1 | # _________________________________________________________________________ |
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2 | # |
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3 | # Coopr: A COmmon Optimization Python Repository |
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4 | # Copyright (c) 2008 Sandia Corporation. |
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5 | # This software is distributed under the BSD License. |
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6 | # Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, |
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7 | # the U.S. Government retains certain rights in this software. |
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8 | # For more information, see the Coopr README.txt file. |
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9 | # _________________________________________________________________________ |
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10 | |
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11 | import sys |
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12 | import types |
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13 | from coopr.pyomo import * |
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14 | from coopr.pyomo.base.expr import * |
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15 | import copy |
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16 | import os.path |
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17 | import traceback |
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18 | import copy |
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19 | from coopr.opt import SolverResults,SolverStatus |
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20 | from coopr.opt.base import SolverFactory |
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21 | from coopr.opt.parallel import SolverManagerFactory |
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22 | import time |
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23 | import types |
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24 | import pickle |
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25 | import gc |
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26 | from math import fabs, log, exp |
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27 | |
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28 | from scenariotree import * |
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29 | from phutils import * |
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30 | from phobjective import * |
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31 | |
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32 | from pyutilib.component.core import ExtensionPoint |
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33 | |
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34 | from coopr.pysp.phextension import IPHExtension |
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35 | |
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36 | class ProgressiveHedging(object): |
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37 | |
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38 | # |
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39 | # a utility intended for folks who are brave enough to script rho setting in a python file. |
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40 | # |
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41 | |
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42 | def setRhoAllScenarios(self, variable_value, rho_expression): |
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43 | |
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44 | variable_name = None |
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45 | variable_index = None |
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46 | |
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47 | if isVariableNameIndexed(variable_value.name) is True: |
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48 | |
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49 | variable_name, variable_index = extractVariableNameAndIndex(variable_value.name) |
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50 | |
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51 | else: |
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52 | |
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53 | variable_name = variable_value.name |
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54 | variable_index = None |
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55 | |
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56 | new_rho_value = None |
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57 | if isinstance(rho_expression, float): |
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58 | new_rho_value = rho_expression |
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59 | else: |
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60 | new_rho_value = rho_expression() |
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61 | |
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62 | if self._verbose is True: |
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63 | print "Setting rho="+str(new_rho_value)+" for variable="+variable_value.name |
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64 | |
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65 | for instance_name, instance in self._instances.items(): |
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66 | |
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67 | rho_param = getattr(instance, "PHRHO_"+variable_name) |
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68 | rho_param[variable_index] = new_rho_value |
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69 | |
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70 | # |
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71 | # a utility intended for folks who are brave enough to script variable bounds setting in a python file. |
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72 | # |
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73 | |
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74 | def setVariableBoundsAllScenarios(self, variable_name, variable_index, lower_bound, upper_bound): |
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75 | |
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76 | if isinstance(lower_bound, float) is False: |
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77 | 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) |
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78 | |
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79 | if isinstance(upper_bound, float) is False: |
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80 | 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) |
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81 | |
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82 | for instance_name, instance in self._instances.items(): |
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83 | |
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84 | variable = getattr(instance, variable_name) |
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85 | variable[variable_index].setlb(lower_bound) |
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86 | variable[variable_index].setub(upper_bound) |
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87 | |
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88 | # |
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89 | # a utility intended for folks who are brave enough to script variable bounds setting in a python file. |
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90 | # same functionality as above, but applied to all indicies of the variable, in all scenarios. |
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91 | # |
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92 | |
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93 | def setVariableBoundsAllIndicesAllScenarios(self, variable_name, lower_bound, upper_bound): |
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94 | |
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95 | if isinstance(lower_bound, float) is False: |
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96 | raise ValueError, "Lower bound supplied to PH method setVariableBoundsAllIndiciesAllScenarios for variable="+variable_name+" must be a constant; value supplied="+str(lower_bound) |
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97 | |
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98 | if isinstance(upper_bound, float) is False: |
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99 | raise ValueError, "Upper bound supplied to PH method setVariableBoundsAllIndicesAllScenarios for variable="+variable_name+" must be a constant; value supplied="+str(upper_bound) |
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100 | |
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101 | for instance_name, instance in self._instances.items(): |
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102 | |
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103 | variable = getattr(instance, variable_name) |
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104 | for index in variable: |
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105 | variable[index].setlb(lower_bound) |
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106 | variable[index].setub(upper_bound) |
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107 | |
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108 | # |
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109 | # checkpoint the current PH state via pickle'ing. the input iteration count |
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110 | # simply serves as a tag to create the output file name. everything with the |
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111 | # exception of the _ph_plugins, _solver_manager, and _solver attributes are |
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112 | # pickled. currently, plugins fail in the pickle process, which is fine as |
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113 | # JPW doesn't think you want to pickle plugins (particularly the solver and |
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114 | # solver manager) anyway. For example, you might want to change those later, |
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115 | # after restoration - and the PH state is independent of how scenario |
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116 | # sub-problems are solved. |
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117 | # |
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118 | |
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119 | def checkpoint(self, iteration_count): |
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120 | |
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121 | checkpoint_filename = "checkpoint."+str(iteration_count) |
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122 | |
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123 | tmp_ph_plugins = self._ph_plugins |
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124 | tmp_solver_manager = self._solver_manager |
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125 | tmp_solver = self._solver |
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126 | |
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127 | self._ph_plugins = None |
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128 | self._solver_manager = None |
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129 | self._solver = None |
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130 | |
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131 | checkpoint_file = open(checkpoint_filename, "w") |
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132 | pickle.dump(self,checkpoint_file) |
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133 | checkpoint_file.close() |
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134 | |
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135 | self._ph_plugins = tmp_ph_plugins |
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136 | self._solver_manager = tmp_solver_manager |
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137 | self._solver = tmp_solver |
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138 | |
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139 | print "Checkpoint written to file="+checkpoint_filename |
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140 | |
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141 | # |
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142 | # a simple utility to count the number of continuous and discrete variables in a set of instances. |
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143 | # unused variables are ignored, and counts include all active indices. returns a pair - num-discrete, |
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144 | # num-continuous. |
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145 | # |
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146 | |
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147 | def compute_variable_counts(self): |
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148 | |
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149 | num_continuous_vars = 0 |
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150 | num_discrete_vars = 0 |
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151 | |
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152 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage |
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153 | |
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154 | for tree_node in stage._tree_nodes: |
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155 | |
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156 | for (variable, index_template) in stage._variables: |
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157 | |
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158 | variable_indices = tree_node._variable_indices[variable.name] |
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159 | |
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160 | for index in variable_indices: |
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161 | |
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162 | is_used = True # until proven otherwise |
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163 | for scenario in tree_node._scenarios: |
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164 | instance = self._instances[scenario._name] |
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165 | if getattr(instance,variable.name)[index].status == VarStatus.unused: |
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166 | is_used = False |
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167 | |
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168 | if is_used is True: |
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169 | |
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170 | if isinstance(variable.domain, IntegerSet) or isinstance(variable.domain, BooleanSet): |
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171 | num_discrete_vars = num_discrete_vars + 1 |
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172 | else: |
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173 | num_continuous_vars = num_continuous_vars + 1 |
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174 | |
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175 | return (num_discrete_vars, num_continuous_vars) |
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176 | |
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177 | # |
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178 | # ditto above, but count the number of fixed discrete and continuous variables. |
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179 | # important: once a variable (value) is fixed, it is flagged as unused in the |
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180 | # course of presolve - because it is no longer referenced. this makes sense, |
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181 | # of course; it's just something to watch for. this is an obvious assumption |
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182 | # that we won't be fixing unused variables, which should not be an issue. |
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183 | # |
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184 | |
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185 | def compute_fixed_variable_counts(self): |
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186 | |
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187 | num_fixed_continuous_vars = 0 |
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188 | num_fixed_discrete_vars = 0 |
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189 | |
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190 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage |
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191 | |
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192 | for tree_node in stage._tree_nodes: |
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193 | |
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194 | for (variable, index_template) in stage._variables: |
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195 | |
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196 | variable_indices = tree_node._variable_indices[variable.name] |
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197 | |
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198 | for index in variable_indices: |
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199 | |
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200 | # implicit assumption is that if a variable value is fixed in one |
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201 | # scenario, it is fixed in all scenarios. |
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202 | |
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203 | is_fixed = False # until proven otherwise |
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204 | for scenario in tree_node._scenarios: |
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205 | instance = self._instances[scenario._name] |
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206 | var_value = getattr(instance,variable.name)[index] |
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207 | if var_value.fixed is True: |
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208 | is_fixed = True |
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209 | |
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210 | if is_fixed is True: |
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211 | |
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212 | if isinstance(variable.domain, IntegerSet) or isinstance(variable.domain, BooleanSet): |
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213 | num_fixed_discrete_vars = num_fixed_discrete_vars + 1 |
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214 | else: |
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215 | num_fixed_continuous_vars = num_fixed_continuous_vars + 1 |
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216 | |
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217 | return (num_fixed_discrete_vars, num_fixed_continuous_vars) |
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218 | |
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219 | # when the quadratic penalty terms are approximated via piecewise linear segments, |
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220 | # we end up (necessarily) "littering" the scenario instances with extra constraints. |
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221 | # these need to and should be cleaned up after PH, for purposes of post-PH manipulation, |
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222 | # e.g., writing the extensive form. |
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223 | def _cleanup_scenario_instances(self): |
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224 | |
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225 | for instance_name, instance in self._instances.items(): |
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226 | |
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227 | for constraint_name in self._instance_augmented_attributes[instance_name]: |
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228 | |
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229 | instance._clear_attribute(constraint_name) |
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230 | |
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231 | # if you don't pre-solve, the name collections won't be updated. |
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232 | instance.preprocess() |
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233 | |
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234 | # create PH weight and xbar vectors, on a per-scenario basis, for each variable that is not in the |
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235 | # final stage, i.e., for all variables that are being blended by PH. the parameters are created |
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236 | # in the space of each scenario instance, so that they can be directly and automatically |
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237 | # incorporated into the (appropriately modified) objective function. |
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238 | |
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239 | def _create_ph_scenario_parameters(self): |
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240 | |
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241 | for (instance_name, instance) in self._instances.items(): |
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242 | |
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243 | new_penalty_variable_names = create_ph_parameters(instance, self._scenario_tree, self._rho, self._linearize_nonbinary_penalty_terms) |
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244 | self._instance_augmented_attributes[instance_name].extend(new_penalty_variable_names) |
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245 | |
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246 | # a simple utility to extract the first-stage cost statistics, e.g., min, average, and max. |
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247 | def _extract_first_stage_cost_statistics(self): |
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248 | |
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249 | maximum_value = 0.0 |
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250 | minimum_value = 0.0 |
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251 | sum_values = 0.0 |
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252 | num_values = 0 |
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253 | first_time = True |
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254 | |
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255 | first_stage = self._scenario_tree._stages[0] |
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256 | (cost_variable, cost_variable_index) = first_stage._cost_variable |
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257 | for scenario_name, instance in self._instances.items(): |
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258 | this_value = getattr(instance, cost_variable.name)[cost_variable_index].value |
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259 | if this_value is not None: # None means not reported by the solver. |
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260 | num_values += 1 |
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261 | sum_values += this_value |
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262 | if first_time is True: |
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263 | first_time = False |
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264 | maximum_value = this_value |
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265 | minimum_value = this_value |
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266 | else: |
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267 | if this_value > maximum_value: |
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268 | maximum_value = this_value |
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269 | if this_value < minimum_value: |
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270 | minimum_value = this_value |
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271 | |
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272 | if num_values > 0: |
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273 | sum_values = sum_values / num_values |
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274 | |
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275 | return minimum_value, sum_values, maximum_value |
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276 | |
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277 | # a utility to transmit - across the PH solver manager - the current weights |
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278 | # and averages for each of my problem instances. used to set up iteration K solves. |
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279 | def _transmit_weights_and_averages(self): |
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280 | |
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281 | for scenario_name, scenario_instance in self._instances.items(): |
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282 | |
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283 | weights_to_transmit = [] |
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284 | averages_to_transmit = [] |
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285 | |
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286 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage, so no weights to worry about. |
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287 | |
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288 | for (variable, index_template) in stage._variables: |
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289 | |
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290 | variable_name = variable.name |
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291 | weight_parameter_name = "PHWEIGHT_"+variable_name |
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292 | weights_to_transmit.append(getattr(scenario_instance, weight_parameter_name)) |
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293 | average_parameter_name = "PHAVG_"+variable_name |
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294 | averages_to_transmit.append(getattr(scenario_instance, average_parameter_name)) |
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295 | |
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296 | self._solver_manager.transmit_weights_and_averages(scenario_instance, weights_to_transmit, averages_to_transmit) |
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297 | |
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298 | # |
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299 | # a utility to transmit - across the PH solver manager - the current rho values |
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300 | # for each of my problem instances. mainly after PH iteration 0 is complete, |
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301 | # in preparation for the iteration K solves. |
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302 | # |
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303 | |
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304 | def _transmit_rhos(self): |
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305 | |
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306 | for scenario_name, scenario_instance in self._instances.items(): |
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307 | |
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308 | rhos_to_transmit = [] |
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309 | |
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310 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage, so no rhos to worry about. |
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311 | |
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312 | for (variable, index_template) in stage._variables: |
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313 | |
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314 | variable_name = variable.name |
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315 | rho_parameter_name = "PHRHO_"+variable_name |
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316 | rhos_to_transmit.append(getattr(scenario_instance, rho_parameter_name)) |
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317 | |
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318 | self._solver_manager.transmit_rhos(scenario_instance, rhos_to_transmit) |
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319 | |
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320 | # |
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321 | # a utility to transmit - across the PH solver manager - the current scenario |
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322 | # tree node statistics to each of my problem instances. done prior to each |
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323 | # PH iteration k. |
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324 | # |
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325 | |
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326 | def _transmit_tree_node_statistics(self): |
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327 | |
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328 | for scenario_name, scenario_instance in self._instances.items(): |
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329 | |
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330 | tree_node_minimums = {} |
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331 | tree_node_maximums = {} |
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332 | |
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333 | scenario = self._scenario_tree._scenario_map[scenario_name] |
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334 | |
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335 | for tree_node in scenario._node_list: |
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336 | |
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337 | tree_node_minimums[tree_node._name] = tree_node._minimums |
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338 | tree_node_maximums[tree_node._name] = tree_node._maximums |
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339 | |
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340 | self._solver_manager.transmit_tree_node_statistics(scenario_instance, tree_node_minimums, tree_node_maximums) |
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341 | |
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342 | # |
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343 | # a utility to enable - across the PH solver manager - weighted penalty objectives. |
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344 | # |
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345 | |
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346 | def _enable_ph_objectives(self): |
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347 | |
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348 | for scenario_name, scenario_instance in self._instances.items(): |
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349 | |
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350 | self._solver_manager.enable_ph_objective(scenario_instance) |
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351 | |
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352 | |
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353 | """ Constructor |
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354 | Arguments: |
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355 | max_iterations the maximum number of iterations to run PH (>= 0). defaults to 0. |
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356 | rho the global rho value (> 0). defaults to 0. |
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357 | rho_setter an optional name of a python file used to set particular variable rho values. |
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358 | solver the solver type that PH uses to solve scenario sub-problems. defaults to "cplex". |
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359 | solver_manager the solver manager type that coordinates scenario sub-problem solves. defaults to "serial". |
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360 | keep_solver_files do I keep intermediate solver files around (for debugging)? defaults to False. |
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361 | output_solver_log do I dump the solver log (as it is being generated) to the screen? defaults to False. |
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362 | output_solver_results do I output (for debugging) the detailed solver results, including solutions, for scenario solves? defaults to False. |
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363 | verbose does the PH object stream debug/status output? defaults to False. |
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364 | 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). |
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365 | checkpoint_interval how many iterations between writing a checkpoint file containing the entire PH state? defaults to 0, indicating never. |
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366 | |
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367 | """ |
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368 | def __init__(self, *args, **kwds): |
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369 | |
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370 | # PH configuration parameters |
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371 | self._rho = 0.0 # a default, global values for rhos. |
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372 | self._rho_setter = None # filename for the modeler to set rho on a per-variable or per-scenario basis. |
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373 | self._bounds_setter = None # filename for the modeler to set rho on a per-variable basis, after all scenarios are available. |
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374 | self._max_iterations = 0 |
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375 | |
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376 | # PH reporting parameters |
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377 | self._verbose = False # do I flood the screen with status output? |
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378 | self._report_solutions = False # do I report solutions after each PH iteration? |
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379 | self._report_weights = False # do I report PH weights prior to each PH iteration? |
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380 | self._report_only_statistics = False # do I report only variable statistics when outputting solutions and weights? |
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381 | self._output_continuous_variable_stats = True # when in verbose mode, do I output weights/averages for continuous variables? |
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382 | self._output_solver_results = False |
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383 | self._output_times = False |
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384 | self._output_scenario_tree_solution = False |
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385 | |
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386 | # PH run-time variables |
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387 | self._current_iteration = 0 # the 'k' |
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388 | self._xbar = {} # current per-variable averages. maps (node_id, variable_name) -> value |
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389 | self._initialized = False # am I ready to call "solve"? Set to True by the initialize() method. |
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390 | |
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391 | # PH solver information / objects. |
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392 | self._solver_type = "cplex" |
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393 | self._solver_manager_type = "serial" # serial or pyro are the options currently available |
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394 | |
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395 | self._solver = None |
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396 | self._solver_manager = None |
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397 | |
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398 | self._keep_solver_files = False |
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399 | self._output_solver_log = False |
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400 | |
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401 | # PH convergence computer/updater. |
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402 | self._converger = None |
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403 | |
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404 | # PH history |
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405 | self._solutions = {} |
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406 | |
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407 | # the checkpoint interval - expensive operation, but worth it for big models. |
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408 | # 0 indicates don't checkpoint. |
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409 | self._checkpoint_interval = 0 |
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410 | |
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411 | # all information related to the scenario tree (implicit and explicit). |
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412 | self._model = None # not instantiated |
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413 | self._model_instance = None # instantiated |
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414 | |
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415 | self._scenario_tree = None |
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416 | |
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417 | self._scenario_data_directory = "" # this the prefix for all scenario data |
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418 | self._instances = {} # maps scenario name to the corresponding model instance |
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419 | 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. |
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420 | |
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421 | # for various reasons (mainly hacks at this point), it's good to know whether we're minimizing or maximizing. |
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422 | self._is_minimizing = None |
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423 | |
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424 | # global handle to ph extension plugins |
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425 | self._ph_plugins = ExtensionPoint(IPHExtension) |
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426 | |
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427 | # PH timing statistics - relative to last invocation. |
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428 | self._init_start_time = None # for initialization() method |
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429 | self._init_end_time = None |
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430 | self._solve_start_time = None # for solve() method |
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431 | self._solve_end_time = None |
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432 | self._cumulative_solve_time = None # seconds, over course of solve() |
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433 | self._cumulative_xbar_time = None # seconds, over course of update_xbars() |
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434 | self._cumulative_weight_time = None # seconds, over course of update_weights() |
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435 | |
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436 | # do I disable warm-start for scenario sub-problem solves during PH iterations >= 1? |
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437 | self._disable_warmstarts = False |
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438 | |
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439 | # do I drop proximal (quadratic penalty) terms from the weighted objective functions? |
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440 | self._drop_proximal_terms = False |
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441 | |
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442 | # do I linearize the quadratic penalty term for continuous variables via a |
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443 | # piecewise linear approximation? the default should always be 0 (off), as the |
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444 | # user should be aware when they are forcing an approximation. |
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445 | self._linearize_nonbinary_penalty_terms = 0 |
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446 | |
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447 | # the breakpoint distribution strategy employed when linearizing. 0 implies uniform |
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448 | # distribution between the variable lower and upper bounds. |
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449 | self._breakpoint_strategy = 0 |
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450 | |
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451 | # do I retain quadratic objective terms associated with binary variables? in general, |
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452 | # there is no good reason to not linearize, but just in case, I introduced the option. |
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453 | self._retain_quadratic_binary_terms = False |
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454 | |
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455 | # PH default tolerances - for use in fixing and testing equality across scenarios, |
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456 | # and other stuff. |
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457 | self._integer_tolerance = 0.00001 |
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458 | |
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459 | # PH maintains a mipgap that is applied to each scenario solve that is performed. |
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460 | # this attribute can be changed by PH extensions, and the change will be applied |
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461 | # on all subsequent solves - until it is modified again. the default is None, |
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462 | # indicating unassigned. |
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463 | self._mipgap = None |
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464 | |
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465 | # should PH simplify expressions after creating them? an example includes |
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466 | # the objective function expression, either with or without linearization. |
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467 | self._simplify_expressions = False |
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468 | |
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469 | # we only store these temporarily... |
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470 | scenario_solver_options = None |
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471 | |
---|
472 | # process the keyword options |
---|
473 | for key in kwds.keys(): |
---|
474 | if key == "max_iterations": |
---|
475 | self._max_iterations = kwds[key] |
---|
476 | elif key == "rho": |
---|
477 | self._rho = kwds[key] |
---|
478 | elif key == "rho_setter": |
---|
479 | self._rho_setter = kwds[key] |
---|
480 | elif key == "bounds_setter": |
---|
481 | self._bounds_setter = kwds[key] |
---|
482 | elif key == "solver": |
---|
483 | self._solver_type = kwds[key] |
---|
484 | elif key == "solver_manager": |
---|
485 | self._solver_manager_type = kwds[key] |
---|
486 | elif key == "scenario_solver_options": |
---|
487 | scenario_solver_options = kwds[key] |
---|
488 | elif key == "scenario_mipgap": |
---|
489 | self._mipgap = kwds[key] |
---|
490 | elif key == "keep_solver_files": |
---|
491 | self._keep_solver_files = kwds[key] |
---|
492 | elif key == "output_solver_results": |
---|
493 | self._output_solver_results = kwds[key] |
---|
494 | elif key == "output_solver_log": |
---|
495 | self._output_solver_log = kwds[key] |
---|
496 | elif key == "verbose": |
---|
497 | self._verbose = kwds[key] |
---|
498 | elif key == "report_solutions": |
---|
499 | self._report_solutions = kwds[key] |
---|
500 | elif key == "report_weights": |
---|
501 | self._report_weights = kwds[key] |
---|
502 | elif key == "report_only_statistics": |
---|
503 | self._report_only_statistics = kwds[key] |
---|
504 | elif key == "output_times": |
---|
505 | self._output_times = kwds[key] |
---|
506 | elif key == "disable_warmstarts": |
---|
507 | self._disable_warmstarts = kwds[key] |
---|
508 | elif key == "drop_proximal_terms": |
---|
509 | self._drop_proximal_terms = kwds[key] |
---|
510 | elif key == "retain_quadratic_binary_terms": |
---|
511 | self._retain_quadratic_binary_terms = kwds[key] |
---|
512 | elif key == "linearize_nonbinary_penalty_terms": |
---|
513 | self._linearize_nonbinary_penalty_terms = kwds[key] |
---|
514 | elif key == "breakpoint_strategy": |
---|
515 | self._breakpoint_strategy = kwds[key] |
---|
516 | elif key == "checkpoint_interval": |
---|
517 | self._checkpoint_interval = kwds[key] |
---|
518 | elif key == "output_scenario_tree_solution": |
---|
519 | self._output_scenario_tree_solution = kwds[key] |
---|
520 | elif key == "simplify_expressions": |
---|
521 | self._simplify_expressions = kwds[key] |
---|
522 | else: |
---|
523 | print "Unknown option=" + key + " specified in call to PH constructor" |
---|
524 | |
---|
525 | # validate all "atomic" options (those that can be validated independently) |
---|
526 | if self._max_iterations < 0: |
---|
527 | raise ValueError, "Maximum number of PH iterations must be non-negative; value specified=" + `self._max_iterations` |
---|
528 | if self._rho <= 0.0: |
---|
529 | raise ValueError, "Value of the rho parameter in PH must be non-zero positive; value specified=" + `self._rho` |
---|
530 | if (self._mipgap is not None) and ((self._mipgap < 0.0) or (self._mipgap > 1.0)): |
---|
531 | raise ValueError, "Value of the mipgap parameter in PH must be on the unit interval; value specified=" + `self._mipgap` |
---|
532 | |
---|
533 | # validate the linearization (number of pieces) and breakpoint distribution parameters. |
---|
534 | if self._linearize_nonbinary_penalty_terms < 0: |
---|
535 | raise ValueError, "Value of linearization parameter for nonbinary penalty terms must be non-negative; value specified=" + `self._linearize_nonbinary_penalty_terms` |
---|
536 | if self._breakpoint_strategy < 0: |
---|
537 | raise ValueError, "Value of the breakpoint distribution strategy parameter must be non-negative; value specified=" + str(self._breakpoint_strategy) |
---|
538 | if self._breakpoint_strategy > 3: |
---|
539 | raise ValueError, "Unknown breakpoint distribution strategy specified - valid values are between 0 and 2, inclusive; value specified=" + str(self._breakpoint_strategy) |
---|
540 | |
---|
541 | # validate rho setter file if specified. |
---|
542 | if self._rho_setter is not None: |
---|
543 | if os.path.exists(self._rho_setter) is False: |
---|
544 | raise ValueError, "The rho setter script file="+self._rho_setter+" does not exist" |
---|
545 | |
---|
546 | # validate bounds setter file if specified. |
---|
547 | if self._bounds_setter is not None: |
---|
548 | if os.path.exists(self._bounds_setter) is False: |
---|
549 | raise ValueError, "The bounds setter script file="+self._bounds_setter+" does not exist" |
---|
550 | |
---|
551 | # validate the checkpoint interval. |
---|
552 | if self._checkpoint_interval < 0: |
---|
553 | raise ValueError, "A negative checkpoint interval with value="+str(self._checkpoint_interval)+" was specified in call to PH constructor" |
---|
554 | |
---|
555 | # construct the sub-problem solver. |
---|
556 | if self._verbose is True: |
---|
557 | print "Constructing solver type="+self._solver_type |
---|
558 | self._solver = SolverFactory(self._solver_type) |
---|
559 | if self._solver == None: |
---|
560 | raise ValueError, "Unknown solver type=" + self._solver_type + " specified in call to PH constructor" |
---|
561 | if self._keep_solver_files is True: |
---|
562 | self._solver.keepFiles = True |
---|
563 | if len(scenario_solver_options) > 0: |
---|
564 | if self._verbose is True: |
---|
565 | print "Initializing scenario sub-problem solver with options="+str(scenario_solver_options) |
---|
566 | self._solver.set_options("".join(scenario_solver_options)) |
---|
567 | if self._output_times is True: |
---|
568 | self._solver._report_timing = True |
---|
569 | |
---|
570 | # construct the solver manager. |
---|
571 | if self._verbose is True: |
---|
572 | print "Constructing solver manager of type="+self._solver_manager_type |
---|
573 | self._solver_manager = SolverManagerFactory(self._solver_manager_type) |
---|
574 | if self._solver_manager is None: |
---|
575 | raise ValueError, "Failed to create solver manager of type="+self._solver_manager_type+" specified in call to PH constructor" |
---|
576 | |
---|
577 | # a set of all valid PH iteration indicies is generally useful for plug-ins, so create it here. |
---|
578 | self._iteration_index_set = Set(name="PHIterations") |
---|
579 | for i in range(0,self._max_iterations + 1): |
---|
580 | self._iteration_index_set.add(i) |
---|
581 | |
---|
582 | # spit out parameterization if verbosity is enabled |
---|
583 | if self._verbose is True: |
---|
584 | print "PH solver configuration: " |
---|
585 | print " Max iterations=" + `self._max_iterations` |
---|
586 | print " Default global rho=" + `self._rho` |
---|
587 | if self._rho_setter is not None: |
---|
588 | print " Rho initialization file=" + self._rho_setter |
---|
589 | if self._bounds_setter is not None: |
---|
590 | print " Variable bounds initialization file=" + self._bounds_setter |
---|
591 | print " Sub-problem solver type=" + `self._solver_type` |
---|
592 | print " Solver manager type=" + `self._solver_manager_type` |
---|
593 | print " Keep solver files? " + str(self._keep_solver_files) |
---|
594 | print " Output solver results? " + str(self._output_solver_results) |
---|
595 | print " Output solver log? " + str(self._output_solver_log) |
---|
596 | print " Output times? " + str(self._output_times) |
---|
597 | print " Checkpoint interval="+str(self._checkpoint_interval) |
---|
598 | |
---|
599 | """ Initialize PH with model and scenario data, in preparation for solve(). |
---|
600 | Constructs and reads instances. |
---|
601 | """ |
---|
602 | def initialize(self, scenario_data_directory_name=".", model=None, model_instance=None, scenario_tree=None, converger=None, linearize=False): |
---|
603 | |
---|
604 | self._init_start_time = time.time() |
---|
605 | |
---|
606 | if self._verbose is True: |
---|
607 | print "Initializing PH" |
---|
608 | print " Scenario data directory=" + scenario_data_directory_name |
---|
609 | |
---|
610 | if not os.path.exists(scenario_data_directory_name): |
---|
611 | raise ValueError, "Scenario data directory=" + scenario_data_directory_name + " either does not exist or cannot be read" |
---|
612 | |
---|
613 | self._scenario_data_directory_name = scenario_data_directory_name |
---|
614 | |
---|
615 | # IMPT: The input model should be an *instance*, as it is very useful (critical!) to know |
---|
616 | # the dimensions of sets, be able to store suffixes on variable values, etc. |
---|
617 | if model is None: |
---|
618 | raise ValueError, "A model must be supplied to the PH initialize() method" |
---|
619 | |
---|
620 | if scenario_tree is None: |
---|
621 | raise ValueError, "A scenario tree must be supplied to the PH initialize() method" |
---|
622 | |
---|
623 | if converger is None: |
---|
624 | raise ValueError, "A convergence computer must be supplied to the PH initialize() method" |
---|
625 | |
---|
626 | self._model = model |
---|
627 | self._model_instance = model_instance |
---|
628 | self._scenario_tree = scenario_tree |
---|
629 | self._converger = converger |
---|
630 | |
---|
631 | model_objective = model.active_components(Objective) |
---|
632 | self._is_minimizing = (model_objective[ model_objective.keys()[0] ].sense == minimize) |
---|
633 | |
---|
634 | self._converger.reset() |
---|
635 | |
---|
636 | # construct the instances for each scenario. |
---|
637 | # |
---|
638 | # garbage collection noticeably slows down PH when dealing with |
---|
639 | # large numbers of scenarios. disable prior to instance construction, |
---|
640 | # and then re-enable. there isn't much collection to do as instances |
---|
641 | # are constructed. |
---|
642 | re_enable_gc = gc.isenabled() |
---|
643 | gc.disable() |
---|
644 | |
---|
645 | if self._verbose is True: |
---|
646 | if self._scenario_tree._scenario_based_data == 1: |
---|
647 | print "Scenario-based instance initialization enabled" |
---|
648 | else: |
---|
649 | print "Node-based instance initialization enabled" |
---|
650 | |
---|
651 | for scenario in self._scenario_tree._scenarios: |
---|
652 | |
---|
653 | scenario_instance = construct_scenario_instance(self._scenario_tree, |
---|
654 | self._scenario_data_directory_name, |
---|
655 | scenario._name, |
---|
656 | self._model, |
---|
657 | self._verbose, |
---|
658 | preprocess=False, |
---|
659 | linearize=linearize, |
---|
660 | simplify=self._simplify_expressions) |
---|
661 | |
---|
662 | # IMPT: disable canonical representation construction for ASL solvers. |
---|
663 | # this is a hack, in that we need to address encodings and |
---|
664 | # the like at a more general level. |
---|
665 | if self._solver_type == "asl": |
---|
666 | scenario_instance.skip_canonical_repn = True |
---|
667 | else: |
---|
668 | scenario_instance.preprocess() |
---|
669 | |
---|
670 | self._instances[scenario._name] = scenario_instance |
---|
671 | self._instances[scenario._name].name = scenario._name |
---|
672 | self._instance_augmented_attributes[scenario._name] = [] |
---|
673 | |
---|
674 | # perform a single pass of garbage collection and re-enable automatic collection. |
---|
675 | if re_enable_gc is True: |
---|
676 | gc.collect() |
---|
677 | gc.enable() |
---|
678 | |
---|
679 | # with the scenario instances now available, have the scenario tree compute the |
---|
680 | # variable match indices at each node. |
---|
681 | self._scenario_tree.defineVariableIndexSets(self._instances) |
---|
682 | |
---|
683 | # let plugins know if they care - this callback point allows |
---|
684 | # users to create/modify the original scenario instances and/or |
---|
685 | # the scenario tree prior to creating PH-related parameters, |
---|
686 | # variables, and the like. |
---|
687 | for plugin in self._ph_plugins: |
---|
688 | plugin.post_instance_creation(self) |
---|
689 | |
---|
690 | # create ph-specific parameters (weights, xbar, etc.) for each instance. |
---|
691 | |
---|
692 | if self._verbose is True: |
---|
693 | print "Creating weight, average, and rho parameter vectors for scenario instances" |
---|
694 | |
---|
695 | self._create_ph_scenario_parameters() |
---|
696 | |
---|
697 | # if specified, run the user script to initialize variable rhos at their whim. |
---|
698 | if self._rho_setter is not None: |
---|
699 | print "Executing user rho set script from filename="+self._rho_setter |
---|
700 | execfile(self._rho_setter) |
---|
701 | |
---|
702 | # with the instances created, run the user script to initialize variable bounds. |
---|
703 | if self._bounds_setter is not None: |
---|
704 | print "Executing user variable bounds set script from filename=", self._bounds_setter |
---|
705 | execfile(self._bounds_setter) |
---|
706 | |
---|
707 | # create parameters to store variable statistics (of general utility) at each node in the scenario tree. |
---|
708 | |
---|
709 | if self._verbose is True: |
---|
710 | print "Creating variable statistic (min/avg/max) parameter vectors for scenario tree nodes" |
---|
711 | |
---|
712 | # do this for all stages, simply for completeness, i.e., to create a fully populated scenario tree. |
---|
713 | for stage in self._scenario_tree._stages: |
---|
714 | |
---|
715 | # first, gather all unique variables referenced in this stage |
---|
716 | # this "gather" step is currently required because we're being lazy |
---|
717 | # in terms of index management in the scenario tree - which |
---|
718 | # should really be done in terms of sets of indices. |
---|
719 | |
---|
720 | stage_variables = {} |
---|
721 | for (reference_variable, index_template) in stage._variables: |
---|
722 | if reference_variable.name not in stage_variables.keys(): |
---|
723 | stage_variables[reference_variable.name] = reference_variable |
---|
724 | |
---|
725 | # next, create min/avg/max parameters for each variable in the corresponding tree node. |
---|
726 | # NOTE: the parameter names below could really be empty, as they are never referenced |
---|
727 | # explicitly. |
---|
728 | for (variable_name, reference_variable) in stage_variables.items(): |
---|
729 | for tree_node in stage._tree_nodes: |
---|
730 | |
---|
731 | new_min_index = reference_variable._index |
---|
732 | new_min_parameter_name = "NODEMIN_"+reference_variable.name |
---|
733 | # this bit of ugliness is due to Pyomo not correctly handling the Param construction |
---|
734 | # case when the supplied index set consists strictly of None, i.e., the source variable |
---|
735 | # is a singleton. this case be cleaned up when the source issue in Pyomo is fixed. |
---|
736 | new_min_parameter = None |
---|
737 | if (len(new_min_index) is 1) and (None in new_min_index): |
---|
738 | new_min_parameter = Param(name=new_min_parameter_name, mutable=True, nochecking=True) |
---|
739 | else: |
---|
740 | new_min_parameter = Param(new_min_index, name=new_min_parameter_name, mutable=True, nochecking=True) |
---|
741 | for index in new_min_index: |
---|
742 | new_min_parameter[index] = 0.0 |
---|
743 | tree_node._minimums[reference_variable.name] = new_min_parameter |
---|
744 | |
---|
745 | new_avg_index = reference_variable._index |
---|
746 | new_avg_parameter_name = "NODEAVG_"+reference_variable.name |
---|
747 | new_avg_parameter = None |
---|
748 | if (len(new_avg_index) is 1) and (None in new_avg_index): |
---|
749 | new_avg_parameter = Param(name=new_avg_parameter_name, mutable=True, nochecking=True) |
---|
750 | else: |
---|
751 | new_avg_parameter = Param(new_avg_index, name=new_avg_parameter_name, mutable=True, nochecking=True) |
---|
752 | for index in new_avg_index: |
---|
753 | new_avg_parameter[index] = 0.0 |
---|
754 | tree_node._averages[reference_variable.name] = new_avg_parameter |
---|
755 | |
---|
756 | new_max_index = reference_variable._index |
---|
757 | new_max_parameter_name = "NODEMAX_"+reference_variable.name |
---|
758 | new_max_parameter = None |
---|
759 | if (len(new_max_index) is 1) and (None in new_max_index): |
---|
760 | new_max_parameter = Param(name=new_max_parameter_name, mutable=True, nochecking=True) |
---|
761 | else: |
---|
762 | new_max_parameter = Param(new_max_index, name=new_max_parameter_name, mutable=True, nochecking=True) |
---|
763 | for index in new_max_index: |
---|
764 | new_max_parameter[index] = 0.0 |
---|
765 | tree_node._maximums[reference_variable.name] = new_max_parameter |
---|
766 | |
---|
767 | # the objective functions are modified throughout the course of PH iterations. |
---|
768 | # save the original, as a baseline to modify in subsequent iterations. reserve |
---|
769 | # the original objectives, for subsequent modification. |
---|
770 | self._original_objective_expression = {} |
---|
771 | for instance_name, instance in self._instances.items(): |
---|
772 | objective_name = instance.active_components(Objective).keys()[0] |
---|
773 | expr = instance.active_components(Objective)[objective_name]._data[None].expr |
---|
774 | if isinstance(expr, Expression) is False: |
---|
775 | expr = _IdentityExpression(expr) |
---|
776 | self._original_objective_expression[instance_name] = expr |
---|
777 | |
---|
778 | # cache the number of discrete and continuous variables in the master instance. this value |
---|
779 | # is of general use, e.g., in the converger classes and in plugins. |
---|
780 | (self._total_discrete_vars,self._total_continuous_vars) = self.compute_variable_counts() |
---|
781 | if self._verbose is True: |
---|
782 | print "Total number of discrete instance variables="+str(self._total_discrete_vars) |
---|
783 | print "Total number of continuous instance variables="+str(self._total_continuous_vars) |
---|
784 | |
---|
785 | # track the total number of fixed variables of each category at the end of each PH iteration. |
---|
786 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
787 | |
---|
788 | # indicate that we're ready to run. |
---|
789 | self._initialized = True |
---|
790 | |
---|
791 | if self._verbose is True: |
---|
792 | print "PH successfully created model instances for all scenarios" |
---|
793 | |
---|
794 | self._init_end_time = time.time() |
---|
795 | |
---|
796 | if self._verbose is True: |
---|
797 | print "PH is successfully initialized" |
---|
798 | if self._output_times is True: |
---|
799 | print "Initialization time=%8.2f seconds" % (self._init_end_time - self._init_start_time) |
---|
800 | |
---|
801 | # let plugins know if they care. |
---|
802 | if self._verbose is True: |
---|
803 | print "Initializing PH plugins" |
---|
804 | for plugin in self._ph_plugins: |
---|
805 | plugin.post_ph_initialization(self) |
---|
806 | if self._verbose is True: |
---|
807 | print "PH plugin initialization complete" |
---|
808 | |
---|
809 | """ Perform the non-weighted scenario solves and form the initial w and xbars. |
---|
810 | """ |
---|
811 | def iteration_0_solve(self): |
---|
812 | |
---|
813 | if self._verbose is True: |
---|
814 | print "------------------------------------------------" |
---|
815 | print "Starting PH iteration 0 solves" |
---|
816 | |
---|
817 | self._current_iteration = 0 |
---|
818 | |
---|
819 | solve_start_time = time.time() |
---|
820 | |
---|
821 | # STEP 0: set up all global solver options. |
---|
822 | self._solver.mipgap = self._mipgap |
---|
823 | |
---|
824 | # STEP 1: queue up the solves for all scenario sub-problems and |
---|
825 | # grab all the action handles for the subsequent barrier sync. |
---|
826 | |
---|
827 | action_handles = [] |
---|
828 | scenario_action_handle_map = {} # maps scenario names to action handles |
---|
829 | action_handle_scenario_map = {} # maps action handles to scenario names |
---|
830 | |
---|
831 | for scenario in self._scenario_tree._scenarios: |
---|
832 | |
---|
833 | instance = self._instances[scenario._name] |
---|
834 | |
---|
835 | if self._verbose is True: |
---|
836 | print "Queuing solve for scenario=" + scenario._name |
---|
837 | |
---|
838 | # IMPT: You have to re-presolve if approximating continuous variable penalty terms with a |
---|
839 | # piecewise linear function. otherwise, the newly introduced variables won't be flagged |
---|
840 | # as unused (as is correct for iteration 0), and the output writer will crater. |
---|
841 | # IMPT: I decided to presolve unconditionally, as PH extensions can add arbitrary components |
---|
842 | # to the base scenario instances - and the variable values/etc. need to be collectged. |
---|
843 | instance.preprocess() |
---|
844 | |
---|
845 | # there's nothing to warm-start from in iteration 0, so don't include the keyword in the solve call. |
---|
846 | # the reason you don't want to include it is that some solvers don't know how to handle the keyword |
---|
847 | # at all (despite it being false). you might want to solve iteration 0 solves using some other solver. |
---|
848 | |
---|
849 | new_action_handle = self._solver_manager.queue(instance, opt=self._solver, tee=self._output_solver_log) |
---|
850 | scenario_action_handle_map[scenario._name] = new_action_handle |
---|
851 | action_handle_scenario_map[new_action_handle] = scenario._name |
---|
852 | |
---|
853 | action_handles.append(new_action_handle) |
---|
854 | |
---|
855 | # STEP 2: loop for the solver results, reading them and loading |
---|
856 | # them into instances as they are available. |
---|
857 | |
---|
858 | if self._verbose is True: |
---|
859 | print "Waiting for scenario sub-problem solves" |
---|
860 | |
---|
861 | num_results_so_far = 0 |
---|
862 | |
---|
863 | while (num_results_so_far < len(self._scenario_tree._scenarios)): |
---|
864 | |
---|
865 | action_handle = self._solver_manager.wait_any() |
---|
866 | results = self._solver_manager.get_results(action_handle) |
---|
867 | scenario_name = action_handle_scenario_map[action_handle] |
---|
868 | instance = self._instances[scenario_name] |
---|
869 | |
---|
870 | if self._verbose is True: |
---|
871 | print "Results obtained for scenario="+scenario_name |
---|
872 | |
---|
873 | if len(results.solution) == 0: |
---|
874 | results.write(num=1) |
---|
875 | raise RuntimeError, "Solve failed for scenario="+scenario_name+"; no solutions generated" |
---|
876 | |
---|
877 | if self._output_solver_results is True: |
---|
878 | print "Results for scenario=",scenario_name |
---|
879 | results.write(num=1) |
---|
880 | |
---|
881 | start_time = time.time() |
---|
882 | instance.load(results) |
---|
883 | end_time = time.time() |
---|
884 | if self._output_times is True: |
---|
885 | print "Time loading results into instance="+str(end_time-start_time)+" seconds" |
---|
886 | |
---|
887 | if self._verbose is True: |
---|
888 | print "Successfully loaded solution for scenario="+scenario_name |
---|
889 | |
---|
890 | num_results_so_far = num_results_so_far + 1 |
---|
891 | |
---|
892 | if self._verbose is True: |
---|
893 | print "Scenario sub-problem solves completed" |
---|
894 | |
---|
895 | solve_end_time = time.time() |
---|
896 | self._cumulative_solve_time += (solve_end_time - solve_start_time) |
---|
897 | |
---|
898 | if self._output_times is True: |
---|
899 | print "Aggregate sub-problem solve time this iteration=%8.2f" % (solve_end_time - solve_start_time) |
---|
900 | |
---|
901 | if self._verbose is True: |
---|
902 | print "Successfully completed PH iteration 0 solves - solution statistics:" |
---|
903 | print " Scenario Objective Value" |
---|
904 | for scenario in self._scenario_tree._scenarios: |
---|
905 | instance = self._instances[scenario._name] |
---|
906 | for objective_name in instance.active_components(Objective): |
---|
907 | objective = instance.active_components(Objective)[objective_name] |
---|
908 | print "%20s %15s %14.4f" % (scenario._name, objective.name, objective._data[None].expr()) |
---|
909 | print "------------------------------------------------" |
---|
910 | |
---|
911 | # |
---|
912 | # recompute the averages, minimum, and maximum statistics for all variables to be blended by PH, i.e., |
---|
913 | # not appearing in the final stage. technically speaking, the min/max aren't required by PH, but they |
---|
914 | # are used often enough to warrant their computation and it's basically free if you're computing the |
---|
915 | # average. |
---|
916 | # |
---|
917 | def update_variable_statistics(self): |
---|
918 | |
---|
919 | start_time = time.time() |
---|
920 | |
---|
921 | # NOTE: the following code has some optimizations that are not normally recommended, in particular |
---|
922 | # the direct access and manipulation of parameters via the .value attribute instead of the |
---|
923 | # user-level-preferred value() method. this is justifiable in this particular instance |
---|
924 | # because we are creating the PH parameters (and therefore can manipulate them safely), and |
---|
925 | # this routine takes a non-trivial amount of the overall run-time. |
---|
926 | |
---|
927 | # compute statistics over all stages, even the last. this is necessary in order to |
---|
928 | # successfully snapshot a scenario tree solution from the average values. |
---|
929 | for stage in self._scenario_tree._stages: |
---|
930 | |
---|
931 | for tree_node in stage._tree_nodes: |
---|
932 | |
---|
933 | for (variable, index_template) in stage._variables: |
---|
934 | |
---|
935 | variable_name = variable.name |
---|
936 | |
---|
937 | variable_indices = tree_node._variable_indices[variable_name] |
---|
938 | |
---|
939 | avg_parameter_name = "PHAVG_"+variable_name |
---|
940 | |
---|
941 | tree_node_var_mins = tree_node._minimums[variable_name] |
---|
942 | tree_node_var_avgs = tree_node._averages[variable_name] |
---|
943 | tree_node_var_maxs = tree_node._maximums[variable_name] |
---|
944 | |
---|
945 | scenario_variables = [] |
---|
946 | for scenario in tree_node._scenarios: |
---|
947 | instance = self._instances[scenario._name] |
---|
948 | scenario_variables.append(getattr(instance, variable_name)) |
---|
949 | |
---|
950 | for index in variable_indices: |
---|
951 | |
---|
952 | min = float("inf") |
---|
953 | avg = 0.0 |
---|
954 | max = float("-inf") |
---|
955 | node_probability = 0.0 |
---|
956 | |
---|
957 | is_used = True # until proven otherwise |
---|
958 | for scenario_variable in scenario_variables: |
---|
959 | |
---|
960 | if scenario_variable[index].status == VarStatus.unused: |
---|
961 | is_used = False |
---|
962 | else: |
---|
963 | node_probability += scenario._probability |
---|
964 | var_value = scenario_variable[index].value |
---|
965 | if var_value < min: |
---|
966 | min = var_value |
---|
967 | avg += (scenario._probability * var_value) |
---|
968 | if var_value > max: |
---|
969 | max = var_value |
---|
970 | |
---|
971 | if is_used is True: |
---|
972 | |
---|
973 | tree_node_var_mins[index].value = min |
---|
974 | tree_node_var_avgs[index].value = avg / node_probability |
---|
975 | tree_node_var_maxs[index].value = max |
---|
976 | |
---|
977 | # distribute the newly computed average to the xbar variable in |
---|
978 | # each instance/scenario associated with this node. only do this |
---|
979 | # if the variable is used! |
---|
980 | for scenario in tree_node._scenarios: |
---|
981 | instance = self._instances[scenario._name] |
---|
982 | try: |
---|
983 | avg_parameter = getattr(instance, avg_parameter_name) |
---|
984 | avg_parameter[index].value = avg / node_probability |
---|
985 | except: |
---|
986 | pass |
---|
987 | |
---|
988 | end_time = time.time() |
---|
989 | self._cumulative_xbar_time += (end_time - start_time) |
---|
990 | |
---|
991 | def update_weights(self): |
---|
992 | |
---|
993 | # because the weight updates rely on the xbars, and the xbars are node-based, |
---|
994 | # I'm looping over the tree nodes and pushing weights into the corresponding scenarios. |
---|
995 | start_time = time.time() |
---|
996 | |
---|
997 | # NOTE: the following code has some optimizations that are not normally recommended, in particular |
---|
998 | # the direct access and manipulation of parameters via the .value attribute instead of the |
---|
999 | # user-level-preferred value() method. this is justifiable in this particular instance |
---|
1000 | # because we are creating the PH parameters (and therefore can manipulate them safely), and |
---|
1001 | # this routine takes a non-trivial amount of the overall run-time. |
---|
1002 | |
---|
1003 | for stage in self._scenario_tree._stages[:-1]: # no blending over the final stage, so no weights to worry about. |
---|
1004 | |
---|
1005 | for tree_node in stage._tree_nodes: |
---|
1006 | |
---|
1007 | for (variable, index_template) in stage._variables: |
---|
1008 | |
---|
1009 | variable_name = variable.name |
---|
1010 | blend_parameter_name = "PHBLEND_"+variable_name |
---|
1011 | weight_parameter_name = "PHWEIGHT_"+variable_name |
---|
1012 | rho_parameter_name = "PHRHO_"+variable_name |
---|
1013 | |
---|
1014 | variable_indices = tree_node._variable_indices[variable_name] |
---|
1015 | |
---|
1016 | for index in variable_indices: |
---|
1017 | |
---|
1018 | tree_node_average = tree_node._averages[variable_name][index].value |
---|
1019 | |
---|
1020 | for scenario in tree_node._scenarios: |
---|
1021 | |
---|
1022 | instance = self._instances[scenario._name] |
---|
1023 | |
---|
1024 | if getattr(instance,variable_name)[index].status != VarStatus.unused: |
---|
1025 | |
---|
1026 | weight_parameter = getattr(instance, weight_parameter_name) |
---|
1027 | |
---|
1028 | # we are currently not updating weights if blending is disabled for a variable. |
---|
1029 | # this is done on the premise that unless you are actively trying to move |
---|
1030 | # the variable toward the mean, the weights will blow up and be huge by the |
---|
1031 | # time that blending is activated. |
---|
1032 | variable_blend_indicator = getattr(instance, blend_parameter_name)[index].value |
---|
1033 | |
---|
1034 | # get the weight and rho parameters for this variable/index combination. |
---|
1035 | rho_value = getattr(instance, rho_parameter_name)[index].value |
---|
1036 | current_variable_weight = weight_parameter[index].value |
---|
1037 | |
---|
1038 | # if I'm maximizing, invert value prior to adding (hack to implement negatives). |
---|
1039 | # probably fixed in Pyomo at this point - I just haven't checked in a long while. |
---|
1040 | if self._is_minimizing is False: |
---|
1041 | current_variable_weight = (-current_variable_weight) |
---|
1042 | current_variable_value = getattr(instance, variable_name)[index]() |
---|
1043 | new_variable_weight = current_variable_weight + variable_blend_indicator * rho_value * (current_variable_value - tree_node_average) |
---|
1044 | # I have the correct updated value, so now invert if maximizing. |
---|
1045 | if self._is_minimizing is False: |
---|
1046 | new_variable_weight = (-new_variable_weight) |
---|
1047 | weight_parameter[index].value = new_variable_weight |
---|
1048 | |
---|
1049 | # we shouldn't have to re-simplify the expression, as we aren't adding any constant-variable terms - just modifying parameters. |
---|
1050 | |
---|
1051 | end_time = time.time() |
---|
1052 | self._cumulative_weight_time += (end_time - start_time) |
---|
1053 | |
---|
1054 | def form_iteration_k_objectives(self): |
---|
1055 | |
---|
1056 | for instance_name, instance in self._instances.items(): |
---|
1057 | |
---|
1058 | new_attrs = form_ph_objective(instance_name, \ |
---|
1059 | instance, \ |
---|
1060 | self._original_objective_expression[instance_name], \ |
---|
1061 | self._scenario_tree, \ |
---|
1062 | self._linearize_nonbinary_penalty_terms, \ |
---|
1063 | self._drop_proximal_terms, \ |
---|
1064 | self._retain_quadratic_binary_terms, \ |
---|
1065 | self._breakpoint_strategy, \ |
---|
1066 | self._integer_tolerance, \ |
---|
1067 | self._simplify_expressions) |
---|
1068 | self._instance_augmented_attributes[instance_name].extend(new_attrs) |
---|
1069 | |
---|
1070 | def iteration_k_solve(self): |
---|
1071 | |
---|
1072 | if self._verbose is True: |
---|
1073 | print "------------------------------------------------" |
---|
1074 | print "Starting PH iteration " + str(self._current_iteration) + " solves" |
---|
1075 | |
---|
1076 | # cache the objective values generated by PH for output at the end of this function. |
---|
1077 | ph_objective_values = {} |
---|
1078 | |
---|
1079 | solve_start_time = time.time() |
---|
1080 | |
---|
1081 | # STEP -1: if using a PH solver manager, propagate current weights/averages to the appropriate solver servers. |
---|
1082 | # ditto the tree node statistics, which are necessary if linearizing (so an optimization could be |
---|
1083 | # performed here). |
---|
1084 | # NOTE: We aren't currently propagating rhos, as they generally don't change - we need to |
---|
1085 | # have a flag, though, indicating whether the rhos have changed, so they can be |
---|
1086 | # transmitted if needed. |
---|
1087 | if self._solver_manager_type == "ph": |
---|
1088 | self._transmit_weights_and_averages() |
---|
1089 | self._transmit_tree_node_statistics() |
---|
1090 | |
---|
1091 | # STEP 0: set up all global solver options. |
---|
1092 | self._solver.mipgap = self._mipgap |
---|
1093 | |
---|
1094 | # STEP 1: queue up the solves for all scenario sub-problems and |
---|
1095 | # grab all of the action handles for the subsequent barrier sync. |
---|
1096 | |
---|
1097 | action_handles = [] |
---|
1098 | scenario_action_handle_map = {} # maps scenario names to action handles |
---|
1099 | action_handle_scenario_map = {} # maps action handles to scenario names |
---|
1100 | |
---|
1101 | for scenario in self._scenario_tree._scenarios: |
---|
1102 | |
---|
1103 | instance = self._instances[scenario._name] |
---|
1104 | |
---|
1105 | if self._verbose is True: |
---|
1106 | print "Queuing solve for scenario=" + scenario._name |
---|
1107 | |
---|
1108 | # IMPT: You have to re-presolve, as the simple presolver collects the linear terms together. If you |
---|
1109 | # don't do this, you won't see any chance in the output files as you vary the problem parameters! |
---|
1110 | # ditto for instance fixing! |
---|
1111 | instance.preprocess() |
---|
1112 | |
---|
1113 | # once past iteration 0, there is always a feasible solution from which to warm-start. |
---|
1114 | # however, you might want to disable warm-start when the solver is behaving badly (which does happen). |
---|
1115 | new_action_handle = None |
---|
1116 | if (self._disable_warmstarts is False) and (self._solver.warm_start_capable() is True): |
---|
1117 | new_action_handle = self._solver_manager.queue(instance, opt=self._solver, warmstart=True, tee=self._output_solver_log) |
---|
1118 | else: |
---|
1119 | new_action_handle = self._solver_manager.queue(instance, opt=self._solver, tee=self._output_solver_log) |
---|
1120 | |
---|
1121 | scenario_action_handle_map[scenario._name] = new_action_handle |
---|
1122 | action_handle_scenario_map[new_action_handle] = scenario._name |
---|
1123 | |
---|
1124 | action_handles.append(new_action_handle) |
---|
1125 | |
---|
1126 | # STEP 2: loop for the solver results, reading them and loading |
---|
1127 | # them into instances as they are available. |
---|
1128 | if self._verbose is True: |
---|
1129 | print "Waiting for scenario sub-problem solves" |
---|
1130 | |
---|
1131 | num_results_so_far = 0 |
---|
1132 | |
---|
1133 | while (num_results_so_far < len(self._scenario_tree._scenarios)): |
---|
1134 | |
---|
1135 | action_handle = self._solver_manager.wait_any() |
---|
1136 | results = self._solver_manager.get_results(action_handle) |
---|
1137 | scenario_name = action_handle_scenario_map[action_handle] |
---|
1138 | instance = self._instances[scenario_name] |
---|
1139 | |
---|
1140 | if self._verbose is True: |
---|
1141 | print "Results obtained for scenario="+scenario_name |
---|
1142 | |
---|
1143 | if len(results.solution) == 0: |
---|
1144 | results.write(num=1) |
---|
1145 | raise RuntimeError, "Solve failed for scenario="+scenario_name+"; no solutions generated" |
---|
1146 | |
---|
1147 | if self._output_solver_results is True: |
---|
1148 | print "Results for scenario=",scenario_name |
---|
1149 | results.write(num=1) |
---|
1150 | |
---|
1151 | start_time = time.time() |
---|
1152 | instance.load(results) |
---|
1153 | end_time = time.time() |
---|
1154 | if self._output_times is True: |
---|
1155 | print "Time loading results into instance="+str(end_time-start_time)+" seconds" |
---|
1156 | |
---|
1157 | if self._verbose is True: |
---|
1158 | print "Successfully loaded solution for scenario="+scenario_name |
---|
1159 | |
---|
1160 | # we're assuming there is a single solution. |
---|
1161 | # the "value" attribute is a pre-defined feature of any solution - it is relative to whatever |
---|
1162 | # objective was selected during optimization, which of course should be the PH objective. |
---|
1163 | try: |
---|
1164 | ph_objective_values[instance.name] = float(results.solution(0).objective['f'].value) |
---|
1165 | except AttributeError: |
---|
1166 | # some solvers (e.g., through the SOL interface) don't report objective function values. |
---|
1167 | ph_objective_values[instance.name] = 0.0 |
---|
1168 | |
---|
1169 | num_results_so_far = num_results_so_far + 1 |
---|
1170 | |
---|
1171 | if self._verbose is True: |
---|
1172 | print "Scenario sub-problem solves completed" |
---|
1173 | |
---|
1174 | solve_end_time = time.time() |
---|
1175 | self._cumulative_solve_time += (solve_end_time - solve_start_time) |
---|
1176 | |
---|
1177 | if self._output_times is True: |
---|
1178 | print "Aggregate sub-problem solve time this iteration=%8.2f" % (solve_end_time - solve_start_time) |
---|
1179 | |
---|
1180 | if self._verbose is True: |
---|
1181 | print "Successfully completed PH iteration " + str(self._current_iteration) + " solves - solution statistics:" |
---|
1182 | print " Scenario PH Objective Cost Objective" |
---|
1183 | for scenario in self._scenario_tree._scenarios: |
---|
1184 | instance = self._instances[scenario._name] |
---|
1185 | for objective_name in instance.active_components(Objective): |
---|
1186 | objective = instance.active_components(Objective)[objective_name] |
---|
1187 | print "%20s %18.4f %14.4f" % (scenario._name, ph_objective_values[scenario._name], self._scenario_tree.compute_scenario_cost(instance)) |
---|
1188 | |
---|
1189 | def solve(self): |
---|
1190 | |
---|
1191 | self._solve_start_time = time.time() |
---|
1192 | self._cumulative_solve_time = 0.0 |
---|
1193 | self._cumulative_xbar_time = 0.0 |
---|
1194 | self._cumulative_weight_time = 0.0 |
---|
1195 | |
---|
1196 | print "Starting PH" |
---|
1197 | |
---|
1198 | if self._initialized == False: |
---|
1199 | raise RuntimeError, "PH is not initialized - cannot invoke solve() method" |
---|
1200 | |
---|
1201 | # garbage collection noticeably slows down PH when dealing with |
---|
1202 | # large numbers of scenarios. fortunately, there are well-defined |
---|
1203 | # points at which garbage collection makes sense (and there isn't a |
---|
1204 | # lot of collection to do). namely, after each PH iteration. |
---|
1205 | re_enable_gc = gc.isenabled() |
---|
1206 | gc.disable() |
---|
1207 | |
---|
1208 | print "Initiating PH iteration=" + `self._current_iteration` |
---|
1209 | |
---|
1210 | self.iteration_0_solve() |
---|
1211 | |
---|
1212 | # update variable statistics prior to any output. |
---|
1213 | self.update_variable_statistics() |
---|
1214 | |
---|
1215 | if (self._verbose is True) or (self._report_solutions is True): |
---|
1216 | print "Variable values following scenario solves:" |
---|
1217 | self.pprint(False, False, True, False, output_only_statistics=self._report_only_statistics) |
---|
1218 | |
---|
1219 | # let plugins know if they care. |
---|
1220 | for plugin in self._ph_plugins: |
---|
1221 | plugin.post_iteration_0_solves(self) |
---|
1222 | |
---|
1223 | # update the fixed variable statistics. |
---|
1224 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
1225 | |
---|
1226 | if self._verbose is True: |
---|
1227 | print "Number of discrete variables fixed="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1228 | print "Number of continuous variables fixed="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1229 | |
---|
1230 | # always output the convergence metric and first-stage cost statistics, to give a sense of progress. |
---|
1231 | self._converger.update(self._current_iteration, self, self._scenario_tree, self._instances) |
---|
1232 | first_stage_min, first_stage_avg, first_stage_max = self._extract_first_stage_cost_statistics() |
---|
1233 | 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) |
---|
1234 | |
---|
1235 | self.update_weights() |
---|
1236 | |
---|
1237 | # let plugins know if they care. |
---|
1238 | for plugin in self._ph_plugins: |
---|
1239 | plugin.post_iteration_0(self) |
---|
1240 | |
---|
1241 | # if using a PH solver server, trasnsmit the rhos prior to the iteration |
---|
1242 | # k solve sequence. for now, we are assuming that the rhos don't change |
---|
1243 | # on a per-iteration basis, but that assumption can be easily relaxed. |
---|
1244 | # it is important to do this after the plugins have a chance to do their |
---|
1245 | # computation. |
---|
1246 | if self._solver_manager_type == "ph": |
---|
1247 | self._transmit_rhos() |
---|
1248 | self._enable_ph_objectives() |
---|
1249 | |
---|
1250 | # checkpoint if it's time - which it always is after iteration 0, |
---|
1251 | # if the interval is >= 1! |
---|
1252 | if (self._checkpoint_interval > 0): |
---|
1253 | self.checkpoint(0) |
---|
1254 | |
---|
1255 | # garbage-collect if it wasn't disabled entirely. |
---|
1256 | if re_enable_gc is True: |
---|
1257 | gc.collect() |
---|
1258 | |
---|
1259 | # there is an upper bound on the number of iterations to execute - |
---|
1260 | # the actual bound depends on the converger supplied by the user. |
---|
1261 | for i in range(1, self._max_iterations+1): |
---|
1262 | |
---|
1263 | self._current_iteration = self._current_iteration + 1 |
---|
1264 | |
---|
1265 | print "Initiating PH iteration=" + `self._current_iteration` |
---|
1266 | |
---|
1267 | if (self._verbose is True) or (self._report_weights is True): |
---|
1268 | print "Variable averages and weights prior to scenario solves:" |
---|
1269 | self.pprint(True, True, False, False, output_only_statistics=self._report_only_statistics) |
---|
1270 | |
---|
1271 | # with the introduction of piecewise linearization, the form of the |
---|
1272 | # penalty-weighted objective is no longer fixed. thus, we need to |
---|
1273 | # create the objectives each PH iteration. we could be fancy in |
---|
1274 | # the case of quadratic objectives (eliminating redundancy), but |
---|
1275 | # if we decide to do this, see the "IMPT" note below. |
---|
1276 | self.form_iteration_k_objectives() |
---|
1277 | |
---|
1278 | # IMPT: for efficiency purposes, the PH objective formation calls above create expression trees by |
---|
1279 | # evaluating the parameters involved in either quadratic or linear expressions directly, e.g., |
---|
1280 | # via value(). as a consequence, if the user modifies PH weights or rhos in the plugin |
---|
1281 | # callback below, it will *not* be propagated to the objective expression. |
---|
1282 | |
---|
1283 | # let plugins know if they care. |
---|
1284 | for plugin in self._ph_plugins: |
---|
1285 | plugin.pre_iteration_k_solves(self) |
---|
1286 | |
---|
1287 | # do the actual solves. |
---|
1288 | self.iteration_k_solve() |
---|
1289 | |
---|
1290 | # update variable statistics prior to any output. |
---|
1291 | self.update_variable_statistics() |
---|
1292 | |
---|
1293 | if (self._verbose is True) or (self._report_solutions is True): |
---|
1294 | print "Variable values following scenario solves:" |
---|
1295 | self.pprint(False, False, True, False, output_only_statistics=self._report_only_statistics) |
---|
1296 | |
---|
1297 | # we don't technically have to do this at the last iteration, |
---|
1298 | # but with checkpointing and re-starts, you're never sure |
---|
1299 | # when you're executing the last iteration. |
---|
1300 | self.update_weights() |
---|
1301 | |
---|
1302 | # let plugins know if they care. |
---|
1303 | for plugin in self._ph_plugins: |
---|
1304 | plugin.post_iteration_k_solves(self) |
---|
1305 | |
---|
1306 | # update the fixed variable statistics. |
---|
1307 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
1308 | |
---|
1309 | if self._verbose is True: |
---|
1310 | print "Number of discrete variables fixed="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1311 | print "Number of continuous variables fixed="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1312 | |
---|
1313 | # let plugins know if they care. |
---|
1314 | for plugin in self._ph_plugins: |
---|
1315 | plugin.post_iteration_k(self) |
---|
1316 | |
---|
1317 | # at this point, all the real work of an iteration is complete. |
---|
1318 | |
---|
1319 | # checkpoint if it's time. |
---|
1320 | if (self._checkpoint_interval > 0) and (i % self._checkpoint_interval is 0): |
---|
1321 | self.checkpoint(i) |
---|
1322 | |
---|
1323 | # check for early termination. |
---|
1324 | self._converger.update(self._current_iteration, self, self._scenario_tree, self._instances) |
---|
1325 | first_stage_min, first_stage_avg, first_stage_max = self._extract_first_stage_cost_statistics() |
---|
1326 | 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) |
---|
1327 | |
---|
1328 | if self._converger.isConverged(self) is True: |
---|
1329 | if self._total_discrete_vars == 0: |
---|
1330 | print "PH converged - convergence metric is below threshold="+str(self._converger._convergence_threshold) |
---|
1331 | else: |
---|
1332 | print "PH converged - convergence metric is below threshold="+str(self._converger._convergence_threshold)+" or all discrete variables are fixed" |
---|
1333 | break |
---|
1334 | |
---|
1335 | # if we're terminating due to exceeding the maximum iteration count, print a message |
---|
1336 | # indicating so - otherwise, you get a quiet, information-free output trace. |
---|
1337 | if i == self._max_iterations: |
---|
1338 | print "Halting PH - reached maximal iteration count="+str(self._max_iterations) |
---|
1339 | |
---|
1340 | # garbage-collect if it wasn't disabled entirely. |
---|
1341 | if re_enable_gc is True: |
---|
1342 | gc.collect() |
---|
1343 | |
---|
1344 | # re-enable the normal garbage collection mode. |
---|
1345 | if re_enable_gc is True: |
---|
1346 | gc.enable() |
---|
1347 | |
---|
1348 | if self._verbose is True: |
---|
1349 | print "Number of discrete variables fixed before final plugin calls="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1350 | print "Number of continuous variables fixed before final plugin calls="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1351 | |
---|
1352 | # let plugins know if they care. do this before |
---|
1353 | # the final solution / statistics output, as the plugins |
---|
1354 | # might do some final tweaking. |
---|
1355 | for plugin in self._ph_plugins: |
---|
1356 | plugin.post_ph_execution(self) |
---|
1357 | |
---|
1358 | # update the fixed variable statistics - the plugins might have done something. |
---|
1359 | (self._total_fixed_discrete_vars,self._total_fixed_continuous_vars) = self.compute_fixed_variable_counts() |
---|
1360 | |
---|
1361 | self._solve_end_time = time.time() |
---|
1362 | |
---|
1363 | print "PH complete" |
---|
1364 | |
---|
1365 | print "Convergence history:" |
---|
1366 | self._converger.pprint() |
---|
1367 | |
---|
1368 | print "Final number of discrete variables fixed="+str(self._total_fixed_discrete_vars)+" (total="+str(self._total_discrete_vars)+")" |
---|
1369 | print "Final number of continuous variables fixed="+str(self._total_fixed_continuous_vars)+" (total="+str(self._total_continuous_vars)+")" |
---|
1370 | |
---|
1371 | # populate the scenario tree solution from the instances - to ensure consistent state |
---|
1372 | # across the scenario tree instance and the scenario instances. |
---|
1373 | self._scenario_tree.snapshotSolutionFromInstances(self._instances) |
---|
1374 | |
---|
1375 | print "Final variable values:" |
---|
1376 | self.pprint(False, False, True, True, output_only_statistics=self._report_only_statistics) |
---|
1377 | |
---|
1378 | print "Final costs:" |
---|
1379 | self._scenario_tree.pprintCosts(self._instances) |
---|
1380 | |
---|
1381 | if self._output_scenario_tree_solution is True: |
---|
1382 | self._scenario_tree.snapshotSolutionFromAverages() |
---|
1383 | print "Final solution (scenario tree format):" |
---|
1384 | self._scenario_tree.pprintSolution() |
---|
1385 | |
---|
1386 | if (self._verbose is True) and (self._output_times is True): |
---|
1387 | print "Overall run-time= %8.2f seconds" % (self._solve_end_time - self._solve_start_time) |
---|
1388 | |
---|
1389 | # cleanup the scenario instances for post-processing - ideally, we want to leave them in |
---|
1390 | # their original state, minus all the PH-specific stuff. we don't do all cleanup (leaving |
---|
1391 | # things like rhos, etc), but we do clean up constraints, as that really hoses up the ef writer. |
---|
1392 | self._cleanup_scenario_instances() |
---|
1393 | |
---|
1394 | # |
---|
1395 | # prints a summary of all collected time statistics |
---|
1396 | # |
---|
1397 | def print_time_stats(self): |
---|
1398 | |
---|
1399 | print "PH run-time statistics (user):" |
---|
1400 | |
---|
1401 | print "Initialization time= %8.2f seconds" % (self._init_end_time - self._init_start_time) |
---|
1402 | print "Overall solve time= %8.2f seconds" % (self._solve_end_time - self._solve_start_time) |
---|
1403 | print "Scenario solve time= %8.2f seconds" % self._cumulative_solve_time |
---|
1404 | print "Average update time= %8.2f seconds" % self._cumulative_xbar_time |
---|
1405 | print "Weight update time= %8.2f seconds" % self._cumulative_weight_time |
---|
1406 | |
---|
1407 | # |
---|
1408 | # a utility to determine whether to output weight / average / etc. information for |
---|
1409 | # a variable/node combination. when the printing is moved into a callback/plugin, |
---|
1410 | # this routine will go there. for now, we don't dive down into the node resolution - |
---|
1411 | # just the variable/stage. |
---|
1412 | # |
---|
1413 | def should_print(self, stage, variable): |
---|
1414 | |
---|
1415 | if self._output_continuous_variable_stats is False: |
---|
1416 | |
---|
1417 | variable_type = variable.domain |
---|
1418 | |
---|
1419 | if (isinstance(variable_type, IntegerSet) is False) and (isinstance(variable_type, BooleanSet) is False): |
---|
1420 | |
---|
1421 | return False |
---|
1422 | |
---|
1423 | return True |
---|
1424 | |
---|
1425 | # |
---|
1426 | # pretty-prints the state of the current variable averages, weights, and values. |
---|
1427 | # inputs are booleans indicating which components should be output. |
---|
1428 | # |
---|
1429 | def pprint(self, output_averages, output_weights, output_values, output_fixed, output_only_statistics=False): |
---|
1430 | |
---|
1431 | if self._initialized is False: |
---|
1432 | raise RuntimeError, "PH is not initialized - cannot invoke pprint() method" |
---|
1433 | |
---|
1434 | # print tree nodes and associated variable/xbar/ph information in stage-order |
---|
1435 | # we don't blend in the last stage, so we don't current care about printing the associated information. |
---|
1436 | for stage in self._scenario_tree._stages[:-1]: |
---|
1437 | |
---|
1438 | print "\tStage=" + str(stage._name) |
---|
1439 | |
---|
1440 | num_outputs_this_stage = 0 # tracks the number of outputs on a per-index basis. |
---|
1441 | |
---|
1442 | for (variable, index_template) in stage._variables: |
---|
1443 | |
---|
1444 | variable_name = variable.name |
---|
1445 | |
---|
1446 | if self.should_print(stage, variable) is True: |
---|
1447 | |
---|
1448 | num_outputs_this_variable = 0 # track, so we don't output the variable names unless there is an entry to report. |
---|
1449 | |
---|
1450 | for tree_node in stage._tree_nodes: |
---|
1451 | |
---|
1452 | variable_indices = tree_node._variable_indices[variable_name] |
---|
1453 | |
---|
1454 | for index in variable_indices: |
---|
1455 | |
---|
1456 | weight_parameter_name = "PHWEIGHT_"+variable_name |
---|
1457 | |
---|
1458 | num_outputs_this_index = 0 # track, so we don't output the variable index more than once. |
---|
1459 | |
---|
1460 | # determine if the variable/index pair is used across the set of scenarios (technically, |
---|
1461 | # it should be good enough to check one scenario). ditto for "fixed" status. fixed does |
---|
1462 | # imply unused (see note below), but we care about the fixed status when outputting |
---|
1463 | # final solutions. |
---|
1464 | |
---|
1465 | is_used = True # should be consistent across scenarios, so one "unused" flags as invalid. |
---|
1466 | is_fixed = False |
---|
1467 | |
---|
1468 | for scenario in tree_node._scenarios: |
---|
1469 | instance = self._instances[scenario._name] |
---|
1470 | variable_value = getattr(instance,variable_name)[index] |
---|
1471 | if variable_value.status == VarStatus.unused: |
---|
1472 | is_used = False |
---|
1473 | if variable_value.fixed is True: |
---|
1474 | is_fixed = True |
---|
1475 | |
---|
1476 | # IMPT: this is far from obvious, but variables that are fixed will - because |
---|
1477 | # presolve will identify them as constants and eliminate them from all |
---|
1478 | # expressions - be flagged as "unused" and therefore not output. |
---|
1479 | |
---|
1480 | if ((output_fixed is True) and (is_fixed is True)) or (is_used is True): |
---|
1481 | |
---|
1482 | minimum_value = None |
---|
1483 | maximum_value = None |
---|
1484 | |
---|
1485 | if index is None: |
---|
1486 | minimum_value = tree_node._minimums[variable_name] |
---|
1487 | maximum_value = tree_node._maximums[variable_name] |
---|
1488 | else: |
---|
1489 | minimum_value = value(tree_node._minimums[variable_name][index]) |
---|
1490 | maximum_value = value(tree_node._maximums[variable_name][index]) |
---|
1491 | |
---|
1492 | # there really isn't a need to output variables whose |
---|
1493 | # values are equal to 0 across-the-board. and there is |
---|
1494 | # good reason not to, i.e., the volume of output. |
---|
1495 | if (fabs(minimum_value) > self._integer_tolerance) or \ |
---|
1496 | (fabs(maximum_value) > self._integer_tolerance): |
---|
1497 | |
---|
1498 | num_outputs_this_stage = num_outputs_this_stage + 1 |
---|
1499 | num_outputs_this_variable = num_outputs_this_variable + 1 |
---|
1500 | num_outputs_this_index = num_outputs_this_index + 1 |
---|
1501 | |
---|
1502 | if num_outputs_this_variable == 1: |
---|
1503 | print "\t\tVariable=" + variable_name |
---|
1504 | |
---|
1505 | if num_outputs_this_index == 1: |
---|
1506 | if index is not None: |
---|
1507 | print "\t\t\tIndex:", indexToString(index), |
---|
1508 | |
---|
1509 | if len(stage._tree_nodes) > 1: |
---|
1510 | print "" |
---|
1511 | print "\t\t\t\tTree Node="+tree_node._name, |
---|
1512 | if output_only_statistics is False: |
---|
1513 | print "\t\t (Scenarios: ", |
---|
1514 | for scenario in tree_node._scenarios: |
---|
1515 | print scenario._name," ", |
---|
1516 | if scenario == tree_node._scenarios[-1]: |
---|
1517 | print ")" |
---|
1518 | |
---|
1519 | if output_values is True: |
---|
1520 | average_value = value(tree_node._averages[variable_name][index]) |
---|
1521 | if output_only_statistics is False: |
---|
1522 | print "\t\t\t\tValues: ", |
---|
1523 | for scenario in tree_node._scenarios: |
---|
1524 | instance = self._instances[scenario._name] |
---|
1525 | this_value = getattr(instance,variable_name)[index].value |
---|
1526 | if output_only_statistics is False: |
---|
1527 | print "%12.4f" % this_value, |
---|
1528 | if scenario == tree_node._scenarios[-1]: |
---|
1529 | if output_only_statistics is True: |
---|
1530 | # there technically isn't any good reason not to always report |
---|
1531 | # the min and max; the only reason we're not doing this currently |
---|
1532 | # is to avoid updating our regression test baseline output. |
---|
1533 | print " Min=%12.4f" % (minimum_value), |
---|
1534 | print " Avg=%12.4f" % (average_value), |
---|
1535 | print " Max=%12.4f" % (maximum_value), |
---|
1536 | else: |
---|
1537 | print " Max-Min=%12.4f" % (maximum_value-minimum_value), |
---|
1538 | print " Avg=%12.4f" % (average_value), |
---|
1539 | print "" |
---|
1540 | if output_weights: |
---|
1541 | print "\t\t\t\tWeights: ", |
---|
1542 | for scenario in tree_node._scenarios: |
---|
1543 | instance = self._instances[scenario._name] |
---|
1544 | print "%12.4f" % value(getattr(instance,weight_parameter_name)[index]), |
---|
1545 | if scenario == tree_node._scenarios[-1]: |
---|
1546 | print "" |
---|
1547 | |
---|
1548 | if output_averages: |
---|
1549 | print "\t\t\t\tAverage: %12.4f" % (tree_node._averages[variable_name][index].value) |
---|
1550 | |
---|
1551 | if num_outputs_this_stage == 0: |
---|
1552 | print "\t\tNo non-converged variables in stage" |
---|
1553 | |
---|
1554 | # cost variables aren't blended, so go through the gory computation of min/max/avg. |
---|
1555 | # we currently always print these. |
---|
1556 | cost_variable_name = stage._cost_variable[0].name |
---|
1557 | cost_variable_index = stage._cost_variable[1] |
---|
1558 | if cost_variable_index is None: |
---|
1559 | print "\t\tCost Variable=" + cost_variable_name |
---|
1560 | else: |
---|
1561 | print "\t\tCost Variable=" + cost_variable_name + indexToString(cost_variable_index) |
---|
1562 | for tree_node in stage._tree_nodes: |
---|
1563 | print "\t\t\tTree Node=" + tree_node._name, |
---|
1564 | if output_only_statistics is False: |
---|
1565 | print "\t\t (Scenarios: ", |
---|
1566 | for scenario in tree_node._scenarios: |
---|
1567 | print scenario._name," ", |
---|
1568 | if scenario == tree_node._scenarios[-1]: |
---|
1569 | print ")" |
---|
1570 | maximum_value = 0.0 |
---|
1571 | minimum_value = 0.0 |
---|
1572 | sum_values = 0.0 |
---|
1573 | num_values = 0 |
---|
1574 | first_time = True |
---|
1575 | if output_only_statistics is False: |
---|
1576 | print "\t\t\tValues: ", |
---|
1577 | else: |
---|
1578 | print "\t\t\t", |
---|
1579 | for scenario in tree_node._scenarios: |
---|
1580 | instance = self._instances[scenario._name] |
---|
1581 | this_value = getattr(instance,cost_variable_name)[cost_variable_index].value |
---|
1582 | if output_only_statistics is False: |
---|
1583 | if this_value is not None: |
---|
1584 | print "%12.4f" % this_value, |
---|
1585 | else: |
---|
1586 | # this is a hack, in case the stage cost variables are not returned. ipopt |
---|
1587 | # does this occasionally, for example, if stage cost variables are constrained |
---|
1588 | # to a constant value (and consequently preprocessed out). |
---|
1589 | print "%12s" % "Not Rprted", |
---|
1590 | if this_value is not None: |
---|
1591 | num_values += 1 |
---|
1592 | sum_values += this_value |
---|
1593 | if first_time is True: |
---|
1594 | first_time = False |
---|
1595 | maximum_value = this_value |
---|
1596 | minimum_value = this_value |
---|
1597 | else: |
---|
1598 | if this_value > maximum_value: |
---|
1599 | maximum_value = this_value |
---|
1600 | if this_value < minimum_value: |
---|
1601 | minimum_value = this_value |
---|
1602 | if scenario == tree_node._scenarios[-1]: |
---|
1603 | if num_values > 0: |
---|
1604 | if output_only_statistics is True: |
---|
1605 | print " Min=%12.4f" % (minimum_value), |
---|
1606 | print " Avg=%12.4f" % (sum_values/num_values), |
---|
1607 | print " Max=%12.4f" % (maximum_value), |
---|
1608 | else: |
---|
1609 | print " Max-Min=%12.4f" % (maximum_value-minimum_value), |
---|
1610 | print " Avg=%12.4f" % (sum_values/num_values), |
---|
1611 | print "" |
---|