Source code for gluonts.mx.block.scaler

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from typing import Tuple, Optional

import mxnet.ndarray as nd
from mxnet.gluon import nn

from gluonts.core.component import validated
from gluonts.mx import Tensor


[docs]class Scaler(nn.HybridBlock): """ Base class for blocks used to scale data. Parameters ---------- keepdims toggle to keep the dimension of the input tensor. axis specify the axis over which to scale. Default is 1 for (N, T, C) shaped input tensor. """ def __init__(self, keepdims: bool = False, axis: int = 1): super().__init__() self.keepdims = keepdims self.axis = axis
[docs] def compute_scale(self, F, data: Tensor, observed_indicator: Tensor): """ Computes the scale of the given input data. Parameters ---------- F A module that can either refer to the Symbol API or the NDArray API in MXNet. data tensor containing the data to be scaled. observed_indicator observed_indicator: binary tensor with the same shape as ``data``, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points. """ raise NotImplementedError()
# noinspection PyMethodOverriding
[docs] def hybrid_forward( self, F, data: Tensor, observed_indicator: Tensor ) -> Tuple[Tensor, Tensor]: """ Parameters ---------- F A module that can either refer to the Symbol API or the NDArray API in MXNet. data tensor containing the data to be scaled. observed_indicator observed_indicator: binary tensor with the same shape as ``data``, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points. Returns ------- Tensor Tensor containing the "scaled" data. Tensor Tensor containing the scale: this has the same shape as the data, except for the axis ``axis`` along which the scale is computed, which is removed if ``keepdims == False``, and kept with length 1 otherwise. For example, if ``data`` has shape ``(N, T, C)`` and ``axis ==1 ``, then ``scale`` has shape ``(N, C)`` if ``keepdims == False``, and ``(N, 1, C)`` otherwise. """ scale = self.compute_scale(F, data, observed_indicator) if self.keepdims: scale = scale.expand_dims(axis=self.axis) return F.broadcast_div(data, scale), scale else: return ( F.broadcast_div(data, scale.expand_dims(axis=self.axis)), scale, )
[docs]class MeanScaler(Scaler): """ The ``MeanScaler`` computes a per-item scale according to the average absolute value over time of each item. The average is computed only among the observed values in the data tensor, as indicated by the second argument. Items with no observed data are assigned a scale based on the global average. Parameters ---------- minimum_scale default scale that is used if the time series has only zeros. """ @validated() def __init__( self, minimum_scale: float = 1e-10, default_scale: Optional[float] = None, *args, **kwargs ): super().__init__(*args, **kwargs) self.minimum_scale = minimum_scale self.default_scale = default_scale
[docs] def compute_scale( self, F, data: Tensor, observed_indicator: Tensor, # shapes (N, T, C) or (N, C, T) ) -> Tensor: """ Parameters ---------- F A module that can either refer to the Symbol API or the NDArray API in MXNet. data tensor containing the data to be scaled. observed_indicator observed_indicator: binary tensor with the same shape as ``data``, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points. Returns ------- Tensor shape (N, C), computed according to the average absolute value over time of the observed values. """ # these will have shape (N, C) num_observed = F.sum(observed_indicator, axis=self.axis) sum_observed = (data.abs() * observed_indicator).sum(axis=self.axis) # first compute a global scale per-dimension total_observed = num_observed.sum(axis=0) denominator = F.maximum(total_observed, 1.0) if self.default_scale is not None: default_scale = self.default_scale * F.ones_like(num_observed) else: # shape (C, ) default_scale = sum_observed.sum(axis=0) / denominator # 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.minimum_scale)
[docs]class MinMax(Scaler): """ The 'MinMax' scales the input data using a min-max approach along the specified axis. """ @validated() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
[docs] def compute_scale( self, F, data: Tensor, observed_indicator: Tensor ) -> Tensor: """ Parameters ---------- F A module that can either refer to the Symbol API or the NDArray API in MXNet. data tensor containing the data to be scaled. observed_indicator observed_indicator: binary tensor with the same shape as ``data``, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points. Returns ------- Tensor shape (N, T, C) or (N, C, T) scaled along the specified axis. """ axis_zero = nd.prod( data == data.zeros_like(), self.axis, keepdims=True ) # Along the specified axis, which array are always at zero axis_zero = nd.broadcast_to( axis_zero, shape=data.shape ) # Broadcast it to the shape of data min_val = nd.where( 1 - observed_indicator, nd.broadcast_to(data.max(keepdims=True), shape=data.shape), data, ).min( axis=self.axis, keepdims=True ) # return the min value along specified axis while ignoring value according to observed_indicator max_val = nd.where( 1 - observed_indicator, nd.broadcast_to(data.min(keepdims=True), shape=data.shape), data, ).max( axis=self.axis, keepdims=True ) # return the max value along specified axis while ignoring value according to observed_indicator scaled_data = (data - min_val) / (max_val - min_val) # Rescale scaled_data = nd.where( axis_zero, scaled_data.zeros_like(), scaled_data ) # Clip Nan values to zero if the data was equal to zero along specified axis scaled_data = nd.where( scaled_data != scaled_data, scaled_data.ones_like(), scaled_data ) # Clip the Nan values to one. scaled_date!=scaled_data tells us where the Nan values are in scaled_data return nd.where( 1 - observed_indicator, scaled_data.zeros_like(), scaled_data ) # Replace data with zero where observed_indicator tells us to.
[docs]class NOPScaler(Scaler): """ The ``NOPScaler`` assigns a scale equals to 1 to each input item, i.e., no scaling is applied upon calling the ``NOPScaler``. """ @validated() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # noinspection PyMethodOverriding
[docs] def compute_scale( self, F, data: Tensor, observed_indicator: Tensor ) -> Tensor: """ Parameters ---------- F A module that can either refer to the Symbol API or the NDArray API in MXNet. data tensor containing the data to be scaled. observed_indicator observed_indicator: binary tensor with the same shape as ``data``, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points. Returns ------- Tensor shape (N, C), identically equal to 1. """ return F.ones_like(data).mean(axis=self.axis)