50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.metrics import classification_report, accuracy_score
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genre_map = {
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0: "剧情",
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1: "喜剧",
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2: "科幻",
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3: "悬疑",
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4: "动作",
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5: "爱情",
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6: "动画",
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7: "犯罪",
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8: "奇幻",
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9: "纪录"
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}
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df = pd.read_csv("movie_data.csv")
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X = df["text"]
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y = df["label"]
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42, stratify=y
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)
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tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
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X_train_tfidf = tfidf.fit_transform(X_train)
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X_test_tfidf = tfidf.transform(X_test)
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model = MultinomialNB()
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model.fit(X_train_tfidf, y_train)
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y_pred = model.predict(X_test_tfidf)
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print(f"准确率: {accuracy_score(y_test, y_pred):.4f}")
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print(classification_report(y_test, y_pred, target_names=genre_map.values()))
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def predict_genre(text):
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text_tfidf = tfidf.transform([text])
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pred_label = model.predict(text_tfidf)[0]
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return genre_map[pred_label]
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new_movie = "一群年轻人在宇宙飞船上探索外星文明，遭遇未知危险"
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print(f"电影简介：{new_movie}")
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print(f"预测类别：{predict_genre(new_movie)}") |