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Adding basic comments to simulate functions.
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@1ozturkbe
1ozturkbe committed Jun 25, 2019
1 parent 76b856f commit 10a48d1
Showing 1 changed file with 54 additions and 3 deletions.
57 changes: 54 additions & 3 deletions robust/simulations/simulate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,29 @@
import multiprocessing as mp
import scipy.stats as stats

def pickleable_robust_solve_time(robust_model,verbosity, min_num_of_linear_sections,
# For the following simulation functions, we define common inputs as the following:
# :model: GP or SP model of interest
# :model_name: string for printing
# :gamma: array of floats to specify set size
# :number_of_iterations: # of MC samples
# :numbers_of_linear_sections: array of integer sections
# :linearization_tolerance: max error of pwl approx
# :verbosity: 0-4 for printout
# :file_name: directory for printing
# :number_of_time_average_solves: # of solves for solution time analysis
# :methods: type of conservative approximation used, dict
# :uncertainty_sets: string defining type of set
# :nominal_solution: solution of model with zero uncertainty
# :nominal_solve_time: solve time of model with zero uncertainty
# :nominal_number_of_constraints:
# :directly_uncertain_vars_subs: dict of uncertain parameter MC samples
# :return:

def pickleable_robust_solve_time(robust_model, verbosity, min_num_of_linear_sections,
max_num_of_linear_sections, linearization_tolerance):
"""
Wrapper for robustsolve for parallelism.
"""
robust_model_solution = robust_model.robustsolve(verbosity=verbosity,
minNumOfLinearSections=min_num_of_linear_sections,
maxNumOfLinearSections=max_num_of_linear_sections,
Expand All @@ -22,6 +43,9 @@ def pickleable_robust_solve_time(robust_model,verbosity, min_num_of_linear_secti

def get_avg_robust_solve_time(number_of_time_average_solves, robust_model, verbosity, min_num_of_linear_sections,
max_num_of_linear_sections, linearization_tolerance, parallel=False):
"""
Given a number of solves, gives the average solution time of a robust model. Parallel option.
"""
if parallel:
pool = mp.Pool(mp.cpu_count()-1)
processes = []
Expand All @@ -42,6 +66,9 @@ def simulate_robust_model(model, method, uncertainty_set, gamma, directly_uncert
number_of_iterations, linearization_tolerance, min_num_of_linear_sections,
max_num_of_linear_sections, verbosity, nominal_solution,
number_of_time_average_solves, parallel=False):
"""
Simulates a robust model given uncertain outcomes.
"""
print(
method[
'name'] + ' under ' + uncertainty_set + ' uncertainty set: \n' + '\t' + 'gamma = %s\n' % gamma
Expand Down Expand Up @@ -100,7 +127,7 @@ def print_simulation_results(robust_model, robust_model_solution, robust_model_s
'\t\t\t' + 'Upper lower relative error: %s\n' % mag(robust_model_solution['upperLowerRelError']))


def generate_variable_gamma_results(model, model_name, gammas, number_of_iterations,
def print_variable_gamma_results(model, model_name, gammas, number_of_iterations,
min_num_of_linear_sections, max_num_of_linear_sections, verbosity,
linearization_tolerance, file_name, number_of_time_average_solves,
methods, uncertainty_sets, nominal_solution, nominal_solve_time,
Expand Down Expand Up @@ -148,6 +175,10 @@ def variable_gamma_results(model, methods, gammas, number_of_iterations,
min_num_of_linear_sections, max_num_of_linear_sections, verbosity,
linearization_tolerance, number_of_time_average_solves,
uncertainty_sets, nominal_solution, directly_uncertain_vars_subs, parallel=False):
"""
Simulates a GP or SP model for a range of gammas, i.e. uncertainty set size.
Outputs are dicts that have the key format: [deltaValue (float), methodName (string), uncertaintySet (string)]
"""
solutions = {}
solve_times = {}
simulations = {}
Expand Down Expand Up @@ -178,6 +209,11 @@ def variable_goal_results(model, methods, deltas, number_of_iterations,
min_num_of_linear_sections, max_num_of_linear_sections, verbosity,
linearization_tolerance, number_of_time_average_solves,
uncertainty_sets, nominal_solution, directly_uncertain_vars_subs, parallel=False):
"""
Simulates a GP or SP model for a range of deltas in the goal programming form.
i.e. maximizes uncertainty set size given an acceptable penalty delta on the objective.
Outputs are dicts that have the key format: [deltaValue (float), methodName (string), uncertaintySet (string)]
"""
solutions = {}
solve_times = {}
simulations = {}
Expand Down Expand Up @@ -222,6 +258,10 @@ def variable_goal_results(model, methods, deltas, number_of_iterations,
return solutions, solve_times, simulations, number_of_constraints

def filter_gamma_result_dict(dict, tupInd1, tupVal1, tupInd2, tupVal2):
"""
Filters the items in outputs of variable_gamma_results or variable_goal_results
with 2 out of 3 keys.
"""
filteredResult = {}
for i in sorted(dict.iterkeys()):
if i[tupInd1] == tupVal1 and i[tupInd2] == tupVal2:
Expand Down Expand Up @@ -256,11 +296,14 @@ def plot_goal_result_PoFandCost(title, objective_name, objective_varkey, objecti
objective_proboffailure_vs_gamma(gammas, objective_costs, objective_name, objective_units,
np.min(objective_costs), np.max(objective_costs), pofs, title, None)

def generate_variable_piecewiselinearsections_results(model, model_name, gamma, number_of_iterations,
def print_variable_pwlsections_results(model, model_name, gamma, number_of_iterations,
numbers_of_linear_sections, linearization_tolerance,
verbosity, file_name, number_of_time_average_solves,
methods, uncertainty_sets, nominal_solution, nominal_solve_time,
nominal_number_of_constraints, directly_uncertain_vars_subs):
"""
Simulates a model for different numbers of PWL sections for each posy.
"""

f = open(file_name, 'w')
f.write(model_name + ' Results: variable piecewise-linear sections\n')
Expand Down Expand Up @@ -301,6 +344,14 @@ def generate_variable_piecewiselinearsections_results(model, model_name, gamma,


def generate_model_properties(model, number_of_time_average_solves, number_of_iterations, distribution = None):
"""
Solves the nominal model, and generates MC samples
:param model: GP or SP model of interest, with uncertainties
:param number_of_time_average_solves: # of solves for solution time analysis
:param number_of_iterations: # of MC samples for simulation
:param distribution: distribution for MC samples, 'normal' or 'uniform otherwise
:return: nominal solution, nominal solve time, nominal number of constraints, and MC samples of uncertain inputs
"""
try:
nominal_solution = model.solve(verbosity=0)
nominal_solve_time = nominal_solution['soltime']
Expand Down

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