Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.
opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
719 if force_all_finite:
720 _assert_all_finite(array,
--> 721 allow_nan=force_all_finite == 'allow-nan')
722
723 if ensure_min_samples > 0:
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/utils/validation.py in _assert_all_finite(X, allow_nan, msg_dtype)
104 msg_err.format
105 (type_err,
--> 106 msg_dtype if msg_dtype is not None else X.dtype)
107 )
108 # for object dtype data, we only check for NaNs (GH-13254)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
报错: