Open JKYtydt opened 4 months ago
elephaint
commented
4 months ago Hi, thanks for using neuralforecast.
Can you include a fully reproducible example of code (a piece of code that I can copy-paste and run standalone on my machine), demonstrating the error? Otherwise it's very difficult for me to help.
JKYtydt
commented
4 months ago 您好，感谢您使用神经预测。
您能否提供一个完全可重现的代码示例（一段我可以复制粘贴并在我的计算机上独立运行的代码）来演示该错误？不然我很难帮忙。
您好，这是完整的代码，麻烦你了 train_df = pd.read_csv('/sdc/jky/llm_demo/coin_BinanceCoin.csv',encoding='utf-8') print('etth1', train_df.head()) model = iTransformer(h=12, input_size=36, n_series=8, hidden_size=128, n_heads=8, e_layers=2, d_layers=1, d_ff=4, factor=1, dropout=0.1, use_norm=True, loss=MSE(), valid_loss=MSE(), early_stop_patience_steps=3, batch_size=24, max_steps=5)
nf = NeuralForecast( models=[model], freq='D' )
nf.fit(df=train_df, val_size=12)
nf.save(path=save_path, model_index=None, overwrite=True, save_dataset=True) print('模型保存完成') Y_hat_insample = nf.predict_insample(step_size=pred_len) print('Y_hat_insample' ,Y_hat_insample.head(),Y_hat_insample.shape) Y_hat_insample = pd.DataFrame(Y_hat_insample)
Y_hat_insample['unique_id'] = Y_hat_insample.index mae = mae(Y_hat_insample,models=['iTransformer'],id_col='unique_id') mse = mse(Y_hat_insample,models=['iTransformer'],id_col='unique_id') validation_df = pd.DataFrame(data={'MAE': mae['iTransformer'], 'MSE': mse['iTransformer']})
JKYtydt
commented
4 months ago 不好意思，我又遇到一个新的问题，在进行iTransformer预测时发现torch版本不能低于2.1.0，如果低于会有如下报错
Traceback (most recent call last):
File "/data/anaconda3/envs/time_py/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 600, in _run_script
exec(code, module.dict)
File "/sdc/jky/llm_demo/pages/2_Forecast.py", line 40, in
elephaint
commented
4 months ago Can you upgrade your Pytorch version to the latest version? I think you might have PyTorch 2.0.0 and it seems there is an issue with our code in that version. So upgrading to 2.1+ should fix the issue.
JKYtydt
commented
4 months ago 你能将你的 Pytorch 版本升级到最新版本吗？我认为你可能使用的是 PyTorch 2.0.0，而且该版本的代码似乎存在问题。因此升级到 2.1+ 应该可以解决问题。
1、您好，您说的我都已经试过了，如果版本更新到2.1+以上，则无法训练，报错信息我已经粘贴在上述问题中 2、关于第一个问题，我依旧无法进行样本内预测
eye4got
commented
3 months ago I was able to reproduce the same error and it appears to be the result of not having consistent sample sizes for each unique_id. cchallu explains potential solutions very effectively in #718. I do think some kind of check during training would be helpful.
JKYtydt
commented
3 months ago @eye4got 您好，非常感谢您提供的建议，您方便告诉一下具体的解决办法么
eye4got
commented
3 months ago @JKYtydt you need to make every unique_id have the same number of time steps. So you can either: 1) cut all the timeseries down to the same length and/or not include some shorter timeseries, so that they are all say 100 days long; 2) use some kind of interpolation or imputation method to fill missing data so they are all the same length. I honestly switched to using the darts package when I found out that neuralforecast doesn't support multivariate forecasting for TFT models. It is much more flexible when it comes to restrictions like this
JKYtydt
commented
3 months ago @eye4got 您好，我的这个数据集unique_id是唯一的，也没有缺失值，但是它仍然存在这个报错，且发现这个形状的差别刚好是输入的序列长度，这有什么关联么
test.py 68 <module>
Y_hat_insample = nf.predict_insample(step_size=12)
core.py 1175 predict_insample
fcsts[:, col_idx : (col_idx + output_length)] = model_fcsts
ValueError:
could not broadcast input array from shape (1416,1) into shape (1440,1)
What happened + What you expected to happen
Traceback (most recent call last): File "/data/anaconda3/envs/time_py/lib/python3.8/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 541, in _run_script exec(code, module.dict) File "/sdc/jky/llm_demo/pages/1_Training.py", line 90, in
Y_hat_insample = nf.predict_insample(step_size=pred_len)
File "/data/anaconda3/envs/time_py/lib/python3.8/site-packages/neuralforecast/core.py", line 1175, in predict_insample
fcsts[:, col_idx : (col_idx + output_length)] = model_fcsts
ValueError: could not broadcast input array from shape (1404,1) into shape (1440,1)
在进行样本内预测时发生了报错，但是书写代码并没有发现问题
Versions / Dependencies
python=3.8 neuralforecast=1.7.2
Reproduction script
nf = NeuralForecast( models=[model], freq=freq )
Issue Severity
None