File size: 2,444 Bytes
4d89104 c65ce49 4d89104 23be25d 27a2e02 23be25d af1525b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | ---
license: cc-by-nc-4.0
---
A curation of datasets for educations purposes.
### California Housing
Since SciKit Learn's [California Housing dataset](https://scikit-learn.org/stable/datasets/real_world.html#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](https://github.com/scikit-learn/scikit-learn/blob/d3898d9d57aeb1e960d266613a2e31b07bca39d7/sklearn/datasets/_california_housing.py#L208-L220) of the [original data](https://web.archive.org/web/20250912205745/https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html).
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:
```python
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:
```python
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)
``` |