gluonts.mx.block.quantile_output module¶
-
class
gluonts.mx.block.quantile_output.
ProjectParams
(num_quantiles, **kwargs)[source]¶ Bases:
mxnet.gluon.block.HybridBlock
Defines a dense layer to compute the projection weights into the quantile space.
- Parameters
num_quantiles – number of quantiles to compute the projection.
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hybrid_forward
(F, x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]¶ - Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
x – input tensor
- Returns
output of the projection layer
- Return type
Tensor
-
class
gluonts.mx.block.quantile_output.
QuantileLoss
(quantiles: List[float], quantile_weights: List[float] = None, weight=None, batch_axis=0, **kwargs)[source]¶ Bases:
mxnet.gluon.loss.Loss
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static
compute_quantile_loss
(F, y_true: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], y_pred_p: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], p: float) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]¶ Compute the quantile loss of the given quantile
- Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
y_true – true target, shape (N1 x N2 x … x Nk x dimension of time series (normally 1)).
y_pred_p – predicted target quantile, shape (N1 x N2 x … x Nk x 1).
p – quantile error to compute the loss.
- Returns
quantile loss, shape: (N1 x N2 x … x Nk x 1)
- Return type
Tensor
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hybrid_forward
(F, y_true: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], y_pred: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], sample_weight=None)[source]¶ Compute the weighted sum of quantile losses.
- Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
y_true – true target, shape (N1 x N2 x … x Nk x dimension of time series (normally 1))
y_pred – predicted target, shape (N1 x N2 x … x Nk x num_quantiles)
sample_weight – sample weights
- Returns
weighted sum of the quantile losses, shape N1 x N1 x … Nk
- Return type
Tensor
-
static
-
class
gluonts.mx.block.quantile_output.
QuantileOutput
(quantiles: List[float], quantile_weights: Optional[List[float]] = None)[source]¶ Bases:
object
Output layer using a quantile loss and projection layer to connect the quantile output to the network.
- Parameters
quantiles – list of quantiles to compute loss over.
quantile_weights – weights of the quantiles.