使用TFT模型进行时序预测的output_quantiles维度问题
IgnoranceSmile opened this issue · 2 comments
是否output_quantiles只支持长度为1和3，测试在其他长度下均会报错。
示例代码：
import pandas as pd
import numpy as np
from paddlets import TSDataset
x = np.linspace(-np.pi, np.pi, 200)
sinx = np.sin(x) * 4 + np.random.randn(200)
df = pd.DataFrame(
{
'time_col': pd.date_range('2022-01-01', periods=200, freq='1h'),
'value': sinx
}
)
target_dataset = TSDataset.load_from_dataframe(
df, #Also can be path to the CSV file
time_col='time_col',
target_cols='value',
freq='1h'
)
new_line = pd.Series(
np.array(range(210)),
index=pd.date_range('2022-01-01', periods=210, freq='1h')
)
target_dataset.set_column(
column='new_b',
value=new_line,
type='known_cov'
)
train_ts, val_ts = target_dataset.split(0.7)
from paddlets.models.forecasting import TransformerModel, TFTModel, RNNBlockRegressor
model = TFTModel(
in_chunk_len = 10,
out_chunk_len = 10,
output_quantiles = [0.1, 0.2]
)
model.fit(train_ts)
报错：
ValueError Traceback (most recent call last)
/tmp/ipykernel_20005/1436158038.py in
6 output_quantiles = [0.1, 0.2]
7 )
----> 8 model.fit(train_ts)
/usr/local/lib/python3.7/dist-packages/paddlets/models/forecasting/dl/paddle_base_impl.py in fit(self, train_tsdataset, valid_tsdataset)
344 self._check_multi_tsdataset(valid_tsdataset)
345 train_dataloader, valid_dataloaders = self._init_fit_dataloaders(train_tsdataset, valid_tsdataset)
--> 346 self._fit(train_dataloader, valid_dataloaders)
347
348 def _fit(
/usr/local/lib/python3.7/dist-packages/paddlets/models/forecasting/dl/paddle_base_impl.py in _fit(self, train_dataloader, valid_dataloaders)
370 # Call the on_epoch_begin method of each callback before the epoch starts.
371 self._callback_container.on_epoch_begin(epoch_idx)
--> 372 self._train_epoch(train_dataloader)
373
374 # Predict for each eval set.
/usr/local/lib/python3.7/dist-packages/paddlets/models/forecasting/dl/paddle_base_impl.py in _train_epoch(self, train_loader)
438 self._callback_container.on_batch_begin(batch_idx)
439 X, y = self._prepare_X_y(data)
--> 440 batch_logs = self._train_batch(X, y)
...
301 comment = OpProtoHolder.instance().get_op_proto(op_type).comment
ValueError: (InvalidArgument) Broadcast dimension mismatch. Operands could not be broadcast together with the shape of X = [3] and the shape of Y = [121, 10, 1, 2]. Received [3] in X is not equal to [2] in Y at i:3.
[Hint: Expected x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 || y_dims_array[i] <= 1 == true, but received x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 || y_dims_array[i] <= 1:0 != true:1.] (at /paddle/paddle/phi/kernels/funcs/common_shape.h:84)
[operator < elementwise_mul > error]
您好，我们跟进复现下您的问题。
您好，这个是因为损失函数使用了默认的3维度的quantiles。如果希望使用2维度，可以修改这行代码的quantiles。这个问题我们也会排期修复。