# Use None or 0 if you want to return all possible neighbors in the select distance.
config.batch_size = 32
# Training batch size of fnn models.
config.epochs = [50, 300]
config.epochs_train2 = 300
config.epochs_interval = 50
# Epochs is a list of len=2 containing the range of epochs after which stop training of M1 models and train a new model M2.
# M1's training will stop after epochs[0]+n*interval such that n>0 and epochs[0]+n*interval<=epochs[1]
# M2's training will last epochs_train2 epochs.
config.epochs_interval_evaluation = 1
# M2's training will stop epochs_interval_evaluation epochs to evaluate performance
# M1's training will stop to evaluate performance only if test1=True
config.folds_number = 10
# Numbers of K-fold CV folds.
config.embedding_name = "tuned_embedding"
# The embedding to be used. There must be a directory containing the embedding in data folder.
config.test1 = False
# True if you want to evaluate M1's performances trainings on test set. Use False to skip the evaluation.
config.OUTPUTS_DIR = None
# The base path in which tests' outputs will be saved. Set as None if you want to store them in project's dir.
config.embedding_dict_to_use = None
# If you want to use the dictionary of another embedding, set this parameter with the embedding name. Use None otherwise.
# There must be a directory containing the embedding in data folder.
config = load_yaml_config(
config,
os.path.join(
os.path.dirname(os.path.abspath(__file__)), "coherence_test_config.yaml"
),
)
