drivendata / countable-care-3rd-place

Users jeongyoonlee and abhishek's 3rd place submission

Countable Care

This is the 3rd prize winning solution by team JYL (Jeong-Yoon Lee and Abhishek Thakur) for the Countable Care competition at DrivenData.org.

The code assumes raw data is available under the data folder, and saves outputs in the build folder.

0. How to Reproduce the Result

To produce the final submission

./run.sh

Then the final submission file will be available at build/tst/final_sub.csv.

1. Requirements

Python Packages

Install python packages listed in requirements.txt - scipy, numpy, scikit-learn, statsmodels, pandas, Kaggler packages

XGBoost 0.3

Install latest XGBoost from source and copy xgboost and wrapper/libxgboostwrapper.so into the system bin and lib folders respectively:

git clone [email protected]:dmlc/xgboost.git
cd xgboost
bash build.sh
(sudo) cp xgboost /usr/local/bin
(sudo) cp wrapper/libxgboostwrapper.so /usr/local/lib

Kaggler 0.3.8

To install latest Kaggler package from source:

git clone [email protected]:jeongyoonlee/Kaggler.git
cd Kaggler
python setup.py build_ext --inplace
(sudo) python setup.py install

2. Features

8 features are used as follows:

• feature1 - impute 0 for missing values for numeric and ordinal features. create dummy variables for values in categorical features appearing 10+ times in training data
• feature2 - same as feature1 except taking log(1 + x) transformation for ordinal features.
• feature3 - same as feature2 except creating dummy variables for values appearing 3+ times in training data.
• feature4 - same as feature3 except treating ordinal features as categorical features.
• feature5 - same as feature4 except taking log2(1 + x) transformation * for ordinal features before treating ordinal features as categorical features.
• feature8 - same as feature4 except normalizing numeric features.
• feature9 - impute -1 for missing values, and label-encode categorical features.
• feature10 - impute 0 for missing values for numeric features, and label-encode categorical features.

How to Generate Features

You can generate feature files manually using relevant Makefiles. For example, to generate feature1 files for class 00 out of 14 classes:

make -f Makefile.feature.feature1 build/feature/feature1.trn00.sps

or you can run an algorithm Makefile that uses featuer1, then feature files will be generated automatically before training:

make -f Makefile.xg_100_8_0.05_feature1

3. Individual Models

Algorithm Implementations

6 different algorithm implementations are used as follows:

• fm - Factorization Machine implementation from Kagger
• nn - Neural Networks implementation from Kaggler
• lr - Logistic Regression implementation from Scikit-Learn
• gbm - Gradient Boosting Machine implementation from Scikit-Learn
• libfm - Factorization Machine implementation from libFM
• xg - Gradient Boosting Machine implementation from XGBoost

Individual Models

From 6 different algorithm implementations and 8 different features (see Features), 19 individual models are built as follows:

• fm_200_8_0.001_feature2
• fm_200_8_0.001_feature2
• fm_200_8_0.001_feature3
• gbm_bagging_40_7_0.1_feature10
• libfm_200_4_0.005_feature2
• libfm_200_4_0.005_feature4
• lr_0.1_feature2
• lr_0.1_feature4
• nn_20_64_0.005_feature8
• nn_20_8_0.01_feature2
• nn_20_8_0.01_feature3
• rf_400_40_feature2
• rf_400_40_feature5
• rf_400_40_feature9
• rf_400_40_feature10
• xg_100_8_0.05_feature1
• xg_100_8_0.05_feature5
• xg_100_8_0.05_feature8
• xg_100_8_0.05_feature9
• xg_100_8_0.05_feature10
• xg_bagging_120_7_0.1_feature9

How to Generate Individual Model Predictions

Each model has its Makefile available for training and prediction. For example, to generate predictions for fm_200_8_0.001_feature2, run:

make -f fm_200_8_0.001_feature2

Predictions for training data with 5-CV and test data will be saved in build/val and build/tst folders respectively.

4. Ensemble

Ensemble Model

Using predictions of 19 individual models (see [Individual Models](individual models)) as inputs, a Gradient Boosting Machine ensemble model is trained as follows:

• esb_xg_grid_colsub

Parameters for the ensemble model are selected for each class by using grid search.

How to Generate Ensemble Prediction

After generating individual model predictions, run the ensemble Makefile as follows:

make -f Makefile.esb.xg_grid_colsub

The prediction and submission files will be available in the build/tst folder.

5. Performance

Ensemble Model Leaderboard

| Model Name | Public Leaderboard | 5-fold CV | Comment | |------------|-------------|-----------|-----------|---------| | esb_xg_grid_esb19_xgb120 | 0.2497 | - | 0.7 * esb_xg_grid_esb19 + 0.3 * sub_xgb120 | | esb_xg_grid_esb19 | 0.2503 | 0.2488 | |

Individual Model Leaderboard

| Model Name | Leaderboard | 5-fold CV | Comment | |------------|-------------|-----------|-----------|---------| | xg_bagging_120_7_0.1_feature9 | - | 0.2564 | | | xg_100_8_0.05_feature9 | - | 0.2568 | | | xg_100_8_0.05_feature1 | - | 0.2575 | | | xg_100_8_0.05_feature8 | - | 0.2575 | | | xg_100_8_0.05_feature10 | - | 0.2618 | | | nn_20_8_0.01_feature3 | - | 0.2660 | | | nn_20_8_0.01_feature2 | - | 0.2669 | | | nn_20_64_0.005_feature8 | - | 0.2675 | | | gbm_bagging_40_7_0.1_feature10 | - | 0.2678 | | | libfm_200_4_0.005_feature4 | - | 0.2694 | | | fm_200_8_0.001_feature3 | - | 0.2717 | | | fm_200_4_0.001_feature2 | - | 0.2720 | | | libfm_200_4_0.005_feature2 | - | 0.2723 | | | rf_400_40_feature9 | - | 0.2755 | | | rf_400_40_feature2 | - | 0.2769 | | | rf_400_40_feature5 | - | 0.2776 | | | rf_400_40_feature10 | - | 0.2881 | | | lr_0.1_feature4 | - | 0.3699 | | | lr_0.1_feature2 | - | 0.3755 | |