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To re-org Q-results

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D-X-Y
2021-03-06 06:13:22 -08:00
parent cc28e1589e
commit 511e2443d0
7 changed files with 172 additions and 40 deletions
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46
lib/trade_models/quant_transformer.py
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@@ -5,6 +5,7 @@ from __future__ import division
from __future__ import print_function
import os
import math
import numpy as np
import pandas as pd
import copy
@@ -43,7 +44,7 @@ class QuantTransformer(Model):
d_feat=6,
hidden_size=48,
depth=5,
dropout=0.0,
pos_dropout=0.1,
n_epochs=200,
lr=0.001,
metric="",
@@ -63,7 +64,7 @@ class QuantTransformer(Model):
self.d_feat = d_feat
self.hidden_size = hidden_size
self.depth = depth
self.dropout = dropout
self.pos_dropout = pos_dropout
self.n_epochs = n_epochs
self.lr = lr
self.metric = metric
@@ -94,7 +95,7 @@ class QuantTransformer(Model):
d_feat,
hidden_size,
depth,
dropout,
pos_dropout,
n_epochs,
lr,
metric,
@@ -114,9 +115,10 @@ class QuantTransformer(Model):
self.model = TransformerModel(d_feat=self.d_feat,
embed_dim=self.hidden_size,
depth=self.depth)
depth=self.depth,
pos_dropout=pos_dropout)
self.logger.info('model: {:}'.format(self.model))
self.logger.info('model size: {:.3f} MB'.format(count_parameters_in_MB(self.model)))
self.logger.info('model size: {:.3f} MB'.format(count_parameters(self.model)))
if optimizer.lower() == "adam":
@@ -129,17 +131,10 @@ class QuantTransformer(Model):
self.fitted = False
self.model.to(self.device)
def mse(self, pred, label):
loss = (pred - label) ** 2
return torch.mean(loss)
def loss_fn(self, pred, label):
mask = ~torch.isnan(label)
if self.loss == "mse":
import pdb; pdb.set_trace()
print('--')
return self.mse(pred[mask], label[mask])
return F.mse_loss(pred[mask], label[mask])
else:
raise ValueError("unknown loss `{:}`".format(self.loss))
@@ -309,7 +304,7 @@ class Attention(nn.Module):
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.scale = qk_scale or math.sqrt(head_dim)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
@@ -333,17 +328,18 @@ class Attention(nn.Module):
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None,
attn_drop=0., mlp_drop=0., drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super(Block, self).__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=mlp_drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = xlayers.DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = xlayers.MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.mlp = xlayers.MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=mlp_drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
@@ -356,12 +352,13 @@ class SimpleEmbed(nn.Module):
def __init__(self, d_feat, embed_dim):
super(SimpleEmbed, self).__init__()
self.d_feat = d_feat
self.embed_dim = embed_dim
self.proj = nn.Linear(d_feat, embed_dim)
def forward(self, x):
x = x.reshape(len(x), 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)
out = self.proj(x) * math.sqrt(self.embed_dim)
return out
@@ -375,7 +372,7 @@ class TransformerModel(nn.Module):
mlp_ratio: float = 4.,
qkv_bias: bool = True,
qk_scale: Optional[float] = None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None):
pos_dropout=0., mlp_drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None):
"""
Args:
d_feat (int, tuple): input image size
@@ -385,7 +382,8 @@ class TransformerModel(nn.Module):
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
drop_rate (float): dropout rate
pos_dropout (float): dropout rate for the positional embedding
mlp_drop_rate (float): the dropout rate for MLP layers in a block
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
norm_layer: (nn.Module): normalization layer
@@ -398,14 +396,13 @@ class TransformerModel(nn.Module):
self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65)
self.pos_drop = nn.Dropout(p=drop_rate)
self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65, dropout=pos_dropout)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
attn_drop=attn_drop_rate, mlp_drop=mlp_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
@@ -431,7 +428,6 @@ class TransformerModel(nn.Module):
cls_tokens = self.cls_token.expand(batch, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
feats_w_tp = self.pos_embed(feats_w_ct)
feats_w_tp = self.pos_drop(feats_w_tp)
xfeats = feats_w_tp
for block in self.blocks: