Add metric and loss modules for RMSE, RMSPE, and AUC #1214
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w4nderlust
reviewed
Jun 24, 2021
ludwig/modules/loss_modules.py
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| def rmspe_loss(targets, predictions): | ||
| if type(predictions) == dict and PREDICTIONS in predictions.keys(): |
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I would remove this, and just have the loss passing the right tensors and the metric passing the tensor associated with predictions key. This way this function is more atomic and reusable.
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@w4nderlust see commit
w4nderlust
reviewed
Jun 24, 2021
ludwig/features/numerical_feature.py
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| elif self.loss[TYPE] == "root_mean_squared_percentage_error": | ||
| self.metric_functions[LOSS] = RMSPEMetric(name="eval_loss") | ||
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| self.metric_functions[ERROR] = ErrorScore(name="metric_error") |
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i think we can get rid of error, it's just not informative. can also get rid of ErrorScore.
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@w4nderlust see commit
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This PR adds support for RMSE, RMSPE, and AUC.
Specifically it adds a metric and loss module for both RMSE and RMSPE and a metric module for AUC.