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20260609.5.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+
+loss_df = pd.read_csv("loss.csv")
+plt.figure(figsize=(10, 5))
+plt.plot(loss_df["epoch"], loss_df["train_loss"], label="训练集loss", color="#2980b9")
+plt.plot(loss_df["epoch"], loss_df["val_loss"], label="验证集loss", color="#e74c3c")
+plt.title("MLP模型训练Loss曲线", fontsize=14)
+plt.xlabel("Epoch")
+plt.ylabel("Loss值")
+plt.legend()
+plt.grid(alpha=0.3)
+plt.tight_layout()
+plt.savefig("images/loss_curve.png", dpi=300)
+plt.show()
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+
+pred_df = pd.read_csv("predictions.csv")
+genre_counts = pred_df["pred_label"].value_counts()
+# 按题目类别顺序排列
+genre_order = ["剧情", "喜剧", "科幻", "悬疑", "动作", "爱情", "动画", "犯罪", "奇幻", "纪录"]
+genre_counts = genre_counts.reindex(genre_order, fill_value=0)
+
+plt.figure(figsize=(12, 6))
+genre_counts.plot(kind="bar", color="#3498db")
+plt.title("测试集10个类别的预测分布", fontsize=14)
+plt.xlabel("电影类别")
+plt.ylabel("预测数量")
+plt.xticks(rotation=45)
+plt.tight_layout()
+plt.savefig("images/category_bar.png", dpi=300)
+plt.show()
+
+from wordcloud import WordCloud
+import pandas as pd
+
+df = pd.read_csv("my_labels.csv")
+all_quotes = " ".join(df["quote"].astype(str))
+
+wordcloud = WordCloud(
+    font_path="msyh.ttc",  # 中文字体路径
+    width=800, height=400, background_color="white"
+).generate(all_quotes)
+
+plt.figure(figsize=(10, 5))
+plt.imshow(wordcloud, interpolation="bilinear")
+plt.axis("off")
+plt.tight_layout()
+plt.savefig("images/wordcloud.png", dpi=300)
+plt.show()