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target_encoding_benchmark.md

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Target Encoding with xfeat

xfeat_target_encoding_image

Target encoding can be greatly accelerated by using GPUs. When using GPUs, xfeat.TargetEncoder accesses cuDF and CuPy internally.

Benchmark

For the detailed experiment scripts and output logs, please refer to /examples/benchmark.

Data

  • record size: 1M ~ 10M records.
  • cardinality: 5,000
  • num folds: 5

Environment

We ran all experiments on a single Linux server (AWS p3.2xlarge) with the following specifications:

  • p3.2xlarge on AWS EC2.
  • OS: Ubuntu 18.04
  • Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
  • GPU: NVIDIA Tesla V100 x1
  • Docker image: smly/xfeat-cudf (Dockerfile)

Results

xfeat_target_encoding_image

Usage of benchmark script

$ docker run -it --rm \
    -v /etc/group:/etc/group:ro \
    -v /etc/passwd:/etc/passwd:ro \
    -u $(id -u $USER):$(id -g $USER) \
    -v /home/ubuntu/xfeat:/root \
    smly/xfeat-cudf bash

ubuntu$ python examples/benchmark/target_encoder.py