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run-multiple-experiments.py
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run-multiple-experiments.py
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import os
#dataset=$1
#scal_com_scal=$2
#emotion=$3
#task=$4
#seed_i=$5
#num_annos=$6
#model_short=$7
#model_class=$8
#run_time_hours=$9
#num_anchors=${10}
#num_anchors_neighbors=${11}
#overwrite_existing_results=${12}
#anchor_v3=${13}
if __name__ == "__main__":
check_existing_results = True
emotions = ['aita'] # lists of datasets to use, each emotion corresponds to a dataset.
# hate aita anger fear joy disgust sadness surprise MD_emotion ArMIS_emotion HSBrexit_emotion ConvAbuse_emotion
tasks = ['multi'] #['multi', 'single']
scal_com_scal = ['scalability'] # ['scalability', 'comment_scalability']
model_classes = ['multi']# ['multi', 'perso'] # multi is multi-tasking model from Davani et al, perso is the model from Plepi et al
sc_perso_multi_model_shorts = ['uid'] #['aa', 'ae', 'uid'] # if we use the models from Plepi et al, we can choose: aa = Authorship attribution, ae = average embedding, uid = user ID
use_full = False # for GE and GHC scalability
overwrite_existing_results = True
save_indi_preds = True
anchor_version = 'NoAnchor' #one of v2, v3, v4, or just sth like NoAnchor
num_anchors = 0
num_anchors_neighbors = 0
seeds = [0,1,2,3,4]
# num_annos_list = [6, 8, 10, 12, 14, 16, 18] + list(range(22, 83, 4)) + list(range(100, 1000, 100)) + list(range(1000, 2510, 300))
num_annos_list = [2500] # the number of annotators to use, given that the a corresponding subset of the dataset exists
anchor_v3 = anchor_version == 'v3' # flag for run time limit: v3 on multi-tasking
uid = model_classes == 'perso' # True # flag for run time limit
assert num_anchors== 0 or anchor_version in ['v2','v3','v4']
for num_annos in num_annos_list:
if anchor_v3: # multi-tasking and v3
if num_annos <= 30:
run_time_hours_minutes="00:20:00"
elif num_annos <= 54:
run_time_hours_minutes="01:00:00"
elif num_annos <= 82:
run_time_hours_minutes="02:00:00"
elif num_annos <= 300:
run_time_hours_minutes="05:00:00"
elif num_annos <= 600:
run_time_hours_minutes="07:00:00"
elif num_annos <= 1000:
run_time_hours_minutes="09:00:00"
elif num_annos <= 1900:
run_time_hours_minutes="18:00:00"
else:
run_time_hours_minutes="24:00:00"
elif uid: # uid with or without anchor
if anchor_version == 'v4':
if num_annos <= 22:
run_time_hours_minutes="0-1"
elif num_annos <= 74:
run_time_hours_minutes="02:00:00"
elif num_annos <= 200:
run_time_hours_minutes="05:00:00"
elif num_annos <= 300:
run_time_hours_minutes="07:00:00"
elif num_annos <= 400:
run_time_hours_minutes="09:00:00"
elif num_annos <= 500:
run_time_hours_minutes="11:00:00"
elif num_annos <= 600:
run_time_hours_minutes="12:00:00"
elif num_annos <= 700:
run_time_hours_minutes="13:00:00"
elif num_annos <= 800:
run_time_hours_minutes="15:00:00"
elif num_annos <= 900:
run_time_hours_minutes="17:00:00"
elif num_annos <= 1000:
run_time_hours_minutes="18:00:00"
elif num_annos <= 1300:
run_time_hours_minutes="23:00:00"
elif num_annos <= 1600:
run_time_hours_minutes="27:00:00"
elif num_annos <= 1900:
run_time_hours_minutes="33:00:00"
elif num_annos <= 2200:
run_time_hours_minutes="37:00:00"
else:
run_time_hours_minutes="42:00:00"
else:
if num_annos <= 100:
run_time_hours_minutes="0-1"
elif num_annos <= 1300:
run_time_hours_minutes="02:00:00"
else:
run_time_hours_minutes="03:30:00"
else: # multi-tasking with other anchor versions
if num_annos <= 50:
run_time_hours_minutes="00:15:00"
elif num_annos <= 82:
run_time_hours_minutes="00:30:00"
elif num_annos <= 300:
run_time_hours_minutes="01:00:00"
elif num_annos <= 600:
run_time_hours_minutes="02:00:00"
elif num_annos <= 1000:
run_time_hours_minutes="04:00:00"
elif num_annos <= 1600:
run_time_hours_minutes="06:00:00"
else:
run_time_hours_minutes="08:00:00"
for emotion in emotions:
if emotion == "hate":
dataset = "GHC"
perso_multi_model_shorts = ["uid"]
perso_single_model_shorts = ["base"]
mt_multi_model_shorts = ["mt"]
mt_single_model_shorts = ["base"]
elif emotion == "aita":
dataset = "SC"
perso_multi_model_shorts = sc_perso_multi_model_shorts
perso_single_model_shorts = ["base"]
mt_multi_model_shorts = ["mt"]
mt_single_model_shorts = ["base"]
else:
dataset = "GE"
perso_multi_model_shorts = ["uid"]
perso_single_model_shorts = ["base"]
mt_multi_model_shorts = ["mt"]
mt_single_model_shorts = ["base"]
for task in tasks:
for model_class in model_classes:
if model_class == "perso":
if task == "multi":
model_short_list = perso_multi_model_shorts
else: # task == "single"
model_short_list = perso_single_model_shorts
else: # model_class == "multi"
if task == "multi":
model_short_list = mt_multi_model_shorts
else: # task == "single"
model_short_list = mt_single_model_shorts
for seed_i in seeds:
for scal_com in scal_com_scal:
for model_short in model_short_list:
# SC scalability aita multi 0 6 uid perso 00:30:00 6 3 True v4 save_indi_preds
command_args = f"{dataset} {scal_com} {emotion} {task} {seed_i} {num_annos} {model_short} {model_class} {run_time_hours_minutes} {num_anchors} {num_anchors_neighbors} {overwrite_existing_results} {anchor_version} {save_indi_preds}"
print(command_args)
if scal_com == 'scalability' and check_existing_results:
emotion_for_dir = ""
if emotion not in ['aita', 'hate']:
emotion_for_dir = "'emotion"
model_short_for_dir = model_short
if model_short_for_dir == 'base':
if model_classes == 'multi':
model_short_for_dir = 'bertbase'
else:
model_short_for_dir = 'sbertbase'
path_to_res = f"results/{model_classes}_{scal_com}_{seed_i}/{dataset}{emotion_for_dir}-{model_short_for_dir}_{num_annos}_annos/"
if not os.path.exists(os.path.join(path_to_res,'model_results.json')):
os.system(f"sbatch run-any-scalability_any.slurm {command_args}")
else:
print(f" results exist: {path_to_res}")
else:
os.system(f"sbatch run-any-scalability_any.slurm {command_args}")