#### 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 len(datanames)>0:
vprint( verbose, "************************************************************************")
vprint( verbose, "****** Attempting to copy files (from res/) for RESULT submission ******")
vprint( verbose, "************************************************************************")
OK = data_io.copy_results(datanames, res_dir, output_dir, verbose) # DO NOT REMOVE!
if OK:
vprint( verbose, "[+] Success")
datanames = [] # Do not proceed with learning and testing
else:
vprint( verbose, "======== Some missing results on current datasets!")
vprint( verbose, "======== Proceeding to train/test:\n")
# =================== End @RESULT SUBMISSION (KEEP THIS) ==================
# ================ @CODE SUBMISSION (SUBTITUTE YOUR CODE) =================
overall_time_budget = 0
for basename in datanames: # Loop over datasets
vprint( verbose, "************************************************")
vprint( verbose, "******** Processing dataset " + basename.capitalize() + " ********")
vprint( verbose, "************************************************")
#### 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 len(datanames)>0:
vprint( verbose, "************************************************************************")
vprint( verbose, "****** Attempting to copy files (from res/) for RESULT submission ******")
vprint( verbose, "************************************************************************")
datanames = data_io.copy_results(datanames, res_dir, output_dir, verbose) # DO NOT REMOVE!
if not datanames:
vprint( verbose, "[+] Results copied to output directory, no model trained/tested")
else:
vprint( verbose, "======== Some missing results on current datasets!")
vprint( verbose, "======== Proceeding to train/test:\n")
# =================== End @RESULT SUBMISSION (KEEP THIS) ==================
# ================ @CODE SUBMISSION =================
overall_time_budget = 0
time_left_over = 0
for basename in datanames: # Loop over datasets
vprint( verbose, "************************************************")
vprint( verbose, "******** Processing dataset " + basename.capitalize() + " ********")
vprint( verbose, "************************************************")
# Input / Output
if len(argv) == 1:
input_dir = os.path.join(CONFIG['root_dir'], '..', 'data')
output_dir = CONFIG['default_output_dir']
else:
input_dir = argv[1]
output_dir = os.path.abspath(argv[2])
data_io.mvdir(output_dir, output_dir + '_' + the_date)
data_io.mkdir(output_dir)
datanames = data_io.inventory_data(input_dir)
# datanames = ["madeline"]
# Result submission : copy files from /res to output dir
OK = data_io.copy_results(datanames, res_dir, output_dir, False)
if OK:
print "Result files copied output dir. Not running train code."
execution_success = True
else:
execution_success = predict(datanames, input_dir)
if execution_success and CONFIG["zipme"]:
submission_filename = "../automl_{}_{}.zip".format(CONFIG['codename'], the_date)
data_io.zipdir(submission_filename, ".")
if CONFIG['running_on_codalab']:
if execution_success:
exit(0)
else:
exit(1)
