python - 如何为 LSTM 和 Keras 构建面板数据？

Question

我试图弄清楚如何构建我的数据集并构建 X 和 y 以便它可以与 Keras 的 Stacked LSTM 一起用于序列分类。

我有面板数据，我试图预测分类。我不完全确定如何理解时间步长或如何根据我的面板数据正确制作数据的形状。

# Libraries
from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np
import pandas as pd

# Here is an example of my data
df = pd.read_csv('https://raw.githubusercontent.com/rocketfish88/democ/master/sample2.csv')
df

# Contains a handful of features, a target, year, and id of the observation
   id        year  x1 x2  x3  y
0   A       2015   1   1   1  1
1   A       2016   2   2   2  1
2   A       2017   3   3   3  2
3   A       2018   4   4   4  2
4   B       2015   1   1   1  3
5   B       2016   2   2   2  2
6   B       2017   3   3   3  1
7   B       2018   4   4   4  1
8   C       2015   1   1   1  2
9   C       2016   2   2   2  2
10  C       2017   3   3   3  3
11  C       2018   4   4   4  2

Keras.io 通过示例提供以下内容：

data_dim = 16
timesteps = 8
num_classes = 10

# expected input data shape: (batch_size, timesteps, data_dim)
model = Sequential()
model.add(LSTM(32, return_sequences=True,
               input_shape=(timesteps, data_dim)))  # returns a sequence of vectors of dimension 32
model.add(LSTM(32, return_sequences=True))  # returns a sequence of vectors of dimension 32
model.add(LSTM(32))  # return a single vector of dimension 32
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# Generate dummy training data
x_train = np.random.random((1000, timesteps, data_dim))
y_train = np.random.random((1000, num_classes))

# Generate dummy validation data
x_val = np.random.random((100, timesteps, data_dim))
y_val = np.random.random((100, num_classes))

model.fit(x_train, y_train,
          batch_size=64, epochs=5,
          validation_data=(x_val, y_val))

对于如何获取我的数据集并将其转换为（大小、时间步长、维度）的正确形状，我相当迷茫

我很感激任何帮助！

标签： pythonkeraspanel-data