from utils import load_yaml_config, Config # ______ # CONFIG: config = Config() config.ocean_traits = [0, 1, 2, 3, 4] # OCEAN personality traits to which perform the coherence test: O:0, C:1, E:2, A:3, N:4. config.distances = [0, 4] # Distances to which perform the coherence test. config.max_neigs = 12500 # Maximum number of unknown neighbors to return in the case of distance>0. # 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.
