example.py

from sklearn.datasets import fetch_mldata
from matplotlib import pyplot as plt
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report

if __name__ == "__main__":
    mnist = fetch_mldata('MNIST original')
    X, y = mnist.data, mnist.target
    #print(mnist.target_name)
    Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)

    plt.imshow(Xtrain[0].reshape((28, 28)))
    plt.show()

    pca = PCA(n_components=128, svd_solver="arpack")
    pca.fit(Xtrain)
    Xtrain = pca.transform(Xtrain)
    knn = KNeighborsClassifier(n_neighbors=5)
    knn.fit(Xtrain, ytrain)

    Xtest = pca.transform(Xtest)
    yhat_test = knn.predict(Xtest)
    print(classification_report(ytest, yhat_test))