Merge pull request #446 from JohnSnowLabs/feature/accuracy-fairness-f…

…or-summarization

Feature/accuracy fairness for summarization

nlptest/datahandler/datasource.py

@@ -483,12 +483,14 @@ def load_data(self):
                     )
 
                 elif (self.task=='summarization'):
+                    expected_results = item.get("summary",None)
+                    if isinstance(expected_results, str) or isinstance(expected_results, bool): expected_results = [str(expected_results)]
                     data.append(
                     SummarizationSample(
                         original = item['document'],
+                        expected_results=expected_results,
                         task=self.task,
                         dataset_name=self._file_path.split('/')[-2]
-
                         )
                 )
 

nlptest/nlptest.py

@@ -290,8 +290,12 @@ def generated_results(self) -> Optional[pd.DataFrame]:
             return
         generated_results_df = pd.DataFrame.from_dict(
             [x.to_dict() for x in self._generated_results])
+        if "test_case" in generated_results_df.columns and "original_question" in generated_results_df.columns:
+            generated_results_df['original_question'].update(generated_results_df.pop('test_case'))
 
-        return generated_results_df.fillna('-')
+        generated_results_df=generated_results_df[generated_results_df.columns.drop("pass").to_list() + ["pass"]]
+
+        return generated_results_df.fillna("-")
 
     def augment(self, input_path: str, output_path: str, inplace: bool = False) -> "Harness":
         """

nlptest/transform/__init__.py

@@ -562,7 +562,7 @@ def transform(self) -> List[Sample]:
                 y_true = pd.Series(data_handler_copy).apply(lambda x: [y.entity for y in x.expected_results.predictions])
             elif isinstance(data_handler_copy[0], SequenceClassificationSample):
                 y_true = pd.Series(data_handler_copy).apply(lambda x: [y.label for y in x.expected_results.predictions])
-            elif isinstance(data_handler_copy[0], QASample):
+            elif data_handler_copy[0].task in ["question-answering", "summarization"]:
                 y_true = pd.Series(data_handler_copy).apply(lambda x: x.expected_results)
 
             y_true = y_true.explode().apply(lambda x: x.split("-")
@@ -628,18 +628,29 @@ def run(cls, sample_list: Dict[str, List[Sample]], model: ModelFactory, raw_data
                     y_true = y_true.explode()
                     y_pred = y_pred.explode()
 
-                elif isinstance(data[0], QASample):
+                elif data[0].task == "question-answering":
                     dataset_name = data[0].dataset_name.split('-')[0].lower()
                     user_prompt = kwargs.get('user_prompt', default_user_prompt.get(dataset_name, ""))
                     prompt_template = """Context: {context}\nQuestion: {question}\n """ + user_prompt
 
                     if data[0].expected_results is None:
-                        logging.warning('The dataset %s does not contain labels and fairness tests cannot be run with it. Skipping the fairness tests.', dataset_name)
-                        return []
+                        raise RuntimeError(f'The dataset {dataset_name} does not contain labels and fairness tests cannot be run with it. Skipping the fairness tests.')
                     y_true = pd.Series(data).apply(lambda x: x.expected_results)
                     X_test = pd.Series(data)
                     y_pred = X_test.apply(lambda sample: model(text={'context':sample.original_context, 'question': sample.original_question}, prompt={"template":prompt_template, 'input_variables':["context", "question"]}))
                     y_pred = y_pred.apply(lambda x: x.strip())
+
+                elif data[0].task == "summarization":
+                    dataset_name = data[0].dataset_name.split('-')[0].lower()
+                    user_prompt = kwargs.get('user_prompt', default_user_prompt.get(dataset_name, ""))
+                    prompt_template =  user_prompt + """Context: {context}\n\n Summary: """
+                    if data[0].expected_results is None:
+                        raise RuntimeError(f'The dataset {dataset_name} does not contain labels and fairness tests cannot be run with it. Skipping the fairness tests.')
+
+                    y_true = pd.Series(data).apply(lambda x: x.expected_results)
+                    X_test = pd.Series(data)
+                    y_pred = X_test.apply(lambda sample: model(text={'context':sample.original}, prompt={"template":prompt_template, 'input_variables':["context"]}))
+                    y_pred = y_pred.apply(lambda x: x.strip())
 
                 if kwargs['is_default']:
                     y_pred = y_pred.apply(lambda x: '1' if x in ['pos', 'LABEL_1', 'POS'] else (
@@ -717,15 +728,15 @@ def transform(self) -> List[Sample]:
         for test_name, params in self.tests.items():
             data_handler_copy = [x.copy() for x in self._data_handler]
 
-            if isinstance(data_handler_copy[0], NERSample):
+            if data_handler_copy[0].task=="ner":
                 y_true = pd.Series(data_handler_copy).apply(lambda x: [y.entity for y in x.expected_results.predictions])
-            elif isinstance(data_handler_copy[0], SequenceClassificationSample):
-                y_true = pd.Series(data_handler_copy).apply(lambda x: [y.label for y in x.expected_results.predictions])
-            elif isinstance(data_handler_copy[0], QASample):
-                y_true = pd.Series(data_handler_copy).apply(lambda x: x.expected_results)
+                y_true = y_true.explode().apply(lambda x: x.split("-")
+                                                [-1] if isinstance(x, str) else x)
+            elif data_handler_copy[0].task=="text-classification":
+                y_true = pd.Series(data_handler_copy).apply(lambda x: [y.label for y in x.expected_results.predictions]).explode()
+            elif data_handler_copy[0].task=="question-answering" or data_handler_copy[0].task=="summarization":
+                y_true = pd.Series(data_handler_copy).apply(lambda x: x.expected_results).explode()
 
-            y_true = y_true.explode().apply(lambda x: x.split("-")
-                                            [-1] if isinstance(x, str) else x)
             y_true = y_true.dropna()
             params["test_name"] = test_name
             transformed_samples = self.supported_tests[test_name].transform(
@@ -781,19 +792,30 @@ def run(cls, sample_list: Dict[str, List[Sample]], model: ModelFactory, raw_data
             y_true = y_true.explode()
             y_pred = y_pred.explode()
 
-        elif isinstance(raw_data[0], QASample):
+        elif raw_data[0].task=="question-answering":
             dataset_name = raw_data[0].dataset_name.split('-')[0].lower()
             user_prompt = kwargs.get('user_prompt', default_user_prompt.get(dataset_name, ""))
             prompt_template = """Context: {context}\nQuestion: {question}\n """ + user_prompt
 
             if raw_data[0].expected_results is None:
-                logging.warning('The dataset %s does not contain labels and accuracy tests cannot be run with it. Skipping the accuracy tests.', dataset_name)
-                return []
+                        raise RuntimeError(f'The dataset {dataset_name} does not contain labels and fairness tests cannot be run with it. Skipping the fairness tests.')
             y_true = pd.Series(raw_data).apply(lambda x: x.expected_results)
             X_test = pd.Series(raw_data)
             y_pred = X_test.apply(lambda sample: model(text={'context':sample.original_context, 'question': sample.original_question}, prompt={"template":prompt_template, 'input_variables':["context", "question"]}))
             y_pred = y_pred.apply(lambda x: x.strip())
 
+        elif raw_data[0].task=="summarization":
+            dataset_name = raw_data[0].dataset_name.split('-')[0].lower()
+            user_prompt = kwargs.get('user_prompt', default_user_prompt.get(dataset_name, ""))
+            prompt_template =  user_prompt + """Context: {context}\n\n Summary: """
+            if raw_data[0].expected_results is None:
+                raise RuntimeError(f'The dataset {dataset_name} does not contain labels and fairness tests cannot be run with it. Skipping the fairness tests.')
+
+            y_true = pd.Series(raw_data).apply(lambda x: x.expected_results)
+            X_test = pd.Series(raw_data)
+            y_pred = X_test.apply(lambda sample: model(text={'context':sample.original}, prompt={"template":prompt_template, 'input_variables':["context"]}))
+            y_pred = y_pred.apply(lambda x: x.strip())
+
         if kwargs['is_default']:
             y_pred = y_pred.apply(lambda x: '1' if x in ['pos', 'LABEL_1', 'POS'] else (
                 '0' if x in ['neg', 'LABEL_0', 'NEG'] else x))

nlptest/transform/accuracy.py

@@ -500,7 +500,7 @@ class MinEMcore(BaseAccuracy):
     """
 
     alias_name = "min_exact_match_score"
-    supported_tasks = ["question-answering"]
+    supported_tasks = ["question-answering", "summarization"]
 
     @staticmethod
     def transform(y_true, params):
@@ -563,7 +563,7 @@ class MinBLEUcore(BaseAccuracy):
     """
 
     alias_name = "min_bleu_score"
-    supported_tasks = ["question-answering"]
+    supported_tasks = ["question-answering", "summarization"]
 
     @staticmethod
     def transform(y_true, params):
@@ -626,7 +626,7 @@ class MinROUGEcore(BaseAccuracy):
     """
 
     alias_name = ["min_rouge1_score","min_rouge2_score","min_rougeL_score","min_rougeLsum_score"]
-    supported_tasks = ["question-answering"]
+    supported_tasks = ["question-answering", "summarization"]
 
     @staticmethod
     def transform(y_true, params):

nlptest/transform/fairness.py

@@ -23,7 +23,7 @@ class BaseFairness(ABC):
         output based on the implemented accuracy measure.
     """
     alias_name = None
-    supported_tasks = ["ner", "text-classification", "question-answering"]
+    supported_tasks = ["ner", "text-classification", "question-answering", "summarization"]
 
     @staticmethod
     @abstractmethod
@@ -100,7 +100,7 @@ def transform(data: List[Sample], params):
         samples = []
         for key, val in min_scores.items():
             sample = MinScoreSample(
-                original="-",
+                original=None,
                 category="fairness",
                 test_type="min_gender_f1_score",
                 test_case=key,
@@ -183,7 +183,7 @@ def transform(data: List[Sample], params):
         samples = []
         for key, val in max_scores.items():
             sample = MaxScoreSample(
-                original="-",
+                original=None,
                 category="fairness",
                 test_type="max_gender_f1_score",
                 test_case=key,

nlptest/utils/custom_types/sample.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Tuple, TypeVar
+from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union
 from copy import deepcopy
 from pydantic import BaseModel, PrivateAttr, validator
 from .helpers import Transformation, Span
@@ -417,7 +417,7 @@ def is_pass(self) -> bool:
 class SummarizationSample(BaseModel):
     original: str = None
     test_case: str = None
-    expected_results: str = None
+    expected_results: Union[str, List] = None
     actual_results: str = None
     state: str = None
     dataset_name: str = None