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eval_baselines.py
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eval_baselines.py
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import os
import torch
from pathlib import Path
from transformers import (
BartForConditionalGeneration,
AutoTokenizer,
HfArgumentParser,
Seq2SeqTrainingArguments,
)
from model_args import ModelArguments
from data_args import DataTrainingArguments
from template_args import TemplateArguments
import utils
from data import TabularData
from tqdm import trange
from load_utils import *
import datasets
parser = HfArgumentParser(
(
ModelArguments,
DataTrainingArguments,
Seq2SeqTrainingArguments,
TemplateArguments,
)
)
(
model_args,
data_args,
training_args,
template_args,
) = parser.parse_args_into_dataclasses()
model, tokenizer, no_space_tokenizer = load_model_and_tokenizer(
model_args, load_template_model=False
)
model.cuda()
os.makedirs(training_args.output_dir, exist_ok=True)
if template_args.evaluation_split == "val":
if "e2e" in data_args.dataset_name:
val_split_name = "validation"
elif "synthbio" in data_args.dataset_name:
val_split_name = "val"
else:
raise ValueError("Invalid dataset: ", data_args.dataset_name)
elif template_args.evaluation_split == "test":
val_split_name = "test"
else:
raise ValueError("Invalid evaluation split: ", template_args.evaluation_split)
# inputs are deduplicated here
assert data_args.dedup_input == False
eval_dataset = TabularData(
data_args.dataset_name,
val_split_name,
tokenizer,
no_space_tokenizer,
data_args,
training_args,
)
# To create multiple references, we deduplicate here to get the relevant information
# dedup by input_ids
dedup_indices = []
seen_input_data = set()
dedup_map = dict()
for i, dt in enumerate(eval_dataset.raw_datasets[val_split_name]):
if "synthbio" in data_args.dataset_name:
dedup_field = dt["input_text"]["context"]
else:
dedup_field = dt["meaning_representation"]
if dedup_field not in seen_input_data:
dedup_indices.append(i)
dedup_map[dedup_field] = [i]
seen_input_data.add(dedup_field)
else:
dedup_map[dedup_field].append(i)
eval_input_ids = eval_dataset.processed_dataset[dedup_indices]["input_ids"]
eval_input_ids = utils.pad_list(eval_input_ids, tokenizer.pad_token_id)
eval_input_ids = torch.LongTensor(eval_input_ids)
# Get baseline BART results
baseline_results = []
for batch_start in trange(
0, eval_input_ids.size(0), template_args.inference_batch_size
):
batch_input_ids = eval_input_ids[
batch_start : batch_start + template_args.inference_batch_size
].cuda()
output_ids = model.generate(
batch_input_ids,
max_length=template_args.max_decode_steps,
num_beams=data_args.num_beams,
num_return_sequences=1,
length_penalty=template_args.length_penalty,
early_stopping=False,
)
for sen_ids in output_ids:
baseline_results.append(
tokenizer.decode(sen_ids, skip_special_tokens=True).strip()
)
if template_args.evaluation_split == "val":
output_dir = Path(training_args.output_dir) / "dev_output"
else:
output_dir = Path(training_args.output_dir) / "test_output"
output_dir.mkdir(exist_ok=True)
with open(output_dir / "baseline_output.txt", "w") as f:
for t in baseline_results:
f.write(t + "\n")
if "synthbio" in data_args.dataset_name:
import numpy as np
from sacrebleu import BLEU
from bert_score import BERTScorer
from rouge_score import rouge_scorer as _rouge_scorer
from preprocessing import untokenize
from collections import defaultdict
bleu_scorer = BLEU()
bert_scorer = BERTScorer(lang="en", rescale_with_baseline=True)
rouge_scorer = _rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
metrics_dict = dict()
maximum_num_ref = max(
[len(per_input_indices) for per_input_indices in dedup_map.values()]
)
bleu_multi_references = [[] for _ in range(maximum_num_ref)]
bert_multi_references = []
# val_sample_i is index of dataset before deduplication
for val_sample_i in dedup_indices:
sample_multi_references = []
val_sample_input = eval_dataset.raw_datasets[template_args.evaluation_split][val_sample_i]["input_text"][
"context"
]
for val_sample_j in dedup_map[val_sample_input]:
sample_multi_references.append(
untokenize(
eval_dataset.raw_datasets[template_args.evaluation_split][val_sample_j]["target_text"].lower()
)
)
bert_multi_references.append(sample_multi_references)
for ref_i in range(maximum_num_ref):
bleu_multi_references[ref_i].append(
sample_multi_references[ref_i]
if ref_i < len(sample_multi_references)
else None
)
bleu_score = bleu_scorer.corpus_score(baseline_results, bleu_multi_references).score
bert_score = list(bert_scorer.score(baseline_results, bert_multi_references))
bert_score = [t.mean().item() for t in bert_score]
all_rouge_scores = [[], [], []]
for pred, refs in zip(baseline_results, bert_multi_references):
best_rouge = None
for r in refs:
temp_rouge = rouge_scorer.score(pred, r)
if best_rouge is None or best_rouge['rougeL'].fmeasure < temp_rouge['rougeL'].fmeasure:
best_rouge = temp_rouge
all_rouge_scores[0].append(best_rouge["rouge1"].fmeasure)
all_rouge_scores[1].append(best_rouge["rouge2"].fmeasure)
all_rouge_scores[2].append(best_rouge["rougeL"].fmeasure)
metrics_dict["full"] = {
"bleu": bleu_score,
"bert_p": bert_score[0],
"bert_r": bert_score[1],
"bert_f": bert_score[2],
"rouge1": np.mean(all_rouge_scores[0]),
"rouge2": np.mean(all_rouge_scores[1]),
"rougeL": np.mean(all_rouge_scores[2])
}
print(metrics_dict)
deduped_dataset = eval_dataset.raw_datasets["val"].select(dedup_indices)
notable_type_to_indices = defaultdict(list)
for i, dt in enumerate(deduped_dataset):
input_table = dict(
zip(
dt["input_text"]["table"]["column_header"],
dt["input_text"]["table"]["content"],
)
)
notable_type_to_indices[input_table["notable_type"]].append(i)
notable_types = list(notable_type_to_indices.keys())
for type_i in range(8):
type_indices = notable_type_to_indices[notable_types[type_i]]
predictions = [baseline_results[i] for i in type_indices]
bleu_multi_references = [[] for _ in range(maximum_num_ref)]
bert_multi_references = []
# sample_i is index of the deduped dataset
for sample_i in type_indices:
sample_multi_references = []
val_sample_input = eval_dataset.raw_datasets[template_args.evaluation_split][dedup_indices[sample_i]][
"input_text"
]["context"]
# val_sample_j is index of dataset before deduplication
for val_sample_j in dedup_map[val_sample_input]:
sample_multi_references.append(
untokenize(
eval_dataset.raw_datasets[template_args.evaluation_split][val_sample_j][
"target_text"
].lower()
)
)
for ref_i in range(maximum_num_ref):
bleu_multi_references[ref_i].append(
sample_multi_references[ref_i]
if ref_i < len(sample_multi_references)
else None
)
bert_multi_references.append(sample_multi_references)
bleu_score = bleu_scorer.corpus_score(predictions, bleu_multi_references).score
bert_score = list(bert_scorer.score(predictions, bert_multi_references))
bert_score = [t.mean().item() for t in bert_score]
all_rouge_scores = [[], [], []]
for pred, refs in zip(predictions, bert_multi_references):
best_rouge = None
for r in refs:
temp_rouge = rouge_scorer.score(pred, r)
if best_rouge is None or best_rouge['rougeL'].fmeasure < temp_rouge['rougeL'].fmeasure:
best_rouge = temp_rouge
all_rouge_scores[0].append(best_rouge["rouge1"].fmeasure)
all_rouge_scores[1].append(best_rouge["rouge2"].fmeasure)
all_rouge_scores[2].append(best_rouge["rougeL"].fmeasure)
metrics_dict[type_i] = {
"bleu": bleu_score,
"bert_p": bert_score[0],
"bert_r": bert_score[1],
"bert_f": bert_score[2],
"rouge1": np.mean(all_rouge_scores[0]),
"rouge2": np.mean(all_rouge_scores[1]),
"rougeL": np.mean(all_rouge_scores[2])
}
print(f"Type {type_i}: {metrics_dict[type_i]}")
torch.save(
metrics_dict, output_dir / "metrics.pt",
)