Add evaluation tests

tests/test_plugins.py

@@ -10,6 +10,8 @@ from senpy.models import Results, Entry, EmotionSet, Emotion, Plugins
 from senpy import plugins
 from senpy.plugins.conversion.emotion.centroids import CentroidConversion
 
+import pandas as pd
+
 
 class ShelfDummyPlugin(plugins.SentimentPlugin, plugins.ShelfMixin):
     '''Dummy plugin for tests.'''
@@ -212,7 +214,7 @@ class PluginsTest(TestCase):
             def input(self, entry, **kwargs):
                 return entry.text
 
-            def predict(self, input):
+            def predict_one(self, input):
                 return 'SIGN' in input
 
             def output(self, output, entry, **kwargs):
@@ -242,7 +244,7 @@ class PluginsTest(TestCase):
 
             mappings = {'happy': 'marl:Positive', 'sad': 'marl:Negative'}
 
-            def predict(self, input, **kwargs):
+            def predict_one(self, input, **kwargs):
                 return 'happy' if ':)' in input else 'sad'
 
             test_cases = [
@@ -309,6 +311,40 @@ class PluginsTest(TestCase):
         res = c._backwards_conversion(e)
         assert res["onyx:hasEmotionCategory"] == "c2"
 
+    def test_evaluation(self):
+        testdata = []
+        for i in range(50):
+            testdata.append(["good", 1])
+        for i in range(50):
+            testdata.append(["bad", 0])
+        dataset = pd.DataFrame(testdata, columns=['text', 'polarity'])
+
+        class DummyPlugin(plugins.TextBox):
+            description = 'Plugin to test evaluation'
+            version = 0
+
+            def predict_one(self, input):
+                return 0
+
+        class SmartPlugin(plugins.TextBox):
+            description = 'Plugin to test evaluation'
+            version = 0
+
+            def predict_one(self, input):
+                if input == 'good':
+                    return 1
+                return 0
+
+        dpipe = DummyPlugin()
+        results = plugins.evaluate(datasets={'testdata': dataset}, plugins=[dpipe], flatten=True)
+        dumb_metrics = results[0].metrics[0]
+        assert abs(dumb_metrics['accuracy'] - 0.5) < 0.01
+
+        spipe = SmartPlugin()
+        results = plugins.evaluate(datasets={'testdata': dataset}, plugins=[spipe], flatten=True)
+        smart_metrics = results[0].metrics[0]
+        assert abs(smart_metrics['accuracy'] - 1) < 0.01
+
 
 def make_mini_test(fpath):
     def mini_test(self):