load_basic_motions#
- load_basic_motions(split=None, return_X_y=True, return_type=None)[source]#
Load the BasicMotions time series classification problem and returns X and y.
This is an equal length multivariate time series classification problem. It loads a 4 class classification problem with number of cases, n, where n = 80 (if split is None) or 40 (if split is “train”/”test”) of series length m = 100.
- 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 class labels for each time series instance in X If return_X_y is False, y is appended to X instead.
- X: sktime data container, following mtype specification
- Raises:
- ValueError if argument “numpy2d”/”numpyflat” is passed as return_type
- Notes
- Dimensionality: multivariate, 6
- Series length: 100
- Train cases: 40
- Test cases: 40
- Number of classes: 4
- The data was generated as part of a student project where four students performed
- four activities whilst wearing a smart watch. The watch collects 3D accelerometer
- and a 3D gyroscope It consists of four classes, which are walking, resting,
- running and badminton. Participants were required to record motion a total of
- five times, and the data is sampled once every tenth of a second, for a ten second
- period.
- Dataset details: http://www.timeseriesclassification.com/description.php?Dataset
- =BasicMotions