请问如何更改新数据预测这一行让他读取这几个文件啊

Question

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

def get_encoding(file):
    encodings = ['utf-8', 'iso-8859-1', 'cp1252', 'gb2312', 'gbk']
    for e in encodings:
        try:
            pd.read_csv(file, encoding=e, nrows=1)  # 尝试读取文件的第一行，如果没有问题则返回该编码
            return e
        except:
            pass
    return None  # 如果所有编码都失败，返回 None
    

def predict_heart_disease(file_path):
    encoding = get_encoding(file_path)

    if encoding is None:
        print(f"无法确定文件 {file_path} 的编码")
        return

    # 数据加载，跳过解析错误的行
    chunksize = 10 ** 6
    chunks = []
    for chunk in pd.read_csv(file_path, chunksize=chunksize, dtype=str, encoding=encoding, error_bad_lines=False):
        chunks.append(chunk)

    data = pd.concat(chunks, axis=0)

    # 分离特征和目标变量
    if '是否患有心血管疾病' in data.columns:
        y = data['是否患有心血管疾病']
        X = data.drop('是否患有心血管疾病', axis=1)

        # 数据预处理
        le = LabelEncoder()
        for column in X.columns:
            if X[column].dtype == 'object':
                X[column] = le.fit_transform(X[column])
        y = le.fit_transform(y)

        scaler = StandardScaler()
        X = scaler.fit_transform(X)

        # 数据划分
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # 80% 训练集, 20% 测试集

        # 构建模型
        model = Sequential()
        model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))
        model.add(Dense(64, activation='relu'))
        model.add(Dense(1, activation='sigmoid'))

        # 模型编译
        model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

        # 模型训练
        model.fit(X_train, y_train, epochs=10, batch_size=32)

        # 模型评估
        score = model.evaluate(X_test, y_test, verbose=0)
        print('Test loss:', score[0])
        print('Test accuracy:', score[1])

        # 保存模型
        model.save("C:\\Users\\1\\Desktop\\heart_disease\\heart_disease_model_{file_path.split('/')[-1]}.h5")

        # 新数据预测
        new_data = [r"C:\Users\1\Desktop\heart_disease\1-首次病程记录.csv", r"C:\Users\1\Desktop\heart_disease\2-日常病程记录.csv",
                  r"C:\Users\1\Desktop\heart_disease\3-出院记录.csv", r"C:\Users\1\Desktop\heart_disease\4-检验记录表.csv",
                  r"C:\Users\1\Desktop\heart_disease\5-检验明细表.csv", r"C:\Users\1\Desktop\heart_disease\6-细菌结果表.csv",
                  r"C:\Users\1\Desktop\heart_disease\7-影像检查报告表.csv"]  # 这个列表需要根据你的实际特征来修改
        new_data = le.transform(new_data)
        new_data = scaler.transform(new_data.reshape(1, -1))
        prediction = model.predict(new_data)
        if prediction[0][0] >= 0.5:
            prediction_result = '有心血管疾病'
        else:
            prediction_result = '无心血管疾病'

        # 返回预测结果
        return prediction_result

# 文件列表
files = [r"C:\Users\1\Desktop\heart_disease\1-首次病程记录.csv", r"C:\Users\1\Desktop\heart_disease\2-日常病程记录.csv",
          r"C:\Users\1\Desktop\heart_disease\3-出院记录.csv", r"C:\Users\1\Desktop\heart_disease\4-检验记录表.csv",
          r"C:\Users\1\Desktop\heart_disease\5-检验明细表.csv", r"C:\Users\1\Desktop\heart_disease\6-细菌结果表.csv",
          r"C:\Users\1\Desktop\heart_disease\7-影像检查报告表.csv", r"C:\Users\1\Desktop\heart_disease\8-输出结果.csv"]

# 用于保存预测结果的列表
prediction_results = []

# 针对每一个文件运行函数】
for file in files:
    print(f"Processing {file}")
    result = predict_heart_disease(file)
    prediction_results.append({r"C:\Users\1\Desktop\result.xlsx": file, '预测结果': result})

# 将预测结果保存为DataFrame
prediction_df = pd.DataFrame(prediction_results)

# 输出预测结果到CSV文件
prediction_df.to_csv('heart_disease_predictions.csv', index=False)

# 输出预测结果到Excel文件
prediction_df.to_excel('heart_disease_predictions.xlsx', index=False)

Answer 1

new_data_files = [r"C:\Users\1\Desktop\heart_disease\1-首次病程记录.csv", 
                  r"C:\Users\1\Desktop\heart_disease\2-日常病程记录.csv",
                  r"C:\Users\1\Desktop\heart_disease\3-出院记录.csv",
                  r"C:\Users\1\Desktop\heart_disease\4-检验记录表.csv",
                  r"C:\Users\1\Desktop\heart_disease\5-检验明细表.csv",
                  r"C:\Users\1\Desktop\heart_disease\6-细菌结果表.csv",
                  r"C:\Users\1\Desktop\heart_disease\7-影像检查报告表.csv"]

for file in new_data_files:
    file = file.replace('\\', '/')  # 将反斜杠替换为正斜杠（如果需要）
    print(f"Processing {file}")
    result = predict_heart_disease(file)
    prediction_results.append({'文件名': file, '预测结果': result})


prediction_df = pd.DataFrame(prediction_results)

prediction_df.to_csv('heart_disease_predictions.csv', index=False)

prediction_df.to_excel('heart_disease_predictions.xlsx', index=False)