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Remove clutter of configfiles in testdata mathfunc #9357

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Remove clutter of configfiles in testdata mathfunc #9357

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36afd13
end of day commit
StephanDeHoop Nov 21, 2024
42c5c69
Replicate existing config files (verify with tests) (still needs clea…
StephanDeHoop Nov 26, 2024
c960695
Small cleanup
StephanDeHoop Nov 26, 2024
969355d
more logical name
StephanDeHoop Nov 26, 2024
acdfae9
rename tests
StephanDeHoop Nov 26, 2024
cd5fc8e
Use minimal and advanced base config generation
StephanDeHoop Nov 26, 2024
a9b9e85
Use pytest.fixtures
StephanDeHoop Nov 26, 2024
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244 changes: 244 additions & 0 deletions tests/everest/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,19 @@

from ert.config import QueueSystem
from ert.ensemble_evaluator import EvaluatorServerConfig
from everest.config import EverestConfig
from everest.config.control_config import ControlConfig
from tests.everest.generate_everest_configs import (
generate_controls_config,
generate_default_advanced_config_dict,
generate_default_minimal_config_dict,
generate_environment_config,
generate_input_constraints_config,
generate_install_jobs_config,
generate_model_config,
generate_objective_function_config,
generate_optimization_config,
)
from tests.everest.utils import relpath


Expand Down Expand Up @@ -137,3 +149,235 @@ def create_evaluator_server_config(run_model):
)

return create_evaluator_server_config


@pytest.fixture
def minimal_everest_config() -> EverestConfig:
return EverestConfig.model_validate(generate_default_minimal_config_dict())


@pytest.fixture
def auto_scaled_controls_everest_config() -> EverestConfig:
everest_config = generate_default_minimal_config_dict()
everest_config["controls"] = [
generate_controls_config(
initial_guess=0.2, auto_scale=True, scaled_range=[0.3, 0.7]
)
]
everest_config["optimization"] = generate_optimization_config(max_batch_num=10)
everest_config["environment"] = generate_environment_config(random_seed=999)
everest_config["forward_model"] = [
"distance3 --point-file point.json --target 0.5 0.5 0.5 --out distance --scaling -1 1 0.3 0.7"
]
everest_config["input_constraints"] = [
generate_input_constraints_config(
weights={"point.x": 1.0, "point.y": 1.0}, upper_bound=0.5
)
]
everest_config["config_path"] = os.path.abspath(
os.path.join(
"./test-data", "everest", "math_func", "config_auto_scaled_controls.yml"
)
)
return EverestConfig.model_validate(everest_config)


@pytest.fixture
def cvar_everest_config() -> EverestConfig:
everest_config = generate_default_minimal_config_dict()
variables = [
{"name": "x", "initial_guess": 0.0},
{"name": "y", "initial_guess": 0.0},
{"name": "z", "initial_guess": 0.0},
]
everest_config["controls"] = [
generate_controls_config(
perturbation_magnitude=0.01,
min=-2.0,
max=2.0,
variables=variables,
initial_guess=None,
)
]
everest_config["optimization"] = generate_optimization_config(
max_batch_num=5,
backend="scipy",
algorithm="slsqp",
cvar={"percentile": 0.5},
convergence_tolerance=None,
)
everest_config["model"] = generate_model_config(realizations=[0, 1])
everest_config["environment"] = generate_environment_config(
random_seed=999, output_folder="distance_output", log_level="info"
)
everest_config["forward_model"] = [
"distance3 --point-file point.json --realization <GEO_ID> --target 0.5 0.5 0.5 --out distance"
]
everest_config["config_path"] = os.path.abspath(
os.path.join("./test-data", "everest", "math_func", "config_cvar.yml")
)
return EverestConfig.model_validate(everest_config)


@pytest.fixture
def advanced_everest_config() -> EverestConfig:
return EverestConfig.model_validate(generate_default_advanced_config_dict())


@pytest.fixture
def advanced_scipy_everest_config() -> EverestConfig:
everest_config = generate_default_advanced_config_dict()
everest_config["optimization"] = generate_optimization_config(
convergence_tolerance=0.001,
constraint_tolerance=0.001,
backend="scipy",
algorithm="SLSQP",
speculative=True,
max_batch_num=4,
perturbation_num=7,
backend_options={"maxiter": 100},
)
everest_config["environment"] = generate_environment_config(
simulation_folder="scratch/advanced/", output_folder="everest_output/"
)

everest_config["config_path"] = os.path.abspath(
os.path.join("./test-data", "everest", "math_func", "config_advanced_scipy.yml")
)
return EverestConfig.model_validate(everest_config)


@pytest.fixture
def minimal_slow_everest_config() -> EverestConfig:
everest_config = generate_default_minimal_config_dict()
everest_config["optimization"] = generate_optimization_config()
everest_config["install_jobs"] = [
generate_install_jobs_config(name="distance3", source="jobs/DISTANCE3"),
generate_install_jobs_config(name="sleep", source="jobs/SLEEP"),
]
everest_config["forward_model"] = [
"distance3 --point-file point.json --target 0.5 0.5 0.5 --out distance",
"sleep --sleep 10",
]
everest_config["config_path"] = os.path.abspath(
os.path.join("./test-data", "everest", "math_func", "config_minimal_slow.yml")
)
return EverestConfig.model_validate(everest_config)


@pytest.fixture
def multiobj_everest_config() -> EverestConfig:
everest_config = generate_default_minimal_config_dict()
everest_config["controls"] = [
generate_controls_config(initial_guess=0, perturbation_magnitude=0.01)
]
everest_config["objective_functions"] = [
generate_objective_function_config(
name="distance_p", weight=0.5, normalization=1.5
),
generate_objective_function_config(
name="distance_q", weight=0.25, normalization=1.0
),
]
everest_config["optimization"] = generate_optimization_config(
convergence_tolerance=0.005, perturbation_num=5, max_batch_num=3
)
everest_config["environment"] = generate_environment_config(
output_folder="everest_output_multiobj", random_seed=999
)
everest_config["forward_model"] = [
"distance3 --point-file point.json --target 0.5 0.5 0.5 --out distance_p",
"distance3 --point-file point.json --target -1.5 -1.5 0.5 --out distance_q",
]
everest_config["config_path"] = os.path.abspath(
os.path.join("./test-data", "everest", "math_func", "config_multiobj.yml")
)
return EverestConfig.model_validate(everest_config)


@pytest.fixture
def one_batch_everest_config() -> EverestConfig:
everest_config = generate_default_minimal_config_dict()
everest_config["optimization"] = generate_optimization_config(
convergence_tolerance=None,
max_batch_num=1,
)
everest_config["environment"] = generate_environment_config(random_seed=999)

everest_config["config_path"] = os.path.abspath(
os.path.join("./test-data", "everest", "math_func", "config_one_batch.yml")
)
return EverestConfig.model_validate(everest_config)


@pytest.fixture
def remove_run_path_everest_config() -> EverestConfig:
everest_config = generate_default_minimal_config_dict()
everest_config["controls"] = [generate_controls_config(initial_guess=0)]
everest_config["optimization"] = generate_optimization_config(
convergence_tolerance=0.005,
max_batch_num=None,
min_realizations_success=1,
min_pert_success=1,
max_iterations=1,
perturbation_num=2,
)
everest_config["environment"] = generate_environment_config(
output_folder="everest_output/", simulation_folder="scratch/advanced/"
)
everest_config["install_jobs"] = [
generate_install_jobs_config(name="distance3", source="jobs/DISTANCE3"),
generate_install_jobs_config(
name="toggle_failure", source="jobs/FAIL_SIMULATION"
),
]
everest_config["forward_model"] = [
"distance3 --point-file point.json --target 0.5 0.5 0.5 --out distance",
"toggle_failure",
]
everest_config["config_path"] = os.path.abspath(
os.path.join(
"./test-data", "everest", "math_func", "config_remove_run_path.yml"
)
)
everest_config["simulator"] = {"delete_run_path": True}
return EverestConfig.model_validate(everest_config)


@pytest.fixture
def stddev_everest_config() -> EverestConfig:
everest_config = generate_default_minimal_config_dict()
variables = [
{"name": "x", "initial_guess": 0.0},
{"name": "y", "initial_guess": 0.0},
{"name": "z", "initial_guess": 0.0},
]
everest_config["controls"] = [
generate_controls_config(
perturbation_magnitude=0.01, variables=variables, initial_guess=None
)
]
everest_config["objective_functions"] = [
generate_objective_function_config(name="distance", weight=1.0),
generate_objective_function_config(
name="stddev", weight=1.0, type="stddev", alias="distance"
),
]
everest_config["optimization"] = generate_optimization_config(
max_batch_num=5,
backend="scipy",
algorithm="slsqp",
convergence_tolerance=0.0001,
perturbation_num=3,
)
everest_config["model"] = generate_model_config(realizations=[0, 1])
everest_config["environment"] = generate_environment_config(
random_seed=999, output_folder="distance_output", log_level=None
)
everest_config["forward_model"] = [
"distance3 --point-file point.json --realization <GEO_ID> --target 0.5 0.5 0.5 --out distance"
]
everest_config["config_path"] = os.path.abspath(
os.path.join("./test-data", "everest", "math_func", "config_stddev.yml")
)
return EverestConfig.model_validate(everest_config)
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