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Remove batchsize and add daily-batch mode

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meng-ustc
2020-11-26 20:22:51 +08:00
parent 99efaadd38
commit ab98f44345
3 changed files with 30 additions and 31 deletions
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57
qlib/contrib/model/pytorch_hats.py
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@@ -54,7 +54,6 @@ class HATS(Model):
n_epochs=200,
lr=0.01,
metric="IC",
batch_size=800,
early_stop=20,
loss="mse",
base_model="GRU",
@@ -76,7 +75,6 @@ class HATS(Model):
self.n_epochs = n_epochs
self.lr = lr
self.metric = metric
self.batch_size = batch_size
self.early_stop = early_stop
self.optimizer = optimizer.lower()
self.loss = loss
@@ -95,7 +93,6 @@ class HATS(Model):
"\nn_epochs : {}"
"\nlr : {}"
"\nmetric : {}"
"\nbatch_size : {}"
"\nearly_stop : {}"
"\noptimizer : {}"
"\nloss_type : {}"
@@ -111,7 +108,6 @@ class HATS(Model):
n_epochs,
lr,
metric,
batch_size,
early_stop,
optimizer.lower(),
loss,
@@ -169,6 +165,18 @@ class HATS(Model):
def cal_ic(self, pred, label):
return torch.mean(pred * label)
def get_daily_inter(self, df, shuffle=False):
# organize the train data into daily inter as daily batches
daily_count = df.groupby(level=0).size().values
daily_index = np.roll(np.cumsum(daily_count), 1)
daily_index[0] = 0
if shuffle:
# shuffle the daily inter data
daily_shuffle = list(zip(daily_index, daily_count))
np.random.shuffle(daily_shuffle)
daily_index, daily_count = zip(*daily_shuffle)
return daily_index, daily_count
def train_epoch(self, x_train, y_train):
x_train_values = x_train.values
@@ -176,16 +184,13 @@ class HATS(Model):
self.HATS_model.train()
indices = np.arange(len(x_train_values))
np.random.shuffle(indices)
# organize the train data into daily inter as daily batches
daily_index, daily_count = self.get_daily_inter(x_train, shuffle=True)
for i in range(len(indices))[:: self.batch_size]:
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()
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
feature = torch.from_numpy(x_train_values[batch]).float()
label = torch.from_numpy(y_train_values[batch]).float()
if self.use_gpu:
feature = feature.cuda()
@@ -210,15 +215,13 @@ class HATS(Model):
scores = []
losses = []
indices = np.arange(len(x_values))
# organize the test data into daily inter as daily batches
daily_index, daily_count = self.get_daily_inter(data_x, shuffle=False)
for i in range(len(indices))[:: self.batch_size]:
if len(indices) - i < self.batch_size:
break
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()
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
feature = torch.from_numpy(x_values[batch]).float()
label = torch.from_numpy(y_values[batch]).float()
if self.use_gpu:
feature = feature.cuda()
@@ -317,14 +320,12 @@ class HATS(Model):
sample_num = x_values.shape[0]
preds = []
for begin in range(sample_num)[:: self.batch_size]:
# organize the data into daily inter as daily batches
daily_index, daily_count = self.get_daily_inter(x_test, shuffle=False)
if sample_num - begin < self.batch_size:
end = sample_num
else:
end = begin + self.batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float()
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
x_batch = torch.from_numpy(x_values[batch]).float()
if self.use_gpu:
x_batch = x_batch.cuda()