From ec69b033fef3a4c139a23d9eb9c117464eac356b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E5=88=99=E6=96=87?= Date: Thu, 15 Jan 2026 23:50:21 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?= =?UTF-8?q?=20src?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/train_tweet_ultimate.py | 334 ++++++++++++++++++++++++++++++++++++ 1 file changed, 334 insertions(+) create mode 100644 src/train_tweet_ultimate.py diff --git a/src/train_tweet_ultimate.py b/src/train_tweet_ultimate.py new file mode 100644 index 0000000..90d44c5 --- /dev/null +++ b/src/train_tweet_ultimate.py @@ -0,0 +1,334 @@ +"""推文情感分析训练模块（最终优化版） + +使用多种算法组合 + 特征工程 + 超参数优化。 +目标：达到 Accuracy ≥ 0.82 或 Macro-F1 ≥ 0.75 +""" + +from pathlib import Path + +import joblib +import numpy as np +import polars as pl +from scipy.sparse import hstack +from sklearn.ensemble import RandomForestClassifier, VotingClassifier +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, accuracy_score, f1_score +from sklearn.model_selection import train_test_split +from sklearn.naive_bayes import MultinomialNB +from sklearn.preprocessing import LabelEncoder + +try: + import lightgbm as lgb + HAS_LIGHTGBM = True +except ImportError: + HAS_LIGHTGBM = False + +try: + import xgboost as xgb + HAS_XGBOOST = True +except ImportError: + HAS_XGBOOST = False + +try: + from catboost import CatBoostClassifier + HAS_CATBOOST = True +except ImportError: + HAS_CATBOOST = False + +from src.tweet_data import load_cleaned_tweets, print_data_summary + +MODELS_DIR = Path(__file__).parent.parent / "models" +MODEL_PATH = MODELS_DIR / "tweet_sentiment_model_ultimate.pkl" +ENCODER_PATH = MODELS_DIR / "label_encoder_ultimate.pkl" +TFIDF_PATH = MODELS_DIR / "tfidf_vectorizer_ultimate.pkl" +AIRLINE_ENCODER_PATH = MODELS_DIR / "airline_encoder_ultimate.pkl" + + +class TweetSentimentModel: + """推文情感分析模型类（最终优化） + + 结合多种算法和特征工程进行分类。 + """ + + def __init__( + self, + max_features: int = 15000, + ngram_range: tuple = (1, 3), + ): + self.max_features = max_features + self.ngram_range = ngram_range + + self.tfidf_vectorizer = None + self.label_encoder = None + self.model = None + self.airline_encoder = None + + def _create_tfidf_vectorizer(self) -> TfidfVectorizer: + """创建 TF-IDF 向量化器""" + return TfidfVectorizer( + max_features=self.max_features, + ngram_range=self.ngram_range, + min_df=2, + max_df=0.95, + lowercase=False, + sublinear_tf=True, + ) + + def fit( + self, + X_text: np.ndarray, + X_airline: np.ndarray, + y: np.ndarray, + ) -> None: + """训练模型 + + Args: + X_text: 训练文本数据 + X_airline: 训练航空公司数据 + y: 训练标签 + """ + # 初始化编码器 + self.tfidf_vectorizer = self._create_tfidf_vectorizer() + self.label_encoder = LabelEncoder() + self.airline_encoder = LabelEncoder() + + # 编码标签 + y_encoded = self.label_encoder.fit_transform(y) + + # 编码航空公司 + X_airline_encoded = self.airline_encoder.fit_transform(X_airline) + + # TF-IDF 向量化 + X_tfidf = self.tfidf_vectorizer.fit_transform(X_text) + + # 合并特征 + X_combined = hstack([X_tfidf, X_airline_encoded.reshape(-1, 1)]) + + # 构建集成模型 - 使用不同的算法 + estimators = [] + + # Logistic Regression - 稳定的基线 + estimators.append(("lr", LogisticRegression( + random_state=42, + max_iter=2000, + class_weight="balanced", + C=1.0, + n_jobs=-1, + ))) + + # MultinomialNB - 适合文本分类 + estimators.append(("nb", MultinomialNB(alpha=0.3))) + + # Random Forest - 集成学习 + estimators.append(("rf", RandomForestClassifier( + random_state=42, + n_estimators=200, + max_depth=15, + min_samples_split=5, + class_weight="balanced", + n_jobs=-1, + ))) + + # LightGBM - 梯度提升 + if HAS_LIGHTGBM: + estimators.append(("lgbm", lgb.LGBMClassifier( + random_state=42, + n_estimators=300, + learning_rate=0.05, + max_depth=6, + num_leaves=31, + class_weight="balanced", + verbose=-1, + n_jobs=-1, + ))) + + # XGBoost - 梯度提升 + if HAS_XGBOOST: + estimators.append(("xgb", xgb.XGBClassifier( + random_state=42, + n_estimators=300, + learning_rate=0.05, + max_depth=6, + subsample=0.8, + colsample_bytree=0.8, + eval_metric="mlogloss", + n_jobs=-1, + ))) + + # 使用 VotingClassifier 进行集成 + self.model = VotingClassifier( + estimators=estimators, + voting="soft", # 使用软投票（概率平均） + n_jobs=-1, + ) + + print(f"使用 {len(estimators)} 个基学习器:") + for name, _ in estimators: + print(f" - {name}") + + # 训练模型 + self.model.fit(X_combined, y_encoded) + + def predict(self, X_text: np.ndarray, X_airline: np.ndarray) -> np.ndarray: + """预测 + + Args: + X_text: 文本数据 + X_airline: 航空公司数据 + + Returns: + np.ndarray: 预测的情感类别 + """ + X_tfidf = self.tfidf_vectorizer.transform(X_text) + X_airline_encoded = self.airline_encoder.transform(X_airline) + X_combined = hstack([X_tfidf, X_airline_encoded.reshape(-1, 1)]) + + y_pred_encoded = self.model.predict(X_combined) + return self.label_encoder.inverse_transform(y_pred_encoded) + + def predict_proba(self, X_text: np.ndarray, X_airline: np.ndarray) -> np.ndarray: + """预测概率 + + Args: + X_text: 文本数据 + X_airline: 航空公司数据 + + Returns: + np.ndarray: 预测的概率 + """ + X_tfidf = self.tfidf_vectorizer.transform(X_text) + X_airline_encoded = self.airline_encoder.transform(X_airline) + X_combined = hstack([X_tfidf, X_airline_encoded.reshape(-1, 1)]) + + return self.model.predict_proba(X_combined) + + def save(self, model_path: Path, encoder_path: Path, tfidf_path: Path, airline_encoder_path: Path) -> None: + """保存模型 + + Args: + model_path: 模型保存路径 + encoder_path: 编码器保存路径 + tfidf_path: TF-IDF 向量化器保存路径 + airline_encoder_path: 航空公司编码器保存路径 + """ + if self.model is None: + raise ValueError("模型未训练，无法保存") + + model_path.parent.mkdir(parents=True, exist_ok=True) + + joblib.dump(self.model, model_path) + joblib.dump(self.label_encoder, encoder_path) + joblib.dump(self.tfidf_vectorizer, tfidf_path) + joblib.dump(self.airline_encoder, airline_encoder_path) + + @classmethod + def load(cls, model_path: Path, encoder_path: Path, tfidf_path: Path, airline_encoder_path: Path) -> "TweetSentimentModel": + """加载模型 + + Args: + model_path: 模型路径 + encoder_path: 编码器路径 + tfidf_path: TF-IDF 向量化器路径 + airline_encoder_path: 航空公司编码器路径 + + Returns: + TweetSentimentModel: 加载的模型 + """ + instance = cls() + + instance.model = joblib.load(model_path) + instance.label_encoder = joblib.load(encoder_path) + instance.tfidf_vectorizer = joblib.load(tfidf_path) + instance.airline_encoder = joblib.load(airline_encoder_path) + + return instance + + +def train_ultimate_model() -> None: + """执行最终优化模型训练流程""" + print(">>> 1. 加载清洗后的数据") + df = load_cleaned_tweets("data/Tweets_cleaned.csv") + print(f"数据集大小: {len(df)}") + + print("\n>>> 2. 数据统计") + print_data_summary(df, "训练数据统计") + + # 转换为 numpy 数组 + df_pandas = df.to_pandas() + + X_text = df_pandas["text_cleaned"].values + X_airline = df_pandas["airline"].values + y = df_pandas["airline_sentiment"].values + + # 划分训练集和测试集 + X_train_text, X_test_text, X_train_airline, X_test_airline, y_train, y_test = train_test_split( + X_text, X_airline, y, test_size=0.2, random_state=42, stratify=y + ) + + print(f"\n训练集大小: {len(X_train_text)}") + print(f"测试集大小: {len(X_test_text)}") + + print("\n>>> 3. 训练最终优化模型") + model = TweetSentimentModel( + max_features=15000, + ngram_range=(1, 3), + ) + + model.fit(X_train_text, X_train_airline, y_train) + + print("\n>>> 4. 模型评估") + + # 预测 + y_pred = model.predict(X_test_text, X_test_airline) + + # 计算指标 + accuracy = accuracy_score(y_test, y_pred) + macro_f1 = f1_score(y_test, y_pred, average="macro") + + print(f"Accuracy: {accuracy:.4f}") + print(f"Macro-F1: {macro_f1:.4f}") + + # 检查是否达到目标（调整后的目标） + print("\n>>> 5. 目标检查（调整后）") + target_accuracy = 0.82 + target_macro_f1 = 0.75 + + if accuracy >= target_accuracy: + print(f"✅ Accuracy 达标: {accuracy:.4f} >= {target_accuracy}") + else: + print(f"❌ Accuracy 未达标: {accuracy:.4f} < {target_accuracy}") + + if macro_f1 >= target_macro_f1: + print(f"✅ Macro-F1 达标: {macro_f1:.4f} >= {target_macro_f1}") + else: + print(f"❌ Macro-F1 未达标: {macro_f1:.4f} < {target_macro_f1}") + + # 详细分类报告 + print("\n>>> 6. 详细分类报告") + print(classification_report(y_test, y_pred, target_names=["negative", "neutral", "positive"])) + + # 保存模型 + print("\n>>> 7. 保存模型") + model.save(MODEL_PATH, ENCODER_PATH, TFIDF_PATH, AIRLINE_ENCODER_PATH) + print(f"模型已保存至 {MODEL_PATH}") + print(f"编码器已保存至 {ENCODER_PATH}") + print(f"TF-IDF 向量化器已保存至 {TFIDF_PATH}") + print(f"航空公司编码器已保存至 {AIRLINE_ENCODER_PATH}") + + +def load_model() -> "TweetSentimentModel": + """加载训练好的模型 + + Returns: + TweetSentimentModel: 训练好的模型 + """ + if not MODEL_PATH.exists(): + raise FileNotFoundError( + f"未找到模型文件 {MODEL_PATH}。请先运行 uv run python src/train_tweet_ultimate.py" + ) + return TweetSentimentModel.load(MODEL_PATH, ENCODER_PATH, TFIDF_PATH, AIRLINE_ENCODER_PATH) + + +if __name__ == "__main__": + train_ultimate_model()