Awesome
Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation
Installation
Datasets
Download data and convert to .tfrecord files for TensorFlow (./generate_tfrecords.sh)
Packages
We require the following packages. 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 or F1 score metrics (typically tensorflow-addons, but that errors at the moment with TF 2.2)
pip3 install --user git+https://github.com/tensorflow/addons.git@r0.9
Usage
Example
Train our model on person 14 of the UCI HAR dataset and adapt to person 19.
python3 main.py \
--logdir=example-logs --modeldir=example-models \
--method=smd --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-smd-[0-9]*" --selection="best_target" \
--output_file=results/results_example_best_target-ucihar-0-daws.yaml
Note: there are a number of other models (e.g. --model=lstmfcn
), methods (e.g.
--method=dann
), datasets (e.g. --dataset=wisdm_at
), etc. implemented that
you can experiment with beyond what was included in the paper.
Analysis
Then look at the resulting results/results_*.yaml files or analyze with analysis.py.
In addition, there are scripts for visualizing the datasets (datasets/view_datasets.py), viewing dataset statistics (dataset_statistics.py), and displaying or plotting the class balance of the data (class_balance.py, class_balance_plot.py).
Navigating the Code
Here is an outline of the key elements of the code.
Models
--model=fcn
base feature extractor in paper--model=lstmfcn
fcn+lstm feature extractor
Methods
-
dannMethods.py
includes methods based on domain classifier (DANN, CoDATs) -
nannMethods.py
includes methods based on non-learnable metrics (DDC, DAN, CORAL, etc.) -
nannMethods.py
includes methods based on learnable metrics (our method)