1 | # _________________________________________________________________________ |
---|
2 | # |
---|
3 | # Coopr: A COmmon Optimization Python Repository |
---|
4 | # Copyright (c) 2008 Sandia Corporation. |
---|
5 | # This software is distributed under the BSD License. |
---|
6 | # Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, |
---|
7 | # the U.S. Government retains certain rights in this software. |
---|
8 | # For more information, see the Coopr README.txt file. |
---|
9 | # _________________________________________________________________________ |
---|
10 | |
---|
11 | import sys |
---|
12 | import types |
---|
13 | from coopr.pyomo import * |
---|
14 | from coopr.pyomo.base.expr import * |
---|
15 | import copy |
---|
16 | import os.path |
---|
17 | import traceback |
---|
18 | import copy |
---|
19 | from coopr.opt import SolverResults,SolverStatus |
---|
20 | from coopr.opt.base import SolverFactory |
---|
21 | from coopr.opt.parallel import SolverManagerFactory |
---|
22 | import time |
---|
23 | import types |
---|
24 | import pickle |
---|
25 | from math import fabs, log, exp |
---|
26 | |
---|
27 | from scenariotree import * |
---|
28 | from phutils import * |
---|
29 | from phobjective import * |
---|
30 | |
---|
31 | from pyutilib.component.core import ExtensionPoint |
---|
32 | |
---|
33 | from coopr.pysp.phextension import IPHExtension |
---|
34 | |
---|
35 | class ProgressiveHedging(object): |
---|
36 | |
---|
37 | # |
---|
38 | # a utility intended for folks who are brave enough to script rho setting in a python file. |
---|
39 | # |
---|
40 | |
---|
41 | def setRhoAllScenarios(self, variable_value, rho_expression): |
---|
42 | |
---|
43 | variable_name = None |
---|
44 | variable_index = None |
---|
45 | |
---|
46 | if isVariableNameIndexed(variable_value.name) is True: |
---|
47 | |
---|
48 | variable_name, variable_index = extractVariableNameAndIndex(variable_value.name) |
---|
49 | |
---|
50 | else: |
---|
51 | |
---|
52 | variable_name = variable_value.name |
---|
53 | variable_index = None |
---|
54 | |
---|
55 | new_rho_value = None |
---|
56 | if isinstance(rho_expression, float): |
---|
57 | new_rho_value = rho_expression |
---|
58 | else: |
---|
59 | new_rho_value = rho_expression() |
---|
60 | |
---|
61 | if self._verbose is True: |
---|
62 | print "Setting rho="+str(new_rho_value)+" for variable="+variable_value.name |
---|
63 | |
---|
64 | for instance_name, instance in self._instances.items(): |
---|
65 | |
---|
66 | rho_param = getattr(instance, "PHRHO_"+variable_name) |
---|
67 | rho_param[variable_index] = new_rho_value |
---|
68 | |
---|
69 | # |
---|
70 | # a utility intended for folks who are brave enough to script variable bounds setting in a python file. |
---|
71 | # |
---|
72 | |
---|
73 | def setVariableBoundsAllScenarios(self, variable_name, variable_index, lower_bound, upper_bound): |
---|
74 | |
---|
75 | if isinstance(lower_bound, float) is False: |
---|
76 | raise ValueError, "Lower bound supplied to PH method setVariableBoundsAllScenarios for variable="+variable_name+indexToString(variable_index)+" must be a constant; value supplied="+str(lower_bound) |
---|
77 | |
---|
78 | if isinstance(upper_bound, float) is False: |
---|
79 | raise ValueError, "Upper bound supplied to PH method setVariableBoundsAllScenarios for variable="+variable_name+indexToString(variable_index)+" must be a constant; value supplied="+str(upper_bound) |
---|
80 | |
---|
81 | for instance_name, instance in self._instances.items(): |
---|
82 | |
---|
83 | variable = getattr(instance, variable_name) |
---|
84 | variable[variable_index].setlb(lower_bound) |
---|
85 | variable[variable_index].setub(upper_bound) |
---|
86 | |
---|
87 | # |
---|
88 | # a utility intended for folks who are brave enough to script variable bounds setting in a python file. |
---|
89 | # same functionality as above, but applied to all indicies of the variable, in all scenarios. |
---|
90 | # |
---|
91 | |
---|
92 | def setVariableBoundsAllIndicesAllScenarios(self, variable_name, lower_bound, upper_bound): |
---|
93 | |
---|
94 | if isinstance(lower_bound, float) is False: |
---|
95 | raise ValueError, "Lower bound supplied to PH method setVariableBoundsAllIndiciesAllScenarios for variable="+variable_name+" must be a constant; value supplied="+str(lower_bound) |
---|
96 | |
---|
97 | if isinstance(upper_bound, float) is False: |
---|
98 | raise ValueError, "Upper bound supplied to PH method setVariableBoundsAllIndicesAllScenarios for variable="+variable_name+" must be a constant; value supplied="+str(upper_bound) |
---|
99 | |
---|
100 | for instance_name, instance in self._instances.items(): |
---|
101 | |
---|
102 | variable = getattr(instance, variable_name) |
---|
103 | for index in variable: |
---|
104 | variable[index].setlb(lower_bound) |
---|
105 | variable[index].setub(upper_bound) |
---|
106 | |
---|
107 | # |
---|
108 | # checkpoint the current PH state via pickle'ing. the input iteration count |
---|
109 | # simply serves as a tag to create the output file name. everything with the |
---|
110 | # exception of the _ph_plugins, _solver_manager, and _solver attributes are |
---|
111 | # pickled. currently, plugins fail in the pickle process, which is fine as |
---|
112 | # JPW doesn't think you want to pickle plugins (particularly the solver and |
---|
113 | # solver manager) anyway. For example, you might want to change those later, |
---|
114 | # after restoration - and the PH state is independent of how scenario |
---|
115 | # sub-problems are solved. |
---|
116 | # |
---|
117 | |
---|
118 | def checkpoint(self, iteration_count): |
---|
119 | |
---|
120 | checkpoint_filename = "checkpoint."+str(iteration_count) |
---|
121 | |
---|
122 | tmp_ph_plugins = self._ph_plugins |
---|
123 | tmp_solver_manager = self._solver_manager |
---|
124 | tmp_solver = self._solver |
---|
125 | |
---|
126 | self._ph_plugins = None |
---|
127 | self._solver_manager = None |
---|
128 | self._solver = None |
---|
129 | |
---|
130 | checkpoint_file = open(checkpoint_filename, "w") |
---|
131 | pickle.dump(self,checkpoint_file) |
---|
132 | checkpoint_file.close() |
---|
133 | |
---|
134 | self._ph_plugins = tmp_ph_plugins |
---|
135 | self._solver_manager = tmp_solver_manager |
---|
136 | self._solver = tmp_solver |
---|
137 | |
---|
138 | print "Checkpoint written to file="+checkpoint_filename |
---|
139 | |
---|
140 | # |
---|
141 | # a simple utility to count the number of continuous and discrete variables in a set of instances. |
---|
142 | # unused variables are ignored, and counts include all active indices. returns a pair - num-discrete, |
---|
143 | # num-continuous. |
---|
144 | # |
---|
145 | |
---|
146 | def compute_variable_counts(self): |
---|
147 | |
---|
148 | num_continuous_vars = 0 |
---|
149 | num_discrete_vars = 0 |
---|
150 | |
---|
151 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage |
---|
152 | |
---|
153 | for tree_node in stage._tree_nodes: |
---|
154 | |
---|
155 | for (variable, index_template, variable_indices) in stage._variables: |
---|
156 | |
---|
157 | for index in variable_indices: |
---|
158 | |
---|
159 | is_used = True # until proven otherwise |
---|
160 | for scenario in tree_node._scenarios: |
---|
161 | instance = self._instances[scenario._name] |
---|
162 | if getattr(instance,variable.name)[index].status == VarStatus.unused: |
---|
163 | is_used = False |
---|
164 | |
---|
165 | if is_used is True: |
---|
166 | |
---|
167 | if isinstance(variable.domain, IntegerSet) or isinstance(variable.domain, BooleanSet): |
---|
168 | num_discrete_vars = num_discrete_vars + 1 |
---|
169 | else: |
---|
170 | num_continuous_vars = num_continuous_vars + 1 |
---|
171 | |
---|
172 | return (num_discrete_vars, num_continuous_vars) |
---|
173 | |
---|
174 | # |
---|
175 | # ditto above, but count the number of fixed discrete and continuous variables. |
---|
176 | # important: once a variable (value) is fixed, it is flagged as unused in the |
---|
177 | # course of presolve - because it is no longer referenced. this makes sense, |
---|
178 | # of course; it's just something to watch for. this is an obvious assumption |
---|
179 | # that we won't be fixing unused variables, which should not be an issue. |
---|
180 | # |
---|
181 | |
---|
182 | def compute_fixed_variable_counts(self): |
---|
183 | |
---|
184 | num_fixed_continuous_vars = 0 |
---|
185 | num_fixed_discrete_vars = 0 |
---|
186 | |
---|
187 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage |
---|
188 | |
---|
189 | for tree_node in stage._tree_nodes: |
---|
190 | |
---|
191 | for (variable, index_template, variable_indices) in stage._variables: |
---|
192 | |
---|
193 | for index in variable_indices: |
---|
194 | |
---|
195 | # implicit assumption is that if a variable value is fixed in one |
---|
196 | # scenario, it is fixed in all scenarios. |
---|
197 | |
---|
198 | is_fixed = False # until proven otherwise |
---|
199 | for scenario in tree_node._scenarios: |
---|
200 | instance = self._instances[scenario._name] |
---|
201 | var_value = getattr(instance,variable.name)[index] |
---|
202 | if var_value.fixed is True: |
---|
203 | is_fixed = True |
---|
204 | |
---|
205 | if is_fixed is True: |
---|
206 | |
---|
207 | if isinstance(variable.domain, IntegerSet) or isinstance(variable.domain, BooleanSet): |
---|
208 | num_fixed_discrete_vars = num_fixed_discrete_vars + 1 |
---|
209 | else: |
---|
210 | num_fixed_continuous_vars = num_fixed_continuous_vars + 1 |
---|
211 | |
---|
212 | return (num_fixed_discrete_vars, num_fixed_continuous_vars) |
---|
213 | |
---|
214 | # when the quadratic penalty terms are approximated via piecewise linear segments, |
---|
215 | # we end up (necessarily) "littering" the scenario instances with extra constraints. |
---|
216 | # these need to and should be cleaned up after PH, for purposes of post-PH manipulation, |
---|
217 | # e.g., writing the extensive form. |
---|
218 | def _cleanup_scenario_instances(self): |
---|
219 | |
---|
220 | for instance_name, instance in self._instances.items(): |
---|
221 | |
---|
222 | for constraint_name in self._instance_augmented_attributes[instance_name]: |
---|
223 | |
---|
224 | instance._clear_attribute(constraint_name) |
---|
225 | |
---|
226 | # if you don't pre-solve, the name collections won't be updated. |
---|
227 | instance.preprocess() |
---|
228 | |
---|
229 | # create PH weight and xbar vectors, on a per-scenario basis, for each variable that is not in the |
---|
230 | # final stage, i.e., for all variables that are being blended by PH. the parameters are created |
---|
231 | # in the space of each scenario instance, so that they can be directly and automatically |
---|
232 | # incorporated into the (appropriately modified) objective function. |
---|
233 | |
---|
234 | def _create_ph_scenario_parameters(self): |
---|
235 | |
---|
236 | for (instance_name, instance) in self._instances.items(): |
---|
237 | |
---|
238 | new_penalty_variable_names = create_ph_parameters(instance, self._scenario_tree, self._rho, self._linearize_nonbinary_penalty_terms) |
---|
239 | self._instance_augmented_attributes[instance_name].extend(new_penalty_variable_names) |
---|
240 | |
---|
241 | # a simple utility to extract the first-stage cost statistics, e.g., min, average, and max. |
---|
242 | def _extract_first_stage_cost_statistics(self): |
---|
243 | |
---|
244 | maximum_value = 0.0 |
---|
245 | minimum_value = 0.0 |
---|
246 | sum_values = 0.0 |
---|
247 | num_values = 0 |
---|
248 | first_time = True |
---|
249 | |
---|
250 | first_stage = self._scenario_tree._stages[0] |
---|
251 | (cost_variable, cost_variable_index) = first_stage._cost_variable |
---|
252 | for scenario_name, instance in self._instances.items(): |
---|
253 | this_value = getattr(instance, cost_variable.name)[cost_variable_index].value |
---|
254 | num_values += 1 |
---|
255 | sum_values += this_value |
---|
256 | if first_time is True: |
---|
257 | first_time = False |
---|
258 | maximum_value = this_value |
---|
259 | minimum_value = this_value |
---|
260 | else: |
---|
261 | if this_value > maximum_value: |
---|
262 | maximum_value = this_value |
---|
263 | if this_value < minimum_value: |
---|
264 | minimum_value = this_value |
---|
265 | return minimum_value, sum_values/num_values, maximum_value |
---|
266 | |
---|
267 | # a utility to transmit - across the PH solver manager - the current weights |
---|
268 | # and averages for each of my problem instances. used to set up iteration K solves. |
---|
269 | def _transmit_weights_and_averages(self): |
---|
270 | |
---|
271 | for scenario_name, scenario_instance in self._instances.items(): |
---|
272 | |
---|
273 | weights_to_transmit = [] |
---|
274 | averages_to_transmit = [] |
---|
275 | |
---|
276 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage, so no weights to worry about. |
---|
277 | |
---|
278 | for (variable, index_template, variable_indices) in stage._variables: |
---|
279 | |
---|
280 | variable_name = variable.name |
---|
281 | weight_parameter_name = "PHWEIGHT_"+variable_name |
---|
282 | weights_to_transmit.append(getattr(scenario_instance, weight_parameter_name)) |
---|
283 | average_parameter_name = "PHAVG_"+variable_name |
---|
284 | averages_to_transmit.append(getattr(scenario_instance, average_parameter_name)) |
---|
285 | |
---|
286 | self._solver_manager.transmit_weights_and_averages(scenario_instance, weights_to_transmit, averages_to_transmit) |
---|
287 | |
---|
288 | # |
---|
289 | # a utility to transmit - across the PH solver manager - the current rho values |
---|
290 | # for each of my problem instances. mainly after PH iteration 0 is complete, |
---|
291 | # in preparation for the iteration K solves. |
---|
292 | # |
---|
293 | |
---|
294 | def _transmit_rhos(self): |
---|
295 | |
---|
296 | for scenario_name, scenario_instance in self._instances.items(): |
---|
297 | |
---|
298 | rhos_to_transmit = [] |
---|
299 | |
---|
300 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage, so no rhos to worry about. |
---|
301 | |
---|
302 | for (variable, index_template, variable_indices) in stage._variables: |
---|
303 | |
---|
304 | variable_name = variable.name |
---|
305 | rho_parameter_name = "PHRHO_"+variable_name |
---|
306 | rhos_to_transmit.append(getattr(scenario_instance, rho_parameter_name)) |
---|
307 | |
---|
308 | self._solver_manager.transmit_rhos(scenario_instance, rhos_to_transmit) |
---|
309 | |
---|
310 | # |
---|
311 | # a utility to transmit - across the PH solver manager - the current scenario |
---|
312 | # tree node statistics to each of my problem instances. done prior to each |
---|
313 | # PH iteration k. |
---|
314 | # |
---|
315 | |
---|
316 | def _transmit_tree_node_statistics(self): |
---|
317 | |
---|
318 | for scenario_name, scenario_instance in self._instances.items(): |
---|
319 | |
---|
320 | tree_node_minimums = {} |
---|
321 | tree_node_maximums = {} |
---|
322 | |
---|
323 | scenario = self._scenario_tree._scenario_map[scenario_name] |
---|
324 | |
---|
325 | for tree_node in scenario._node_list: |
---|
326 | |
---|
327 | tree_node_minimums[tree_node._name] = tree_node._minimums |
---|
328 | tree_node_maximums[tree_node._name] = tree_node._maximums |
---|
329 | |
---|
330 | self._solver_manager.transmit_tree_node_statistics(scenario_instance, tree_node_minimums, tree_node_maximums) |
---|
331 | |
---|
332 | # |
---|
333 | # a utility to enable - across the PH solver manager - weighted penalty objectives. |
---|
334 | # |
---|
335 | |
---|
336 | def _enable_ph_objectives(self): |
---|
337 | |
---|
338 | for scenario_name, scenario_instance in self._instances.items(): |
---|
339 | |
---|
340 | self._solver_manager.enable_ph_objective(scenario_instance) |
---|
341 | |
---|
342 | |
---|
343 | """ Constructor |
---|
344 | Arguments: |
---|
345 | max_iterations the maximum number of iterations to run PH (>= 0). defaults to 0. |
---|
346 | rho the global rho value (> 0). defaults to 0. |
---|
347 | rho_setter an optional name of a python file used to set particular variable rho values. |
---|
348 | solver the solver type that PH uses to solve scenario sub-problems. defaults to "cplex". |
---|
349 | solver_manager the solver manager type that coordinates scenario sub-problem solves. defaults to "serial". |
---|
350 | keep_solver_files do I keep intermediate solver files around (for debugging)? defaults to False. |
---|
351 | output_solver_log do I dump the solver log (as it is being generated) to the screen? defaults to False. |
---|
352 | output_solver_results do I output (for debugging) the detailed solver results, including solutions, for scenario solves? defaults to False. |
---|
353 | verbose does the PH object stream debug/status output? defaults to False. |
---|
354 | output_times do I output timing statistics? defaults to False (e.g., useful in the case where you want to regression test against baseline output). |
---|
355 | checkpoint_interval how many iterations between writing a checkpoint file containing the entire PH state? defaults to 0, indicating never. |
---|
356 | |
---|
357 | """ |
---|
358 | def __init__(self, *args, **kwds): |
---|
359 | |
---|
360 | # PH configuration parameters |
---|
361 | self._rho = 0.0 # a default, global values for rhos. |
---|
362 | self._rho_setter = None # filename for the modeler to set rho on a per-variable or per-scenario basis. |
---|
363 | self._bounds_setter = None # filename for the modeler to set rho on a per-variable basis, after all scenarios are available. |
---|
364 | self._max_iterations = 0 |
---|
365 | |
---|
366 | # PH reporting parameters |
---|
367 | self._verbose = False # do I flood the screen with status output? |
---|
368 | self._report_solutions = False # do I report solutions after each PH iteration? |
---|
369 | self._report_weights = False # do I report PH weights prior to each PH iteration? |
---|
370 | self._output_continuous_variable_stats = True # when in verbose mode, do I output weights/averages for continuous variables? |
---|
371 | self._output_solver_results = False |
---|
372 | self._output_times = False |
---|
373 | |
---|
374 | # PH run-time variables |
---|
375 | self._current_iteration = 0 # the 'k' |
---|
376 | self._xbar = {} # current per-variable averages. maps (node_id, variable_name) -> value |
---|
377 | self._initialized = False # am I ready to call "solve"? Set to True by the initialize() method. |
---|
378 | |
---|
379 | # PH solver information / objects. |
---|
380 | self._solver_type = "cplex" |
---|
381 | self._solver_manager_type = "serial" # serial or pyro are the options currently available |
---|
382 | |
---|
383 | self._solver = None |
---|
384 | self._solver_manager = None |
---|
385 | |
---|
386 | self._keep_solver_files = False |
---|
387 | self._output_solver_log = False |
---|
388 | |
---|
389 | # PH convergence computer/updater. |
---|
390 | self._converger = None |
---|
391 | |
---|
392 | # PH history |
---|
393 | self._solutions = {} |
---|
394 | |
---|
395 | # the checkpoint interval - expensive operation, but worth it for big models. |
---|
396 | # 0 indicates don't checkpoint. |
---|
397 | self._checkpoint_interval = 0 |
---|
398 | |
---|
399 | # all information related to the scenario tree (implicit and explicit). |
---|
400 | self._model = None # not instantiated |
---|
401 | self._model_instance = None # instantiated |
---|
402 | |
---|
403 | self._scenario_tree = None |
---|
404 | |
---|
405 | self._scenario_data_directory = "" # this the prefix for all scenario data |
---|
406 | self._instances = {} # maps scenario name to the corresponding model instance |
---|
407 | self._instance_augmented_attributes = {} # maps scenario name to a list of the constraints added (e.g., for piecewise linear approximation) to the instance by PH. |
---|
408 | |
---|
409 | # for various reasons (mainly hacks at this point), it's good to know whether we're minimizing or maximizing. |
---|
410 | self._is_minimizing = None |
---|
411 | |
---|
412 | # global handle to ph extension plugins |
---|
413 | self._ph_plugins = ExtensionPoint(IPHExtension) |
---|
414 | |
---|
415 | # PH timing statistics - relative to last invocation. |
---|
416 | self._init_start_time = None # for initialization() method |
---|
417 | self._init_end_time = None |
---|
418 | self._solve_start_time = None # for solve() method |
---|
419 | self._solve_end_time = None |
---|
420 | self._cumulative_solve_time = None # seconds, over course of solve() |
---|
421 | self._cumulative_xbar_time = None # seconds, over course of update_xbars() |
---|
422 | self._cumulative_weight_time = None # seconds, over course of update_weights() |
---|
423 | |
---|
424 | # do I disable warm-start for scenario sub-problem solves during PH iterations >= 1? |
---|
425 | self._disable_warmstarts = False |
---|
426 | |
---|
427 | # do I drop proximal (quadratic penalty) terms from the weighted objective functions? |
---|
428 | self._drop_proximal_terms = False |
---|
429 | |
---|
430 | # do I linearize the quadratic penalty term for continuous variables via a |
---|
431 | # piecewise linear approximation? the default should always be 0 (off), as the |
---|
432 | # user should be aware when they are forcing an approximation. |
---|
433 | self._linearize_nonbinary_penalty_terms = 0 |
---|
434 | |
---|
435 | # the breakpoint distribution strategy employed when linearizing. 0 implies uniform |
---|
436 | # distribution between the variable lower and upper bounds. |
---|
437 | self._breakpoint_strategy = 0 |
---|
438 | |
---|
439 | # do I retain quadratic objective terms associated with binary variables? in general, |
---|
440 | # there is no good reason to not linearize, but just in case, I introduced the option. |
---|
441 | self._retain_quadratic_binary_terms = False |
---|
442 | |
---|
443 | # PH default tolerances - for use in fixing and testing equality across scenarios, |
---|
444 | # and other stuff. |
---|
445 | self._integer_tolerance = 0.00001 |
---|
446 | |
---|
447 | # PH maintains a mipgap that is applied to each scenario solve that is performed. |
---|
448 | # this attribute can be changed by PH extensions, and the change will be applied |
---|
449 | # on all subsequent solves - until it is modified again. the default is None, |
---|
450 | # indicating unassigned. |
---|
451 | self._mipgap = None |
---|
452 | |
---|
453 | # we only store these temporarily... |
---|
454 | scenario_solver_options = None |
---|
455 | |
---|
456 | # process the keyword options |
---|
457 | for key in kwds.keys(): |
---|
458 | if key == "max_iterations": |
---|
459 | self._max_iterations = kwds[key] |
---|
460 | elif key == "rho": |
---|
461 | self._rho = kwds[key] |
---|
462 | elif key == "rho_setter": |
---|
463 | self._rho_setter = kwds[key] |
---|
464 | elif key == "bounds_setter": |
---|
465 | self._bounds_setter = kwds[key] |
---|
466 | elif key == "solver": |
---|
467 | self._solver_type = kwds[key] |
---|
468 | elif key == "solver_manager": |
---|
469 | self._solver_manager_type = kwds[key] |
---|
470 | elif key == "scenario_solver_options": |
---|
471 | scenario_solver_options = kwds[key] |
---|
472 | elif key == "scenario_mipgap": |
---|
473 | self._mipgap = kwds[key] |
---|
474 | elif key == "keep_solver_files": |
---|
475 | self._keep_solver_files = kwds[key] |
---|
476 | elif key == "output_solver_results": |
---|
477 | self._output_solver_results = kwds[key] |
---|
478 | elif key == "output_solver_log": |
---|
479 | self._output_solver_log = kwds[key] |
---|
480 | elif key == "verbose": |
---|
481 | self._verbose = kwds[key] |
---|
482 | elif key == "report_solutions": |
---|
483 | self._report_solutions = kwds[key] |
---|
484 | elif key == "report_weights": |
---|
485 | self._report_weights = kwds[key] |
---|
486 | elif key == "output_times": |
---|
487 | self._output_times = kwds[key] |
---|
488 | elif key == "disable_warmstarts": |
---|
489 | self._disable_warmstarts = kwds[key] |
---|
490 | elif key == "drop_proximal_terms": |
---|
491 | self._drop_proximal_terms = kwds[key] |
---|
492 | elif key == "retain_quadratic_binary_terms": |
---|
493 | self._retain_quadratic_binary_terms = kwds[key] |
---|
494 | elif key == "linearize_nonbinary_penalty_terms": |
---|
495 | self._linearize_nonbinary_penalty_terms = kwds[key] |
---|
496 | elif key == "breakpoint_strategy": |
---|
497 | self._breakpoint_strategy = kwds[key] |
---|
498 | elif key == "checkpoint_interval": |
---|
499 | self._checkpoint_interval = kwds[key] |
---|
500 | else: |
---|
501 | print "Unknown option=" + key + " specified in call to PH constructor" |
---|
502 | |
---|
503 | # validate all "atomic" options (those that can be validated independently) |
---|
504 | if self._max_iterations < 0: |
---|
505 | raise ValueError, "Maximum number of PH iterations must be non-negative; value specified=" + `self._max_iterations` |
---|
506 | if self._rho <= 0.0: |
---|
507 | raise ValueError, "Value of the rho parameter in PH must be non-zero positive; value specified=" + `self._rho` |
---|
508 | if (self._mipgap is not None) and ((self._mipgap < 0.0) or (self._mipgap > 1.0)): |
---|
509 | raise ValueError, "Value of the mipgap parameter in PH must be on the unit interval; value specified=" + `self._mipgap` |
---|
510 | |
---|
511 | # validate the linearization (number of pieces) and breakpoint distribution parameters. |
---|
512 | if self._linearize_nonbinary_penalty_terms < 0: |
---|
513 | raise ValueError, "Value of linearization parameter for nonbinary penalty terms must be non-negative; value specified=" + `self._linearize_nonbinary_penalty_terms` |
---|
514 | if self._breakpoint_strategy < 0: |
---|
515 | raise ValueError, "Value of the breakpoint distribution strategy parameter must be non-negative; value specified=" + str(self._breakpoint_strategy) |
---|
516 | if self._breakpoint_strategy > 3: |
---|
517 | raise ValueError, "Unknown breakpoint distribution strategy specified - valid values are between 0 and 2, inclusive; value specified=" + str(self._breakpoint_strategy) |
---|
518 | |
---|
519 | # validate rho setter file if specified. |
---|
520 | if self._rho_setter is not None: |
---|
521 | if os.path.exists(self._rho_setter) is False: |
---|
522 | raise ValueError, "The rho setter script file="+self._rho_setter+" does not exist" |
---|
523 | |
---|
524 | # validate bounds setter file if specified. |
---|
525 | if self._bounds_setter is not None: |
---|
526 | if os.path.exists(self._bounds_setter) is False: |
---|
527 | raise ValueError, "The bounds setter script file="+self._bounds_setter+" does not exist" |
---|
528 | |
---|
529 | # validate the checkpoint interval. |
---|
530 | if self._checkpoint_interval < 0: |
---|
531 | raise ValueError, "A negative checkpoint interval with value="+str(self._checkpoint_interval)+" was specified in call to PH constructor" |
---|
532 | |
---|
533 | # construct the sub-problem solver. |
---|
534 | if self._verbose is True: |
---|
535 | print "Constructing solver type="+self._solver_type |
---|
536 | self._solver = SolverFactory(self._solver_type) |
---|
537 | if self._solver == None: |
---|
538 | raise ValueError, "Unknown solver type=" + self._solver_type + " specified in call to PH constructor" |
---|
539 | if self._keep_solver_files is True: |
---|
540 | self._solver.keepFiles = True |
---|
541 | if len(scenario_solver_options) > 0: |
---|
542 | if self._verbose is True: |
---|
543 | print "Initializing scenario sub-problem solver with options="+str(scenario_solver_options) |
---|
544 | self._solver.set_options("".join(scenario_solver_options)) |
---|
545 | if self._output_times is True: |
---|
546 | self._solver._report_timing = True |
---|
547 | |
---|
548 | # construct the solver manager. |
---|
549 | if self._verbose is True: |
---|
550 | print "Constructing solver manager of type="+self._solver_manager_type |
---|
551 | self._solver_manager = SolverManagerFactory(self._solver_manager_type) |
---|
552 | if self._solver_manager is None: |
---|
553 | raise ValueError, "Failed to create solver manager of type="+self._solver_manager_type+" specified in call to PH constructor" |
---|
554 | |
---|
555 | # a set of all valid PH iteration indicies is generally useful for plug-ins, so create it here. |
---|
556 | self._iteration_index_set = Set(name="PHIterations") |
---|
557 | for i in range(0,self._max_iterations + 1): |
---|
558 | self._iteration_index_set.add(i) |
---|
559 | |
---|
560 | # spit out parameterization if verbosity is enabled |
---|
561 | if self._verbose is True: |
---|
562 | print "PH solver configuration: " |
---|
563 | print " Max iterations=" + `self._max_iterations` |
---|
564 | print " Default global rho=" + `self._rho` |
---|
565 | if self._rho_setter is not None: |
---|
566 | print " Rho initialization file=" + self._rho_setter |
---|
567 | if self._bounds_setter is not None: |
---|
568 | print " Variable bounds initialization file=" + self._bounds_setter |
---|
569 | print " Sub-problem solver type=" + `self._solver_type` |
---|
570 | print " Solver manager type=" + `self._solver_manager_type` |
---|
571 | print " Keep solver files? " + str(self._keep_solver_files) |
---|
572 | print " Output solver results? " + str(self._output_solver_results) |
---|
573 | print " Output solver log? " + str(self._output_solver_log) |
---|
574 | print " Output times? " + str(self._output_times) |
---|
575 | print " Checkpoint interval="+str(self._checkpoint_interval) |
---|
576 | |
---|
577 | """ Initialize PH with model and scenario data, in preparation for solve(). |
---|
578 | Constructs and reads instances. |
---|
579 | """ |
---|
580 | def initialize(self, scenario_data_directory_name=".", model=None, model_instance=None, scenario_tree=None, converger=None): |
---|
581 | |
---|
582 | self._init_start_time = time.time() |
---|
583 | |
---|
584 | if self._verbose is True: |
---|
585 | print "Initializing PH" |
---|
586 | print " Scenario data directory=" + scenario_data_directory_name |
---|
587 | |
---|
588 | if not os.path.exists(scenario_data_directory_name): |
---|
589 | raise ValueError, "Scenario data directory=" + scenario_data_directory_name + " either does not exist or cannot be read" |
---|
590 | |
---|
591 | self._scenario_data_directory_name = scenario_data_directory_name |
---|
592 | |
---|
593 | # IMPT: The input model should be an *instance*, as it is very useful (critical!) to know |
---|
594 | # the dimensions of sets, be able to store suffixes on variable values, etc. |
---|
595 | if model is None: |
---|
596 | raise ValueError, "A model must be supplied to the PH initialize() method" |
---|
597 | |
---|
598 | if scenario_tree is None: |
---|
599 | raise ValueError, "A scenario tree must be supplied to the PH initialize() method" |
---|
600 | |
---|
601 | if converger is None: |
---|
602 | raise ValueError, "A convergence computer must be supplied to the PH initialize() method" |
---|
603 | |
---|
604 | self._model = model |
---|
605 | self._model_instance = model_instance |
---|
606 | self._scenario_tree = scenario_tree |
---|
607 | self._converger = converger |
---|
608 | |
---|
609 | model_objective = model.active_components(Objective) |
---|
610 | self._is_minimizing = (model_objective[ model_objective.keys()[0] ].sense == minimize) |
---|
611 | |
---|
612 | self._converger.reset() |
---|
613 | |
---|
614 | # construct the instances for each scenario |
---|
615 | if self._verbose is True: |
---|
616 | if self._scenario_tree._scenario_based_data == 1: |
---|
617 | print "Scenario-based instance initialization enabled" |
---|
618 | else: |
---|
619 | print "Node-based instance initialization enabled" |
---|
620 | |
---|
621 | for scenario in self._scenario_tree._scenarios: |
---|
622 | |
---|
623 | scenario_instance = construct_scenario_instance(self._scenario_tree, |
---|
624 | self._scenario_data_directory_name, |
---|
625 | scenario._name, |
---|
626 | self._model, |
---|
627 | self._verbose) |
---|
628 | |
---|
629 | self._instances[scenario._name] = scenario_instance |
---|
630 | self._instances[scenario._name].name = scenario._name |
---|
631 | self._instance_augmented_attributes[scenario._name] = [] |
---|
632 | |
---|
633 | # let plugins know if they care - this callback point allows |
---|
634 | # users to create/modify the original scenario instances and/or |
---|
635 | # the scenario tree prior to creating PH-related parameters, |
---|
636 | # variables, and the like. |
---|
637 | for plugin in self._ph_plugins: |
---|
638 | plugin.post_instance_creation(self) |
---|
639 | |
---|
640 | # create ph-specific parameters (weights, xbar, etc.) for each instance. |
---|
641 | |
---|
642 | if self._verbose is True: |
---|
643 | print "Creating weight, average, and rho parameter vectors for scenario instances" |
---|
644 | |
---|
645 | self._create_ph_scenario_parameters() |
---|
646 | |
---|
647 | # if specified, run the user script to initialize variable rhos at their whim. |
---|
648 | if self._rho_setter is not None: |
---|
649 | print "Executing user rho set script from filename="+self._rho_setter |
---|
650 | execfile(self._rho_setter) |
---|
651 | |
---|
652 | # with the instances created, run the user script to initialize variable bounds. |
---|
653 | if self._bounds_setter is not None: |
---|
654 | print "Executing user variable bounds set script from filename=", self._bounds_setter |
---|
655 | execfile(self._bounds_setter) |
---|
656 | |
---|
657 | # create parameters to store variable statistics (of general utility) at each node in the scenario tree. |
---|
658 | |
---|
659 | if self._verbose is True: |
---|
660 | print "Creating variable statistic (min/avg/max) parameter vectors for scenario tree nodes" |
---|
661 | |
---|
662 | for stage in self._scenario_tree._stages[:-1]: |
---|
663 | |
---|
664 | # first, gather all unique variables referenced in this stage |
---|
665 | # this "gather" step is currently required because we're being lazy |
---|
666 | # in terms of index management in the scenario tree - which |
---|
667 | # should really be done in terms of sets of indices. |
---|
668 | |
---|
669 | stage_variables = {} |
---|
670 | for (reference_variable, index_template, reference_index) in stage._variables: |
---|
671 | if reference_variable.name not in stage_variables.keys(): |
---|
672 | stage_variables[reference_variable.name] = reference_variable |
---|
673 | |
---|
674 | # next, create min/avg/max parameters for each variable in the corresponding tree node. |
---|
675 | # NOTE: the parameter names below could really be empty, as they are never referenced |
---|
676 | # explicitly. |
---|
677 | for (variable_name, reference_variable) in stage_variables.items(): |
---|
678 | for tree_node in stage._tree_nodes: |
---|
679 | |
---|
680 | new_min_index = reference_variable._index |
---|
681 | new_min_parameter_name = "NODEMIN_"+reference_variable.name |
---|
682 | # this bit of ugliness is due to Pyomo not correctly handling the Param construction |
---|
683 | # case when the supplied index set consists strictly of None, i.e., the source variable |
---|
684 | # is a singleton. this case be cleaned up when the source issue in Pyomo is fixed. |
---|
685 | new_min_parameter = None |
---|
686 | if (len(new_min_index) is 1) and (None in new_min_index): |
---|
687 | new_min_parameter = Param(name=new_min_parameter_name) |
---|
688 | else: |
---|
689 | new_min_parameter = Param(new_min_index,name=new_min_parameter_name) |
---|
690 | for index in new_min_index: |
---|
691 | new_min_parameter[index] = 0.0 |
---|
692 | tree_node._minimums[reference_variable.name] = new_min_parameter |
---|
693 | |
---|
694 | new_avg_index = reference_variable._index |
---|
695 | new_avg_parameter_name = "NODEAVG_"+reference_variable.name |
---|
696 | new_avg_parameter = None |
---|
697 | if (len(new_avg_index) is 1) and (None in new_avg_index): |
---|
698 | new_avg_parameter = Param(name=new_avg_parameter_name) |
---|
699 | else: |
---|
700 | new_avg_parameter = Param(new_avg_index,name=new_avg_parameter_name) |
---|
701 | for index in new_avg_index: |
---|
702 | new_avg_parameter[index] = 0.0 |
---|
703 | tree_node._averages[reference_variable.name] = new_avg_parameter |
---|
704 | |
---|
705 | new_max_index = reference_variable._index |
---|
706 | new_max_parameter_name = "NODEMAX_"+reference_variable.name |
---|
707 | new_max_parameter = None |
---|
708 | if (len(new_max_index) is 1) and (None in new_max_index): |
---|
709 | new_max_parameter = Param(name=new_max_parameter_name) |
---|
710 | else: |
---|
711 | new_max_parameter = Param(new_max_index,name=new_max_parameter_name) |
---|
712 | for index in new_max_index: |
---|
713 | new_max_parameter[index] = 0.0 |
---|
714 | tree_node._maximums[reference_variable.name] = new_max_parameter |
---|
715 | |
---|
716 | # the objective functions are modified throughout the course of PH iterations. |
---|
717 | # save the original, as a baseline to modify in subsequent iterations. reserve |
---|
718 | # the original objectives, for subsequent modification. |
---|
719 | self._original_objective_expression = {} |
---|
720 | for instance_name, instance in self._instances.items(): |
---|
721 | objective_name = instance.active_components(Objective).keys()[0] |
---|
722 | expr = instance.active_components(Objective)[objective_name]._data[None].expr |
---|
723 | if isinstance(expr, Expression) is False: |
---|
724 | expr = _IdentityExpression(expr) |
---|
725 | self._original_objective_expression[instance_name] = expr |
---|
726 | |
---|
727 | # cache the number of discrete and continuous variables in the master instance. this value |
---|
728 | # is of general use, e.g., in the converger classes and in plugins. |
---|
729 | (self._total_discrete_vars,self._total_continuous_vars) = self.compute_variable_counts() |
---|
730 | if self._verbose is True: |
---|
731 | print "Total number of discrete instance variables="+str(self._total_discrete_vars) |
---|
732 | print "Total number of continuous instance variables="+str(self._total_continuous_vars) |
---|
733 | |
---|
734 | # track the total number of fixed variables of each category at the end of each PH iteration. |
---|
735 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
736 | |
---|
737 | # indicate that we're ready to run. |
---|
738 | self._initialized = True |
---|
739 | |
---|
740 | if self._verbose is True: |
---|
741 | print "PH successfully created model instances for all scenarios" |
---|
742 | |
---|
743 | self._init_end_time = time.time() |
---|
744 | |
---|
745 | if self._verbose is True: |
---|
746 | print "PH is successfully initialized" |
---|
747 | if self._output_times is True: |
---|
748 | print "Initialization time=%8.2f seconds" % (self._init_end_time - self._init_start_time) |
---|
749 | |
---|
750 | # let plugins know if they care. |
---|
751 | if self._verbose is True: |
---|
752 | print "Initializing PH plugins" |
---|
753 | for plugin in self._ph_plugins: |
---|
754 | plugin.post_ph_initialization(self) |
---|
755 | if self._verbose is True: |
---|
756 | print "PH plugin initialization complete" |
---|
757 | |
---|
758 | """ Perform the non-weighted scenario solves and form the initial w and xbars. |
---|
759 | """ |
---|
760 | def iteration_0_solve(self): |
---|
761 | |
---|
762 | if self._verbose is True: |
---|
763 | print "------------------------------------------------" |
---|
764 | print "Starting PH iteration 0 solves" |
---|
765 | |
---|
766 | self._current_iteration = 0 |
---|
767 | |
---|
768 | solve_start_time = time.time() |
---|
769 | |
---|
770 | # STEP 0: set up all global solver options. |
---|
771 | self._solver.mipgap = self._mipgap |
---|
772 | |
---|
773 | # STEP 1: queue up the solves for all scenario sub-problems and |
---|
774 | # grab all the action handles for the subsequent barrier sync. |
---|
775 | |
---|
776 | action_handles = [] |
---|
777 | scenario_action_handle_map = {} # maps scenario names to action handles |
---|
778 | action_handle_scenario_map = {} # maps action handles to scenario names |
---|
779 | |
---|
780 | for scenario in self._scenario_tree._scenarios: |
---|
781 | |
---|
782 | instance = self._instances[scenario._name] |
---|
783 | |
---|
784 | if self._verbose is True: |
---|
785 | print "Queuing solve for scenario=" + scenario._name |
---|
786 | |
---|
787 | # IMPT: You have to re-presolve if approximating continuous variable penalty terms with a |
---|
788 | # piecewise linear function. otherwise, the newly introduced variables won't be flagged |
---|
789 | # as unused (as is correct for iteration 0), and the output writer will crater. |
---|
790 | # IMPT: I decided to presolve unconditionally, as PH extensions can add arbitrary components |
---|
791 | # to the base scenario instances - and the variable values/etc. need to be collectged. |
---|
792 | instance.preprocess() |
---|
793 | |
---|
794 | # there's nothing to warm-start from in iteration 0, so don't include the keyword in the solve call. |
---|
795 | # the reason you don't want to include it is that some solvers don't know how to handle the keyword |
---|
796 | # at all (despite it being false). you might want to solve iteration 0 solves using some other solver. |
---|
797 | |
---|
798 | new_action_handle = self._solver_manager.queue(instance, opt=self._solver, tee=self._output_solver_log) |
---|
799 | scenario_action_handle_map[scenario._name] = new_action_handle |
---|
800 | action_handle_scenario_map[new_action_handle] = scenario._name |
---|
801 | |
---|
802 | action_handles.append(new_action_handle) |
---|
803 | |
---|
804 | # STEP 2: loop for the solver results, reading them and loading |
---|
805 | # them into instances as they are available. |
---|
806 | |
---|
807 | if self._verbose is True: |
---|
808 | print "Waiting for scenario sub-problem solves" |
---|
809 | |
---|
810 | num_results_so_far = 0 |
---|
811 | |
---|
812 | while (num_results_so_far < len(self._scenario_tree._scenarios)): |
---|
813 | |
---|
814 | action_handle = self._solver_manager.wait_any() |
---|
815 | results = self._solver_manager.get_results(action_handle) |
---|
816 | scenario_name = action_handle_scenario_map[action_handle] |
---|
817 | instance = self._instances[scenario_name] |
---|
818 | |
---|
819 | if self._verbose is True: |
---|
820 | print "Results obtained for scenario="+scenario_name |
---|
821 | |
---|
822 | if len(results.solution) == 0: |
---|
823 | results.write(num=1) |
---|
824 | raise RuntimeError, "Solve failed for scenario="+scenario_name+"; no solutions generated" |
---|
825 | |
---|
826 | if self._output_solver_results is True: |
---|
827 | print "Results for scenario=",scenario_name |
---|
828 | results.write(num=1) |
---|
829 | |
---|
830 | start_time = time.time() |
---|
831 | instance.load(results) |
---|
832 | end_time = time.time() |
---|
833 | if self._output_times is True: |
---|
834 | print "Time loading results into instance="+str(end_time-start_time)+" seconds" |
---|
835 | |
---|
836 | if self._verbose is True: |
---|
837 | print "Successfully loaded solution for scenario="+scenario_name |
---|
838 | |
---|
839 | num_results_so_far = num_results_so_far + 1 |
---|
840 | |
---|
841 | if self._verbose is True: |
---|
842 | print "Scenario sub-problem solves completed" |
---|
843 | |
---|
844 | solve_end_time = time.time() |
---|
845 | self._cumulative_solve_time += (solve_end_time - solve_start_time) |
---|
846 | |
---|
847 | if self._output_times is True: |
---|
848 | print "Aggregate sub-problem solve time this iteration=%8.2f" % (solve_end_time - solve_start_time) |
---|
849 | |
---|
850 | if self._verbose is True: |
---|
851 | print "Successfully completed PH iteration 0 solves - solution statistics:" |
---|
852 | print " Scenario Objective Value" |
---|
853 | for scenario in self._scenario_tree._scenarios: |
---|
854 | instance = self._instances[scenario._name] |
---|
855 | for objective_name in instance.active_components(Objective): |
---|
856 | objective = instance.active_components(Objective)[objective_name] |
---|
857 | print "%20s %15s %14.4f" % (scenario._name, objective.name, objective._data[None].expr()) |
---|
858 | print "------------------------------------------------" |
---|
859 | |
---|
860 | # |
---|
861 | # recompute the averages, minimum, and maximum statistics for all variables to be blended by PH, i.e., |
---|
862 | # not appearing in the final stage. technically speaking, the min/max aren't required by PH, but they |
---|
863 | # are used often enough to warrant their computation and it's basically free if you're computing the |
---|
864 | # average. |
---|
865 | # |
---|
866 | def update_variable_statistics(self): |
---|
867 | |
---|
868 | start_time = time.time() |
---|
869 | |
---|
870 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage |
---|
871 | |
---|
872 | for tree_node in stage._tree_nodes: |
---|
873 | |
---|
874 | for (variable, index_template, variable_indices) in stage._variables: |
---|
875 | |
---|
876 | variable_name = variable.name |
---|
877 | |
---|
878 | avg_parameter_name = "PHAVG_"+variable_name |
---|
879 | |
---|
880 | for index in variable_indices: |
---|
881 | min = float("inf") |
---|
882 | avg = 0.0 |
---|
883 | max = float("-inf") |
---|
884 | node_probability = 0.0 |
---|
885 | |
---|
886 | is_used = True # until proven otherwise |
---|
887 | for scenario in tree_node._scenarios: |
---|
888 | |
---|
889 | instance = self._instances[scenario._name] |
---|
890 | |
---|
891 | if getattr(instance,variable_name)[index].status == VarStatus.unused: |
---|
892 | is_used = False |
---|
893 | else: |
---|
894 | node_probability += scenario._probability |
---|
895 | var_value = getattr(instance, variable.name)[index].value |
---|
896 | if var_value < min: |
---|
897 | min = var_value |
---|
898 | avg += (scenario._probability * var_value) |
---|
899 | if var_value > max: |
---|
900 | max = var_value |
---|
901 | |
---|
902 | if is_used is True: |
---|
903 | |
---|
904 | tree_node._minimums[variable.name][index] = min |
---|
905 | tree_node._averages[variable.name][index] = avg / node_probability |
---|
906 | tree_node._maximums[variable.name][index] = max |
---|
907 | |
---|
908 | # distribute the newly computed average to the xbar variable in |
---|
909 | # each instance/scenario associated with this node. only do this |
---|
910 | # if the variable is used! |
---|
911 | for scenario in tree_node._scenarios: |
---|
912 | instance = self._instances[scenario._name] |
---|
913 | avg_parameter = getattr(instance, avg_parameter_name) |
---|
914 | avg_parameter[index] = avg / node_probability |
---|
915 | |
---|
916 | end_time = time.time() |
---|
917 | self._cumulative_xbar_time += (end_time - start_time) |
---|
918 | |
---|
919 | def update_weights(self): |
---|
920 | |
---|
921 | # because the weight updates rely on the xbars, and the xbars are node-based, |
---|
922 | # I'm looping over the tree nodes and pushing weights into the corresponding scenarios. |
---|
923 | start_time = time.time() |
---|
924 | |
---|
925 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage, so no weights to worry about. |
---|
926 | |
---|
927 | for tree_node in stage._tree_nodes: |
---|
928 | |
---|
929 | for (variable, index_template, variable_indices) in stage._variables: |
---|
930 | |
---|
931 | variable_name = variable.name |
---|
932 | blend_parameter_name = "PHBLEND_"+variable_name |
---|
933 | weight_parameter_name = "PHWEIGHT_"+variable_name |
---|
934 | rho_parameter_name = "PHRHO_"+variable_name |
---|
935 | |
---|
936 | for index in variable_indices: |
---|
937 | |
---|
938 | tree_node_average = tree_node._averages[variable.name][index]() |
---|
939 | |
---|
940 | for scenario in tree_node._scenarios: |
---|
941 | |
---|
942 | instance = self._instances[scenario._name] |
---|
943 | |
---|
944 | if getattr(instance,variable.name)[index].status != VarStatus.unused: |
---|
945 | |
---|
946 | # we are currently not updating weights if blending is disabled for a variable. |
---|
947 | # this is done on the premise that unless you are actively trying to move |
---|
948 | # the variable toward the mean, the weights will blow up and be huge by the |
---|
949 | # time that blending is activated. |
---|
950 | variable_blend_indicator = getattr(instance, blend_parameter_name)[index]() |
---|
951 | |
---|
952 | # get the weight and rho parameters for this variable/index combination. |
---|
953 | rho_value = getattr(instance, rho_parameter_name)[index]() |
---|
954 | current_variable_weight = getattr(instance, weight_parameter_name)[index]() |
---|
955 | |
---|
956 | # if I'm maximizing, invert value prior to adding (hack to implement negatives). |
---|
957 | # probably fixed in Pyomo at this point - I just haven't checked in a long while. |
---|
958 | if self._is_minimizing is False: |
---|
959 | current_variable_weight = (-current_variable_weight) |
---|
960 | current_variable_value = getattr(instance,variable.name)[index]() |
---|
961 | new_variable_weight = current_variable_weight + variable_blend_indicator * rho_value * (current_variable_value - tree_node_average) |
---|
962 | # I have the correct updated value, so now invert if maximizing. |
---|
963 | if self._is_minimizing is False: |
---|
964 | new_variable_weight = (-new_variable_weight) |
---|
965 | getattr(instance, weight_parameter_name)[index].value = new_variable_weight |
---|
966 | |
---|
967 | # we shouldn't have to re-simplify the expression, as we aren't adding any constant-variable terms - just modifying parameters. |
---|
968 | |
---|
969 | end_time = time.time() |
---|
970 | self._cumulative_weight_time += (end_time - start_time) |
---|
971 | |
---|
972 | def form_iteration_k_objectives(self): |
---|
973 | |
---|
974 | for instance_name, instance in self._instances.items(): |
---|
975 | |
---|
976 | new_attrs = form_ph_objective(instance_name, \ |
---|
977 | instance, \ |
---|
978 | self._original_objective_expression[instance_name], \ |
---|
979 | self._scenario_tree, \ |
---|
980 | self._linearize_nonbinary_penalty_terms, \ |
---|
981 | self._drop_proximal_terms, \ |
---|
982 | self._retain_quadratic_binary_terms, \ |
---|
983 | self._breakpoint_strategy, \ |
---|
984 | self._integer_tolerance) |
---|
985 | self._instance_augmented_attributes[instance_name].extend(new_attrs) |
---|
986 | |
---|
987 | def iteration_k_solve(self): |
---|
988 | |
---|
989 | if self._verbose is True: |
---|
990 | print "------------------------------------------------" |
---|
991 | print "Starting PH iteration " + str(self._current_iteration) + " solves" |
---|
992 | |
---|
993 | # cache the objective values generated by PH for output at the end of this function. |
---|
994 | ph_objective_values = {} |
---|
995 | |
---|
996 | solve_start_time = time.time() |
---|
997 | |
---|
998 | # STEP -1: if using a PH solver manager, propagate current weights/averages to the appropriate solver servers. |
---|
999 | # ditto the tree node statistics, which are necessary if linearizing (so an optimization could be |
---|
1000 | # performed here). |
---|
1001 | # NOTE: We aren't currently propagating rhos, as they generally don't change - we need to |
---|
1002 | # have a flag, though, indicating whether the rhos have changed, so they can be |
---|
1003 | # transmitted if needed. |
---|
1004 | if self._solver_manager_type == "ph": |
---|
1005 | self._transmit_weights_and_averages() |
---|
1006 | self._transmit_tree_node_statistics() |
---|
1007 | |
---|
1008 | # STEP 0: set up all global solver options. |
---|
1009 | self._solver.mipgap = self._mipgap |
---|
1010 | |
---|
1011 | # STEP 1: queue up the solves for all scenario sub-problems and |
---|
1012 | # grab all of the action handles for the subsequent barrier sync. |
---|
1013 | |
---|
1014 | action_handles = [] |
---|
1015 | scenario_action_handle_map = {} # maps scenario names to action handles |
---|
1016 | action_handle_scenario_map = {} # maps action handles to scenario names |
---|
1017 | |
---|
1018 | for scenario in self._scenario_tree._scenarios: |
---|
1019 | |
---|
1020 | instance = self._instances[scenario._name] |
---|
1021 | |
---|
1022 | if self._verbose is True: |
---|
1023 | print "Queuing solve for scenario=" + scenario._name |
---|
1024 | |
---|
1025 | # IMPT: You have to re-presolve, as the simple presolver collects the linear terms together. If you |
---|
1026 | # don't do this, you won't see any chance in the output files as you vary the problem parameters! |
---|
1027 | # ditto for instance fixing! |
---|
1028 | instance.preprocess() |
---|
1029 | |
---|
1030 | # once past iteration 0, there is always a feasible solution from which to warm-start. |
---|
1031 | # however, you might want to disable warm-start when the solver is behaving badly (which does happen). |
---|
1032 | new_action_handle = None |
---|
1033 | if (self._disable_warmstarts is False) and (self._solver.warm_start_capable() is True): |
---|
1034 | new_action_handle = self._solver_manager.queue(instance, opt=self._solver, warmstart=True, tee=self._output_solver_log) |
---|
1035 | else: |
---|
1036 | new_action_handle = self._solver_manager.queue(instance, opt=self._solver, tee=self._output_solver_log) |
---|
1037 | |
---|
1038 | scenario_action_handle_map[scenario._name] = new_action_handle |
---|
1039 | action_handle_scenario_map[new_action_handle] = scenario._name |
---|
1040 | |
---|
1041 | action_handles.append(new_action_handle) |
---|
1042 | |
---|
1043 | # STEP 2: loop for the solver results, reading them and loading |
---|
1044 | # them into instances as they are available. |
---|
1045 | if self._verbose is True: |
---|
1046 | print "Waiting for scenario sub-problem solves" |
---|
1047 | |
---|
1048 | num_results_so_far = 0 |
---|
1049 | |
---|
1050 | while (num_results_so_far < len(self._scenario_tree._scenarios)): |
---|
1051 | |
---|
1052 | action_handle = self._solver_manager.wait_any() |
---|
1053 | results = self._solver_manager.get_results(action_handle) |
---|
1054 | scenario_name = action_handle_scenario_map[action_handle] |
---|
1055 | instance = self._instances[scenario_name] |
---|
1056 | |
---|
1057 | if self._verbose is True: |
---|
1058 | print "Results obtained for scenario="+scenario_name |
---|
1059 | |
---|
1060 | if len(results.solution) == 0: |
---|
1061 | results.write(num=1) |
---|
1062 | raise RuntimeError, "Solve failed for scenario="+scenario_name+"; no solutions generated" |
---|
1063 | |
---|
1064 | if self._output_solver_results is True: |
---|
1065 | print "Results for scenario=",scenario_name |
---|
1066 | results.write(num=1) |
---|
1067 | |
---|
1068 | start_time = time.time() |
---|
1069 | instance.load(results) |
---|
1070 | end_time = time.time() |
---|
1071 | if self._output_times is True: |
---|
1072 | print "Time loading results into instance="+str(end_time-start_time)+" seconds" |
---|
1073 | |
---|
1074 | if self._verbose is True: |
---|
1075 | print "Successfully loaded solution for scenario="+scenario_name |
---|
1076 | |
---|
1077 | # we're assuming there is a single solution. |
---|
1078 | # the "value" attribute is a pre-defined feature of any solution - it is relative to whatever |
---|
1079 | # objective was selected during optimization, which of course should be the PH objective. |
---|
1080 | ph_objective_values[instance.name] = float(results.solution(0).objective['f'].value) |
---|
1081 | |
---|
1082 | num_results_so_far = num_results_so_far + 1 |
---|
1083 | |
---|
1084 | if self._verbose is True: |
---|
1085 | print "Scenario sub-problem solves completed" |
---|
1086 | |
---|
1087 | solve_end_time = time.time() |
---|
1088 | self._cumulative_solve_time += (solve_end_time - solve_start_time) |
---|
1089 | |
---|
1090 | if self._output_times is True: |
---|
1091 | print "Aggregate sub-problem solve time this iteration=%8.2f" % (solve_end_time - solve_start_time) |
---|
1092 | |
---|
1093 | if self._verbose is True: |
---|
1094 | print "Successfully completed PH iteration " + str(self._current_iteration) + " solves - solution statistics:" |
---|
1095 | print " Scenario PH Objective Cost Objective" |
---|
1096 | for scenario in self._scenario_tree._scenarios: |
---|
1097 | instance = self._instances[scenario._name] |
---|
1098 | for objective_name in instance.active_components(Objective): |
---|
1099 | objective = instance.active_components(Objective)[objective_name] |
---|
1100 | print "%20s %18.4f %14.4f" % (scenario._name, ph_objective_values[scenario._name], self._scenario_tree.compute_scenario_cost(instance)) |
---|
1101 | |
---|
1102 | def solve(self): |
---|
1103 | |
---|
1104 | self._solve_start_time = time.time() |
---|
1105 | self._cumulative_solve_time = 0.0 |
---|
1106 | self._cumulative_xbar_time = 0.0 |
---|
1107 | self._cumulative_weight_time = 0.0 |
---|
1108 | |
---|
1109 | print "Starting PH" |
---|
1110 | |
---|
1111 | if self._initialized == False: |
---|
1112 | raise RuntimeError, "PH is not initialized - cannot invoke solve() method" |
---|
1113 | |
---|
1114 | print "Initiating PH iteration=" + `self._current_iteration` |
---|
1115 | |
---|
1116 | self.iteration_0_solve() |
---|
1117 | |
---|
1118 | # update variable statistics prior to any output. |
---|
1119 | self.update_variable_statistics() |
---|
1120 | |
---|
1121 | if (self._verbose is True) or (self._report_solutions is True): |
---|
1122 | print "Variable values following scenario solves:" |
---|
1123 | self.pprint(False,False,True,False) |
---|
1124 | |
---|
1125 | # let plugins know if they care. |
---|
1126 | for plugin in self._ph_plugins: |
---|
1127 | plugin.post_iteration_0_solves(self) |
---|
1128 | |
---|
1129 | # update the fixed variable statistics. |
---|
1130 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
1131 | |
---|
1132 | if self._verbose is True: |
---|
1133 | print "Number of discrete variables fixed="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1134 | print "Number of continuous variables fixed="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1135 | |
---|
1136 | # always output the convergence metric and first-stage cost statistics, to give a sense of progress. |
---|
1137 | self._converger.update(self._current_iteration, self, self._scenario_tree, self._instances) |
---|
1138 | first_stage_min, first_stage_avg, first_stage_max = self._extract_first_stage_cost_statistics() |
---|
1139 | print "Convergence metric=%12.4f First stage cost avg=%12.4f Max-Min=%8.2f" % (self._converger.lastMetric(), first_stage_avg, first_stage_max-first_stage_min) |
---|
1140 | |
---|
1141 | self.update_weights() |
---|
1142 | |
---|
1143 | # let plugins know if they care. |
---|
1144 | for plugin in self._ph_plugins: |
---|
1145 | plugin.post_iteration_0(self) |
---|
1146 | |
---|
1147 | # if using a PH solver server, trasnsmit the rhos prior to the iteration |
---|
1148 | # k solve sequence. for now, we are assuming that the rhos don't change |
---|
1149 | # on a per-iteration basis, but that assumption can be easily relaxed. |
---|
1150 | # it is important to do this after the plugins have a chance to do their |
---|
1151 | # computation. |
---|
1152 | if self._solver_manager_type == "ph": |
---|
1153 | self._transmit_rhos() |
---|
1154 | self._enable_ph_objectives() |
---|
1155 | |
---|
1156 | # checkpoint if it's time - which it always is after iteration 0, |
---|
1157 | # if the interval is >= 1! |
---|
1158 | if (self._checkpoint_interval > 0): |
---|
1159 | self.checkpoint(0) |
---|
1160 | |
---|
1161 | # there is an upper bound on the number of iterations to execute - |
---|
1162 | # the actual bound depends on the converger supplied by the user. |
---|
1163 | for i in range(1, self._max_iterations+1): |
---|
1164 | |
---|
1165 | self._current_iteration = self._current_iteration + 1 |
---|
1166 | |
---|
1167 | print "Initiating PH iteration=" + `self._current_iteration` |
---|
1168 | |
---|
1169 | if (self._verbose is True) or (self._report_weights is True): |
---|
1170 | print "Variable averages and weights prior to scenario solves:" |
---|
1171 | self.pprint(True,True,False,False) |
---|
1172 | |
---|
1173 | # with the introduction of piecewise linearization, the form of the |
---|
1174 | # penalty-weighted objective is no longer fixed. thus, we need to |
---|
1175 | # create the objectives each PH iteration. |
---|
1176 | self.form_iteration_k_objectives() |
---|
1177 | |
---|
1178 | # let plugins know if they care. |
---|
1179 | for plugin in self._ph_plugins: |
---|
1180 | plugin.pre_iteration_k_solves(self) |
---|
1181 | |
---|
1182 | # do the actual solves. |
---|
1183 | self.iteration_k_solve() |
---|
1184 | |
---|
1185 | # update variable statistics prior to any output. |
---|
1186 | self.update_variable_statistics() |
---|
1187 | |
---|
1188 | if (self._verbose is True) or (self._report_solutions is True): |
---|
1189 | print "Variable values following scenario solves:" |
---|
1190 | self.pprint(False,False,True,False) |
---|
1191 | |
---|
1192 | # we don't technically have to do this at the last iteration, |
---|
1193 | # but with checkpointing and re-starts, you're never sure |
---|
1194 | # when you're executing the last iteration. |
---|
1195 | self.update_weights() |
---|
1196 | |
---|
1197 | # let plugins know if they care. |
---|
1198 | for plugin in self._ph_plugins: |
---|
1199 | plugin.post_iteration_k_solves(self) |
---|
1200 | |
---|
1201 | # update the fixed variable statistics. |
---|
1202 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
1203 | |
---|
1204 | if self._verbose is True: |
---|
1205 | print "Number of discrete variables fixed="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1206 | print "Number of continuous variables fixed="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1207 | |
---|
1208 | # let plugins know if they care. |
---|
1209 | for plugin in self._ph_plugins: |
---|
1210 | plugin.post_iteration_k(self) |
---|
1211 | |
---|
1212 | # at this point, all the real work of an iteration is complete. |
---|
1213 | |
---|
1214 | # checkpoint if it's time. |
---|
1215 | if (self._checkpoint_interval > 0) and (i % self._checkpoint_interval is 0): |
---|
1216 | self.checkpoint(i) |
---|
1217 | |
---|
1218 | # check for early termination. |
---|
1219 | self._converger.update(self._current_iteration, self, self._scenario_tree, self._instances) |
---|
1220 | first_stage_min, first_stage_avg, first_stage_max = self._extract_first_stage_cost_statistics() |
---|
1221 | print "Convergence metric=%12.4f First stage cost avg=%12.4f Max-Min=%8.2f" % (self._converger.lastMetric(), first_stage_avg, first_stage_max-first_stage_min) |
---|
1222 | |
---|
1223 | if self._converger.isConverged(self) is True: |
---|
1224 | if self._total_discrete_vars == 0: |
---|
1225 | print "PH converged - convergence metric is below threshold="+str(self._converger._convergence_threshold) |
---|
1226 | else: |
---|
1227 | print "PH converged - convergence metric is below threshold="+str(self._converger._convergence_threshold)+" or all discrete variables are fixed" |
---|
1228 | break |
---|
1229 | |
---|
1230 | # if we're terminating due to exceeding the maximum iteration count, print a message |
---|
1231 | # indicating so - otherwise, you get a quiet, information-free output trace. |
---|
1232 | if i == self._max_iterations: |
---|
1233 | print "Halting PH - reached maximal iteration count="+str(self._max_iterations) |
---|
1234 | |
---|
1235 | if self._verbose is True: |
---|
1236 | print "Number of discrete variables fixed before final plugin calls="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1237 | print "Number of continuous variables fixed before final plugin calls="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1238 | |
---|
1239 | # let plugins know if they care. do this before |
---|
1240 | # the final solution / statistics output, as the plugins |
---|
1241 | # might do some final tweaking. |
---|
1242 | for plugin in self._ph_plugins: |
---|
1243 | plugin.post_ph_execution(self) |
---|
1244 | |
---|
1245 | # update the fixed variable statistics - the plugins might have done something. |
---|
1246 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
1247 | |
---|
1248 | print "PH complete" |
---|
1249 | |
---|
1250 | print "Convergence history:" |
---|
1251 | self._converger.pprint() |
---|
1252 | |
---|
1253 | print "Final number of discrete variables fixed="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1254 | print "Final number of continuous variables fixed="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1255 | |
---|
1256 | print "Final variable values:" |
---|
1257 | self.pprint(False,False,True,True) |
---|
1258 | |
---|
1259 | print "Final costs:" |
---|
1260 | self._scenario_tree.pprintCosts(self._instances) |
---|
1261 | |
---|
1262 | self._solve_end_time = time.time() |
---|
1263 | |
---|
1264 | if (self._verbose is True) and (self._output_times is True): |
---|
1265 | print "Overall run-time= %8.2f seconds" % (self._solve_end_time - self._solve_start_time) |
---|
1266 | |
---|
1267 | # cleanup the scenario instances for post-processing - ideally, we want to leave them in |
---|
1268 | # their original state, minus all the PH-specific stuff. we don't do all cleanup (leaving |
---|
1269 | # things like rhos, etc), but we do clean up constraints, as that really hoses up the ef writer. |
---|
1270 | self._cleanup_scenario_instances() |
---|
1271 | |
---|
1272 | # |
---|
1273 | # prints a summary of all collected time statistics |
---|
1274 | # |
---|
1275 | def print_time_stats(self): |
---|
1276 | |
---|
1277 | print "PH run-time statistics (user):" |
---|
1278 | |
---|
1279 | print "Initialization time= %8.2f seconds" % (self._init_end_time - self._init_start_time) |
---|
1280 | print "Overall solve time= %8.2f seconds" % (self._solve_end_time - self._solve_start_time) |
---|
1281 | print "Scenario solve time= %8.2f seconds" % self._cumulative_solve_time |
---|
1282 | print "Average update time= %8.2f seconds" % self._cumulative_xbar_time |
---|
1283 | print "Weight update time= %8.2f seconds" % self._cumulative_weight_time |
---|
1284 | |
---|
1285 | # |
---|
1286 | # a utility to determine whether to output weight / average / etc. information for |
---|
1287 | # a variable/node combination. when the printing is moved into a callback/plugin, |
---|
1288 | # this routine will go there. for now, we don't dive down into the node resolution - |
---|
1289 | # just the variable/stage. |
---|
1290 | # |
---|
1291 | def should_print(self, stage, variable, variable_indices): |
---|
1292 | |
---|
1293 | if self._output_continuous_variable_stats is False: |
---|
1294 | |
---|
1295 | variable_type = variable.domain |
---|
1296 | |
---|
1297 | if (isinstance(variable_type, IntegerSet) is False) and (isinstance(variable_type, BooleanSet) is False): |
---|
1298 | |
---|
1299 | return False |
---|
1300 | |
---|
1301 | return True |
---|
1302 | |
---|
1303 | # |
---|
1304 | # pretty-prints the state of the current variable averages, weights, and values. |
---|
1305 | # inputs are booleans indicating which components should be output. |
---|
1306 | # |
---|
1307 | def pprint(self, output_averages, output_weights, output_values, output_fixed): |
---|
1308 | |
---|
1309 | if self._initialized is False: |
---|
1310 | raise RuntimeError, "PH is not initialized - cannot invoke pprint() method" |
---|
1311 | |
---|
1312 | # print tree nodes and associated variable/xbar/ph information in stage-order |
---|
1313 | # we don't blend in the last stage, so we don't current care about printing the associated information. |
---|
1314 | for stage in self._scenario_tree._stages[:-1]: |
---|
1315 | |
---|
1316 | print "\tStage=" + stage._name |
---|
1317 | |
---|
1318 | num_outputs_this_stage = 0 # tracks the number of outputs on a per-index basis. |
---|
1319 | |
---|
1320 | for (variable, index_template, variable_indices) in stage._variables: |
---|
1321 | |
---|
1322 | variable_name = variable.name |
---|
1323 | |
---|
1324 | if self.should_print(stage, variable, variable_indices) is True: |
---|
1325 | |
---|
1326 | num_outputs_this_variable = 0 # track, so we don't output the variable names unless there is an entry to report. |
---|
1327 | |
---|
1328 | for index in variable_indices: |
---|
1329 | |
---|
1330 | weight_parameter_name = "PHWEIGHT_"+variable_name |
---|
1331 | |
---|
1332 | num_outputs_this_index = 0 # track, so we don't output the variable index more than once. |
---|
1333 | |
---|
1334 | for tree_node in stage._tree_nodes: |
---|
1335 | |
---|
1336 | # determine if the variable/index pair is used across the set of scenarios (technically, |
---|
1337 | # it should be good enough to check one scenario). ditto for "fixed" status. fixed does |
---|
1338 | # imply unused (see note below), but we care about the fixed status when outputting |
---|
1339 | # final solutions. |
---|
1340 | |
---|
1341 | is_used = True # should be consistent across scenarios, so one "unused" flags as invalid. |
---|
1342 | is_fixed = False |
---|
1343 | |
---|
1344 | for scenario in tree_node._scenarios: |
---|
1345 | instance = self._instances[scenario._name] |
---|
1346 | variable_value = getattr(instance,variable_name)[index] |
---|
1347 | if variable_value.status == VarStatus.unused: |
---|
1348 | is_used = False |
---|
1349 | if variable_value.fixed is True: |
---|
1350 | is_fixed = True |
---|
1351 | |
---|
1352 | # IMPT: this is far from obvious, but variables that are fixed will - because |
---|
1353 | # presolve will identify them as constants and eliminate them from all |
---|
1354 | # expressions - be flagged as "unused" and therefore not output. |
---|
1355 | |
---|
1356 | if ((output_fixed is True) and (is_fixed is True)) or (is_used is True): |
---|
1357 | |
---|
1358 | minimum_value = None |
---|
1359 | maximum_value = None |
---|
1360 | |
---|
1361 | if index is None: |
---|
1362 | minimum_value = tree_node._minimums[variable_name] |
---|
1363 | maximum_value = tree_node._maximums[variable_name] |
---|
1364 | else: |
---|
1365 | minimum_value = tree_node._minimums[variable_name][index]() |
---|
1366 | maximum_value = tree_node._maximums[variable_name][index]() |
---|
1367 | |
---|
1368 | # there really isn't a need to output variables whose |
---|
1369 | # values are equal to 0 across-the-board. and there is |
---|
1370 | # good reason not to, i.e., the volume of output. |
---|
1371 | if (fabs(minimum_value) > self._integer_tolerance) or \ |
---|
1372 | (fabs(maximum_value) > self._integer_tolerance): |
---|
1373 | |
---|
1374 | num_outputs_this_stage = num_outputs_this_stage + 1 |
---|
1375 | num_outputs_this_variable = num_outputs_this_variable + 1 |
---|
1376 | num_outputs_this_index = num_outputs_this_index + 1 |
---|
1377 | |
---|
1378 | if num_outputs_this_variable == 1: |
---|
1379 | print "\t\tVariable=" + variable_name |
---|
1380 | |
---|
1381 | if num_outputs_this_index == 1: |
---|
1382 | if index is not None: |
---|
1383 | print "\t\t\tIndex:", indexToString(index) |
---|
1384 | |
---|
1385 | print "\t\t\t\tTree Node="+tree_node._name+"\t\t (Scenarios: ", |
---|
1386 | for scenario in tree_node._scenarios: |
---|
1387 | print scenario._name," ", |
---|
1388 | if scenario == tree_node._scenarios[-1]: |
---|
1389 | print ")" |
---|
1390 | |
---|
1391 | if output_values is True: |
---|
1392 | average_value = tree_node._averages[variable_name][index]() |
---|
1393 | print "\t\t\t\tValues: ", |
---|
1394 | for scenario in tree_node._scenarios: |
---|
1395 | instance = self._instances[scenario._name] |
---|
1396 | this_value = getattr(instance,variable_name)[index].value |
---|
1397 | print "%12.4f" % this_value, |
---|
1398 | if scenario == tree_node._scenarios[-1]: |
---|
1399 | print " Max-Min=%12.4f" % (maximum_value-minimum_value), |
---|
1400 | print " Avg=%12.4f" % (average_value), |
---|
1401 | print "" |
---|
1402 | if output_weights: |
---|
1403 | print "\t\t\t\tWeights: ", |
---|
1404 | for scenario in tree_node._scenarios: |
---|
1405 | instance = self._instances[scenario._name] |
---|
1406 | print "%12.4f" % getattr(instance,weight_parameter_name)[index].value, |
---|
1407 | if scenario == tree_node._scenarios[-1]: |
---|
1408 | print "" |
---|
1409 | |
---|
1410 | if output_averages: |
---|
1411 | print "\t\t\t\tAverage: %12.4f" % (tree_node._averages[variable_name][index].value) |
---|
1412 | |
---|
1413 | if num_outputs_this_stage == 0: |
---|
1414 | print "\t\tNo non-converged variables in stage" |
---|
1415 | |
---|
1416 | # cost variables aren't blended, so go through the gory computation of min/max/avg. |
---|
1417 | # we currently always print these. |
---|
1418 | cost_variable_name = stage._cost_variable[0].name |
---|
1419 | cost_variable_index = stage._cost_variable[1] |
---|
1420 | if cost_variable_index is None: |
---|
1421 | print "\t\tCost Variable=" + cost_variable_name |
---|
1422 | else: |
---|
1423 | print "\t\tCost Variable=" + cost_variable_name + indexToString(cost_variable_index) |
---|
1424 | for tree_node in stage._tree_nodes: |
---|
1425 | print "\t\t\tTree Node=" + tree_node._name + "\t\t (Scenarios: ", |
---|
1426 | for scenario in tree_node._scenarios: |
---|
1427 | print scenario._name," ", |
---|
1428 | if scenario == tree_node._scenarios[-1]: |
---|
1429 | print ")" |
---|
1430 | maximum_value = 0.0 |
---|
1431 | minimum_value = 0.0 |
---|
1432 | sum_values = 0.0 |
---|
1433 | num_values = 0 |
---|
1434 | first_time = True |
---|
1435 | print "\t\t\tValues: ", |
---|
1436 | for scenario in tree_node._scenarios: |
---|
1437 | instance = self._instances[scenario._name] |
---|
1438 | this_value = getattr(instance,cost_variable_name)[cost_variable_index].value |
---|
1439 | print "%12.4f" % this_value, |
---|
1440 | num_values += 1 |
---|
1441 | sum_values += this_value |
---|
1442 | if first_time is True: |
---|
1443 | first_time = False |
---|
1444 | maximum_value = this_value |
---|
1445 | minimum_value = this_value |
---|
1446 | else: |
---|
1447 | if this_value > maximum_value: |
---|
1448 | maximum_value = this_value |
---|
1449 | if this_value < minimum_value: |
---|
1450 | minimum_value = this_value |
---|
1451 | if scenario == tree_node._scenarios[-1]: |
---|
1452 | print " Max-Min=%12.4f" % (maximum_value-minimum_value), |
---|
1453 | print " Avg=%12.4f" % (sum_values/num_values), |
---|
1454 | print "" |
---|
1455 | |
---|