from modeling import Model
from cross_validation import CrossValidationDQNN
if __name__ == '__main__':
from config import config
model = Model(config=config)
model.add_csv_data('data/csv/test.csv')
model.train_model()
model.init_onehot()
cross_validation = CrossValidationDQNN(config=config)
estimate = cross_validation.run()
if __name__ == '__main__':
# create untrained Model object based on config.py
from config import config
model = Model(config=config)
# or load previously created (using the same DB) model with its own configuration
# model = Model(model_file="/home/user/repos/python_deep_reinforcement_sequence/data/LDA/model/1d439b93-fbc9-4ae4-b76d-7d1332259344_model.pickle")
# create main dictionary from .csv: this complete replaces data in DB.
# Call add_csv_data ONLY ONCE before the first launch of training!!
# model.add_csv_data('data/csv/test_seasons.csv') # this drops DB, but not deletes saved model files, built using it
# or create it from all .csv files in a given folder, assuming all of them have the proper format
model.add_csv_data('data/csv/test.csv') # this drops db, but not deletes saved model files, built using it
# a, b = model.get_part_of_data(parts=10)
model.compute_context_features([datetime.timedelta(days=2), datetime.timedelta(hours=1)])
print(1)
# train model
# If the description of a model with appropriate config parameters is found in db, the training phase will be skipped
# Otherwise, the new model will be trained and saved for further usage, model_id and config parameters will be added to DB
# Also this function creates word_id index according to occurrences_threshold parameter
model.train_model()
# get vector for the given text
text = "jun jul aug sep" # "This is the random text the following words are added in order to be recognized by dictionary: read, blog, post"
vectors = model.transforming(text)
print(vectors)
