Source code for

# Copyright 2018, 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
# or in the "license" file accompanying this file. This file is distributed
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.

# Standard library imports
from typing import List, Optional, Tuple

import mxnet as mx

# First-party imports
from gluonts.core.component import validated
from gluonts.model.common import Tensor

from .representation import Representation

[docs]class MeanScaling(Representation): """ A class representing a mean scaling approach. Inputs are simply rescaled based on their mean. Parameters ---------- minimum_scale The minimum value to which re-scaled values will be clipped to. (default: 1e-10) clip_max The maximum value to which re-scaled values will be clipped to. Negative values will be clipped at -clip_max and positive values at clip_max. (default: None) """ @validated() def __init__( self, scale_min: float = 1e-10, clip_max: Optional[float] = None, *args, **kwargs, ): super().__init__(*args, **kwargs) self.scale_min = scale_min self.clip_max = clip_max
[docs] def compute_scale( self, F, data: Tensor, observed_indicator: Tensor # shapes (N, T, C) ) -> Tensor: # these will have shape (N, C) num_observed = F.sum(observed_indicator, axis=1) sum_observed = (data.abs() * observed_indicator).sum(axis=1) # first compute a global scale per-dimension total_observed = num_observed.sum(axis=0) denominator = F.maximum(total_observed, 1.0) default_scale = sum_observed.sum(axis=0) / denominator # shape (C, ) # then compute a per-item, per-dimension scale denominator = F.maximum(num_observed, 1.0) scale = sum_observed / denominator # shape (N, C) # use per-batch scale when no element is observed # or when the sequence contains only zeros cond = F.broadcast_greater(sum_observed, F.zeros_like(sum_observed)) scale = F.where( cond, scale, F.broadcast_mul(default_scale, F.ones_like(num_observed)), ) return F.maximum(scale, self.scale_min)
# noinspection PyMethodOverriding
[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 = F.cast(data, dtype="float32") if scale is None: scale = self.compute_scale(F, data, observed_indicator) scale = scale.expand_dims(axis=1) scaled_data = F.broadcast_div(data, scale) if self.clip_max is not None: scaled_data = F.clip(scaled_data, -self.clip_max, self.clip_max) return scaled_data, scale, []
[docs] def post_transform( self, F, samples: Tensor, scale: Tensor, rep_params: List[Tensor] ) -> Tensor: transf_samples = F.broadcast_mul(samples, scale) return transf_samples