-
Notifications
You must be signed in to change notification settings - Fork 0
/
IEFC_distilroberta_4767_5.py
154 lines (137 loc) · 6.32 KB
/
IEFC_distilroberta_4767_5.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
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
primaryto3 = {
"anticipation": "positive",
"joy": "positive",
"trust": "positive",
"surprise": "neutral",
"neutral": "neutral",
"fear": "negative",
"sadness": "negative",
"disgust": "negative",
"anger": "negative"
}
emotions_train_valid = pd.read_csv("memor/data_preproc_2.csv").replace({'elicited_emotion': primaryto3})
emotions_train_valid = emotions_train_valid[emotions_train_valid["split"]!="test"][["split", "history", "utterance", "elicited_emotion"]]
emotions_test = pd.read_csv("memor/test_data.csv").replace({'elicited_emotion': primaryto3})
emotions_test = emotions_test[["split", "history", "utterance", "elicited_emotion"]]
emotions = pd.concat([emotions_train_valid, emotions_test], ignore_index=True)
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, random_state):
if mode not in set(["full-history", "last-uttr", "no-history"]):
print("mode is invalid")
return
if mode == "full-history":
df = deepcopy(emotions)
df["history"] = df["history"].apply(lambda x: " ".join(eval(x)))
df.rename(columns={"history": "text", "elicited_emotion": "label"}, inplace=True)
elif mode == "last-uttr":
df = deepcopy(emotions)
df["history"] = df["history"].apply(lambda x: " ".join(eval(x)[-2:]) if len(eval(x)) >= 2 else eval(x)[-1])
df.rename(columns={"history": "text", "elicited_emotion": "label"}, inplace=True)
else:
df = deepcopy(emotions)
df.rename(columns={"utterance": "text", "elicited_emotion": "label"}, inplace=True)
df["label"].replace({"positive": 0, "neutral": 1, "negative": 2}, inplace=True)
df_train = df[df["split"]=="train"].sample(4767, random_state=random_state)
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)
for i in range(5):
model = AutoModelForSequenceClassification.from_pretrained(
model_ckpt,
num_labels=num_labels,
id2label=id2label,
label2id=label2id
).to(device)
label_ratio, emotions_dataset = make_dataset(mode, i)
emotions_encoded = emotions_dataset.map(tokenize, batched=True)
training_args = TrainingArguments(
output_dir=f"IEFC-{model_ckpt}-{mode}-4767-{i}",
evaluation_strategy="epoch",
per_device_train_batch_size=128,
per_device_eval_batch_size=128,
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"IEFC-{model_ckpt}-{mode}-4767-{i}")
print("Results on Test Data:")
preds_output = trainer.predict(emotions_encoded["test"])
print(preds_output.metrics)