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Update TCTS. (#495)

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* Update TCTS Model.

Co-authored-by: lewwang <lwwang@microsoft.com>
This commit is contained in:
Lewen Wang
2021-07-04 16:45:05 +08:00
committed by GitHub
parent 2d4f0e80f9
commit ace7484304
10 changed files with 117 additions and 156 deletions
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107
qlib/contrib/model/pytorch_tcts.py
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@@ -9,12 +9,13 @@ import os
import numpy as np
import pandas as pd
import copy
import random
from sklearn.metrics import roc_auc_score, mean_squared_error
import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -60,8 +61,9 @@ class TCTS(Model):
weight_lr=5e-7,
steps=3,
GPU=0,
seed=None,
seed=0,
target_label=0,
lowest_valid_performance=0.993,
**kwargs
):
# Set logger.
@@ -85,6 +87,9 @@ class TCTS(Model):
self.weight_lr = weight_lr
self.steps = steps
self.target_label = target_label
self.lowest_valid_performance = lowest_valid_performance
self._fore_optimizer = fore_optimizer
self._weight_optimizer = weight_optimizer
self.logger.info(
"TCTS parameters setting:"
@@ -113,40 +118,6 @@ class TCTS(Model):
)
)
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.fore_model = GRUModel(
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
)
self.weight_model = MLPModel(
d_feat=360 + 2 * self.output_dim + 1,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
output_dim=self.output_dim,
)
if fore_optimizer.lower() == "adam":
self.fore_optimizer = optim.Adam(self.fore_model.parameters(), lr=self.fore_lr)
elif fore_optimizer.lower() == "gd":
self.fore_optimizer = optim.SGD(self.fore_model.parameters(), lr=self.fore_lr)
else:
raise NotImplementedError("optimizer {} is not supported!".format(fore_optimizer))
if weight_optimizer.lower() == "adam":
self.weight_optimizer = optim.Adam(self.weight_model.parameters(), lr=self.weight_lr)
elif weight_optimizer.lower() == "gd":
self.weight_optimizer = optim.SGD(self.weight_model.parameters(), lr=self.weight_lr)
else:
raise NotImplementedError("optimizer {} is not supported!".format(weight_optimizer))
self.fitted = False
self.fore_model.to(self.device)
self.weight_model.to(self.device)
def loss_fn(self, pred, label, weight):
loc = torch.argmax(weight, 1)
@@ -258,11 +229,9 @@ class TCTS(Model):
def fit(
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"],
col_set=["feature", "label"],
@@ -274,7 +243,62 @@ class TCTS(Model):
x_test, y_test = df_test["feature"], df_test["label"]
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
best_loss = np.inf
while best_loss > self.lowest_valid_performance:
if best_loss < np.inf:
print("Failed! Start retraining.")
self.seed = random.randint(0, 1000) # reset random seed
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
best_loss = self.training(
x_train, y_train, x_valid, y_valid, x_test, y_test, verbose=verbose, save_path=save_path
)
def training(
self,
x_train,
y_train,
x_valid,
y_valid,
x_test,
y_test,
verbose=True,
save_path=None,
):
self.fore_model = GRUModel(
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
)
self.weight_model = MLPModel(
d_feat=360 + 2 * self.output_dim + 1,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
output_dim=self.output_dim,
)
if self._fore_optimizer.lower() == "adam":
self.fore_optimizer = optim.Adam(self.fore_model.parameters(), lr=self.fore_lr)
elif self._fore_optimizer.lower() == "gd":
self.fore_optimizer = optim.SGD(self.fore_model.parameters(), lr=self.fore_lr)
else:
raise NotImplementedError("optimizer {} is not supported!".format(self._fore_optimizer))
if self._weight_optimizer.lower() == "adam":
self.weight_optimizer = optim.Adam(self.weight_model.parameters(), lr=self.weight_lr)
elif self._weight_optimizer.lower() == "gd":
self.weight_optimizer = optim.SGD(self.weight_model.parameters(), lr=self.weight_lr)
else:
raise NotImplementedError("optimizer {} is not supported!".format(self._weight_optimizer))
self.fitted = False
self.fore_model.to(self.device)
self.weight_model.to(self.device)
best_loss = np.inf
best_epoch = 0
@@ -291,7 +315,8 @@ class TCTS(Model):
val_loss = self.test_epoch(x_valid, y_valid)
test_loss = self.test_epoch(x_test, y_test)
print("valid %.6f, test %.6f" % (val_loss, test_loss))
if verbose:
print("valid %.6f, test %.6f" % (val_loss, test_loss))
if val_loss < best_loss:
best_loss = val_loss
@@ -316,6 +341,8 @@ class TCTS(Model):
if self.use_gpu:
torch.cuda.empty_cache()
return best_loss
def predict(self, dataset):
if not self.fitted:
raise ValueError("model is not fitted yet!")