def computeMetric(self, ph, scenario_tree, instances):
normalized_term_diff = 0.0
for stage, tree_node, variable_id, variable_values, is_fixed, is_stale \
in scenario_tree_node_variables_generator_noinstances(
scenario_tree,
includeDerivedVariables=False,
includeLastStage=False):
average_value = tree_node._averages[variable_id]
# should think about nixing the magic constant below (not
# sure how to best pararamterize it).
if ((not is_stale) or (is_fixed)) and \
(fabs(average_value) > 0.0001):
for var_value, scenario_probability in variable_values:
normalized_term_diff += \
scenario_probability * \
fabs((var_value - average_value)/average_value)
normalized_term_diff = \
normalized_term_diff / \
(ph._total_discrete_vars + ph._total_continuous_vars)
return normalized_term_diff
def _w_printing(self, ofile, ph=None):
# print the w values in a useful way to the open file ofile
# if ph is None, just write the header
if ph is None:
ofile.write("iteration; tree node; scenario; variable; ID; W\n")
else:
root_node_name = ph._scenario_tree.findRootNode()._name
for stage, tree_node, variable_id, variable_values, is_fixed, is_stale in \
scenario_tree_node_variables_generator_noinstances(ph._scenario_tree,
includeDerivedVariables=False,
includeLastStage=False):
if is_stale is False \
and (OnlyRootNode is False or tree_node._name == root_node_name):
for scenario in tree_node._scenarios:
scen_name = scenario._name
weight_value = scenario._w[
tree_node._name][variable_id]
variable_name, index = tree_node._variable_ids[
variable_id]
full_variable_name = variable_name + indexToString(
index)
ofile.write(
str(ph._current_iteration) + ';' +
tree_node._name + ';' + scen_name + ';' +
full_variable_name + ';' + str(variable_id) + ';' +
str(weight_value) + '\n')
def _inspect_variable_convergence(self, ph, ph_iter):
# collect termdiff by node and variable so we can
# report and possibly sort
term_diff = dict((tree_node._name, {}) \
for stage in ph._scenario_tree._stages[:-1]
for tree_node in stage._tree_nodes)
# Track these for reporting purposes
node_fixed_cnt = dict((tree_node._name, 0) \
for stage in ph._scenario_tree._stages[:-1]
for tree_node in stage._tree_nodes)
total_fixed_cnt = 0
for stage, tree_node, variable_id, variable_values, is_fixed, is_stale \
in scenario_tree_node_variables_generator_noinstances(
ph._scenario_tree,
includeDerivedVariables=False,
includeLastStage=False):
if is_fixed:
node_fixed_cnt[tree_node._name] += 1
total_fixed_cnt += 1
# Depending on preprocessing options, stale may indicate
# fixed or unused in the model, either way we can skip it
if (not is_stale):
var_node_avg = 0.0
for var_value, scenario_probability in variable_values:
var_node_avg += scenario_probability * var_value
var_term_diff = 0.0
for var_value, scenario_probability in variable_values:
var_term_diff += \
scenario_probability * \
fabs(var_value - var_node_avg)
term_diff[tree_node._name][variable_id] = var_term_diff
# Print individual variable term diffs by node
# and sorted highest to lowest
# skip the leaf stage
ofile = open(self.wonly_file, 'a')
for stage in ph._scenario_tree._stages[:-1]:
for tree_node in stage._tree_nodes:
for variable_id, var_term_diff in sorted(
term_diff[tree_node._name].items(),
key=itemgetter(1),
reverse=True):
variable_name, index = tree_node._variable_ids[variable_id]
ofile.write(
str(ph_iter) + "; " + tree_node._name + "; " +
variable_name + indexToString(index) + "; " +
str(var_term_diff) + '\n')
ofile.close()
def _inspect_variable_convergence(self, ph, ph_iter):
# collect termdiff by node and variable so we can
# report and possibly sort
term_diff = dict((tree_node._name, {}) \
for stage in ph._scenario_tree._stages[:-1]
for tree_node in stage._tree_nodes)
# Track these for reporting purposes
node_fixed_cnt = dict((tree_node._name, 0) \
for stage in ph._scenario_tree._stages[:-1]
for tree_node in stage._tree_nodes)
total_fixed_cnt = 0
for stage, tree_node, variable_id, variable_values, is_fixed, is_stale \
in scenario_tree_node_variables_generator_noinstances(
ph._scenario_tree,
includeDerivedVariables=False,
includeLastStage=False):
if is_fixed:
node_fixed_cnt[tree_node._name] += 1
total_fixed_cnt += 1
# Depending on preprocessing options, stale may indicate
# fixed or unused in the model, either way we can skip it
if (not is_stale):
var_node_avg = 0.0
for var_value, scenario_probability in variable_values:
var_node_avg += scenario_probability * var_value
var_term_diff = 0.0
for var_value, scenario_probability in variable_values:
var_term_diff += \
scenario_probability * \
fabs(var_value - var_node_avg)
term_diff[tree_node._name][variable_id] = var_term_diff
# Print individual variable term diffs by node
# and sorted highest to lowest
# skip the leaf stage
ofile = open(self.wonly_file, 'a')
for stage in ph._scenario_tree._stages[:-1]:
for tree_node in stage._tree_nodes:
for variable_id, var_term_diff in sorted(term_diff[tree_node._name].items(),
key=itemgetter(1),
reverse=True):
variable_name, index = tree_node._variable_ids[variable_id]
ofile.write(str(ph_iter)+"; "+tree_node._name+"; "+variable_name+indexToString(index)+"; "+str(var_term_diff)+'\n')
ofile.close()
def _w_printing(self, ofile, ph=None):
# print the w values in a useful way to the open file ofile
# if ph is None, just write the header
if ph is None:
ofile.write("iteration; tree node; scenario; variable; W\n")
else:
for stage, tree_node, variable_id, variable_values, is_fixed, is_stale in \
scenario_tree_node_variables_generator_noinstances(ph._scenario_tree,
includeDerivedVariables=False,
includeLastStage=False):
if (is_stale is False):
for scenario in tree_node._scenarios:
scen_name = scenario._name
weight_value = scenario._w[tree_node._name][variable_id]
variable_name, index = tree_node._variable_ids[variable_id]
full_variable_name = variable_name+indexToString(index)
ofile.write(str(ph._current_iteration) + ';' + tree_node._name + ';' + scen_name + ';' + full_variable_name + ';' + str(weight_value)+'\n')
def computeMetric(self, ph, scenario_tree, instances):
term_diff = 0.0
for stage, tree_node, variable_id, variable_values, is_fixed, is_stale \
in scenario_tree_node_variables_generator_noinstances(
scenario_tree,
includeDerivedVariables=False,
includeLastStage=False):
if (not is_stale) or (is_fixed):
for var_value, scenario_probability in variable_values:
term_diff += \
scenario_probability * \
fabs(var_value - tree_node._averages[variable_id])
return term_diff
