superglue.py

"""
To-do:
    - WSC requires free-form generation
    - ReCoRD
"""
import numpy as np
from . common import HFTask, yesno
from lm_eval.base import rf, mean, acc_all, metric_max_over_ground_truths
import sklearn
import transformers.data.metrics.squad_metrics as squad_metrics


class BoolQ(HFTask):
    DATASET_PATH = "super_glue"
    DATASET_NAME = "boolq"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def fewshot_description(self):
        # TODO: figure out actual description
        return "Read the following passages and answer each question with a yes or a no."

    def doc_to_text(self, doc):
        return f"{doc['passage']}\nquestion: {doc['question']}\nanswer: "
    
    def doc_to_target(self, doc):
        return yesno(doc['label']) 

    def construct_requests(self, doc, ctx):

        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
        ll_no, _ = rf.loglikelihood(ctx, ' no')

        return ll_yes, ll_no

    def process_results(self, doc, results):
        ll_yes, ll_no = results
        gold = doc["label"]

        acc = 1. if (ll_yes > ll_no) == gold else 0.

        return {
            "acc": acc
        }
    
    def higher_is_better(self):
        return {
            "acc": True
        }
    
    def aggregation(self):
        return {
            "acc": mean
        }


class CommitmentBank(HFTask):
    DATASET_PATH = "super_glue"
    DATASET_NAME = "cb"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def fewshot_description(self):
        return "Given a premise and a hypothesis, classify whether the author of the premise is committed" \
            "to the truth of the hypothesis. The three possible labels are true, false or neither."

    def doc_to_text(self, doc):
        return "{}\nquestion: {} true, false or neither?\nanswer:".format(
            doc["premise"],
            doc["hypothesis"],
        )

    def doc_to_target(self, doc):
        # True = entailment
        # False = contradiction
        # Neither = neutral
        return " {}".format({0: "true", 1: "neither", 2: "false"}[doc["label"]])

    def construct_requests(self, doc, ctx):
        ll_true, _ = rf.loglikelihood(ctx, ' true')
        ll_neither, _ = rf.loglikelihood(ctx, ' neither')
        ll_false, _ = rf.loglikelihood(ctx, ' false')

        return ll_true, ll_neither, ll_false

    def process_results(self, doc, results):
        gold = doc["label"]
        pred = np.argmax(results)
        acc = 1. if pred == gold else 0.

        return {
            "acc": acc,
            "f1": (pred, gold)
        }
    
    def higher_is_better(self):
        return {
            "acc": True,
            "f1": True
        }

    @classmethod
    def cb_multi_fi(cls, items):
        preds, golds = zip(*items)
        preds = np.array(preds)
        golds = np.array(golds)
        f11 = sklearn.metrics.f1_score(y_true=golds == 0, y_pred=preds == 0)
        f12 = sklearn.metrics.f1_score(y_true=golds == 1, y_pred=preds == 1)
        f13 = sklearn.metrics.f1_score(y_true=golds == 2, y_pred=preds == 2)
        avg_f1 = mean([f11, f12, f13])
        return avg_f1
    
    def aggregation(self):
        return {
            "acc": mean,
            "f1": self.cb_multi_fi,
        }


class Copa(HFTask):
    DATASET_PATH = "super_glue"
    DATASET_NAME = "copa"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def fewshot_description(self):
        return "Given a premise and one alternative with a causal relation to the premise and another without," \
            "choose the more plausible alternative"

    def doc_to_text(self, doc):
        # Drop the period
        connector = {
            "cause": "because",
            "effect": "therefore",
        }[doc["question"]]
        return doc["premise"].strip()[:-1] + f" {connector} "

    def doc_to_target(self, doc):
        correct_choice = doc["choice1"] if doc["label"] == 0 else doc["choice2"]
        # Connect the sentences
        return self.convert_choice(correct_choice)

    def construct_requests(self, doc, ctx):
        choice1 = " " + self.convert_choice(doc["choice1"])
        choice2 = " " + self.convert_choice(doc["choice2"])
        
        ll_choice1, _ = rf.loglikelihood(ctx, choice1)
        ll_choice2, _ = rf.loglikelihood(ctx, choice2)

        return ll_choice1, ll_choice2

    def process_results(self, doc, results):
        gold = doc["label"]
        pred = np.argmax(results)
        acc = 1. if pred == gold else 0.

        return {
            "acc": acc
        }
    
    def higher_is_better(self):
        return {
            "acc": True
        }
    
    def aggregation(self):
        return {
            "acc": mean
        }

    @staticmethod
    def convert_choice(choice):
        return choice[0].lower() + choice[1:]


class MultiRC(HFTask):
    DATASET_PATH = "super_glue"
    DATASET_NAME = "multirc"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def fewshot_description(self):
        return "READING COMPREHENSION ANSWER KEY"

    def doc_to_text(self, doc):
        return f"{doc['paragraph']}\n\n{doc['question']}\n"

    def doc_to_target(self, doc):
        return self.format_answer(answer=doc["answer"], label=doc["label"])

    @staticmethod
    def format_answer(answer, label):
        label_str = "True" if label else "False"
        return f"[{label_str}] {answer}"

    def construct_requests(self, doc, ctx):
        true_choice = self.format_answer(answer=doc["answer"], label=True)
        false_choice = self.format_answer(answer=doc["answer"], label=False)
        
        ll_true_choice, _ = rf.loglikelihood(ctx, f' {true_choice}')
        ll_false_choice, _ = rf.loglikelihood(ctx, f' {false_choice}')

        return ll_true_choice, ll_false_choice

    def process_results(self, doc, results):
        pred = np.argmax(results)
        return {
            "acc": (pred, doc)
        }
    
    def higher_is_better(self):
        return {
            "acc": True
        }
    
    def aggregation(self):
        return {
            "acc": acc_all
        }


class ReCoRD(HFTask):
    DATASET_PATH = "super_glue"
    DATASET_NAME = "record"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def training_docs(self):
        # In ReCoRD, each doc manifests multiple "examples" in the context of few shot example packing.
        # Each doc consists of multiple answer candidates, each of which is scored yes/no.
        # Hence, we one "doc" for each (context + passage, answer) pair.
        # Moreover, we only use the correct answers for context packing
        # (This is not an issue for evaluation, where we can directly score multiple candidates at once).
        if self.has_training_docs():
            if self._training_docs is None:
                self._training_docs = []
                for doc in self.data["train"]:
                    for entity in list(set(doc["entities"])):
                        self._training_docs.append({
                            "passage": doc["passage"],
                            "query": doc["query"],
                            "entity": entity,
                            "label": entity in doc["answers"],
                        })
            return self._training_docs

    def doc_to_text(self, doc):
        initial_text, *highlights = doc["passage"].strip().split("\n@highlight\n")
        text = initial_text + "\n\n"
        for highlight in highlights:
            text += f"  - {highlight}.\n"
        return text

    @classmethod
    def format_answer(cls, query, entity):
        return f'  - {query}'.replace("@placeholder", entity)

    def doc_to_target(self, doc):
        return self.format_answer(query=doc["query"], entity=doc["entity"])

    def construct_requests(self, doc, ctx):
        requests = [
            rf.loglikelihood(ctx, self.format_answer(query=doc["query"], entity=entity))
            for entity in doc["entities"]
        ]
        return requests

    def process_results(self, doc, results):
        # ReCoRD's evaluation is actually deceptively simple:
        # - Pick the maximum likelihood prediction entity
        # - Evaluate the accuracy and token F1 PER EXAMPLE
        # - Average over all examples
        max_idx = np.argmax(np.array(results))
        prediction = doc["entities"][max_idx]
        gold_label_set = list(set(doc["answers"]))
        f1 = metric_max_over_ground_truths(squad_metrics.compute_f1, prediction, gold_label_set)
        em = metric_max_over_ground_truths(squad_metrics.compute_exact, prediction, gold_label_set)

        return {
            "f1": f1,
            "em": em,
        }

    def higher_is_better(self):
        return {
            "f1": True,
            "em": True,
        }

    def aggregation(self):
        return {
            "f1": mean,
            "em": mean,
        }


class WordsInContext(HFTask):
    DATASET_PATH = "super_glue"
    DATASET_NAME = "wic"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def doc_to_text(self, doc):
        return "{}\n{}\nQuestion: Is the word '{}' used in the same way in the" \
               " two sentences above?\nanswer:".format(
                    doc["sentence1"],
                    doc["sentence2"],
                    doc["sentence1"][doc["start1"]:doc["end1"]],
                )

    def doc_to_target(self, doc):
        return " {}".format({0: "no", 1: "yes"}[doc["label"]])

    def construct_requests(self, doc, ctx):
        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
        ll_no, _ = rf.loglikelihood(ctx, ' no')

        return ll_yes, ll_no

    def process_results(self, doc, results):
        ll_yes, ll_no = results
        gold = doc["label"]

        acc = 1. if (ll_yes > ll_no) == gold else 0.

        return {
            "acc": acc
        }

    def higher_is_better(self):
        return {
            "acc": True
        }

    def aggregation(self):
        return {
            "acc": mean
        }


class SGWinogradSchemaChallenge(HFTask):
    DATASET_PATH = "super_glue"
    DATASET_NAME = "wsc"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def training_docs(self):
        if self.has_training_docs():
            if self._training_docs is None:
                # GPT-3 Paper's format only uses positive examples for fewshot "training"
                self._training_docs = [
                    doc for doc in
                    self.data["train"]
                    if doc["label"]
                ]
            return self._training_docs

    def fewshot_description(self):
        return "Final Exam with Answer Key\n" \
           "Instructions: Please carefully read the following passages. " \
           "For each passage, you must identify which noun the pronoun marked in *bold*" \
           " refers to.\n====="

    def doc_to_text(self, doc):
        raw_passage = doc["text"]
        passage = (
            raw_passage[:doc["span2_index"]]
            + "*{}*".format(doc["span2_text"])
            + raw_passage[doc["span2_index"] + len(doc["span2_text"]):]
        )
        pronoun = doc["span2_text"]
        text = (
            f"Passage: {passage}\n"
            + f"Question: In the passage above, what does the pronoun \"*{pronoun}*\" refer to?\n"
            + "Answer:"
        )
        return text

    def doc_to_target(self, doc):
        return " {}".format(doc["span1_text"])

    def construct_requests(self, doc, ctx):
        # Evaluate probability of generating answer based on span1_text (coref target)
        raise NotImplementedError("requires free-form generation")

    def process_results(self, doc, results):
        # Evaluate probability of generating answer based on span1_text (coref target)
        raise NotImplementedError("requires evaluation from free-form generation")

    def higher_is_better(self):
        return {
            "acc": True
        }

    def aggregation(self):
        return {
            "acc": mean
        }