_text(self):
+        iterations = 0
+        while iterations < 15:
+            status = self.natural_language_classifier.get_classifier(self.classifier_id)
+            iterations += 1
+            if status['status'] != 'Available':
+                time.sleep(FIVE_SECONDS)
+
+        if status['status'] != 'Available':
+            assert False, 'Classifier is not available'
+
+        classes = self.natural_language_classifier.classify(self.classifier_id, 'How hot will it be tomorrow?')
+        assert classes is not None
+
+        collection = ['{"text":"How hot will it be today?"}', '{"text":"Is it hot outside?"}']
+        classes = self.natural_language_classifier.classify_collection(
+            self.classifier_id, collection)
+        assert classes is not None