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train_mlp.py

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+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import precision_score
+import csv
+
+# 1. 读取标注数据
+df = pd.read_csv("my_labels.csv")
+texts = df["text"].tolist()
+labels = df["label"].tolist()
+
+# 2. TF-IDF文本特征提取
+tfidf = TfidfVectorizer()
+X = tfidf.fit_transform(texts)
+y = np.array(labels)
+
+# 3. 划分训练集、验证集
+X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 4. 训练MLP，记录每轮loss
+mlp = MLPClassifier(hidden_layer_sizes=(128,64), max_iter=100, random_state=42)
+train_loss_list = []
+val_precision_list = []
+
+for epoch in range(1, mlp.max_iter+1):
+    mlp.partial_fit(X_train, y_train, classes=np.unique(y))
+    # 记录训练loss
+    train_loss_list.append({"epoch": epoch, "loss": mlp.loss_})
+    # 验证集预测、计算precision
+    y_pred_val = mlp.predict(X_val)
+    val_prec = precision_score(y_val, y_pred_val, average="macro", zero_division=0)
+    val_precision_list.append({"epoch": epoch, "precision": val_prec})
+
+# 保存loss.csv
+with open("loss.csv", "w", newline="", encoding="utf-8") as f:
+    writer = csv.DictWriter(f, fieldnames=["epoch", "loss"])
+    writer.writeheader()
+    writer.writerows(train_loss_list)
+
+# 保存predictions.csv
+with open("predictions.csv", "w", newline="", encoding="utf-8") as f:
+    writer = csv.DictWriter(f, fieldnames=["epoch", "precision"])
+    writer.writeheader()
+    writer.writerows(val_precision_list)
+
+print("模型训练完成，已输出 loss.csv、predictions.csv")