elif opt == "--output_format":
out_format = val
elif opt == "--core_in_dir":
core_in_dir = val
elif opt == "--core_filter":
core_filter = val
elif opt in ("-h", "--help"):
usage()
sys.exit(0)
else:
usage(1)
log_utils.config_logging(log_file)
utils.assert_option_not_none(in_file_prefix, "Input file prefix required", usage)
utils.assert_option_not_none(out_dir, "Output directory required", usage)
utils.assert_option_not_none(in_format, "Input file format required", usage)
gz = eval(gz)
utils.assert_bool(gz, "--gz value must be True/False", usage)
if not core_in_dir is None:
build_space_batch(in_file_prefix, in_format, out_dir, out_format, core_in_dir, core_filter, gz)
else:
utils.assert_option_not_none(core_space_file, "Input file required", usage)
build_space(in_file_prefix, in_format, out_dir, out_format, core_space_file, gz)
if __name__ == "__main__":
main(sys.argv)
usage()
sys.exit(0)
else:
usage(1)
log_utils.config_logging(log_file)
utils.assert_option_not_none(in_file, "Input file required", usage)
utils.assert_option_not_none(out_dir, "Output directory required", usage)
utils.assert_option_not_none(model, "Model to be trained required", usage)
utils.assert_option_not_none(arg_space, "Argument space(s) file(s) required", usage)
utils.assert_option_not_none(phrase_space, "Phrase space file required", usage)
crossvalidation = eval(crossvalidation)
intercept = eval(intercept)
utils.assert_bool(intercept, "intercept must be True/False", usage)
utils.assert_bool(crossvalidation, "crossvalidation must be True/False", usage)
export_params = eval(export_params)
utils.assert_bool(export_params, "export_params must be True/False", usage)
if not param is None:
param = float(param)
if not param_range is None:
param_range = [float(param) for param in param_range]
if not crossvalidation and regression == "ridge":
utils.assert_option_not_none(param, "Cannot run (no-crossvalidation) RidgeRegression with no lambda value!", usage)
train_model(in_file, out_dir, model, arg_space, phrase_space, regression,
crossvalidation, intercept, param, param_range, export_params)
else:
usage(1)
log_utils.config_logging(log_file)
utils.assert_option_not_none(in_file, "Input file required", usage)
utils.assert_option_not_none(out_dir, "Output directory required", usage)
utils.assert_option_not_none(model, "Model to be trained required", usage)
utils.assert_option_not_none(arg_space,
"Argument space(s) file(s) required", usage)
utils.assert_option_not_none(phrase_space, "Phrase space file required",
usage)
crossvalidation = eval(crossvalidation)
intercept = eval(intercept)
utils.assert_bool(intercept, "intercept must be True/False", usage)
utils.assert_bool(crossvalidation, "crossvalidation must be True/False",
usage)
export_params = eval(export_params)
utils.assert_bool(export_params, "export_params must be True/False", usage)
if not param is None:
param = float(param)
if not param_range is None:
param_range = [float(param) for param in param_range]
if not crossvalidation and regression == "ridge":
utils.assert_option_not_none(
param,
"Cannot run (no-crossvalidation) RidgeRegression with no lambda value!",
usage)
normalizations = val.split(",")
elif opt in ("-l", "--log"):
log_file = val
elif opt == "--input_format":
in_format = val
elif opt == "--output_format":
out_format = val
elif opt in ("-h", "--help"):
usage(0)
else:
usage(1)
if not log_file is None:
log_utils.config_logging(log_file)
utils.assert_option_not_none(in_file_prefix, "Input file prefix required",
usage)
utils.assert_option_not_none(out_dir, "Output directory required", usage)
utils.assert_option_not_none(in_format, "Input format required", usage)
gz = eval(gz)
utils.assert_bool(gz, "--gz value must be True/False", usage)
build_spaces(in_file_prefix, in_format, out_dir, out_format, weightings,
selections, reductions, normalizations, gz)
if __name__ == '__main__':
main(sys.argv)
