# 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.
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"
),
)
config = Config()
config.ocean_traits = [0, 1, 2, 3, 4]
# OCEAN personality traits to which tune the embedding: O:0, C:1, E:2, A:3, N:4
config.epochs_number = 10
# NLP model's training epochs
config.num_reviews = 1500000
# number of reviews to use for training (training set + test set)
config.voc_dim = 6 * 10**4
# number of terms in the tuned embedding
config.train_zeros = False
# use True if you want to train weights representing padding's tokens, use False otherwise.
config.output_type = "mean"
# target of the model: 'mean' or 'sum' of known terms' scores in the review.
config.shuffle = True
# if True review from yelp dataset will be shuffled before extracting num_reviews reviews.
# if False the first num_reviews of yelp dataset will be extracted.
config.features_config = [100, int(100 / 2), int(100 / 4)]
# configuration of NLP model's architecture: features, filters and hidden units.
config.embedding_name = "new_tuned_embedding"
# name of the dir to be created that stores the tuned embedding.
config.load_reviews_from_scratch = False
# use False if you have already loaded and stored reviews, use True if you want to reload and restore reviews.
config.tune_embedding = True
# use True to train the model, use False otherwise (eg if you just want to load reviews).
config = load_yaml_config(
config,
os.path.join(os.path.dirname(os.path.abspath(__file__)),
"tune_embedding_config.yaml"),
)
raise Exception("The project dir's name must be 'personality_prediction'. Rename it.")
sys.path.append(os.getcwd())
# ______
# IMPORT:
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.batch_size = 32
# training batch size of fnn models.
config.folds_number = 10
# numbers of K-fold CV folds.
config.embedding_name = "glove"
# the embedding to be used. There must be a directory containing the embedding in data folder.
config.epochs = 300
# training's epochs number.
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__)), "kfcv_test_config.yaml"),
)
