def test_grid_search():
gridsearch = strategy.GridSearch()
engine = MockExecutionEngine()
_reset_execution_engine(engine)
gridsearch.run(*_get_model_and_mutators())
wait_models(*engine.models)
selection = set()
for model in engine.models:
selection.add((model.graphs['_model__fc1'].hidden_nodes[0].operation.
parameters['bias'], model.graphs['_model__fc2'].
hidden_nodes[0].operation.parameters['bias']))
assert len(selection) == 4
_reset_execution_engine()
def test_random_search():
random = strategy.Random()
engine = MockExecutionEngine()
_reset_execution_engine(engine)
random.run(*_get_model_and_mutators())
wait_models(*engine.models)
selection = set()
for model in engine.models:
selection.add((
model.get_node_by_name('_model__fc1').operation.parameters['bias'],
model.get_node_by_name('_model__fc2').operation.parameters['bias']
))
assert len(selection) == 4
_reset_execution_engine()
def test_evolution():
evolution = strategy.RegularizedEvolution(population_size=5, sample_size=3, cycles=10, mutation_prob=0.5, on_failure='ignore')
engine = MockExecutionEngine(failure_prob=0.2)
_reset_execution_engine(engine)
evolution.run(*_get_model_and_mutators())
wait_models(*engine.models)
_reset_execution_engine()
evolution = strategy.RegularizedEvolution(population_size=5, sample_size=3, cycles=10, mutation_prob=0.5, on_failure='worst')
engine = MockExecutionEngine(failure_prob=0.4)
_reset_execution_engine(engine)
evolution.run(*_get_model_and_mutators())
wait_models(*engine.models)
_reset_execution_engine()
def test_rl():
rl = strategy.PolicyBasedRL(max_collect=2, trial_per_collect=10)
engine = MockExecutionEngine(failure_prob=0.2)
_reset_execution_engine(engine)
rl.run(*_get_model_and_mutators(diff_size=True))
wait_models(*engine.models)
_reset_execution_engine()
rl = strategy.PolicyBasedRL(max_collect=2, trial_per_collect=10)
engine = MockExecutionEngine(failure_prob=0.2)
_reset_execution_engine(engine)
rl.run(*_get_model_and_mutators())
wait_models(*engine.models)
_reset_execution_engine()