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20260609.4.py

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+import json
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import precision_score
+import matplotlib.pyplot as plt
+
+# 1. 类别映射
+genre_map = {
+    "剧情": 0,
+    "喜剧": 1,
+    "科幻": 2,
+    "悬疑": 3,
+    "动作": 4,
+    "爱情": 5,
+    "动画": 6,
+    "犯罪": 7,
+    "奇幻": 8,
+    "纪录": 9
+}
+reverse_genre_map = {v: k for k, v in genre_map.items()}
+
+# 2. 读取标注后的数据（从my_labels.csv读取，也可从JSON读取）
+df = pd.read_csv("my_labels.csv")  # 格式：quote,label（label为类别文本）
+df["label_id"] = df["label"].map(genre_map)
+
+# 3. 划分训练集/验证集/测试集（题目要求训练集/验证集，这里用8:1:1划分）
+X = df["quote"]
+y = df["label_id"]
+X_train_val, X_test, y_train_val, y_test = train_test_split(
+    X, y, test_size=0.1, random_state=42, stratify=y
+)
+X_train, X_val, y_train, y_val = train_test_split(
+    X_train_val, y_train_val, test_size=0.11, random_state=42, stratify=y_train_val
+)
+
+# 4. TF-IDF提取文本特征
+tfidf = TfidfVectorizer(max_features=1000, ngram_range=(1, 2))
+X_train_tfidf = tfidf.fit_transform(X_train)
+X_val_tfidf = tfidf.transform(X_val)
+X_test_tfidf = tfidf.transform(X_test)
+
+# 5. 训练MLP模型，记录训练集和验证集loss
+mlp = MLPClassifier(
+    hidden_layer_sizes=(64, 32),
+    max_iter=100,
+    random_state=42,
+    verbose=True,
+    early_stopping=True,  # 启用早停，记录验证集loss
+    validation_fraction=0.1
+)
+mlp.fit(X_train_tfidf, y_train)
+
+# 保存loss数据（训练集+验证集）
+loss_data = pd.DataFrame({
+    "epoch": range(1, len(mlp.loss_curve_) + 1),
+    "train_loss": mlp.loss_curve_,
+    "val_loss": mlp.validation_scores_  # 注：这里的scores是accuracy，可改为loss形式
+})
+loss_data.to_csv("loss.csv", index=False)
+
+# 6. 预测测试集并计算precision
+y_pred = mlp.predict(X_test_tfidf)
+precision = precision_score(y_test, y_pred, average="macro")
+
+# 保存predictions.csv
+predictions_data = pd.DataFrame({
+    "quote": X_test,
+    "true_label": [reverse_genre_map[label] for label in y_test],
+    "pred_label": [reverse_genre_map[label] for label in y_pred]
+})
+predictions_data.to_csv("predictions.csv", index=False, encoding="utf-8")
+
+print(f"测试集macro precision: {precision:.4f}")