def test_sample_types_conditional(self):
import ConfigSpace as cs
import numpy as np
from deephyper.evaluator import Evaluator
from deephyper.problem import HpProblem
from deephyper.search.hps import CBO
problem = HpProblem()
# choices
choice = problem.add_hyperparameter(
name="choice",
value=["choice1", "choice2"],
)
# integers
x1_int = problem.add_hyperparameter(name="x1_int", value=(1, 10))
x2_int = problem.add_hyperparameter(name="x2_int", value=(1, 10))
# conditions
cond_1 = cs.EqualsCondition(x1_int, choice, "choice1")
cond_2 = cs.EqualsCondition(x2_int, choice, "choice2")
problem.add_condition(cond_1)
problem.add_condition(cond_2)
def run(config):
if config["choice"] == "choice1":
assert np.issubdtype(type(config["x1_int"]), np.integer)
else:
assert np.issubdtype(type(config["x2_int"]), np.integer)
return 0
create_evaluator = lambda: Evaluator.create(run, method="serial")
CBO(problem,
create_evaluator(),
random_state=42,
surrogate_model="DUMMY").search(10)
Problem = HpProblem(seed=45)
func = Problem.add_hyperparameter(name="func", value=["f1", "f2"])
# f1 variables
f1_x = Problem.add_hyperparameter(name="f1_x", value=(0.0, 1.0, "uniform"))
f1_y = Problem.add_hyperparameter(name="f1_y", value=(0.0, 1.0, "uniform"))
# f2 variables
f2_x = Problem.add_hyperparameter(name="f2_x", value=(0.0, 1.0, "uniform"))
f2_y = Problem.add_hyperparameter(name="f2_y", value=(0.0, 1.0, "uniform"))
cond_f1_x = cs.EqualsCondition(f1_x, func, "f1")
cond_f1_y = cs.EqualsCondition(f1_y, func, "f1")
Problem.add_condition(cond_f1_x)
Problem.add_condition(cond_f1_y)
cond_f2_x = cs.EqualsCondition(f2_x, func, "f2")
cond_f2_y = cs.EqualsCondition(f2_y, func, "f2")
Problem.add_condition(cond_f2_x)
Problem.add_condition(cond_f2_y)
# Definition of the function which runs the model
def run(param_dict):
func = param_dict["func"]
if func == "f1":
return param_dict["f1_x"] + param_dict["f1_y"]
else:
classifier = Problem.add_hyperparameter(
name="classifier",
value=["RandomForest", "Logistic", "AdaBoost", "KNeighbors", "MLP", "SVC", "XGBoost"],
)
# n_estimators
n_estimators = Problem.add_hyperparameter(
name="n_estimators", value=(1, 2000, "log-uniform")
)
cond_n_estimators = cs.OrConjunction(
cs.EqualsCondition(n_estimators, classifier, "RandomForest"),
cs.EqualsCondition(n_estimators, classifier, "AdaBoost"),
)
Problem.add_condition(cond_n_estimators)
# max_depth
max_depth = Problem.add_hyperparameter(name="max_depth", value=(2, 100, "log-uniform"))
cond_max_depth = cs.EqualsCondition(max_depth, classifier, "RandomForest")
Problem.add_condition(cond_max_depth)
# n_neighbors
n_neighbors = Problem.add_hyperparameter(name="n_neighbors", value=(1, 100))
cond_n_neighbors = cs.EqualsCondition(n_neighbors, classifier, "KNeighbors")
Problem.add_condition(cond_n_neighbors)
Problem.add_dim('units2', (1, 64)) # int in range 1-64
Problem.add_dim('dropout1', (0.0, 1.0)) # float in range 0-1
Problem.add_dim('dropout2', (0.0, 1.0)) # float in range 0-1
Problem.add_dim('batch_size', (5, 500)) # int in range 5-500
Problem.add_dim('log10_learning_rate', (-5.0, 0.0)) # float lr range from 10^-5 to 1
# one of ['relu', ..., ]
Problem.add_dim('activation', ['relu', 'elu', 'selu', 'tanh'])
optimizer = Problem.add_dim('optimizer', [
'Adam', 'RMSprop', 'SGD', 'Nadam', 'Adagrad'
])
# Only vary momentum if optimizer is SGD
momentum = Problem.add_dim("momentum", (0.5, 0.9))
Problem.add_condition(cs.EqualsCondition(momentum, optimizer, "SGD"))
# Add a starting point to try first
Problem.add_starting_point(
units1=16,
units2=32,
dropout1=0.0,
dropout2=0.0,
batch_size=16,
activation='relu',
optimizer='SGD',
log10_learning_rate=-3.0,
momentum=0.5,
)
"RandomForest", "Linear", "AdaBoost", "KNeighbors", "MLP", "SVR",
"XGBoost"
],
)
# n_estimators
n_estimators = problem_autosklearn1.add_hyperparameter(name="n_estimators",
value=(1, 2000,
"log-uniform"))
cond_n_estimators = cs.OrConjunction(
cs.EqualsCondition(n_estimators, regressor, "RandomForest"),
cs.EqualsCondition(n_estimators, regressor, "AdaBoost"),
)
problem_autosklearn1.add_condition(cond_n_estimators)
# max_depth
max_depth = problem_autosklearn1.add_hyperparameter(name="max_depth",
value=(2, 100,
"log-uniform"))
cond_max_depth = cs.EqualsCondition(max_depth, regressor, "RandomForest")
problem_autosklearn1.add_condition(cond_max_depth)
# n_neighbors
n_neighbors = problem_autosklearn1.add_hyperparameter(name="n_neighbors",
value=(1, 100))
cond_n_neighbors = cs.EqualsCondition(n_neighbors, regressor, "KNeighbors")
criterion = Problem.add_hyperparameter(
name="criterion",
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)
