fetch_octane

skfda.datasets.fetch_octane(*, return_X_y: typing_extensions.Literal[False] = False, as_frame: bool = False) → Bunch

skfda.datasets.fetch_octane(*, return_X_y: typing_extensions.Literal[True], as_frame: typing_extensions.Literal[False] = False) → Tuple[FDataGrid, ndarray[Any, dtype[int64]]]

skfda.datasets.fetch_octane(*, return_X_y: typing_extensions.Literal[True], as_frame: typing_extensions.Literal[True]) → Tuple[DataFrame, Series]

Load near infrared spectra of gasoline samples.

This function fetchs the octane dataset from the R package ‘mrfDepth’ from CRAN.

Near infrared (NIR) spectra of gasoline samples, with wavelengths ranging from 1102nm to 1552nm with measurements every two nm. This dataset contains six outliers to which ethanol was added, which is required in some states. See [RDEH2006] and [HuRS2015] for further details.

The data is labeled according to this different composition.

Source:

Esbensen K. (2001). Multivariate data analysis in practice. 5th edn. Camo Software, Trondheim, Norway.

References

[RDEH2006]

Rousseeuw, Peter & Debruyne, Michiel & Engelen, Sanne & Hubert, Mia. (2006). Robustness and Outlier Detection in Chemometrics. Critical Reviews in Analytical Chemistry. 36. 221-242. 10.1080/10408340600969403.

[HuRS2015]

Hubert, Mia & Rousseeuw, Peter & Segaert, Pieter. (2015). Multivariate functional outlier detection. Statistical Methods and Applications. 24. 177-202. 10.1007/s10260-015-0297-8.

Parameters:

• return_X_y – Return only the data and target as a tuple.

• as_frame – Return the data in a Pandas Dataframe or Series.