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exp2.py
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import datetime
from pathlib import Path
import numpy as np
import yaml
from emukit.core import ContinuousParameter
from emukit.core import ParameterSpace
from joblib import Parallel
from joblib import delayed
from active_learning import convert_all_numpy_to_float
from active_learning import run_experiment
from metrics import avg_precision
from metrics import create_f1_evaluator
from models import Hierarchical
from models import MaskedGPClassifier
from models import MaskedModel
from plots import plot_ground_truth
from plots import plot_metrics
from test_functions import car_performance_2
from utils import summarise_results
def main(
misclassification_threshold: float = 0.02,
n_repeats: int = 5,
save_dir: Path = Path("tmp"),
n_jobs: int = 15,
max_iter: int = 150,
):
np.random.seed(0)
random_state = np.random.randint(np.iinfo(np.int32).max, size=5 * n_repeats)
save_dir = save_dir / datetime.datetime.now().strftime("%Y_%B_%d_%p%I:%M")
save_dir = save_dir / "car"
save_dir.mkdir(parents=True, exist_ok=True)
parameter_space = ParameterSpace([ContinuousParameter("x_0", -100, -0), ContinuousParameter("vx", 10, 15)])
offset = np.min(parameter_space.get_bounds(), axis=-1)
space_range = np.diff(parameter_space.get_bounds(), axis=-1).squeeze()
parameter_space_rescaled = ParameterSpace([ContinuousParameter("x_0", 0, 1), ContinuousParameter("vx", 0, 1)])
def car_performance_2_rescaled(x):
return car_performance_2(*np.split(space_range * x + offset, x.shape[-1], -1))
metric_fns = {
"f1": create_f1_evaluator(parameter_space_rescaled, car_performance_2_rescaled),
"Average Precision": avg_precision(parameter_space_rescaled, car_performance_2_rescaled),
}
(save_dir).mkdir(parents=True, exist_ok=True)
plot_ground_truth(car_performance_2_rescaled, parameter_space_rescaled, save_dir)
analytic_failure_probability = 0.0382019
# https://joblib.readthedocs.io/en/latest/auto_examples/parallel_random_state.html
all_results = Parallel(n_jobs=n_jobs)(
list(
(
*(
delayed(run_experiment)(
performance_function=car_performance_2_rescaled,
model=Hierarchical(),
parameter_space=parameter_space_rescaled,
save_dir=save_dir / f"hierarchical_{idx}",
metric_fns=metric_fns,
misclassification_threshold=misclassification_threshold,
true_pf=analytic_failure_probability,
seed=random_state[idx],
max_iterations=max_iter,
)
for idx in range(n_repeats)
),
*(
delayed(run_experiment)(
performance_function=car_performance_2_rescaled,
model=MaskedGPClassifier(),
parameter_space=parameter_space_rescaled,
save_dir=save_dir / f"masked_gp_cls_{idx}",
metric_fns=metric_fns,
misclassification_threshold=misclassification_threshold,
true_pf=analytic_failure_probability,
seed=random_state[idx + n_repeats],
max_iterations=max_iter,
)
for idx in range(n_repeats)
),
*(
delayed(run_experiment)(
performance_function=car_performance_2_rescaled,
model=MaskedModel(mask_with=1.0),
parameter_space=parameter_space_rescaled,
save_dir=save_dir / f"masked_{idx}",
metric_fns=metric_fns,
misclassification_threshold=misclassification_threshold,
true_pf=analytic_failure_probability,
seed=random_state[idx + 2 * n_repeats],
max_iterations=max_iter,
)
for idx in range(n_repeats)
),
*(
delayed(run_experiment)(
performance_function=car_performance_2_rescaled,
model=MaskedModel(mask_with=0.5),
parameter_space=parameter_space_rescaled,
save_dir=save_dir / f"masked05_{idx}",
metric_fns=metric_fns,
misclassification_threshold=misclassification_threshold,
true_pf=analytic_failure_probability,
seed=random_state[idx + 3 * n_repeats],
max_iterations=max_iter,
)
for idx in range(n_repeats)
),
*(
delayed(run_experiment)(
performance_function=car_performance_2_rescaled,
model=MaskedModel(mask_with=0.1),
parameter_space=parameter_space_rescaled,
save_dir=save_dir / f"masked01_{idx}",
metric_fns=metric_fns,
misclassification_threshold=misclassification_threshold,
true_pf=analytic_failure_probability,
seed=random_state[idx + 4 * n_repeats],
max_iterations=max_iter,
)
for idx in range(n_repeats)
),
)
)
)
metric_results_hierarchical = all_results[:n_repeats]
metric_results_masked_gpcls = all_results[n_repeats : 2 * n_repeats]
metric_results_masked = all_results[2 * n_repeats : 3 * n_repeats]
metric_results_masked05 = all_results[3 * n_repeats : 4 * n_repeats]
metric_results_masked01 = all_results[4 * n_repeats : 5 * n_repeats]
result_dict = {
"Hierarchical GP": metric_results_hierarchical,
r"Masked GP $\alpha=1$": metric_results_masked,
r"Masked GP $\alpha=0.5$": metric_results_masked05,
r"Masked GP $\alpha=0.1$": metric_results_masked01,
"Masked GP Classification": metric_results_masked_gpcls,
}
plot_metrics(
result_dict,
save_dir,
analytic_failure_probability,
)
with open(save_dir / "results.yaml", "w") as f:
yaml.dump(convert_all_numpy_to_float(summarise_results(result_dict)), f)
if __name__ == "__main__":
main()