FaithTrip

Brief Introduction

FaithTrip leverages Denoising Diffusion Probabilistic Models (DDPMs) to gradually align the generated trajectory with the tourist’s intent. The core idea stems from one of the characteristics of DDPM: the progressive data generation process. We employ an explicit condition-injecting strategy during the inference stage to achieve the alignment. This strategy progressively substitutes the source and destination of the generated trajectory with the ground truth of the source/destination (from the tourist’s query), enabling the model to iteratively refine itself and ultimately produce realistic, intent-consistent trajectories.

Environmental Requirements

We run the code on a computer with RTX3060, i5 12400F, and 16G memory. Please Install the dependencies via anaconda:

Create virtual environment

conda create -n FaithTrip python=3.9.18

Activate environment

conda activate FaithTrip

Install pytorch and cuda toolkit

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia

Install other requirements

conda install numpy pandas
pip install scikit-learn

Folder Structure

Folder Name	Description
asset	Metadata and preprocessing process
data	Preprocessed input data
results	Storage related experimental results
BASE	Transformer-Based model(BASE) and the Base using clipping-merging strategy(BASE-CM)
BASE-WSE	Transformer-Based model using weighted classification loss in start and end points
FaithTrip	the source code of FaithTrip with fast-sampling
FaithTrip-Z	FaithTrip predicts epsilon(EPS) and calculate x0
FaithTrip-S	FaithTrip w/o fast-sampling
README.md	This instruction document
run.bat	Script file for running this code

How to run our programs

The detailed operation mode and parameter settings of each model can be found in run.bat.

@echo off

REM Setting Python Interpreter Path
set python_path = Python location of your virtual environment

REM Run FaithTrip with Fast-Sampling
python .\FaithTrip\train_diffusion.py --dataset Osak --lr 0.01 --batch_size 4 --d_model 16 --step 32 --max_noise 0.02 --sampling 2

python .\FaithTrip\train_diffusion.py --dataset Glas --lr 0.01 --batch_size 4 --d_model 64 --step 32 --max_noise 0.02 --sampling 2

python .\FaithTrip\train_diffusion.py --dataset Edin --lr 0.01 --batch_size 16 --d_model 64 --step 32 --max_noise 0.06 --sampling 4

python .\FaithTrip\train_diffusion.py --dataset Toro --lr 0.01 --batch_size 8 --d_model 64 --step 32 --max_noise 0.06 --sampling 4

REM Run FaithTrip Standard w/o Fast-Sampling to predict x_0
REM The FaithTrip set default values T(step) in 32 and beta_t(max_noise) in 0.02, respectively. 
REM If you want to change it, try to add --step and --max_noise
python .\FaithTrip-S\train_diffusion.py --dataset Osak --lr 0.01 --batch_size 4 --d_model 16

python .\FaithTrip-S\train_diffusion.py --dataset Glas --lr 0.01 --batch_size 4 --d_model 64

python .\FaithTrip-S\train_diffusion.py --dataset Edin --lr 0.01 --batch_size 16 --d_model 64

python .\FaithTrip-S\train_diffusion.py --dataset Toro --lr 0.01 --batch_size 8 --d_model 64

REM Run FaithTrip-Z to predict noise
python .\FaithTrip-Z\train_diffusion.py --dataset Osak --lr 0.005 --batch_size 4 --d_model 32

python .\FaithTrip-Z\train_diffusion.py --dataset Glas --lr 0.005 --batch_size 4 --d_model 32

python .\FaithTrip-Ztrain_diffusion.py --dataset Edin --lr 0.005 --batch_size 16 --d_model 32

python .\FaithTrip-Z\train_diffusion.py --dataset Toro --lr 0.005 --batch_size 8 --d_model 32

REM Run BASE and BASE-CM
REM PS: max f1(pairs-f1) represent the results of BASE-CM and total f1(pairs-f1) represent the results of BASE
python .\BASE\train_base.py --dataset Osak --lr 0.001 --batch_size 4 --d_model 128

python .\BASE\train_base.py --dataset Glas --lr 0.001 --batch_size 4 --d_model 128

python .\BASE\train_base.py --dataset Edin --lr 0.001 --batch_size 16 --d_model 128

python .\BASE\train_base.py --dataset Toro --lr 0.001 --batch_size 8 --d_model 64

REM Run BASE-WSE
python .\BASE-WSE\train_base.py --dataset Osak --lr 0.001 --batch_size 4 --d_model 128 --se_weight 5

python .\BASE-WSE\train_base.py --dataset Glas --lr 0.001 --batch_size 4 --d_model 128 --se_weight 5

python .\BASE-WSE\train_base.py --dataset Edin --lr 0.001 --batch_size 16 --d_model 128 --se_weight 5

python .\BASE-WSE\train_base.py --dataset Toro --lr 0.001 --batch_size 8 --d_model 64 --se_weight 5

If your operating system is Windows, you can use the command In the working directory as

.\run.bat

to run this script file directly. You can also directly paste commands into the terminal to run the program just like

python .\FaithTrip\train_diffusion.py --dataset Osak --lr 0.01 --batch_size 4 --d_model 16 --step 32 --max_noise 0.02 --sampling 2

Hope such an implementation could help you on your projects. Any comments and feedback are appreciated.