#!/bin/bash

# . activate xformer-multisource-domain-adaptation

# . setenv.sh

run_name="(emnlp-sentiment)"
model_dir="./emnlp_sentiment_experiments"
tags="emnlp sentiment experiments"
# for i in 1000,1 1001,2 666,3 7,4 50,5; do IFS=","; set -- $i;
# 1) Basic
python emnlp_final_experiments/sentiment-analysis/train_basic.py \
--dataset_loc data/sentiment-dataset \
--train_pct 0.9 \
--n_gpu 1 \
--n_epochs 5 \
--domains books dvd electronics kitchen_\&_housewares \
--seed 1 \
--run_name "basic-distilbert-${2}" \
--model_dir ${model_dir}/basic_distilbert \
--tags ${tags} \
--batch_size 8 \
--lr 0.00003
# indices_dir=`ls -d -t ${model_dir}/basic_distilbert/*/ | head -1`

  # 2) Adv-6
#   python emnlp_final_experiments/sentiment-analysis/train_basic_domain_adversarial.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-adversarial-6-${2}" \
#     --model_dir ${model_dir}/distilbert_adversarial_6 \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --supervision_layer 6 \
#     --indices_dir ${indices_dir}

#   # 3) Adv-3
#   python emnlp_final_experiments/sentiment-analysis/train_basic_domain_adversarial.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-adversarial-3-${2}" \
#     --model_dir ${model_dir}/distilbert_adversarial_3 \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --supervision_layer 3 \
#     --indices_dir ${indices_dir}

#   # 4) Independent-Avg
#   python emnlp_final_experiments/sentiment-analysis/train_multi_view_averaging_individuals.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-ensemble-averaging-individuals-${2}" \
#     --model_dir ${model_dir}/distilbert_ensemble_averaging_individuals \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --indices_dir ${indices_dir}
#   avg_model=`ls -d -t ${model_dir}/distilbert_ensemble_averaging_individuals/*/ | head -1`

#   # 5) Independent-Ft
#   python emnlp_final_experiments/sentiment-analysis/train_multi_view_selective_weighting.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 30 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-ensemble-selective-attention-${2}" \
#     --model_dir ${model_dir}/distilbert_ensemble_selective_attention \
#     --tags ${tags} \
#     --pretrained_model ${avg_model} \
#     --indices_dir ${indices_dir}

#   # 6) MoE-DC
#   python emnlp_final_experiments/sentiment-analysis/train_multi_view_domainclassifier_individuals.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-ensemble-domainclassifier-individuals-${2}" \
#     --model_dir ${model_dir}/distilbert_ensemble_domainclassifier_individuals \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --indices_dir ${indices_dir} \
#     --pretrained_model ${avg_model}

#   # 7) MoE-Avg
#   python emnlp_final_experiments/sentiment-analysis/train_multi_view.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-ensemble-averaging-${2}" \
#     --model_dir ${model_dir}/distilbert_ensemble_averaging \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --ensemble_basic \
#     --indices_dir ${indices_dir}

#   # 8) MoE-Att
#   python emnlp_final_experiments/sentiment-analysis/train_multi_view.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-ensemble-attention-${2}" \
#     --model_dir ${model_dir}/distilbert_ensemble_attention \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --indices_dir ${indices_dir}

#   # 9) MoE-Att-Adv-6
#   python emnlp_final_experiments/sentiment-analysis/train_multi_view_domain_adversarial.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-ensemble-attention-adversarial-6-${2}" \
#     --model_dir ${model_dir}/distilbert_ensemble_attention_adversarial_6 \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --supervision_layer 6 \
#     --indices_dir ${indices_dir}

#   # 10) MoE-Att-Adv-3
#   python emnlp_final_experiments/sentiment-analysis/train_multi_view_domain_adversarial.py \
#     --dataset_loc data/sentiment-dataset \
#     --train_pct 0.9 \
#     --n_gpu 1 \
#     --n_epochs 5 \
#     --domains books dvd electronics kitchen_\&_housewares \
#     --seed ${1} \
#     --run_name "distilbert-ensemble-attention-adversarial-3-${2}" \
#     --model_dir ${model_dir}/distilbert_ensemble_attention_adversarial_4 \
#     --tags ${tags} \
#     --batch_size 8 \
#     --lr 0.00003 \
#     --supervision_layer 3 \
#     --indices_dir ${indices_dir}

# done