from deephyper.problem import HpProblem
from deephyper.problem import config_space as cs
# Problem definition
Problem = HpProblem()
x_hp = Problem.add_hyperparameter(
name="x", value=(0, 10)) # or Problem.add_dim("x", (0, 10))
y_hp = Problem.add_hyperparameter(
name="y", value=(0, 10)) # or Problem.add_dim("y", (0, 10))
not_zero = cs.ForbiddenAndConjunction(cs.ForbiddenEqualsClause(x_hp, 0),
cs.ForbiddenEqualsClause(y_hp, 0))
Problem.add_forbidden_clause(not_zero)
Problem.add_starting_point(x=1, y=1)
# Definition of the function which runs the model
def run(param_dict):
x = param_dict["x"]
y = param_dict["y"]
res = np.log(1 / (y + x))
return res # the objective
value=["friedman_mse", "mse", "mae", "gini", "entropy"],
default_value="gini",
)
# GradientBoosting
loss = Problem.add_hyperparameter(name="loss",
value=["deviance", "exponential"])
learning_rate = Problem.add_hyperparameter(name="learning_rate",
value=(0.01, 1.0))
subsample = Problem.add_hyperparameter(name="subsample", value=(0.01, 1.0))
gradient_boosting_hp = [loss, learning_rate, subsample]
for hp_i in gradient_boosting_hp:
Problem.add_condition(
cs.EqualsCondition(hp_i, classifier, "GradientBoosting"))
forbidden_criterion_rf = cs.ForbiddenAndConjunction(
cs.ForbiddenEqualsClause(classifier, "RandomForest"),
cs.ForbiddenInClause(criterion, ["friedman_mse", "mse", "mae"]),
)
Problem.add_forbidden_clause(forbidden_criterion_rf)
forbidden_criterion_gb = cs.ForbiddenAndConjunction(
cs.ForbiddenEqualsClause(classifier, "GradientBoosting"),
cs.ForbiddenInClause(criterion, ["gini", "entropy"]),
)
Problem.add_forbidden_clause(forbidden_criterion_gb)
if __name__ == "__main__":
print(Problem)