2021-03-21 13:52:22 +01:00
|
|
|
#####################################################
|
|
|
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
|
|
|
|
|
#####################################################
|
|
|
|
|
from __future__ import division
|
|
|
|
|
from __future__ import print_function
|
|
|
|
|
|
|
|
|
|
import math
|
|
|
|
|
from functools import partial
|
|
|
|
|
from typing import Optional, Text
|
|
|
|
|
|
|
|
|
|
import torch
|
|
|
|
|
import torch.nn as nn
|
|
|
|
|
import torch.nn.functional as F
|
|
|
|
|
|
2021-05-19 07:00:33 +02:00
|
|
|
from xautodl import spaces
|
2021-03-21 13:52:22 +01:00
|
|
|
from .super_linear import SuperLinear
|
|
|
|
|
from .super_module import SuperModule
|
|
|
|
|
from .super_module import IntSpaceType
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class SuperAlphaEBDv1(SuperModule):
|
|
|
|
|
"""A simple layer to convert the raw trading data from 1-D to 2-D data and apply an FC layer."""
|
|
|
|
|
|
|
|
|
|
def __init__(self, d_feat: int, embed_dim: IntSpaceType):
|
|
|
|
|
super(SuperAlphaEBDv1, self).__init__()
|
|
|
|
|
self._d_feat = d_feat
|
|
|
|
|
self._embed_dim = embed_dim
|
|
|
|
|
self.proj = SuperLinear(d_feat, embed_dim)
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
|
def embed_dim(self):
|
|
|
|
|
return spaces.get_max(self._embed_dim)
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
|
def abstract_search_space(self):
|
|
|
|
|
root_node = spaces.VirtualNode(id(self))
|
|
|
|
|
space = self.proj.abstract_search_space
|
|
|
|
|
if not spaces.is_determined(space):
|
|
|
|
|
root_node.append("proj", space)
|
|
|
|
|
if not spaces.is_determined(self._embed_dim):
|
|
|
|
|
root_node.append("_embed_dim", self._embed_dim.abstract(reuse_last=True))
|
|
|
|
|
return root_node
|
|
|
|
|
|
|
|
|
|
def apply_candidate(self, abstract_child: spaces.VirtualNode):
|
|
|
|
|
super(SuperAlphaEBDv1, self).apply_candidate(abstract_child)
|
|
|
|
|
if "proj" in abstract_child:
|
|
|
|
|
self.proj.apply_candidate(abstract_child["proj"])
|
|
|
|
|
|
|
|
|
|
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
|
|
|
|
x = input.reshape(len(input), self._d_feat, -1) # [N, F*T] -> [N, F, T]
|
|
|
|
|
x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
|
|
|
|
|
if not spaces.is_determined(self._embed_dim):
|
|
|
|
|
embed_dim = self.abstract_child["_embed_dim"].value
|
|
|
|
|
else:
|
|
|
|
|
embed_dim = spaces.get_determined_value(self._embed_dim)
|
|
|
|
|
out = self.proj(x) * math.sqrt(embed_dim)
|
|
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
|
|
|
|
x = input.reshape(len(input), self._d_feat, -1) # [N, F*T] -> [N, F, T]
|
|
|
|
|
x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
|
|
|
|
|
out = self.proj(x) * math.sqrt(self.embed_dim)
|
|
|
|
|
return out
|
