Estimated Time of Arrival Evaluator
==================================

We have implemented several evaluation loss functions so that different models under the same task can be compared under the same standard.

Evaluation Metrics
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For the task of estimated time of arrival, this evaluator implements a series of evaluation indicators:

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Evaluation Metrics                Formula
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MAE(Mean Absolute Error)          .. math:: MAE=\frac{1}{n}\sum_{i=1}^n|\hat{y_{i}}-y_i|
MSE(Mean Squared Error)           .. math:: MSE=\frac{1}{n}\sum_{i=1}^n(\hat{y_{i}}-y_i)^2
RMSE(Rooted Mean Squared Error)   .. math:: RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^n(\hat{y_{i}}-y_i)^2}
MAPE(Mean Absolute Percent Error) .. math:: MAPE=\frac{1}{n}\sum_{i=1}^n|\frac{\hat{y_{i}}-y_i}{y_i}|*100\%
R2(Coefficient of Determination)  .. math:: R^2=1-\frac{\sum_{i=1}^n(y_i-\hat{y_i})^2}{\sum_{i=1}^n(y_i-\bar{y})^2}
EVAR(Explained variance score)    .. math:: EVAR =1-\frac{Var(y_i-\hat{y_i})}{Var(y_i)}
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The ground-truth value is \ :math:`y=\{y_1,y_2,...,y_n\}`\, the prediction value is \ :math:`\hat{y} = \{\hat{y_1}, \hat{y_2}, ..., \hat{y_n}\}`\ ，\ :math:`n`\ is the number of samples, the mean value is \ :math:`\bar{y}=\frac{1}{n}\sum_{i=1}^ny_i`\, the variance is \ :math:`Var(y_i)=\frac{1}{n}\sum_{i=1}^n(y_{i}-\bar{y})^2`\ .

Evaluation Settings
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The following are parameters involved in the evaluator:

Location: libcity/config/evaluator/ETAEvaluator.json

- ``metrics``\ : Array of evaluation metrics, \ ``allowed_metrics``\ in evaluator class indicates the type of metrics that the task can accept, and ``metrics`` cannot exceed this range.