def main(result, data, redo, name_of_folder):
common_result_path = result
output_path = os.path.join(result, name_of_folder)
exhaustive_approach_results_path = os.path.join(result,
"exhaustive_approach")
try:
data_graph = nx.read_gpickle(data)
except:
data_graph = nx.read_gml(data)
#data_graph=nx.read_gpickle(data)
pattern = nx.read_gml(os.path.join(output_path, 'input_pattern.gml'))
#load Plist
pkl_file = open(os.path.join(output_path, 'Plist_base.pickle'), 'rb')
Plist_base = pickle.load(pkl_file)
#load monitoring marks
pkl_file = open(os.path.join(output_path, 'monitoring_marks.pickle'), 'rb')
monitoring_marks = pickle.load(pkl_file)
#load monitoring_reports
pkl_file = open(os.path.join(output_path, 'monitoring_reports.pickle'),
'rb')
monitoring_reports = pickle.load(pkl_file)
detailed_result_path = os.path.join(output_path, "monitoring")
if (not redo) and os.path.exists(detailed_result_path) and len(
os.listdir(detailed_result_path)) >= 120:
row = csv_report.get_row(result, output_path, "false_furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, name_of_folder + ".info"),
'w') as f:
f.write(str(row))
sys.exit()
pattern_file_name = common_result_path.split("/")[-1]
if pattern_file_name == "":
pattern_file_name = common_result_path.split("/")[-2]
picklename = os.path.join(exhaustive_approach_results_path,
"fdict_exhaustive_%s.pickle" % pattern_file_name)
pickin = open(picklename, 'rb')
fdict_exhaustive = pickle.load(pickin)
approaches.globals_sampling.output_path = output_path
if pattern_file_name.startswith("dblp"):
approaches.globals_sampling.experiment_name = "dblp"
else:
approaches.globals_sampling.experiment_name = "yeast"
my_version_report_online(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path,
detailed_result_path, monitoring_reports,
exhaustive_approach_results_path, Plist_base, 1,
pattern_file_name) #print monitoring_reports
row = csv_report.get_row(result, output_path, "false_furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, name_of_folder + ".info"), 'w') as f:
f.write(str(row))
def main(pattern_path, output_folder, result, data, redo, write,
monitoring_reports):
common_result_path = result
output_path = os.path.join(result, output_folder)
detailed_result_path = os.path.join(output_path, "monitoring")
if (not redo) and os.path.exists(detailed_result_path) and len(
os.listdir(detailed_result_path)) >= 100:
row = csv_report.get_row(result, output_path, "furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "furer_row.info"), 'w') as f:
f.write(str(row))
sys.exit()
exhaustive_approach_results_path = os.path.join(common_result_path,
"exhaustive_results")
try:
data_graph = nx.read_gpickle(data)
except:
data_graph = nx.read_gml(data)
pattern = nx.read_gml(pattern_path)
#load Plist
pkl_file = open(os.path.join(output_path, 'Plist.pickle'), 'rb')
Plist = pickle.load(pkl_file)
#load monitoring marks
pkl_file = open(os.path.join(output_path, 'monitoring_marks.pickle'), 'rb')
monitoring_marks = pickle.load(pkl_file)
#load monitoring_reports
if os.path.exists(os.path.join(output_path, 'monitoring_reports.pickle')):
pkl_file = open(os.path.join(output_path, 'monitoring_reports.pickle'),
'rb')
monitoring_reports = pickle.load(pkl_file)
pattern_file_name = common_result_path.split("/")[-1]
if pattern_file_name == "":
pattern_file_name = common_result_path.split("/")[-2]
fdict_exhaustive = None
if os.path.exists(
os.path.join(exhaustive_approach_results_path, "fudict.pickle")):
pickin = open(
os.path.join(exhaustive_approach_results_path, "fudict.pickle"))
fdict_exhaustive = pickle.load(pickin)
approaches.globals_sampling.output_path = output_path
my_version_report(fdict_exhaustive, data_graph, pattern, monitoring_marks,
output_path, detailed_result_path, monitoring_reports,
exhaustive_approach_results_path, Plist, 1,
pattern_file_name, write) #print monitoring_reports
row = csv_report.get_row(pattern_path, result, output_path, "furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "furer_row.info"), 'w') as f:
f.write(str(row))
def main(result, data, redo, write, monitoring_reports):
flag_version = 'my'
common_result_path = result
output_path = os.path.join(result, 'results_false_furer')
exhaustive_approach_results_path = os.path.join(common_result_path,
"exhaustive_approach")
detailed_result_path = os.path.join(output_path, "monitoring")
if (not redo) and os.path.exists(detailed_result_path) and len(
os.listdir(detailed_result_path)) >= 120:
print "Results already post-processed"
row = csv_report.get_row(result, output_path, "false_furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "false_furer_row.info"), 'w') as f:
f.write(str(row))
sys.exit()
try:
data_graph = nx.read_gpickle(data)
except:
data_graph = nx.read_gml(data)
#data_graph=nx.read_gpickle(data)
pattern = nx.read_gml(os.path.join(common_result_path,
'input_pattern.gml'))
#load Plist
pkl_file = open(os.path.join(output_path, 'Plist.pickle'), 'rb')
Plist = pickle.load(pkl_file)
#load monitoring marks
pkl_file = open(os.path.join(output_path, 'monitoring_marks.pickle'), 'rb')
monitoring_marks = pickle.load(pkl_file)
#load monitoring_reports
pattern_file_name = common_result_path.split("/")[-1]
fdict_exhaustive = None
if pattern_file_name == "":
pattern_file_name = common_result_path.split("/")[-2]
if os.path.exists(os.path.join(output_path, 'monitoring_reports.pickle')):
pkl_file = open(os.path.join(output_path, 'monitoring_reports.pickle'),
'rb')
monitoring_reports = pickle.load(pkl_file)
if write == True:
picklename = os.path.join(
exhaustive_approach_results_path,
"fdict_exhaustive_%s.pickle" % pattern_file_name)
pickin = open(picklename, 'rb')
fdict_exhaustive = pickle.load(pickin)
print common_result_path.split("/")
experiments.globals.output_path = output_path
if pattern_file_name.startswith("dblp"):
experiments.globals.experiment_name = "dblp"
else:
experiments.globals.experiment_name = "yeast"
#smplr.complete_combinations(fdict_exhaustive, data_graph, pattern, Plist) # add zeros to all not present combinations
#smplr.smooth(fdict_exhaustive, fdict_exhaustive) # Laplace smoothing also for the exhaustive
#Report the results
print "Monitoring report..."
if (flag_version == 'my'):
my_version_report(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path, detailed_result_path,
monitoring_reports, exhaustive_approach_results_path,
Plist, 1, pattern_file_name,
write) #print monitoring_reports
if (flag_version == 'martin'):
martin_version_report(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path,
detailed_result_path, monitoring_reports,
exhaustive_approach_results_path, Plist, 1,
pattern_file_name) #print monitoring_reports
#report.report_monitoring(monitoring_marks,output_path,detailed_result_path,monitoring_reports,exhaustive_approach_results_path,data_graph,pattern,Plist,1,pattern_file_name) #print monitoring_reports
row = csv_report.get_row(result, output_path, "false_furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "false_furer_row.info"), 'w') as f:
f.write(str(row))
def main(result, data, redo, write, monitoring_reports):
print "reporting furer"
flag_version = 'my'
common_result_path = result
output_path = os.path.join(result, 'results_furer')
detailed_result_path = os.path.join(output_path, "monitoring")
if (not redo) and os.path.exists(detailed_result_path) and len(
os.listdir(detailed_result_path)) >= 100:
print "Results already post-processed"
row = csv_report.get_row(result, output_path, "furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "furer_row.info"), 'w') as f:
f.write(str(row))
sys.exit()
exhaustive_approach_results_path = os.path.join(common_result_path,
"exhaustive_approach")
try:
data_graph = nx.read_gpickle(data)
except:
data_graph = nx.read_gml(data)
#data_graph=nx.read_gpickle(data)
pattern = nx.read_gml(os.path.join(common_result_path,
'input_pattern.gml'))
#load Plist
pkl_file = open(os.path.join(output_path, 'Plist.pickle'), 'rb')
Plist = pickle.load(pkl_file)
#load monitoring marks
pkl_file = open(os.path.join(output_path, 'monitoring_marks.pickle'), 'rb')
monitoring_marks = pickle.load(pkl_file)
#load monitoring_reports
if os.path.exists(os.path.join(output_path, 'monitoring_reports.pickle')):
pkl_file = open(os.path.join(output_path, 'monitoring_reports.pickle'),
'rb')
monitoring_reports = pickle.load(pkl_file)
print common_result_path, common_result_path.split("/")
pattern_file_name = common_result_path.split("/")[-1]
if pattern_file_name == "":
pattern_file_name = common_result_path.split("/")[-2]
print "Number of reports: ", len(monitoring_reports)
print "pattern file name: ", pattern_file_name
print "Do we need exhaustive dict: ", write
fdict_exhaustive = None
if write == True:
picklename = os.path.join(
exhaustive_approach_results_path,
"fdict_exhaustive_%s.pickle" % pattern_file_name)
pickin = open(picklename, 'rb')
fdict_exhaustive = pickle.load(pickin)
experiments.globals.output_path = output_path
if pattern_file_name.startswith("dblp"):
experiments.globals.experiment_name = "dblp"
else:
experiments.globals.experiment_name = "yeast"
if (flag_version == 'my'):
my_version_report(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path, detailed_result_path,
monitoring_reports, exhaustive_approach_results_path,
Plist, 1, pattern_file_name,
write) #print monitoring_reports
if (flag_version == 'martin'):
martin_version_report(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path,
detailed_result_path, monitoring_reports,
exhaustive_approach_results_path, Plist, 1,
pattern_file_name) #print monitoring_reports
row = csv_report.get_row(result, output_path, "furer",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "furer_row.info"), 'w') as f:
f.write(str(row))
def main(result, data, redo, exp, write, monitoring_reports):
print "************************************** Reporting random for ", result, "*************************************************"
flag_version = exp
print "Running version: ", flag_version
output_path = os.path.join(result, 'random_vertex_approach')
common_result_path = result
detailed_result_path = os.path.join(output_path, "monitoring")
exhaustive_approach_results_path = os.path.join(common_result_path,
"exhaustive_approach")
if (not redo) and os.path.exists(detailed_result_path) and len(
os.listdir(detailed_result_path)) >= 120:
print "Results already post-processed"
row = csv_report.get_row(result, output_path, "random",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "random_row.info"), 'w') as f:
f.write(str(row))
sys.exit()
try:
data_graph = nx.read_gpickle(data)
except:
data_graph = nx.read_gml(data)
#data_graph=nx.read_gpickle(data)
pattern = nx.read_gml(os.path.join(common_result_path,
'input_pattern.gml'))
with open(os.path.join(result, 'root_node.dec'), 'r') as f:
for line in f.readlines():
root_node = int(line.split(" ")[0])
root_node_predicate_name = str(
line.split(" ")[1].rstrip().lstrip())
break
root_nodes = [
x for x in data_graph.nodes() if data_graph.node[x]['predicate'] ==
pattern.node[root_node]['predicate']
]
print "NR root nodeS: ", len(root_nodes)
experiments.globals.nr_root_nodes = len(root_nodes)
fdict_exhaustive = None
pattern_file_name = common_result_path.split("/")[-1]
if pattern_file_name == "":
pattern_file_name = common_result_path.split("/")[-2]
if os.path.exists(os.path.join(output_path, 'monitoring_reports.pickle')):
pkl_file = open(os.path.join(output_path, 'monitoring_reports.pickle'),
'rb')
monitoring_reports = pickle.load(pkl_file)
if write == True:
picklename = os.path.join(
exhaustive_approach_results_path,
"fdict_exhaustive_%s.pickle" % pattern_file_name)
pickin = open(picklename, 'rb')
fdict_exhaustive = pickle.load(pickin)
print "Nr monitoring reports: ", len(monitoring_reports)
#load Plist
pkl_file = open(os.path.join(output_path, 'Plist.pickle'), 'rb')
Plist = pickle.load(pkl_file)
#load monitoring marks
pkl_file = open(os.path.join(output_path, 'monitoring_marks.pickle'), 'rb')
monitoring_marks = pickle.load(pkl_file)
experiments.globals.output_path = output_path
if pattern_file_name.startswith("dblp"):
experiments.globals.experiment_name = "dblp"
else:
experiments.globals.experiment_name = "yeast"
if (flag_version == 'my'):
my_version_report(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path, detailed_result_path,
monitoring_reports, exhaustive_approach_results_path,
Plist, 1, pattern_file_name,
write) #print monitoring_reports
if (flag_version == 'martin'):
martin_version_report(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path,
detailed_result_path, monitoring_reports,
exhaustive_approach_results_path, Plist, 1,
pattern_file_name) #print monitoring_reports
#smplr.complete_combinations(fdict_exhaustive, data_graph, pattern, Plist) # add zeros to all not present combinations
#smplr.smooth(fdict_exhaustive, fdict_exhaustive) # Laplace smoothing also for the exhaustive
#Report the results
#print "Monitoring report..."
#report.report_monitoring(monitoring_marks,output_path,detailed_result_path,monitoring_reports,exhaustive_approach_results_path,data_graph,pattern,Plist,1,pattern_file_name) #print monitoring_reports
row = csv_report.get_row(result, output_path, "random",
result.replace("RESULTS", "PATTERNS"))
with open(os.path.join(output_path, "random_row.info"), 'w') as f:
f.write(str(row))