def get_user_indices()

in flsim/active_user_selectors/simple_user_selector.py [0:0]

• 51 lines of code
• 4 McCabe index (conditional complexity)

    def get_user_indices(self, **kwargs) -> List[int]:
        required_inputs = [
            "num_total_users",
            "users_per_round",
            "data_provider",
            "global_model",
            "epoch",
        ]
        (
            num_total_users,
            users_per_round,
            data_provider,
            global_model,
            epoch,
        ) = self.unpack_required_inputs(required_inputs, kwargs)

        # pyre-fixme[16]: `HighLossActiveUserSelector` has no attribute `cfg`.
        if epoch < self.cfg.epochs_before_active:
            selected_indices = self._non_active_sampling(
                num_total_users, users_per_round
            )
            return selected_indices

        if self.user_losses.nelement() == 0:
            (
                self.user_losses,
                self.user_sample_counts,
            ) = self._get_initial_losses_and_counts(
                num_total_users, data_provider, global_model
            )

        user_utility = ActiveUserSelectorUtils.normalize_by_sample_count(
            user_utility=self.user_losses,
            user_sample_counts=self.user_sample_counts,
            averaging_exponent=self.cfg.count_normalization_exponent,
        )
        probs = ActiveUserSelectorUtils.convert_to_probability(
            user_utility=user_utility,
            fraction_with_zero_prob=self.cfg.fraction_with_zero_prob,
            softmax_temperature=self.cfg.softmax_temperature,
        )
        selected_indices = ActiveUserSelectorUtils.select_users(
            users_per_round=users_per_round,
            probs=probs,
            fraction_uniformly_random=self.cfg.fraction_uniformly_random,
            rng=self.rng,
        )

        for i in selected_indices:
            (
                self.user_losses[i],
                self.user_sample_counts[i],
            ) = self._get_user_loss_and_sample_count(
                data_provider.get_user_data(i), global_model
            )

        return selected_indices