move large sentiment models to separate tests

Author: DevinTDHa

.github/workflows/nlu_test_flow.yaml

@@ -76,6 +76,14 @@ jobs:
         if: always()
         run: |
           python -m unittest  'tests/nlu_core_tests/component_tests/classifier_tests/sentiment_tests.py'
+      - name: NLU Classifier Sentiment test Imdb -
+        if: always()
+        run: |
+          python -m unittest  'tests/nlu_core_tests/component_tests/classifier_tests/sentiment_test_imdb.py'
+      - name: NLU Classifier Sentiment test Twitter -
+        if: always()
+        run: |
+          python -m unittest  'tests/nlu_core_tests/component_tests/classifier_tests/sentiment_test_twitter.py'
       - name: NLU Classifier Spam tests -
         if: always()
         run: |

tests/nlu_core_tests/component_tests/classifier_tests/sentiment_test_imdb.py

@@ -0,0 +1,19 @@
+import unittest
+from nlu import *
+
+
+class TestSentimentImdb(unittest.TestCase):
+    def test_sentiment_imdb_model(self):
+        pipe = nlu.load('sentiment.imdb', verbose=True)
+        df = pipe.predict(['I love pancaces. I hate Mondays',
+                           'I love Fridays'])
+        print(df.columns)
+        for c in df.columns: print(df[c])
+        df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'],
+                          output_level='document')
+        print(df.columns)
+        for c in df.columns: print(df[c])
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/nlu_core_tests/component_tests/classifier_tests/sentiment_test_twitter.py

@@ -0,0 +1,19 @@
+import unittest
+from nlu import *
+
+
+class TestSentimentTwitter(unittest.TestCase):
+    def test_sentiment_twitter_model(self):
+        pipe = nlu.load('sentiment.twitter', verbose=True)
+        df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'],
+                          output_level='sentence')
+        print(df.columns)
+        for c in df.columns: print(df[c])
+        df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'],
+                          output_level='document')
+        print(df.columns)
+        for c in df.columns: print(df[c])
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/nlu_core_tests/component_tests/classifier_tests/sentiment_tests.py

@@ -10,27 +10,6 @@ def test_sentiment_model(self):
         df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'], output_level='document',drop_irrelevant_cols=False, metadata=True, )
         for c in df.columns: print(df[c])
 
-    def test_sentiment_imdb_model(self):
-        pipe = nlu.load('sentiment.twitter',verbose=True)
-        df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'])#, output_level='document',drop_irrelevant_cols=False, metadata=True, )
-        print(df.columns)
-        for c in df.columns: print(df[c])
-        df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'], output_level='document')
-        print(df.columns)
-        for c in df.columns: print(df[c])
-
-
-
-    def test_sentiment_twitter_model(self):
-        pipe = nlu.load('sentiment.imdb',verbose=True)
-        df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'], output_level='sentence')
-        print(df.columns)
-        for c in df.columns: print(df[c])
-        df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'], output_level='document')
-        print(df.columns)
-        for c in df.columns: print(df[c])
-
-
     def test_sentiment_detector_model(self):
         pipe = nlu.load('sentiment.imdb',verbose=True)
         df = pipe.predict(['I love pancaces. I hate Mondays', 'I love Fridays'], output_level='sentence')