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Add the HIST and IGMTF model on Alpha360 #1040

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merged 12 commits into from
Apr 13, 2022
Merged

Add the HIST and IGMTF model on Alpha360 #1040

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708f352
Commit the code of HIST and IGMTF on Alpha360
Wentao-Xu Apr 10, 2022
c321a68
add stock index
Wentao-Xu Apr 10, 2022
d08089d
Update README.md
Wentao-Xu Apr 10, 2022
985204a
delete useless code
Wentao-Xu Apr 10, 2022
e444304
Merge https://github.com/Wentao-Xu/qlib
Wentao-Xu Apr 10, 2022
70f937d
fix the bug of code format with black
Wentao-Xu Apr 11, 2022
6fc4539
Merge branch 'main' into main
Wentao-Xu Apr 11, 2022
15c584a
fix pylint bugs
Wentao-Xu Apr 12, 2022
04d68dc
Merge https://github.com/Wentao-Xu/qlib
Wentao-Xu Apr 12, 2022
8827e11
fix the bugs of pylint
Wentao-Xu Apr 13, 2022
6b2370d
fix pylint bugs
Wentao-Xu Apr 13, 2022
b1eb894
fix flake8
Wentao-Xu Apr 13, 2022
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fix the bug of code format with black
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@Wentao-Xu
Wentao-Xu committed Apr 11, 2022
commit 70f937d9fafd4fd1b90c24e378f452fcdc374480
114 changes: 57 additions & 57 deletions qlib/contrib/model/pytorch_hist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from typing import Text, Union
import urllib.request
import copy
from ...utils import get_or_create_path
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
import torch.nn as nn
Expand All @@ -24,6 +24,7 @@
from ...contrib.model.pytorch_lstm import LSTMModel
from ...contrib.model.pytorch_gru import GRUModel


class HIST(Model):
"""HIST Model

Expand Down Expand Up @@ -54,8 +55,8 @@ def __init__(
loss="mse",
base_model="GRU",
model_path=None,
stock2concept = None,
stock_index = None,
stock2concept=None,
stock_index=None,
optimizer="adam",
GPU=0,
seed=None,
Expand Down Expand Up @@ -130,7 +131,7 @@ def __init__(
dropout=self.dropout,
base_model=self.base_model,
)
self.logger.info("model:\n{:}".format(self.HIST_model))
self.logger.info("model:\n{:}".format(self.HIST_model))
self.logger.info("model size: {:.4f} MB".format(count_parameters(self.HIST_model)))
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.HIST_model.parameters(), lr=self.lr)
Expand All @@ -141,9 +142,9 @@ def __init__(

self.fitted = False
self.HIST_model.to(self.device)
@property
def use_gpu(self):

@property
def use_gpu(self):
return self.device != torch.device("cpu")

def mse(self, pred, label):
Expand All @@ -168,7 +169,7 @@ def metric_fn(self, pred, label):

vx = x - torch.mean(x)
vy = y - torch.mean(y)
return torch.sum(vx * vy) / (torch.sqrt(torch.sum(vx ** 2)) * torch.sqrt(torch.sum(vy ** 2)))
return torch.sum(vx * vy) / (torch.sqrt(torch.sum(vx**2)) * torch.sqrt(torch.sum(vy**2)))

if self.metric == "" or self.metric == "loss":
return -self.loss_fn(pred[mask], label[mask])
Expand All @@ -189,7 +190,7 @@ def get_daily_inter(self, df, shuffle=False):

def train_epoch(self, x_train, y_train, stock_index):

stock2concept_matrix = np.load(self.stock2concept)
stock2concept_matrix = np.load(self.stock2concept)
x_train_values = x_train.values
y_train_values = np.squeeze(y_train.values)
stock_index = stock_index.values
Expand All @@ -198,11 +199,11 @@ def train_epoch(self, x_train, y_train, stock_index):

# organize the train data into daily batches
daily_index, daily_count = self.get_daily_inter(x_train, shuffle=True)

for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
feature = torch.from_numpy(x_train_values[batch]).float().to(self.device)
concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index[batch]]).float().to(self.device)
concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index[batch]]).float().to(self.device)
label = torch.from_numpy(y_train_values[batch]).float().to(self.device)
pred = self.HIST_model(feature, concept_matrix)
loss = self.loss_fn(pred, label)
Expand Down Expand Up @@ -231,7 +232,7 @@ def test_epoch(self, data_x, data_y, stock_index):
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
feature = torch.from_numpy(x_values[batch]).float().to(self.device)
concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index[batch]]).float().to(self.device)
concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index[batch]]).float().to(self.device)
label = torch.from_numpy(y_values[batch]).float().to(self.device)
with torch.no_grad():
pred = self.HIST_model(feature, concept_matrix)
Expand All @@ -254,19 +255,18 @@ def fit(
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
if df_train.empty or df_valid.empty:
if df_train.empty or df_valid.empty:
raise ValueError("Empty data from dataset, please check your dataset config.")

if not os.path.exists(self.stock2concept):
url = 'http://fintech.msra.cn/stock_data/downloads/qlib_csi300_stock2concept.npy'
url = "http://fintech.msra.cn/stock_data/downloads/qlib_csi300_stock2concept.npy"
urllib.request.urlretrieve(url, self.stock2concept)


stock_index = np.load(self.stock_index, allow_pickle=True).item()
df_train['stock_index'] = 733
df_train['stock_index'] = df_train.index.get_level_values('instrument').map(stock_index)
df_valid['stock_index'] = 733
df_valid['stock_index'] = df_valid.index.get_level_values('instrument').map(stock_index)
df_train["stock_index"] = 733
df_train["stock_index"] = df_train.index.get_level_values("instrument").map(stock_index)
df_valid["stock_index"] = 733
df_valid["stock_index"] = df_valid.index.get_level_values("instrument").map(stock_index)

x_train, y_train, stock_index_train = df_train["feature"], df_train["label"], df_train["stock_index"]
x_valid, y_valid, stock_index_valid = df_valid["feature"], df_valid["label"], df_valid["stock_index"]
Expand Down Expand Up @@ -300,7 +300,7 @@ def fit(
# train
self.logger.info("training...")
self.fitted = True

for step in range(self.n_epochs):
self.logger.info("Epoch%d:", step)
self.logger.info("training...")
Expand Down Expand Up @@ -328,20 +328,19 @@ def fit(
self.HIST_model.load_state_dict(best_param)
torch.save(best_param, save_path)


def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")

stock2concept_matrix = np.load(self.stock2concept)
stock_index = np.load(self.stock_index, allow_pickle=True).item()
df_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
df_test['stock_index'] = 733
df_test['stock_index'] = df_test.index.get_level_values('instrument').map(stock_index)
stock_index_test = df_test['stock_index'].values
stock_index_test[np.isnan(stock_index_test)]=733
stock_index_test = stock_index_test.astype('int')
df_test = df_test.drop(['stock_index'], axis=1)
df_test["stock_index"] = 733
df_test["stock_index"] = df_test.index.get_level_values("instrument").map(stock_index)
stock_index_test = df_test["stock_index"].values
stock_index_test[np.isnan(stock_index_test)] = 733
stock_index_test = stock_index_test.astype("int")
df_test = df_test.drop(["stock_index"], axis=1)
index = df_test.index

self.HIST_model.eval()
Expand All @@ -354,7 +353,7 @@ def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
x_batch = torch.from_numpy(x_values[batch]).float().to(self.device)
concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index_test[batch]]).float().to(self.device)
concept_matrix = torch.from_numpy(stock2concept_matrix[stock_index_test[batch]]).float().to(self.device)

with torch.no_grad():
pred = self.HIST_model(x_batch, concept_matrix).detach().cpu().numpy()
Expand Down Expand Up @@ -415,44 +414,46 @@ def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_mod
torch.nn.init.xavier_uniform_(self.fc_indi.weight)

self.leaky_relu = nn.LeakyReLU()
self.softmax_s2t = torch.nn.Softmax(dim = 0)
self.softmax_t2s = torch.nn.Softmax(dim = 1)
self.softmax_s2t = torch.nn.Softmax(dim=0)
self.softmax_t2s = torch.nn.Softmax(dim=1)

self.fc_out_es = nn.Linear(hidden_size, 1)
self.fc_out_is = nn.Linear(hidden_size, 1)
self.fc_out_indi = nn.Linear(hidden_size, 1)
self.fc_out = nn.Linear(hidden_size, 1)

def cal_cos_similarity(self, x, y): # the 2nd dimension of x and y are the same
def cal_cos_similarity(self, x, y): # the 2nd dimension of x and y are the same
xy = x.mm(torch.t(y))
x_norm = torch.sqrt(torch.sum(x*x, dim =1)).reshape(-1, 1)
y_norm = torch.sqrt(torch.sum(y*y, dim =1)).reshape(-1, 1)
cos_similarity = xy/x_norm.mm(torch.t(y_norm))
x_norm = torch.sqrt(torch.sum(x * x, dim=1)).reshape(-1, 1)
y_norm = torch.sqrt(torch.sum(y * y, dim=1)).reshape(-1, 1)
cos_similarity = xy / x_norm.mm(torch.t(y_norm))
cos_similarity[cos_similarity != cos_similarity] = 0
return cos_similarity

def forward(self, x, concept_matrix):
device = torch.device(torch.get_device(x))

x_hidden = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
x_hidden = x_hidden.permute(0, 2, 1) # [N, T, F]
x_hidden = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
x_hidden = x_hidden.permute(0, 2, 1) # [N, T, F]
x_hidden, _ = self.rnn(x_hidden)
x_hidden = x_hidden[:, -1, :]

# Predefined Concept Module
stock_to_concept = concept_matrix

stock_to_concept = concept_matrix

stock_to_concept_sum = torch.sum(stock_to_concept, 0).reshape(1, -1).repeat(stock_to_concept.shape[0], 1)
stock_to_concept_sum = stock_to_concept_sum.mul(concept_matrix)

stock_to_concept_sum = stock_to_concept_sum + (torch.ones(stock_to_concept.shape[0], stock_to_concept.shape[1]).to(device))
stock_to_concept = stock_to_concept / stock_to_concept_sum #股票到tag的权重
hidden = torch.t(stock_to_concept).mm(x_hidden) #

hidden = hidden[hidden.sum(1)!=0]

concept_to_stock = self.cal_cos_similarity(x_hidden, hidden)
stock_to_concept_sum = stock_to_concept_sum + (
torch.ones(stock_to_concept.shape[0], stock_to_concept.shape[1]).to(device)
)
stock_to_concept = stock_to_concept / stock_to_concept_sum # 股票到tag的权重
hidden = torch.t(stock_to_concept).mm(x_hidden) #

hidden = hidden[hidden.sum(1) != 0]

concept_to_stock = self.cal_cos_similarity(x_hidden, hidden)
concept_to_stock = self.softmax_t2s(concept_to_stock)

e_shared_info = concept_to_stock.mm(hidden)
Expand All @@ -462,23 +463,22 @@ def forward(self, x, concept_matrix):
output_es = self.fc_es_fore(e_shared_info)
output_es = self.leaky_relu(output_es)


# Hidden Concept Module
i_shared_info = x_hidden - e_shared_back
hidden = i_shared_info #每个股票都有一个hidden的tag，所以有280个hidden tags。
i_stock_to_concept = self.cal_cos_similarity(i_shared_info, hidden)
hidden = i_shared_info # 每个股票都有一个hidden的tag，所以有280个hidden tags。
i_stock_to_concept = self.cal_cos_similarity(i_shared_info, hidden)
dim = i_stock_to_concept.shape[0]
diag = i_stock_to_concept.diagonal(0)
i_stock_to_concept = i_stock_to_concept * (torch.ones(dim, dim) - torch.eye(dim)).to(device)
row = torch.linspace(0,dim-1,dim).to(device).long()
column =i_stock_to_concept.max(1)[1].long()
row = torch.linspace(0, dim - 1, dim).to(device).long()
column = i_stock_to_concept.max(1)[1].long()
value = i_stock_to_concept.max(1)[0]
i_stock_to_concept[row, column] = 10
i_stock_to_concept[i_stock_to_concept!=10]=0
i_stock_to_concept[i_stock_to_concept != 10] = 0
i_stock_to_concept[row, column] = value
i_stock_to_concept = i_stock_to_concept + torch.diag_embed((i_stock_to_concept.sum(0)!=0).float()*diag)
i_stock_to_concept = i_stock_to_concept + torch.diag_embed((i_stock_to_concept.sum(0) != 0).float() * diag)
hidden = torch.t(i_shared_info).mm(i_stock_to_concept).t()
hidden = hidden[hidden.sum(1)!=0]
hidden = hidden[hidden.sum(1) != 0]

i_concept_to_stock = self.cal_cos_similarity(i_shared_info, hidden)
i_concept_to_stock = self.softmax_t2s(i_concept_to_stock)
Expand All @@ -490,7 +490,7 @@ def forward(self, x, concept_matrix):
output_is = self.leaky_relu(output_is)

# Individual Information Module
individual_info = x_hidden - e_shared_back - i_shared_back
individual_info = x_hidden - e_shared_back - i_shared_back
output_indi = individual_info
output_indi = self.fc_indi(output_indi)
output_indi = self.leaky_relu(output_indi)
Expand All @@ -500,4 +500,4 @@ def forward(self, x, concept_matrix):
all_info = output_es + output_is + output_indi
pred_all = self.fc_out(all_info).squeeze()

return pred_all
return pred_all
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