print(c_matrix)
print(
classification_report(results_data["actual"],
results_data["predicted"]))
logger.log_message(results_data.head(20))
logger.log_message(c_matrix)
logger.log_message(
classification_report(results_data["actual"],
results_data["predicted"]))
if __name__ == '__main__':
# training
logger.log_message("start training")
utils.update_files_list(os.path.join(data_path, "train"))
train_data = prepare_dataset(utils.get_files())
logger.log_message("Train data procession complete")
modeller = prepare_model(train_data)
# save model
logger.log_message("Training model saved at model_" + session_id)
modeller.get_model().save(
os.path.join(save_model_path, "model_" + session_id))
# testing
logger.log_message("start testing")
utils.update_files_list(os.path.join(data_path, "test"))
test_data = prepare_dataset(utils.get_files(), False)
logger.log_message("Test data procession complete")
predicted_results = modeller.predict_output(test_data)
