使用 sklearn 在 MNIST 数据集上进行手写数字识别

Question

I want to build a Handwritten Digit Recognition on MNIST dataset using sklearn and I wanted to shuffle my train set for both features(x) and label(y). But it shows a KeyError. Let me know what is the correct way to do it.

    from sklearn.datasets import fetch_openml
    mnist = fetch_openml('mnist_784')
    x,y=mnist['data'],mnist['target']
    x.shape
    y.shape
    import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    digit = np.array(x.iloc[45])
    digit_img = digit.reshape(28,28)
    plt.imshow(digit_img,cmap=matplotlib.cm.binary , interpolation="nearest")
    plt.axis("off")
    y.iloc[45]
    x_train, x_test = x[:60000],x[60000:]
    y_train, y_test=y[:60000],y[60000:]
    import numpy as np
    shuffled = np.random.permutation(60000)
    x_train=x_train[shuffled] -->
    y_train = y_train[shuffled] --> these two lines are throwing error

Answer 1

Please check if type(x_train) is numpy.ndarray or DataFrame. Since Scikit-Learn 0.24, fetch_openml() returns a Pandas DataFrame by default. If it is dataframe, in that case you can not use x_train[shuffled] , which is meant for arrays. Instead use x_train.iloc[shuffled]