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import torch
import numpy as np
from src.quere import OpenEndedExplanationDataset, SquadExplanationDataset, ClosedEndedExplanationDataset
from baselines.rep_dataset import RepDataset
from src.utils import normalize_data, get_linear_results
def run_transfer_model(dataset, base_lm, transfer_lm, b=True):
if dataset == "nq":
base_dataset = OpenEndedExplanationDataset(base_lm, load_quere=True)
transfer_dataset = OpenEndedExplanationDataset(transfer_lm, load_quere=True)
elif dataset == "squad":
base_dataset = SquadExplanationDataset(base_lm, load_quere=True)
transfer_dataset = SquadExplanationDataset(transfer_lm, load_quere=True)
base_train_data, base_train_labels, base_train_log_probs = \
base_dataset.train_data, base_dataset.train_labels, base_dataset.train_log_probs
base_train_logits, base_train_pre_conf, base_train_post_conf = base_dataset.train_logits, base_dataset.train_pre_confs, base_dataset.train_post_confs
base_test_data, base_test_labels, base_test_log_probs = \
base_dataset.test_data, base_dataset.test_labels, base_dataset.test_log_probs
base_test_logits, base_test_pre_conf, base_test_post_conf = base_dataset.test_logits, base_dataset.test_pre_confs, base_dataset.test_post_confs
transfer_train_data, transfer_train_labels, transfer_train_log_probs = \
transfer_dataset.train_data, transfer_dataset.train_labels, transfer_dataset.train_log_probs
transfer_train_logits, transfer_train_pre_conf, transfer_train_post_conf = transfer_dataset.train_logits, transfer_dataset.train_pre_confs, transfer_dataset.train_post_confs
transfer_test_data, transfer_test_labels, transfer_test_log_probs = \
transfer_dataset.test_data, transfer_dataset.test_labels, transfer_dataset.test_log_probs
transfer_test_logits, transfer_test_pre_conf, transfer_test_post_conf = transfer_dataset.test_logits, transfer_dataset.test_pre_confs, transfer_dataset.test_post_confs
results = {
"logprob_auroc": [],
"logits_auroc": [],
"preconf_auroc": [],
"postconf_auroc": [],
"exp_auroc": [],
"exp_all_auroc": [],
"transfer_logprob_auroc": [],
"transfer_logits_auroc": [],
"transfer_preconf_auroc": [],
"transfer_postconf_auroc": [],
"transfer_exp_auroc": [],
"transfer_exp_all_auroc": []
}
seeds = range(5)
# unsqueeze 2nd dim of 1d outputs
base_train_pre_conf = base_train_pre_conf.reshape(-1, 1)
base_test_pre_conf = base_test_pre_conf.reshape(-1, 1)
base_train_post_conf = base_train_post_conf.reshape(-1, 1)
base_test_post_conf = base_test_post_conf.reshape(-1, 1)
base_train_log_probs = base_train_log_probs.reshape(-1, 1)
base_test_log_probs = base_test_log_probs.reshape(-1, 1)
transfer_train_pre_conf = transfer_train_pre_conf.reshape(-1, 1)
transfer_test_pre_conf = transfer_test_pre_conf.reshape(-1, 1)
transfer_train_post_conf = transfer_train_post_conf.reshape(-1, 1)
transfer_test_post_conf = transfer_test_post_conf.reshape(-1, 1)
transfer_train_log_probs = transfer_train_log_probs.reshape(-1, 1)
transfer_test_log_probs = transfer_test_log_probs.reshape(-1, 1)
# standard z-score normalize all data with train mean and std
base_train_data, base_test_data = normalize_data(base_train_data, base_test_data)
base_train_log_probs, base_test_log_probs = normalize_data(base_train_log_probs, base_test_log_probs)
base_train_pre_conf, base_test_pre_conf = normalize_data(base_train_pre_conf, base_test_pre_conf)
base_train_post_conf, base_test_post_conf = normalize_data(base_train_post_conf, base_test_post_conf)
base_train_logits, base_test_logits = normalize_data(base_train_logits, base_test_logits)
transfer_train_data, transfer_test_data = normalize_data(transfer_train_data, transfer_test_data)
transfer_train_log_probs, transfer_test_log_probs = normalize_data(transfer_train_log_probs, transfer_test_log_probs)
transfer_train_pre_conf, transfer_test_pre_conf = normalize_data(transfer_train_pre_conf, transfer_test_pre_conf)
transfer_train_post_conf, transfer_test_post_conf = normalize_data(transfer_train_post_conf, transfer_test_post_conf)
transfer_train_logits, transfer_test_logits = normalize_data(transfer_train_logits, transfer_test_logits)
for seed in seeds:
# set random seed
np.random.seed(seed)
torch.manual_seed(seed)
# get results for logprob
acc, f1, ece, auroc = get_linear_results(base_train_log_probs, base_train_labels, transfer_test_log_probs, transfer_test_labels, seed=seed, balanced=b)
results["transfer_logprob_auroc"].append(auroc)
# get base result logprob -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_log_probs, transfer_train_labels, transfer_test_log_probs, transfer_test_labels, seed=seed, balanced=b)
results["logprob_auroc"].append(auroc)
# get results for preconf
acc, f1, ece, auroc = get_linear_results(base_train_pre_conf, base_train_labels, transfer_test_pre_conf, transfer_test_labels, seed=seed, balanced=b)
results["transfer_preconf_auroc"].append(auroc)
# get base result preconf -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_pre_conf, transfer_train_labels, transfer_test_pre_conf, transfer_test_labels, seed=seed, balanced=b)
results["preconf_auroc"].append(auroc)
# get results for postconf
acc, f1, ece, auroc = get_linear_results(base_train_post_conf, base_train_labels, transfer_test_post_conf, transfer_test_labels, seed=seed, balanced=b)
results["transfer_postconf_auroc"].append(auroc)
# get base result postconf -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_post_conf, transfer_train_labels, transfer_test_post_conf, transfer_test_labels, seed=seed, balanced=b)
results["postconf_auroc"].append(auroc)
# get results for logits
acc, f1, ece, auroc = get_linear_results(base_train_logits, base_train_labels, transfer_test_logits, transfer_test_labels, seed=seed, balanced=b)
results["transfer_logits_auroc"].append(auroc)
# get base result logits -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_logits, transfer_train_labels, transfer_test_logits, transfer_test_labels, seed=seed, balanced=b)
results["logits_auroc"].append(auroc)
# get results for exp
acc, f1, ece, auroc = get_linear_results(base_train_data, base_train_labels, transfer_test_data, transfer_test_labels, seed=seed, balanced=b)
results["transfer_exp_auroc"].append(auroc)
# get base result exp -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_data, transfer_train_labels, transfer_test_data, transfer_test_labels, seed=seed, balanced=b)
results["exp_auroc"].append(auroc)
# get reuslts for exp_all
base_train_data_all = np.concatenate([base_train_data, base_train_log_probs, base_train_pre_conf, base_train_post_conf], axis=1)
transfer_test_data_all = np.concatenate([transfer_test_data, transfer_test_log_probs, transfer_test_pre_conf, transfer_test_post_conf], axis=1)
acc, f1, ece, auroc = get_linear_results(base_train_data_all, base_train_labels, transfer_test_data_all, transfer_test_labels, seed=seed, balanced=b)
results["transfer_exp_all_auroc"].append(auroc)
# get base result exp_all -> train and test with transfer data
transfer_train_data_all = np.concatenate([transfer_train_data, transfer_train_log_probs, transfer_train_pre_conf, transfer_train_post_conf], axis=1)
acc, f1, ece, auroc = get_linear_results(transfer_train_data_all, transfer_train_labels, transfer_test_data_all, transfer_test_labels, seed=seed, balanced=b)
results["exp_all_auroc"].append(auroc)
# compute means
results = {k: np.mean(v) for k, v in results.items()}
results = {k: round(v, 4) for k, v in results.items()}
# for k in ["logits_f1", "logprob_f1", "preconf_f1", "postconf_f1", "exp_f1", "exp_all_f1"]:
for k in ["logits_auroc", "logprob_auroc", "preconf_auroc", "postconf_auroc", "exp_auroc", "exp_all_auroc", "transfer_logprob_auroc", "transfer_logits_auroc", "transfer_preconf_auroc", "transfer_postconf_auroc", "transfer_exp_auroc", "transfer_exp_all_auroc"]:
print(k, results[k])
def run_transfer_dataset(dataset_base, dataset_transfer, llm, b=True):
if dataset_base == "nq":
base_dataset = OpenEndedExplanationDataset(llm)
elif dataset_base == "squad":
base_dataset = SquadExplanationDataset(llm)
elif dataset_base == "BooIQ":
base_dataset = ClosedEndedExplanationDataset("BooIQ", llm, load_quere=True)
elif dataset_base == "CommonsenseQA":
base_dataset = ClosedEndedExplanationDataset("CommonsenseQA", load_quere=True)
elif dataset_base == "HaluEval":
base_dataset = ClosedEndedExplanationDataset("HaluEval", load_quere=True)
elif dataset_base == "ToxicEval":
base_dataset = ClosedEndedExplanationDataset("ToxicEval", load_quere=True)
if dataset_transfer == "nq":
transfer_dataset = OpenEndedExplanationDataset(llm)
elif dataset_transfer == "squad":
transfer_dataset = SquadExplanationDataset(llm)
elif dataset_transfer == "BooIQ":
transfer_dataset = ClosedEndedExplanationDataset("BooIQ", load_quere=True)
elif dataset_transfer == "CommonsenseQA":
transfer_dataset = ClosedEndedExplanationDataset("CommonsenseQA", load_quere=True)
elif dataset_transfer == "HaluEval":
transfer_dataset = ClosedEndedExplanationDataset("HaluEval", load_quere=True)
elif dataset_transfer == "ToxicEval":
transfer_dataset = ClosedEndedExplanationDataset("ToxicEval", load_quere=True)
# load base and transfer reps
base_rep_dataset = RepDataset(dataset_base, llm)
transfer_rep_dataset = RepDataset(dataset_transfer, llm)
base_train_rep = base_rep_dataset.train_rep
base_test_rep = base_rep_dataset.test_rep
transfer_train_rep = transfer_rep_dataset.train_rep
transfer_test_rep = transfer_rep_dataset.test_rep
base_train_data, base_train_labels, base_train_log_probs = \
base_dataset.train_data, base_dataset.train_labels, base_dataset.train_log_probs
base_train_logits, base_train_pre_conf, base_train_post_conf = base_dataset.train_logits, base_dataset.train_pre_confs, base_dataset.train_post_confs
base_test_data, base_test_labels, base_test_log_probs = \
base_dataset.test_data, base_dataset.test_labels, base_dataset.test_log_probs
base_test_logits, base_test_pre_conf, base_test_post_conf = base_dataset.test_logits, base_dataset.test_pre_confs, base_dataset.test_post_confs
transfer_train_data, transfer_train_labels, transfer_train_log_probs = \
transfer_dataset.train_data, transfer_dataset.train_labels, transfer_dataset.train_log_probs
transfer_train_logits, transfer_train_pre_conf, transfer_train_post_conf = transfer_dataset.train_logits, transfer_dataset.train_pre_confs, transfer_dataset.train_post_confs
transfer_test_data, transfer_test_labels, transfer_test_log_probs = \
transfer_dataset.test_data, transfer_dataset.test_labels, transfer_dataset.test_log_probs
transfer_test_logits, transfer_test_pre_conf, transfer_test_post_conf = transfer_dataset.test_logits, transfer_dataset.test_pre_confs, transfer_dataset.test_post_confs
results = {
"logprob_auroc": [],
"logits_auroc": [],
"preconf_auroc": [],
"postconf_auroc": [],
"exp_auroc": [],
"exp_all_auroc": [],
"transfer_logprob_auroc": [],
"transfer_logits_auroc": [],
"transfer_preconf_auroc": [],
"transfer_postconf_auroc": [],
"transfer_exp_auroc": [],
"transfer_exp_all_auroc": [],
"rep_auroc": [],
"transfer_rep_auroc": []
}
seeds = range(5)
# unsqueeze 2nd dim of 1d outputs
base_train_pre_conf = base_train_pre_conf.reshape(-1, 1)
base_test_pre_conf = base_test_pre_conf.reshape(-1, 1)
base_train_post_conf = base_train_post_conf.reshape(base_train_labels.shape[0], -1)
base_test_post_conf = base_test_post_conf.reshape(base_test_labels.shape[0], -1)
base_train_log_probs = base_train_log_probs.reshape(base_train_labels.shape[0], -1)
base_test_log_probs = base_test_log_probs.reshape(base_test_labels.shape[0], -1)
transfer_train_pre_conf = transfer_train_pre_conf.reshape(-1, 1)
transfer_test_pre_conf = transfer_test_pre_conf.reshape(-1, 1)
transfer_train_post_conf = transfer_train_post_conf.reshape(transfer_train_labels.shape[0], -1)
transfer_test_post_conf = transfer_test_post_conf.reshape(transfer_test_labels.shape[0], -1)
transfer_train_log_probs = transfer_train_log_probs.reshape(transfer_train_labels.shape[0], -1)
transfer_test_log_probs = transfer_test_log_probs.reshape(transfer_test_labels.shape[0], -1)
# standard z-score normalize all data with train mean and std
base_train_data, base_test_data = normalize_data(base_train_data, base_test_data)
base_train_log_probs, base_test_log_probs = normalize_data(base_train_log_probs, base_test_log_probs)
base_train_pre_conf, base_test_pre_conf = normalize_data(base_train_pre_conf, base_test_pre_conf)
base_train_post_conf, base_test_post_conf = normalize_data(base_train_post_conf, base_test_post_conf)
base_train_logits, base_test_logits = normalize_data(base_train_logits, base_test_logits)
_, transfer_test_data = normalize_data(base_train_data, transfer_test_data)
_, transfer_test_log_probs = normalize_data(base_train_log_probs, transfer_test_log_probs)
_, transfer_test_pre_conf = normalize_data(base_train_pre_conf, transfer_test_pre_conf)
_, transfer_test_post_conf = normalize_data(base_train_post_conf, transfer_test_post_conf)
_, transfer_test_logits = normalize_data(base_train_logits, transfer_test_logits)
for seed in seeds:
# set random seed
np.random.seed(seed)
torch.manual_seed(seed)
# get results for logprob
acc, f1, ece, auroc = get_linear_results(base_train_log_probs, base_train_labels, transfer_test_log_probs, transfer_test_labels, seed=seed, balanced=b)
results["transfer_logprob_auroc"].append(auroc)
# get base result logprob -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_log_probs, transfer_train_labels, transfer_test_log_probs, transfer_test_labels, seed=seed, balanced=b)
results["logprob_auroc"].append(auroc)
# get results for preconf
acc, f1, ece, auroc = get_linear_results(base_train_pre_conf, base_train_labels, transfer_test_pre_conf, transfer_test_labels, seed=seed, balanced=b)
results["transfer_preconf_auroc"].append(auroc)
# get base result preconf -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_pre_conf, transfer_train_labels, transfer_test_pre_conf, transfer_test_labels, seed=seed, balanced=b)
results["preconf_auroc"].append(auroc)
# get results for postconf
acc, f1, ece, auroc = get_linear_results(base_train_post_conf, base_train_labels, transfer_test_post_conf, transfer_test_labels, seed=seed, balanced=b)
results["transfer_postconf_auroc"].append(auroc)
# get base result postconf -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_post_conf, transfer_train_labels, transfer_test_post_conf, transfer_test_labels, seed=seed, balanced=b)
results["postconf_auroc"].append(auroc)
# get results for logits
acc, f1, ece, auroc = get_linear_results(base_train_logits, base_train_labels, transfer_test_logits, transfer_test_labels, seed=seed, balanced=b)
results["transfer_logits_auroc"].append(auroc)
# get base result logits -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_logits, transfer_train_labels, transfer_test_logits, transfer_test_labels, seed=seed, balanced=b)
results["logits_auroc"].append(auroc)
# get results for exp
acc, f1, ece, auroc = get_linear_results(base_train_data, base_train_labels, transfer_test_data, transfer_test_labels, seed=seed, balanced=b)
results["transfer_exp_auroc"].append(auroc)
# get base result exp -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_data, transfer_train_labels, transfer_test_data, transfer_test_labels, seed=seed, balanced=b)
results["exp_auroc"].append(auroc)
# get reuslts for exp_all
base_train_data_all = np.concatenate([base_train_data, base_train_log_probs, base_train_pre_conf, base_train_post_conf], axis=1)
transfer_test_data_all = np.concatenate([transfer_test_data, transfer_test_log_probs, transfer_test_pre_conf, transfer_test_post_conf], axis=1)
acc, f1, ece, auroc = get_linear_results(base_train_data_all, base_train_labels, transfer_test_data_all, transfer_test_labels, seed=seed, balanced=b)
results["transfer_exp_all_auroc"].append(auroc)
# get base result exp_all -> train and test with transfer data
transfer_train_data_all = np.concatenate([transfer_train_data, transfer_train_log_probs, transfer_train_pre_conf, transfer_train_post_conf], axis=1)
acc, f1, ece, auroc = get_linear_results(transfer_train_data_all, transfer_train_labels, transfer_test_data_all, transfer_test_labels, seed=seed, balanced=b)
results["exp_all_auroc"].append(auroc)
# get results for rep
acc, f1, ece, auroc = get_linear_results(base_train_rep, base_train_labels, transfer_test_rep, transfer_test_labels, seed=seed, balanced=b)
results["transfer_rep_auroc"].append(auroc)
# get base result rep -> train and test with transfer data
acc, f1, ece, auroc = get_linear_results(transfer_train_rep, transfer_train_labels, transfer_test_rep, transfer_test_labels, seed=seed, balanced=b)
results["rep_auroc"].append(auroc)
# compute means
results = {k: np.mean(v) for k, v in results.items()}
results = {k: round(v, 4) for k, v in results.items()}
# for k in ["logits_f1", "logprob_f1", "preconf_f1", "postconf_f1", "exp_f1", "exp_all_f1"]:
for k in ["logits_auroc", "logprob_auroc", "preconf_auroc", "postconf_auroc", "exp_auroc", "exp_all_auroc", "transfer_logits_auroc", "transfer_logprob_auroc", "transfer_preconf_auroc", "transfer_postconf_auroc", "transfer_exp_auroc", "transfer_exp_all_auroc", "rep_auroc", "transfer_rep_auroc"]:
print(k, results[k])
if __name__ == "__main__":
# dataset = "nq"
dataset = "squad"
# base_lm = "llama3-8b"
# transfer_lm = "llama3-70b"
base_lm = "llama3-3b"
transfer_lm = "llama3-8b"
run_transfer_model(dataset, base_lm, transfer_lm)
# base_dataset = "nq"
# transfer_dataset = "squad"
# base_dataset = "squad"
# transfer_dataset = "nq"
# base_dataset = "HaluEval"
# transfer_dataset = "ToxicEval"
# base_dataset = "ToxicEval"
# transfer_dataset = "HaluEval"
# llm = "llama3-70b"
# run_transfer_dataset(base_dataset, transfer_dataset, llm)