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‎README.md

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# EEG Classification using Deep and Transfer Learning
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###### Requirements
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#### Requirements
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* install pytorch
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* pip install braindecode
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* pip install numpy
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* preprocess raw data
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Dependencies can be found in:
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* requirements.txt
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* environment.yml (for conda virt env)
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\#MasterThesis :bowtie: :tophat:
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#### Data:
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Download EEG MI datasets from:
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* BCI Competition IV 2a: http://www.bbci.de/competition/iv/#dataset2a
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* High-Gamma: https://web.gin.g-node.org/robintibor/high-gamma-dataset
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#### Create dirs:
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In the root of the project:
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* Make a directory for the (processed) data: "/data"
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* Make a directory for the results: "/results"
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#### Data preprocessing:
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To preprocess the data got to "/data_loader/process_data/.."
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For both datasets there is separate preprocess script.
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#### Config the experiment:
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In "config.yaml" (in the project root) you can configure the experiment.
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* experiment->dataset: [hgd, bciciv2a]
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* model->name: [eegnet, deep, shallow]
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* experiment->type: [no_tl, loo_tl, ccsa_da]
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*no_tl: no transfer learning, just dataset+CNN model
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*loo_tl: leave-one-out transfer leaning
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*ccsa_da: feature space alignment, siamese network with contrastive loss,
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ccsa: classification and contrastive semantic alignment loss
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* server->full_cv: True=trains model for all subjects 4-fold (sequentially),
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False=trains only model for subject_id and i_valid_fold given.
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#### Run experiment:
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Run the file "/experiment/run.py" to start the experiment.
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Results get put into "/results/" dir.
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:bowtie: :tophat:

‎environment.yml

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name: eeg
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channels:
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- conda-forge
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- pytorch
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- defaults
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dependencies:
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- tslearn=0.1.29=py37h1e5eb4f_0
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- _tflow_select=2.3.0=mkl
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- absl-py=0.7.1=py37_0
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- astor=0.7.1=py37_0
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- astroid=2.1.0=py37_0
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- blas=1.0=mkl
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- c-ares=1.15.0=h1de35cc_1
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- ca-certificates=2019.1.23=0
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- certifi=2019.3.9=py37_0
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- cffi=1.12.1=py37hb5b8e2f_0
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- freetype=2.9.1=hb4e5f40_0
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- gast=0.2.2=py37_0
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- grpcio=1.16.1=py37h044775b_1
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- h5py=2.9.0=py37h3134771_0
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- hdf5=1.10.4=hfa1e0ec_0
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- intel-openmp=2019.1=144
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- isort=4.3.8=py37_0
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- jpeg=9b=he5867d9_2
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- keras=2.2.4=0
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- keras-applications=1.0.7=py_0
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- keras-base=2.2.4=py37_0
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- keras-preprocessing=1.0.9=py_0
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- lazy-object-proxy=1.3.1=py37h1de35cc_2
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- libcxx=4.0.1=hcfea43d_1
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- libcxxabi=4.0.1=hcfea43d_1
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- libedit=3.1.20181209=hb402a30_0
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- libffi=3.2.1=h475c297_4
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- libgfortran=3.0.1=h93005f0_2
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- libpng=1.6.36=ha441bb4_0
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- libprotobuf=3.7.1=hd9629dc_0
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- libtiff=4.0.10=hcb84e12_2
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- markdown=3.1=py37_0
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- mccabe=0.6.1=py37_1
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- mkl=2019.1=144
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- mkl_fft=1.0.10=py37h5e564d8_0
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- mkl_random=1.0.2=py37h27c97d8_0
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- mock=2.0.0=py37_0
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- ncurses=6.1=h0a44026_1
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- ninja=1.8.2=py37h04f5b5a_1
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- numpy=1.16.2=py37hacdab7b_0
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- numpy-base=1.16.2=py37h6575580_0
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- olefile=0.46=py37_0
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- openssl=1.1.1b=h1de35cc_1
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- pbr=5.1.3=py_0
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- pillow=5.4.1=py37hb68e598_0
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- pip=19.0.3=py37_0
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- protobuf=3.7.1=py37h0a44026_0
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- pycparser=2.19=py37_0
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- pylint=2.2.2=py37_0
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- python=3.7.2=haf84260_0
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- pyyaml=5.1=py37h1de35cc_0
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- readline=7.0=h1de35cc_5
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- scikit-learn=0.20.3=py37h27c97d8_0
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- scipy=1.2.1=py37h1410ff5_0
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- setuptools=40.8.0=py37_0
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- six=1.12.0=py37_0
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- sqlite=3.26.0=ha441bb4_0
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- tensorboard=1.13.1=py37haf313ee_0
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- tensorflow=1.13.1=mkl_py37h70c3834_0
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- tensorflow-base=1.13.1=mkl_py37h66b1bf0_0
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- tensorflow-estimator=1.13.0=py_0
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- termcolor=1.1.0=py37_1
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- tk=8.6.8=ha441bb4_0
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- werkzeug=0.15.2=py_0
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- wheel=0.33.1=py37_0
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- wrapt=1.11.1=py37h1de35cc_0
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- xz=5.2.4=h1de35cc_4
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- yaml=0.1.7=hc338f04_2
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- zlib=1.2.11=h1de35cc_3
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- zstd=1.3.7=h5bba6e5_0
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- pytorch=1.0.1=py3.7_2
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- torchvision=0.2.2=py_3
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- pip:
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- autopep8==1.4.3
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- autopy==2.1.0
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- bandit==1.5.1
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- braindecode==0.4.7
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- cycler==0.10.0
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- docopt==0.6.2
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- gitdb2==2.0.5
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- gitpython==2.1.11
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- jsonpickle==0.9.6
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- kiwisolver==1.0.1
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- llvmlite==0.27.1
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- mako==1.0.8
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- markupsafe==1.1.1
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- matplotlib==3.0.3
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- mne==0.17.1
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- munch==2.3.2
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- numba==0.42.1
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- pandas==0.24.1
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- py-cpuinfo==5.0.0
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- pycodestyle==2.5.0
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- pydocstyle==3.0.0
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- pyflakes==2.1.1
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- pylama==7.6.6
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- pymongo==3.7.2
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- pyparsing==2.3.1
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- python-dateutil==2.8.0
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- pytz==2018.9
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- resampy==0.2.1
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- sacred==0.7.4
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- smmap2==2.0.5
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- snowballstemmer==1.2.1
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- stevedore==1.30.1
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- torch==1.0.1.post2
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- typed-ast==1.4.0
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‎requirements.txt

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absl-py==0.7.1
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astor==0.7.1
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astroid==2.2.5
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autopep8==1.4.3
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autopy==2.1.0
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bandit==1.5.1
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Braindecode==0.4.7
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certifi==2019.3.9
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cffi==1.12.1
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cycler==0.10.0
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docopt==0.6.2
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gast==0.2.2
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gitdb2==2.0.5
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GitPython==2.1.11
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grpcio==1.16.1
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h5py==2.9.0
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isort==4.3.8
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jsonpickle==0.9.6
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Keras==2.2.4
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Keras-Applications==1.0.7
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Keras-Preprocessing==1.0.9
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kiwisolver==1.0.1
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lazy-object-proxy==1.3.1
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llvmlite==0.27.1
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Mako==1.0.8
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Markdown==3.1
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MarkupSafe==1.1.1
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matplotlib==3.0.3
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mccabe==0.6.1
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mkl-fft==1.0.10
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mkl-random==1.0.2
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mne==0.17.1
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mock==2.0.0
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munch==2.3.2
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numba==0.42.1
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numpy==1.16.2
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olefile==0.46
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pandas==0.24.1
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pbr==5.1.3
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Pillow==5.4.1
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protobuf==3.7.1
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py-cpuinfo==5.0.0
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pycodestyle==2.5.0
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pycparser==2.19
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pydocstyle==3.0.0
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pyflakes==2.1.1
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pylama==7.6.6
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pylint==2.3.1
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pymongo==3.7.2
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pyparsing==2.3.1
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python-dateutil==2.8.0
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pytz==2018.9
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PyYAML==5.1
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resampy==0.2.1
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sacred==0.7.4
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scikit-learn==0.20.3
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scipy==1.2.1
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six==1.12.0
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smmap2==2.0.5
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snowballstemmer==1.2.1
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stevedore==1.30.1
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tensorboard==1.13.1
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tensorflow==1.13.1
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tensorflow-estimator==1.13.0
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termcolor==1.1.0
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torch==1.0.1.post2
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torchvision==0.2.2
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tslearn==0.1.29
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typed-ast==1.4.0
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Werkzeug==0.15.2
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wrapt==1.11.1

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