- load_arrow_head(split=None, return_X_y=True, return_type=None)#
Load the ArrowHead time series classification problem and returns X and y.
- 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.
- 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 class labels for each time series instance in X If return_X_y is False, y is appended to X instead.
Dimensionality: univariate Series length: 251 Train cases: 36 Test cases: 175 Number of classes: 3
The arrowhead data consists of outlines of the images of arrowheads. The shapes of the projectile points are converted into a time series using the angle-based method. The classification of projectile points is an important topic in anthropology. The classes are based on shape distinctions such as the presence and location of a notch in the arrow. The problem in the repository is a length normalised version of that used in Ye09shapelets. The three classes are called “Avonlea”, “Clovis” and “Mix”.”
Dataset details: http://timeseriesclassification.com/description.php ?Dataset=ArrowHead
>>> from sktime.datasets import load_arrow_head >>> X, y = load_arrow_head()