def main():
# choices = {'a': 1, 'b': 2}
# result = choices.get(key, 'default')
user_input = input("Type: \n 1 for Random Search \n 2 for GA \n 3 for ABC \n 4 for Local Search \n")
print('Target solution: {}'.format(TARGET))
if user_input == '1':
random_search(TARGET, NUMBER_OF_ITERATIONS)
elif user_input == '2':
ga_search(TARGET, NUMBER_OF_ITERATIONS, POPULATION_SIZE)
elif user_input == '3':
abc_search(TARGET, NUMBER_OF_ITERATIONS)
elif user_input == '4':
local_search(TARGET, NUMBER_OF_ITERATIONS)
def grasp_by_max_time(cost_function,
alpha,
greedy_loss_function,
max_time_in_seconds,
input,
params_glf=None):
best_cost_solution = math.inf
best_solution = None
print("Initiating GRASP with time constraint...")
time_start = time.time()
time_current = time_start
k = 0
while time_current - time_start <= max_time_in_seconds:
k = k + 1
feasible_solution = construct(greedy_loss_function, alpha, input,
params_glf)
cost_of_new_solution, best_solution_in_neighbourhood = local_search(
cost_function, input, feasible_solution)
if (cost_of_new_solution < best_cost_solution):
best_solution = best_solution_in_neighbourhood
best_cost_solution = cost_of_new_solution
time_current = time.time()
time_spent_computing = time_current - time_start
return (best_solution, best_cost_solution, time_spent_computing, k)
def random_scalarizations(config,
data_array,
param_space,
fast_addressing_of_data_array,
regression_models,
iteration_number,
objective_weights,
objective_limits,
classification_model=None):
"""
Run one iteration of bayesian optimization with random scalarizations.
:param config: dictionary containing all the configuration parameters of this optimization.
:param data_array: a dictionary containing previously explored points and their function values.
:param param_space: parameter space object for the current application.
:param fast_addressing_of_data_array: dictionary for quick-access to previously explored configurations.
:param regression_models: the surrogate models used to evaluate points.
:param iteration_number: the current iteration number.
:param objective_weights: objective weights for multi-objective optimization. Not implemented yet.
:param objective_limits: estimated minimum and maximum limits for each objective.
:param classification_model: feasibility classifier for constrained optimization.
"""
optimization_metrics = config["optimization_objectives"]
number_of_objectives = len(optimization_metrics)
local_search_starting_points = config["local_search_starting_points"]
local_search_random_points = config["local_search_random_points"]
scalarization_key = config["scalarization_key"]
optimization_function_parameters = {}
optimization_function_parameters['regression_models'] = regression_models
optimization_function_parameters['iteration_number'] = iteration_number
optimization_function_parameters['data_array'] = data_array
optimization_function_parameters[
'classification_model'] = classification_model
optimization_function_parameters['param_space'] = param_space
optimization_function_parameters['objective_weights'] = objective_weights
optimization_function_parameters['model_type'] = config["models"]["model"]
optimization_function_parameters['objective_limits'] = objective_limits
optimization_function_parameters['acquisition_function'] = config[
"acquisition_function"]
optimization_function_parameters['scalarization_method'] = config[
"scalarization_method"]
optimization_function_parameters['number_of_cpus'] = config[
"number_of_cpus"]
_, best_configuration = local_search(
local_search_starting_points,
local_search_random_points,
param_space,
fast_addressing_of_data_array,
False, # we do not want the local search to consider feasibility constraints, only the acquisition functions
run_acquisition_function,
optimization_function_parameters,
scalarization_key,
previous_points=data_array)
return best_configuration
def run(self):
start = time.time()
process_gop_list = []
process_item_list = []
process_util_list = []
process_pair_list = []
search_counter = 0
print("Process " + str(self.processIdx) + " starts global search.")
for idx, item in enumerate(
partition_to_k(self.layer_list, self.acc_cluster_num, False),
0):
if idx % PROCESS_NUM == self.processIdx:
sub_gop_list = []
search_counter = search_counter + 1
sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag \
= model_split_by_list(self.conv_N, self.conv_M, self.conv_r, self.conv_R, self.conv_K, self.conv_S, self.flag, item)
sub_pair_list, sub_lat_list, sub_util_list = \
local_search(sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag)
for i in range(0, len(sub_conv_N)):
sub_gop_list.append(
gop_calculate(sub_conv_N[i], sub_conv_M[i],
sub_conv_R[i], sub_conv_K[i]))
if max(sub_lat_list) < self.overall_lat:
overall_lat = max(sub_lat_list)
if len(process_pair_list) < 6:
process_item_list.append(item)
process_pair_list.append(sub_pair_list)
# process_pair_list.append([overall_lat])
process_util_list.append([overall_lat])
process_gop_list.append(sub_gop_list)
# process_util_list.append(sub_util_list)
# process_pair_list.append(sub_util_list)
# else:
# max_among_mins = process_pair_list.index(max(overall_lat))
# process_pair_list.remove(process_pair_list[max_among_mins])
# process_pair_list.append(sub_pair_list)
# process_pair_list.append([overall_lat])
# process_pair_list.append(sub_util_list)
# print "For set ID: " + str(idx) + ", the final explored points = ", search_counter
if len(process_pair_list) != 0:
self.result_Q.put((process_pair_list, process_item_list,
process_gop_list, process_util_list))
end = time.time()
print("Thread ", self.processIdx, " :", (end - start))
def grasp_by_max_time(cost_function,
alpha,
greedy_loss_function,
max_time_in_seconds,
input,
params_glf=None):
best_cost_solution = math.inf
best_solution = None
print("Initiating GRASP with time constraint...")
time_start = time.time()
time_current = time_start
k = 0
while time_current - time_start <= max_time_in_seconds:
k = k + 1
feasible_solution = construct(greedy_loss_function, alpha, input,
params_glf)
if input["stop_after_some_time"] == True:
if time.time() - input["time_start"] > input["max_time"]:
return (best_solution, best_cost_solution,
time.time() - time_start, k)
cost_of_new_solution, best_solution_in_neighbourhood = local_search(
cost_function, input, feasible_solution)
# TODO: Remove after debug
check_if_solution_makes_sense(best_solution_in_neighbourhood)
# TODO: Remove after debug
if (cost_of_new_solution < best_cost_solution):
best_solution = best_solution_in_neighbourhood
best_cost_solution = cost_of_new_solution
print(f"Run {k} iteration!!!")
time_current = time.time()
time_spent_computing = time_current - time_start
print(f"Run {k} iterations!!!")
return (best_solution, best_cost_solution, time_spent_computing, k)
def grasp(cost_function, alpha, greedy_cost_function, max_iterations, input):
best_cost_solution = math.inf
best_solution = None
print("Initiating GRASP...")
start = time.time()
for k in range(max_iterations):
feasible_solution = construct(greedy_cost_function, alpha, input)
cost_of_new_solution, best_solution_in_neighbourhood = local_search(
cost_function, input, feasible_solution)
print(f"Cost of new solution: {cost_of_new_solution}")
print(
f"Highest load of new solution: {best_solution_in_neighbourhood['highest_loaded_truck_load']}"
)
if (cost_of_new_solution < best_cost_solution):
best_solution = best_solution_in_neighbourhood
best_cost_solution = cost_of_new_solution
return (best_solution, best_cost_solution, time.time() - start,
max_iterations)
def grasp(cost_function, alpha, greedy_loss_function, max_iterations, input):
best_cost_solution = math.inf
best_solution = None
print("Initiating GRASP...")
for k in range(max_iterations):
#print(f"Running {k} iteration: construct")
feasible_solution = construct(greedy_loss_function, alpha, input)
#print(f"Running {k} iteration: local_search")
cost_of_new_solution, best_solution_in_neighbourhood = local_search(
cost_function, input, feasible_solution)
print(f"Cost of new solution: {cost_of_new_solution}")
print(
f"Highest load of new solution: {feasible_solution['highest_loaded_truck_load']}"
)
sum_in_rows = best_solution_in_neighbourhood['pt'].sum(axis=1)
if np.any(sum_in_rows > 1):
print(best_solution_in_neighbourhood)
print("Local search f****d up for row > 1")
sys.exit()
if np.any(sum_in_rows == 0):
print(best_solution_in_neighbourhood)
print("Local search f****d up for row = 0")
sys.exit()
if (cost_of_new_solution < best_cost_solution):
best_solution = best_solution_in_neighbourhood
best_cost_solution = cost_of_new_solution
print(f"Run {k} iteration!!!")
return (best_solution, best_cost_solution, None, max_iterations)
groups = create_groups_with_bfs(graph, nb_groups=3)
print(groups.shape)
print(groups)
plot_graph(graph,
groups,
title='gaussian kernel graph with 3 groups got with BFS',
file_name='gaussian_kernel_graph.png')
weights = get_adjacency_matrix(graph)
# best_permutation = smb2.spectral_sequencing(weights)
best_permutation = smb2.mc_allister(weights)
print(
f'value of bandwith sum found : {smb2.bandwidth_sum(best_permutation, weights)}'
)
best_permutation, best_value = local_search(best_permutation, graph)
print(best_permutation)
print(
f'value of bandwith sum found : {smb2.bandwidth_sum(best_permutation, weights)}'
)
print(best_permutation)
spectrogram_with_groups(
graph,
groups,
permutation=best_permutation,
file_name='spectrogram_gaussian_kernel_graph_groups_bfs.png',
title=
'mc_allister then local search on gaussian kernel graph with 3 groups got with BFS'
)
def main(w0 = None):
# tm should translate unknown words as-is with probability 1
w = w0
if w is None:
# lm_logprob, distortion penenalty, direct translate logprob, direct lexicon logprob, inverse translation logprob, inverse lexicon logprob
if opts.weights == "no weights specify":
w = [1.0/7] * 7
# w = [1.76846735947, 0.352553835525, 1.00071564481, 1.49937872683, 0.562198294709, -0.701483985454, 1.80395218437]
else:
w = [float(line.strip()) for line in open(opts.weights)]
sys.stderr.write(str(w) + '\n')
tm = models.TM(opts.tm, opts.k, opts.mute)
lm = models.LM(opts.lm, opts.mute)
# ibm_t = {}
ibm_t = init('./data/ibm.t.gz')
french = [tuple(line.strip().split()) for line in open(opts.input).readlines()[:opts.num_sents]]
french = french[opts.start : opts.end]
bound_width = float(opts.bwidth)
for word in set(sum(french,())):
if (word,) not in tm:
tm[(word,)] = [models.phrase(word, [0.0, 0.0, 0.0, 0.0])]
nbest_output = []
total_prob = 0
if opts.mute == 0:
sys.stderr.write("Start decoding %s ...\n" % (opts.input,))
for idx,f in enumerate(french):
if opts.mute == 0:
sys.stderr.write("Decoding sentence #%s ...\n" % (str(idx)))
initial_hypothesis = hypothesis(lm.begin(), 0.0, 0, 0, None, None, None)
heaps = [{} for _ in f] + [{}]
heaps[0][lm.begin(), 0, 0] = initial_hypothesis
for i, heap in enumerate(heaps[:-1]):
# maintain beam heap
# front_item = sorted(heap.itervalues(), key=lambda h: -h.logprob)[0]
for h in sorted(heap.itervalues(),key=lambda h: -h.logprob)[:opts.s]: # prune
# if h.logprob < front_item.logprob - float(opts.bwidth):
# continue
fopen = prefix1bits(h.coverage)
for j in xrange(fopen,min(fopen+1+opts.disord, len(f)+1)):
for k in xrange(j+1, len(f)+1):
if f[j:k] in tm:
if (h.coverage & bitmap(range(j, k))) == 0:
for phrase in tm[f[j:k]]:
lm_prob = 0
lm_state = h.lm_state
for word in phrase.english.split():
(lm_state, prob) = lm.score(lm_state, word)
lm_prob += prob
lm_prob += lm.end(lm_state) if k == len(f) else 0.0
coverage = h.coverage | bitmap(range(j, k))
# logprob = h.logprob + lm_prob*w[0] + getDotProduct(phrase.several_logprob, w[2:6]) + abs(h.end+1-j)*w[1] + ibm_model_1_w_score(ibm_t, f, phrase.english)*w[6]
logprob = h.logprob
logprob += lm_prob*w[0]
logprob += getDotProduct(phrase.several_logprob, w[1:5])
# logprob += opts.diseta*abs(h.end+1-j)*w[1]
logprob += ibm_model_1_w_score(ibm_t, f, phrase.english)*w[5]
logprob += (len(phrase.english.split()) - (k - j)) * w[6]
new_hypothesis = hypothesis(lm_state, logprob, coverage, k, h, phrase, abs(h.end + 1 - j))
# add to heap
num = onbits(coverage)
if (lm_state, coverage, k) not in heaps[num] or new_hypothesis.logprob > heaps[num][lm_state, coverage, k].logprob:
heaps[num][lm_state, coverage, k] = new_hypothesis
winners = sorted(heaps[-1].itervalues(), key=lambda h: -h.logprob)[0:opts.nbest]
def get_lm_logprob(test_list):
stance = []
for i in test_list:
stance += (i.split())
stance = tuple(stance)
lm_state = ("<s>",)
score = 0.0
for word in stance:
(lm_state, word_score) = lm.score(lm_state, word)
score += word_score
return score
def get_list_and_features(h, idx_self):
lst = [];
features = [0, 0, 0, 0, 0, 0, 0]
current_h = h;
while current_h.phrase is not None:
# print current_h
lst.append(current_h.phrase.english)
# features[1] += current_h.distortionPenalty
features[1] += current_h.phrase.several_logprob[0] # translation feature 1
features[2] += current_h.phrase.several_logprob[1] # translation feature 2
features[3] += current_h.phrase.several_logprob[2] # translation feature 3
features[4] += current_h.phrase.several_logprob[3] # translation feature 4
current_h = current_h.predecessor
lst.reverse()
features[0] = get_lm_logprob(lst) # language model score
features[5] = ibm_model_1_score(ibm_t, f, lst)
features[6] = len(lst) - len(french[idx_self])
return (lst, features)
for win in winners:
# s = str(idx) + " ||| "
(lst, features) = get_list_and_features(win, idx)
print local_search.local_search(lst, lm)
def prior_guided_optimization(config,
data_array,
param_space,
fast_addressing_of_data_array,
regression_models,
iteration_number,
objective_weights,
objective_limits,
classification_model=None):
"""
Run a prior-guided bayesian optimization iteration.
:param config: dictionary containing all the configuration parameters of this optimization.
:param data_array: a dictionary containing previously explored points and their function values.
:param param_space: parameter space object for the current application.
:param fast_addressing_of_data_array: dictionary for quick-access to previously explored configurations.
:param regression_models: the surrogate models used to evaluate points.
:param iteration_number: the current iteration number.
:param objective_weights: objective weights for multi-objective optimization. Not implemented yet.
:param objective_limits: estimated minimum and maximum limits for each objective.
:param classification_model: feasibility classifier for constrained optimization.
"""
local_search_starting_points = config["local_search_starting_points"]
local_search_random_points = config["local_search_random_points"]
scalarization_key = config["scalarization_key"]
function_parameters = {}
function_parameters["param_space"] = param_space
function_parameters["iteration_number"] = iteration_number
function_parameters["regression_models"] = regression_models
function_parameters['classification_model'] = classification_model
function_parameters["objective_weights"] = objective_weights
function_parameters["objective_limits"] = objective_limits
function_parameters['model_type'] = config["models"]["model"]
function_parameters["model_weight"] = config["model_posterior_weight"]
function_parameters["posterior_floor"] = config[
"posterior_computation_lower_limit"]
model_good_quantile = config["model_good_quantile"]
function_parameters["threshold"] = {}
optimization_metrics = param_space.get_optimization_parameters()
for objective in optimization_metrics:
function_parameters["threshold"][objective] = np.quantile(
data_array[objective], model_good_quantile)
if param_space.get_prior_normalization_flag() is True:
prior_limit_estimation_points = config["prior_limit_estimation_points"]
good_prior_normalization_limits = estimate_prior_limits(
param_space, prior_limit_estimation_points, objective_weights)
else:
good_prior_normalization_limits = None
function_parameters[
"good_prior_normalization_limits"] = good_prior_normalization_limits
if classification_model is not None:
function_parameters["posterior_normalization_limits"] = [
float("inf"), float("-inf")
]
_, best_configuration = local_search(
local_search_starting_points,
local_search_random_points,
param_space,
fast_addressing_of_data_array,
False, # set feasibility to false, we handle it inside the acquisition function
compute_EI_from_posteriors,
function_parameters,
scalarization_key,
previous_points=data_array)
return best_configuration
def test():
facilities = read_and_parse_text("test_cases/test0.txt")
#local_search(facilities, frozenset())
#local_search(facilities, frozenset({'A','B','C','D','E'}))
local_search(facilities, frozenset({'B', 'D'}))
def __init__(self, w, target_est_count=None, target_moe_count=None, target_th_count=None,\
target_est_prop=None, target_moe_prop=None, target_th_prop=None,\
target_est_ratio=None, target_moe_ratio=None, target_th_ratio=None,\
target_th_all=None, count_est=None, count_th_min=None, count_th_max=None,\
exclude=None, auto_exclude=0, base_solutions=100,\
zscore=True, pca=True, local_improvement=True, local_params=None,\
compactness=None, points=None, anchor=None, cardinality=False,\
cv_exclude_count=0, cv_exclude_prop=0, cv_exclude_ratio=0):
time1 = time.time()
time_output = {
'prep': 0,
'base': 0,
'base_wrapup': 0,
'local': 0,
'local_wrapup': 0,
'wrapup': 0,
'total': 0
}
# convert arbitrary IDs in W object to integers
id2i = w.id2i
neighbors = {
id2i[key]: [id2i[neigh] for neigh in w.neighbors[key]]
for key in w.id_order
}
w = ps.W(neighbors)
# build KDTree for use in finding base solution
if issubclass(type(points), scipy.spatial.KDTree):
kd = points
points = kd.data
elif type(points).__name__ == 'ndarray':
kd = ps.common.KDTree(points)
elif issubclass(type(points),
ps.core.IOHandlers.pyShpIO.PurePyShpWrapper):
#loop to find centroids, need to be sure order matches W and data
centroids = []
for i in points:
centroids.append(i.centroid)
kd = ps.common.KDTree(centroids)
points = kd.data
elif points is None:
kd = None
else:
raise Exception, 'Unsupported type passed to points'
# dictionary allowing multivariate and univariate flexibility
target_parts = {'target_est_count':target_est_count,\
'target_est_prop':target_est_prop,\
'target_est_ratio':target_est_ratio,\
'target_sde_count':target_moe_count ,\
'target_sde_prop':target_moe_prop,\
'target_sde_ratio':target_moe_ratio}
# setup the holder for the variables to minimize; later we will put all
# the count, ratio and proportion variables into this array.
# Also, convert MOEs to standard errors when appropriate
total_vars = 0
rows = 0
if target_est_count is not None:
rows, cols = target_est_count.shape
total_vars += cols
target_parts['target_est_count'] = target_est_count * 1.0
target_parts['target_sde_count'] = target_moe_count / 1.645
if target_est_prop is not None:
rows, cols = target_est_prop.shape
total_vars += cols / 2
target_parts['target_est_prop'] = target_est_prop * 1.0
target_parts['target_sde_prop'] = target_moe_prop / 1.645
if target_est_ratio is not None:
rows, cols = target_est_ratio.shape
total_vars += cols / 2
target_parts['target_est_ratio'] = target_est_ratio * 1.0
target_parts['target_sde_ratio'] = target_moe_ratio / 1.645
if total_vars == 0:
target_est = None
print 'warning: optimization steps will not be run since no target_est variables provided'
else:
target_est = np.ones((rows, total_vars)) * -999
# organize and check the input data; prep data for actual computations
position = 0
target_th = []
# IMPORTANT: maintain the order of count then proportion then ratio
if target_est_count is not None:
target_est, target_th, position = mv_data_prep(target_est_count,\
target_th_count, target_th_all,\
target_est, target_th, position,\
scale=1, ratio=False)
if target_est_prop is not None:
target_est, target_th, position = mv_data_prep(target_est_prop,\
target_th_prop, target_th_all,\
target_est, target_th, position,\
scale=2, ratio=False)
if target_est_ratio is not None:
target_est, target_th, position = mv_data_prep(target_est_ratio,\
target_th_ratio, target_th_all,\
target_est, target_th, position,\
scale=2, ratio=True)
target_th = np.array(target_th)
# compute zscores
# NOTE: zscores computed using all data, i.e. we do not screen out
# observations in the exclude list.
if zscore and target_est is not None:
if pca:
# Python does not currently have a widely used tool for
# computing PCA with missing values. In principle,
# NIPALS (Nonlinear Iterative Partial Least Squares)
# can accommodate missing values, but the implementation in MDP
# 3.4 will return a matrix of NAN values if there is an NAN
# value in the input data.
# http://sourceforge.net/p/mdp-toolkit/mailman/mdp-toolkit-users/?viewmonth=201111
# http://stats.stackexchange.com/questions/35561/imputation-of-missing-values-for-pca
# Therefore, we impute the missing values when the user
# requests PCA; compute the z-scores on the imputed data; and
# then pass this on to the PCA step.
# The imputation replaces a missing value with the average of
# its neighbors (i.e., its spatial lag). If missing values
# remain (due to missing values in a missing value's neighbor
# set), then that value is replaced by the column average.
w_standardized = copy.deepcopy(w)
w_standardized.transform = 'r'
target_est_lag = ps.lag_spatial(w_standardized, target_est)
# replace troublemakers with their spatial lag
trouble = np.isfinite(target_est)
trouble = np.bitwise_not(trouble)
target_est[trouble] = target_est_lag[trouble]
del target_est_lag
del trouble
# Pandas ignores missing values by default, so we can
# compute the z-score and retain the missing values
target_est = pd.DataFrame(target_est)
target_est = (target_est -
target_est.mean(axis=0)) / target_est.std(axis=0)
target_est = target_est.values
if pca:
# For the PCA case we need to replace any remaining missing
# values with their column average. Since we now have z-scores,
# we know that the average of every column is zero.
# If it's not the PCA case, then we can leave the missing
# values in as they will be ignored down the line.
if np.isfinite(target_est.sum()) == False:
trouble = np.isfinite(target_est)
trouble = np.bitwise_not(trouble)
target_est[trouble] = 0.
del trouble
# run principle components on target data (skip PCA if pca=False)
# NOTE: matplotlib has deprecated PCA function, also it only uses SVD
# which can get tripped up by bad data
# NOTE: the logic here is to first identify the principle components and
# then weight each component in preparation for future SSD
# computations; we weight the data here so that we don't need to
# weight the data each time the SSD is computed; in effect we want
# to compute the SSD on each raw component and then weight that
# component's contribution to the total SSD by the component's share
# of total variance explained, since the SSD computation has a
# squared term we can take the square root of the data now and then
# not have to weight it later
# NOTE: PCA computed using all data, i.e. we do not screen out
# observations in the exclude list.
if pca and target_est is not None:
try:
# eigenvector approach
pca_node = MDP.nodes.PCANode()
target_est = pca_node.execute(
target_est) # get principle components
except:
try:
# singular value decomposition approach
pca_node = MDP.nodes.PCANode(svd=True)
target_est = pca_node.execute(
target_est) # get principle components
except:
# NIPALS would be a better approach than imputing
# missing values entirely, but MDP 3.4 does not handle
# missing values. Leaving this code as a place holder in
# case MDP is updated later.
###pca_node = MDP.nodes.NIPALSNode()
###target_est = pca_node.execute(target_est) # get principle components
raise Exception, "PCA not possible given input data and settings. Set zscore=True to automatically impute missing values or address missing values in advance."
pca_variance = np.sqrt(pca_node.d / pca_node.total_variance)
target_est = target_est * pca_variance # weighting for SSD
# NOTE: the target_est variable is passed to the SSD function, and the
# target_parts variable is passed to the feasibility test function
# set the appropriate objective function plan
build_region, enclave_test, local_test = function_picker(count_est,\
count_th_min, count_th_max, target_th_count,\
target_th_prop, target_th_ratio, target_th_all)
# setup the CV computation
get_cv = UTILS.get_mv_cv
cv_exclude = [cv_exclude_count, cv_exclude_prop, cv_exclude_ratio]
# setup areas to be excluded from computations
if exclude:
exclude = [id2i[j] for j in exclude]
original_exclude = exclude[:] # in integer ID form
else:
original_exclude = []
# might consider an automated process to drop observations where
# count_est=0; at this time the user would be expected to add these
# observations to the exclude list
time2 = time.time()
time_output['prep'] = time2 - time1
# find the feasible solution with the most number of regions
regions, id2region, exclude, enclaves = BASE.base_region_iterator(\
w, count_th_min, count_th_max, count_est, target_th, target_est,\
exclude, auto_exclude, get_cv, base_solutions,\
target_parts, build_region, enclave_test, kd, points,
anchor, cardinality, cv_exclude)
time3 = time.time()
time_output['base'] = time3 - time2
problem_ids = list(set(exclude).difference(original_exclude))
if id2region == False:
# Infeasible base run
exit = "no feasible solution"
time3a = time4 = time4a = time.time()
else:
if target_est is not None:
# only compute SSDs if there are target_est variables
start_ssds = np.array([
UTILS.sum_squares(region, target_est) for region in regions
])
else:
start_ssds = np.ones(len(regions)) * -999.0
if compactness:
# capture compactness from base solution
start_compactness = UTILS.compactness_global(
regions, compactness)
if local_improvement and len(regions) > 1:
# only run the local improvement if the appropriate flag is set
# (local_improvement=True) and if there is more then one region to
# swap areas between
# swap areas along region borders that improve SSD
time3a = time.time()
regions, id2region, exit = \
LOCAL.local_search(regions, id2region, w, count_th_min, count_th_max,\
count_est, target_th, target_parts,\
target_est, exclude, get_cv,\
local_test, local_params, cv_exclude)
time4 = time.time()
# collect stats on SSD for each region
end_ssds = np.array([
UTILS.sum_squares(region, target_est) for region in regions
])
ssd_improvement = (end_ssds - start_ssds) / start_ssds
ssd_improvement[np.isnan(
ssd_improvement
)] = 0.0 # makes singleton regions have 0 improvement
ssds = np.vstack((start_ssds, end_ssds, ssd_improvement)).T
if compactness:
# capture compactness from final solution
end_compactness = UTILS.compactness_global(
regions, compactness)
compact_change = \
(end_compactness - start_compactness) / start_compactness
compacts = np.vstack(
(start_compactness, end_compactness, compact_change)).T
else:
compacts = np.ones((len(regions), 3)) * -999.0
time4a = time.time()
else:
time3a = time4 = time.time()
# capture start SSDs and compactness, insert -999 for "improvements"
ssds = np.vstack((start_ssds, np.ones(start_ssds.shape)*-999,\
np.ones(start_ssds.shape)*-999)).T
if compactness:
compacts = np.vstack((start_compactness, np.ones(start_compactness.shape)*-999,\
np.ones(start_compactness.shape)*-999)).T
else:
compacts = np.ones((len(regions), 3)) * -999.0
exit = 'no local improvement'
print "Did not run local improvement"
time4a = time.time()
time_output['base_wrapup'] = time3a - time3
time_output['local'] = time4 - time3a
time_output['local_wrapup'] = time4a - time4
####################
# process regionalization results for user output
####################
# setup header for the pandas dataframes (estimates, MOEs, CVs)
header = []
if target_est_count is not None:
if 'pandas' in str(type(target_est_count)):
header.extend(target_est_count.columns.tolist())
else:
header.extend([
'count_var' + str(i)
for i in range(target_est_count.shape[1])
])
if target_est_prop is not None:
if 'pandas' in str(type(target_est_prop)):
header.extend(target_est_count.prop.tolist())
else:
header.extend([
'prop_var' + str(i)
for i in range(target_est_prop.shape[1] / 2)
])
if target_est_ratio is not None:
if 'pandas' in str(type(target_est_ratio)):
header.extend(target_est_ratio.columns.tolist())
else:
header.extend([
'ratio_var' + str(i)
for i in range(target_est_ratio.shape[1] / 2)
])
# initialize pandas dataframes (estimates, MOEs, CVs; regions and areas)
regionID = pd.Index(range(len(regions)), name='regionID')
ests_region = pd.DataFrame(index=regionID, columns=header)
moes_region = pd.DataFrame(index=regionID, columns=header)
cvs_region = pd.DataFrame(index=regionID, columns=header)
areaID = pd.Index(range(w.n), name='areaID')
ests_area = pd.DataFrame(index=areaID, columns=header)
moes_area = pd.DataFrame(index=areaID, columns=header)
cvs_area = pd.DataFrame(index=areaID, columns=header)
# setup header and pandas dataframe (count variable, if applicable)
header = ['count']
if count_est is not None:
if 'pandas' in str(type(count_est)):
header = [count_est.columns[0]]
counts_region = pd.DataFrame(index=range(len(regions)), columns=header)
counts_area = pd.DataFrame(index=range(w.n), columns=header)
# create SSD and compactness dataframes
if id2region == False:
# Infeasible base run
ssds = None
compacts = None
else:
ssds = pd.DataFrame(
ssds,
index=regionID,
columns=['start_ssd', 'end_ssd', 'ssd_improvement'])
compacts = pd.DataFrame(compacts,
index=regionID,
columns=[
'start_compactness', 'end_compactness',
'compactness_improvement'
])
# this one-dimensional list will contain the region IDs (ordered by area)
ordered_region_ids = np.ones(w.n) * -9999
for i, region in enumerate(regions):
if count_est is not None:
# get region totals for count variable
counts_region.ix[i] = count_est[region].sum()
for j in region:
counts_area.ix[j] = count_est[j]
ests = []
sdes = []
if target_est_count is not None:
# est, MOE and CV for count data
est, sde = UTILS.get_est_sde_count(region, target_parts)
est[np.isnan(est)] = 0.0 # clean up 0/0 case
sde[np.isnan(sde)] = 0.0 # clean up 0/0 case
ests.extend(est)
sdes.extend(sde)
if target_est_prop is not None:
# est, MOE and CV for proportion data
est, sde = UTILS.get_est_sde_prop(region, target_parts)
est[np.isnan(est)] = 0.0 # clean up 0/0 case
sde[np.isnan(sde)] = 0.0 # clean up 0/0 case
ests.extend(est)
sdes.extend(sde)
if target_est_ratio is not None:
# est, MOE and CV for ratio data
est, sde = UTILS.get_est_sde_ratio(region, target_parts)
est[np.isnan(est)] = 0.0 # clean up 0/0 case
sde[np.isnan(sde)] = 0.0 # clean up 0/0 case
ests.extend(est)
sdes.extend(sde)
ests_region, moes_region, cvs_region = wrapup_region(\
i, ests, sdes, target_parts,
ests_region, moes_region, cvs_region)
ests_area, moes_area, cvs_area = wrapup_areas(\
region, target_parts,
ests_area, moes_area, cvs_area)
ordered_region_ids[region] = i
# set excluded areas to region ID -999
ordered_region_ids[exclude] = -999
time5 = time.time()
time_output['wrapup'] = time5 - time4
time_output['total'] = time5 - time1
self.exit = exit
self.time = time_output
self.enclaves = enclaves
self.p = len(regions)
self.regions = regions
self.region_ids = ordered_region_ids.tolist()
self.ssds = ssds
self.compactness = compacts
self.ests_region = ests_region
self.moes_region = moes_region
self.cvs_region = cvs_region
self.ests_area = ests_area
self.moes_area = moes_area
self.cvs_area = cvs_area
self.counts_region = counts_region
self.counts_area = counts_area
self.problem_ids = problem_ids
def solve_it(input_data):
# Modify this code to run your optimization algorithm
# parse the input
lines = input_data.split('\n')
parts = lines[0].split()
facility_count = int(parts[0])
customer_count = int(parts[1])
facilities = []
for i in range(1, facility_count + 1):
parts = lines[i].split()
facilities.append(
Facility(i - 1, float(parts[0]), int(parts[1]),
Point(float(parts[2]), float(parts[3]))))
customers = []
for i in range(facility_count + 1, facility_count + 1 + customer_count):
parts = lines[i].split()
customers.append(
Customer(i - 1 - facility_count, int(parts[0]),
Point(float(parts[1]), float(parts[2]))))
from mip1 import run1
from mip1 import weight_random, weight_length, weight_length_plus_some_setup
#solution = run1(facilities, customers, weight_random, 20)
#from mip2 import run2
#solution = run2(facilities, customers, num_fac=2, iterations=500)
from local_search import local_search
solution = local_search(facilities, customers)
# build a trivial solution
# pack the facilities one by one until all the customers are served
'''solution = [-1]*len(customers)
capacity_remaining = [f.capacity for f in facilities]
facility_index = 0
for customer in customers:
if capacity_remaining[facility_index] >= customer.demand:
solution[customer.index] = facility_index
capacity_remaining[facility_index] -= customer.demand
else:
facility_index += 1
assert capacity_remaining[facility_index] >= customer.demand
solution[customer.index] = facility_index
capacity_remaining[facility_index] -= customer.demand
'''
used = [0] * len(facilities)
for facility_index in solution:
used[facility_index] = 1
# calculate the cost of the solution
obj = sum([f.setup_cost * used[f.index] for f in facilities])
for customer in customers:
obj += length(customer.location,
facilities[solution[customer.index]].location)
# prepare the solution in the specified output format
output_data = '%.2f' % obj + ' ' + str(0) + '\n'
output_data += ' '.join(map(str, solution))
return output_data
visualise_sol( w_price, w_space, w_capacity, solution )
# ITERATIVE
if task == "iterative":
for i in range(8):
if m_time:
print "@ Start constructive h.: %s" % (
stops.start_time()
)
init_solution = constructive_random_heuristic(
w_price, w_space, w_capacity
)
if m_time:
print "@ Start iterative ls.: %s" % (
stops.current_time()
)
frame = local_search( init_solution, w_price, w_space, w_capacity )
solution = frame.run_iteration()
temp_price = calculate_price( solution, w_price )
if temp_price < best_sol:
best_sol = temp_price
best_type = "iterative"
if m_time:
print "@ Finish: %s" % ( stops.current_time() )
print "\t\tIterative: %s" % (str(temp_price))
if stats:
print solution_table( solution )
if visualise:
visualise_sol( w_price, w_space, w_capacity, solution )
# GREAT DELUGE
if task in ("deluge", "delugeall", \
"delugerandom", "delugegreedy", "delugepeckish"):
def main(config, black_box_function=None, output_file=""):
"""
Run design-space exploration using prior injection.
:param config: dictionary containing all the configuration parameters of this design-space exploration.
:param black_box_function: black-box function to optimize if running on default mode.
:param output_file: a name for the file used to save the optimization results.
"""
debug = False
sys.stdout.write_to_logfile(str(config) + "\n")
param_space = space.Space(config)
random_time = datetime.datetime.now()
run_directory = config["run_directory"]
application_name = config["application_name"]
hypermapper_mode = config["hypermapper_mode"]["mode"]
log_file = deal_with_relative_and_absolute_path(run_directory,
config["log_file"])
sys.stdout.change_log_file(log_file)
if (hypermapper_mode == 'client-server'):
sys.stdout.switch_log_only_on_file(True)
if hypermapper_mode == "default":
if black_box_function == None:
print("Error: the black box function must be provided")
raise SystemExit
if not callable(black_box_function):
print("Error: the black box function parameter is not callable")
raise SystemExit
input_params = param_space.get_input_parameters()
optimization_metrics = config["optimization_objectives"]
if len(optimization_metrics) > 1:
print(
"Error: prior optimization does not support multi-objective optimization yet"
)
exit()
number_of_objectives = len(optimization_metrics)
optimization_iterations = config["optimization_iterations"]
evaluations_per_optimization_iteration = config[
"evaluations_per_optimization_iteration"]
number_of_cpus = config["number_of_cpus"]
if number_of_cpus > 1:
print(
"Warning: this mode supports only sequential execution for now. Running on a single cpu."
)
number_of_cpus = 1
print_importances = config["print_parameter_importance"]
epsilon_greedy_threshold = config["epsilon_greedy_threshold"]
if "feasible_output" in config:
feasible_output = config["feasible_output"]
feasible_output_name = feasible_output["name"]
enable_feasible_predictor = feasible_output[
"enable_feasible_predictor"]
enable_feasible_predictor_grid_search_on_recall_and_precision = feasible_output[
"enable_feasible_predictor_grid_search_on_recall_and_precision"]
feasible_predictor_grid_search_validation_file = feasible_output[
"feasible_predictor_grid_search_validation_file"]
feasible_parameter = param_space.get_feasible_parameter()
acquisition_function_optimizer = config["acquisition_function_optimizer"]
if acquisition_function_optimizer == "local_search":
local_search_random_points = config["local_search_random_points"]
local_search_starting_points = config["local_search_starting_points"]
elif acquisition_function_optimizer == "posterior_sampling":
posterior_sampling_tuning_points = config[
"posterior_sampling_tuning_points"]
posterior_sampling_final_samples = config[
"posterior_sampling_final_samples"]
posterior_sampling_mcmc_chains = config[
"posterior_sampling_mcmc_chains"]
else:
print(
"Unrecognized acquisition function optimization method in the configuration file:",
acquisition_function_optimizer)
raise SystemExit
exhaustive_search_data_array = None
exhaustive_search_fast_addressing_of_data_array = None
scalarization_key = config["scalarization_key"]
scalarization_method = config["scalarization_method"]
model_weight = config["model_posterior_weight"]
model_good_quantile = config["model_good_quantile"]
weight_sampling = config["weight_sampling"]
objective_limits = {}
for objective in optimization_metrics:
objective_limits[objective] = [float("inf"), float("-inf")]
number_of_doe_samples = config["design_of_experiment"]["number_of_samples"]
model_type = config["models"]["model"]
regression_model_parameters = {}
if model_type == "random_forest":
regression_model_parameters["n_estimators"] = config["models"][
"number_of_trees"]
regression_model_parameters["max_features"] = config["models"][
"max_features"]
regression_model_parameters["bootstrap"] = config["models"][
"bootstrap"]
regression_model_parameters["min_samples_split"] = config["models"][
"min_samples_split"]
tree_means_per_leaf = None
tree_vars_per_leaf = None
if output_file == "":
output_data_file = config["output_data_file"]
if output_data_file == "output_samples.csv":
output_data_file = application_name + "_" + output_data_file
else:
output_data_file = output_file
beginning_of_time = param_space.current_milli_time()
absolute_configuration_index = 0
if param_space.get_prior_normalization_flag() is True:
prior_limit_estimation_points = config["prior_limit_estimation_points"]
objective_weights = sample_weight_flat(
optimization_metrics, 1
)[0] # this will do fine for 1 objective cases, but for multi-objective optimization it might break
good_prior_normalization_limits = estimate_prior_limits(
param_space, prior_limit_estimation_points, objective_weights)
else:
good_prior_normalization_limits = None
# Design of experiments/resume optimization phase
doe_t0 = datetime.datetime.now()
if config["resume_optimization"] == True:
resume_data_file = config["resume_optimization_data"]
if not resume_data_file.endswith('.csv'):
print("Error: resume data file must be a CSV")
raise SystemExit
if resume_data_file == "output_samples.csv":
resume_data_file = application_name + "_" + resume_data_file
data_array, fast_addressing_of_data_array = param_space.load_data_file(
resume_data_file, debug=False, number_of_cpus=number_of_cpus)
absolute_configuration_index = len(data_array[list(data_array.keys(
))[0]]) # get the number of points evaluated in the previous run
beginning_of_time = beginning_of_time - data_array[
param_space.get_timestamp_parameter()[0]][
-1] # Set the timestamp back to match the previous run
print("Resumed optimization, number of samples = %d ......." %
absolute_configuration_index)
if absolute_configuration_index < number_of_doe_samples:
configurations = param_space.get_doe_sample_configurations(
fast_addressing_of_data_array,
number_of_doe_samples - absolute_configuration_index,
"random sampling")
print(
"Design of experiment phase, number of new doe samples = %d ......."
% (number_of_doe_samples - absolute_configuration_index))
new_data_array = param_space.run_configurations(
hypermapper_mode, configurations, beginning_of_time,
black_box_function, exhaustive_search_data_array,
exhaustive_search_fast_addressing_of_data_array, run_directory)
data_array = concatenate_data_dictionaries(
data_array, new_data_array,
param_space.input_output_and_timestamp_parameter_names)
absolute_configuration_index = number_of_doe_samples
iteration_number = 1
else:
iteration_number = absolute_configuration_index - number_of_doe_samples + 1
else:
fast_addressing_of_data_array = {}
default_configuration = param_space.get_default_or_random_configuration(
)
str_data = param_space.get_unique_hash_string_from_values(
default_configuration)
fast_addressing_of_data_array[str_data] = absolute_configuration_index
if number_of_doe_samples - 1 > 0:
configurations = param_space.get_doe_sample_configurations(
fast_addressing_of_data_array, number_of_doe_samples - 1,
"random sampling") + [default_configuration]
else:
configurations = [default_configuration]
print(
"Design of experiment phase, number of doe samples = %d ......." %
number_of_doe_samples)
data_array = param_space.run_configurations(
hypermapper_mode, configurations, beginning_of_time,
black_box_function, exhaustive_search_data_array,
exhaustive_search_fast_addressing_of_data_array, run_directory)
absolute_configuration_index += number_of_doe_samples
iteration_number = 1
for objective in optimization_metrics:
lower_bound = min(objective_limits[objective][0],
min(data_array[objective]))
upper_bound = max(objective_limits[objective][1],
max(data_array[objective]))
objective_limits[objective] = [lower_bound, upper_bound]
if enable_feasible_predictor:
# HyperMapper needs at least one valid and one invalid sample for its feasibility classifier
# i.e. they cannot all be equal
while are_all_elements_equal(data_array[
feasible_parameter[0]]) and optimization_iterations > 0:
print(
"Warning: all points are either valid or invalid, random sampling more configurations."
)
print("Number of doe samples so far:",
absolute_configuration_index)
configurations = param_space.get_doe_sample_configurations(
fast_addressing_of_data_array, 1, "random sampling")
new_data_array = param_space.run_configurations(
hypermapper_mode, configurations, beginning_of_time,
black_box_function, exhaustive_search_data_array,
exhaustive_search_fast_addressing_of_data_array, run_directory)
data_array = concatenate_data_dictionaries(
new_data_array, data_array,
param_space.input_output_and_timestamp_parameter_names)
absolute_configuration_index += 1
optimization_iterations -= 1
print(
"\nEnd of doe/resume phase, the number of configuration runs is: %d\n"
% absolute_configuration_index)
sys.stdout.write_to_logfile(
("DoE time %10.4f sec\n" %
((datetime.datetime.now() - doe_t0).total_seconds())))
with open(
deal_with_relative_and_absolute_path(run_directory,
output_data_file), 'w') as f:
w = csv.writer(f)
w.writerow(param_space.get_input_output_and_timestamp_parameters())
tmp_list = [
param_space.convert_types_to_string(j, data_array)
for j in param_space.get_input_output_and_timestamp_parameters()
]
tmp_list = list(zip(*tmp_list))
for i in range(len(data_array[optimization_metrics[0]])):
w.writerow(tmp_list[i])
if evaluations_per_optimization_iteration > 1:
print("Warning, number of evaluations per iteration > 1")
print(
"HyperMapper's prior optimization currently does not support multiple runs per iteration, setting evaluations per iteration to 1"
)
function_parameters = {}
function_parameters["param_space"] = param_space
function_parameters["model_weight"] = model_weight
function_parameters["threshold"] = {}
function_parameters[
"good_prior_normalization_limits"] = good_prior_normalization_limits
function_parameters["posterior_floor"] = config[
"posterior_computation_lower_limit"]
function_parameters['model_type'] = model_type
bo_t0 = datetime.datetime.now()
while iteration_number <= optimization_iterations:
print("Starting optimization iteration", iteration_number)
model_t0 = datetime.datetime.now()
iteration_t0 = datetime.datetime.now()
regression_models, _, _ = models.generate_mono_output_regression_models(
data_array,
param_space,
input_params,
optimization_metrics,
1.00,
model_type=model_type,
number_of_cpus=number_of_cpus,
print_importances=print_importances,
**regression_model_parameters)
normalized_objectives = {}
for objective in optimization_metrics:
if objective_limits[objective][1] == objective_limits[objective][
0]:
normalized_objectives[objective] = [0] * len(
data_array[objective])
else:
normalized_objectives[objective] = [(x - objective_limits[objective][0]) \
/(objective_limits[objective][1] - objective_limits[objective][0]) for x in data_array[objective]]
function_parameters["threshold"][objective] = np.quantile(
data_array[objective], model_good_quantile)
if model_type == "random_forest":
# Change splits of each node from (lower_bound + upper_bound)/2 to a uniformly sampled split in (lower_bound, upper_bound)
bufferx = [data_array[input_param] for input_param in input_params]
bufferx = list(map(list, list(zip(*bufferx))))
tree_means_per_leaf = {}
tree_vars_per_leaf = {}
leaf_per_sample = models.get_leaves_per_sample(
bufferx, regression_models, param_space)
for objective in optimization_metrics:
tree_means_per_leaf[objective] = models.get_mean_per_leaf(
data_array[objective], leaf_per_sample[objective])
tree_vars_per_leaf[objective] = models.get_var_per_leaf(
data_array[objective], leaf_per_sample[objective])
regression_models = models.transform_rf_using_uniform_splits(
regression_models, data_array, param_space)
function_parameters["tree_means_per_leaf"] = tree_means_per_leaf
function_parameters["tree_vars_per_leaf"] = tree_vars_per_leaf
classification_model = None
if enable_feasible_predictor:
classification_model, _, _ = models.generate_classification_model(
application_name,
param_space,
data_array,
input_params,
feasible_parameter,
1.00,
debug,
n_estimators=10,
max_features=0.75,
number_of_cpus=number_of_cpus,
data_array_exhaustive=exhaustive_search_data_array,
enable_feasible_predictor_grid_search_on_recall_and_precision=
enable_feasible_predictor_grid_search_on_recall_and_precision,
feasible_predictor_grid_search_validation_file=
feasible_predictor_grid_search_validation_file,
print_importances=print_importances)
sys.stdout.write_to_logfile(
("Model fitting time %10.4f sec\n" %
((datetime.datetime.now() - model_t0).total_seconds())))
objective_weights = sample_weight_flat(optimization_metrics, 1)[0]
data_array_scalarization, objective_limits = compute_data_array_scalarization(
data_array, objective_weights, objective_limits, None,
scalarization_method)
data_array[scalarization_key] = data_array_scalarization.tolist()
epsilon = random.uniform(0, 1)
if epsilon > epsilon_greedy_threshold:
function_parameters["objective_weights"] = objective_weights
function_parameters["objective_limits"] = objective_limits
function_parameters["iteration_number"] = iteration_number
function_parameters["objective_weights"] = objective_weights
function_parameters["regression_models"] = regression_models
function_parameters['classification_model'] = classification_model
if enable_feasible_predictor:
function_parameters["posterior_normalization_limits"] = [
float("inf"), float("-inf")
]
if acquisition_function_optimizer == "local_search":
_, best_configuration = local_search(
local_search_starting_points,
local_search_random_points,
param_space,
fast_addressing_of_data_array,
False, # set feasibility to false, we handle it inside the acquisition function
compute_EI_from_posteriors,
function_parameters,
scalarization_key,
previous_points=data_array)
else:
print(
"Unrecognized acquisition function optimization method in the configuration file:",
acquisition_function_optimizer)
raise SystemExit
str_data = param_space.get_unique_hash_string_from_values(
best_configuration)
fast_addressing_of_data_array[
str_data] = absolute_configuration_index
absolute_configuration_index += 1
else:
sys.stdout.write_to_logfile(
str(epsilon) + " < " + str(epsilon_greedy_threshold) +
" random sampling a configuration to run\n")
best_configuration = param_space.random_sample_configurations_without_repetitions(
fast_addressing_of_data_array, 1, use_priors=False)[0]
black_box_t0 = datetime.datetime.now()
best_configuration = [best_configuration]
new_data_array = param_space.run_configurations(
hypermapper_mode, best_configuration, beginning_of_time,
black_box_function, exhaustive_search_data_array,
exhaustive_search_fast_addressing_of_data_array, run_directory)
sys.stdout.write_to_logfile(
("Black box time %10.4f sec\n" %
((datetime.datetime.now() - black_box_t0).total_seconds())))
with open(
deal_with_relative_and_absolute_path(run_directory,
output_data_file),
'a') as f:
w = csv.writer(f)
tmp_list = [
param_space.convert_types_to_string(j, new_data_array) for j in
list(param_space.get_input_output_and_timestamp_parameters())
]
tmp_list = list(zip(*tmp_list))
for i in range(len(new_data_array[optimization_metrics[0]])):
w.writerow(tmp_list[i])
data_array = concatenate_data_dictionaries(
new_data_array, data_array,
param_space.input_output_and_timestamp_parameter_names)
for objective in optimization_metrics:
lower_bound = min(objective_limits[objective][0],
min(data_array[objective]))
upper_bound = max(objective_limits[objective][1],
max(data_array[objective]))
objective_limits[objective] = [lower_bound, upper_bound]
iteration_number += 1
sys.stdout.write_to_logfile(
("BO iteration time %10.4f sec\n" %
((datetime.datetime.now() - iteration_t0).total_seconds())))
sys.stdout.write_to_logfile(
("BO total time %10.4f sec\n" %
((datetime.datetime.now() - bo_t0).total_seconds())))
print("End of Prior Optimization")
