def consolidate(self):
"""
Consolidates the results obtained by the single WEKA processes into
a consistent structure of collections that are stored on the
file system.
"""
self._log("Consolidating results ...")
# We load and store the results once into a PerformanceResultSummary.
# From_multiple csv does the necessary consolidation
# and mixes and parses the table.
self._log("Reading intermediate results...")
result_collection = PerformanceResultSummary(dataset_dir=self.result_directory)
self._log("done")
self._log("Storing result collection")
result_collection.store(self.result_directory)
self._log("done")
# Write the specification of this operation
# to the result directory in order to make later
# analysis of results more easy
source_operation_file = open(os.path.join(self.result_directory,
"source_operation.yaml"), 'w')
yaml.dump(self.operation_spec, source_operation_file)
source_operation_file.close()
def consolidate(self):
"""
Consolidates the results of the single processes into a consistent result of the whole
operation
"""
assert (self.state == "retrieved")
if ((self.ProcessingSuccessful == True)
and (len(self.CrashedProcesses) == 0)):
self.current_operation.consolidate()
if ((self.ProcessingSuccessful == True)
and (len(self.CrashedProcesses) != 0)):
import pySPACE.resources.dataset_defs.performance_result.PerformanceResultSummary as PerformanceResultSummary
# merge the remaining files
print "***************************************************************************************************"
print "Starting merging . . ."
PerformanceResultSummary.merge_performance_results(
self.current_operation.result_directory)
print "Merging complete . . ."
print "***************************************************************************************************"
self._log("Operation - consolidated")
self.state = "consolidated"
def main():
import sys, os
file_path = os.path.dirname(os.path.abspath(__file__))
pyspace_path = file_path[:file_path.rfind('pySPACE')-1]
if not pyspace_path in sys.path:
sys.path.append(pyspace_path)
from pySPACE.resources.dataset_defs.performance_result import PerformanceResultSummary as PerformanceResultSummary
input_dir = sys.argv[1]
PerformanceResultSummary.merge_performance_results(input_dir)
def consolidate(self):
"""
Consolidates the results of the single processes into a consistent result of the whole
operation
"""
assert(self.state == "retrieved")
if ((self.ProcessingSuccessful ==True) and (len(self.CrashedProcesses) == 0)):
self.current_operation.consolidate()
if ((self.ProcessingSuccessful ==True) and (len(self.CrashedProcesses) != 0)):
import pySPACE.resources.dataset_defs.performance_result.PerformanceResultSummary as PerformanceResultSummary
# merge the remaining files
print "***************************************************************************************************"
print "Starting merging . . ."
PerformanceResultSummary.merge_performance_results(self.current_operation.result_directory)
print "Merging complete . . ."
print "***************************************************************************************************"
self._log("Operation - consolidated")
self.state = "consolidated"
def _load_results_collection_from_file(self, file_name=None):
""" Load results collection from file """
if file_name is None:
# Let the user specify a file to be loaded
self.file_name = \
str(QtGui.QFileDialog.getOpenFileName(
parent=self, caption="Select a results file",
filter="results files (*.csv)"))
else:
self.file_name = file_name
# Try to load specified file
dirname, filename = os.path.split(self.file_name)
self.result_collection = PerformanceResultSummary(dataset_dir=dirname,
csv_filename=filename)
# Create working copy that can be modified
self.current_collection = copy.deepcopy(self.result_collection)
def retrieve(self):
"""
Returns the result of the operation.
"""
self.state = "executing"
self._log("Operation - executing")
if (self.NumberOfProcessesToRunAtBeginning > len(self.process_args_list)):
args = ([self.COMMAND_MPI] +
['--loadbalance']+
['--nolocal']+
['--hostfile'] +
[pySPACE.configuration.root_dir+ "/" +'hostsfile'] +
['-n', str(len(self.process_args_list))] +
[self.COMMAND_PYTHON] +
[self.runner_script] +
self.process_args_list)
# Start the processes.
p =subprocess.Popen(args)
#self.pids.append(p)
self.IndexCopyStart += self.NumberOfProcessesToRunAtBeginning
#print args
else:
#copy the arguments of the processes to run
sub_process_args_list = (self.process_args_list[self.IndexCopyStart:
self.NumberOfProcessesToRunAtBeginning])
args = ([self.COMMAND_MPI] +
['--loadbalance']+
['--nolocal']+
['--hostfile'] +
[pySPACE.configuration.root_dir+ "/" +'hostsfile'] +
['-n', str(len(sub_process_args_list))] +
[self.COMMAND_PYTHON] +
[self.runner_script] +
sub_process_args_list)
# Start the processes.
p = subprocess.Popen(args)
#self.pids.append(p) # TODO: call p.poll() for p in self.pids after all processes have exited
self.IndexCopyStart += self.NumberOfProcessesToRunAtBeginning
#print args
# Create a list of boolean for processes which are finished.
# First we assume that all processes are not started, so we set
# every element of the list to false
FinishedProcesses=[False for i in range(len(self.process_args_list))]
# Wait until all processes finish and start new processes
# when old ones finish
print "Waiting for the processes to finish...."
# Counter for the processes which are finished. It will be reset
# after 'NumberOfProcessesToRunLater' processes are finished
CounterProcessesFinished = 0
processes_Finished = False
while not processes_Finished:
try:
processes_Finished = True
for LoopCounter, process_args in enumerate(self.process_args_list):
if (self.not_xor (os.path.isfile(process_args+"_Finished"),
os.path.isfile(process_args+"_Crashed"))):
processes_Finished = False
else:
if (FinishedProcesses[LoopCounter] == False):
# Record that the process is finished
FinishedProcesses[LoopCounter] = True
# If the process is crashed take note of that
if (os.path.isfile(process_args+"_Crashed")):
self.CrashedProcesses.append(process_args)
# Increment the counter for the number of processes finished
# by one
CounterProcessesFinished += 1
self.TotalProcessesFinished += 1
# update the progress bar
self.progress_bar.update(self.TotalProcessesFinished)
if (CounterProcessesFinished == self.NumberOfProcessesToRunLater):
# Define a variable for a subset of processes to run
sub_process_args_list = []
if (self.IndexCopyStart==len(self.process_args_list)):
break
elif ((self.IndexCopyStart+self.NumberOfProcessesToRunLater)< len(self.process_args_list)):
sub_process_args_list = (self.process_args_list[self.IndexCopyStart:
self.IndexCopyStart +self.NumberOfProcessesToRunLater])
else:
sub_process_args_list = self.process_args_list[self.IndexCopyStart:len(self.process_args_list)]
args = ([self.COMMAND_MPI] +
['--loadbalance']+
['--nolocal']+
['--hostfile'] +
[pySPACE.configuration.root_dir+ "/" +'hostsfile'] +
['-n', str(len(sub_process_args_list))] +
[self.COMMAND_PYTHON] +
[self.runner_script] +
sub_process_args_list)
# Start the processes
if (len(sub_process_args_list) > 0):
p = subprocess.Popen(args)
#print args
# Adjust the start index
self.IndexCopyStart += self.NumberOfProcessesToRunLater
# Reset the counter for processes finished
CounterProcessesFinished = 0
# sleep for one second
time.sleep(1)
except (KeyboardInterrupt, SystemExit): # if processes hang forever
self.ProcessingSuccessful = False
print "*********************************************************************************************************"
print "pySPACE forced to stop ..."
print "Please wait until mpi_backend is finished with consolidating the results generated and with clean up ..."
print "**********************************************************************************************************"
import pySPACE.resources.dataset_defs.performance_result.PerformanceResultSummary as PerformanceResultSummary
# merge the remaining files
print "***************************************************************************************************"
print "Starting merging . . ."
PerformanceResultSummary.merge_performance_results(self.current_operation.result_directory)
print "Merging complete . . ."
print "***************************************************************************************************"
break #The while loop will break
self._log("Operation - processing finished")
# Change the state to retrieved
self.state = "retrieved"
return None
def consolidate(self):
""" Consolidates the results obtained by the single processes into a consistent structure
of collections that are stored on the file system.
"""
# Consolidate the results
directory_pattern = os.sep.join([self.result_directory, "{*",])
dataset_pathes = glob.glob(directory_pattern)
# For all collections found
for dataset_path in dataset_pathes:
# Load their meta_data
meta_data = BaseDataset.load_meta_data(dataset_path)
# Determine author and date
try:
author = pwd.getpwuid(os.getuid())[4]
except:
author = "unknown"
self._log("Author could not be resolved.",level=logging.WARNING)
date = time.strftime("%Y%m%d_%H_%M_%S")
# Update meta data and store it
meta_data.update({"author" : author, "date" : date})
BaseDataset.store_meta_data(dataset_path, meta_data)
# Copy the input dataset specification file to the result
# directory in order to make later analysis of
# the results more easy
input_meta_path = os.sep.join([pySPACE.configuration.storage,
meta_data["input_collection_name"]])
input_meta = BaseDataset.load_meta_data(input_meta_path)
BaseDataset.store_meta_data(dataset_path,input_meta,
file_name="input_metadata.yaml")
# Check if some results consist of several runs
# and update the meta data in this case
# TODO: This is not a clean solution
for dataset_dir in glob.glob(os.sep.join([self.result_directory,
"*"])):
if not os.path.isdir(dataset_dir): continue
# There can be either run dirs, persistency dirs, or both of them.
# Check of whichever there are more. If both exist, their numbers
# are supposed to be equal.
nr_run_dirs = len(glob.glob(os.sep.join([dataset_dir,
"data_run*"])))
nr_per_dirs = len(glob.glob(os.sep.join([dataset_dir,
"persistency_run*"])))
nr_runs = max(nr_run_dirs, nr_per_dirs)
if nr_runs > 1:
collection_meta = BaseDataset.load_meta_data(dataset_dir)
collection_meta["runs"] = nr_runs
BaseDataset.store_meta_data(dataset_dir,collection_meta)
# If we don't create a feature vector or time series collection,
# we evaluated our classification using a classification performance sink.
# The resulting files should be merged to one csv tabular.
pathlist = glob.glob(os.path.join(self.result_directory,"results_*"))
if len(pathlist)>0:
# Do the consolidation the same way as for WekaClassificationOperation
self._log("Consolidating results ...")
# We load and store the results once into a PerformanceResultSummary
# This does the necessary consolidation...
self._log("Reading intermediate results...")
result_collection = PerformanceResultSummary(dataset_dir=self.result_directory)
self._log("done")
self._log("Storing result collection")
result_collection.store(self.result_directory)
self._log("done")
PerformanceResultSummary.merge_traces(self.result_directory)
if not(self.compression == False):
# Since we get one result summary,
# we don't need the numerous folders.
# So we zip them to make the whole folder more easy visible.
import zipfile
cwd=os.getcwd()
os.chdir(self.result_directory)
# If there are to many or to large folders, problems may occur.
# This case we want to log, try 64 bit mode, and then skip the zipping.
try:
pathlist = glob.glob(os.path.join(self.result_directory,"{*}"))
if not self.compression == "delete":
save_file=zipfile.ZipFile(self.result_directory+'/result_folders.zip',mode="w",compression=self.compression)
# we want to have the zipped file relative to the result directory
for path in pathlist:
for node in os.walk(path):
rel_path=os.path.relpath(node[0],self.result_directory)
save_file.write(rel_path)
for data in node[2]:
save_file.write(os.path.join(rel_path,data))
save_file.close()
# To still have an easy access to the history of the processing,
# we keep one folder.
pathlist.pop()
for path in pathlist:
shutil.rmtree(path)
except:
self._log("Result files could not be compressed with 32 bit mode, switching to 64 bit mode.", level=logging.CRITICAL)
# nearly total code copy, only difference with 64 bit mode
try:
pathlist = glob.glob(os.path.join(self.result_directory,"{*}"))
save_file=zipfile.ZipFile(self.result_directory+'/result_folders.zip',mode="w",compression=self.compression, allowZip64=True)
# we want to have the zipped file relative to the result directory
for path in pathlist:
for node in os.walk(path):
rel_path=os.path.relpath(node[0],self.result_directory)
save_file.write(rel_path)
for data in node[2]:
save_file.write(os.path.join(rel_path,data))
save_file.close()
# To still have an easy access to the history of the processing,
# we keep one folder.
pathlist.pop()
for path in pathlist:
shutil.rmtree(path)
except:
self._log("64 bit mode also failed. Please check your files and your code or contact your local programmer!", level=logging.CRITICAL)
os.chdir(cwd)
def retrieve(self, timeout=1e6):
"""
Returns the result of the operation.
"""
self.state = "executing"
self._log("Operation - executing")
if (self.NumberOfProcessesToRunAtBeginning > len(
self.process_args_list)):
args = ([self.COMMAND_MPI] + ['--loadbalance'] + ['--nolocal'] +
['--hostfile'] +
[pySPACE.configuration.root_dir + "/" + 'hostsfile'] +
['-n', str(len(self.process_args_list))] +
[self.COMMAND_PYTHON] + [self.runner_script] +
self.process_args_list)
# Start the processes.
self._log("mpi-parameters: %s" % args, level=logging.DEBUG)
self._log("mpi-parameters-joined: %s" % os.path.join(args),
level=logging.DEBUG)
p = subprocess.Popen(args)
#self.pids.append(p)
self.IndexCopyStart += self.NumberOfProcessesToRunAtBeginning
#print args
else:
#copy the arguments of the processes to run
sub_process_args_list = (
self.process_args_list[self.IndexCopyStart:self.
NumberOfProcessesToRunAtBeginning])
args = ([self.COMMAND_MPI] + ['--loadbalance'] + ['--nolocal'] +
['--hostfile'] +
[pySPACE.configuration.root_dir + "/" + 'hostsfile'] +
['-n', str(len(sub_process_args_list))] +
[self.COMMAND_PYTHON] + [self.runner_script] +
sub_process_args_list)
# Start the processes.
p = subprocess.Popen(args)
#self.pids.append(p) # TODO: call p.poll() for p in self.pids after all processes have exited
self.IndexCopyStart += self.NumberOfProcessesToRunAtBeginning
#print args
# Create a list of boolean for processes which are finished.
# First we assume that all processes are not started, so we set
# every element of the list to false
FinishedProcesses = [False for i in range(len(self.process_args_list))]
# Wait until all processes finish and start new processes
# when old ones finish
print "Waiting for the processes to finish...."
# Counter for the processes which are finished. It will be reset
# after 'NumberOfProcessesToRunLater' processes are finished
CounterProcessesFinished = 0
processes_Finished = False
while not processes_Finished:
try:
processes_Finished = True
for LoopCounter, process_args in enumerate(
self.process_args_list):
if (self.not_xor(
os.path.isfile(process_args + "_Finished"),
os.path.isfile(process_args + "_Crashed"))):
processes_Finished = False
else:
if (FinishedProcesses[LoopCounter] == False):
# Record that the process is finished
FinishedProcesses[LoopCounter] = True
# If the process is crashed take note of that
if (os.path.isfile(process_args + "_Crashed")):
self.CrashedProcesses.append(process_args)
# Increment the counter for the number of processes finished
# by one
CounterProcessesFinished += 1
self.TotalProcessesFinished += 1
# update the progress bar
self.progress_bar.update(
self.TotalProcessesFinished)
if (CounterProcessesFinished ==
self.NumberOfProcessesToRunLater):
# Define a variable for a subset of processes to run
sub_process_args_list = []
if (self.IndexCopyStart == len(
self.process_args_list)):
break
elif ((self.IndexCopyStart +
self.NumberOfProcessesToRunLater) < len(
self.process_args_list)):
sub_process_args_list = (
self.process_args_list[
self.IndexCopyStart:self.
IndexCopyStart +
self.NumberOfProcessesToRunLater])
else:
sub_process_args_list = self.process_args_list[
self.IndexCopyStart:len(
self.process_args_list)]
args = (
[self.COMMAND_MPI] + ['--loadbalance'] +
['--nolocal'] + ['--hostfile'] + [
pySPACE.configuration.root_dir + "/" +
'hostsfile'
] +
['-n',
str(len(sub_process_args_list))] +
[self.COMMAND_PYTHON] +
[self.runner_script] +
sub_process_args_list)
# Start the processes
if (len(sub_process_args_list) > 0):
p = subprocess.Popen(args)
#print args
# Adjust the start index
self.IndexCopyStart += self.NumberOfProcessesToRunLater
# Reset the counter for processes finished
CounterProcessesFinished = 0
# sleep for one second
time.sleep(1)
except (KeyboardInterrupt,
SystemExit): # if processes hang forever
self.ProcessingSuccessful = False
print "*********************************************************************************************************"
print "pySPACE forced to stop ..."
print "Please wait until mpi_backend is finished with consolidating the results generated and with clean up ..."
print "**********************************************************************************************************"
import pySPACE.resources.dataset_defs.performance_result.PerformanceResultSummary as PerformanceResultSummary
# merge the remaining files
print "***************************************************************************************************"
print "Starting merging . . ."
PerformanceResultSummary.merge_performance_results(
self.current_operation.result_directory)
print "Merging complete . . ."
print "***************************************************************************************************"
break #The while loop will break
self._log("Operation - processing finished")
# Change the state to retrieved
self.state = "retrieved"
return None
def consolidate(self, _=None):
""" Consolidates the results obtained by the single processes into a consistent structure
of collections that are stored on the file system.
"""
# Consolidate the results
directory_pattern = os.sep.join([
self.result_directory,
"{*",
])
dataset_pathes = glob.glob(directory_pattern)
# For all collections found
for dataset_path in dataset_pathes:
try:
# Load their meta_data
meta_data = BaseDataset.load_meta_data(dataset_path)
# Determine author and date
author = get_author()
date = time.strftime("%Y%m%d_%H_%M_%S")
# Update meta data and store it
meta_data.update({"author": author, "date": date})
# There can be either run dirs, persistency dirs, or both of them.
# Check of whichever there are more. If both exist, their numbers
# are supposed to be equal.
nr_run_dirs = len(
glob.glob(os.path.join(dataset_path, "data_run*")))
nr_per_dirs = len(
glob.glob(os.path.join(dataset_path, "persistency_run*")))
nr_runs = max(nr_run_dirs, nr_per_dirs)
if nr_runs > 1:
meta_data["runs"] = nr_runs
# Store the metadata
BaseDataset.store_meta_data(dataset_path, meta_data)
# Copy the input dataset specification file to the result
# directory in order to make later analysis of
# the results more easy
# THA: Split the first "/" from the input collection name, because otherwise it will be treated
# as an absolute path
input_collection_name = meta_data["input_collection_name"][1:] if \
meta_data["input_collection_name"][0] == os.sep else meta_data["input_collection_name"]
input_meta_path = os.path.join(pySPACE.configuration.storage,
input_collection_name)
try:
input_meta = BaseDataset.load_meta_data(input_meta_path)
BaseDataset.store_meta_data(
dataset_path,
input_meta,
file_name="input_metadata.yaml")
except (IOError, OSError) as e:
self._log("Error copying the input_metadata.yaml: {error}".
format(error=e.message),
level=logging.CRITICAL)
except Exception as e:
logging.getLogger("%s" % self).exception(
"Error updating the metadata: {error!s}".format(error=e))
raise e
# If we don't create a feature vector or time series collection,
# we evaluated our classification using a classification performance sink.
# The resulting files should be merged to one csv tabular.
pathlist = glob.glob(os.path.join(self.result_directory, "results_*"))
if len(pathlist) > 0:
# Do the consolidation the same way as for WekaClassificationOperation
self._log("Consolidating results ...")
# We load and store the results once into a PerformanceResultSummary
# This does the necessary consolidation...
self._log("Reading intermediate results...")
try:
result_collection = PerformanceResultSummary(
dataset_dir=self.result_directory)
self._log("done")
self._log("Storing result collection")
result_collection.store(self.result_directory)
self._log("done")
PerformanceResultSummary.merge_traces(self.result_directory)
except Exception as e:
logging.getLogger("%s" % self).exception(
"Error merging the result collection: {error!s}".format(
error=e))
if self.compression:
# Since we get one result summary,
# we don't need the numerous folders.
# So we zip them to make the whole folder more easy visible.
import zipfile
cwd = os.getcwd()
os.chdir(self.result_directory)
# If there are to many or to large folders, problems may occur.
# This case we want to log, try 64 bit mode,
# and then skip the zipping.
try:
pathlist = glob.glob(
os.path.join(self.result_directory, "{*}"))
if not self.compression == "delete":
save_file = zipfile.ZipFile(
self.result_directory + '/result_folders.zip',
mode="w",
compression=self.compression)
# we want to have the zipped file relative to the
# result directory
for path in pathlist:
for node in os.walk(path):
rel_path = os.path.relpath(
node[0], self.result_directory)
save_file.write(rel_path)
for data in node[2]:
save_file.write(
os.path.join(rel_path, data))
save_file.close()
# To still have an easy access to the history of the
# processing, we keep one folder.
pathlist.pop()
for path in pathlist:
shutil.rmtree(path)
except Exception, e:
self._log("Result files could not be compressed with 32" +
" bit mode, switching to 64 bit mode",
level=logging.CRITICAL)
# nearly total code copy, only difference with 64 bit mode
try:
pathlist = glob.glob(
os.path.join(self.result_directory, "{*}"))
save_file = zipfile.ZipFile(
self.result_directory + '/result_folders.zip',
mode="w",
compression=self.compression,
allowZip64=True)
# we want to have the zipped file relative to the
# result directory
for path in pathlist:
for node in os.walk(path):
rel_path = os.path.relpath(
node[0], self.result_directory)
save_file.write(rel_path)
for data in node[2]:
save_file.write(
os.path.join(rel_path, data))
save_file.close()
# To still have an easy access to the history of the
# processing, we keep one folder.
pathlist.pop()
for path in pathlist:
shutil.rmtree(path)
except:
self._log(
"64 bit mode also failed. Please check your files and your code or contact your local programmer!",
level=logging.CRITICAL)
os.chdir(cwd)
from PyQt4 import QtGui
# Copy the csv-files to a temporary directory such that there is no risk of
# interfering with the running operation
input_dir = sys.argv[1]
temp_dir = tempfile.mkdtemp()
for filename in fnmatch.filter(os.listdir(os.path.abspath(input_dir)),
"*.csv"):
shutil.copy(input_dir + os.sep + filename, temp_dir)
file_path = os.path.dirname(os.path.abspath(__file__))
pyspace_path = file_path[:file_path.rfind('pySPACE') - 1]
if not pyspace_path in sys.path:
sys.path.append(pyspace_path)
# Import csv-analysis and merge csv files
from pySPACE.resources.dataset_defs.performance_result import PerformanceResultSummary
PerformanceResultSummary.merge_performance_results(temp_dir)
# Invoke results analysis gui
from pySPACE.run.gui.performance_results_analysis import PerformanceResultsAnalysisMainWindow
app = QtGui.QApplication(sys.argv)
performance_results_analysis = \
PerformanceResultsAnalysisMainWindow(temp_dir + os.sep + "results.csv")
performance_results_analysis.show()
# Clean up
shutil.rmtree(temp_dir)
sys.exit(app.exec_())
def consolidate(self, _=None):
""" Consolidates the results obtained by the single processes into a consistent structure
of collections that are stored on the file system.
"""
# Consolidate the results
directory_pattern = os.sep.join([self.result_directory, "{*",])
dataset_pathes = glob.glob(directory_pattern)
# For all collections found
for dataset_path in dataset_pathes:
try:
# Load their meta_data
meta_data = BaseDataset.load_meta_data(dataset_path)
# Determine author and date
author = get_author()
date = time.strftime("%Y%m%d_%H_%M_%S")
# Update meta data and store it
meta_data.update({"author": author, "date": date})
# There can be either run dirs, persistency dirs, or both of them.
# Check of whichever there are more. If both exist, their numbers
# are supposed to be equal.
nr_run_dirs = len(glob.glob(os.path.join(dataset_path, "data_run*")))
nr_per_dirs = len(glob.glob(os.path.join(dataset_path, "persistency_run*")))
nr_runs = max(nr_run_dirs, nr_per_dirs)
if nr_runs > 1:
meta_data["runs"] = nr_runs
# Store the metadata
BaseDataset.store_meta_data(dataset_path, meta_data)
# Copy the input dataset specification file to the result
# directory in order to make later analysis of
# the results more easy
# THA: Split the first "/" from the input collection name, because otherwise it will be treated
# as an absolute path
input_collection_name = meta_data["input_dataset_name"][1:] if \
meta_data["input_dataset_name"][0] == os.sep else meta_data["input_dataset_name"]
input_meta_path = os.path.join(pySPACE.configuration.storage, input_collection_name)
try:
input_meta = BaseDataset.load_meta_data(input_meta_path)
BaseDataset.store_meta_data(dataset_path, input_meta, file_name="input_metadata.yaml")
except (IOError, OSError) as e:
self._log("Error copying the input_metadata.yaml: {error}".format(error=e.message),
level=logging.CRITICAL)
except Exception as e:
logging.getLogger("%s" % self).exception("Error updating the metadata: {error!s}".format(error=e))
raise e
# If we don't create a feature vector or time series collection,
# we evaluated our classification using a classification performance sink.
# The resulting files should be merged to one csv tabular.
pathlist = glob.glob(os.path.join(self.result_directory,"results_*"))
if len(pathlist)>0:
# Do the consolidation the same way as for WekaClassificationOperation
self._log("Consolidating results ...")
# We load and store the results once into a PerformanceResultSummary
# This does the necessary consolidation...
self._log("Reading intermediate results...")
try:
result_collection = PerformanceResultSummary(dataset_dir=self.result_directory)
self._log("done")
self._log("Storing result collection")
result_collection.store(self.result_directory)
self._log("done")
PerformanceResultSummary.merge_traces(self.result_directory)
except Exception as e:
logging.getLogger("%s" % self).exception("Error merging the result collection: {error!s}".format(
error=e))
if self.compression:
# Since we get one result summary,
# we don't need the numerous folders.
# So we zip them to make the whole folder more easy visible.
import zipfile
cwd = os.getcwd()
os.chdir(self.result_directory)
# If there are to many or to large folders, problems may occur.
# This case we want to log, try 64 bit mode,
# and then skip the zipping.
try:
pathlist = glob.glob(os.path.join(self.result_directory,"{*}"))
if not self.compression == "delete":
save_file = zipfile.ZipFile(
self.result_directory+'/result_folders.zip',
mode="w", compression=self.compression)
# we want to have the zipped file relative to the
# result directory
for path in pathlist:
for node in os.walk(path):
rel_path=os.path.relpath(node[0],
self.result_directory)
save_file.write(rel_path)
for data in node[2]:
save_file.write(os.path.join(rel_path,
data))
save_file.close()
# To still have an easy access to the history of the
# processing, we keep one folder.
pathlist.pop()
for path in pathlist:
shutil.rmtree(path)
except Exception, e:
self._log("Result files could not be compressed with 32"+
" bit mode, switching to 64 bit mode",
level=logging.CRITICAL)
# nearly total code copy, only difference with 64 bit mode
try:
pathlist = glob.glob(os.path.join(self.result_directory,"{*}"))
save_file=zipfile.ZipFile(
self.result_directory+'/result_folders.zip',
mode="w", compression=self.compression,
allowZip64=True)
# we want to have the zipped file relative to the
# result directory
for path in pathlist:
for node in os.walk(path):
rel_path = os.path.relpath(node[0],
self.result_directory)
save_file.write(rel_path)
for data in node[2]:
save_file.write(os.path.join(rel_path,data))
save_file.close()
# To still have an easy access to the history of the
# processing, we keep one folder.
pathlist.pop()
for path in pathlist:
shutil.rmtree(path)
except:
self._log("64 bit mode also failed. Please check your files and your code or contact your local programmer!", level=logging.CRITICAL)
os.chdir(cwd)
# Copy the csv-files to a temporary directory such that there is no risk of
# interfering with the running operation
input_dir = sys.argv[1]
temp_dir = tempfile.mkdtemp()
for filename in fnmatch.filter(os.listdir(os.path.abspath(input_dir)),
"*.csv"):
shutil.copy(input_dir + os.sep + filename, temp_dir)
file_path = os.path.dirname(os.path.abspath(__file__))
pyspace_path = file_path[:file_path.rfind('pySPACE')-1]
if not pyspace_path in sys.path:
sys.path.append(pyspace_path)
# Import csv-analysis and merge csv files
from pySPACE.resources.dataset_defs.performance_result import PerformanceResultSummary
PerformanceResultSummary.merge_performance_results(temp_dir)
# Invoke results analysis gui
from pySPACE.run.gui.performance_results_analysis import PerformanceResultsAnalysisMainWindow
app = QtGui.QApplication(sys.argv)
performance_results_analysis = \
PerformanceResultsAnalysisMainWindow(temp_dir + os.sep + "results.csv")
performance_results_analysis.show()
# Clean up
shutil.rmtree(temp_dir)
sys.exit(app.exec_())