evaluation/long_32k_eval/dataset_evaluator_retro_nv.py

import os
import argparse
import json
import shutil
import re

from datasets import load_dataset, load_metric
from huggingface_hub import hf_hub_download

from nv.evaluate_f1_sft_zeroshot import evaluate_f1

DATASETS = [
    'doc2dial_full_dialogue',
]

PATTERN = re.compile(r'\b[A-D]\b')

def find_answer(s):
    match = PATTERN.search(s)
    if match is None:
        return None # None is a signal of not find! NOTE
    return match.group()

def read_json_data(data_path):
    references = []
    questions = []
    id_to_labels = dict()
    id_list = list() 
    idx = 0
    with open(data_path, "r") as f:
        examples = json.load(f)
        for data_item in examples: # dict_keys(['source', 'paragraph_id', 'question', 'answer', 'sub-paragraphs', 'word_count', 'id', 'ctxs'])
            idx_str = str(idx) if 'id' not in data_item else str(data_item['id'])
            idx += 1
            id_list.append(idx_str)

            questions.append(data_item['question'])
            if "answers" in data_item:
                references.append(data_item['answers']) # NOTE take all the answers!
                answer_list = [answer_str for answer_str in data_item['answers']]
                id_to_labels[idx_str] = answer_list

            elif "answer" in data_item:
                references.append([data_item['answer']]) # take the single answer, as a list
                id_to_labels[idx_str] = [data_item['answer']]
            else:
                raise ValueError("need answer or answers from input json")
    return id_to_labels, id_list, questions, references #answers

def convert_to_seq(aquestion, apred):
    if apred is None:
        apred = ""

    matched_pred = find_answer(apred)
    if matched_pred is None:
        matched_pred = apred

    apred = '({})'.format(matched_pred)

    alist = aquestion.split('\n')
    for aitem in alist:
        aitem = aitem.strip()
        if aitem.startswith(apred):
            pred_out = ' '.join(aitem.split(' ')[1:])
            print('from {} to [{}]'.format(apred, pred_out))
            return pred_out
    print('Warning: could not find ({}) from question {}'.format(apred, aquestion))

    return apred

# 500 -> 100 
def load_prediction(test_file, id_list, id_to_labels, questions, dataset_name):
    predictions = []
    with open(test_file, "r") as f:
        for line in f.readlines():
            predictions.append(line.strip())
    if len(predictions) != len(id_list):
        print("NOTE: different number of samples, {} in prediction, yet {} in reference".format(
            len(predictions), len(id_list)))
        id_list = id_list[0: len(predictions)]

    id_to_prediction = dict()
    for aid, apred in zip(id_list, predictions):
        id_to_prediction[aid] = apred

    if dataset_name.startswith('quality'):
        print('quality dataset, and rewriting the prediction to the full textual sequence...')
        questions = questions[0: len(predictions)]
        id_to_prediction = dict()
        for aid, aquestion, apred in zip(id_list, questions, predictions):
            apred_seq = convert_to_seq(aquestion, apred)
            id_to_prediction[aid] = apred_seq

    return id_to_prediction, id_list, predictions

def main(args):
    datasets = [args.dataset] if args.dataset in DATASETS else DATASETS 
    for dataset_name in datasets: 
        print(dataset_name)

        ground_truth_file = args.datapath
        prediction_file = args.gen_test_file
       
        evaluate_f1(ground_truth_file, prediction_file, dataset_name)


def main_orig(args, raise_on_errors=False):
    datasets = [args.dataset] if args.dataset in DATASETS else DATASETS 
    for dataset_name in datasets: 
        print(dataset_name)

        id_to_labels, id_list, questions, answers = read_json_data(args.datapath) 
        id_to_pred, id_list, predictions = load_prediction(args.gen_test_file, 
                id_list, id_to_labels, questions,
                dataset_name)

        if len(id_to_labels) > len(id_list):
            print('NOTE: prune the reference set from {} to {}'.format(
                len(id_to_labels), len(id_list)))
            id_to_labels = {aid:id_to_labels[aid] for aid in id_list}

        errors, details = verify(id_to_pred, id_to_labels)

        if len(errors) == 0:
            score = scorer(dataset_name, predictions, answers, all_classes=None)
            print('final display:', dataset_name, score)
        elif len(errors) > 0:
            errors_msg = errors[0] if len(errors) == 1 else " ".join(f"{i}: {err}" for i, err in enumerate(errors))
            print(json.dumps(errors, indent=4))
            raise ValueError(f"Failed to evaluate due to: {errors_msg}")


def download_metric():
    scrolls_metric_path = hf_hub_download(repo_id="tau/scrolls", filename="metrics/scrolls.py", repo_type="dataset")
    updated_scrolls_metric_path = (
        os.path.dirname(scrolls_metric_path) + os.path.basename(scrolls_metric_path).replace(".", "_") + ".py"
    )
    shutil.copy(scrolls_metric_path, updated_scrolls_metric_path)
    return updated_scrolls_metric_path

def verify(id_to_pred, id_to_labels):
    errors = []
    details = {"missing_keys": [], "redundant_keys": []}
    if not isinstance(id_to_pred, dict):
        errors.append('The predictions must be saved a JSON object: {"id1": "prediction1", "id2": "prediction2", ...}')
    else:
        if not all(isinstance(key, str) for key in id_to_pred.keys()):
            errors.append("All keys of the predictions dictionary must be strings")
        if not all(isinstance(value, str) for value in id_to_pred.values()):
            errors.append("All values of the predictions dictionary must be strings")
        if len(errors) == 0:
            predictions_keys, reference_keys = set(id_to_pred.keys()), set(id_to_labels.keys())
            missing_keys = reference_keys - predictions_keys
            redundant_keys = predictions_keys - reference_keys

            if len(missing_keys) > 0:
                details["missing_keys"] = list(missing_keys)
                errors.append(f"There are missing example IDs.")
            else:
                del details["missing_keys"]

            if len(redundant_keys) > 0:
                details["redundant_keys"] = list(redundant_keys)
                errors.append(f"There are redundant example IDs.")
            else:
                del details["redundant_keys"]

    return errors, details

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Evaluate SCROLLS predictions per dataset")

    parser.add_argument("--datapath", type=str, 
            default=None, help="datapath for test json file [reference]")
    parser.add_argument("--gen_test_file", type=str, 
            default=None, help="generations for test file [system prediction]")
    parser.add_argument("--dataset", type=str, 
            default=None, help="name of the dataset used in scrolls: {}".format(DATASETS))

    args = parser.parse_args()
    main(args)