Spaces:
Runtime error
Runtime error
File size: 3,166 Bytes
ee21b96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import random
import numpy as np
from fairseq import options
from examples.noisychannel import rerank, rerank_options
def random_search(args):
param_values = []
tuneable_parameters = ["lenpen", "weight1", "weight2", "weight3"]
initial_params = [args.lenpen, args.weight1, args.weight2, args.weight3]
for i, elem in enumerate(initial_params):
if type(elem) is not list:
initial_params[i] = [elem]
else:
initial_params[i] = elem
tune_parameters = args.tune_param.copy()
for i in range(len(args.tune_param)):
assert args.upper_bound[i] >= args.lower_bound[i]
index = tuneable_parameters.index(args.tune_param[i])
del tuneable_parameters[index]
del initial_params[index]
tune_parameters += tuneable_parameters
param_values += initial_params
random.seed(args.seed)
random_params = np.array(
[
[
random.uniform(args.lower_bound[i], args.upper_bound[i])
for i in range(len(args.tune_param))
]
for k in range(args.num_trials)
]
)
set_params = np.array(
[
[initial_params[i][0] for i in range(len(tuneable_parameters))]
for k in range(args.num_trials)
]
)
random_params = np.concatenate((random_params, set_params), 1)
rerank_args = vars(args).copy()
if args.nbest_list:
rerank_args["gen_subset"] = "test"
else:
rerank_args["gen_subset"] = args.tune_subset
for k in range(len(tune_parameters)):
rerank_args[tune_parameters[k]] = list(random_params[:, k])
if args.share_weights:
k = tune_parameters.index("weight2")
rerank_args["weight3"] = list(random_params[:, k])
rerank_args = argparse.Namespace(**rerank_args)
best_lenpen, best_weight1, best_weight2, best_weight3, best_score = rerank.rerank(
rerank_args
)
rerank_args = vars(args).copy()
rerank_args["lenpen"] = [best_lenpen]
rerank_args["weight1"] = [best_weight1]
rerank_args["weight2"] = [best_weight2]
rerank_args["weight3"] = [best_weight3]
# write the hypothesis from the valid set from the best trial
if args.gen_subset != "valid":
rerank_args["gen_subset"] = "valid"
rerank_args = argparse.Namespace(**rerank_args)
rerank.rerank(rerank_args)
# test with the best hyperparameters on gen subset
rerank_args = vars(args).copy()
rerank_args["gen_subset"] = args.gen_subset
rerank_args["lenpen"] = [best_lenpen]
rerank_args["weight1"] = [best_weight1]
rerank_args["weight2"] = [best_weight2]
rerank_args["weight3"] = [best_weight3]
rerank_args = argparse.Namespace(**rerank_args)
rerank.rerank(rerank_args)
def cli_main():
parser = rerank_options.get_tuning_parser()
args = options.parse_args_and_arch(parser)
random_search(args)
if __name__ == "__main__":
cli_main()
|