adapters

sktime_interface module

cbr_fox.adapters.sktime_interface.compute_distance_interface(input_data_dictionary, metric: str | Callable[[ndarray, ndarray], float], kwargs)

Interface for computing pairwise distances using sktime metrics or custom callable functions.

This function attempts to calculate the distance or correlation between arrays using the specified metric. It first tries to execute the metric as a sktime-compatible distance function. If that fails, it falls back to using the metric as a user-defined callable function. Any errors during the process are logged, and an exception is raised if all attempts fail.

Parameters:

• input_data_dictionary (dict) – The dictionary containing preprocessed input data. It includes keys for forecasted_window, training_windows, windows_len, and components_len.

• metric (str or callable) – A string or callable object used to compute the distance or correlation between two arrays.

• kwargs (dict) – Additional keyword arguments to pass to the metric. Default is an empty dictionary.

Returns:

A numpy array containing the computed distances or correlations for each component or window.

Return type:

ndarray

Raises:

• ValueError – If both the sktime metric and the custom callable fail to compute the distance.

• TypeError – If metric is not a string or callable.

Notes

• The sktime metric interface is tried first. If it fails, the function attempts to execute metric as a callable.

• All errors are logged for debugging purposes.

cbr_fox.adapters.sktime_interface.distance_sktime_interface(input_data_dictionary, metric, kwargs={})

Interface for computing pairwise distances using a custom or built-in sktime metric.

This function calculates the correlation or distance between two arrays for each component of each window. It allows the use of either sktime’s built-in distance metrics or user-defined custom metrics, offering flexibility in the analysis. If the computation results in any NaN values, the function logs an error and raises an exception to prevent invalid further calculations.

Parameters:

• input_data_dictionary (dict) – Dictionary containing the input data. The dictionary should contain keys for forecasted_window, training_windows, windows_len, and components_len.

• metric (str or callable) – The metric to be used for computing distances. This can either be a built-in sktime metric or a custom function.

• kwargs (dict, optional) – Additional arguments passed to the metric function (default is an empty dictionary).

Returns:

A numpy array containing the pairwise distances or correlations between the forecasted and training windows for each component.

Return type:

ndarray

Raises:

ValueError – If the computation results in NaN values, an error will be raised.

cbr_fox.adapters.sktime_interface.pearson(x, y)

Computes the Pearson correlation coefficient between two arrays.

This function uses NumPy’s corrcoef method to calculate the Pearson correlation matrix and extracts the correlation coefficient between x and y. The value returned is a measure of linear correlation between the two input arrays, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation).

Parameters:

• x (array_like) – First input array, can be any shape that is compatible with NumPy’s corrcoef method.

• y (array_like) – Second input array, should have the same shape as x.

Returns:

Pearson correlation coefficient between x and y.

Return type:

float

Notes

The function uses NumPy’s corrcoef which computes the correlation matrix, and the coefficient is extracted from this matrix.