コード例 #1
0
    learner_name = job_description["learner"]
    fit_params = copy.deepcopy(job_description["fit_params"])
    learner_params = job_description["learner_params"]
    hp_iters = int(job_description["hp_iters"])
    hp_ranges = copy.deepcopy(job_description["hp_ranges"])
    hp_fit_params = copy.deepcopy(job_description["hp_fit_params"])
    learning_problem = job_description["learning_problem"]
    experiment_schema = job_description["experiment_schema"]
    experiment_table = job_description["experiment_table"]
    validation_loss = job_description["validation_loss"]
    hash_value = job_description["hash_value"]
    random_state = np.random.RandomState(seed=seed + fold_id)
    optimizer_path = os.path.join(DIR_PATH, OPTIMIZER_FOLDER, "{}".format(hash_value))
    hash_file = os.path.join(DIR_PATH, MODEL_FOLDER, "{}.h5".format(hash_value))

    configure_numpy_keras(seed=seed)
    dataset_params['random_state'] = random_state
    dataset_params['fold_id'] = fold_id
    dataset_reader = get_dataset_reader(dataset_name, dataset_params)
    inner_cv = ShuffleSplit(n_splits=n_inner_folds, test_size=0.1, random_state=random_state)
    if learner_name in [MNL, PCL, NLM, GEV, MLM]:
        fit_params['random_seed'] = seed + fold_id
        fit_params['vi_params']["callbacks"] = [pm.callbacks.CheckParametersConvergence(diff="absolute", tolerance=0.01
                                                                                        , every=50)]
    optimizer = load(optimizer_path)
    if "ps" in optimizer.acq_func:
        best_i = np.argmin(np.array(optimizer.yi)[:, 0])
    else:
        best_i = np.argmin(optimizer.yi)
    best_point = optimizer.Xi[best_i]
    best_loss = optimizer.yi[best_i]
コード例 #2
0
import inspect
import logging
import os

from csrank import TagGenomeObjectRankingDatasetReader
from csrank.tensorflow_util import configure_numpy_keras
from csrank.util import setup_logging

if __name__ == '__main__':
    DIR_PATH = os.path.dirname(
        os.path.abspath(inspect.getfile(inspect.currentframe())))
    log_path = os.path.join(DIR_PATH, 'logs', 'prepare_tag_genome.log')
    setup_logging(log_path=log_path)
    configure_numpy_keras(seed=42)
    logger = logging.getLogger('TagGenome')
    logger.info("ccs req id : {}".format(os.environ['CCS_REQID']))
    tg = TagGenomeObjectRankingDatasetReader()