Tecator

Tecator

class Tecator[source]

Load the Tecator time series regression problem.

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.regression import Tecator
>>> dataset = Tecator()
>>> X, y = dataset.load("X", "y")

Methods

cache_files_directory()	Get the directory where cache files are stored.
cleanup_cache_files()	Cleanup cache files from the cache directory.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the skbase object.
is_composite()	Check if the object is composed of other BaseObjects.
keys()	Return a list of available sets.
load(*args)	Load the dataset.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
loader_func([return_X_y, return_type, y_dtype])	Load the Tecator time series regression problem and returns X and y.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.

get_loader_func