atrcheema / ai4water Goto Github PK

def _inverse_transform_y(self, true_outputs, predicted):
    """inverse transformation of y/labels for both true and predicted"""

    if self.config['y_transformation']:
        from ai4water.preprocessing import Transformations
        y_transformer = Transformations.from_config(self.config['y_transformer_'])

        if self.config['y_transformation']:  # only if we apply transformation on y
            # both x,and true_y were given
            true_outputs = self.__inverse_transform_y(true_outputs, y_transformer)
            # because observed y may have -ves or zeros which would have been
            # removed during fit and are put back into it during inverse transform, so
            # in such case predicted y should not be treated by zero indices or negative indices
            # of true y. In other words, parameters of true y should not have impact on inverse
            # transformation of predicted y.
            predicted = self.__inverse_transform_y(predicted, y_transformer, postprocess=False)

    return true_outputs, predicted

def __inverse_transform_y(self,
                          y,
                          transformer,
                          method="inverse_transform",
                          postprocess=True
                          )->np.ndarray:
    """inverse transforms y where y is supposed to be true or predicted output
    from model."""
    # todo, if train_y had zeros or negatives then this will be wrong
    if isinstance(y, np.ndarray):
        # it is ndarray, either num_outs>1 or quantiles>1 or forecast_len>1 some combination of them
        if y.size > len(y):
            if y.ndim == 2:
                # todo, following line was problematic
                y = getattr(transformer, method)(y, postprocess=postprocess)
            else:
                # (exs, outs, quantiles) or (exs, outs, forecast_len) or (exs, forecast_len, quantiles)
                for out in range(y.shape[1]):
                    for q in range(y.shape[2]):
                        y[:, out, q] = getattr(transformer, method)(y[:, out, q],
                                                                    postprocess=postprocess).reshape(-1, )
        else:  # 1d array
            y = getattr(transformer, method)(y, postprocess=postprocess)
    # y can be None for example when we call model.predict(x=x),
    # in this case we don't know what is y
    elif y is not None:
        raise ValueError(f"can't inverse transform y of type {type(y)}")

    return y