def test_get_range_hyps():
with pytest.raises(AssertionError) as error:
utils_covariance.get_range_hyps(1.0, 2)
with pytest.raises(AssertionError) as error:
utils_covariance.get_range_hyps('abc', 2)
with pytest.raises(AssertionError) as error:
utils_covariance.get_range_hyps('se', 1.2)
with pytest.raises(AssertionError) as error:
utils_covariance.get_range_hyps('se', 2, is_ard='abc')
with pytest.raises(AssertionError) as error:
utils_covariance.get_range_hyps('se', 2, is_ard=1)
with pytest.raises(AssertionError) as error:
utils_covariance.get_range_hyps('se', 2, is_fixed_noise=1)
with pytest.raises(AssertionError) as error:
utils_covariance.get_range_hyps('se', 2, is_fixed_noise='abc')
cur_range = utils_covariance.get_range_hyps('se',
2,
is_ard=False,
is_fixed_noise=False)
assert isinstance(cur_range, list)
assert cur_range == [[0.01, 1000.0], [0.01, 1000.0], [0.001, 10.0]]
print(type(cur_range))
print(cur_range)
def get_optimized_kernel(
X_train: np.ndarray,
Y_train: np.ndarray,
prior_mu: constants.TYPING_UNION_CALLABLE_NONE,
str_cov: str,
str_optimizer_method: str = constants.STR_OPTIMIZER_METHOD_GP,
str_modelselection_method: str = constants.STR_MODELSELECTION_METHOD,
use_ard: bool = constants.USE_ARD,
fix_noise: bool = constants.FIX_GP_NOISE,
debug: bool = False) -> constants.TYPING_TUPLE_TWO_ARRAYS_DICT:
"""
This function computes the kernel matrix optimized by optimization
method specified, its inverse matrix, and the optimized hyperparameters.
:param X_train: inputs. Shape: (n, d) or (n, m, d).
:type X_train: numpy.ndarray
:param Y_train: outputs. Shape: (n, 1).
:type Y_train: numpy.ndarray
:param prior_mu: prior mean function or None.
:type prior_mu: callable or NoneType
:param str_cov: the name of covariance function.
:type str_cov: str.
:param str_optimizer_method: the name of optimization method.
:type str_optimizer_method: str., optional
:param str_modelselection_method: the name of model selection method.
:type str_modelselection_method: str., optional
:param use_ard: flag for using automatic relevance determination.
:type use_ard: bool., optional
:param fix_noise: flag for fixing a noise.
:type fix_noise: bool., optional
:param debug: flag for printing log messages.
:type debug: bool., optional
:returns: a tuple of kernel matrix over `X_train`, kernel matrix
inverse, and dictionary of hyperparameters.
:rtype: tuple of (numpy.ndarray, numpy.ndarray, dict.)
:raises: AssertionError, ValueError
"""
# TODO: check to input same fix_noise to convert_hyps and restore_hyps
utils_gp.validate_common_args(X_train, Y_train, str_cov, prior_mu, debug)
assert isinstance(str_optimizer_method, str)
assert isinstance(str_modelselection_method, str)
assert isinstance(use_ard, bool)
assert isinstance(fix_noise, bool)
utils_covariance.check_str_cov('get_optimized_kernel', str_cov,
X_train.shape)
assert str_optimizer_method in constants.ALLOWED_OPTIMIZER_METHOD_GP
assert str_modelselection_method in constants.ALLOWED_MODELSELECTION_METHOD
use_gradient = bool(str_optimizer_method != 'Nelder-Mead')
# TODO: Now, use_gradient is fixed as False.
# use_gradient = False
time_start = time.time()
if debug:
logger.debug('str_optimizer_method: %s', str_optimizer_method)
logger.debug('str_modelselection_method: %s',
str_modelselection_method)
logger.debug('use_gradient: %s', use_gradient)
prior_mu_train = utils_gp.get_prior_mu(prior_mu, X_train)
if str_cov in constants.ALLOWED_COV_BASE:
num_dim = X_train.shape[1]
elif str_cov in constants.ALLOWED_COV_SET:
num_dim = X_train.shape[2]
use_gradient = False
if str_modelselection_method == 'ml':
neg_log_ml_ = lambda hyps: gp_likelihood.neg_log_ml(
X_train,
Y_train,
hyps,
str_cov,
prior_mu_train,
use_ard=use_ard,
fix_noise=fix_noise,
use_gradient=use_gradient,
debug=debug)
elif str_modelselection_method == 'loocv':
# TODO: add use_ard.
neg_log_ml_ = lambda hyps: gp_likelihood.neg_log_pseudo_l_loocv(
X_train,
Y_train,
hyps,
str_cov,
prior_mu_train,
fix_noise=fix_noise,
debug=debug)
use_gradient = False
else: # pragma: no cover
raise ValueError(
'get_optimized_kernel: missing conditions for str_modelselection_method.'
)
hyps_converted = utils_covariance.convert_hyps(str_cov,
utils_covariance.get_hyps(
str_cov,
num_dim,
use_ard=use_ard),
fix_noise=fix_noise)
if str_optimizer_method in ['BFGS', 'SLSQP']:
result_optimized = scipy.optimize.minimize(neg_log_ml_,
hyps_converted,
method=str_optimizer_method,
jac=use_gradient,
options={'disp': False})
if debug:
logger.debug('negative log marginal likelihood: %.6f',
result_optimized.fun)
logger.debug('scipy message: %s', result_optimized.message)
result_optimized = result_optimized.x
elif str_optimizer_method in ['L-BFGS-B', 'SLSQP-Bounded']:
if str_optimizer_method == 'SLSQP-Bounded':
str_optimizer_method = 'SLSQP'
bounds = utils_covariance.get_range_hyps(str_cov,
num_dim,
use_ard=use_ard,
fix_noise=fix_noise)
result_optimized = scipy.optimize.minimize(neg_log_ml_,
hyps_converted,
method=str_optimizer_method,
bounds=bounds,
jac=use_gradient,
options={'disp': False})
if debug:
logger.debug('negative log marginal likelihood: %.6f',
result_optimized.fun)
logger.debug('scipy message: %s', result_optimized.message)
result_optimized = result_optimized.x
elif str_optimizer_method in ['Nelder-Mead']:
result_optimized = scipy.optimize.minimize(neg_log_ml_,
hyps_converted,
method=str_optimizer_method,
options={'disp': False})
if debug:
logger.debug('negative log marginal likelihood: %.6f',
result_optimized.fun)
logger.debug('scipy message: %s', result_optimized.message)
result_optimized = result_optimized.x
else: # pragma: no cover
raise ValueError(
'get_optimized_kernel: missing conditions for str_optimizer_method'
)
hyps = utils_covariance.restore_hyps(str_cov,
result_optimized,
use_ard=use_ard,
fix_noise=fix_noise)
hyps = utils_covariance.validate_hyps_dict(hyps, str_cov, num_dim)
cov_X_X, inv_cov_X_X, _ = covariance.get_kernel_inverse(
X_train, hyps, str_cov, fix_noise=fix_noise, debug=debug)
time_end = time.time()
if debug:
logger.debug('hyps optimized: %s', utils_logger.get_str_hyps(hyps))
logger.debug('time consumed to construct gpr: %.4f sec.',
time_end - time_start)
return cov_X_X, inv_cov_X_X, hyps
def get_optimized_kernel(
X_train,
Y_train,
prior_mu,
str_cov,
str_optimizer_method=constants.STR_OPTIMIZER_METHOD_GP,
str_modelselection_method=constants.STR_MODELSELECTION_METHOD,
is_fixed_noise=constants.IS_FIXED_GP_NOISE,
debug=False):
"""
This function computes the kernel matrix optimized by optimization method specified, its inverse matrix, and the optimized hyperparameters.
:param X_train: inputs. Shape: (n, d) or (n, m, d).
:type X_train: numpy.ndarray
:param Y_train: outputs. Shape: (n, 1).
:type Y_train: numpy.ndarray
:param prior_mu: prior mean function or None.
:type prior_mu: function or NoneType
:param str_cov: the name of covariance function.
:type str_cov: str.
:param str_optimizer_method: the name of optimization method.
:type str_optimizer_method: str., optional
:param str_modelselection_method: the name of model selection method.
:type str_modelselection_method: str., optional
:param is_fixed_noise: flag for fixing a noise.
:type is_fixed_noise: bool., optional
:param debug: flag for printing log messages.
:type debug: bool., optional
:returns: a tuple of kernel matrix over `X_train`, kernel matrix inverse, and dictionary of hyperparameters.
:rtype: tuple of (numpy.ndarray, numpy.ndarray, dict.)
:raises: AssertionError, ValueError
"""
# TODO: check to input same is_fixed_noise to convert_hyps and restore_hyps
assert isinstance(X_train, np.ndarray)
assert isinstance(Y_train, np.ndarray)
assert callable(prior_mu) or prior_mu is None
assert isinstance(str_cov, str)
assert isinstance(str_optimizer_method, str)
assert isinstance(str_modelselection_method, str)
assert isinstance(is_fixed_noise, bool)
assert isinstance(debug, bool)
assert len(Y_train.shape) == 2
assert X_train.shape[0] == Y_train.shape[0]
_check_str_cov('get_optimized_kernel', str_cov, X_train.shape)
assert str_optimizer_method in constants.ALLOWED_OPTIMIZER_METHOD_GP
assert str_modelselection_method in constants.ALLOWED_MODELSELECTION_METHOD
# TODO: fix this.
is_gradient = True
time_start = time.time()
if debug:
print('[DEBUG] get_optimized_kernel in gp.py: str_optimizer_method {}'.
format(str_optimizer_method))
print(
'[DEBUG] get_optimized_kernel in gp.py: str_modelselection_method {}'
.format(str_modelselection_method))
prior_mu_train = get_prior_mu(prior_mu, X_train)
if str_cov in constants.ALLOWED_GP_COV_BASE:
num_dim = X_train.shape[1]
elif str_cov in constants.ALLOWED_GP_COV_SET:
num_dim = X_train.shape[2]
is_gradient = False
if str_modelselection_method == 'ml':
neg_log_ml_ = lambda hyps: neg_log_ml(X_train,
Y_train,
hyps,
str_cov,
prior_mu_train,
is_fixed_noise=is_fixed_noise,
is_gradient=is_gradient,
debug=debug)
elif str_modelselection_method == 'loocv':
neg_log_ml_ = lambda hyps: neg_log_pseudo_l_loocv(X_train,
Y_train,
hyps,
str_cov,
prior_mu_train,
is_fixed_noise=
is_fixed_noise,
debug=debug)
is_gradient = False
else: # pragma: no cover
raise ValueError(
'get_optimized_kernel: missing conditions for str_modelselection_method.'
)
hyps_converted = utils_covariance.convert_hyps(
str_cov,
utils_covariance.get_hyps(str_cov, num_dim),
is_fixed_noise=is_fixed_noise,
)
if str_optimizer_method == 'BFGS':
result_optimized = scipy.optimize.minimize(neg_log_ml_,
hyps_converted,
method=str_optimizer_method,
jac=is_gradient,
options={'disp': False})
result_optimized = result_optimized.x
elif str_optimizer_method == 'L-BFGS-B':
bounds = utils_covariance.get_range_hyps(str_cov,
num_dim,
is_fixed_noise=is_fixed_noise)
result_optimized = scipy.optimize.minimize(neg_log_ml_,
hyps_converted,
method=str_optimizer_method,
bounds=bounds,
jac=is_gradient,
options={'disp': False})
result_optimized = result_optimized.x
# TODO: Fill this conditions
elif str_optimizer_method == 'DIRECT': # pragma: no cover
raise NotImplementedError(
'get_optimized_kernel: allowed str_optimizer_method, but it is not implemented.'
)
elif str_optimizer_method == 'CMA-ES': # pragma: no cover
raise NotImplementedError(
'get_optimized_kernel: allowed str_optimizer_method, but it is not implemented.'
)
else: # pragma: no cover
raise ValueError(
'get_optimized_kernel: missing conditions for str_optimizer_method'
)
hyps = utils_covariance.restore_hyps(str_cov,
result_optimized,
is_fixed_noise=is_fixed_noise)
hyps, _ = utils_covariance.validate_hyps_dict(hyps, str_cov, num_dim)
cov_X_X, inv_cov_X_X, grad_cov_X_X = get_kernel_inverse(
X_train, hyps, str_cov, is_fixed_noise=is_fixed_noise, debug=debug)
time_end = time.time()
if debug:
print('[DEBUG] get_optimized_kernel in gp.py: optimized hyps for gpr',
hyps)
print('[DEBUG] get_optimized_kernel in gp.py: time consumed',
time_end - time_start, 'sec.')
return cov_X_X, inv_cov_X_X, hyps
def get_optimized_kernel(
X_train,
Y_train,
prior_mu,
str_cov,
str_optimizer_method=constants.STR_OPTIMIZER_METHOD_GP,
str_modelselection_method=constants.STR_MODELSELECTION_METHOD,
is_fixed_noise=constants.IS_FIXED_GP_NOISE,
debug=False):
# TODO: check to input same is_fixed_noise to convert_hyps and restore_hyps
assert isinstance(X_train, np.ndarray)
assert isinstance(Y_train, np.ndarray)
assert callable(prior_mu) or prior_mu is None
assert isinstance(str_cov, str)
assert isinstance(str_optimizer_method, str)
assert isinstance(str_modelselection_method, str)
assert isinstance(is_fixed_noise, bool)
assert isinstance(debug, bool)
assert len(Y_train.shape) == 2
assert X_train.shape[0] == Y_train.shape[0]
_check_str_cov('get_optimized_kernel', str_cov, X_train.shape)
assert str_optimizer_method in constants.ALLOWED_OPTIMIZER_METHOD_GP
assert str_modelselection_method in constants.ALLOWED_MODELSELECTION_METHOD
time_start = time.time()
if debug:
print('[DEBUG] get_optimized_kernel in gp.py: str_optimizer_method {}'.
format(str_optimizer_method))
print(
'[DEBUG] get_optimized_kernel in gp.py: str_modelselection_method {}'
.format(str_modelselection_method))
prior_mu_train = get_prior_mu(prior_mu, X_train)
if str_cov in constants.ALLOWED_GP_COV_BASE:
num_dim = X_train.shape[1]
elif str_cov in constants.ALLOWED_GP_COV_SET:
num_dim = X_train.shape[2]
if str_modelselection_method == 'ml':
neg_log_ml = lambda hyps: -1.0 * log_ml(X_train,
Y_train,
hyps,
str_cov,
prior_mu_train,
is_fixed_noise=is_fixed_noise,
debug=debug)
elif str_modelselection_method == 'loocv':
neg_log_ml = lambda hyps: -1.0 * log_pseudo_l_loocv(X_train,
Y_train,
hyps,
str_cov,
prior_mu_train,
is_fixed_noise=
is_fixed_noise,
debug=debug)
else: # pragma: no cover
raise ValueError(
'get_optimized_kernel: missing conditions for str_modelselection_method.'
)
hyps_converted = utils_covariance.convert_hyps(
str_cov,
utils_covariance.get_hyps(str_cov, num_dim),
is_fixed_noise=is_fixed_noise,
)
if str_optimizer_method == 'BFGS':
result_optimized = scipy.optimize.minimize(neg_log_ml,
hyps_converted,
method=str_optimizer_method)
result_optimized = result_optimized.x
elif str_optimizer_method == 'L-BFGS-B':
bounds = utils_covariance.get_range_hyps(str_cov,
num_dim,
is_fixed_noise=is_fixed_noise)
result_optimized = scipy.optimize.minimize(neg_log_ml,
hyps_converted,
method=str_optimizer_method,
bounds=bounds)
result_optimized = result_optimized.x
# TODO: Fill this conditions
elif str_optimizer_method == 'DIRECT': # pragma: no cover
raise NotImplementedError(
'get_optimized_kernel: allowed str_optimizer_method, but it is not implemented.'
)
elif str_optimizer_method == 'CMA-ES': # pragma: no cover
raise NotImplementedError(
'get_optimized_kernel: allowed str_optimizer_method, but it is not implemented.'
)
# INFO: It is allowed, but a condition is missed.
else: # pragma: no cover
raise ValueError(
'get_optimized_kernel: missing conditions for str_optimizer_method'
)
hyps = utils_covariance.restore_hyps(str_cov,
result_optimized,
is_fixed_noise=is_fixed_noise)
hyps, _ = utils_covariance.validate_hyps_dict(hyps, str_cov, num_dim)
cov_X_X, inv_cov_X_X = get_kernel_inverse(X_train,
hyps,
str_cov,
debug=debug)
time_end = time.time()
if debug:
print('[DEBUG] get_optimized_kernel in gp.py: optimized hyps for gpr',
hyps)
print('[DEBUG] get_optimized_kernel in gp.py: time consumed',
time_end - time_start, 'sec.')
return cov_X_X, inv_cov_X_X, hyps
def test_get_range_hyps():
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps(1.0, 2)
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('abc', 2)
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('se', 1.2)
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('se', 2, use_ard='abc')
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('se', 2, use_ard=1)
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('se', 2, use_gp='abc')
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('se', 2, use_gp=1)
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('se', 2, fix_noise=1)
with pytest.raises(AssertionError) as error:
package_target.get_range_hyps('se', 2, fix_noise='abc')
cur_range = package_target.get_range_hyps('se',
2,
use_ard=False,
fix_noise=False)
print(type(cur_range))
print(cur_range)
assert isinstance(cur_range, list)
assert cur_range == [[0.001, 10.0], [0.01, 1000.0], [0.01, 1000.0]]
cur_range = package_target.get_range_hyps('se',
2,
use_ard=False,
fix_noise=False,
use_gp=False)
print(type(cur_range))
print(cur_range)
assert isinstance(cur_range, list)
assert cur_range == [[0.001, 10.0], [2.00001, 200.0], [0.01, 1000.0],
[0.01, 1000.0]]
cur_range = package_target.get_range_hyps('se',
2,
use_ard=True,
fix_noise=False,
use_gp=True)
print(type(cur_range))
print(cur_range)
assert isinstance(cur_range, list)
assert cur_range == [[0.001, 10.0], [0.01, 1000.0], [0.01, 1000.0],
[0.01, 1000.0]]
cur_range = package_target.get_range_hyps('se',
2,
use_ard=False,
fix_noise=True,
use_gp=True)
print(type(cur_range))
print(cur_range)
assert isinstance(cur_range, list)
assert cur_range == [[0.01, 1000.0], [0.01, 1000.0]]