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update

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Alex Wang
2020-11-25 19:46:48 +08:00
parent 2c403943b2
commit 64b7748033
2 changed files with 91 additions and 65 deletions
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32
examples/workflow_by_code_sfm.py
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@@ -1,5 +1,15 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# Copyright (c) Microsoft Corporation.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from pathlib import Path
@@ -61,22 +71,22 @@ if __name__ == "__main__":
"module_path": "qlib.contrib.model.pytorch_sfm",
"kwargs": {
"d_feat": 6,
"hidden_size": 64,
"output_dim": 1,
"freq_dim": 15,
"hidden_size": 32,
"output_dim" : 16,
"freq_dim" : 25,
"dropout_W": 0.5,
"dropout_U": 0.5,
"n_epochs": 10,
"n_epochs": 200,
"lr": 1e-3,
"batch_size": 800,
"batch_size": 200,
"early_stop": 20,
"eval_steps": 5,
"loss": "mse",
"lr_decay": 0.96,
"lr_decay_steps": 100,
"optimizer": "gd",
"lr_decay" : 0.96,
"lr_decay_steps" : 100,
"optimizer" : "adam",
"GPU": 1,
"seed": 0,
"seed": 710,
},
},
"dataset": {

124
qlib/contrib/model/pytorch_sfm.py
View File

@@ -21,12 +21,11 @@ from ...model.base import Model
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
class SFM_Model(nn.Module):
def __init__(self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu"):
def __init__(self, d_feat=6, output_dim = 1, freq_dim = 10, hidden_size = 64, dropout_W = 0.0, dropout_U = 0.0, device = "cpu"):
super().__init__()
self.input_dim = d_feat
self.input_dim = d_feat
self.output_dim = output_dim
self.freq_dim = freq_dim
self.hidden_dim = hidden_size
@@ -57,22 +56,22 @@ class SFM_Model(nn.Module):
self.W_p = nn.Parameter(init.xavier_uniform_(torch.empty(self.hidden_dim, self.output_dim)))
self.b_p = nn.Parameter(torch.zeros(self.output_dim))
self.activation = nn.Tanh()
self.inner_activation = nn.Hardsigmoid()
self.dropout_W, self.dropout_U = (dropout_W, dropout_U)
self.fc_out = nn.Linear(self.output_dim, 1)
self.states = []
def forward(self, input):
input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
input = input.permute(0, 2, 1) # [N, T, F]
input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
input = input.permute(0, 2, 1) # [N, T, F]
time_step = input.shape[1]
for ts in range(time_step):
x = input[:, ts, :]
if len(self.states) == 0: # hasn't initialized yet
x = input[:, ts,:]
if(len(self.states)==0): #hasn't initialized yet
self.init_states(x)
self.get_constants(x)
p_tm1 = self.states[0]
@@ -89,79 +88,77 @@ class SFM_Model(nn.Module):
x_fre = torch.matmul(x * B_W[0], self.W_fre) + self.b_fre
x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
i = self.inner_activation(
x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)
) # not sure whether I am doing in the right unsquuze
i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)) # not sure whether I am doing in the right unsquuze
ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
ste = torch.reshape(ste, (-1, self.hidden_dim, 1))
fre = torch.reshape(fre, (-1, 1, self.freq_dim))
f = ste * fre
c = i * self.activation(x_c + torch.matmul(h_tm1 * B_U[0], self.U_c))
time = time_tm1 + 1
omega = torch.tensor(2 * np.pi) * time * frequency
re = torch.cos(omega)
re = torch.cos(omega)
im = torch.sin(omega)
c = torch.reshape(c, (-1, self.hidden_dim, 1))
S_re = f * S_re_tm1 + c * re
S_im = f * S_im_tm1 + c * im
A = torch.square(S_re) + torch.square(S_im)
A = torch.reshape(A, (-1, self.freq_dim)).float()
A_a = torch.matmul(A * B_U[0], self.U_a)
A_a = torch.reshape(A_a, (-1, self.hidden_dim))
a = self.activation(A_a + self.b_a)
o = self.inner_activation(x_o + torch.matmul(h_tm1 * B_U[0], self.U_o))
h = o * a
p = torch.matmul(h, self.W_p) + self.b_p
self.states = [p, h, S_re, S_im, time, None, None, None]
self.states = []
self.states = []
return self.fc_out(p).squeeze()
def init_states(self, x):
reducer_f = torch.zeros((self.hidden_dim, self.freq_dim)).to(self.device)
reducer_p = torch.zeros((self.hidden_dim, self.output_dim)).to(self.device)
init_state_h = torch.zeros(self.hidden_dim).to(self.device)
init_state_p = torch.matmul(init_state_h, reducer_p)
init_state = torch.zeros_like(init_state_h).to(self.device)
init_freq = torch.matmul(init_state_h, reducer_f)
init_state = torch.reshape(init_state, (-1, self.hidden_dim, 1))
init_freq = torch.reshape(init_freq, (-1, 1, self.freq_dim))
init_state_S_re = init_state * init_freq
init_state_S_im = init_state * init_freq
init_state_time = torch.tensor(0).to(self.device)
self.states = [init_state_p, init_state_h, init_state_S_re, init_state_S_im, init_state_time, None, None, None]
def get_constants(self, x):
constants = []
constants.append([torch.tensor(1.0).to(self.device) for _ in range(6)])
constants.append([torch.tensor(1.0).to(self.device) for _ in range(7)])
array = np.array([float(ii) / self.freq_dim for ii in range(self.freq_dim)])
constants.append([torch.tensor(1.).to(self.device) for _ in range(6)])
constants.append([torch.tensor(1.).to(self.device) for _ in range(7)])
array = np.array([float(ii)/self.freq_dim for ii in range(self.freq_dim)])
constants.append(torch.tensor(array).to(self.device))
self.states[5:] = constants
class SFM(Model):
"""SFM Model
@@ -188,7 +185,7 @@ class SFM(Model):
d_feat=6,
hidden_size=64,
output_dim=1,
freq_dim=10,
freq_dim = 10,
dropout_W=0.0,
dropout_U=0.0,
n_epochs=200,
@@ -224,7 +221,7 @@ class SFM(Model):
self.lr_decay_steps = lr_decay_steps
self.optimizer = optimizer.lower()
self.loss_type = loss
self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.device = 'cuda:%d'%(GPU) if torch.cuda.is_available() else 'cpu'
self.use_gpu = torch.cuda.is_available()
self.seed = seed
@@ -232,7 +229,8 @@ class SFM(Model):
"SFM parameters setting:"
"\nd_feat : {}"
"\nhidden_size : {}"
"\nfrequency_dimension : {}"
"\noutput_size : {}"
"\nfrequency_dimension : {}"
"\ndropout_W: {}"
"\ndropout_U: {}"
"\nn_epochs : {}"
@@ -249,6 +247,7 @@ class SFM(Model):
"\nseed : {}".format(
d_feat,
hidden_size,
output_dim,
freq_dim,
dropout_W,
dropout_U,
@@ -272,14 +271,14 @@ class SFM(Model):
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.sfm_model = SFM_Model(
d_feat=self.d_feat,
output_dim=self.output_dim,
hidden_size=self.hidden_size,
freq_dim=self.freq_dim,
dropout_W=self.dropout_W,
dropout_U=self.dropout_U,
device=self.device,
)
d_feat=self.d_feat,
output_dim = self.output_dim,
hidden_size = self.hidden_size,
freq_dim = self.freq_dim,
dropout_W=self.dropout_W,
dropout_U = self.dropout_U,
device = self.device
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
elif optimizer.lower() == "gd":
@@ -304,7 +303,14 @@ class SFM(Model):
self._fitted = False
self.sfm_model.to(self.device)
def fit(self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, **kwargs):
def fit(
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
**kwargs
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
@@ -360,7 +366,6 @@ class SFM(Model):
# validation
train_loss += loss.val
# print(loss.val)
if step and step % self.eval_steps == 0:
stop_steps += 1
train_loss /= self.eval_steps
@@ -394,12 +399,12 @@ class SFM(Model):
# update learning rate
self.scheduler.step(cur_loss_val)
if self.device != "cpu":
if self.device != 'cpu':
torch.cuda.empty_cache()
def get_loss(self, pred, target, loss_type):
if loss_type == "mse":
sqr_loss = (pred - target) ** 2
sqr_loss = (pred - target)**2
loss = sqr_loss.mean()
return loss
elif loss_type == "binary":
@@ -414,17 +419,30 @@ class SFM(Model):
x_test = dataset.prepare("test", col_set="feature")
index = x_test.index
x_test = torch.from_numpy(x_test.values).float()
x_test = x_test.to(self.device)
self.sfm_model.eval()
x_values = x_test.values
sample_num = x_values.shape[0]
preds = []
with torch.no_grad():
if self.device != "cpu":
preds = self.sfm_model(x_test).detach().cpu().numpy()
for begin in range(sample_num)[::self.batch_size]:
if sample_num-begin<self.batch_size:
end = sample_num
else:
preds = self.sfm_model(x_test).detach().numpy()
return pd.Series(preds, index=index)
end = begin + self.batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float()
if self.device != 'cpu':
x_batch = x_batch.to(self.device)
with torch.no_grad():
if self.device != 'cpu':
pred = self.sfm_model(x_batch).detach().cpu().numpy()
else:
pred = self.sfm_model(x_batch).detach().cpu().numpy()
preds.append(pred)
return pd.Series(np.concatenate(preds), index=index)
def save(self, filename, **kwargs):
with save_multiple_parts_file(filename) as model_dir:
@@ -443,10 +461,8 @@ class SFM(Model):
self.sfm_model.load_state_dict(torch.load(_model_path))
self._fitted = True
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()