Datasets:

Royi
/

DataSets

Since SciKit Learn's California Housing dataset often fails to download this is the Data Frame of the data in CSV format.
By default SciKit Learn use some pre processing of the original data.
This CSV is the data after the processing of SciKit Learn.

CIFAR 10

Since using SciKit Learn's fetch_openml() fails to download the CIFAR_10 dataset this is an alternative.
It was generated by:

dsTrain = torchvision.datasets.CIFAR10(root = dataFolderPath, train = True,  download = True)
dsVal   = torchvision.datasets.CIFAR10(root = dataFolderPath, train = False, download = True)

numSamples = len(dsTrain)
tXTrain = np.zeros((numSamples, 32, 32, 3), dtype = np.uint8)
vYTrain = np.zeros((numSamples,), dtype = np.uint8)

for ii in range(numSamples):
    tXi, valY = dsTrain[ii]
    tXTrain[ii] = tXi
    vYTrain[ii] = valY

numSamples = len(dsVal)
tXVal = np.zeros((numSamples, 32, 32, 3), dtype = np.uint8)
vYVal = np.zeros((numSamples,), dtype = np.uint8)

for ii in range(numSamples):
    tXi, valY = dsVal[ii]
    tXVal[ii] = tXi
    vYVal[ii] = valY

tX = np.concatenate((tXTrain, tXVal), axis = 0)
vY = np.concatenate((vYTrain, vYVal), axis = 0)

mX = np.reshape(tX, (tX.shape[0], -1))

dfData = pd.DataFrame(np.concatenate((mX, vY[:, np.newaxis]), axis = 1), columns = [f'Pixel_{ii:04d}' for ii in range(mX.shape[1])] + ['Label'])
dfData.to_parquet('CIFAR10.parquet', index = False)

MNIST

A dataframe where the first 60,000 rows are the train set and the last 10,000 are the test set.
The last column is the label.
Images are row major, hence a np.reshape(dfX.iloc[0, :-1], (28, 28)) will generate the image.

Generated by:

import numpy as np
from sklearn.datasets import fetch_openml

dfX, dsY = fetch_openml('mnist_784', version = 1, return_X_y = True, as_frame = True)

dfX.columns = [f'{ii:04d}' for ii in range(dfX.shape[1])]
dfX['Label'] = dsY.astype(np.uint8)
dfX = dfX.astype(np.uint8)

dfX.to_parquet('MNIST.parquet', index = False)