#!/bin/bash

DATASET=$1

VERSION=$2

LABEL_EMB=pifa-tfidf

DATA_DIR=./datasets/${DATASET}

PRED_NPZ_PATHS=""

SEED_LIST=( 0 1 2 )

for SEED in "${SEED_LIST[@]}"; do

# indexer (for reproducibility, use clusters from pretrained_dir)

OUTPUT_DIR=pretrained_models/${DATASET}/${LABEL_EMB}-s${SEED}

INDEXER_DIR=${OUTPUT_DIR}/indexer

RANKER_DIR=${OUTPUT_DIR}/ranker/linear-${VERSION}

mkdir -p ${RANKER_DIR}

# ranker train and predict

PRED_NPZ_PATH=${RANKER_DIR}/tst.pred.npz

# x_emb=TF-IDF, model=Parabel

if [ ${VERSION} == 'v0' ]; then

python -m xbert.ranker train \

-x ${DATA_DIR}/X.trn.npz \

-y ${DATA_DIR}/Y.trn.npz \

-c ${INDEXER_DIR}/code.npz \

-o ${RANKER_DIR} -t 0.01

python -m xbert.ranker predict \

-m ${RANKER_DIR} -o ${PRED_NPZ_PATH} \

-x ${DATA_DIR}/X.tst.npz \

-y ${DATA_DIR}/Y.tst.npz

# x_emb=xlnet_finetuned+TF-IDF, model=Parabel

elif [ ${VERSION} == 'v1' ]; then

python -m xbert.ranker train \

-x ${DATA_DIR}/X.trn.npz \

-x2 ${DATA_DIR}/X.trn.finetune.xlnet.npy \

-y ${DATA_DIR}/Y.trn.npz \

-c ${INDEXER_DIR}/code.npz \

-o ${RANKER_DIR} -t 0.01 -f 0

python -m xbert.ranker predict \

-m ${RANKER_DIR} -o ${PRED_NPZ_PATH} \

-x ${DATA_DIR}/X.tst.npz \

-x2 ${DATA_DIR}/X.tst.finetune.xlnet.npy \

-y ${DATA_DIR}/Y.tst.npz -f 0

else

echo 'unknown linear version'

exit

fi

# append all prediction path

PRED_NPZ_PATHS="${PRED_NPZ_PATHS} ${PRED_NPZ_PATH}"

done

# final eval

EVAL_DIR=results_linear

mkdir -p ${EVAL_DIR}

python -u -m xbert.evaluator \

-y datasets/${DATASET}/Y.tst.npz \

-e -p ${PRED_NPZ_PATHS} \

|& tee ${EVAL_DIR}/${DATASET}.${VERSION}.txt