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rerank_tune.py 2.81 KB
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Nathan Ng's avatar
Backward reranking public (#667)  
Nathan Ng committed
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import rerank
import argparse
import numpy as np
import random
from examples.noisychannel import rerank_options
from fairseq import 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)
Myle Ott's avatar
Small fixes  
Myle Ott committed
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    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)
    ])
Nathan Ng's avatar
Backward reranking public (#667)  
Nathan Ng committed
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    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()