如何把预测的结果输出到电脑中

Question

怎么把我预测的结果输出到D盘中

import pandas as pd
def parse_date(date_string):
    return pd.Timestamp(date_string.replace('_', '-'))
df = pd.read_csv('D:/findata.csv', index_col='Date', parse_dates=True, date_parser=parse_date)
df.sort_index(inplace=True)
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)
print(len(X))
print(len(y))
print(len(X_lately))
pre_days = 10
# mem_days=[5,10,15]
# lstm_layers=[1,2,3]
# dense_layers=[1,2,3]
# units = [16,32]
mem_days=[5]
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=filepath=f"./bestmodel1/{{val_mape:.2f}}{{epoch:02d}}men{the_mem_days}lstm{the_lstm_layers}dense{the_dense_layers}unit{the_units}.keras"
                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('./bestmodel1/4.0804men5lstm3dense2unit32.keras')
best_model.summary()
best_model.evaluate(X_test,y_test)
pre = best_model.predict(X_test)
print(len(pre))
import matplotlib.pyplot as plt
df_time = df.index[- len(y_test):]
plt.plot(df_time,y_test,color='red',label='price')
plt.plot(df_time,pre,color='green',label='predict')
plt.show()

Answer 1

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在您提供的代码基础上，要将预测结果pre保存到D盘，您可以使用pandas的to_csv()函数。我会在预测部分的代码后添加一个部分，用于保存预测结果到CSV文件。

首先，在导入模块时添加pandas：

import pandas as pd

然后，在模型评估和绘制图形之后，添加以下代码来保存预测结果：

# ... (之前的代码)

# 预测部分
pre = best_model.predict(X_test)

# 将预测结果转换为DataFrame，并设置时间戳作为索引
prediction_df = pd.DataFrame(pre, columns=["Predicted Price"])
prediction_df["Date"] = df_time
prediction_df.set_index("Date", inplace=True)

# 保存预测结果到D盘
prediction_df.to_csv(f"D:/predictions_{pre_days}_days.csv")

这段代码会创建一个新的DataFrame，其中包含预测价格和对应的时间戳，然后将其保存为CSV文件，文件名为predictions_{pre_days}_days.csv，其中pre_days是您的预测天数。这样，预测结果就会被保存到D盘上。