This is improvement version of the official clone for the implementation of gcForest.

Package Official Website: http://lamda.nju.edu.cn/code_gcForest.ashx

Reference: [1] Z.-H. Zhou and J. Feng. Deep Forest: Towards an Alternative to Deep Neural Networks.
In IJCAI-2017. (https://arxiv.org/abs/1702.08835v2 )

Requirements: This package is developed with Python 3.x, please make sure all the dependencies are installed, which is specified in requirements.txt

• Train Driver DataSet:
  • python tools/train_cascade.py --model models/driver/gcforest/ca-tree500-n4x2-3folds.json --log_dir logs/gcforest/driver/ca-tree500-n4x2-3folds/
  • python tools/train_cascade.py --model .\models\driver\gcforest\ca-tree50-deep10-n1x2-3folds.json --log_dir logs/gcforest/driver/ca-tree50-n1x2-3folds/
• ​1. Change Python 2.7 to Python 3.x (FINISH)
  • basestring to str
  • / to //
• 2. Add metrics (FINISH)
  • auc
  • nor-gini
• 3. Add Best Layer ID Select (FINISH)
  • train dataset and test dataset best result layer id
• 4. Add GDBT (FINISH)
  • {"n_folds":3,"type":"GradientBoostingClassifier","n_estimators":50,"max_depth":10,"loss":"exponential","learning_rate":0.01,"warm_start":"True"}
• 5. Add XGBoost (FINISH)
  • python tools/train_cascade.py --model .\models\driver\gcforest\ca-tree50-deep10-n1x1-3folds.json --log_dir logs/gcforest/driver/ca-tree50-n1x1-3folds/
• 6. Add Feature Not Reduce (FINISH)
• 7. Add Output Test Data (FINISH)
  • Stage-1: IPython
  • Stage-2: predict_test in train_cascade
• 8. Add Output the Class Vector & Tree Paths

• lib/gcforest
  • code for the implementations for gcforest
• tools/train_fg.py
  • the demo script used for training Fine grained Layers
• tools/train_cascade.py
  • the demo script used for training Cascade Layers
• models/
  • folder to save models which can be used in tools/train_fg.py and tools/train_cascade.py
  • the gcForest structure is saved in json format
• logs
  • folder logs/gcforest is used to save the logfiles produced by demo scripts

Happy Hacking.