Active Learning Module for Bootstrapping Alan
- Create a Conda Environment for Python 3.7.3:
conda create --name <EVN_NAME> python=3.7.3 conda activate <EVN_NAME>
- Git Clone:
git clone https://<USR_NAME>@bitbucket.org/rbcmllab/alan-framework.git
- Install Python Dependencies
cd alan-framework/modules/boostrap/active_learning pip install -r requirements.txt pip install alanbal
> python bin/ast_al_bin.py -h usage: ast_al_bin.py [-h] -i INIT -p POOL -o OUTPUT [-d DEVSET] Parameters for AST classification active learner. The model will be persisted into the output folder at every iteration. If a dev dataset was provided, the classification accuracy score would be calculated on this dataset at each iteration and a performance plot would be saved into the output folder. optional arguments: -h, --help show this help message and exit -i INIT, --init INIT Shared-AST json file path; ASTs in this file will be used to initialize the active learner. Every AST in this file must contain a target value and a list of complexity_features. -p POOL, --pool POOL Shared-AST json file path; Active learner will sample ASTs from this file Every AST in this file must contain a list of complexity_features. -o OUTPUT, --output OUTPUT Absolute path of the model persisting folder. -d DEVSET, --devset DEVSET Shared-AST json file path; ASTs in this file will be used to evaluate the active learner. Every AST in this file must contain a target value and a list of complexity_features.
> python bin/ast_al_bin.py \ -i $DATA_DIR/ast_train.json \ -p $DATA_DIR/ast_pool.json \ -o $DATA_DIR \ -d $DATA_DIR/ast_dev.json
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.