fetch_digits_optical_penbased

mlquantify.datasets.fetch_digits_optical_penbased(*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False, n_retries=3, delay=1.0, protocol=None, n_samples=1000, sample_size=500, random_state=None, target_col=None, which='optical')

Optical / Pen-based handwritten digits (10-class, easy).

Two UCI handwritten-digit datasets selected with which. optical (id 80): 5620 samples x 64 integer pixel-block counts. penbased (id 81): 10992 samples x 16 pen-trajectory coordinates. Both are 10-class and almost perfectly class-balanced.

Quantification: a well-separated 10-class multiclass baseline.

Samples	5620 (opt) / 10992 (pen)
Features	64 (opt) / 16 (pen)
Classes	10 (balanced)
Missing	0

Source: https://archive.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits

Parameters:

data_homestr or path-like, default=None

Folder used to cache the downloaded file(s); defaults to _data/ next to the package.

download_if_missingbool, default=True

If False, raise instead of downloading when the cache is empty.

return_X_ybool, default=False

Return (X, y) instead of a Bunch.

as_framebool, default=False

Return .data as a DataFrame, .target as a Series, and a combined .frame (features + a "target" column).

n_retriesint, default=3

Number of download attempts before giving up.

delayfloat, default=1.0

Seconds to wait between attempts.

protocol{None, “app”, “npp”, “upp”, “ppp”} or mlquantify protocol, default=None

If set, draw evaluation sample-bags with an mlquantify protocol; the Bunch then also has .samples (index bags into .data), .prevalences and .protocol.

n_samplesint, default=1000

Number of prevalence points (bags) generated by the protocol.

sample_sizeint, default=500

Instances per bag (the protocol batch_size).

random_stateint or None, default=None

Seed forwarded to the protocol.

which{‘optical’, ‘penbased’}, default=’optical’

Which UCI digit dataset to load.

Returns:

dataBunch

Dictionary-like object. Attributes: data (features), target (labels), feature_names, target_names, DESCR; frame when as_frame=True; and samples / prevalences / protocol when protocol is set.

(X, y)tuple

Returned instead when return_X_y=True.

References

Alpaydin, E. & Kaynak, C. (1998). UCI ML Repository (#80, #81).

Examples

>>> b = fetch_digits_optical_penbased(which='penbased'); b.data.shape  
(10992, 16)