def _speedup_analysis(self, N_generator=None, filename=None, **kwargs):
if N_generator is None:
def N_generator(n):
return n
N = self.SCM_approximation.N
print(TextBox("SCM speedup analysis begins", fill="="))
print("")
speedup_analysis_table = SpeedupAnalysisTable(self.testing_set)
speedup_analysis_table.set_Nmax(N)
speedup_analysis_table.add_column("speedup",
group_name="speedup",
operations=("min", "mean", "max"))
exact_timer = Timer("parallel")
SCM_timer = Timer("serial")
for (mu_index, mu) in enumerate(self.testing_set):
print(TextLine("SCM " + str(mu_index), fill="~"))
self.SCM_approximation.set_mu(mu)
exact_timer.start()
self.SCM_approximation.evaluate_stability_factor()
elapsed_exact = exact_timer.stop()
for n in range(1, N + 1): # n = 1, ... N
n_arg = N_generator(n)
if n_arg is not None:
SCM_timer.start()
self.SCM_approximation.get_stability_factor_lower_bound(
n_arg)
self.SCM_approximation.get_stability_factor_upper_bound(
n_arg)
elapsed_SCM = SCM_timer.stop()
speedup_analysis_table[
"speedup", n, mu_index] = elapsed_exact / elapsed_SCM
else:
speedup_analysis_table["speedup", n,
mu_index] = NotImplemented
# Print
print("")
print(speedup_analysis_table)
print("")
print(TextBox("SCM speedup analysis ends", fill="="))
print("")
# Export speedup analysis table
speedup_analysis_table.save(
self.folder["speedup_analysis"],
"speedup_analysis" if filename is None else filename)
def _speedup_analysis(self, N_generator=None, filename=None, **kwargs):
if N_generator is None:
def N_generator():
N = self.EIM_approximation.N
for n in range(1, N + 1): # n = 1, ... N
yield n
def N_generator_max():
*_, Nmax = N_generator()
return Nmax
interpolation_method_name = self.EIM_approximation.parametrized_expression.interpolation_method_name()
description = self.EIM_approximation.parametrized_expression.description()
print(TextBox(interpolation_method_name + " speedup analysis begins for" + "\n"
+ "\n".join(description), fill="="))
print("")
speedup_analysis_table = SpeedupAnalysisTable(self.testing_set)
speedup_analysis_table.set_Nmax(N_generator_max())
speedup_analysis_table.add_column("speedup", group_name="speedup", operations=("min", "mean", "max"))
evaluate_timer = Timer("parallel")
EIM_timer = Timer("serial")
for (mu_index, mu) in enumerate(self.testing_set):
print(TextLine(interpolation_method_name + " " + str(mu_index), fill=":"))
self.EIM_approximation.set_mu(mu)
# Evaluate the exact function on the truth grid
evaluate_timer.start()
self.EIM_approximation.evaluate_parametrized_expression()
elapsed_evaluate = evaluate_timer.stop()
for n in N_generator():
EIM_timer.start()
self.EIM_approximation.solve(n)
elapsed_EIM = EIM_timer.stop()
speedup_analysis_table["speedup", n, mu_index] = elapsed_evaluate / elapsed_EIM
# Print
print("")
print(speedup_analysis_table)
print("")
print(TextBox(interpolation_method_name + " speedup analysis ends for" + "\n"
+ "\n".join(description), fill="="))
print("")
# Export speedup analysis table
speedup_analysis_table.save(self.folder["speedup_analysis"],
"speedup_analysis" if filename is None else filename)
def _speedup_analysis(self, N_generator=None, filename=None, **kwargs):
if N_generator is None:
def N_generator():
N = self.reduced_problem.N
if isinstance(N, dict):
N = min(N.values())
for n in range(1, N + 1): # n = 1, ... N
yield n
def N_generator_items():
for n in N_generator():
assert isinstance(n, (dict, int))
if isinstance(n, int):
yield (n, n)
elif isinstance(n, dict):
assert len(n) == 1
(n_int, n_online_size_dict) = n.popitem()
assert isinstance(n_int, int)
assert isinstance(n_online_size_dict, OnlineSizeDict)
yield (n_int, n_online_size_dict)
else:
raise TypeError(
"Invalid item generated by N_generator")
def N_generator_max():
*_, Nmax = N_generator_items()
assert isinstance(Nmax, tuple)
assert len(Nmax) == 2
assert isinstance(Nmax[0], int)
return Nmax[0]
print(
TextBox(self.truth_problem.name() + " " + self.label +
" speedup analysis begins",
fill="="))
print("")
speedup_analysis_table = SpeedupAnalysisTable(self.testing_set)
speedup_analysis_table.set_Nmax(N_generator_max())
speedup_analysis_table.add_column("speedup_solve",
group_name="speedup_solve",
operations=("min", "mean",
"max"))
speedup_analysis_table.add_column("speedup_output",
group_name="speedup_output",
operations=("min", "mean",
"max"))
truth_timer = Timer("parallel")
reduced_timer = Timer("serial")
for (mu_index, mu) in enumerate(self.testing_set):
print(TextLine(str(mu_index), fill="#"))
self.reduced_problem.set_mu(mu)
truth_timer.start()
self.truth_problem.solve(**kwargs)
elapsed_truth_solve = truth_timer.stop()
truth_timer.start()
self.truth_problem.compute_output()
elapsed_truth_output = truth_timer.stop()
for (n_int, n_arg) in N_generator_items():
reduced_timer.start()
solution = self.reduced_problem.solve(n_arg, **kwargs)
elapsed_reduced_solve = reduced_timer.stop()
reduced_timer.start()
output = self.reduced_problem.compute_output()
elapsed_reduced_output = reduced_timer.stop()
if solution is not NotImplemented:
speedup_analysis_table[
"speedup_solve", n_int,
mu_index] = elapsed_truth_solve / elapsed_reduced_solve
else:
speedup_analysis_table["speedup_solve", n_int,
mu_index] = NotImplemented
if output is not NotImplemented:
speedup_analysis_table[
"speedup_output", n_int,
mu_index] = (elapsed_truth_solve +
elapsed_truth_output) / (
elapsed_reduced_solve +
elapsed_reduced_output)
else:
speedup_analysis_table["speedup_output", n_int,
mu_index] = NotImplemented
# Print
print("")
print(speedup_analysis_table)
print("")
print(
TextBox(self.truth_problem.name() + " " + self.label +
" speedup analysis ends",
fill="="))
print("")
# Export speedup analysis table
speedup_analysis_table.save(
self.folder["speedup_analysis"],
"speedup_analysis" if filename is None else filename)
def _speedup_analysis(self, N_generator=None, filename=None, **kwargs):
if N_generator is None:
def N_generator(n):
return n
N = self.reduced_problem.N
if isinstance(N, dict):
N = min(N.values())
print(TextBox(self.truth_problem.name() + " " + self.label + " speedup analysis begins", fill="="))
print("")
speedup_analysis_table = SpeedupAnalysisTable(self.testing_set)
speedup_analysis_table.set_Nmax(N)
speedup_analysis_table.add_column("speedup_solve", group_name="speedup_solve", operations=("min", "mean", "max"))
speedup_analysis_table.add_column("speedup_output", group_name="speedup_output", operations=("min", "mean", "max"))
truth_timer = Timer("parallel")
reduced_timer = Timer("serial")
for (mu_index, mu) in enumerate(self.testing_set):
print(TextLine(str(mu_index), fill="#"))
self.reduced_problem.set_mu(mu)
truth_timer.start()
self.truth_problem.solve(**kwargs)
elapsed_truth_solve = truth_timer.stop()
truth_timer.start()
self.truth_problem.compute_output()
elapsed_truth_output = truth_timer.stop()
for n in range(1, N + 1): # n = 1, ... N
n_arg = N_generator(n)
if n_arg is not None:
reduced_timer.start()
solution = self.reduced_problem.solve(n_arg, **kwargs)
elapsed_reduced_solve = reduced_timer.stop()
reduced_timer.start()
output = self.reduced_problem.compute_output()
elapsed_reduced_output = reduced_timer.stop()
else:
solution = NotImplemented
output = NotImplemented
if solution is not NotImplemented:
speedup_analysis_table["speedup_solve", n, mu_index] = elapsed_truth_solve/elapsed_reduced_solve
else:
speedup_analysis_table["speedup_solve", n, mu_index] = NotImplemented
if output is not NotImplemented:
speedup_analysis_table["speedup_output", n, mu_index] = (elapsed_truth_solve + elapsed_truth_output)/(elapsed_reduced_solve + elapsed_reduced_output)
else:
speedup_analysis_table["speedup_output", n, mu_index] = NotImplemented
# Print
print("")
print(speedup_analysis_table)
print("")
print(TextBox(self.truth_problem.name() + " " + self.label + " speedup analysis ends", fill="="))
print("")
# Export speedup analysis table
speedup_analysis_table.save(self.folder["speedup_analysis"], "speedup_analysis" if filename is None else filename)