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Shopee Data Science Competition - ShopeeSense (2019)
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README.md

Shopee Data Science Competition - ShopeeSense

This is the codebase for ShopeeSense submission for Shopee Data Science Competition. The competition requires the participants to extract attributes from the image and title description of a listed product. The product was from three categories - Mobile, Beauty, and Fashion.

For each of the categories, we built four algorithms which we ensemble to output the final prediction. The four algorithms are listed as follows:

  1. Term Frequency - Inverse Document Frequency, Single Value Decomposition, and Gradient Boosted Decision Tree for product title.
  2. Recurrent Neural Network with ASGD Weighted-Dropped Long Short Term Memory architecture for product title.
  3. Convolution Neural Network with pretrained ResNet34 for product image.
  4. Heuristic Algorithm based on the product title.

The architecture of the model is listed as follows:

alt text

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and deployment purposes.

Prerequisites

What things you need to install the software and how to install them.

Managing Project Dependencies using Pyenv + Pipenv

We use pipenv for managing project dependencies and Python environments (i.e. virtual environments). All direct packages dependencies (e.g. NumPy may be used in a User Defined Function), as well as all the packages used during development (e.g. PySpark, flake8 for code linting, IPython for interactive console sessions, etc.), are described in the Pipfile. Their precise downstream dependencies are described in Pipfile.lock.

[OPTIONAL] Installing Pyenv

sudo apt install pyenv

Other installation instructions here.

[OPTIONAL] Automatically initialize pyenv when terminal loads

echo eval "$(pyenv init -)" >> ~/.bash_profile
source ~/.bash_profile

[OPTIONAL] Installing python

To see all available python versions

pyenv install --list

To install Python 3.7.2

pyenv install 3.7.2

To install Python 3.7.2 in this directory

cd sparkflow
pyenv install 3.7.2
pyenv local 3.7.2

[OPTIONAL] Installing Pipenv

To get started with Pipenv, first of all download it - assuming that there is a global version of Python available on your system and on the PATH, then this can be achieved by running the following command,

pip install pipenv

Setting up your pipenv for the first time

pipenv --python 3.7.2

For more information, including advanced configuration options, see the official pipenv documentation.

Kaggle API

To use the Kaggle API, sign up for a Kaggle account at https://www.kaggle.com. Then go to the 'Account' tab of your user profile (https://www.kaggle.com/<username>/account) and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location ~/.kaggle/kaggle.json (on Windows in the location C:\Users\<Windows-username>\.kaggle\kaggle.json - you can check the exact location, sans drive, with echo %HOMEPATH%). You can define a shell environment variable KAGGLE_CONFIG_DIR to change this location to $KAGGLE_CONFIG_DIR/kaggle.json (on Windows it will be %KAGGLE_CONFIG_DIR%\kaggle.json).

For your security, ensure that other users of your computer do not have read access to your credentials. On Unix-based systems you can do this with the following command:

chmod 600 ~/.kaggle/kaggle.json

You can also choose to export your Kaggle username and token to the environment:

export KAGGLE_USERNAME=datadinosaur
export KAGGLE_KEY=xxxxxxxxxxxxxx

In addition, you can export any other configuration value that normally would be in the $HOME/.kaggle/kaggle.json in the format 'KAGGLE_' (note uppercase).
For example, if the file had the variable "proxy" you would export KAGGLE_PROXY and it would be discovered by the client.

Installation

Make sure that you're in the project's root directory (the same one in which the Pipfile resides), and then run,

make setup

This single command will ensure that pyenv and pipenv is installed within your computer, and it installs all of the package dependencies for the project.

make prepare-data

This will do the following:

  1. Creates /data folder
  2. Download all the data from Shopee Kaggle Dataset into /data, and unzip all of them
  3. Renaming all files that ends with _info_val_competition.csv to _info_test_competition.csv
  4. Splitting the training files as into Development dataset and Validation dataset, _info_dev_competition.csv and _info_val_competition.csv

Development Dataset and Validation Dataset is a subset of the Training Dataset, where we will train the model on Development Dataset, and Use the Validation Dataset to get the estimated Performance of the Model

bash bins/dl_large_files.sh

This script will try to download the Beauty, Fashion, and Mobile image files.

Running

Project Structure

├── bins
│   ├── data.sh
│   ├── dl_large_files.sh
│   ├── fastai.sh
│   ├── img.sh
│   ├── lgb.sh
│   └── setup_linux.sh
├── config.sh
├── data
│   └── fono_api
├── __init__.py
├── LICENSE
├── Makefile
├── model
│   ├── common
│   │   ├── __init__.py
│   │   ├── split_data.py
│   │   └── topic.py
│   ├── heuristic
│   │   ├── fashion_library.json
│   │   ├── __init__.py
│   │   └── mobile
│   │       ├── enricher.py
│   │       ├── extractor.py
│   │       └── __init__.py
│   ├── image
│   │   ├── fastai
│   │   │   ├── __init__.py
│   │   │   ├── main.py
│   │   │   └── ml_model.py
│   │   ├── __init__.py
│   │   └── pytorch
│   │       ├── dataset.py
│   │       ├── __init__.py
│   │       ├── test.py
│   │       ├── train.py
│   │       └── validation.py
│   ├── __init__.py
│   ├── leak
│   │   ├── __init__.py
│   │   └── main.py
│   └── text
│       ├── common
│       │   ├── __init__.py
│       │   └── prediction.py
│       ├── fastai
│       │   ├── class_model.py
│       │   ├── __init__.py
│       │   ├── lm_model.py
│       │   └── main.py
│       ├── __init__.py
│       ├── lgb
│       │   ├── config.py
│       │   ├── eta_zoo.py
│       │   ├── __init__.py
│       │   ├── lgb_model.py
│       │   ├── main.py
│       │   └── tuning.py
│       └── utils
│           ├── common.py
│           └── __init__.py
├── notebooks
│   ├── adi
│   │   ├── combine_answer.ipynb
│   │   ├── concat_submission.ipynb
│   │   ├── fastai_clasifier.ipynb
│   │   ├── fastai_lm.ipynb
│   │   ├── ida_beauty_prediction.ipynb
│   │   ├── ida_fashion_prediction.ipynb
│   │   ├── image_fastai.ipynb
│   │   ├── image_fastai_pycharm.ipynb
│   │   ├── image_folder_management.ipynb
│   │   ├── __init__.py
│   │   ├── kyle_enricher.ipynb
│   │   ├── Kyle LM.ipynb
│   │   ├── kyle_prediction.ipynb
│   │   ├── kyle_validation.ipynb
│   │   ├── leak_answer.ipynb
│   │   ├── lgb_model.ipynb
│   │   └── submission.ipynb
│   ├── ida
│   │   └── fashion_heuristics_generate_review_heuristics_library.ipynb
│   ├── kyle
│   │   ├── first.py
│   │   └── __init__.py
│   └── placeholder
├── output
│   ├── logs
│   │   └── placeholder
│   ├── model_checkpoint
│   │   └── placeholder
│   ├── result
│   │   └── placeholder
│   └── result_metadata
│       └── placeholder
├── Pipfile
├── Pipfile.lock
├── README.md
├── try.png
└── utils
    ├── api_keys.py
    ├── common.py
    ├── envs.py
    ├── fonAPI.py
    ├── __init__.py
    ├── logger.py
    └── pytorch
        ├── callbacks
        │   ├── callback.py
        │   ├── __init__.py
        │   ├── loss.py
        │   └── optim.py
        ├── __init__.py
        └── utils
            ├── checkpoint.py
            ├── common.py
            ├── __init__.py
            └── lr_finder.py

All of the main algorithms is located under model/ directory. Each of the main algorithms (model/image/fastai, model/image/pytorch, model/text/lgb, model/text/fastai) has a main.py which can be ran directly to obtain the final predictions. config.sh is needed to be run before running each of the main python script to initialize the environment variables

Ensembling the predictions from each of the ML model can be done using the following jupyter notebook: notebooks/adi/concat_submission.ipynb

Source codes to Mobile, Beauty and Fashion heuristics algorithms under model/heuristics.

Built With

  • Numpy - NumPy is the fundamental package for scientific computing with Python.
  • tqdm - A fast, extensible progress bar for Python and CLI
  • pandas - powerful Python data analysis toolkit
  • seaborn - statistical data visualization
  • Pytorch - Deep Learning Framework for Python
  • torchvision - Image Toolkit for Pytorch
  • ipykernel - IPython Kernel for Jupyter
  • kaggle - Official Kaggle API
  • scikit-learn - machine Learning in Python
  • scikit-image - image processing in python
  • category-encoders - Categorical Encoding for Python
  • xgboost - optimized distributed gradient boosting library
  • lightgbm - gradient boosting framework that uses tree based learning algorithms
  • argparse - Parser for command-line options, arguments and sub-commands
  • fastai - making neural nets uncool again

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Authors

License

This project is licensed under the MIT License - see the LICENSE file for details

Acknowledgments

A huge thank you to Shopee Data Science team for organizing this hackathon.

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