学习神经网络并保存结果

Posted 2023-03-12

【中文标题】学习神经网络并保存结果

【英文标题】：Learning neural network and saving the result

【发布时间】：2019-10-17 08:02:34

【问题描述】：

我认为这是一个简单的问题，但对我来说不是（df中有一个表：

Date        X1  X2  Y1
07.02.2019  5   1   1
08.02.2019  6   2   1
09.02.2019  1   3   0
10.02.2019  4   4   1
11.02.2019  1   1   0
12.02.2019  4   2   1
13.02.2019  5   5   1
14.02.2019  6   5   1
15.02.2019  1   1   0
16.02.2019  4   5   1
17.02.2019  1   2   0
18.02.2019  1   1   
19.02.2019  2   1   
20.02.2019  3   2   
21.02.2019  4   14

我需要从参数 X1 和 X2 为 Y1 构建一个神经网络，然后将其应用于日期大于 17.02.2019 的行，并将网络预测结果保存在单独的 df2 中

 import pandas as pd
    import numpy as np
    import re
    from sklearn.neural_network import MLPClassifier 

    df = pd.read_csv("ob.csv", encoding = 'cp1251', sep = ';')
    df['Date'] = pd.to_datetime(df['Date'], format='%d.%m.%Y')
    startdate = pd.to_datetime('2019-02-17') 


    X = ['X1', 'X2'] ????
    y = ['Y1'] ????
    clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1) 
    clf.fit(x, y) 
    clf.predict(???????)  ????? df2 = ????

在哪里？？？？ - 我不知道如何正确设置条件

【参考方案1】：

import pandas as pd
import numpy as np
import re
from sklearn.neural_network import MLPClassifier 

df = pd.read_csv("ob.csv", encoding = 'cp1251', sep = ';')
df['Date'] = pd.to_datetime(df['Date'], format='%d.%m.%Y')
startdate = pd.to_datetime('2019-02-17') 

train = df[df['Date'] <= '2019-02-17']
test = df[df['Date'] > '2019-02-17']

X_train = train[['X1', 'X2']]
y_train = train[['Y1']]

X_test = test[['X1', 'X2']]
y_test = test[['Y1']]

clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1) 
clf.fit(X_train, y_train) 
df2 = pd.DataFrame(clf.predict(X_test))
df2.to_csv('prediction.csv')