删除 1.py

1.py

@@ -1,65 +0,0 @@
-import warnings
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-warnings.filterwarnings("ignore", category=UserWarning)
-
-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.svm import LinearSVC
-from sklearn.metrics import accuracy_score, classification_report
-
-genre_dict = {
-    0: "剧情",
-    1: "喜剧",
-    2: "科幻",
-    3: "悬疑",
-    4: "动作",
-    5: "爱情",
-    6: "动画",
-    7: "犯罪",
-    8: "奇幻",
-    9: "纪录"
-}
-num_classes = len(genre_dict)
-
-def load_data(file_path="movie_data.csv"):
-    df = pd.read_csv(file_path)
-    texts = df["text"].astype(str).tolist()
-    labels = df["label"].astype(int).tolist()
-    return texts, labels
-
-def text_feature_extraction(texts):
-    vectorizer = TfidfVectorizer(
-        max_features=10000,
-        stop_words="english",
-        ngram_range=(1, 2)
-    )
-    features = vectorizer.fit_transform(texts)
-    return features, vectorizer
-
-def train_and_evaluate(features, labels):
-    X_train, X_test, y_train, y_test = train_test_split(
-        features, labels, test_size=0.2, random_state=42, stratify=labels
-    )
-    model = LinearSVC(random_state=42, max_iter=10000)
-    model.fit(X_train, y_train)
-    y_pred = model.predict(X_test)
-    acc = accuracy_score(y_test, y_pred)
-    print(f"测试集准确率: {acc:.4f}")
-    print("\n分类报告:")
-    print(classification_report(y_test, y_pred, target_names=genre_dict.values()))
-    return model
-
-def predict_genre(model, vectorizer, new_text):
-    new_feature = vectorizer.transform([new_text])
-    pred_label = model.predict(new_feature)[0]
-    return genre_dict[pred_label]
-
-if __name__ == "__main__":
-    texts, labels = load_data()
-    features, vectorizer = text_feature_extraction(texts)
-    model = train_and_evaluate(features, labels)
-    sample_text = "一个孤独的科学家发明了时间机器，却在穿梭时空的过程中陷入了悖论..."
-    print(f"\n示例文本: {sample_text}")
-    print(f"预测类型: {predict_genre(model, vectorizer, sample_text)}")