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Upgrade spaces and add more tests

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This commit is contained in:
D-X-Y
2021-03-18 15:04:14 +08:00
parent 85ee0ad4eb
commit 38409e602f
12 changed files with 386 additions and 84 deletions
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24
lib/trade_models/naive_v1_model.py
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@@ -1,6 +1,8 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021 #
##################################################
# Use noise as prediction #
##################################################
from __future__ import division
from __future__ import print_function
@@ -27,7 +29,11 @@ class NAIVE_V1(Model):
self.d_feat = d_feat
self.seed = seed
self.logger.info("NAIVE-V1 parameters setting: d_feat={:}, seed={:}".format(self.d_feat, self.seed))
self.logger.info(
"NAIVE-V1 parameters setting: d_feat={:}, seed={:}".format(
self.d_feat, self.seed
)
)
if self.seed is not None:
random.seed(self.seed)
@@ -49,7 +55,9 @@ class NAIVE_V1(Model):
def model(self, x):
num = len(x)
return np.random.normal(loc=self._mean, scale=self._std, size=num).astype(x.dtype)
return np.random.normal(loc=self._mean, scale=self._std, size=num).astype(
x.dtype
)
def fit(self, dataset: DatasetH):
def _prepare_dataset(df_data):
@@ -71,9 +79,15 @@ class NAIVE_V1(Model):
# df_train['feature']['CLOSE1'].values
# train_dataset['features'][:, -1]
masks = ~np.isnan(train_dataset["labels"])
self._mean, self._std = np.mean(train_dataset["labels"][masks]), np.std(train_dataset["labels"][masks])
train_mse_loss = self.mse(self.model(train_dataset["features"]), train_dataset["labels"])
valid_mse_loss = self.mse(self.model(valid_dataset["features"]), valid_dataset["labels"])
self._mean, self._std = np.mean(train_dataset["labels"][masks]), np.std(
train_dataset["labels"][masks]
)
train_mse_loss = self.mse(
self.model(train_dataset["features"]), train_dataset["labels"]
)
valid_mse_loss = self.mse(
self.model(valid_dataset["features"]), valid_dataset["labels"]
)
self.logger.info("Training MSE loss: {:}".format(train_mse_loss))
self.logger.info("Validation MSE loss: {:}".format(valid_mse_loss))
self.fitted = True