def test_multiprocessing_works(self):
experiment = _setup_experiment()
experiment.num_workers = 5
experiment.num_steps = 10
experiment.num_runs = 5
result = attention_allocation_experiment.run(experiment)
result = json.loads(result)
def mle_agent_epsilon_5_resource_all_dynamics():
"""Run experiments on a greedy-epsilon mle agent, epsilon=0.6, across dynamics."""
dynamic_values_to_test = [0.0, 0.01, 0.05, 0.1, 0.15]
experiment = _setup_experiment()
experiment.agent_class = allocation_agents.MLEProbabilityMatchingAgent
experiment.agent_params = allocation_agents.MLEProbabilityMatchingAgentParams(
)
experiment.agent_params.burn_steps = 25
experiment.agent_params.epsilon = 0.5
experiment.agent_params.window = 100
reports_dict = {}
for value in dynamic_values_to_test:
experiment.env_params.dynamic_rate = value
json_report = attention_allocation_experiment.run(experiment)
report = json.loads(json_report)
print('\n\nMLE Agent, 6 attention units, epsilon=0.5')
_print_discovered_missed_incidents_report(value, report)
output_filename = 'mle_epsilon.5_6units_%f.json' % value
with open(os.path.join(FLAGS.output_dir, output_filename), 'w') as f:
json.dump(report, f)
reports_dict[value] = json_report
return reports_dict
def test_MLEProbabilityMatchingAgent_works(self):
experiment = _setup_experiment()
experiment.agent_class = allocation_agents.MLEProbabilityMatchingAgent
experiment.agent_params = allocation_agents.MLEProbabilityMatchingAgentParams(
burn_steps=25, window=100)
result = attention_allocation_experiment.run(experiment)
# Tests that the result is a valid json string.
result = json.loads(result)
def test_MLEGreedyAgent_works(self):
experiment = _setup_experiment()
experiment.agent_class = allocation_agents.MLEGreedyAgent
experiment.agent_params = allocation_agents.MLEGreedyAgentParams(
burn_steps=5, window=10, alpha=5.0)
result = attention_allocation_experiment.run(experiment)
# Tests that the result is a valid json string.
result = json.loads(result)
def uniform_agent_resource_all_dynamics():
"""Run experiments on a uniform agent across dynamic rates."""
dynamic_values_to_test = [0.0, 0.01, 0.05, 0.1, 0.15]
experiment = _setup_experiment()
experiment.agent_class = test_util.DummyAgent
reports_dict = {}
for value in dynamic_values_to_test:
print('Running an experiment...')
experiment.env_params.dynamic_rate = value
json_report = attention_allocation_experiment.run(experiment)
report = json.loads(json_report)
print('\n\nUniform Random Agent, 6 attention units')
_print_discovered_missed_incidents_report(value, report)
output_filename = 'uniform_6units_%f.json' % value
with open(os.path.join(FLAGS.output_dir, output_filename), 'w') as f:
json.dump(report, f)
reports_dict[value] = json_report
return reports_dict
def mle_greedy_alpha5_agent_resource_all_dynamics():
"""Run experiments on a greedy-epsilon mle agent, epsilon=0.1, across dynamics."""
dynamic_values_to_test = [0.0, 0.01, 0.05, 0.1, 0.15]
experiment = _setup_experiment()
experiment.agent_class = allocation_agents.MLEGreedyAgent
experiment.agent_params = allocation_agents.MLEGreedyAgentParams(
burn_steps=25, window=100, alpha=0.75)
reports_dict = {}
for value in dynamic_values_to_test:
print('Running an experiment...')
experiment.env_params.dynamic_rate = value
json_report = attention_allocation_experiment.run(experiment)
report = json.loads(json_report)
print('\n\nMLE Greedy Fair Agent, 6 attention units, alpha=0.75')
_print_discovered_missed_incidents_report(value, report)
output_filename = 'mle_greedy_fair_alpha75_6units_%f.json' % value
with open(os.path.join(FLAGS.output_dir, output_filename), 'w') as f:
json.dump(report, f)
reports_dict[value] = json_report
return reports_dict
def test_report_is_replicable(self):
experiment = _setup_experiment()
json_report = attention_allocation_experiment.run(experiment)
json_report_second = attention_allocation_experiment.run(experiment)
self.assertEqual(json_report, json_report_second)
def test_report_valid_json(self):
# Tests that the experiment can run.
experiment = _setup_experiment()
result = attention_allocation_experiment.run(experiment)
# Tests that the result is a valid json string.
result = json.loads(result)
