Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Steps:

• Download data and convert to .tfrecord files for TensorFlow (./generate_tfrecords.sh)
• Train models (main.py or kamiak_train_*.srun)
• Evaluate models (main_eval.py or kamiak_eval_*.srun)
• Analyze results (analysis.py)

Installation

We require the following packages (module load for Kamiak). Adjust for your computer setup.

module load cuda/10.1.105 cudnn/7.6.4.38_cuda10.1 python3/3.7.4
pip install --user --upgrade pip
export PATH="$HOME/.local/bin:$PATH"
pip3 install --user --upgrade pip
pip3 install --user --upgrade numpy cython
pip3 install --user --upgrade tensorflow-gpu pillow lxml jupyter matplotlib pandas scikit-learn scipy tensorboard rarfile tqdm pyyaml grpcio absl-py

# If using --moving_average (typically tensorflow-addons, but that errors at the moment with TF 2.2)
pip3 install --user git+https://github.com/tensorflow/[email protected]

Or, to use only the CPU, set up as follows and modify the train scripts to source kamiak_tensorflow_cpu.sh.

module load python3/3.7.4
export PATH="$HOME/.local/bin:$PATH"
pip3 install --user --upgrade virtualenvwrapper
export VIRTUALENVWRAPPER_PYTHON="$(which python3)"
mkdir -p ~/Envs
export WORKON_HOME=~/Envs
source ~/.local/bin/virtualenvwrapper.sh
mkvirtualenv -p python3 tensorflow_cpu

which pip # check it's ~/Envs/tensorflow_cpu/bin/pip
which python3 # check it's ~/Envs/tensorflow_cpu/bin/python3

# Note: we don't use --user for virtual environments
pip install --upgrade numpy cython
# Note: here it's "tensorflow" not "tensorflow-gpu" -- the rest is the same.
pip install --upgrade tensorflow pillow lxml jupyter matplotlib pandas scikit-learn scipy tensorboard rarfile tqdm pyyaml grpcio absl-py

# If using --moving_average
pip install git+https://github.com/tensorflow/[email protected]

Usage

Example

Train a CoDATS-WS model (i.e. using DA-WS) on person 14 of the UCI HAR dataset and adapt to person 19.

python3 main.py \
    --logdir=example-logs --modeldir=example-models \
    --method=daws --dataset=ucihar --sources=14 \
    --target=19 --uid=0 --debugnum=0 --gpumem=0

Then evaluate that model on the holdout test data, outputting the results to a YAML file.

mkdir -p results
python3 main_eval.py \
    --logdir=example-logs --modeldir=example-models \
    --jobs=1 --gpus=1 --gpumem=0 \
    --match="ucihar-0-daws-[0-9]*" --selection="best_target" \
    --output_file=results/results_example_best_target-ucihar-0-daws.yaml

Note: there are a number of other options (e.g. --ensemble=5, --moving_average), models (e.g. --model=inceptiontime), methods (e.g. --method=dann_pad), datasets (e.g. --dataset=wisdm_at), etc. implemented that you can experiment with beyond what was included in the paper.

All Experiments

To run all the experiments in the paper, see the Slurm scripts for training (kamiak_train_*.srun) and evaluation (kamiak_eval_*.srun). Tweak for your cluster. Then, you can run the experiments, e.g. for the single-source experiments (excluding the upper bound, which has its own scripts):

sbatch -J train kamiak_train_ssda.srun adapt
sbatch -J eval kamiak_eval_ssda.srun adapt

Analysis

Then look at the resulting results/results_*.yaml files or analyze with analysis.py.