Initial skeleton refactoring

lm_eval/tasks/coqa.py

@@ -4,19 +4,23 @@ import json
 import random
 from lm_eval.base import Dataset
 from ..utils import sh
-
+import itertools
 
 class CoQA(Dataset):
     def __init__(self):
         self.download()
     def download(self):
-        #TODO: don't download if files already there
-        sh("""
+        sh ("""
             mkdir -p data/coqa 
-            wget http://downloads.cs.stanford.edu/nlp/data/coqa/coqa-train-v1.0.json -O data/coqa/coqa-train-v1.0.json
-            wget http://downloads.cs.stanford.edu/nlp/data/coqa/coqa-dev-v1.0.json -O data/coqa/coqa-dev-v1.0.json
+            wget --no-clobber http://downloads.cs.stanford.edu/nlp/data/coqa/coqa-train-v1.0.json -O data/coqa/coqa-train-v1.0.json
+            wget --no-clobber http://downloads.cs.stanford.edu/nlp/data/coqa/coqa-dev-v1.0.json -O data/coqa/coqa-dev-v1.0.json
             """)
 
+    @classmethod
+    def get_answers(cls, doc, turn_id):
+        answers = zip(doc["answers"], zip(doc["additional_answers"]))
+        return answers[turn_id - 1]
+
     def has_training_docs(self):
         return True
 
@@ -36,16 +40,15 @@ class CoQA(Dataset):
         pass
     
     def fewshot_description(self):
-        # TODO: figure out description
-        return ""
+        return "Given a passage and a conversation so far, answer the next question in the conversation."
     
     def doc_to_text(self, doc):
-        # TODO: implement.
-        raise NotImplementedError('doc_to_text not implemented')
-
+        qa_pairs = [(q, a) in zip_longest(doc["questions"], doc["answers"][:-1])]  # truncate target answer
+        return "{}\n\n{}".format(doc["story"], f"Q: {q}"+ '\n\n' + f"A: {a}")
+    
     def doc_to_target(self, doc):
-        # TODO: implement.
-        raise NotImplementedError('doc_to_target not implemented')
+        # TODO: all distinct answers taking into account whitespace?
+        return get_answers(doc, len(doc["questions"]))
 
     def construct_requests(self, doc, ctx):
         """ Uses RequestFactory to construct Requests and returns an iterable of 
@@ -58,8 +61,11 @@ class CoQA(Dataset):
             language description, as well as the few shot examples, and the question
             part of the document for `doc`. 
         """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        ll_alternative_answers = [
+            rf.loglikelihood(ctx, " " + answer) for answer in get_answers(doc, len(doc["questions"]))
+        ]
+
+        return ll_alternative_answers
     
     def process_results(self, doc, results):
         """Take a single document and the LM results and evaluates, returning a 
@@ -71,8 +77,12 @@ class CoQA(Dataset):
         :param results:
             The results of the requests created in construct_requests.
         """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        golds = get_answers(doc, len(doc["questions"]))
+        pred = np.argmax(results)
+        return {
+            "acc": pred in golds,
+            # "f1": (golds, pred),    # TODO:  Fix
+        }
 
     def aggregation(self):
         """