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initial lstnet multivariate point forecasting model (#9)

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* initial lstnet

* lstnet network

* fixed forward

* fix splitter

* fix prediction

* rename argument to what it is i.e. time_first

* fixed scaling and some default values

* scaler can now take time_first=False tensors
This commit is contained in:
Kashif Rasul
2020-04-17 17:13:44 +02:00
committed by GitHub Enterprise
parent 2d8f6d31f0
commit c5fac32bb2
6 changed files with 367 additions and 20 deletions
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pts/modules/scaler.py
+26 -10
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@@ -6,9 +6,10 @@ import torch.nn as nn
class Scaler(ABC, nn.Module):
def __init__(self, keepdim: bool = False):
def __init__(self, keepdim: bool = False, time_first: bool = True):
super().__init__()
self.keepdim = keepdim
self.time_first = time_first
@abstractmethod
def compute_scale(
@@ -23,7 +24,8 @@ class Scaler(ABC, nn.Module):
Parameters
----------
data
tensor of shape (N, T, C) containing the data to be scaled
tensor of shape (N, T, C) if ``time_first == True`` or (N, C, T)
if ``time_first == False`` containing the data to be scaled
observed_indicator
observed_indicator: binary tensor with the same shape as
@@ -33,19 +35,23 @@ class Scaler(ABC, nn.Module):
Returns
-------
Tensor
Tensor containing the "scaled" data, shape: (N, T, C).
Tensor containing the "scaled" data, shape: (N, T, C) or (N, C, T).
Tensor
Tensor containing the scale, of shape (N, C) if ``keepdim == False``, and shape
(N, 1, C) if ``keepdim == True``.
Tensor containing the scale, of shape (N, C) if ``keepdim == False``,
and shape (N, 1, C) or (N, C, 1) if ``keepdim == True``.
"""
scale = self.compute_scale(data, observed_indicator)
if self.time_first:
dim = 1
else:
dim = 2
if self.keepdim:
scale = scale.unsqueeze(1)
scale = scale.unsqueeze(dim=dim)
return data / scale, scale
else:
return data / scale.unsqueeze(1), scale
return data / scale.unsqueeze(dim=dim), scale
class MeanScaler(Scaler):
@@ -69,9 +75,15 @@ class MeanScaler(Scaler):
def compute_scale(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> torch.Tensor:
if self.time_first:
dim = 1
else:
dim = 2
# these will have shape (N, C)
num_observed = observed_indicator.sum(dim=1)
sum_observed = (data.abs() * observed_indicator).sum(dim=1)
num_observed = observed_indicator.sum(dim=dim)
sum_observed = (data.abs() * observed_indicator).sum(dim=dim)
# first compute a global scale per-dimension
total_observed = num_observed.sum(dim=0)
@@ -105,4 +117,8 @@ class NOPScaler(Scaler):
def compute_scale(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> torch.Tensor:
return torch.ones_like(data).mean(dim=1)
if self.time_first:
dim = 1
else:
dim = 2
return torch.ones_like(data).mean(dim=dim)