load_tecator

load_tecator(split=None, return_X_y=True, return_type=None)

Load the Tecator time series regression problem and returns X and y.

Parameters:

split: None or one of “TRAIN”, “TEST”, optional (default=None)

Whether to load the train or test instances of the problem. By default it loads both train and test instances (in a single container).

return_X_y: bool, optional (default=True)

If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.

return_type: valid Panel mtype str or None, optional (default=None=”nested_univ”)

Memory data format specification to return X in, None = “nested_univ” type. str can be any supported sktime Panel mtype,

for list of mtypes, see datatypes.MTYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

commonly used specifications:

“nested_univ: nested pd.DataFrame, pd.Series in cells “numpy3D”/”numpy3d”/”np3D”: 3D np.ndarray (instance, variable, time index) “numpy2d”/”np2d”/”numpyflat”: 2D np.ndarray (instance, time index) “pd-multiindex”: pd.DataFrame with 2-level (instance, time) MultiIndex

Exception is raised if the data cannot be stored in the requested type.

Returns:

X: sktime data container, following mtype specification return_type

The time series data for the problem, with n instances

y: 1D numpy array of length n, only returned if return_X_y if True

The target values for each time series instance in X If return_X_y is False, y is appended to X instead.

Notes

Dimensionality: univariate Series length: 100 Train cases: 172 Test cases: 43

The purpose of this dataset is to measure the fat content of meat based off its near

infrared absorbance spectrum.

The absorbance spectrum is measured in the wavelength range of 850 nm to 1050 nm. The fat content is measured by standard chemical analysis methods. The dataset contains 215 samples of meat, each with 100 spectral measurements. For more information see: https://www.openml.org/search?type=data&sort=runs&id=505&status=active

References

[1] C.Borggaard and H.H.Thodberg, “Optimal Minimal Neural Interpretation of Spectra” , Analytical Chemistry 64 (1992), p 545-551. [2] H.H.Thodberg, “Ace of Bayes: Application of Neural Networks with Pruning” Manuscript 1132, Danish Meat Research Institute (1993), p 1-12.

Examples

>>> from sktime.datasets import load_tecator
>>> X, y = load_tecator()