# gluonts.mx.distribution.binned module¶

class gluonts.mx.distribution.binned.Binned(bin_log_probs: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], bin_centers: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], label_smoothing: Optional[float] = None)[source]

A binned distribution defined by a set of bins via bin centers and bin probabilities.

Parameters
• bin_log_probs – Tensor containing log probabilities of the bins, of shape (*batch_shape, num_bins).

• bin_centers – Tensor containing the bin centers, of shape (*batch_shape, num_bins).

• F

• label_smoothing – The label smoothing weight, real number in [0, 1). Default None. If not None, then the loss of the distribution will be “label smoothed” cross-entropy. For example, instead of computing cross-entropy loss between the estimated bin probabilities and a hard-label (one-hot encoding) [1, 0, 0], a soft label of [0.9, 0.05, 0.05] is taken as the ground truth (when label_smoothing=0.15). See (Muller et al., 2019) [MKH19], for further reference.

property F
arg_names = None
property args
property batch_shape

Layout of the set of events contemplated by the distribution.

Invoking sample() from a distribution yields a tensor of shape batch_shape + event_shape, and computing log_prob (or loss more in general) on such sample will yield a tensor of shape batch_shape.

This property is available in general only in mx.ndarray mode, when the shape of the distribution arguments can be accessed.

property bin_probs
cdf(x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

Returns the value of the cumulative distribution function evaluated at x

property event_dim

Number of event dimensions, i.e., length of the event_shape tuple.

This is 0 for distributions over scalars, 1 over vectors, 2 over matrices, and so on.

property event_shape

Shape of each individual event contemplated by the distribution.

For example, distributions over scalars have event_shape = (), over vectors have event_shape = (d, ) where d is the length of the vectors, over matrices have event_shape = (d1, d2), and so on.

Invoking sample() from a distribution yields a tensor of shape batch_shape + event_shape.

This property is available in general only in mx.ndarray mode, when the shape of the distribution arguments can be accessed.

is_reparameterizable = False
log_prob(x)[source]

Compute the log-density of the distribution at x.

Parameters

x – Tensor of shape (*batch_shape, *event_shape).

Returns

Tensor of shape batch_shape containing the log-density of the distribution for each event in x.

Return type

Tensor

loss(x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

Compute the loss at x according to the distribution.

By default, this method returns the negative of log_prob. For some distributions, however, the log-density is not easily computable and therefore other loss functions are computed.

Parameters

x – Tensor of shape (*batch_shape, *event_shape).

Returns

Tensor of shape batch_shape containing the value of the loss for each event in x.

Return type

Tensor

property mean

Tensor containing the mean of the distribution.

quantile(level: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

Calculates quantiles for the given levels.

Parameters

level – Level values to use for computing the quantiles. level should be a 1d tensor of level values between 0 and 1.

Returns

Quantile values corresponding to the levels passed. The return shape is

(num_levels, …DISTRIBUTION_SHAPE…),

where DISTRIBUTION_SHAPE is the shape of the underlying distribution.

Return type

quantiles

sample(num_samples=None, dtype=<class 'numpy.float32'>)[source]

Draw samples from the distribution.

If num_samples is given the first dimension of the output will be num_samples.

Parameters
• num_samples – Number of samples to to be drawn.

• dtype – Data-type of the samples.

Returns

A tensor containing samples. This has shape (*batch_shape, *eval_shape) if num_samples = None and (num_samples, *batch_shape, *eval_shape) otherwise.

Return type

Tensor

smooth_ce_loss(x)[source]

Cross-entropy loss with a “smooth” label.

property stddev

Tensor containing the standard deviation of the distribution.

class gluonts.mx.distribution.binned.BinnedArgs(num_bins: int, bin_centers: mxnet.ndarray.ndarray.NDArray, **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

hybrid_forward(F, x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], bin_centers: 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]][source]

Overrides to construct symbolic graph for this Block.

Parameters
• x (Symbol or NDArray) – The first input tensor.

• *args (list of Symbol or list of NDArray) – Additional input tensors.

class gluonts.mx.distribution.binned.BinnedOutput(bin_centers: mxnet.ndarray.ndarray.NDArray, label_smoothing: Optional[float] = None)[source]
distr_cls

alias of Binned

distribution(args, loc=None, scale=None) → gluonts.mx.distribution.binned.Binned[source]

Construct the associated distribution, given the collection of constructor arguments and, optionally, a scale tensor.

Parameters
• distr_args – Constructor arguments for the underlying Distribution type.

• loc – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.

• scale – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.

property event_shape

Shape of each individual event contemplated by the distributions that this object constructs.

get_args_proj(*args, **kwargs) → mxnet.gluon.block.HybridBlock[source]