def run_benchmark_simulation(study, baseline_name):
experiment = baseline_name
baseline_configuration = baselines.get_baseline_configuration(baseline_name)
task_id = baseline_configuration["task_id"]
seed = 1
epochs = 25
steps = 200
max_time = 60 * 60
population_size = 20
path = get_simulations_path().joinpath(study).joinpath(experiment)
utility.prepare_OpenML()
simulation = make_openml_light_classifier_simulation(study, experiment, baseline_name, task_id, seed, population_size, path)
run_simulation(simulation, steps, epochs, max_time)
autem.ReportManager(path).update_combined_reports()
def run_test_simulation(baseline_name=None, seed=None):
baseline_name = get_test_baseline_name(
) if baseline_name is None else baseline_name
experiment = baseline_name if seed is None else "%s_%d" % (baseline_name,
seed)
study = get_test_study()
seed = seed if not seed is None else 2
version = get_test_version()
configuration = baselines.get_baseline_configuration(baseline_name)
path = get_test_simulations_path().joinpath(study).joinpath(experiment)
memory = str(path.joinpath("cache"))
utility.prepare_OpenML()
task_id = configuration["task_id"]
task = openml.tasks.get_task(task_id)
data_id = task.dataset_id
dataset = openml.datasets.get_dataset(data_id)
dataset_name = dataset.name
simulation_name = "Test %s_%s_v%d" % (study, experiment, version)
identity = {
'study': study,
'experiment': experiment,
'dataset': dataset_name,
'version': version
}
simulation = autem.Simulation(simulation_name, [
loaders.OpenMLLoader(data_id),
scorers.Accuracy(),
workflows.Snapshot(),
baselines.BaselineStats(baseline_name),
reporters.Csv(path),
hyper_learners.ClassificationSnapshot(),
],
seed=seed,
n_jobs=4,
identity=identity,
memory=memory)
simulation.run()
def run_balance_scale_mastery(seed):
baseline_name = "balance-scale"
experiment = "mastery_%s_s%d" % (baseline_name, seed)
study = "DEV"
version = benchmark.get_version()
simulation_name = "%s_%s_v%d" % (study, experiment, version)
configuration = baselines.get_baseline_configuration(baseline_name)
path = benchmark.get_simulations_path().joinpath(study).joinpath(
experiment)
utility.prepare_OpenML()
task_id = configuration["task_id"]
task = openml.tasks.get_task(task_id)
data_id = task.dataset_id
dataset = openml.datasets.get_dataset(data_id)
dataset_name = dataset.name
identity = {
'study': study,
'experiment': experiment,
'dataset': dataset_name,
'version': version
}
simulation = autem.Simulation(simulation_name, [
loaders.OpenMLLoader(data_id),
scorers.LeagueScorer(scorers.accuracy_score),
workflows.MasteryWorkflow(),
baselines.BaselineStats(baseline_name),
hyper_learners.ClassificationSVM(),
reporters.Csv(path),
])
settings = autem.SimulationSettings(simulation)
settings.set_identity(identity)
settings.set_n_jobs(4)
settings.set_seed(seed)
simulation.run()
def make_benchmark_simulation(study, baseline_name, configuration, learner):
experiment = baseline_name
baseline_configuration = baselines.get_baseline_configuration(baseline_name)
task_id = baseline_configuration["task_id"]
task = openml.tasks.get_task(task_id)
data_id = task.dataset_id
version = get_version()
configuration = baseline_configuration["Configuration"] if configuration is None else configuration
learner = baseline_configuration["Learner"] if learner is None else learner
configuration_valid = configuration in simulation_builders
if not configuration_valid:
print("Baseline %s configuration %s does not exist" % (baseline_name, configuration))
return None
name = "'%s_%s_%s v%d'" % (study, experiment, configuration, version)
identity = {
'study': study,
'experiment': experiment,
'dataset': baseline_name,
'version': version,
'configuration': configuration,
}
n_jobs = get_n_jobs()
seed = 1
path = get_simulations_path().joinpath(study).joinpath(experiment)
memory = str(path.joinpath("cache"))
utility.prepare_OpenML()
simulation_builder = simulation_builders[configuration]
simulation = simulation_builder(name, identity, data_id, learner, path)
settings = autem.SimulationSettings(simulation)
settings.set_identity(identity)
settings.set_n_jobs(4)
settings.set_seed(seed)
settings.set_memory(memory)
return simulation
def run_cylinder_bands_mastery():
seed = 1
study = "DEV"
baseline_name = "cylinder-bands"
experiment = baseline_name
version = benchmark.get_version()
simulation_name = "%s_%s_v%d" % (study, experiment, version)
configuration = baselines.get_baseline_configuration(baseline_name)
path = benchmark.get_simulations_path().joinpath(study).joinpath(
experiment)
utility.prepare_OpenML()
task_id = configuration["task_id"]
task = openml.tasks.get_task(task_id)
data_id = task.dataset_id
dataset = openml.datasets.get_dataset(data_id)
dataset_name = dataset.name
identity = {
'study': study,
'experiment': experiment,
'dataset': dataset_name,
'version': version
}
simulation = autem.Simulation(simulation_name, [
loaders.OpenMLLoader(data_id),
scorers.Accuracy(),
workflows.Mastery(["Learner"]),
baselines.BaselineStats(baseline_name),
hyper_learners.ClassificationSVM(),
reporters.Csv(path),
],
seed=seed,
n_jobs=4,
identity=identity)
simulation.run()
def run_cylinder_bands_custom():
seed = 1
study = "DEV"
baseline_name = "cylinder-bands"
experiment = baseline_name
version = benchmark.get_version()
simulation_name = "%s_%s_v%d" % (study, experiment, version)
configuration = baselines.get_baseline_configuration(baseline_name)
path = benchmark.get_simulations_path().joinpath(study).joinpath(
experiment)
utility.prepare_OpenML()
task_id = configuration["task_id"]
task = openml.tasks.get_task(task_id)
data_id = task.dataset_id
dataset = openml.datasets.get_dataset(data_id)
dataset_name = dataset.name
identity = {
'study': study,
'experiment': experiment,
'dataset': dataset_name,
'version': version
}
simulation = autem.Simulation(
simulation_name,
[
loaders.OpenMLLoader(data_id),
scorers.Accuracy(),
workflows.Standard(),
baselines.BaselineStats(baseline_name),
# Scalers
autem.Choice("Scaler", [
preprocessors.RobustScaler(),
preprocessors.StandardScaler(),
preprocessors.BoxCoxTransform(),
preprocessors.YeoJohnsonTransform()
]),
# Feature Selectors
autem.Choice(
"Selector",
[
#preprocessors.NoSelector(),
#preprocessors.SelectPercentile(),
preprocessors.VarianceThreshold()
]),
# Feature Reducers
autem.Choice(
"Reducer",
[
preprocessors.NoReducer(),
#preprocessors.FastICA(),
#preprocessors.FeatureAgglomeration(),
#preprocessors.PCA(),
]),
# Approximators
autem.Choice("Approximator", [
preprocessors.NoApproximator(),
]),
autem.Choice(
"Learner",
[
# learners.LinearSVC(),
learners.PolySVC(),
# learners.RadialBasisSVC(),
]),
reporters.Csv(path),
],
seed=seed,
n_jobs=4,
identity=identity)
simulation.run()