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1.8 KiB
1.8 KiB
install environment
# try with pip torch WORKS!
export PROJ=deeptime
conda create -n $PROJ python=3.8 -y
conda activate $PROJ
mamba install -y ipykernel pip ipywidgets
pip install torch==1.10.0+cu113 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
# 117 does not exist yet
python -m ipykernel install --user --name $PROJ
pip install gin-config fire pandas matplotlib numpy scikit-learn einops tensorboard yapf
pip install tsai
# note that I've also recorded the env in requirements
python -m experiments.forecast --config_path=storage/experiments/Exchange/192S/repeat=0/config.gin run | tee -a storage/experiments/Exchange/192S/repeat=0/instance.log 2>&1%
run
python -m experiments.forecast --config_path=storage/experiments/Exchange/96S/repeat=0/config.gin run
python -m experiments.forecast --config_path=storage/experiments/Exchange/96Splus/repeat=0/config.gin run
python -m experiments.forecast --config_path=storage/experiments/Exchange/96Splusshort/repeat=0/config.gin run
python -m experiments.forecast --config_path=storage/experiments/Exchange/96Sshort/repeat=0/config.gin run
Lessons
Single variate works much better. The output is not just a straight line. Likely because we have limited the output, not the input
stocks
python -m experiments.forecast --config_path=experiments/configs/Stocks/96S.gin build_experiment
python -m experiments.forecast --config_path=storage/experiments/Stocks/96S/repeat=0/config.gin run
make build-all path=experiments/configs/Stocks
./run.sh
So how does deeptime work?
original:
- inr(coords)
- RR
My mods (I added past other variables):
- inr(concat([x, coords]))
- RR
Where INR is one of [mlp, lstm, lstm2, transformer, transforme2, inceptioncausal]
TODO:
- M2S mode
- add other INR's
- add None as learner