得到ValueError:“只能具有MultiIndex的元组索引" [英] getting ValueError : "Can only tuple-index with a MultiIndex "

问题描述

我正在尝试实现一个简单的RNN以预测整数序列中的下一个整数.因此，我有一个数据集，如下所示:

I am trying to implement a simple RNN to predict the next integer in an integer sequence. So, I have a data set that is as below:

Id  Sequence
1   1,0,0,2,24,552,21280,103760,70299264,5792853248,587159944704
2   1,1,5,11,35,93,269,747,2115,5933,16717,47003,132291,372157,1047181,2946251,8289731,23323853,65624397,184640891,519507267,1461688413,4112616845,11571284395,32557042499,91602704493,257733967693
4   0,1,101,2,15,102,73,3,40,16,47,103,51,74,116,4,57,41,125,17,12,48,9,104,30,52,141,75,107,117,69,5,148,58,88,42,33,126,152,18,160,13,38,49,55,10,28,105,146,31,158
5   1,4,14,23,42,33,35,34,63,66,87,116,84,101,126,164,128,102,135,143,149,155,203,224,186,204,210,237,261,218,219,286,257,266,361,355,336,302,374,339,371,398,340,409,348,388,494,436,407,406
6   1,1,2,5,4,2,6,13,11,4,10,10,12,6,8,29,16,11,18,20,12,10,22,26,29,12,38,30,28,8,30,61,20,16,24,55,36,18,24,52,40,12,42,50,44,22,46,58,55,29,32,60,52,38,40,78,36,28,58,40,60,30,66,125,48,20,66,80,44,24
9   0,31,59,90,120,151,181,212,243,273,304,334,365,396,424,455,485,516,546,577,608,638,669,699,730,761,789,820,850,881,911,942,973,1003,1034,1064,1095,1126,1155,1186,1216,1247,1277,1308,1339,1369,1400,1430
10  1,1,2,5,13,36,111,347,1134,3832,13126,46281,165283,598401,2202404,8168642,30653724,116082962,442503542,1701654889,6580937039,25603715395,100223117080,394001755683,1556876401398,6178202068457,24608353860698,98421159688268,394901524823138,1589722790850089
12  0,0,0,0,112,40286,5485032,534844548,45066853496,3538771308282,267882021563464,19861835713621616,1453175611052688600,105278656040052332838,7564280930105061931496

到目前为止，我的代码是:

My code so far is:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import SimpleRNN
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.preprocessing.sequence import pad_sequences

def stoarray(data = [], sep = ','):
    return data.map(lambda x: np.array(x.split(sep), dtype=float))

def create_dataset(dataset, window_size=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-window_size-1):
        a = dataset[i:(i+window_size), 0]
        dataX.append(a)
        dataY.append(dataset[i + window_size, 0]) #gives the ValueError : Can only tuple index with multi index
    return np.array(dataX), np.array(dataY)

# fix random seed for reproducibility
np.random.seed(7)

# loading data
colna = ['id', 'seq']
train_data = pd.read_csv('G:/Python/integer_sequencing/testfile.csv', header=1)
train_data.columns = colna
dataset = train_data['seq']
#print(dataset)
window_size = 1
X_train, Y_train = create_dataset(dataset, window_size)

print(X_train.head(5))
print(Y_train.head(5))

我正在尝试使用X_train作为包含完整集合的输入来拆分每个序列，除了最后一项和Y_train被视为输出将仅包含最后一位的情况之外，其余组成完整的集合. 但是，当我运行代码时，我得到了ValueError:只能使用MultiIndex进行元组索引. 任何人都可以解释它对我的代码的含义以及我该怎么做才能解决它.

I am trying to split each sequence with X_train as input that consists the complete set except the last term and Y_train be treated as an output will consist only the last digit. But when I run the code, I get the ValueError : Can only tuple-index with a MultiIndex. Can anybody explain what it means with respect to my code and what must I do to resolve it.

回溯调用:

PS-我是堆栈溢出和深度学习的新手，因此，如果您能提出建议并帮助我设置问题格式，我将不胜感激.

PS - I am new to stack overflow and deep learning, so I would be very grateful if you could suggest and help me with formatting of my question.

推荐答案

您在dataset[i:(i+window_size), 0]中的i:(i+window_size), 0上遇到了问题.

You have problem with i:(i+window_size), 0 in dataset[i:(i+window_size), 0].

在您的代码中dataset表示train_data['seq']，它是单列-一维Series-但您像在二维DataFrame中一样使用i:(i+window_size), 0.

In your code dataset means train_data['seq'] which is single column - single-dimensional Series - but you use i:(i+window_size), 0 like in two-dimensional DataFrame.

您只能使用单个整数，例如dataset[0]或切片dataset[i:(i+window_size)]

You can use only single integer like dataset[0] or slice dataset[i:(i+window_size)]

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