def evaluate_main():
positive_class_weight = 2.0
label = 'label_needsmoderation'
logger.info(f'Label: {label}')
data = m.Posts()
data.set_label(label=label)
data.set_balance_method(balance_method='translate', sampling_strategy=0.9)
state_dict = 'model_gbertbase_label_needsmoderation_210423_014254'
logger.info(f"Loading model from state_dict ./models/{state_dict}.bin")
load_model_and_evaluate(state_dict,data, label, 2.0)
def grid_search_main():
TARGET_LABELS = ['label_discriminating', 'label_inappropriate',
'label_sentimentnegative', 'label_needsmoderation']
#TARGET_LABELS = ['label_needsmoderation']
TRANS_OS = {'translate':[0.9], 'oversample':[0.9]}
LEARNING_RATES = [1e-5, 5e-6, 25e-7, 125e-8]
POSITIVE_WEIGHTS = [2., 3.]
for positive_class_weight in POSITIVE_WEIGHTS:
for learning_rate in LEARNING_RATES:
for label in TARGET_LABELS:
for method, strat in TRANS_OS.items():
for strategy in strat:
data = m.Posts()
data.set_label(label=label)
data.set_balance_method(balance_method=method, sampling_strategy=strategy)
logger.info('-' * 50)
logger.info(f'positive_class_weight: {positive_class_weight}, learning-rate: {learning_rate}')
logger.info(f'Label: {label}')
logger.info(f'Balance-method: {method}, Balance-strategy: {strategy}')
logger.info('-' * 50)
make_model(data, label, learning_rate, positive_class_weight)
import mlflow
from modeling.config import TRACKING_URI, EXPERIMENT_NAME #, TRACKING_URI_DEV
import logging
from time import time
# set logging
logger = logging.getLogger(__name__)
logging.basicConfig(format="%(asctime)s: %(message)s")
logging.getLogger("pyhive").setLevel(logging.CRITICAL) # avoid excessive logs
logger.setLevel(logging.INFO)
if __name__ == "__main__":
data = m.Posts()
#embedding_dict_glove = transformers.load_embedding_vectors(embedding_style='glove')
#embedding_dict_w2v = transformers.load_embedding_vectors(embedding_style='word2vec')
trans_os = {
None: [None],
'translate': [0.8, 0.9, 1.0],
'oversample': [0.8, 0.9, 1.0]
}
#vecs = {CountVectorizer(): 'count',
# TfidfVectorizer(): 'tfidf',
# transformers.MeanEmbeddingVectorizer(embedding_dict=embedding_dict_glove): 'glove',
# transformers.MeanEmbeddingVectorizer(embedding_dict=embedding_dict_w2v): 'word2vec',
# }
vecs = {