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EERC_distilroberta.py
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EERC_distilroberta.py
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import pandas as pd
from datasets import Dataset, DatasetDict
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer, TrainerCallback
import evaluate
import torch
import torch.nn as nn
import numpy as np
from copy import deepcopy
model_ckpt = "distilroberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_ckpt, truncation_side='left')
# load data
emotions_train_valid = pd.read_csv("dailydialog/data_preproc.csv")
emotions_train_valid = emotions_train_valid[emotions_train_valid["split"]!="test"][["split", "history", "utterance", "emotion"]]
emotions_test = pd.read_csv("dailydialog/data_preproc_next_emotion.csv")
emotions_test = emotions_test[emotions_test["split"]=="test"][["split", "history", "next_utterance", "next_uttr_emotion"]]
def tokenize(batch):
return tokenizer(batch["text"], padding=True, truncation=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
num_labels = 3
id2label = {
"0": "positive",
"1": "neutral",
"2": "negative"
}
label2id = {
"positive": 0,
"neutral": 1,
"negative": 2
}
accuracy = evaluate.load("accuracy")
precision = evaluate.load("precision")
recall = evaluate.load("recall")
f1score = evaluate.load("f1")
def compute_metrics(eval_pred):
predictions, labels = eval_pred
predictions = np.argmax(predictions, axis=1)
acc = accuracy.compute(predictions=predictions, references=labels)["accuracy"]
prec = precision.compute(predictions=predictions, references=labels, average='macro', zero_division=0.0)["precision"]
rec = recall.compute(predictions=predictions, references=labels, average='macro')["recall"]
f1 = f1score.compute(predictions=predictions, references=labels, average='macro')["f1"]
predictions2 = [predictions[i] for i, label in enumerate(labels) if label != 1]
labels2 = [label for label in labels if label != 1]
f1_2 = f1score.compute(predictions=predictions2, references=labels2, average='macro')["f1"]
f1_3 = f1score.compute(predictions=predictions, references=labels, labels=[0, 2], average='macro')["f1"]
return {"accuracy": acc, "precision": prec, "recall": rec, "f1": f1, "f1_wo_neu_old": f1_2, "f1_wo_neu": f1_3}
class CustomCallback(TrainerCallback):
def __init__(self, trainer) -> None:
super().__init__()
self._trainer = trainer
def on_epoch_end(self, args, state, control, **kwargs):
if control.should_evaluate:
control_copy = deepcopy(control)
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset, metric_key_prefix="train")
return control_copy
def make_dataset(mode):
if mode not in set(["full-history", "last-uttr", "no-history"]):
print("mode is invalid")
return
if mode == "full-history":
df_train_valid = deepcopy(emotions_train_valid)
df_train_valid["history"] = df_train_valid["history"].apply(lambda x: " ".join(eval(x)))
df_test = deepcopy(emotions_test)
df_test["history"] = df_test["history"].apply(lambda x: " ".join(eval(x))) + " " + df_test["next_utterance"]
df = pd.concat([df_train_valid[["split", "history", "emotion"]],
df_test[["split", "history", "next_uttr_emotion"]].rename(columns={"next_uttr_emotion": "emotion"})], ignore_index=True)
df.rename(columns={"history": "text", "emotion": "label"}, inplace=True)
elif mode == "last-uttr":
df_train_valid = deepcopy(emotions_train_valid)
df_train_valid["history"] = df_train_valid["history"].apply(lambda x: " ".join(eval(x)[-2:]) if len(eval(x)) >= 2 else eval(x)[-1])
df_test = deepcopy(emotions_test)
df_test["history"] = df_test["history"].apply(lambda x: eval(x)[-1]) + " " + df_test["next_utterance"]
df = pd.concat([df_train_valid[["split", "history", "emotion"]],
df_test[["split", "history", "next_uttr_emotion"]].rename(columns={"next_uttr_emotion": "emotion"})], ignore_index=True)
df.rename(columns={"history": "text", "emotion": "label"}, inplace=True)
else:
df_train_valid = deepcopy(emotions_train_valid)
df_test = deepcopy(emotions_test)
df = pd.concat([df_train_valid[["split", "utterance", "emotion"]],
df_test[["split", "next_utterance", "next_uttr_emotion"]].rename(columns={"next_utterance": "utterance", "next_uttr_emotion": "emotion"})], ignore_index=True)
df.rename(columns={"utterance": "text", "emotion": "label"}, inplace=True)
df["label"].replace({"positive": 0, "neutral": 1, "negative": 2}, inplace=True)
df_train = df[df["split"]=="train"]
df_valid = df[df["split"]=="valid"]
df_test = df[df["split"]=="test"]
label_ratio = df_train.value_counts("label", sort=False, normalize=True)
emotions_dataset = DatasetDict({
"train": Dataset.from_pandas(df_train[["text", "label"]], preserve_index=False).shuffle(),
"valid": Dataset.from_pandas(df_valid[["text", "label"]], preserve_index=False),
"test": Dataset.from_pandas(df_test[["text", "label"]], preserve_index=False)
})
return label_ratio, emotions_dataset
for mode in ["full-history", "last-uttr", "no-history"]:
print(mode)
model = AutoModelForSequenceClassification.from_pretrained(
model_ckpt,
num_labels=num_labels,
id2label=id2label,
label2id=label2id
).to(device)
label_ratio, emotions_dataset = make_dataset(mode)
emotions_encoded = emotions_dataset.map(tokenize, batched=True)
training_args = TrainingArguments(
output_dir=f"EERC-{model_ckpt}-{mode}",
evaluation_strategy="epoch",
per_device_train_batch_size=64,
per_device_eval_batch_size=64,
num_train_epochs=40,
weight_decay=0.01,
warmup_steps=500,
metric_for_best_model="f1",
load_best_model_at_end=True
)
class_weight = torch.tensor(1/label_ratio).clone().to(device, torch.float32)
class CustomTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
labels = inputs.get("labels")
outputs = model(**inputs)
logits = outputs.get("logits")
loss_fct = nn.CrossEntropyLoss(weight=class_weight)
loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
return (loss, outputs) if return_outputs else loss
trainer = CustomTrainer(
model=model,
args=training_args,
train_dataset=emotions_encoded["train"],
eval_dataset=emotions_encoded["valid"],
tokenizer=tokenizer,
compute_metrics=compute_metrics
)
trainer.add_callback(CustomCallback(trainer))
trainer.train()
trainer.save_model(f"EERC-{model_ckpt}-{mode}")
print("Results on Test Data:")
preds_output = trainer.predict(emotions_encoded["test"])
print(preds_output.metrics)