pyepo.metric.metrics

Metrics for SKlearn model

Functions

SPOError(pred_cost, true_cost, model_type, args)	A function to calculate normalized true regret
makeSkScorer(optmodel)	A function to create sklearn scorer
makeAutoSkScorer(optmodel)	A function to create Auto-SKlearn scorer
testMSE(pred_cost, true_cost, model_type, args)	A function to calculate MSE for testing
makeTestMSEScorer(optmodel)	A function to create MSE scorer for testing

Module Contents

pyepo.metric.metrics.SPOError(pred_cost, true_cost, model_type, args)

A function to calculate normalized true regret

Parameters:

• pred_cost (numpy.array) – predicted costs

• true_cost (numpy.array) – true costs

• model_type (ABCMeta) – optModel class type

• args (dict) – optModel args

Returns:

regret loss

Return type:

float

pyepo.metric.metrics.makeSkScorer(optmodel)

A function to create sklearn scorer

Parameters:

optmodel (optModel) – optimization model

Returns:

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

Return type:

scorer

pyepo.metric.metrics.makeAutoSkScorer(optmodel)

A function to create Auto-SKlearn scorer

Parameters:

optmodel (optModel) – optimization model

Returns:

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

Return type:

scorer

pyepo.metric.metrics.testMSE(pred_cost, true_cost, model_type, args)

A function to calculate MSE for testing

Parameters:

• pred_cost (array) – predicted costs

• true_cost (array) – true costs

• model_type (ABCMeta) – optModel class type

• args (dict) – optModel args

Returns:

mse

Return type:

float

pyepo.metric.metrics.makeTestMSEScorer(optmodel)

A function to create MSE scorer for testing

Parameters:

optmodel (optModel) – optimization model

Returns:

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

Return type:

scorer