change directory name

# coding=utf-8

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

"""Model evaluation"""

from megatron import get_args

from megatron import print_rank_0

from tasks.knwl_dialo.metrics import F1Metric

from tqdm import tqdm

def evaluate_f1(guess_file, answer_file):

    """Evaluating F1 Score"""

    guess_list = []

    print_rank_0('reading %s' % guess_file)

    with open(guess_file, "r") as f:

        for i, line in enumerate(tqdm(f)):

            line = line.strip()

            if "<|endoftext|>" in line:

                line = line.replace("<|endoftext|>", "")

            guess_list.append(line)

    answer_list = []

    print_rank_0('reading %s' % answer_file)

    with open(answer_file, "r") as f:

        for i, line in enumerate(tqdm(f)):

            line = line.strip()

            if line == "no_passages_used":

                line = ""

            answer_list.append(line)

    assert len(guess_list) == len(answer_list), \

        "lengths of guess and answer are different!"

    precision, recall, f1 = F1Metric.compute_all_pairs(guess_list, answer_list)

    print_rank_0('Precision: %.4f; recall: %.4f; f1: %.4f' % (precision, recall, f1))

    print_rank_0('done :-)')

def main():

    args = get_args()

    evaluate_f1(args.guess_file, args.answer_file)

# The following code is adapted from

# https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/metrics.py, 

# which is licensed under the MIT license. More details on the license can be 

# found at https://github.com/facebookresearch/ParlAI/blob/master/LICENSE.

"""Provides standard metric evaluations for dialog."""

from collections import Counter

from typing import List

import numpy as np

import re

re_art = re.compile(r'\b(a|an|the)\b')

re_punc = re.compile(r'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']')

def normalize_answer(s):

    """

    Lower text and remove punctuation, articles and extra whitespace.

    """

    s = s.lower()

    s = re_punc.sub(' ', s)

    s = re_art.sub(' ', s)

    s = ' '.join(s.split())

    return s

class F1Metric:

    """

    Helper class which computes token-level F1.

    """

    @staticmethod

    def _prec_recall_f1_score(pred_items, gold_items):

        """

        Compute precision, recall and f1 given a set of gold and prediction items.

        :param pred_items: iterable of predicted values

        :param gold_items: iterable of gold values

        :return: tuple (p, r, f1) for precision, recall, f1

        """

        common = Counter(gold_items) & Counter(pred_items)

        num_same = sum(common.values())

        if num_same == 0:

            return 0, 0, 0

        precision = 1.0 * num_same / len(pred_items)

        recall = 1.0 * num_same / len(gold_items)

        f1 = (2 * precision * recall) / (precision + recall)

        return precision, recall, f1

    @staticmethod

    def compute_each_pair(guess: str, answer: str):

        if answer == "":

            return None, None, None

        if guess == "":

            return 0, 0, 0

        g_tokens = normalize_answer(guess).split()

        a_tokens = normalize_answer(answer).split()

        precision, recall, f1 = F1Metric._prec_recall_f1_score(g_tokens, a_tokens)

        return precision, recall, f1

    @staticmethod

    def compute_all_pairs(guesses: List[str], answers: List[str]):

        # additional augment:

        assert len(guesses) == len(answers)

        precision_list, recall_list, f1_list = [], [], []

        for guess, answer in zip(guesses, answers):

            precision, recall, f1 = F1Metric.compute_each_pair(guess, answer)

            if precision is None or recall is None or f1 is None:

                continue

            precision_list.append(precision)

            recall_list.append(recall)

            f1_list.append(f1)

        return np.mean(precision_list), np.mean(recall_list), np.mean(f1_list)