pyepo.metric.metrics

Metrics for SKlearn model

Functions

SPOError(→ float)	A function to calculate normalized true regret
makeSkScorer(→ Callable)	A function to create sklearn scorer
makeAutoSkScorer(→ Callable)	A function to create Auto-SKlearn scorer
testMSE(→ float)	A function to calculate MSE for testing
makeTestMSEScorer(→ Callable)	A function to create MSE scorer for testing

Module Contents

pyepo.metric.metrics.SPOError(pred_cost: numpy.ndarray, true_cost: numpy.ndarray, model_type: type, args: dict) → float

A function to calculate normalized true regret

Parameters:

• pred_cost – predicted costs

• true_cost – true costs

• model_type – optModel class type

• args – optModel args

Returns:

regret loss

Return type:

float

pyepo.metric.metrics.makeSkScorer(optmodel: pyepo.model.opt.optModel) → Callable

A function to create sklearn scorer

Parameters:

optmodel – optimization model

Returns:

callable object that returns a scalar score; less is better.

Return type:

scorer

pyepo.metric.metrics.makeAutoSkScorer(optmodel: pyepo.model.opt.optModel) → Callable

A function to create Auto-SKlearn scorer

Parameters:

optmodel – optimization model

Returns:

callable object that returns a scalar score; less is better.

Return type:

scorer

pyepo.metric.metrics.testMSE(pred_cost: numpy.ndarray, true_cost: numpy.ndarray, model_type: type, args: dict) → float

A function to calculate MSE for testing

Parameters:

• pred_cost – predicted costs

• true_cost – true costs

• model_type – optModel class type

• args – optModel args

Returns:

mse

Return type:

float

pyepo.metric.metrics.makeTestMSEScorer(optmodel: pyepo.model.opt.optModel) → Callable

A function to create MSE scorer for testing

Parameters:

optmodel – optimization model

Returns:

callable object that returns a scalar score; less is better.

Return type:

scorer