python怎么将预测的数据输出到电脑上

Question

import pandas as pd
file_path = r"D:\\findata.csv"  
df = pd.read_csv(file_path)
def Stock_Price_LSTM_Data_Precesing(df,mem_his_days,pre_days):
    df.dropna(inplace=True)
    df.sort_index(inplace=True)
    df['label']= df['Close'].shift(-pre_days)
    from sklearn.preprocessing import StandardScaler
    scaler = StandardScaler()
    sca_X=scaler.fit_transform(df.iloc[:,:-1])   
    mem_his_days = 10
    
    from collections import deque
    deq = deque(maxlen=mem_his_days)
    
    X = []
    for i in sca_X:
        deq.append(list(i))
        if len(deq)==mem_his_days:
            X.append(list(deq))
    X_lately = X[-pre_days:]
    X = X[:-pre_days]
    y = df['label'].values[mem_his_days-1:-pre_days]
    
    
    import numpy as np
    X = np.array(X)
    y = np.array(y)
    return X,y,X_lately
 
X,y,X_lately = Stock_Price_LSTM_Data_Precesing(df,5,10)
pre_days = 10
# mem_days=[5,10,15]
# lstm_layers=[1,2,3]
# dense_layers=[1,2,3]
# units = [16,32]
mem_days=[15]
lstm_layers=[3]
dense_layers=[2]
units =[32]
from tensorflow.keras.callbacks import ModelCheckpoint
 
for the_mem_days in mem_days:
    for the_lstm_layers in lstm_layers:
        for the_dense_layers in dense_layers:
            for the_units in units:
                filepath=f"./thebestmodel/{{val_mape:.2f}}{{epoch:02d}}men{the_mem_days}lstm{the_lstm_layers}dense{the_dense_layers}unit{the_units}.kreas"
                checkpoint = ModelCheckpoint(
                    filepath=filepath,
                    save_weights_only=False,
                    monitor='val_mape',
                    mode='min',
                    save_best_only=True)
                X,y,X_lately = Stock_Price_LSTM_Data_Precesing(df,the_mem_days,pre_days)
                from sklearn.model_selection import train_test_split
                X_train,X_test,y_train,y_test = train_test_split(X,y,shuffle=False,test_size=0.1)
                import tensorflow as tf
                from tensorflow.keras.models import Sequential
                from tensorflow.keras.layers import LSTM,Dense,Dropout
                model = Sequential()
                model.add(LSTM(the_units,input_shape=X.shape[1:],activation='relu',return_sequences=True))
                model.add(Dropout(0.1))
                for i in range(the_lstm_layers):
                    model.add(LSTM(the_units,activation='relu',return_sequences=True))
                    model.add(Dropout(0.1))
                
                model.add(LSTM(the_units,activation='relu'))
                model.add(Dropout(0.1))
                for i in range(the_dense_layers):
                    model.add(Dense(the_units,activation='relu'))
                    model.add(Dropout(0.1))
                
                model.add(Dense(1))
                model.compile(optimizer='adam',
                             loss='mse',
                             metrics=['mape'])
                model.fit(X_train,y_train,batch_size=32,epochs=50,validation_data=(X_test,y_test),callbacks=[checkpoint])
from tensorflow.keras.models import load_model
best_model = load_model('./thebestmodel/7.8713men15lstm3dense2unit32.keras')
best_model.summary()
best_model.evaluate(X_test,y_test)
pre = best_model.predict(X_test)
print(len(pre))
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model
 
# 假设CSV文件的第一列是日期，且格式正确可被pd.to_datetime解析
file_path = r"D:\\findata.csv"  
df = pd.read_csv(file_path)
df['Date'] = pd.to_datetime(df.pop(df.columns[0]), format='%Y_%m_%d')
 
def extract_dates_for_prediction(df, pre_days):
    # 获取用于预测的日期范围，这里简化处理，实际情况需根据数据切分逻辑调整
    last_dates = df['Date'][-(pre_days + len(y_test)):]  # 包含最后一个用于预测的日期及其前pre_days天
    return last_dates[-len(y_test):]  # 取出与预测结果对应的部分
 
# ... （你的数据预处理和模型训练代码保持不变）
 
# 加载最佳模型并进行预测
best_model = load_model('./thebestmodel/7.8713men15lstm3dense2unit32.keras')
pre = best_model.predict(X_test)
 
# 提取正确的日期范围
correct_dates = extract_dates_for_prediction(df, pre_days)
 
# 绘制图表
plt.figure(figsize=(14, 7))
plt.plot(correct_dates, y_test, color='red', label='Actual Price')
plt.plot(correct_dates, pre, color='green', label='Predicted Price')
plt.title('Stock Price Prediction')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid(True)
plt.xticks(rotation=45)  # 旋转X轴标签以便更好地显示
plt.show()

以上是我的代码，怎么将预测的数据输出到我电脑上D盘中，格式是csv文件

Answer 1

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要将预测的数据输出到电脑上的D盘中，并保存为CSV格式，您可以使用pandas的to_csv函数。首先，确保您的预测结果pre和对应的日期（我们假设您已经正确提取了这些日期并存储在correct_dates变量中）被整合成一个DataFrame。然后，您可以直接调用to_csv方法将这个DataFrame保存为CSV文件。下面是将预测结果保存到CSV的代码段，假设您已经有了预测值pre和对应的日期correct_dates：

# 假设correct_dates和pre的长度相同，且correct_dates已经包含了预测日期
# 将预测价格与日期合并成一个DataFrame
predicted_data = pd.DataFrame({
    'Date': correct_dates,
    'Predicted_Price': pre.flatten()  # 确保pre是一维的
})

# 指定保存路径
output_file_path = r"D:\predicted_prices.csv"

# 将DataFrame保存为CSV文件
predicted_data.to_csv(output_file_path, index=False)

这段代码会创建一个包含两列（'Date' 和 'Predicted_Price'）的DataFrame，然后将其保存到D盘根目录下的predicted_prices.csv文件中。注意，这里假设correct_dates已经被适当地生成，以匹配预测值的时间序列。如果您需要从y_test和预测结果pre中提取日期或其他特定处理步骤，请根据实际情况调整代码。