Boostrap Module for Alan
Project description
Alan Bootstrap
Active Learning
Setup Environment
- Create a Conda Environment for Python 3.7.3:
conda create --name <EVN_NAME> python=3.7.3
- Git Clone:
git clone https://<USR_NAME>@bitbucket.org/rbcmllab/data_bootstrap.git
- Install Python Dependencies
cd data_bootstrap/alanbootstrap
pip install -r requirements.txt
pip install alanbootstrap
Run
> 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.
Example:
> 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
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