Method log_summary

textattack/loggers/attack_log_manager.py:93–174

(self)

        self.log_summary_rows(attack_detail_rows, "Attack Details", "attack_details")

    def log_summary(self):
        total_attacks = len(self.results)
        if total_attacks == 0:
            return

        # Default metrics - calculated on every attack
        attack_success_stats = AttackSuccessRate().calculate(self.results)
        words_perturbed_stats = WordsPerturbed().calculate(self.results)
        attack_query_stats = AttackQueries().calculate(self.results)

        # @TODO generate this table based on user input - each column in specific class
        # Example to demonstrate:
        # summary_table_rows = attack_success_stats.display_row() + words_perturbed_stats.display_row() + ...
        summary_table_rows = [
            [
                "Number of successful attacks:",
                attack_success_stats["successful_attacks"],
            ],
            ["Number of failed attacks:", attack_success_stats["failed_attacks"]],
            ["Number of skipped attacks:", attack_success_stats["skipped_attacks"]],
            [
                "Original accuracy:",
                str(attack_success_stats["original_accuracy"]) + "%",
            ],
            [
                "Accuracy under attack:",
                str(attack_success_stats["attack_accuracy_perc"]) + "%",
            ],
            [
                "Attack success rate:",
                str(attack_success_stats["attack_success_rate"]) + "%",
            ],
            [
                "Average perturbed word %:",
                str(words_perturbed_stats["avg_word_perturbed_perc"]) + "%",
            ],
            [
                "Average num. words per input:",
                words_perturbed_stats["avg_word_perturbed"],
            ],
        ]

        summary_table_rows.append(
            ["Avg num queries:", attack_query_stats["avg_num_queries"]]
        )

        for metric_name, metric in self.metrics.items():
            summary_table_rows.append([metric_name, metric.calculate(self.results)])

        if self.enable_advance_metrics:
            perplexity_stats = Perplexity().calculate(self.results)
            use_stats = USEMetric().calculate(self.results)

            summary_table_rows.append(
                [
                    "Average Original Perplexity:",
                    perplexity_stats["avg_original_perplexity"],
                ]