def read_ground_truth(self, file_):
'''
read ground truths of all instances
and save them in self.instances
@RELATION GROUND_TRUTH_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE SATUNSAT {SAT,UNSAT}
@ATTRIBUTE OPTIMAL_VALUE NUMERIC
'''
Printer.print_c("Read %s" % (file_))
with open(file_, "rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e(
"Parsing of arff file failed (%s) - maybe conflict of header and data."
% (file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %
(file_))
# extract feature names
for attr in arff_dict["attributes"][1:]:
self.metainfo.ground_truths[attr[0]] = attr[1]
insts = []
for data in arff_dict["data"]:
inst_name = data[0]
truth = data[1:]
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w(
"Instance \"%s\" has ground truths but was not found in performance file"
% (inst_name))
continue
truth_dict = {}
for truth_name, truth_value in zip(arff_dict["attributes"][1:],
truth):
if type(truth_name[1]) is list:
truth_dict[truth_name[0]] = self.metainfo.ground_truths[
truth_name[0]].index(
truth_value) if truth_value else -1
else:
truth_dict[truth_name[0]] = truth_value
inst_._ground_truth = truth_dict
if inst_name in insts:
Printer.print_e("Instance \"%s\" is not unique in %s" %
(inst_name, file_))
else:
insts.append(inst_name)
def read_feature_runstatus(self, file_):
'''
reads run stati of all pairs instance x feature step
and saves them self.instances
Expected header:
@RELATION FEATURE_RUNSTATUS_2013 - SAT - Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE preprocessing { ok , timeout , memout , presolved , crash , other }
@ATTRIBUTE local_search_probing { ok , timeout , memout , presolved , crash , other }
'''
Printer.print_c("Read %s" %(file_))
with open(file_,"rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e("Parsing of arff file failed (%s) - maybe conflict of header and data." %(file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %(file_))
for f_name in arff_dict["attributes"][2:]:
f_name = f_name[0]
if not f_name in self.metainfo.feature_group_dict.keys():
Printer.print_e("Feature step \"%s\" was not defined in feature steps" %(f_name))
if len(self.metainfo.feature_group_dict.keys()) != len(arff_dict["attributes"][2:]):
Printer.print_e("Number of feature steps in description.txt (%d) and feature_runstatus.arff (%d) does not match." %(len(self.metainfo.feature_group_dict.keys()), len(arff_dict["attributes"][2:-1])))
pairs_inst_rep = []
for data in arff_dict["data"]:
inst_name = data[0]
repetition = data[1]
stati = data[2:]
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w("Instance \"%s\" has feature step status but was not found in performance file" %(inst_name))
continue
if (inst_name,repetition) in pairs_inst_rep:
Printer.print_w("Pair (%s,%s) is not unique in %s" %(inst_name,repetition,file_))
else:
pairs_inst_rep.append((inst_name,repetition))
#===================================================================
# # if runstatus of feature vector is not always ok, remove feature vector
# if reduce(lambda x,y: False if ((not x) and y == "ok") else True, stati, False):
# inst_._features = None
#===================================================================
for status, f_step in zip(stati,arff_dict["attributes"][2:]):
inst_._features_status[f_step[0]] = status
def read_feature_runstatus(self, file_):
'''
reads run stati of all pairs instance x feature step
and saves them self.instances
Expected header:
@RELATION FEATURE_RUNSTATUS_2013 - SAT - Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE preprocessing { ok , timeout , memout , presolved , crash , other }
@ATTRIBUTE local_search_probing { ok , timeout , memout , presolved , crash , other }
'''
Printer.print_c("Read %s" %(file_))
fp = open(file_,"rb")
arff_dict = arff.load(fp)
fp.close()
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %(file_))
for f_name in arff_dict["attributes"][2:]:
f_name = f_name[0]
if not f_name in self.metainfo.feature_group_dict.keys():
Printer.print_e("Feature step \"%s\" was not defined in feature steps" %(f_name))
if len(self.metainfo.feature_group_dict.keys()) != len(arff_dict["attributes"][2:]):
Printer.print_e("Number of feature steps in description.txt (%d) and feature_runstatus.arff (%d) does not match." %(len(self.metainfo.feature_group_dict.keys()), len(arff_dict["attributes"][2:-1])))
pairs_inst_rep = []
for data in arff_dict["data"]:
inst_name = data[0]
repetition = data[1]
stati = data[2:]
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w("Instance \"%s\" has feature step status but was not found in performance file" %(inst_name))
continue
if (inst_name,repetition) in pairs_inst_rep:
Printer.print_w("Pair (%s,%s) is not unique in %s" %(inst_name,repetition,file_))
else:
pairs_inst_rep.append((inst_name,repetition))
#===================================================================
# # if runstatus of feature vector is not always ok, remove feature vector
# if reduce(lambda x,y: False if ((not x) and y == "ok") else True, stati, False):
# inst_._features = None
#===================================================================
for status, f_step in zip(stati,arff_dict["attributes"][2:]):
inst_._features_status[f_step[0]] = status
def read_cv(self, file_):
'''
read cross validation <file_>
@RELATION CV_2013 - SAT - Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE fold NUMERIC
'''
Printer.print_c("Read %s" % (file_))
self.metainfo.cv_given = True
with open(file_, "rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e(
"Parsing of arff file failed (%s) - maybe conflict of header and data."
% (file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %
(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %
(file_))
if arff_dict["attributes"][2][0] != "fold":
Printer.print_e("fold as third attribute is missing in %s" %
(file_))
rep_fold_dict = {}
for data in arff_dict["data"]:
inst_name = data[0]
rep = int(data[1])
fold = int(data[2])
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w(
"Instance \"%s\" has ground truths but was not found in performance file"
)
continue
inst_._fold[rep] = fold
fold_distribution = rep_fold_dict.get(rep, {})
rep_fold_dict[rep] = fold_distribution
fold_distribution[fold] = fold_distribution.get(fold, 0)
fold_distribution[fold] += 1
for rep, fold_dist in rep_fold_dict.items():
Printer.print_c(
"%d-th repetition: %s distribution" %
(rep, ",".join(map(str, list(fold_dist.values())))))
def read_ground_truth(self,file_):
'''
read ground truths of all instances
and save them in self.instances
@RELATION GROUND_TRUTH_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE SATUNSAT {SAT,UNSAT}
@ATTRIBUTE OPTIMAL_VALUE NUMERIC
'''
Printer.print_c("Read %s" %(file_))
with open(file_,"rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e("Parsing of arff file failed (%s) - maybe conflict of header and data." %(file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %(file_))
# extract feature names
for attr in arff_dict["attributes"][1:]:
self.metainfo.ground_truths[attr[0]] = attr[1]
insts = []
for data in arff_dict["data"]:
inst_name = data[0]
truth = data[1:]
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w("Instance \"%s\" has ground truths but was not found in performance file" %(inst_name))
continue
truth_dict = {}
for truth_name, truth_value in zip(arff_dict["attributes"][1:], truth):
if type(truth_name[1]) is list:
truth_dict[truth_name[0]] = self.metainfo.ground_truths[truth_name[0]].index(truth_value) if truth_value else -1
else:
truth_dict[truth_name[0]] = truth_value
inst_._ground_truth = truth_dict
if inst_name in insts:
Printer.print_e("Instance \"%s\" is not unique in %s" %(inst_name,file_))
else:
insts.append(inst_name)
def read_feature_costs(self, file_):
'''
reads feature time file
and saves in self.instances
Expected header:
@RELATION FEATURE_COSTS_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE preprocessing NUMERIC
@ATTRIBUTE local_search_probing NUMERIC
'''
Printer.print_c("Read %s" %(file_))
with open(file_,"rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e("Parsing of arff file failed (%s) - maybe conflict of header and data." %(file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("\"instance_id\" as first attribute is missing in %s" %(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("\"repetition\" as second attribute is missing in %s" %(file_))
found_groups = map(str,sorted(map(lambda x: x[0], arff_dict["attributes"][2:])))
for meta_group in self.metainfo.feature_group_dict.keys():
if meta_group not in found_groups:
Printer.print_e("\"%s\" as attribute is missing in %s" %(meta_group, file_))
pairs_inst_rep = []
for data in arff_dict["data"]:
inst_name = str(data[0])
repetition = data[1]
feature_cost = data[2:]
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w("Instance \"%s\" has feature cost but was not found in algorithm_runs.arff" %(inst_name))
continue
for cost, f_group in zip(feature_cost,arff_dict["attributes"][2:]):
inst_._feature_group_cost_dict[str(f_group[0])] = cost
if (inst_name,repetition) in pairs_inst_rep:
Printer.print_w("Pair (%s,%s) is not unique in %s" %(inst_name,repetition, file_))
else:
pairs_inst_rep.append((inst_name,repetition))
def read_feature_costs(self, file_):
'''
reads feature time file
and saves in self.instances
Expected header:
@RELATION FEATURE_COSTS_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE preprocessing NUMERIC
@ATTRIBUTE local_search_probing NUMERIC
'''
Printer.print_c("Read %s" %(file_))
fp = open(file_,"rb")
arff_dict = arff.load(fp)
fp.close()
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("\"instance_id\" as first attribute is missing in %s" %(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("\"repetition\" as second attribute is missing in %s" %(file_))
found_groups = map(str,sorted(map(lambda x: x[0], arff_dict["attributes"][2:])))
for meta_group in self.metainfo.feature_group_dict.keys():
if meta_group not in found_groups:
Printer.print_e("\"%s\" as attribute is missing in %s" %(meta_group, file_))
pairs_inst_rep = []
for data in arff_dict["data"]:
inst_name = str(data[0])
repetition = data[1]
feature_cost = data[2:]
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w("Instance \"%s\" has feature cost but was not found in algorithm_runs.arff" %(inst_name))
continue
for cost, f_group in zip(feature_cost,arff_dict["attributes"][2:]):
inst_._feature_group_cost_dict[str(f_group[0])] = cost
if (inst_name,repetition) in pairs_inst_rep:
Printer.print_w("Pair (%s,%s) is not unique in %s" %(inst_name,repetition, file_))
else:
pairs_inst_rep.append((inst_name,repetition))
def remove_features(self):
'''
inst_dict: instance name -> Instance()
meta_info: parsed coseal meta information and command line arguments (meta_info.options)
'''
feature_steps = self.metainfo.options.feature_steps
feature_group_dict = self.metainfo.feature_group_dict
if not feature_steps:
feature_steps = list(feature_group_dict.keys()) # if no steps are specified, use all
empty_check = set(feature_steps).difference(set(feature_group_dict.keys()))
if empty_check:
Printer.print_e("Feature steps (--feature-steps [list]) are not defined in data: %s" %(",".join(empty_check)), -2)
unused_features = set()
unused_steps = set(feature_group_dict.keys()).difference(set(feature_steps))
for u_step in unused_steps:
not_processed_features = feature_group_dict[u_step]
unused_features = unused_features.union(set(not_processed_features))
Printer.print_nearly_verbose("Remove features: %s\n" %(",".join(unused_features)))
used_features = set(self.metainfo.features).difference(unused_features)
Printer.print_c("Used features: %s\n" %(",".join(used_features)))
unused_index_features = sorted(list(map(str,self.metainfo.features).index(un_feature) for un_feature in unused_features), reverse=True)
for inst_ in self.instances.values():
for un_feature_indx in unused_index_features:
inst_._features.pop(un_feature_indx)
total_cost = 0
previous_presolved = False
for f_step in feature_steps:
if inst_._feature_group_cost_dict.get(f_step) and not previous_presolved: # feature costs are maybe None
total_cost += inst_._feature_group_cost_dict[f_step]
if inst_._features_status[f_step] == "presolved":
previous_presolved = True
for un_step in unused_steps: # remove step status if unused
del inst_._features_status[un_step]
inst_._feature_cost_total = total_cost
inst_._pre_solved = "presolved" in map(lambda x: x, inst_._features_status.values())
for un_feature_indx in unused_index_features:
self.metainfo.features.pop(un_feature_indx)
def read_cv(self, file_):
'''
read cross validation <file_>
@RELATION CV_2013 - SAT - Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE fold NUMERIC
'''
Printer.print_c("Read %s" %(file_))
self.metainfo.cv_given = True
with open(file_,"rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e("Parsing of arff file failed (%s) - maybe conflict of header and data." %(file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %(file_))
if arff_dict["attributes"][2][0] != "fold":
Printer.print_e("fold as third attribute is missing in %s" %(file_))
rep_fold_dict = {}
for data in arff_dict["data"]:
inst_name = data[0]
rep = int(data[1])
fold = int(data[2])
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w("Instance \"%s\" has ground truths but was not found in performance file")
continue
inst_._fold[rep] = fold
fold_distribution = rep_fold_dict.get(rep,{})
rep_fold_dict[rep] = fold_distribution
fold_distribution[fold] = fold_distribution.get(fold,0)
fold_distribution[fold] += 1
for rep, fold_dist in rep_fold_dict.items():
Printer.print_c("%d-th repetition: %s distribution" %(rep, ",".join(map(str,list(fold_dist.values())))))
def read_ground_truth(self,file_):
'''
read ground truths of all instances
and save them in self.instances
@RELATION GROUND_TRUTH_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE SATUNSAT {SAT,UNSAT}
@ATTRIBUTE OPTIMAL_VALUE NUMERIC
'''
Printer.print_c("Read %s" %(file_))
fp = open(file_,"rb")
arff_dict = arff.load(fp)
fp.close()
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %(file_))
# extract feature names
for attr in arff_dict["attributes"][1:]:
self.metainfo.ground_truths[attr[0]] = attr[1]
insts = []
for data in arff_dict["data"]:
inst_name = data[0]
truth = data[1:]
inst_ = self.instances.get(inst_name)
if not inst_:
Printer.print_w("Instance \"%s\" has ground truths but was not found in performance file")
continue
truth_dict = dict((truth_name[0], self.metainfo.ground_truths[truth_name[0]].index(truth_value) if truth_value else -1)\
for truth_name, truth_value in zip(arff_dict["attributes"][1:], truth))
inst_._ground_truth = truth_dict
if inst_name in insts:
Printer.print_e("Instance \"%s\" is not unique in %s" %(inst_name,file_))
else:
insts.append(inst_name)
def read_feature_values(self, file_):
'''
reads feature file
and saves them in self.instances
Expected Header:
@RELATION FEATURE_VALUES_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE number_of_variables NUMERIC
@ATTRIBUTE number_of_clauses NUMERIC
@ATTRIBUTE first_local_min_steps NUMERIC
'''
Printer.print_c("Read %s" % (file_))
with open(file_, "rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e(
"Parsing of arff file failed (%s) - maybe conflict of header and data."
% (file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %
(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %
(file_))
feature_set = set(self.metainfo.features_deterministic).union(
self.metainfo.features_stochastic)
for f_name in arff_dict["attributes"][2:]:
f_name = f_name[0]
self.metainfo.features.append(f_name)
if not f_name in feature_set:
Printer.print_e(
"Feature \"%s\" was not defined as deterministic or stochastic"
% (f_name))
pairs_inst_rep = []
encoutered_features = []
for data in arff_dict["data"]:
inst_name = data[0]
repetition = data[1]
features = data[2:]
if len(features) != len(self.metainfo.features):
Printer.print_e(
"Number of features in attributes does not match number of found features; instance: %s"
% (inst_name))
if not self.instances.get(inst_name):
Printer.print_w(
"Instance \"%s\" has features but was not found in performance file"
% (inst_name))
continue
inst_ = self.instances[inst_name]
inst_._features = features #TODO: handle feature repetitions
# not only Nones in feature vector and previously seen
if reduce(lambda x, y: True if (x or y) else False, features,
False) and features in encoutered_features:
Printer.print_w("Feature vector found twice: %s" %
(",".join(map(str, features))))
else:
encoutered_features.append(features)
if (inst_name, repetition) in pairs_inst_rep:
Printer.print_w("Pair (%s,%s) is not unique in %s" %
(inst_name, repetition, file_))
else:
pairs_inst_rep.append((inst_name, repetition))
def read_algorithm_runs(self, file_):
'''
read performance file
and saves information
add Instance() in self.instances
unsuccessful runs are replaced by algorithm_cutoff_time if performance_type is runtime
EXPECTED HEADER:
@RELATION ALGORITHM_RUNS_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE algorithm STRING
@ATTRIBUTE PAR10 NUMERIC
@ATTRIBUTE Number_of_satisfied_clauses NUMERIC
@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
'''
Printer.print_c("Read %s" % (file_))
with open(file_, "rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e(
"Parsing of arff file failed (%s) - maybe conflict of header and data."
% (file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %
(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %
(file_))
if arff_dict["attributes"][2][0] != "algorithm":
Printer.print_e("algorithm as third attribute is missing in %s" %
(file_))
listed_metrics = map(
lambda x: x[0],
arff_dict["attributes"][3:3 +
len(self.metainfo.performance_measure)])
diff_set = set(
self.metainfo.performance_measure).difference(listed_metrics)
if diff_set:
Printer.print_e("\"%s\" as attribute is missing in %s" %
(diff_set, file_))
if arff_dict["attributes"][-1][0] != "runstatus":
Printer.print_e("runstatus as last attribute is missing in %s" %
(file_))
pairs_inst_rep_alg = []
for data in arff_dict["data"]:
inst_name = str(data[0])
repetition = data[1]
algorithm = str(data[2])
perf_list = data[3:-1]
status = data[-1]
inst_ = self.instances.get(inst_name, Instance(inst_name))
for p_measure, p_type, perf in zip(
self.metainfo.performance_measure,
self.metainfo.performance_type, perf_list):
if perf is None:
Printer.print_e(
"The following performance data has missing values. Please impute all missing values.\n"
+ "%s" % (",".join(map(str, data))))
if p_type == "runtime" and (
perf is None or status != "ok"
): # if broken run, replace with cutoff time
perf = self.metainfo.algorithm_cutoff_time
inst_._cost[p_measure] = inst_._cost.get(p_measure, {})
perf_measure_dict = inst_._cost[p_measure]
perf_measure_dict[algorithm] = perf_measure_dict.get(
algorithm, [])
perf_measure_dict[algorithm].append(max(float(perf), 0.00001))
inst_._status[algorithm] = status
self.instances[inst_name] = inst_
if (inst_name, repetition, algorithm) in pairs_inst_rep_alg:
Printer.print_w("Pair (%s,%s,%s) is not unique in %s" %
(inst_name, repetition, algorithm, file_))
else:
pairs_inst_rep_alg.append((inst_name, repetition, algorithm))
def read_description(self, file_):
'''
reads description file
and saves all meta information
'''
Printer.print_c("Read %s" % (file_))
with open(file_, "r") as fh:
description = yaml.load(fh)
self.metainfo.scenario = description.get('scenario_id')
self.metainfo.performance_measure = description.get(
'performance_measures')
if not isinstance(description.get('performance_measures'), list):
Printer.print_e("'performance_measures' has to be list")
self.metainfo.maximize = description.get('maximize')
if not isinstance(description.get('maximize'), list):
Printer.print_e("'maximize' has to be list")
self.metainfo.performance_type = description.get('performance_type')
if not isinstance(description.get('performance_type'), list):
Printer.print_e("'performance_type' has to be list")
self.metainfo.algorithm_cutoff_time = description.get(
'algorithm_cutoff_time')
self.metainfo.features_cutoff_memory = description.get(
'algorithm_cutoff_memory')
self.metainfo.features_cutoff_time = description.get(
'features_cutoff_time')
self.metainfo.features_cutoff_memory = description.get(
'features_cutoff_memory')
self.metainfo.features_deterministic = description.get(
'features_deterministic')
if self.metainfo.features_deterministic is None:
self.metainfo.features_deterministic = set()
self.metainfo.features_stochastic = description.get(
'features_stochastic')
if self.metainfo.features_stochastic is None:
self.metainfo.features_stochastic = set()
self.metainfo.algortihms_deterministics = description.get(
'algorithms_deterministic')
if self.metainfo.algortihms_deterministics is None:
self.metainfo.algortihms_deterministics = set()
self.metainfo.algorithms_stochastic = description.get(
'algorithms_stochastic')
if self.metainfo.algorithms_stochastic is None:
self.metainfo.algorithms_stochastic = set()
self.metainfo.feature_group_dict = description.get('feature_steps')
self.metainfo.feature_steps = description.get('default_steps')
self.metainfo.algorithms = list(
set(self.metainfo.algorithms_stochastic).union(
self.metainfo.algortihms_deterministics))
if not self.metainfo.scenario:
Printer.print_w("Have not found SCENARIO_ID")
if not self.metainfo.performance_measure:
Printer.print_w("Have not found PERFORMANCE_MEASURE")
if not self.metainfo.performance_type:
Printer.print_w("Have not found PERFORMANCE_TYPE")
if not self.metainfo.maximize:
Printer.print_w("Have not found MAXIMIZE")
if not self.metainfo.algorithm_cutoff_time:
Printer.print_e("Have not found algorithm_cutoff_time")
if not self.metainfo.algorithm_cutoff_memory:
Printer.print_w("Have not found algorithm_cutoff_memory")
if not self.metainfo.features_cutoff_time:
Printer.print_w("Have not found features_cutoff_time")
Printer.print_w(
"Assumption FEATURES_CUTOFF_TIME == ALGORITHM_CUTOFF_TIME ")
self.metainfo.features_cutoff_time = self.metainfo.algorithm_cutoff_time
if not self.metainfo.features_cutoff_memory:
Printer.print_w("Have not found features_cutoff_memory")
if not self.metainfo.features_deterministic:
Printer.print_w("Have not found features_deterministic")
if not self.metainfo.features_stochastic:
Printer.print_w("Have not found features_stochastic")
if not self.metainfo.algortihms_deterministics:
Printer.print_w("Have not found algortihms_deterministics")
if not self.metainfo.algorithms_stochastic:
Printer.print_w("Have not found algorithms_stochastic")
if not self.metainfo.feature_group_dict:
Printer.print_e("Have not found any feature step")
if not self.metainfo.feature_steps:
Printer.print_e("Have not found default feature step")
for step, d in self.metainfo.feature_group_dict.items():
if d.get("requires") and not isinstance(d["requires"], list):
Printer.print_e(
"'requires' of a feature step (%s) has to be list." %
(step))
if self.metainfo.feature_steps:
f_groups = set(self.metainfo.feature_group_dict.keys())
if set(self.metainfo.feature_steps).difference(f_groups):
Printer.print_e(
"Default feature steps are not listed (%s)" %
(set(self.metainfo.feature_steps).difference(f_groups)))
feature_intersec = set(
self.metainfo.features_deterministic).intersection(
self.metainfo.features_stochastic)
if feature_intersec:
Printer.print_w(
"Intersection of deterministic and stochastic features is not empty: %s"
% (str(feature_intersec)))
algo_intersec = set(
self.metainfo.algortihms_deterministics).intersection(
self.metainfo.algorithms_stochastic)
if algo_intersec:
Printer.print_w(
"Intersection of deterministic and stochastic algorithms is not empty: %s"
% (str(algo_intersec)))
def run_extractor(self, args_dic, instance):
'''
run extractor and look for instance features
Parameter:
args_dic : dictionary with options
instance : instance to solve
'''
# run claspre extractor
ret = Claspre2.run_extractor(self, args_dic, instance)
Printer.print_c("\nExtended Stats Feature Extraction:")
self._set_args(args_dic, instance)
cmd = self._cmd
cmd[0] = cmd[0].replace("claspre", "exst")
del cmd[1]
del cmd[1]
cmd.append("--outf=2")
cmd.append("--stats=2")
cmd.append("--time-limit=120")
Printer.print_c(" ".join(cmd))
signal.signal(signal.SIGINT, self.__clean_up_with_signal)
signal.signal(signal.SIGHUP, self.__clean_up_with_signal)
signal.signal(signal.SIGQUIT, self.__clean_up_with_signal)
signal.signal(signal.SIGSEGV, self.__clean_up_with_signal)
signal.signal(signal.SIGTERM, self.__clean_up_with_signal)
signal.signal(signal.SIGXCPU, self.__clean_up_with_signal)
signal.signal(signal.SIGXFSZ, self.__clean_up_with_signal)
try:
# start exst
self._popen_ = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE)
self._instance.seek(0)
if isinstance(self._instance,
file) and self._instance.name.endswith(".gz"):
zcat_popen = Popen(["zcat", self._instance.name], stdout=PIPE)
input_ = zcat_popen.stdout.read()
else:
input_ = self._instance.read()
# get exst output
(out_, err_) = self._popen_.communicate(input=input_)
except OSError:
Printer.print_w(
"Feature extractor was unable to compute features (path correct?): %s"
% (cmd))
try:
# extract features from output
feature_dict = json.loads(out_)
except:
try:
Printer.print_w("Could not parse features. %s!" %
(self._instance.name))
except AttributeError:
Printer.print_w("Could not parse features from stdin!")
return ret
# get Extended Stats element from json
preprocessing_feats = feature_dict["Stats"]["Extended Stats"]
flat_feats = []
flat_feats.extend([y for (x, y) in preprocessing_feats])
# combine claspre and extended features
ret += flat_feats
return ret
def remove_features(self):
'''
inst_dict: instance name -> Instance()
meta_info: parsed coseal meta information and command line arguments (meta_info.options)
'''
feature_steps = self.metainfo.options.feature_steps
feature_group_dict = self.metainfo.feature_group_dict
features = None #self.metainfo.options.features
if features: #the user specified a subset of features (disables given feature_steps)
empty_check = set(features).difference(self.metainfo.features)
if empty_check:
Printer.print_e("Features (--features [list]) are not defined in data: %s" %(",".join(empty_check)), -2)
unused_features = set(self.metainfo.features).difference(features)
# find the corresponding feature steps
feature_steps = set()
for f in features:
for f_group, f_list in feature_group_dict.iteritems():
if f in f_list["provides"]:
feature_steps.add(f_group)
changed = True
while changed:
changed = False
for step in feature_steps:
missing_steps = set(feature_group_dict[step].get("requires",set())).difference(feature_steps)
if missing_steps:
changed = True
feature_steps = feature_steps.union(missing_steps)
Printer.print_w("Adding missing feature step because of a pre-condition: %s" %(",".join(missing_steps)))
unused_steps = set(feature_group_dict.keys()).difference(set(feature_steps))
Printer.print_c("Used feature steps (%d): %s" %(len(feature_steps), ",".join(feature_steps)))
else:
if not feature_steps:
feature_steps = list(self.metainfo.feature_steps) # if no steps are specified, use default
empty_check = set(feature_steps).difference(set(feature_group_dict.keys()))
if empty_check:
Printer.print_e("Feature steps (--feature-steps [list]) are not defined in data: %s" %(",".join(empty_check)), -2)
# check preconditions of features
available_steps = set()
used_features = set()
for step in feature_steps: #TODO: order of feature steps could be an issue
req_steps = set(feature_group_dict[step].get("requires", set()))
miss_steps = req_steps.difference(feature_steps)
if miss_steps:
Printer.print_w("Feature Step %s does not met his pre-conditions (%s). Adding feature step to set (but not to feature set!)." %(step, ",".join(miss_steps)))
available_steps.add(step)
for f in feature_group_dict[step]["provides"]:
used_features.add(f)
for ms in miss_steps:
available_steps.add(ms)
for f in feature_group_dict[ms]["provides"]:
used_features.add(f)
feature_steps = available_steps
Printer.print_c("Used Feature Steps (%d): %s" % (len(feature_steps), ",".join(feature_steps)))
unused_features = set(self.metainfo.features).difference(set(used_features))
unused_steps = set(feature_group_dict.keys()).difference(set(available_steps))
Printer.print_nearly_verbose("Remove features: %s\n" %(",".join(unused_features)))
used_features = set(self.metainfo.features).difference(unused_features)
Printer.print_c("Used features (%d): %s\n" %(len(used_features), ",".join(used_features)))
if not used_features:
Printer.print_w("Empty feature set - fall back to default feature set.")
return False
unused_index_features = sorted(list(map(str,self.metainfo.features).index(un_feature) for un_feature in unused_features), reverse=True)
# remove unused features
for inst_ in self.instances.values():
for un_feature_indx in unused_index_features:
inst_._features.pop(un_feature_indx)
# compute feature costs
for inst_ in self.instances.values():
total_cost = 0
previous_presolved = False
for f_step in feature_steps:
if inst_._feature_group_cost_dict.get(f_step) and not previous_presolved: # feature costs are maybe None
total_cost += inst_._feature_group_cost_dict[f_step]
if inst_._features_status[f_step] == "PRESOLVED":
previous_presolved = True
for un_step in unused_steps: # remove step status if unused
del inst_._features_status[un_step]
inst_._feature_cost_total = total_cost
inst_._pre_solved = "PRESOLVED" in map(lambda x: x.upper(), inst_._features_status.values())
for un_feature_indx in unused_index_features:
self.metainfo.features.pop(un_feature_indx)
#=======================================================================
# if self.metainfo.options.impute == "none":
# for inst_ in self.instances.values():
# if reduce(lambda x,y: False if ((not x) and y.upper() == "OK") else True, inst_._features_status.values(), False):
# inst_._features = None
#=======================================================================
return True
def check_instances(self):
'''
check each instances of completeness and soundness
'''
Printer.print_c("Check Consistency and Completeness of input data")
n_instances = len(self.instances)
n_no_feats = 0
n_unsolvable = 0
n_unsolvable2 = 0
n_valid = 0
n_presolved = 0
feature_costs = 0
for inst_ in self.instances.values():
valid = True
unsolvable = "ok" not in list(inst_._status.values())
if unsolvable:
n_unsolvable += 1
valid = False
if not inst_._cost:
Printer.print_e("Missing algorithm cost for instance \"%s\"" %(inst_._name))
valid = False
inst_.finished_input(self.metainfo.algorithms)
if not inst_._features:
Printer.print_verbose("Missing features values for instance \"%s\"" %(inst_._name))
n_no_feats += 1
valid = False
if inst_._pre_solved:
n_presolved += 1
if valid:
n_valid += 1
times = filter(lambda x: x < self.metainfo.algorithm_cutoff_time, inst_._cost_vec)
#===================================================================
# if not times:
# n_unsolvable2 += 1
#===================================================================
feature_costs += inst_._feature_cost_total
#if not times and not unsolvable:
# print(inst_._name)
# print(inst_._cost_vec)
# print(inst_._status.values())
#===================================================================
# if not unsolvable and not times:
# print(inst_)
#===================================================================
#if not inst_._feature_runtimes:
# Printer.print_e("Miss feature costs for instance %s" %(inst_._name))
#if not inst_._stati:
# Printer.print_e("Miss run status for instance %s" %(inst_._name), type_="w")
#if not inst_._ground_truth:
# Printer.print_e("Miss ground truth for instance %s" %(inst_._name), type_="w")
Printer.print_c("Instances: \t\t %d" %(n_instances))
Printer.print_c("Incomplete Feature Vector: \t %d" %(n_no_feats))
Printer.print_c("Unsolvable Instances (status): \t %d" %(n_unsolvable))
#Printer.print_c("Unsolvable Instances (runtime): \t %d" %(n_unsolvable2))
Printer.print_c("Valid Instances: \t %d" %(n_valid))
Printer.print_c("Presolved: \t\t %d" %(n_presolved))
Printer.print_c("Average Feature Costs on all features: \t %.4f" %(feature_costs / n_instances))
#=======================================================================
# if n_unsolvable != n_unsolvable2:
# Printer.print_w("Number of unsolvable instances regarding status and runtime is not consistent.")
#=======================================================================
if not n_valid:
Printer.print_e("Have not found valid instances",-10)
def check_instances(self):
'''
check each instances of completeness and soundness
'''
Printer.print_c("Check Consistency and Completeness of input data")
n_instances = len(self.instances)
n_no_feats = 0
n_unsolvable = 0
n_unsolvable2 = 0
n_valid = 0
n_presolved = 0
feature_costs = 0
for inst_ in self.instances.values():
valid = True
unsolvable = "ok" not in list(inst_._status.values())
if unsolvable:
n_unsolvable += 1
valid = False
if not inst_._cost:
Printer.print_e("Missing algorithm cost for instance \"%s\"" %
(inst_._name))
valid = False
inst_.finished_input(self.metainfo.algorithms)
if not inst_._features:
Printer.print_verbose(
"Missing features values for instance \"%s\"" %
(inst_._name))
n_no_feats += 1
valid = False
if inst_._pre_solved:
n_presolved += 1
if valid:
n_valid += 1
times = filter(lambda x: x < self.metainfo.algorithm_cutoff_time,
inst_._cost_vec)
#===================================================================
# if not times:
# n_unsolvable2 += 1
#===================================================================
feature_costs += inst_._feature_cost_total
#if not times and not unsolvable:
# print(inst_._name)
# print(inst_._cost_vec)
# print(inst_._status.values())
#===================================================================
# if not unsolvable and not times:
# print(inst_)
#===================================================================
#if not inst_._feature_runtimes:
# Printer.print_e("Miss feature costs for instance %s" %(inst_._name))
#if not inst_._stati:
# Printer.print_e("Miss run status for instance %s" %(inst_._name), type_="w")
#if not inst_._ground_truth:
# Printer.print_e("Miss ground truth for instance %s" %(inst_._name), type_="w")
Printer.print_c("Instances: \t\t %d" % (n_instances))
Printer.print_c("Incomplete Feature Vector: \t %d" % (n_no_feats))
Printer.print_c("Unsolvable Instances (status): \t %d" %
(n_unsolvable))
#Printer.print_c("Unsolvable Instances (runtime): \t %d" %(n_unsolvable2))
Printer.print_c("Valid Instances: \t %d" % (n_valid))
Printer.print_c("Presolved: \t\t %d" % (n_presolved))
Printer.print_c("Average Feature Costs on all features: \t %.4f" %
(feature_costs / n_instances))
#=======================================================================
# if n_unsolvable != n_unsolvable2:
# Printer.print_w("Number of unsolvable instances regarding status and runtime is not consistent.")
#=======================================================================
if not n_valid:
Printer.print_e("Have not found valid instances", -10)
def read_feature_values(self, file_):
'''
reads feature file
and saves them in self.instances
Expected Header:
@RELATION FEATURE_VALUES_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE number_of_variables NUMERIC
@ATTRIBUTE number_of_clauses NUMERIC
@ATTRIBUTE first_local_min_steps NUMERIC
'''
Printer.print_c("Read %s" %(file_))
with open(file_,"rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e("Parsing of arff file failed (%s) - maybe conflict of header and data." %(file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %(file_))
feature_set = set(self.metainfo.features_deterministic).union(self.metainfo.features_stochastic)
for f_name in arff_dict["attributes"][2:]:
f_name = f_name[0]
self.metainfo.features.append(f_name)
if not f_name in feature_set:
Printer.print_e("Feature \"%s\" was not defined as deterministic or stochastic" %(f_name))
pairs_inst_rep = []
encoutered_features = []
for data in arff_dict["data"]:
inst_name = data[0]
repetition = data[1]
features = data[2:]
if len(features) != len(self.metainfo.features):
Printer.print_e("Number of features in attributes does not match number of found features; instance: %s" %(inst_name))
if not self.instances.get(inst_name):
Printer.print_w("Instance \"%s\" has features but was not found in performance file" %(inst_name))
continue
inst_ = self.instances[inst_name]
inst_._features = features #TODO: handle feature repetitions
# not only Nones in feature vector and previously seen
if reduce(lambda x,y: True if (x or y) else False, features, False) and features in encoutered_features:
Printer.print_w("Feature vector found twice: %s" %(",".join(map(str,features))))
else:
encoutered_features.append(features)
if (inst_name,repetition) in pairs_inst_rep:
Printer.print_w("Pair (%s,%s) is not unique in %s" %(inst_name,repetition, file_))
else:
pairs_inst_rep.append((inst_name,repetition))
def read_algorithm_runs(self, file_):
'''
read performance file
and saves information
add Instance() in self.instances
unsuccessful runs are replaced by algorithm_cutoff_time if performance_type is runtime
EXPECTED HEADER:
@RELATION ALGORITHM_RUNS_2013-SAT-Competition
@ATTRIBUTE instance_id STRING
@ATTRIBUTE repetition NUMERIC
@ATTRIBUTE algorithm STRING
@ATTRIBUTE PAR10 NUMERIC
@ATTRIBUTE Number_of_satisfied_clauses NUMERIC
@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
'''
Printer.print_c("Read %s" %(file_))
with open(file_,"rb") as fp:
try:
arff_dict = arff.load(fp)
except arff.BadNominalValue:
Printer.print_e("Parsing of arff file failed (%s) - maybe conflict of header and data." %(file_))
if arff_dict["attributes"][0][0] != "instance_id":
Printer.print_e("instance_id as first attribute is missing in %s" %(file_))
if arff_dict["attributes"][1][0] != "repetition":
Printer.print_e("repetition as second attribute is missing in %s" %(file_))
if arff_dict["attributes"][2][0] != "algorithm":
Printer.print_e("algorithm as third attribute is missing in %s" %(file_))
listed_metrics = map(lambda x: x[0], arff_dict["attributes"][3 : 3 + len(self.metainfo.performance_measure)])
diff_set = set(self.metainfo.performance_measure).difference(listed_metrics)
if diff_set:
Printer.print_e("\"%s\" as attribute is missing in %s" %(diff_set, file_))
if arff_dict["attributes"][-1][0] != "runstatus":
Printer.print_e("runstatus as last attribute is missing in %s" %(file_))
pairs_inst_rep_alg = []
for data in arff_dict["data"]:
inst_name = str(data[0])
repetition = data[1]
algorithm = str(data[2])
perf_list = data[3:-1]
status = data[-1]
inst_ = self.instances.get(inst_name,Instance(inst_name))
for p_measure, p_type, perf in zip(self.metainfo.performance_measure, self.metainfo.performance_type, perf_list):
if perf is None:
Printer.print_e("The following performance data has missing values. Please impute all missing values.\n"+
"%s" % (",".join(map(str,data))))
if p_type == "runtime" and (perf is None or status != "ok"): # if broken run, replace with cutoff time
perf = self.metainfo.algorithm_cutoff_time
inst_._cost[p_measure] = inst_._cost.get(p_measure,{})
perf_measure_dict = inst_._cost[p_measure]
perf_measure_dict[algorithm] = perf_measure_dict.get(algorithm,[])
perf_measure_dict[algorithm].append(max(float(perf),0.00001))
inst_._status[algorithm] = status
self.instances[inst_name] = inst_
if (inst_name,repetition, algorithm) in pairs_inst_rep_alg:
Printer.print_w("Pair (%s,%s,%s) is not unique in %s" %(inst_name, repetition, algorithm, file_))
else:
pairs_inst_rep_alg.append((inst_name,repetition, algorithm))
def read_description(self, file_):
'''
reads description file
and saves all meta information
'''
Printer.print_c("Read %s" %(file_))
with open(file_, "r") as fh:
description = yaml.load(fh)
self.metainfo.scenario = description.get('scenario_id')
self.metainfo.performance_measure = description.get('performance_measures')
if not isinstance(description.get('performance_measures'), list):
Printer.print_e("'performance_measures' has to be list")
self.metainfo.maximize = description.get('maximize')
if not isinstance(description.get('maximize'), list):
Printer.print_e("'maximize' has to be list")
self.metainfo.performance_type = description.get('performance_type')
if not isinstance(description.get('performance_type'), list):
Printer.print_e("'performance_type' has to be list")
self.metainfo.algorithm_cutoff_time = description.get('algorithm_cutoff_time')
self.metainfo.features_cutoff_memory = description.get('algorithm_cutoff_memory')
self.metainfo.features_cutoff_time = description.get('features_cutoff_time')
self.metainfo.features_cutoff_memory = description.get('features_cutoff_memory')
self.metainfo.features_deterministic = description.get('features_deterministic')
if self.metainfo.features_deterministic is None:
self.metainfo.features_deterministic = set()
self.metainfo.features_stochastic = description.get('features_stochastic')
if self.metainfo.features_stochastic is None:
self.metainfo.features_stochastic = set()
self.metainfo.algortihms_deterministics = description.get('algorithms_deterministic')
if self.metainfo.algortihms_deterministics is None:
self.metainfo.algortihms_deterministics = set()
self.metainfo.algorithms_stochastic = description.get('algorithms_stochastic')
if self.metainfo.algorithms_stochastic is None:
self.metainfo.algorithms_stochastic = set()
self.metainfo.feature_group_dict = description.get('feature_steps')
self.metainfo.feature_steps = description.get('default_steps')
self.metainfo.algorithms = list(
set(self.metainfo.algorithms_stochastic).union(
self.metainfo.algortihms_deterministics))
if not self.metainfo.scenario:
Printer.print_w("Have not found SCENARIO_ID")
if not self.metainfo.performance_measure:
Printer.print_w("Have not found PERFORMANCE_MEASURE")
if not self.metainfo.performance_type:
Printer.print_w("Have not found PERFORMANCE_TYPE")
if not self.metainfo.maximize:
Printer.print_w("Have not found MAXIMIZE")
if not self.metainfo.algorithm_cutoff_time:
Printer.print_e("Have not found algorithm_cutoff_time")
if not self.metainfo.algorithm_cutoff_memory:
Printer.print_w("Have not found algorithm_cutoff_memory")
if not self.metainfo.features_cutoff_time:
Printer.print_w("Have not found features_cutoff_time")
Printer.print_w("Assumption FEATURES_CUTOFF_TIME == ALGORITHM_CUTOFF_TIME ")
self.metainfo.features_cutoff_time = self.metainfo.algorithm_cutoff_time
if not self.metainfo.features_cutoff_memory:
Printer.print_w("Have not found features_cutoff_memory")
if not self.metainfo.features_deterministic:
Printer.print_w("Have not found features_deterministic")
if not self.metainfo.features_stochastic:
Printer.print_w("Have not found features_stochastic")
if not self.metainfo.algortihms_deterministics:
Printer.print_w("Have not found algortihms_deterministics")
if not self.metainfo.algorithms_stochastic:
Printer.print_w("Have not found algorithms_stochastic")
if not self.metainfo.feature_group_dict:
Printer.print_e("Have not found any feature step")
if not self.metainfo.feature_steps:
Printer.print_e("Have not found default feature step")
for step, d in self.metainfo.feature_group_dict.items():
if d.get("requires") and not isinstance(d["requires"], list):
Printer.print_e("'requires' of a feature step (%s) has to be list." %(step))
if self.metainfo.feature_steps:
f_groups = set(self.metainfo.feature_group_dict.keys())
if set(self.metainfo.feature_steps).difference(f_groups):
Printer.print_e("Default feature steps are not listed (%s)" %(set(self.metainfo.feature_steps).difference(f_groups)))
feature_intersec = set(self.metainfo.features_deterministic).intersection(self.metainfo.features_stochastic)
if feature_intersec:
Printer.print_w("Intersection of deterministic and stochastic features is not empty: %s" %(str(feature_intersec)))
algo_intersec = set(self.metainfo.algortihms_deterministics).intersection(self.metainfo.algorithms_stochastic)
if algo_intersec:
Printer.print_w("Intersection of deterministic and stochastic algorithms is not empty: %s" %(str(algo_intersec)))
def remove_features(self):
'''
inst_dict: instance name -> Instance()
meta_info: parsed coseal meta information and command line arguments (meta_info.options)
'''
feature_steps = self.metainfo.options.feature_steps
feature_group_dict = self.metainfo.feature_group_dict
features = None #self.metainfo.options.features
if features: #the user specified a subset of features (disables given feature_steps)
empty_check = set(features).difference(self.metainfo.features)
if empty_check:
Printer.print_e(
"Features (--features [list]) are not defined in data: %s"
% (",".join(empty_check)), -2)
unused_features = set(self.metainfo.features).difference(features)
# find the corresponding feature steps
feature_steps = set()
for f in features:
for f_group, f_list in feature_group_dict.iteritems():
if f in f_list["provides"]:
feature_steps.add(f_group)
changed = True
while changed:
changed = False
for step in feature_steps:
missing_steps = set(feature_group_dict[step].get(
"requires", set())).difference(feature_steps)
if missing_steps:
changed = True
feature_steps = feature_steps.union(missing_steps)
Printer.print_w(
"Adding missing feature step because of a pre-condition: %s"
% (",".join(missing_steps)))
unused_steps = set(feature_group_dict.keys()).difference(
set(feature_steps))
Printer.print_c("Used feature steps (%d): %s" %
(len(feature_steps), ",".join(feature_steps)))
else:
if not feature_steps:
feature_steps = list(
self.metainfo.feature_steps
) # if no steps are specified, use default
empty_check = set(feature_steps).difference(
set(feature_group_dict.keys()))
if empty_check:
Printer.print_e(
"Feature steps (--feature-steps [list]) are not defined in data: %s"
% (",".join(empty_check)), -2)
# check preconditions of features
available_steps = set()
used_features = set()
for step in feature_steps: #TODO: order of feature steps could be an issue
req_steps = set(feature_group_dict[step].get(
"requires", set()))
miss_steps = req_steps.difference(feature_steps)
if miss_steps:
Printer.print_w(
"Feature Step %s does not met his pre-conditions (%s). Adding feature step to set (but not to feature set!)."
% (step, ",".join(miss_steps)))
available_steps.add(step)
for f in feature_group_dict[step]["provides"]:
used_features.add(f)
for ms in miss_steps:
available_steps.add(ms)
for f in feature_group_dict[ms]["provides"]:
used_features.add(f)
feature_steps = available_steps
Printer.print_c("Used Feature Steps (%d): %s" %
(len(feature_steps), ",".join(feature_steps)))
unused_features = set(self.metainfo.features).difference(
set(used_features))
unused_steps = set(feature_group_dict.keys()).difference(
set(available_steps))
Printer.print_nearly_verbose("Remove features: %s\n" %
(",".join(unused_features)))
used_features = set(self.metainfo.features).difference(unused_features)
Printer.print_c("Used features (%d): %s\n" %
(len(used_features), ",".join(used_features)))
if not used_features:
Printer.print_w(
"Empty feature set - fall back to default feature set.")
return False
unused_index_features = sorted(list(
map(str, self.metainfo.features).index(un_feature)
for un_feature in unused_features),
reverse=True)
# remove unused features
for inst_ in self.instances.values():
for un_feature_indx in unused_index_features:
inst_._features.pop(un_feature_indx)
# compute feature costs
for inst_ in self.instances.values():
total_cost = 0
previous_presolved = False
for f_step in feature_steps:
if inst_._feature_group_cost_dict.get(
f_step
) and not previous_presolved: # feature costs are maybe None
total_cost += inst_._feature_group_cost_dict[f_step]
if inst_._features_status[f_step] == "PRESOLVED":
previous_presolved = True
for un_step in unused_steps: # remove step status if unused
del inst_._features_status[un_step]
inst_._feature_cost_total = total_cost
inst_._pre_solved = "PRESOLVED" in map(
lambda x: x.upper(), inst_._features_status.values())
for un_feature_indx in unused_index_features:
self.metainfo.features.pop(un_feature_indx)
#=======================================================================
# if self.metainfo.options.impute == "none":
# for inst_ in self.instances.values():
# if reduce(lambda x,y: False if ((not x) and y.upper() == "OK") else True, inst_._features_status.values(), False):
# inst_._features = None
#=======================================================================
return True
def read_description(self, file_):
'''
reads description file
and saves all meta information
'''
Printer.print_c("Read %s" %(file_))
with open(file_,"r") as fp:
for line in fp:
line = line.replace("\n","").strip(" ")
if line.startswith("scenario_id"):
self.metainfo.scenario = line.split(":")[1].strip(" ")
elif line.startswith("performance_measures" ):
self.metainfo.performance_measure = map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(","))
elif line.startswith("maximize"):
try:
self.metainfo.maximize = line.split(":")[1].strip(" ").split(",")
except ValueError:
Printer.print_w("Cannot read MAXIMIZE")
elif line.startswith("performance_type"):
self.metainfo.performance_type = map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(","))
elif line.startswith("algorithm_cutoff_time"):
try:
self.metainfo.algorithm_cutoff_time= float(line.split(":")[1])
except ValueError:
Printer.print_w("Cannot read ALGORITHM_CUTOFF_TIME")
elif line.startswith("algorithm_cutoff_memory"):
try:
self.metainfo.algorithm_cutoff_memory = float(line.split(":")[1])
except ValueError:
Printer.print_w("Cannot read ALGORITHM_CUTOFF_MEMORY")
elif line.startswith("features_cutoff_time"):
try:
self.metainfo.features_cutoff_time = float(line.split(":")[1])
except ValueError:
Printer.print_w("Cannot read FEATURES_CUTOFF_TIME")
elif line.startswith("features_cutoff_memory"):
try:
self.metainfo.features_cutoff_memory = float(line.split(":")[1])
except ValueError:
Printer.print_w("Cannot read FEATURES_CUTOFF_MEMORY")
elif line.startswith("features_deterministic"):
try:
self.metainfo.features_deterministic = map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(","))
except ValueError:
Printer.print_w("Cannot read FEATURES_DETERMINISTIC")
elif line.startswith("features_stochastic"):
try:
self.metainfo.features_stochastic = map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(","))
except ValueError:
Printer.print_w("Cannot read FEATURES_STOCHASTIC")
elif line.startswith("algorithms_deterministic"):
try:
self.metainfo.algortihms_deterministics = filter(lambda x: True if x else False, map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(",")))
except ValueError:
Printer.print_w("Cannot read ALGORTIHMS_DETERMINISTIC")
elif line.startswith("algorithms_stochastic"):
try:
self.metainfo.algorithms_stochastic = filter(lambda x: True if x else False, map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(",")))
except ValueError:
Printer.print_w("Cannot read ALGORITHMS_STOCHASTIC")
elif line.startswith("feature_step"):
try:
group_name = line.split(":")[0][12:].strip(" ")
features = map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(","))
self.metainfo.feature_group_dict[group_name] = features
except ValueError:
Printer.print_w("Cannot read Feature_Step")
elif line.startswith("default_step"):
try:
self.metainfo.feature_steps = filter(lambda x: True if x else False, map(lambda x: x.strip(" "), line.split(":")[1].strip(" ").split(",")))
except ValueError:
Printer.print_w("Cannot read DEFAULT_STEPS")
self.metainfo.algorithms = list(set(self.metainfo.algorithms_stochastic).union(self.metainfo.algortihms_deterministics))
if not self.metainfo.scenario:
Printer.print_w("Have not found SCENARIO_ID")
if not self.metainfo.performance_measure:
Printer.print_w("Have not found PERFORMANCE_MEASURE")
if not self.metainfo.performance_type:
Printer.print_w("Have not found PERFORMANCE_TYPE")
if not self.metainfo.maximize:
Printer.print_w("Have not found MAXIMIZE")
if not self.metainfo.algorithm_cutoff_time:
Printer.print_e("Have not found algorithm_cutoff_time")
if not self.metainfo.algorithm_cutoff_memory:
Printer.print_w("Have not found algorithm_cutoff_memory")
if not self.metainfo.features_cutoff_time:
Printer.print_w("Have not found features_cutoff_time")
Printer.print_w("Assumption FEATURES_CUTOFF_TIME == ALGORITHM_CUTOFF_TIME ")
self.metainfo.features_cutoff_time = self.metainfo.algorithm_cutoff_time
if not self.metainfo.features_cutoff_memory:
Printer.print_w("Have not found features_cutoff_memory")
if not self.metainfo.features_deterministic:
Printer.print_w("Have not found features_deterministic")
if not self.metainfo.features_stochastic:
Printer.print_w("Have not found features_stochastic")
if not self.metainfo.algortihms_deterministics:
Printer.print_w("Have not found algortihms_deterministics")
if not self.metainfo.algorithms_stochastic:
Printer.print_w("Have not found algorithms_stochastic")
if not self.metainfo.feature_group_dict:
Printer.print_e("Have not found any feature step")
if not self.metainfo.feature_steps:
Printer.print_e("Have not found default feature step")
feature_intersec = set(self.metainfo.features_deterministic).intersection(self.metainfo.features_stochastic)
if feature_intersec:
Printer.print_w("Intersection of deterministic and stochastic features is not empty: %s" %(str(feature_intersec)))
algo_intersec = set(self.metainfo.algortihms_deterministics).intersection(self.metainfo.algorithms_stochastic)
if algo_intersec:
Printer.print_w("Intersection of deterministic and stochastic algorithms is not empty: %s" %(str(algo_intersec)))