# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
from typing import List, Optional, Tuple
from mxnet.gluon import nn
from gluonts.core.component import validated
from gluonts.mx import Tensor
from .representation import Representation
# LearnedBinning = Union[GlobalRelativeBinning, LocalAbsoluteBinning]
[docs]class DiscretePIT(Representation):
"""
A class representing a discrete probability integral transform of a given
quantile-based learned binning. Note that this representation is intended
to be applied on top of a quantile-based binning representation.
Parameters
----------
num_bins
Number of bins used by the data on which this representation is
applied.
mlp_tranf
Whether we want to post-process the pit-transformed valued using a MLP
which can learn an appropriate binning, which would ensure that pit
models have the same expressiveness as standard quantile binning with
embedding.
(default: False)
embedding_size
The desired layer output size if mlp_tranf=True. By default, the
following heuristic is used:
https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html
(default: round(num_bins**(1/4)))
"""
@validated()
def __init__(
self,
num_bins: int,
mlp_transf: bool = False,
embedding_size: Optional[int] = None,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
self.num_bins = num_bins
self.mlp_transf = mlp_transf
if embedding_size is None:
self.embedding_size = round(self.num_bins ** (1 / 4))
else:
self.embedding_size = embedding_size
if mlp_transf:
self.mlp = nn.HybridSequential()
self.mlp.add(
nn.Dense(units=self.num_bins, activation="relu", flatten=False)
)
self.mlp.add(nn.Dense(units=self.embedding_size, flatten=False))
else:
self.mlp = None
[docs] def hybrid_forward(
self,
F,
data: Tensor,
observed_indicator: Tensor,
scale: Optional[Tensor],
rep_params: List[Tensor],
**kwargs,
) -> Tuple[Tensor, Tensor, List[Tensor]]:
data = data / self.num_bins
if self.mlp_transf:
data = F.expand_dims(data, axis=-1)
data = self.mlp(data)
return data, scale, rep_params
[docs] def post_transform(
self, F, samples: Tensor, scale: Tensor, rep_params: List[Tensor]
) -> Tensor:
samples = samples * F.full(1, self.num_bins)
samples = F.Custom(
samples, F.arange(self.num_bins), op_type="digitize"
)
return samples