wehub-resource-sync
103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import random
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import numpy as np
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from fairseq import options
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from examples.noisychannel import rerank, rerank_options
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def random_search(args):
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param_values = []
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tuneable_parameters = ["lenpen", "weight1", "weight2", "weight3"]
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initial_params = [args.lenpen, args.weight1, args.weight2, args.weight3]
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for i, elem in enumerate(initial_params):
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if type(elem) is not list:
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initial_params[i] = [elem]
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else:
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initial_params[i] = elem
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tune_parameters = args.tune_param.copy()
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for i in range(len(args.tune_param)):
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assert args.upper_bound[i] >= args.lower_bound[i]
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index = tuneable_parameters.index(args.tune_param[i])
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del tuneable_parameters[index]
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del initial_params[index]
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tune_parameters += tuneable_parameters
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param_values += initial_params
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random.seed(args.seed)
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random_params = np.array(
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[
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[
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random.uniform(args.lower_bound[i], args.upper_bound[i])
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for i in range(len(args.tune_param))
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]
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for k in range(args.num_trials)
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]
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)
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set_params = np.array(
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[
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[initial_params[i][0] for i in range(len(tuneable_parameters))]
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for k in range(args.num_trials)
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]
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)
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random_params = np.concatenate((random_params, set_params), 1)
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rerank_args = vars(args).copy()
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if args.nbest_list:
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rerank_args["gen_subset"] = "test"
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else:
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rerank_args["gen_subset"] = args.tune_subset
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for k in range(len(tune_parameters)):
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rerank_args[tune_parameters[k]] = list(random_params[:, k])
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if args.share_weights:
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k = tune_parameters.index("weight2")
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rerank_args["weight3"] = list(random_params[:, k])
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rerank_args = argparse.Namespace(**rerank_args)
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best_lenpen, best_weight1, best_weight2, best_weight3, best_score = rerank.rerank(
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rerank_args
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)
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rerank_args = vars(args).copy()
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rerank_args["lenpen"] = [best_lenpen]
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rerank_args["weight1"] = [best_weight1]
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rerank_args["weight2"] = [best_weight2]
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rerank_args["weight3"] = [best_weight3]
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# write the hypothesis from the valid set from the best trial
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if args.gen_subset != "valid":
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rerank_args["gen_subset"] = "valid"
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rerank_args = argparse.Namespace(**rerank_args)
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rerank.rerank(rerank_args)
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# test with the best hyperparameters on gen subset
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rerank_args = vars(args).copy()
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rerank_args["gen_subset"] = args.gen_subset
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rerank_args["lenpen"] = [best_lenpen]
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rerank_args["weight1"] = [best_weight1]
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rerank_args["weight2"] = [best_weight2]
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rerank_args["weight3"] = [best_weight3]
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rerank_args = argparse.Namespace(**rerank_args)
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rerank.rerank(rerank_args)
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def cli_main():
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parser = rerank_options.get_tuning_parser()
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args = options.parse_args_and_arch(parser)
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random_search(args)
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if __name__ == "__main__":
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cli_main()
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