execution_success = True
#### INPUT/OUTPUT: Get input and output directory names
if len(argv)==1: # Use the default input and output directories if no arguments are provided
input_dir = default_input_dir
output_dir = default_output_dir
else:
input_dir = argv[1]
output_dir = os.path.abspath(argv[2]);
vprint( verbose, "Using input_dir: " + input_dir)
vprint( verbose, "Using output_dir: " + output_dir)
# Move old results and create a new output directory
if not(running_on_codalab) and save_previous_results:
data_io.mvdir(output_dir, output_dir+'_'+the_date)
data_io.mkdir(output_dir)
#### INVENTORY DATA (and sort dataset names alphabetically)
datanames = data_io.inventory_data(input_dir)
# Overwrite the "natural" order
#### DEBUG MODE: Show dataset list and STOP
if debug_mode>=3:
data_io.show_io(input_dir, output_dir)
print('\n****** Sample code version ' + str(version) + ' ******\n\n' + '========== DATASETS ==========\n')
data_io.write_list(datanames)
datanames = [] # Do not proceed with learning and testing
# ==================== @RESULT SUBMISSION (KEEP THIS) =====================
# Always keep this code to enable result submission of pre-calculated results
for key in configuration:
configuration[key] = str(configuration[key])
try:
configuration[key] = float(configuration[key])
if np.abs(np.round(configuration[key], 0) -
configuration[key]) < 0.00001:
configuration[key] = np.round(configuration[key], 0).astype(int)
except Exception:
pass
# Move old results and create a new output directory
if not(running_on_codalab) and save_previous_results:
data_io.mvdir(res_dir, res_dir+'_'+the_date)
data_io.mkdir(res_dir)
#### INVENTORY DATA (and sort dataset names alphabetically)
datanames = data_io.inventory_data(data_dir)
# Overwrite the "natural" order
#### DEBUG MODE: Show dataset list and STOP
if debug_mode>=3:
data_io.show_io(data_dir, res_dir)
logger.info('****** Sample code version ' + str(version) + '******')
logger.info('========== DATASETS ==========')
data_io.write_list(datanames)
datanames = [] # Do not proceed with learning and testing
# =========================== BEGIN PROGRAM ================================
if __name__=="__main__" and debug_mode<4:
#### Check whether everything went well (no time exceeded)
execution_success = True
#### INPUT/OUTPUT: Get input and output directory names
if len(argv)==1: # Use the default input and output directories if no arguments are provided
input_dir = default_input_dir
output_dir = default_output_dir
else:
input_dir = argv[1]
output_dir = os.path.abspath(argv[2])
# Move old results and create a new output directory
if not(running_on_codalab):
data_io.mvdir(output_dir, '../'+output_dir+'_'+the_date)
data_io.mkdir(output_dir)
#### INVENTORY DATA (and sort dataset names alphabetically)
datanames = data_io.inventory_data(input_dir)
#### DEBUG MODE: Show dataset list and STOP
if debug_mode>=3:
data_io.show_io(input_dir, output_dir)
print('\n****** Sample code version ' + str(version) + ' ******\n\n' + '========== DATASETS ==========\n')
data_io.write_list(datanames)
datanames = [] # Do not proceed with learning and testing
# ==================== @RESULT SUBMISSION (KEEP THIS) =====================
# Always keep this code to enable result submission of pre-calculated results
# deposited in the res/ subdirectory.
if __name__ == "__main__" and debug_mode < 4:
#### Check whether everything went well (no time exceeded)
execution_success = True
#### INPUT/OUTPUT: Get input and output directory names
if len(
argv
) == 1: # Use the default input and output directories if no arguments are provided
input_dir = default_input_dir
output_dir = default_output_dir
else:
input_dir = argv[1]
output_dir = os.path.abspath(argv[2])
# Move old results and create a new output directory
if not (running_on_codalab):
data_io.mvdir(output_dir, '../' + output_dir + '_' + the_date)
data_io.mkdir(output_dir)
#### INVENTORY DATA (and sort dataset names alphabetically)
datanames = data_io.inventory_data(input_dir)
#### DEBUG MODE: Show dataset list and STOP
if debug_mode >= 3:
data_io.show_io(input_dir, output_dir)
print('\n****** Sample code version ' + str(version) + ' ******\n\n' +
'========== DATASETS ==========\n')
data_io.write_list(datanames)
datanames = [] # Do not proceed with learning and testing
# ==================== @RESULT SUBMISSION (KEEP THIS) =====================
# Always keep this code to enable result submission of pre-calculated results
print("Using submission_dir: " + submission_dir)
# Our libraries
path.append (program_dir)
path.append (submission_dir)
import data_io # general purpose input/output functions
from data_io import vprint # print only in verbose mode
from data_manager import DataManager # load/save data and get info about them
from model import model # example model, in scikit-learn style
if debug_mode >= 4: # Show library version and directory structure
data_io.show_dir(".")
# Move old results and create a new output directory (useful if you run locally)
if save_previous_results:
data_io.mvdir(output_dir, output_dir+'_'+the_date)
data_io.mkdir(output_dir)
#### INVENTORY DATA (and sort dataset names alphabetically)
datanames = data_io.inventory_data(input_dir)
# Overwrite the "natural" order
#### DEBUG MODE: Show dataset list and STOP
if debug_mode>=3:
data_io.show_version()
data_io.show_io(input_dir, output_dir)
print('\n****** Ingestion program version ' + str(version) + ' ******\n\n' + '========== DATASETS ==========\n')
data_io.write_list(datanames)
datanames = [] # Do not proceed with learning and testing
#### MAIN LOOP OVER DATASETS:
