avito-demand-prediction

[1] Convert to feather format

jupyter nbconvert --execute make_feather.ipynb --ExecutePreprocessor.timeout=0

[2] Download image features

[3] Download fasttext binaries

[4] Create train2.csv and test2.csv for classic ML models

perl -p -e 's/\/\n//' train.csv > train2.csv

perl -p -e 's/\/\n//' test.csv > test2.csv

[5] Quantum Gravity Callback Installation

Name		Name	Last commit message	Last commit date
Latest commit History 91 Commits
README.md		README.md
apply_quantum_gravity_to_sub.py		apply_quantum_gravity_to_sub.py
avito_region_city_features.csv		avito_region_city_features.csv
debug_callback.py		debug_callback.py
geomean_csv.py		geomean_csv.py
geomean_layer.py		geomean_layer.py
geomean_regression.py		geomean_regression.py
gravity.py		gravity.py
iterm.py		iterm.py
make_feather.ipynb		make_feather.ipynb
mean_layer.py		mean_layer.py
multi_gpu_keras.py		multi_gpu_keras.py
optimizer_callback.py		optimizer_callback.py
quantum_gravity_callback.py		quantum_gravity_callback.py
train-ft-images.ipynb		train-ft-images.ipynb
train-ft-images.py		train-ft-images.py
train.ipynb		train.ipynb
train_submission.csv		train_submission.csv
validation_callback.py		validation_callback.py

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