def parse_timing_to_csv(filename, outfile='parsed_timing.csv'):
#drive, path = os.path.splitdrive(filename)
#outpath, file_nameonly = os.path.split(path)
with open(filename) as fh:
lines = []
for line in fh:
lines.append(xu.parse_hadoop_line(line))
header = [
'num_rows', 'num_cols', 'num_clusters', 'num_views', 'time_per_step',
'which_kernel'
]
reduced_lines = map(lambda x: x[1], lines)
with open(outfile, 'w') as csvfile:
csvwriter = csv.writer(csvfile, delimiter=',')
csvwriter.writerow(header)
for reduced_line in reduced_lines:
try:
parsed_line = parse_reduced_line(reduced_line)
csvwriter.writerow(parsed_line)
except Exception as e:
pass
def process_line(line, table_data):
key, dict_in = xu.parse_hadoop_line(line)
if dict_in is None:
return None, None
command = dict_in['command']
method = method_lookup[command]
ret_dict = method(table_data, dict_in)
return key, ret_dict
def process_line(line, table_data):
key, dict_in = xu.parse_hadoop_line(line)
if dict_in is None:
return None, None
command = dict_in['command']
method = method_lookup[command]
ret_dict = method(table_data, dict_in)
return key, ret_dict
def parse_to_csv(in_filename, out_filename='parsed_convergence.csv'):
variable_names_to_extract = ['num_rows', 'num_cols', 'num_clusters', 'num_views',
'max_mean', 'n_steps', 'block_size','column_ari_list',
'generative_mean_test_log_likelihood','mean_test_ll_list',
'elapsed_seconds_list']
header = ['experiment'] + variable_names_to_extract
with open(in_filename) as in_fh:
with open(out_filename,'w') as out_fh:
csvwriter = csv.writer(out_fh)
csvwriter.writerow(header)
for line in in_fh:
try:
parsed_line = xu.parse_hadoop_line(line)
output_row = parsed_line_to_output_row(parsed_line,
variable_names_to_extract=variable_names_to_extract)
csvwriter.writerow(output_row)
except Exception as e:
sys.stderr.write(line + '\n' + str(e) + '\n')
def parse_timing_to_csv(filename, outfile='parsed_timing.csv'):
#drive, path = os.path.splitdrive(filename)
#outpath, file_nameonly = os.path.split(path)
with open(filename) as fh:
lines = []
for line in fh:
lines.append(xu.parse_hadoop_line(line))
header = ['num_rows', 'num_cols', 'num_clusters', 'num_views', 'time_per_step', 'which_kernel']
reduced_lines = map(lambda x: x[1], lines)
with open(outfile,'w') as csvfile:
csvwriter = csv.writer(csvfile,delimiter=',')
csvwriter.writerow(header)
for reduced_line in reduced_lines:
try:
parsed_line = parse_reduced_line(reduced_line)
csvwriter.writerow(parsed_line)
except Exception as e:
pass
def read_hadoop_output_file(hadoop_output_filename):
with open(hadoop_output_filename) as fh:
ret_dict = dict([xu.parse_hadoop_line(line) for line in fh])
return ret_dict
def hadoop_to_dict_generator(test_key_file_object): # return read cursor to the start (or this generator cannot be called again) test_key_file_object.seek(0) for line in test_key_file_object: dict_line = xu.parse_hadoop_line(line) yield dict_line
for reduced_line in reduced_lines:
try:
parsed_line = parse_reduced_line(reduced_line)
csvwriter.writerow(parsed_line)
except Exception as e:
pass
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('filename', type=str)
args = parser.parse_args()
filename = args.filename
with open(filename) as fh:
lines = []
for line in fh:
lines.append(xu.parse_hadoop_line(line))
header = 'num_rows,num_cols,num_clusters,num_views,time_per_step,which_kernel'
print(header)
reduced_lines = map(lambda x: x[1], lines)
for reduced_line in reduced_lines:
try:
parsed_line = parse_reduced_line(reduced_line)
print(','.join(map(str, parsed_line)))
except Exception as e:
pass
csvwriter.writerow(header)
for reduced_line in reduced_lines:
try:
parsed_line = parse_reduced_line(reduced_line)
csvwriter.writerow(parsed_line)
except Exception as e:
pass
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('filename', type=str)
args = parser.parse_args()
filename = args.filename
with open(filename) as fh:
lines = []
for line in fh:
lines.append(xu.parse_hadoop_line(line))
header = 'num_rows,num_cols,num_clusters,num_views,time_per_step,which_kernel'
print(header)
reduced_lines = map(lambda x: x[1], lines)
for reduced_line in reduced_lines:
try:
parsed_line = parse_reduced_line(reduced_line)
print(','.join(map(str, parsed_line)))
except Exception as e:
pass
testlist.append(impute_run_parameters)
test_idx += 1
print "Done."
# table data is empty because we generate it in the mapper
table_data=dict(T=[],M_c=[],X_L=[],X_D=[])
fu.pickle(table_data, table_data_filename)
#####################
if do_local:
output_filename = os.path.join(directory_path, "output_local")
output_file_object = open(output_filename, 'ab')
with open(input_filename,'rb') as infile:
for line in infile:
key, test_dict = xu.parse_hadoop_line(line)
ret_dict = run_mi_test_local.run_mi_test_local(test_dict)
xu.write_hadoop_line(output_file_object, key, ret_dict)
print "%s\n\t%s" % (str(test_dict), str(ret_dict))
output_file_object.close()
# generate the csv
parse_mi.parse_data_to_csv(input_filename, params_dict, test_idx, output_filename)
print "Done."
elif do_remote:
# generate the massive hadoop files
hadoop_engine = HE.HadoopEngine(output_path=output_path,
input_filename=input_filename,
table_data_filename=table_data_filename,
which_engine_binary=which_engine_binary,
hdfs_uri=hdfs_uri,
def hadoop_to_dict_generator(test_key_file_object):
# return read cursor to the start (or this generator cannot be called again)
test_key_file_object.seek(0)
for line in test_key_file_object:
dict_line = xu.parse_hadoop_line(line)
yield dict_line
def read_hadoop_output_file(hadoop_output_filename):
with open(hadoop_output_filename) as fh:
ret_dict = dict([xu.parse_hadoop_line(line) for line in fh])
return ret_dict
analyze=analyze_helper,
time_analyze=time_analyze_helper,
convergence_analyze=convergence_analyze_helper,
chunk_analyze=chunk_analyze_helper,
mi_analyze=mi_analyze_helper)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--table_data_filename',
type=str,
default=hs.default_table_data_filename)
parser.add_argument('--command_dict_filename',
type=str,
default=hs.default_command_dict_filename)
args = parser.parse_args()
table_data_filename = args.table_data_filename
command_dict_filename = args.command_dict_filename
table_data = fu.unpickle(table_data_filename)
command_dict = fu.unpickle(command_dict_filename)
command = command_dict['command']
method = method_lookup[command]
#
from signal import signal, SIGPIPE, SIG_DFL
signal(SIGPIPE, SIG_DFL)
for line in sys.stdin:
key, data_dict = xu.parse_hadoop_line(line)
ret_dict = method(table_data, data_dict, command_dict)
xu.write_hadoop_line(sys.stdout, key, ret_dict)
time_analyze=time_analyze_helper,
convergence_analyze=convergence_analyze_helper,
chunk_analyze=chunk_analyze_helper,
mi_analyze=mi_analyze_helper
)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--table_data_filename', type=str,
default=hs.default_table_data_filename)
parser.add_argument('--command_dict_filename', type=str,
default=hs.default_command_dict_filename)
args = parser.parse_args()
table_data_filename = args.table_data_filename
command_dict_filename = args.command_dict_filename
table_data = fu.unpickle(table_data_filename)
command_dict = fu.unpickle(command_dict_filename)
command = command_dict['command']
method = method_lookup[command]
#
from signal import signal, SIGPIPE, SIG_DFL
signal(SIGPIPE,SIG_DFL)
for line in sys.stdin:
key, data_dict = xu.parse_hadoop_line(line)
ret_dict = method(table_data, data_dict, command_dict)
xu.write_hadoop_line(sys.stdout, key, ret_dict)
