Python 属性错误：“int”对象没有属性“reshape”

Question

我正在尝试使用 python 预测股票价格，同时尝试将数据集重塑为“fit”函数的 2D num 数组，并将其用作参考：sklearn Logistic Regression "ValueError: Found array with dim 3. Estimator expected <= 2."

next_day_open_values, nx, ny = next_day_open_values.shape
next_day_open_values = next_day_open_values.reshape((next_day_open_values,nx*ny))
y_normaliser = preprocessing.MinMaxScaler()
y_normaliser.fit((np.expand_dims( next_day_open_values, -1 )))

我遇到了这个错误：

    <ipython-input-42-6ea43c55dc18> in csv_to_dataset(csv_path)
     20 
     21     next_day_open_values, nx, ny = next_day_open_values.shape
---> 22     next_day_open_values = next_day_open_values.reshape((next_day_open_values,nx*ny))
     23     y_normaliser = preprocessing.MinMaxScaler()
     24     y_normaliser.fit((np.expand_dims( next_day_open_values, -1 )))

AttributeError: 'int' object has no attribute 'reshape'

出了什么问题？我该如何解决？感谢您提供详细的答案。

到目前为止的代码如下（我使用的是 Jupyter notebook）：

import keras
from keras.models import Model
from keras.layers import Dense, Dropout, LSTM, Input, Activation
from keras import optimizers
import numpy as np
np.random.seed(4)
import tensorflow
tensorflow.random.set_seed(4)

import pandas as pd
from sklearn import preprocessing
import numpy as np

history_points = 50

def csv_to_dataset(csv_path):
    data = pd.read_csv(csv_path)
    data = data.drop('Date', axis=1)
    data = data.drop(0, axis=0)
    data_normaliser = preprocessing.MinMaxScaler()
    data_normalised = data_normaliser.fit_transform(data)
    # using the last {history_points} open high low close volume data points, predict the next open value
    ohlcv_histories_normalised =      np.array([data_normalised[i  : i + history_points].copy() for i in range(len(data_normalised) - history_points)])
    next_day_open_values_normalised = np.array([data_normalised[:,0][i + history_points].copy() for i in range(len(data_normalised) - history_points)])
    next_day_open_values_normalised = np.expand_dims(next_day_open_values_normalised, -1)

    next_day_open_values = np.array([data.iloc[:,0][i + history_points].copy() for i in range(len(data) - history_points)])
    next_day_open_values = np.expand_dims(next_day_open_values_normalised, axis=-1)

    next_day_open_values, nx, ny = next_day_open_values.shape
    next_day_open_values = next_day_open_values.reshape((next_day_open_values,nx*ny))
    y_normaliser = preprocessing.MinMaxScaler()
    y_normaliser.fit((np.expand_dims( next_day_open_values, -1 )))

    assert ohlcv_histories_normalised.shape[0] == next_day_open_values_normalised.shape[0]
    return ohlcv_histories_normalised, next_day_open_values_normalised, next_day_open_values, y_normaliser

#dataset
hlcv_histories, next_day_open_values, unscaled_y, y_normaliser = csv_to_dataset('AMZN1.csv')

test_split = 0.9 # the percent of data to be used for testing
n = int(ohlcv_histories.shape[0] * test_split)

# splitting the dataset up into train and test sets

ohlcv_train = ohlcv_histories[:n]
y_train = next_day_open_values[:n]

ohlcv_test = ohlcv_histories[n:]
y_test = next_day_open_values[n:]

unscaled_y_test = unscaled_y[n:]

请随时更正/编辑。

谢谢

Answer 1

您提供了大量的代码行，但这实际上归结为一个问题：您正在从next_day_open_values, nx, ny = next_day_open_values.shape 中提取 3 个整数。 Numpy 的 reshape 需要一个数组作为输入，而不是整数或单个值。

参数：numpy.reshape(a, newshape, order='C')

a : array_like - 要重塑的数组。

我怀疑您是否正在尝试为 nx*ny 形状获取重复的单个整数的向量。此外，如果将输入转换为数组并执行相同的操作，您将遇到ValueError，因为您无法将大小为 1 的数组重新整形为特定形状。

我相信这可能有效，但我不知道 next_day_open_values 是什么：

next_day_open_values_s, nx, ny = next_day_open_values.shape
next_day_open_values = next_day_open_values.reshape(next_day_open_values_s,nx*ny)