def tune_model_weights():
parser = generate.get_parser_with_args()
parser = add_tune_args(parser)
args = options.parse_args_and_arch(parser)
print(args.model_weights)
n_models = len(args.path.split(CHECKPOINT_PATHS_DELIMITER))
print(n_models)
weight_grid = np.linspace(
args.weight_lower_bound, args.weight_upper_bound, args.n_grid + 1
)
weight_vec_aux = list(itertools.product(weight_grid, weight_grid))
weight_vec = []
for w1, w2 in weight_vec_aux:
weight_sum = w1 + w2
if weight_sum <= 1:
w3 = 1 - weight_sum
weight_vec.append(str(w1) + "," + str(w2) + "," + str(w3))
print(len(weight_vec))
output = pd.DataFrame()
for weight in weight_vec:
args.model_weights = weight
print(args.model_weights)
generate.validate_args(args)
score = generate.generate(args)
print(score)
output = output.append(
{"weight": args.model_weights, "bleu_score": score}, ignore_index=True
)
output.to_csv(args.output_file_name)
return output
def generate_main(data_dir, extra_flags=None):
parser = generate.get_parser_with_args()
args = options.parse_args_and_arch(
parser,
[
"--source-vocab-file",
os.path.join(data_dir, "dictionary-in.txt"),
"--target-vocab-file",
os.path.join(data_dir, "dictionary-out.txt"),
"--source-text-file",
os.path.join(data_dir, "test.in"),
"--target-text-file",
os.path.join(data_dir, "test.out"),
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--beam",
"3",
"--length-penalty",
"0.0",
"--batch-size",
"64",
"--max-len-b",
"5",
"--no-progress-bar",
] + (extra_flags or []),
)
generate.validate_args(args)
generate.generate(args)
def evaluation_function(parameterization):
w1 = parameterization.get("w1")
w2 = parameterization.get("w2")
w3 = parameterization.get("w3")
weight = str(w1) + "," + str(w2) + "," + str(w3)
args.model_weights = weight
generate.validate_args(args)
score = generate.generate(args)
return {"bleu_score": (score, 0.0)}
