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analysis.py
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analysis.py
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# -*- coding: utf-8 -*-
"""
Created on March 7 2023
@author: Justin Starreveld: j.s.starreveld@uva.nl
"""
# import external packages
import copy
import numpy as np
# import internal packages
...
class analysis:
"""
robustness & sensitivity analysis algorithm(s) for uncertain multistage optimization problems
INPUT:
...
OUTPUT:
...
"""
def __init__(self, decision_stages, scenarios, problem_instance,
create_model, solve_model, evaluate_feasibility,
evaluate_objective, get_solution, alter_instance,
analysis_method='RA', record_solutions=False,
verbose=False):
self.decision_stages = decision_stages
self.scenarios = scenarios
self.problem_instance = problem_instance
self.create_model = create_model
self.solve_model = solve_model
self.evaluate_feasibility = evaluate_feasibility
self.evaluate_objective = evaluate_objective
self.get_solution = get_solution
self.alter_instance = alter_instance
self.analysis_method = analysis_method
self.record_solutions = record_solutions
self.verbose = verbose
def run(self, solution):
if self.analysis_method == "RA":
return self.robustness_analysis(solution)
elif self.analysis_method == "SA":
return self.sensitivity_analysis(solution)
else:
print("Error in RA, don't recognize setting")
return None
def sensitivity_analysis(self, solution):
N = len(self.scenarios)
feasibility_results = np.empty(N)
feasibility_results.fill(np.nan)
objective_results = np.empty(N)
objective_results.fill(np.nan)
if self.record_solutions:
solution_results = np.empty(N)
solution_results.fill(np.nan)
for s in range(N):
scenario = self.scenarios[s]
problem_instance = self.alter_instance(self.problem_instance, scenario, None, None)
model = self.create_model(problem_instance)
results = self.solve_model(model)
feas_yn = self.evaluate_feasibility(results)
if feas_yn:
feasibility_results[s] = 1
objective_results[s] = self.evaluate_objective(model)
if self.record_solutions:
solution_results[s] = self.get_solution(model)
else:
feasibility_results[s] = 0
if self.verbose:
print("Scenario "+(str(s))+": " + str(round(objective_results[s],1)))
if self.record_solutions:
return feasibility_results, objective_results, solution_results
return feasibility_results, objective_results
def robustness_analysis(self, solution):
N = len(self.scenarios)
num_stages = len(self.decision_stages)
feasibility_results = np.empty(N)
feasibility_results.fill(np.nan)
objective_results = np.empty(N)
objective_results.fill(np.nan)
if self.record_solutions:
solution_results = np.empty(N)
solution_results.fill(np.nan)
for s in range(N):
scenario = self.scenarios[s]
# fix x0 decisions
fixed_variable_info = {}
fixed_variable_info = self.fix_decisions(solution, fixed_variable_info, 0)
if num_stages == 1:
problem_instance = self.alter_instance(self.problem_instance, scenario, self.decision_stages, 0)
model = self.create_model(problem_instance, fixed_variable_info=fixed_variable_info)
results = self.solve_model(model)
solution = self.get_solution(model)
else:
for stage in range(1, num_stages):
problem_instance = self.alter_instance(self.problem_instance, scenario, self.decision_stages, stage)
model = self.create_model(problem_instance, fixed_variable_info=fixed_variable_info)
results = self.solve_model(model)
solution = self.get_solution(model)
fixed_variable_info = self.fix_decisions(solution, fixed_variable_info, stage)
feas_yn = self.evaluate_feasibility(results)
if feas_yn:
feasibility_results[s] = 1
objective_results[s] = self.evaluate_objective(model)
if self.record_solutions:
solution_results[s] = solution
else:
feasibility_results[s] = 0
if self.verbose:
print("Scenario "+(str(s))+": " + str(round(objective_results[s],1)))
if self.record_solutions:
return feasibility_results, objective_results, solution_results
return feasibility_results, objective_results
def fix_decisions(self, solution, fixed_variable_info, stage):
vars_to_be_fixed = self.decision_stages[stage]
for var_name, index in vars_to_be_fixed:
solution_value = solution[var_name][index]
if var_name not in fixed_variable_info.keys():
fixed_variable_info[var_name] = {index: solution_value}
else:
fixed_variable_info[var_name][index] = solution_value
return fixed_variable_info