Closed roijalbaker closed 1 month ago
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@roijalbaker This error is caused by a recent TSDB update. To make things work, you should install tsdb==0.3.1, rather than v0.4.
@WenjieDu 我也遇到了同样的问题,安装了tsdb==0.3.1还是报错
@WenjieDu 我print了key值,如图所示
@WenjieDu 我也遇到了同样的问题,安装了tsdb==0.3.1还是报错
@V50-ikun After downgrading from v0.4 to v0.3.1, please remove all files with .pkl
suffix under ~/.tsdb
. You can run with the command rm -rf ~/.tsdb/*/*.pkl
, or directly delete ~/.tsdb
the whole directory.
@WenjieDu 问题解决了,非常感谢!
Sadly, i've installed v 0.3.1 and there isn't any file '.pkl' in tsbd. Nevertheless, same issue is occouring.
@Gabriel-Lucena96 It works fine. Take a look at the below screenshot.
@WenjieDu, thank you. It works just fine! One more thing I'd like to know... what's the difference among 'test_X', 'test_X_ori' and 'test_X_indicating_mask' (boolean matrix)?
@Gabriel-Lucena96 no problem. If PyPOTS is useful to you, please start 🌟 our repositories to help more people notice our work. You can also follow me on GitHub to receive the latest news from PyPOTS in the future.
X_ori
stands for X original
, which is from original X with of course original missing values if any. X
is X_ori
added with additional artificial missing data, X
is for model input, X_ori
is for error calculation and model validation, indicating_mask
tells the difference between X and X_ori, i.e. which part is artificially masked out.
Already gave 🌟 and followed :) I'm sorry the insistance, just trying to understand the data... But specifically obout physionet2012, original data has no missing values, correct? I checked:
print(np.isnan(data['test_X_ori']).any()) # False
If I understood correctly, the mask should only be 'True' if it is a added missing value (difference between original and test). Nevertheless, I've checked
bool_ori = np.isnan(data['test_X_ori'])
bool_test = np.isnan(data['test_X'])
MASK = ~(bool_ori == bool_test) # Only True when added missing data
equal = (data['test_X_indicating_mask'] == MASK) # Should be all True
print(equal.all()) # False
and it didn't matchup. Am I getting the wrong idea?
Ohh, and have you tested your algorithm in univariated TS?
@Gabriel-Lucena96 physionet2012 original data has ~80% missing data. In the old version of gene_physionet2012(), we gene indicating mask then fill X_ori with 0, hence your testing has no problem. We use metric functions in pypots.utils.metrics
that you can refer to.
1. System Info
Using the Google Colab space
2. Information
3. Reproduction
Run this script in Google CoLab
4. Expected behavior
I expect no error in the second cell of the notebook