Requirmenets:

• Python3
• Pytorch==1.7
• Numpy==1.20.1
• scikit-learn==0.24.1
• Pandas==1.2.4
• skorch==0.10.0
• openpyxl==3.0.7

Datasets

Loading and Preparing Benchmark Datasets

Create a folder and download the pre-processed versions of the datasets WISDM, HAR, HHAR, Boiler, and Sleep-EDF.

To add new dataset (e.g., NewData), it should be placed in a folder named: NewData in the datasets directory.

Since "NewData" has several domains, each domain should be split into train/test splits with naming style as "train_x.pt" and "test_x.pt".

The structure of data files should in dictionary form as follows: train.pt = {"samples": data, "labels: labels}, and similarly for test.pt.

Configurations

Next, you have to add a class with the name NewData in the configs/data_model_configs.py file. You can find similar classes for existing datasets as guidelines. Also, you have to specify the cross-domain scenarios in self.scenarios variable.

Last, you have to add another class with the name NewData in the configs/hparams.py file to specify the training parameters.

Closed-Set Domain Adaptation Algorithms

Baselines

• Deep Coral
• CDAN
• DIRT-T
• HoMM
• CoDATS
• AdvSKM
• CLUDA

Universal Domain Adaptation Algorithms

Existing Algorithms

• UniDA
• DANCE
• OVANet
• UniOT

RAINCOAT

Model

Our main model architecture can be found here.

Algorithm

Our training algorithm can be found here.

Training procedure

The experiments are organised in a hierarchical way such that:

• Several experiments are collected under one directory assigned by --experiment_description.
• Each experiment could have different trials, each is specified by --run_description.
• For example, if we want to experiment different UDA methods with CNN backbone, we can assign --experiment_description CNN_backnones --run_description DANN and --experiment_description CNN_backnones --run_description DDC and so on.

Training a model

To train a model:

python main.py  --experiment_description exp1  \
                --run_description run_1 \
                --da_method DANN \
                --dataset HHAR \
                --backbone CNN \
                --num_runs 5 \