src/evaluate.py

from utils import jaccard
from datasets import load_dataset
from transformers import (
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    HfArgumentParser
)
from preprocess import DatasetArguments, ProcessedArguments, get_words
from shared import device, GeneralArguments
from predict import ClassifierArguments, predict, TrainingOutputArguments
from segment import word_start, word_end, SegmentationArguments, add_labels_to_words
import pandas as pd
from dataclasses import dataclass, field
from typing import Optional
from tqdm import tqdm
import json
import os
import random


@dataclass
class EvaluationArguments(TrainingOutputArguments):
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """
    max_videos: Optional[int] = field(
        default=None,
        metadata={
            'help': 'The number of videos to test on'
        }
    )

    data_dir: Optional[str] = DatasetArguments.__dataclass_fields__['data_dir']
    dataset: Optional[str] = DatasetArguments.__dataclass_fields__[
        'validation_file']

    output_file: Optional[str] = field(
        default='metrics.csv',
        metadata={
            'help': 'Save metrics to output file'
        }
    )


def attach_predictions_to_sponsor_segments(predictions, sponsor_segments):
    """Attach sponsor segments to closest prediction"""
    for prediction in predictions:
        prediction['best_overlap'] = 0
        prediction['best_sponsorship'] = None

    # Assign predictions to actual (labelled) sponsored segments
    for sponsor_segment in sponsor_segments:
        sponsor_segment['best_overlap'] = 0
        sponsor_segment['best_prediction'] = None

        for prediction in predictions:

            j = jaccard(prediction['start'], prediction['end'],
                        sponsor_segment['start'], sponsor_segment['end'])
            if sponsor_segment['best_overlap'] < j:
                sponsor_segment['best_overlap'] = j
                sponsor_segment['best_prediction'] = prediction

            if prediction['best_overlap'] < j:
                prediction['best_overlap'] = j
                prediction['best_sponsorship'] = sponsor_segment

    return sponsor_segments


def calculate_metrics(labelled_words, predictions):

    metrics = {
        'true_positive': 0,  # Is sponsor, predicted sponsor
        # Is sponsor, predicted not sponsor (i.e., missed it - bad)
        'false_negative': 0,
        # Is not sponsor, predicted sponsor (classified incorectly, not that bad since we do manual checking afterwards)
        'false_positive': 0,
        'true_negative': 0,  # Is not sponsor, predicted not sponsor
    }

    metrics['video_duration'] = word_end(
        labelled_words[-1])-word_start(labelled_words[0])

    for index, word in enumerate(labelled_words):
        if index >= len(labelled_words) - 1:
            continue

        # TODO make sure words with manual transcripts
        duration = labelled_words[index+1]['start'] - word['start']

        predicted_sponsor = False
        for p in predictions:
            # Is in some prediction
            if p['start'] <= word['start'] <= p['end']:
                predicted_sponsor = True
                break

        if predicted_sponsor:
            # total_positive_time += duration
            if word['category'] is not None:  # Is actual sponsor
                metrics['true_positive'] += duration
            else:
                metrics['false_positive'] += duration
        else:
            # total_negative_time += duration
            if word['category'] is not None:  # Is actual sponsor
                metrics['false_negative'] += duration
            else:
                metrics['true_negative'] += duration

    # NOTE In cases where we encounter division by 0, we say that the value is 1
    # https://stats.stackexchange.com/a/1775
    # (Precision) TP+FP=0: means that all instances were predicted as negative
    # (Recall)    TP+FN=0: means that there were no positive cases in the input data

    # The fraction of predictions our model got right
    # Can simplify, but use full formula
    z = metrics['true_positive'] + metrics['true_negative'] + \
        metrics['false_positive'] + metrics['false_negative']
    metrics['accuracy'] = (
        (metrics['true_positive'] + metrics['true_negative']) / z) if z > 0 else 1

    # What proportion of positive identifications was actually correct?
    z = metrics['true_positive'] + metrics['false_positive']
    metrics['precision'] = (metrics['true_positive'] / z) if z > 0 else 1

    # What proportion of actual positives was identified correctly?
    z = metrics['true_positive'] + metrics['false_negative']
    metrics['recall'] = (metrics['true_positive'] / z) if z > 0 else 1

    # https://deepai.org/machine-learning-glossary-and-terms/f-score

    s = metrics['precision'] + metrics['recall']
    metrics['f-score'] = (2 * (metrics['precision'] *
                               metrics['recall']) / s) if s > 0 else 0

    return metrics


def main():
    hf_parser = HfArgumentParser((
        EvaluationArguments,
        ProcessedArguments,
        SegmentationArguments,
        ClassifierArguments,
        GeneralArguments
    ))

    evaluation_args, processed_args, segmentation_args, classifier_args, _ = hf_parser.parse_args_into_dataclasses()

    model = AutoModelForSeq2SeqLM.from_pretrained(evaluation_args.model_path)
    model.to(device())

    tokenizer = AutoTokenizer.from_pretrained(evaluation_args.model_path)

    dataset = load_dataset('json', data_files=os.path.join(
        evaluation_args.data_dir, evaluation_args.dataset))['train']

    video_ids = [row['video_id'] for row in dataset]
    random.shuffle(video_ids)  # TODO Make param

    if evaluation_args.max_videos is not None:
        video_ids = video_ids[:evaluation_args.max_videos]

    # Load labelled data:
    final_path = os.path.join(
        processed_args.processed_dir, processed_args.processed_file)

    with open(final_path) as fp:
        final_data = json.load(fp)

    total_accuracy = 0
    total_precision = 0
    total_recall = 0
    total_fscore = 0

    out_metrics = []

    try:
        with tqdm(video_ids) as progress:
            for video_id in progress:
                progress.set_description(f'Processing {video_id}')
                sponsor_segments = final_data.get(video_id, [])

                words = get_words(video_id)
                if not words:
                    continue

                # Make predictions
                predictions = predict(video_id, model, tokenizer,
                                      segmentation_args, words, classifier_args)

                labelled_words = add_labels_to_words(words, sponsor_segments)
                met = calculate_metrics(labelled_words, predictions)
                met['video_id'] = video_id

                out_metrics.append(met)

                total_accuracy += met['accuracy']
                total_precision += met['precision']
                total_recall += met['recall']
                total_fscore += met['f-score']

                progress.set_postfix({
                    'accuracy': total_accuracy/len(out_metrics),
                    'precision':  total_precision/len(out_metrics),
                    'recall':  total_recall/len(out_metrics),
                    'f-score': total_fscore/len(out_metrics)
                })

                labelled_predicted_segments = attach_predictions_to_sponsor_segments(
                    predictions, sponsor_segments)
                for seg in labelled_predicted_segments:
                    if seg['best_prediction'] is None:
                        print('\nNo match found for', seg)

    except KeyboardInterrupt:
        pass

    df = pd.DataFrame(out_metrics)

    df.to_csv(evaluation_args.output_file)
    print(df.mean())


if __name__ == '__main__':
    main()