SumMeSummaryEvaluator.py

import numpy as np
from BaseEvaluator import BaseEvaluator


class SumMeSummaryEvaluator(BaseEvaluator):
    def __init__(self, generated_summaries, dataset, metric):
        super().__init__(generated_summaries, dataset, metric)

    def evaluate(self, metric='kendalltau'):
        rc_func = self.get_rc_func(metric)
        taus = []
        for video_name in self.dataset.keys():
            if video_name in self.summaries.keys():
                tau, p_value = rc_func(self.dataset[video_name]['mean_user_score'], self.summaries[video_name])
                taus.append(tau)
        return np.asarray(taus).mean()