ForeTiS.evaluation.eval_metrics

Module Contents

Functions

smape(y_true, y_pred)	Function delivering Symmetric Mean Absolute Percentage Error between prediction and actual values
mape(y_true, y_pred)	Function delivering Mean Absolute Percentage Error between prediction and actual values
get_evaluation_report(y_true, y_pred[, prefix, ...])	Get values for common evaluation metrics

ForeTiS.evaluation.eval_metrics.smape(y_true, y_pred)

Function delivering Symmetric Mean Absolute Percentage Error between prediction and actual values :param y_true: actual values :param y_pred: prediction values :return: sMAPE between prediction and actual values

Parameters:

• y_true (numpy.array) –

• y_pred (numpy.array) –

Return type:

float

ForeTiS.evaluation.eval_metrics.mape(y_true, y_pred)

Function delivering Mean Absolute Percentage Error between prediction and actual values :param y_true: actual values :param y_pred: prediction values :return: MAPE between prediction and actual values

Parameters:

• y_true (numpy.array) –

• y_pred (numpy.array) –

Return type:

float

ForeTiS.evaluation.eval_metrics.get_evaluation_report(y_true, y_pred, prefix='', current_model_name=None)

Get values for common evaluation metrics

Parameters:

• y_true (numpy.array) – true values

• y_pred (numpy.array) – predicted values

• prefix (str) – prefix to be added to the key if multiple eval metrics are collected

• current_model_name (str) – name of the current model according to naming of .py file in package model

Returns:

dictionary with common metrics

Return type:

dict