- Download parsed python dataset from here, unarchive and place under
PYTHON150K_DIR:
# Replace with desired path.
>>> PYTHON150K_DIR=/path/to/data/dir
>>> mkdir -p $PYTHON150K_DIR
>>> cd $PYTHON150K_DIR
>>> wget http://files.srl.inf.ethz.ch/data/py150.tar.gz
...
>>> tar -xzvf py150.tar.gz
...
- Extract samples to
DATA_DIR:
# Replace with desired path.
>>> DATA_DIR=$(pwd)/data/default
>>> SEED=239
>>> python extract.py \
--data_dir=$PYTHON150K_DIR \
--output_dir=$DATA_DIR \
--seed=$SEED
...
- Preprocess for training:
>>> ./preprocess.sh $DATA_DIR
...
- Train:
>>> cd ..
>>> DESC=default
>>> CUDA=0
>>> ./train_python150k.sh $DATA_DIR $DESC $CUDA $SEED
...
setup#2: batch_size=64
setup#3: embedding_size=256,use_momentum=False
setup#4: batch_size=32,embedding_size=256,embeddings_dropout_keep_prob=0.5,use_momentum=False
| params |
Precision |
Recall |
F1 |
ROUGE-2 |
ROUGE-L |
| default |
0.37 |
0.27 |
0.31 |
0.06 |
0.38 |
| setup#2 |
0.40 |
0.31 |
0.34 |
0.08 |
0.41 |
| setup#3 |
0.36 |
0.31 |
0.33 |
0.09 |
0.38 |
| setup#4 |
0.33 |
0.25 |
0.28 |
0.05 |
0.34 |
| params |
Precision |
Recall |
F1 |
ROUGE-2 |
ROUGE-L |
| default |
0.37 |
0.27 |
0.31 |
0.06 |
0.38 |
| no ast nodes (5th epoch) |
0.27 |
0.16 |
0.20 |
0.02 |
0.28 |
| no token split (4th epoch) |
0.60 |
0.09 |
0.15 |
0.00 |
0.60 |