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)