def __call__()

in activemri/baselines/ddqn.py [0:0]

• 39 lines of code
• 6 McCabe index (conditional complexity)

    def __call__(self):
        training_done = False
        while not training_done:
            training_done = self.check_if_train_done()
            self.logger.info(f"Is training done? {training_done}.")
            checkpoint_episode, timestamp = self.load_latest_policy()

            if timestamp is None or timestamp <= self.last_time_stamp:
                # No new policy checkpoint to evaluate
                self.logger.info(
                    "No new policy to evaluate. "
                    "I will wait for 10 minutes before trying again."
                )
                time.sleep(600)
                continue

            self.logger.info(
                f"Found a new checkpoint with timestamp {timestamp}, "
                f"I will start evaluation now."
            )
            test_scores, _ = evaluation.evaluate(
                self.env,
                self.policy,
                self.options.num_test_episodes,
                self.options.seed,
                "val",
                verbose=True,
            )
            auc_score = test_scores[self.options.reward_metric].sum(axis=1).mean()
            if "mse" in self.options.reward_metric:
                auc_score *= -1
            self.logger.info(f"The test score for the model was {auc_score}.")
            self.last_time_stamp = timestamp
            if auc_score > self.best_test_score:
                self.save_tester_checkpoint()
                policy_path = os.path.join(self.evaluation_dir, "policy_best.pt")
                self.save_policy(policy_path, checkpoint_episode)
                self.best_test_score = auc_score
                self.logger.info(
                    f"Saved DQN model with score {self.best_test_score} to {policy_path}, "
                    f"corresponding to episode {checkpoint_episode}."
                )