your_AI_DIY_parameters.py

# Copyright (C) 2024 Antonio Rodriguez
# 
# This file is part of Personalized-AI-Based-Do-It-Yourself-Glucose-Prediction-tool.
# 
# Personalized-AI-Based-Do-It-Yourself-Glucose-Prediction-tool is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# 
# Personalized-AI-Based-Do-It-Yourself-Glucose-Prediction-tool is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
# 
# You should have received a copy of the GNU General Public License
# along with Personalized-AI-Based-Do-It-Yourself-Glucose-Prediction-tool.  If not, see <http://www.gnu.org/licenses/>.

# your_AI_DIY_parameters.py
# This module contains the FINAL PARAMETERS for the DIY tool.
# This is, the once that will be used for the generation and 
# execution of the personalized AI model. This file is intented
# to be changed just BY DEVELOPERS/RESEARCHERS that have validated
# the obtained results. Otherwise, leave it as it is.  

from sensor_params import *

first_DIY_version = {'N' : 96, # Input window length
                'step' : 1, # Step to sweep the CGM data to generate the dataset
                'PH' : 30, # Prediction Horizon 
                'single_multi_step' : 'multi', # 'multi' if more than one step ahead is performed. 'single' for one step. 
                'partition' : 'month-wise-4-folds', # Cross-Validation partition (to add more, the correspondent functions must be implemented)
                'input_features' : 2, # Currently 1 means only CGM, and 2 means CGM and its derivative
                'normalization' : 'min-max', # Normalization (to add more, the correspondent functions must be implemented)
                'under_over_sampling' : None, # Currently only undersampling was implemented, without positive results
                'model' : 'LSTM', # Currently: 'DIL-1D-UNET' or 'StackedLSTM' supported. Add whatever you want in 'models' folder and add it in the main_libreview.py
                'loss_function' : 'ISO_loss' # Loss function to generate your model. 'root_mean_squared_error' (actually MSE) is also supported 
                }

# PLEASE, CHECK THE IMPLICATIONS OF THESE CHANGES ON THE DIY_top_module.py code, since not all of this is currently parametrized.