fetch_wine_quality

mlquantify.datasets.fetch_wine_quality(*, 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)

Wine Quality: sensory score 3-9 from physicochemistry (ORDINAL).

6497 red and white vinho verde wines, each with 11 physicochemical measurements (acidity, sugar, sulphur dioxide, alcohol, …) plus a wine-colour indicator. The target is the median expert quality score on a 0-10 scale (observed range 3-9): a naturally ordinal label, dominated by scores 5-6.

Quantification: re-use as an ordinal-quantification benchmark (ordered, imbalanced classes).

Samples	6497
Features	12 (11 physchem + colour)
Classes	7 ordinal (quality 3-9)
Missing	0

Source: https://archive.ics.uci.edu/dataset/186/wine+quality

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.

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

Cortez, P. et al. (2009). Decis. Support Syst. 47. UCI #186.

Examples

>>> b = fetch_wine_quality(); sorted(set(b.target))  
[3, 4, 5, 6, 7, 8, 9]