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Update all baseline models.

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This commit is contained in:
lwwang1995
2020-11-27 22:30:05 +08:00
parent 7952d79932
commit bebce24a7c
17 changed files with 282 additions and 856 deletions
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34
qlib/contrib/model/pytorch_gru.py
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@@ -11,7 +11,12 @@ import pandas as pd
import copy
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, drop_nan_by_y_index
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
import torch
@@ -109,14 +114,19 @@ class GRU(Model):
)
self.gru_model = GRUModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr)
elif optimizer.lower() == "gd":
self.train_optimizer = optim.SGD(self.gru_model.parameters(), lr=self.lr)
else:
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
raise NotImplementedError(
"optimizer {} is not supported!".format(optimizer)
)
self._fitted = False
if self.use_gpu:
@@ -141,7 +151,7 @@ class GRU(Model):
mask = torch.isfinite(label)
if self.metric == "" or self.metric == "loss": # use loss
if self.metric == "" or self.metric == "loss":
return -self.loss_fn(pred[mask], label[mask])
raise ValueError("unknown metric `%s`" % self.metric)
@@ -161,8 +171,12 @@ class GRU(Model):
if len(indices) - i < self.batch_size:
break
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float()
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float()
feature = torch.from_numpy(
x_train_values[indices[i : i + self.batch_size]]
).float()
label = torch.from_numpy(
y_train_values[indices[i : i + self.batch_size]]
).float()
if self.use_gpu:
feature = feature.cuda()
@@ -194,7 +208,9 @@ class GRU(Model):
if len(indices) - i < self.batch_size:
break
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float()
feature = torch.from_numpy(
x_values[indices[i : i + self.batch_size]]
).float()
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float()
if self.use_gpu:
@@ -219,7 +235,9 @@ class GRU(Model):
):
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
x_train, y_train = df_train["feature"], df_train["label"]