method2/final_stacking_V2.py

# ==============================================================================
# 脚本 2: final_stacking_V2.py (增强版 Stacking 完整代码)
#
# 目的：运行增强版 Stacking，将 Model A/B 概率与 6 个人工特征结合。
# 核心: 元特征 = [Prob_A, Prob_B, VADER Scores, Lengthening, Punctuation]
# ==============================================================================

import pandas as pd
import numpy as np
import torch
import joblib
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score
from datasets import Dataset
from transformers import (
    # 明确指定 DebertaV2Tokenizer 解决兼容性问题
    DebertaV2Tokenizer,
    AutoModelForSequenceClassification,
    TrainingArguments,
    Trainer
)
import os
import time
import warnings

# --- 1. 定义路径和常量 ---
warnings.filterwarnings("ignore")
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# 原始数据和新特征的路径 (使用脚本 1 的输出)
TRAIN_FILE_PATH = "train_features.csv" 
VALID_FILE_PATH = "val_features.csv"   

# 依赖路径 (基础模型概率的来源，假设已复制到 method2 目录下)
MODEL_A_DIR = "final_model_tfidf"
VECTORIZER_PATH = os.path.join(MODEL_A_DIR, "vectorizer.joblib")
MODEL_A_PATH = os.path.join(MODEL_A_DIR, "model_A.joblib")
DEBERTA_MODEL_PATH = "final_model_deberta_weighted" 

# 新增的人工特征列表
NEW_FEATURES = [
    'vader_compound_score', 'vader_neg_score', 'vader_pos_score', 
    'lengthening_ratio', 'extreme_punctuation'
]


# --- 2. 加载增强后的数据 ---
print("--- 方案二：增强版集成学习 ---")
print("加载增强特征后的数据...")
train_df = pd.read_csv(TRAIN_FILE_PATH)
eval_df = pd.read_csv(VALID_FILE_PATH)

# 将 label 映射为数值 (由于 extract_features.py 已经处理，这里只做确认)
y_train = train_df['label']
y_eval = eval_df['label'] 
X_train_text = train_df['text']
X_eval_text = eval_df['text']


# (核心步骤) 提取新的人工特征作为 Meta-Features 的一部分
X_train_new_features = train_df[NEW_FEATURES].values
X_eval_new_features = eval_df[NEW_FEATURES].values

print(f"已提取 {len(NEW_FEATURES)} 个人工特征。")

# --- 3: 获取 Model A 的预测概率 (元特征 1) ---
print(f"\n--- 1/4：加载 Model A ({MODEL_A_DIR}) 并获取预测概率 ---")
try:
    vectorizer = joblib.load(VECTORIZER_PATH)
    model_A = joblib.load(MODEL_A_PATH)
except FileNotFoundError:
    print("！！！错误：Model A 文件缺失。请将 Model A 文件夹复制到当前目录。！！！")
    exit()

X_train_tfidf = vectorizer.transform(X_train_text)
X_eval_tfidf = vectorizer.transform(X_eval_text)

probs_A_train = model_A.predict_proba(X_train_tfidf)[:, 1]
probs_A_eval = model_A.predict_proba(X_eval_tfidf)[:, 1]

# --- 4: 获取 Model B 的预测概率 (元特征 2) ---
print(f"\n--- 2/4：加载 Model B ({DEBERTA_MODEL_PATH}) 并获取预测概率 ---")
try:
    model_B = AutoModelForSequenceClassification.from_pretrained(DEBERTA_MODEL_PATH)
    tokenizer_B = DebertaV2Tokenizer.from_pretrained(DEBERTA_MODEL_PATH)
except Exception:
    print("！！！错误：Model B 文件缺失或加载失败。请确保 Model B 文件夹已复制且依赖已安装。！！！")
    exit()

# 准备 Trainer 和数据函数
trainer_B = Trainer(
    model=model_B,
    args=TrainingArguments(output_dir="./tmp_ensemble_eval", per_device_eval_batch_size=32, eval_strategy="no", fp16=True)
)

def tokenize_function(examples):
    return tokenizer_B(examples["text"], padding="max_length", truncation=True, max_length=512)

# Tokenize 数据集 (需要用到原始的 text 列)
train_dataset_hf = Dataset.from_pandas(train_df)
eval_dataset_hf = Dataset.from_pandas(eval_df)

# 移除不需要的列，只保留 text 和 label 用于预测
cols_to_remove = [col for col in train_df.columns if col not in ['text', 'label']]
tokenized_train_dataset = train_dataset_hf.remove_columns(cols_to_remove).map(tokenize_function, batched=True, num_proc=4)
tokenized_eval_dataset = eval_dataset_hf.remove_columns(cols_to_remove).map(tokenize_function, batched=True, num_proc=4)

# 预测训练集和验证集
predictions_B_train = trainer_B.predict(tokenized_train_dataset.remove_columns(["text", "label"]))
probs_B_train = torch.nn.Softmax(dim=1)(torch.from_numpy(predictions_B_train.predictions))[:, 1].numpy()

predictions_B_eval = trainer_B.predict(tokenized_eval_dataset.remove_columns(["text", "label"]))
probs_B_eval = torch.nn.Softmax(dim=1)(torch.from_numpy(predictions_B_eval.predictions))[:, 1].numpy()

# --- 5: 训练增强版元模型 ---
print("\n--- 3/4：创建增强版元特征并训练 Meta-Model ---")

# (核心步骤) 堆叠所有元特征: Model A 概率 + Model B 概率 + 6个人工特征 (共 8 个特征)
X_train_meta_V2 = np.column_stack((probs_A_train, probs_B_train, X_train_new_features))
X_eval_meta_V2 = np.column_stack((probs_A_eval, probs_B_eval, X_eval_new_features))

print(f"增强版元特征维度: {X_eval_meta_V2.shape} (包含 2 个概率 + 5 个人工特征 = 7 个特征)")

# 训练元模型：使用 LR 并加入类别权重 (高召回潜力)
# 我们回到 V1 的 LR 模型，但这次有更多特征来帮助它做决策。
meta_model_V2 = LogisticRegression(
    class_weight='balanced', 
    random_state=42,
    max_iter=500 
)
meta_model_V2.fit(X_train_meta_V2, y_train)

print("增强版元模型 (LR) 训练完毕！")


# --- 6: 最终评估：阈值调优 (继续使用 Thr=0.65) ---
print("\n--- 4/4：最终评估：阈值调优 (使用 Thr=0.65) ---")

# 获取 LR 模型的预测概率 (scores)
meta_scores_V2 = meta_model_V2.predict_proba(X_eval_meta_V2)[:, 1] 

# 使用 V3 方案中表现略有提升的阈值 0.65
BEST_THRESHOLD = 0.65 

# 根据新阈值生成最终预测
preds_meta_tuned_V2 = (meta_scores_V2 >= BEST_THRESHOLD).astype(int)

# 输出详细的分类报告
print(f"--- 评估阈值: {BEST_THRESHOLD:.2f} ---")
report = classification_report(
    y_eval, 
    preds_meta_tuned_V2, 
    target_names=['real (0)', 'fake (1)'],
    digits=4,
    zero_division=0
)
print(report)

f1_ensemble_V2 = f1_score(y_eval, preds_meta_tuned_V2, pos_label=1)
print(f"--- 最终集成 F1 分数 (Fake): {f1_ensemble_V2:.4f} ---")
print("--- 脚本 final_stacking_V2.py 运行结束 ---")