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To re-org Q-results

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This commit is contained in:
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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2
.latent-data/qlib

Submodule .latent-data/qlib updated: 49697b1f15...91fd53ab4d

14
exps/trading/baselines.py
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@@ -65,16 +65,16 @@ def update_market(config, market):
def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
# model initiaiton
print('')
print('[{:}] - [{:}]: {:}'.format(experiment_name, recorder_name, uri))
print('dataset={:}'.format(dataset))
print("")
print("[{:}] - [{:}]: {:}".format(experiment_name, recorder_name, uri))
print("dataset={:}".format(dataset))
model = init_instance_by_config(task_config["model"])
# start exp
with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri):
log_file = R.get_recorder().root_uri / '{:}.log'.format(experiment_name)
log_file = R.get_recorder().root_uri / "{:}.log".format(experiment_name)
set_log_basic_config(log_file)
# train model
@@ -109,12 +109,14 @@ def main(xargs, exp_yaml):
qlib.init(**config.get("qlib_init"))
dataset_config = config.get("task").get("dataset")
dataset = init_instance_by_config(dataset_config)
pprint('args: {:}'.format(xargs))
pprint("args: {:}".format(xargs))
pprint(dataset_config)
pprint(dataset)
for irun in range(xargs.times):
run_exp(config.get("task"), dataset, xargs.alg, "recorder-{:02d}-{:02d}".format(irun, xargs.times), xargs.save_dir)
run_exp(
config.get("task"), dataset, xargs.alg, "recorder-{:02d}-{:02d}".format(irun, xargs.times), xargs.save_dir
)
if __name__ == "__main__":

135
exps/trading/organize_results.py Normal file
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@@ -0,0 +1,135 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
#####################################################
# python exps/trading/organize_results.py
#####################################################
import sys, argparse
import numpy as np
from typing import List, Text
from collections import defaultdict, OrderedDict
from pathlib import Path
from pprint import pprint
import ruamel.yaml as yaml
lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
import qlib
from qlib.config import REG_CN
from qlib.workflow import R
class QResult:
def __init__(self):
self._result = defaultdict(list)
def append(self, key, value):
self._result[key].append(value)
@property
def result(self):
return self._result
def update(self, metrics, filter_keys=None):
for key, value in metrics.items():
if filter_keys is not None and key in filter_keys:
key = filter_keys[key]
elif filter_keys is not None:
continue
self.append(key, value)
@staticmethod
def full_str(xstr, space):
xformat = '{:' + str(space) + 's}'
return xformat.format(str(xstr))
def info(self, keys: List[Text], separate: Text = '', space: int = 25, show=True):
avaliable_keys = []
for key in keys:
if key not in self.result:
print('There are invalid key [{:}].'.format(key))
else:
avaliable_keys.append(key)
head_str = separate.join([self.full_str(x, space) for x in avaliable_keys])
values = []
for key in avaliable_keys:
current_values = self._result[key]
mean = np.mean(current_values)
std = np.std(current_values)
values.append('{:.4f} $\pm$ {:.4f}'.format(mean, std))
value_str = separate.join([self.full_str(x, space) for x in values])
if show:
print(head_str)
print(value_str)
else:
return head_str, value_str
def compare_results(heads, values, names, space=10):
for idx, x in enumerate(heads):
assert x == heads[0], '[{:}] {:} vs {:}'.format(idx, x, heads[0])
new_head = QResult.full_str('Name', space) + heads[0]
print(new_head)
for name, value in zip(names, values):
xline = QResult.full_str(name, space) + value
print(xline)
def filter_finished(recorders):
returned_recorders = dict()
not_finished = 0
for key, recorder in recorders.items():
if recorder.status == "FINISHED":
returned_recorders[key] = recorder
else:
not_finished += 1
return returned_recorders, not_finished
def main(xargs):
R.reset_default_uri(xargs.save_dir)
experiments = R.list_experiments()
key_map = {"IC": "IC",
"ICIR": "ICIR",
"Rank IC": "Rank_IC",
"Rank ICIR": "Rank_ICIR",
"excess_return_with_cost.annualized_return": "Annualized_Return",
"excess_return_with_cost.information_ratio": "Information_Ratio",
"excess_return_with_cost.max_drawdown": "Max_Drawdown"}
all_keys = list(key_map.values())
print("There are {:} experiments.".format(len(experiments)))
head_strs, value_strs, names = [], [], []
for idx, (key, experiment) in enumerate(experiments.items()):
if experiment.id == '0':
continue
recorders = experiment.list_recorders()
recorders, not_finished = filter_finished(recorders)
print(
"====>>>> {:02d}/{:02d}-th experiment {:9s} has {:02d}/{:02d} finished recorders.".format(
idx, len(experiments), experiment.name, len(recorders), len(recorders) + not_finished
)
)
result = QResult()
for recorder_id, recorder in recorders.items():
result.update(recorder.list_metrics(), key_map)
head_str, value_str = result.info(all_keys, show=False)
head_strs.append(head_str)
value_strs.append(value_str)
names.append(experiment.name)
compare_results(head_strs, value_strs, names, space=10)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Show Results")
parser.add_argument("--save_dir", type=str, default="./outputs/qlib-baselines", help="The checkpoint directory.")
args = parser.parse_args()
provider_uri = "~/.qlib/qlib_data/cn_data"
qlib.init(provider_uri=provider_uri, region=REG_CN)
main(args)

9
exps/trading/workflow_tt.py
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@@ -4,7 +4,7 @@
# Refer to:
# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.ipynb
# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
# python exps/trading/workflow_tt.py
# python exps/trading/workflow_tt.py --market all
#####################################################
import sys, argparse
from pathlib import Path
@@ -102,10 +102,9 @@ def main(xargs):
task = dict(model=model_config, dataset=dataset_config, record=record_config)
# start exp to train model
with R.start(experiment_name="tt_model", uri=xargs.save_dir):
set_log_basic_config(R.get_recorder().root_uri / 'log.log')
with R.start(experiment_name="tt_model", uri=xargs.save_dir + "-" + xargs.market):
set_log_basic_config(R.get_recorder().root_uri / "log.log")
model = init_instance_by_config(model_config)
dataset = init_instance_by_config(dataset_config)
@@ -138,7 +137,7 @@ if __name__ == "__main__":
parser.add_argument("--market", type=str, default="csi300", help="The market indicator.")
args = parser.parse_args()
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
provider_uri = "~/.qlib/qlib_data/cn_data"
qlib.init(provider_uri=provider_uri, region=REG_CN)
main(args)

4
lib/layers/positional_embedding.py
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@@ -24,12 +24,12 @@ class PositionalEncoder(nn.Module):
else:
pe[pos, i] = math.cos(value)
pe = pe.unsqueeze(0)
self.dropout = nn.Dropout(p=dropout)
self.register_buffer('pe', pe)
def forward(self, x):
batch, seq, fdim = x.shape[:3]
embeddings = self.pe[:, :seq, :fdim]
import pdb; pdb.set_trace()
outs = self.dropout(x + embeddings)
return x + embeddings
return outs

46
lib/trade_models/quant_transformer.py
View File

@@ -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:

2
lib/utils/__init__.py
View File

@@ -1,6 +1,6 @@
from .evaluation_utils import obtain_accuracy
from .gpu_manager import GPUManager
from .flop_benchmark import get_model_infos, count_parameters_in_MB
from .flop_benchmark import get_model_infos, count_parameters, count_parameters_in_MB
from .affine_utils import normalize_points, denormalize_points
from .affine_utils import identity2affine, solve2theta, affine2image
from .hash_utils import get_md5_file