ddop.metrics.total_costs

ddop.metrics.total_costs(y_true, y_pred, cu, co, multioutput='cumulated')

Compute total costs based on the the difference between y_true and y_pred and the given underage and overage costs.

Parameters

• y_true (array-like) – The true values

• y_pred (array-like) – The predicted vales

• cu (int or float) – the underage costs per unit.

• co (int or float) – the overage costs per unit.

• multioutput ({"raw_values", "cumulated"}, default="cumulated") –

  Defines aggregating of multiple output values. Default is “cumulated”.

  ’raw_values’ :

  Returns a full set of cost values in case of multioutput input.

  ’cumulated’ :

  Costs of all outputs are cumulated.

Returns

total_costs – The total costs. If multioutput is ‘raw_values’, then the total costs are returned for each output separately. If multioutput is ‘cumulated’, then the cumulated costs of all outputs is returned. The total costs are non-negative floating points. The best value is 0.0.

Return type

float or ndarray of floats

Examples

>>> from ddop.metrics import total_costs
>>> y_true = [[2,2], [2,4], [3,6]]
>>> y_pred = [[1,2], [3,3], [4,7]]
>>> cu = [2,4]
>>> co = [1,1]
>>> total_costs(y_true, y_pred, cu, co, multioutput="raw_values")
array([4,5])
>>> total_costs(y_true, y_pred, cu, co, multioutput="cumulated")
9