ZDstandup / kaggle-quora-question-pairs-1

My solution to Kaggle Quora Question Pairs competition (Top 2%, Private LB log loss 0.13497).

kaggle-quora-question-pairs

My solution to Kaggle Quora Question Pairs competition (Top 2%, Private LB log loss 0.13497).

Overview

The solution uses a mixture of purely statistical features, classical NLP features, and deep learning. Almost 200 handcrafted features are combined with out-of-fold predictions from 4 neural networks having different architectures.

The final model is a GBM (LightGBM), trained with early stopping and a very small learning rate, using stratified K-fold cross validation.

Reproducing the Solution

Hardware Requirements

Almost all code (with the exception of some 3rd-party scripts) can efficiently utilize multi-core machines. At the same time, some of them might be memory-hungry. All code has been tested on a machine with 64 GB RAM. For all non-neural notebooks, a c4.8xlarge AWS instance should do excellent.

For neural networks, a GPU is highly recommended. On a GTX 1080 Ti, it takes about 8-9 hours to complete all 4 "neural" notebooks.

You'll need about 30 GB of free disk space to store the pre-trained word embeddings and the extracted features.

Software Requirements

1. Python >= 3.6.
2. LightGBM (compiled from sources).
3. FastText (compiled from sources).
4. Python packages from requirements.txt.
5. (Recommended) NVIDIA CUDA and a GPU version of TensorFlow.

Environment Provisioning

You can spin up a fresh Ubuntu 16.04 AWS instance and use Ansible to make all the necessary software installation and configuration (except the GPU-related stuff).

1. Make sure to open the ports 22 and 8888 on the target machine.
2. Navigate to provisioning directory.
3. Edit config.yml:
   • jupyter_plaintext_password: the password to set for the Jupyter server on the target machine.
   • kaggle_username, kaggle_password: your Kaggle credentials (required to download the competition datasets). Otherwise, download them to the data folder manually.
4. Edit inventory.ini and specify your instance DNS and the private key file (*.pem) to access it.
5. Run:

   $ ansible-galaxy install -r requirements.yml
   $ ansible-playbook playbook.yml -i inventory.ini
   

Running the Code

Automatic

Run run-all.sh from the repository root. Check notebooks/output for execution progress and data/submissions for the final results.

Manual

Start a Jupyter server in the notebooks directory. If you used the Ansible playbook, the server will already be running on port 8888.

Run the notebooks in the following order:

1. Preprocessing.

   1) preproc-tokenize-spellcheck.ipynb
   2) preproc-extract-unique-questions.ipynb
   3) preproc-embeddings-fasttext.ipynb
   4) preproc-nn-sequences-fasttext.ipynb
   

2. Feature extraction.

   Run all feature-*.ipynb notebooks in arbitrary order.

   Note: for faster execution, run all feature-oofp-nn-*.ipynb notebooks on a machine with a GPU and NVIDIA CUDA.

3. Prediction.

   Run classify-lightgbm-cv-pred.ipynb. The output file will be saved as DATETIME-submission-draft-CVSCORE.csv