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import numpy as np
from cmaes.safe_cma import SafeCMA
def example1():
"""
example with a single safety function
"""
# number of dimensions
dim = 5
# objective function
def quadratic(x):
coef = 1000 ** (np.arange(dim) / float(dim - 1))
return np.sum((x * coef) ** 2)
# safety function
def safe_function(x):
return x[0]
# safe seeds
safe_seeds_num = 10
safe_seeds = (np.random.rand(safe_seeds_num, dim) * 2 - 1) * 5
safe_seeds[:, 0] = -np.abs(safe_seeds[:, 0])
# evaluation of safe seeds (with a single safety function)
seeds_evals = np.array([quadratic(x) for x in safe_seeds])
seeds_safe_evals = np.stack([[safe_function(x)] for x in safe_seeds])
safety_threshold = np.array([0])
# optimizer (safe CMA-ES)
optimizer = SafeCMA(
sigma=1.0,
safety_threshold=safety_threshold,
safe_seeds=safe_seeds,
seeds_evals=seeds_evals,
seeds_safe_evals=seeds_safe_evals,
)
unsafe_eval_counts = 0
best_eval = np.inf
for generation in range(400):
solutions = []
for _ in range(optimizer.population_size):
# Ask a parameter
x = optimizer.ask()
value = quadratic(x)
safe_value = np.array([safe_function(x)])
# save best eval
best_eval = np.min((best_eval, value))
unsafe_eval_counts += safe_value > safety_threshold
solutions.append((x, value, safe_value))
# Tell evaluation values.
optimizer.tell(solutions)
print(f"#{generation} ({best_eval} {unsafe_eval_counts})")
if optimizer.should_stop():
break
def example2():
"""
example with multiple safety functions
"""
# number of dimensions
dim = 5
# objective function
def quadratic(x):
coef = 1000 ** (np.arange(dim) / float(dim - 1))
return np.sum((x * coef) ** 2)
# safety functions
def safe_function1(x):
return x[0]
def safe_function2(x):
return x[1]
# safe seeds
safe_seeds_num = 10
safe_seeds = (np.random.rand(safe_seeds_num, dim) * 2 - 1) * 5
safe_seeds[:, 0] = -np.abs(safe_seeds[:, 0])
safe_seeds[:, 1] = -np.abs(safe_seeds[:, 1])
# evaluation of safe seeds (with multiple safety functions)
seeds_evals = np.array([quadratic(x) for x in safe_seeds])
seeds_safe_evals = np.stack(
[[safe_function1(x), safe_function2(x)] for x in safe_seeds]
)
safety_threshold = np.array([0, 0])
# optimizer (safe CMA-ES)
optimizer = SafeCMA(
sigma=1.0,
safety_threshold=safety_threshold,
safe_seeds=safe_seeds,
seeds_evals=seeds_evals,
seeds_safe_evals=seeds_safe_evals,
)
unsafe_eval_counts = 0
best_eval = np.inf
for generation in range(400):
solutions = []
for _ in range(optimizer.population_size):
# Ask a parameter
x = optimizer.ask()
value = quadratic(x)
safe_value = np.array([safe_function1(x), safe_function2(x)])
# save best eval
best_eval = np.min((best_eval, value))
unsafe_eval_counts += safe_value > safety_threshold
solutions.append((x, value, safe_value))
# Tell evaluation values.
optimizer.tell(solutions)
print(f"#{generation} ({best_eval} {unsafe_eval_counts})")
if optimizer.should_stop():
break
if __name__ == "__main__":
example1()
example2()
|
