simulation/decai/simulation/simulate_bhp_dt.py [17:66]:
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sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))


class Runner(object):
    @inject
    def __init__(self,
                 data: DataLoader,
                 simulator: Simulator,
                 ):
        self._data = data
        self._s = simulator

    def run(self):
        init_train_data_portion = 0.10

        # Set up the agents that will act in the simulation.
        agents = [
            # Good
            Agent(address="Good",
                  start_balance=10_000,
                  mean_deposit=5,
                  stdev_deposit=1,
                  mean_update_wait_s=10 * 60,
                  ),
            # Malicious: determined  with the goal of disrupting others.
            Agent(address="Bad",
                  start_balance=10_000,
                  mean_deposit=10,
                  stdev_deposit=3,
                  mean_update_wait_s=1 * 60 * 60,
                  good=False,
                  ),
        ]

        # Start the simulation.
        self._s.simulate(agents,
                         baseline_accuracy=0.44,
                         init_train_data_portion=init_train_data_portion,
                         accuracy_plot_wait_s=math.inf,
                         )


# Run with `bokeh serve PATH`.
if __name__.startswith('bk_script_'):
    # Set up the data, model, and incentive mechanism.
    inj = Injector([
        DecisionTreeModule,
        DefaultCollaborativeTrainerModule,
        LoggingModule,
        StakeableImModule,
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simulation/decai/simulation/simulate_ttt_dt.py [22:71]:
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sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))


class Runner(object):
    @inject
    def __init__(self,
                 data: DataLoader,
                 simulator: Simulator,
                 ):
        self._data = data
        self._s = simulator

    def run(self):
        init_train_data_portion = 0.10

        # Set up the agents that will act in the simulation.
        agents = [
            # Good
            Agent(address="Good",
                  start_balance=10_000,
                  mean_deposit=5,
                  stdev_deposit=1,
                  mean_update_wait_s=10 * 60,
                  ),
            # Malicious: determined  with the goal of disrupting others.
            Agent(address="Bad",
                  start_balance=10_000,
                  mean_deposit=10,
                  stdev_deposit=3,
                  mean_update_wait_s=1 * 60 * 60,
                  good=False,
                  ),
        ]

        # Start the simulation.
        self._s.simulate(agents,
                         baseline_accuracy=0.44,
                         init_train_data_portion=init_train_data_portion,
                         accuracy_plot_wait_s=math.inf,
                         )


# Run with `bokeh serve PATH`.
if __name__.startswith('bk_script_'):
    # Set up the data, model, and incentive mechanism.
    inj = Injector([
        DecisionTreeModule,
        DefaultCollaborativeTrainerModule,
        LoggingModule,
        StakeableImModule,
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