You provide a huge amount of lines of code, but this actually sums to a single issue: You are extracting 3 integers from next_day_open_values, nx, ny = next_day_open_values.shape. Numpy's reshape expects an array as input, not an integer or single value.

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

a : array_like - Array to be reshaped.

I doubt you are trying to get a vector of a single integer repeating for the nx*ny shape. Furthermore, if you turn the input into an array, and perform the same operation, you'll run into ValueError because you cannot reshape an array of size 1 into a specific shape.

I believe this might work, but I don't know what next_day_open_values is:

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)

You can convert your list to a numpy array and then reshape it with numpy.reshape

import numpy as np

# Convert to numpy array
w_train = np.array(w_train)
w_test = np.array(w_train)

# Reshape
w_train = np.reshape(w_train, (2404,28,224,224,3))
w_test = np.reshape(w_test, (601,28,224,224,3))

If you read the docs for DecisionTreeClassifier.predict, you can see you are passing the wrong data:

predict(X, check_input=True)

Predict class or regression value for X. For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned. Parameters:

X : array-like or sparse matrix of shape = [n_samples, n_features]

Solution was linked on reshaped method on documentation page.

Insted of Y.reshape(-1,1) you need to use:

Y.values.reshape(-1,1)

Your current approach involves nested loops and reshaping, which can be quite slow. Instead of using np.append within loops, consider using numpy.roll for efficient pixel shifting. Using numpy.roll, you can achieve the desired image shifts using numpy.roll. Here’s an example of how to do it:

import numpy as np
from scipy.ndimage.interpolation import shift
from keras.datasets import mnist

# Load MNIST dataset
(x_train, y_train), _ = mnist.load_data()

def shift_image(image, shift_x, shift_y):
    return np.roll(image, (shift_x, shift_y), axis=(0, 1)).reshape(-1)

# Create shifted copies for each direction
X_train_expansion = []
y_train_expansion = []

for shift_x, shift_y in ((0, 1), (0, -1), (1, 0), (-1, 0)):
    for image, label in zip(x_train, y_train):
        shifted_image = shift_image(image, shift_x, shift_y)
        X_train_expansion.append(shifted_image)
        y_train_expansion.append(label)

X_train_expansion = np.array(X_train_expansion)
y_train_expansion = np.array(y_train_expansion)