TalkingData AdTracking Fraud Detection Challenge
models and scores
model definition can be found in scripts/model_lib.py
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model1 LGBM with 83 (76 numerical, 7 categorical) features.
-
model2 keras with 27(18 numerical, 9 categorical) features, You can see network structure in model.png
| model | private score | public score |
|---|---|---|
| model1 | 0.9836325 | 0.9828896 |
| model2 | 0.9830595 | 0.9822785 |
feature engineering and scripts
Most of these features have already been discussed on the kaggle forum.
-
counting features
- mk_feat_count.py
- mk_feat_count_time.py
- mk_feat_countRatio.py
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cumulative count
- mk_feat_cumcount.py
- mk_feat_recumcount.py
- mk_feat_cumratio.py
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time to next click
- mk_feat_nextClick_leak_day.py
- mk_feat_nextClick_filter.py
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time bucket count.(make multiple time intervals, and count the number of buckets which the IP exists)
- mk_feat_rangecount.py
- mk_feat_rangecount_minute.py
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variance
- mk_feat_var.py
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common IP
- mk_feat_common_ip.py
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unique count
- mk_feat_uniq_count2.py
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target encoding: woe
- mk_feat_woe_all_prev.py
- mk_feat_woe_bound.py
Features will be calculated once and saved to disk.
Importance from LGBM is found in importance.txt.
Requirements
I used following environment
Hardware:
- Memory: 256GB RAM, 256GB SWAP
- CPU: 20 core, 2.10GHz
- GPU: 1080Ti
Python3 packages:
- numpy==1.14.2
- pandas==0.22.0
- lightgbm==2.1.0
- keras==2.1.5
How to run
At first, put sample_submission.csv test.csv test_supplement.csv train.csv to input directory.
Then run shell scripts as follows,
$ cd scripts/
$ ./run_mk_feats.sh
$ ./run_mk_model1.sh
$ ./run_mk_model2.sh
Output prediction files will be in csv directory.
It took about one day for feature extraction(run_mk_feats.sh).
It needs large memory(~256GB) to build model1(run_mk_model1.sh), sorry.
GPU is required to build model2(run_mk_model2.sh)