PyPOTS - A Python Toolbox for Data Mining on Partially-Observed Time Series

Last update: Dec 31, 2022

Overview

Welcome to PyPOTS

A Python Toolbox for Data Mining on Partially-Observed Time Series

⦿ Motivation: Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this blank.

⦿ Mission: PyPOTS is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS is going to have unified APIs together with detailed documentation and interactive examples across algorithms as tutorials.

To make various open-source time-series datasets readily available to our users, PyPOTS gets supported by project TSDB (Time-Series DataBase), a toolbox making loading time-series datasets super easy!

Visit TSDB right now to know more about this handy tool 🛠 !

❖ Installation

Install the latest release from PyPI:

pip install pypots

Install with the latest code on GitHub:

pip install https://github.com/WenjieDu/PyPOTS/archive/main.zip

❖ Available Algorithms

Task	Type	Algorithm	Year	Reference
Imputation	Neural Network	SAITS: Self-Attention-based Imputation for Time Series	2022	¹
Imputation	Neural Network	Transformer	2017	² ¹
Imputation, Classification	Neural Network	BRITS (Bidirectional Recurrent Imputation for Time Series)	2018	³
Imputation	Naive	LOCF (Last Observation Carried Forward)	-	-
Classification	Neural Network	GRU-D	2018	⁴
Classification	Neural Network	Raindrop	2022	⁵
Clustering	Neural Network	CRLI (Clustering Representation Learning on Incomplete time-series data)	2021	⁶
Clustering	Neural Network	VaDER (Variational Deep Embedding with Recurrence)	2019	⁷
Forecasting	Probabilistic	BTTF (Bayesian Temporal Tensor Factorization)	2021	⁸

‼️ PyPOTS is currently under developing. If you like it and look forward to its growth, please give PyPOTS a star and watch it to keep you posted on its progress and to let me know that its development is meaningful. If you have any feedback, or want to contribute ideas/suggestions or share time-series related algorithms/papers, please join PyPOTS community and , or drop me an email.

Thank you all for your attention! 😃

Du, W., Cote, D., & Liu, Y. (2022). SAITS: Self-Attention-based Imputation for Time Series. ArXiv, abs/2202.08516. ↩ ↩ ²
Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention is All you Need. NeurIPS 2017. ↩
Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018). BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018. ↩
Che, Z., Purushotham, S., Cho, K., Sontag, D.A., & Liu, Y. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports, 8. ↩
Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022). Graph-Guided Network for Irregularly Sampled Multivariate Time Series. ICLR 2022. ↩
Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021). Learning Representations for Incomplete Time Series Clustering. AAAI 2021. ↩
Jong, J.D., Emon, M.A., Wu, P., Karki, R., Sood, M., Godard, P., Ahmad, A., Vrooman, H.A., Hofmann-Apitius, M., & Fröhlich, H. (2019). Deep learning for clustering of multivariate clinical patient trajectories with missing values. GigaScience, 8. ↩
Sun, L., & Chen, X. (2021). Bayesian Temporal Factorization for Multidimensional Time Series Prediction. IEEE transactions on pattern analysis and machine intelligence, PP. ↩

Comments

[Feature request] Is it possible to "warm-up" the transformer?

Thank you for creating this wonderful resource! This is an amazing and useful tool!

Regarding SAITS, is it possible to pass a learning rate scheduler, rather than a fixed learning rate, for the transformer to pre-train?

I ask this because I compared the outputs of training 100 epochs vs 1000 epochs. The loss continues to decrease, but the error on holdout timepoints does not change between 100 vs 1000 epochs. Strangely, the prediction (after 100 & 1000 epochs) is less accurate than linear interpolation...! I wondered if it is because the transformers have too many parameters, and it needs some help learning initially.

opened by b2jia 9
can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

PS C:\Users\Lyc\Downloads\PyPOTS-main\PyPOTS-main> & C:/Users/Lyc/AppData/Local/Programs/Python/Python39/python.exe c:/Users/Lyc/Downloads/PyPOTS-main/PyPOTS-main/pypots/tests/test_imputation.py Running test cases for BRITS... Model initialized successfully. Number of the trainable parameters: 580976 epoch 0: training loss 1.2366, validating loss 0.4201 epoch 1: training loss 0.8974, validating loss 0.3540 epoch 2: training loss 0.7426, validating loss 0.2919 epoch 3: training loss 0.6147, validating loss 0.2414 epoch 4: training loss 0.5411, validating loss 0.2157 ERunning test cases for BRITS... Model initialized successfully. Number of the trainable parameters: 580976 epoch 0: training loss 1.2054, validating loss 0.4022 epoch 1: training loss 0.8631, validating loss 0.3399 epoch 2: training loss 0.7204, validating loss 0.2863 epoch 3: training loss 0.5995, validating loss 0.2399 epoch 4: training loss 0.5325, validating loss 0.2123 ERunning test cases for LOCF... LOCF test_MAE: 0.17510570872656786 .Running test cases for LOCF... .Running test cases for SAITS... Model initialized successfully. Number of the trainable parameters: 1332704 epoch 0: training loss 0.9181, validating loss 0.2936 epoch 1: training loss 0.6287, validating loss 0.2303 epoch 2: training loss 0.5345, validating loss 0.2086 epoch 3: training loss 0.4735, validating loss 0.1895 epoch 4: training loss 0.4224, validating loss 0.1744 ERunning test cases for SAITS... Model initialized successfully. Number of the trainable parameters: 1332704 epoch 0: training loss 0.7823, validating loss 0.2779 epoch 1: training loss 0.5015, validating loss 0.2250 epoch 2: training loss 0.4418, validating loss 0.2097 epoch 3: training loss 0.4119, validating loss 0.1994 epoch 4: training loss 0.3866, validating loss 0.1815 ERunning test cases for Transformer... Model initialized successfully. Number of the trainable parameters: 666122 epoch 0: training loss 0.7715, validating loss 0.2843 epoch 1: training loss 0.4861, validating loss 0.2271 epoch 2: training loss 0.4176, validating loss 0.2077 epoch 3: training loss 0.3822, validating loss 0.2005 epoch 4: training loss 0.3592, validating loss 0.1961 ERunning test cases for Transformer... Model initialized successfully. Number of the trainable parameters: 666122 epoch 0: training loss 0.8033, validating loss 0.2910 epoch 1: training loss 0.4856, validating loss 0.2345 epoch 2: training loss 0.4282, validating loss 0.2157 epoch 3: training loss 0.3882, validating loss 0.2051 epoch 4: training loss 0.3599, validating loss 0.1942 E

ERROR: test_impute (main.TestBRITS)

Traceback (most recent call last): File "c:\Users\Lyc\Downloads\PyPOTS-main\PyPOTS-main\pypots\tests\test_imputation.py", line 125, in setUp self.brits.fit(self.train_X, self.val_X) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\brits.py", line 504, in fit self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\base.py", line 142, in _train_model if np.equal(self.best_loss, float('inf')): File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\torch_tensor.py", line 732, in array return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

====================================================================== ERROR: test_parameters (main.TestBRITS)

Traceback (most recent call last): File "c:\Users\Lyc\Downloads\PyPOTS-main\PyPOTS-main\pypots\tests\test_imputation.py", line 125, in setUp self.brits.fit(self.train_X, self.val_X) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\brits.py", line 504, in fit self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\base.py", line 142, in _train_model if np.equal(self.best_loss, float('inf')): File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\torch_tensor.py", line 732, in array return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

====================================================================== ERROR: test_impute (main.TestSAITS)

Traceback (most recent call last): File "c:\Users\Lyc\Downloads\PyPOTS-main\PyPOTS-main\pypots\tests\test_imputation.py", line 45, in setUp self.saits.fit(self.train_X, self.val_X) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\saits.py", line 170, in fit self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\base.py", line 142, in _train_model if np.equal(self.best_loss, float('inf')): File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\torch_tensor.py", line 732, in array return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

====================================================================== ERROR: test_parameters (main.TestSAITS)

Traceback (most recent call last): File "c:\Users\Lyc\Downloads\PyPOTS-main\PyPOTS-main\pypots\tests\test_imputation.py", line 45, in setUp self.saits.fit(self.train_X, self.val_X) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\saits.py", line 170, in fit self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\base.py", line 142, in _train_model if np.equal(self.best_loss, float('inf')): File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\torch_tensor.py", line 732, in array return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

====================================================================== ERROR: test_impute (main.TestTransformer)

Traceback (most recent call last): File "c:\Users\Lyc\Downloads\PyPOTS-main\PyPOTS-main\pypots\tests\test_imputation.py", line 89, in setUp self.transformer.fit(self.train_X, self.val_X) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\transformer.py", line 256, in fit self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\base.py", line 142, in _train_model if np.equal(self.best_loss, float('inf')): File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\torch_tensor.py", line 732, in array return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

====================================================================== ERROR: test_parameters (main.TestTransformer)

Traceback (most recent call last): File "c:\Users\Lyc\Downloads\PyPOTS-main\PyPOTS-main\pypots\tests\test_imputation.py", line 89, in setUp self.transformer.fit(self.train_X, self.val_X) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\transformer.py", line 256, in fit self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask) File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\pypots\imputation\base.py", line 142, in _train_model if np.equal(self.best_loss, float('inf')): File "C:\Users\Lyc\AppData\Local\Programs\Python\Python39\lib\site-packages\torch_tensor.py", line 732, in array return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

Ran 8 tests in 176.311s

FAILED (errors=6)

opened by BasinChen 8

BRITS imputation test fails on cuda device mismatch

Hi, when trying to run imputation tests with commit 6dcc8942459094e3a3fc5e11363f5d712ee8e742 on dev branch.

py3.9_cuda11.3_cudnn8.2.0_0

$ python -m pytest tests/test_imputation.py

./tests/test_imputation.py::TestBRITS::test_parameters Failed with Error: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
  File ".../unittest/case.py", line 59, in testPartExecutor
    yield
  File ".../unittest/case.py", line 588, in run
    self._callSetUp()
  File ".../unittest/case.py", line 547, in _callSetUp
    self.setUp()
  File ".../PyPOTS/pypots/tests/test_imputation.py", line 98, in setUp
    self.brits.fit(self.train_X, self.val_X)
  File "/PyPOTS/pypots/imputation/brits.py", line 504, in fit
    self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask)
  File "/PyPOTS/pypots/imputation/base.py", line 154, in _train_model
    if np.equal(self.best_loss, float("inf")):
  File .../lib/python3.9/site-packages/torch/_tensor.py", line 732, in __array__
    return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

opened by MaciejSkrabski 4

GPU enabled model raises Exception: expected self and mask to be on the same device, but got mask on cpu and self on cuda:0

Hello, great library, but using gpu enabled machine results in errors.

pypots version = 0.0.6 (the one available in PyPI)

code to replicate problem:

import unittest
from pypots.tests.test_imputation import TestBRITS, TestLOCF, TestSAITS, TestTransformer
from pypots import __version__


if __name__ == "__main__":
    print(__version__)
    unittest.main()

results:

0.0.6
Running test cases for BRITS...
Model initialized successfully. Number of the trainable parameters: 580976
ERunning test cases for BRITS...
Model initialized successfully. Number of the trainable parameters: 580976
ERunning test cases for LOCF...
LOCF test_MAE: 0.1712224306027283
.Running test cases for LOCF...
.Running test cases for SAITS...
Model initialized successfully. Number of the trainable parameters: 1332704
Exception: expected self and mask to be on the same device, but got mask on cpu and self on cuda:0
ERunning test cases for SAITS...
Model initialized successfully. Number of the trainable parameters: 1332704
Exception: expected self and mask to be on the same device, but got mask on cpu and self on cuda:0
ERunning test cases for Transformer...
Model initialized successfully. Number of the trainable parameters: 666122
epoch 0: training loss 0.7681, validating loss 0.2941
epoch 1: training loss 0.4731, validating loss 0.2395
epoch 2: training loss 0.4235, validating loss 0.2069
epoch 3: training loss 0.3781, validating loss 0.1914
epoch 4: training loss 0.3530, validating loss 0.1837
ERunning test cases for Transformer...
Model initialized successfully. Number of the trainable parameters: 666122
epoch 0: training loss 0.7826, validating loss 0.2820
epoch 1: training loss 0.4687, validating loss 0.2352
epoch 2: training loss 0.4188, validating loss 0.2132
epoch 3: training loss 0.3857, validating loss 0.1977
epoch 4: training loss 0.3604, validating loss 0.1945
E
======================================================================
ERROR: test_impute (pypots.tests.test_imputation.TestBRITS)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/tests/test_imputation.py", line 99, in setUp
    self.brits.fit(self.train_X, self.val_X)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/brits.py", line 494, in fit
    training_set = DatasetForBRITS(train_X)  # time_gaps is necessary for BRITS
  File "mydirs(...)/python3.9/site-packages/pypots/data/dataset_for_brits.py", line 62, in __init__
    forward_delta = parse_delta(forward_missing_mask)
  File "mydirs(...)/python3.9/site-packages/pypots/data/dataset_for_brits.py", line 36, in parse_delta
    delta.append(torch.ones(1, n_features) + (1 - m_mask[step]) * delta[-1])
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

======================================================================
ERROR: test_parameters (pypots.tests.test_imputation.TestBRITS)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/tests/test_imputation.py", line 99, in setUp
    self.brits.fit(self.train_X, self.val_X)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/brits.py", line 494, in fit
    training_set = DatasetForBRITS(train_X)  # time_gaps is necessary for BRITS
  File "mydirs(...)/python3.9/site-packages/pypots/data/dataset_for_brits.py", line 62, in __init__
    forward_delta = parse_delta(forward_missing_mask)
  File "mydirs(...)/python3.9/site-packages/pypots/data/dataset_for_brits.py", line 36, in parse_delta
    delta.append(torch.ones(1, n_features) + (1 - m_mask[step]) * delta[-1])
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

======================================================================
ERROR: test_impute (pypots.tests.test_imputation.TestSAITS)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/base.py", line 83, in _train_model
    results = self.model.forward(inputs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/saits.py", line 95, in forward
    imputed_data, [X_tilde_1, X_tilde_2, X_tilde_3] = self.impute(inputs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/saits.py", line 62, in impute
    enc_output, _ = encoder_layer(enc_output)
  File "mydirs(...)/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 122, in forward
    enc_output, attn_weights = self.slf_attn(enc_input, enc_input, enc_input, attn_mask=mask_time)
  File "mydirs(...)/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 72, in forward
    v, attn_weights = self.attention(q, k, v, attn_mask)
  File "mydirs(...)/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 32, in forward
    attn = attn.masked_fill(attn_mask == 1, -1e9)
RuntimeError: expected self and mask to be on the same device, but got mask on cpu and self on cuda:0

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/tests/test_imputation.py", line 35, in setUp
    self.saits.fit(self.train_X, self.val_X)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/saits.py", line 171, in fit
    self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/base.py", line 123, in _train_model
    raise RuntimeError('Training got interrupted. Model was not get trained. Please try fit() again.')
RuntimeError: Training got interrupted. Model was not get trained. Please try fit() again.

======================================================================
ERROR: test_parameters (pypots.tests.test_imputation.TestSAITS)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/base.py", line 83, in _train_model
    results = self.model.forward(inputs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/saits.py", line 95, in forward
    imputed_data, [X_tilde_1, X_tilde_2, X_tilde_3] = self.impute(inputs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/saits.py", line 62, in impute
    enc_output, _ = encoder_layer(enc_output)
  File "mydirs(...)/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 122, in forward
    enc_output, attn_weights = self.slf_attn(enc_input, enc_input, enc_input, attn_mask=mask_time)
  File "mydirs(...)/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 72, in forward
    v, attn_weights = self.attention(q, k, v, attn_mask)
  File "mydirs(...)/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 32, in forward
    attn = attn.masked_fill(attn_mask == 1, -1e9)
RuntimeError: expected self and mask to be on the same device, but got mask on cpu and self on cuda:0

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/tests/test_imputation.py", line 35, in setUp
    self.saits.fit(self.train_X, self.val_X)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/saits.py", line 171, in fit
    self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/base.py", line 123, in _train_model
    raise RuntimeError('Training got interrupted. Model was not get trained. Please try fit() again.')
RuntimeError: Training got interrupted. Model was not get trained. Please try fit() again.

======================================================================
ERROR: test_impute (pypots.tests.test_imputation.TestTransformer)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/tests/test_imputation.py", line 68, in setUp
    self.transformer.fit(self.train_X, self.val_X)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 257, in fit
    self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/base.py", line 129, in _train_model
    if np.equal(self.best_loss, float('inf')):
  File "mydirs(...)/python3.9/site-packages/torch/_tensor.py", line 732, in __array__
    return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

======================================================================
ERROR: test_parameters (pypots.tests.test_imputation.TestTransformer)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "mydirs(...)/python3.9/site-packages/pypots/tests/test_imputation.py", line 68, in setUp
    self.transformer.fit(self.train_X, self.val_X)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/transformer.py", line 257, in fit
    self._train_model(training_loader, val_loader, val_X_intact, val_X_indicating_mask)
  File "mydirs(...)/python3.9/site-packages/pypots/imputation/base.py", line 129, in _train_model
    if np.equal(self.best_loss, float('inf')):
  File "mydirs(...)/python3.9/site-packages/torch/_tensor.py", line 732, in __array__
    return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

----------------------------------------------------------------------
Ran 8 tests in 20.239s

FAILED (errors=6)

i suspect that you call .to(device) too early on data. You might also override device parameter when initiating new tensors (i.e. in torch.ones in parse_delta)

Best regards!

opened by MaciejSkrabski 4

Early stop

Wenjie,

I tried the PyPOTS with the Beijing Air quality database. For the dataset preparation, I follow the gene_UCI_BeijingAirQuality_dataset. The following is the PyPOTS setup.

saits_base = SAITS(seq_len=seq_len, n_features=132, 
                   n_layers=2,  # num of group-inner layers
                   d_model=256, # model hidden dim
                   d_inner=128, # hidden size of feed forward layer
                   n_head=4, # head num of self-attention
                   d_k=64, d_v=64, # key dim, value dim
                   dropout=0, 
                   epochs=200,
                   patience=30,
                   batch_size=32,
                   weight_decay=1e-5,
                   ORT_weight=1,
                   MIT_weight=1,
                  )

saits_base.fit(train_set_X)

PyPOTS stops earlier than the epochs specified (stops around epoch 80), without triggering either print('Exceeded the training patience. Terminating the training procedure...') or print('Finished all training epochs.').

epoch 0: training loss 0.9637 
epoch 1: training loss 0.6161 
epoch 2: training loss 0.5177 
epoch 3: training loss 0.4783 
epoch 4: training loss 0.4489 
...
epoch 73: training loss 0.2462 
epoch 74: training loss 0.2460 
epoch 75: training loss 0.2480 
epoch 76: training loss 0.2452 
epoch 77: training loss 0.2452 
epoch 78: training loss 0.2458 
epoch 79: training loss 0.2449 
epoch 80: training loss 0.2423 
epoch 81: training loss 0.2425 
epoch 82: training loss 0.2443 
epoch 83: training loss 0.2403 
epoch 84: training loss 0.2406

Then I evaluate the model performance (not knowing why the model stops early) on test_set as

test_set_mae = cal_mae(test_set_imputation, test_set_X_intact, test_set_indicating_mask)
0.21866121846582318

I have a few questions:

What could be the cause for the early stop?
In addition, is there any object in saits_base that stores the loss history?
Does the function cal_mae calculate the same MAE in your paper? For this Beijing air quality case, I should be able to tune the hyperparameter to get the test_set_mae down to around 0.146?

Thank you, Haochen

opened by Rdfing 2

fix: brits on cuda

Some tensors created on the fly (mainly in base.py and dataset_for_brits.py ) used to ignore the model's and data's device (cpu or gpu). This caused BRITS to throw errors whenever users wanted to run it on cuda enabled machine.

opened by MaciejSkrabski 1
Update and fix the dependencies in the development env

As mentioned in #7, I am trying to update and fix the dependencies of the development environment for testing cases. Expecting this can help speed up the processing of setting up Conda when running tests.

opened by WenjieDu 1
refactor: explicit channels in conda env ymls

Hi! You may have noticed that, when creating a new conda environment from *.yml file, it takes ages to solve package dependencies. I attempt to speed the process up by explicitly defining channel in which to search for a package. I also defined minimal pandas version to be 1.4.1 - the things were weird before that. I also allow for python versions newer than 3.7.13 and I believe you'll find it acceptable.

Please let me know if this is in any way helpful.

opened by MaciejSkrabski 9

Releases(v0.0.9)

v0.0.9(Dec 20, 2022)
In this version, we speed up the installation process of PyPOTS. We noticed that torch_geometric and related dependencies take too much time to install. Therefore, they're removed from the list of requirements. They're necessary for the graph model RainDrop. Hence, users who need RainDrop have to install torch_geometric manually after they set up PyPOTS.

What's Changed

Merge updates by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/23

Update README and add the configurations of docs by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/24

Merge dev into main by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/26

Merge dev into main by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/28

Full Changelog: https://github.com/WenjieDu/PyPOTS/compare/v0.0.8...v0.0.9
Source code(tar.gz)
Source code(zip)
v0.0.8(Sep 13, 2022)
Fixed bugs with running on CUDA devices;

What's Changed

fix: brits imputation test device mismatch by @MaciejSkrabski in https://github.com/WenjieDu/PyPOTS/pull/11

Merge branch 'dev' into main by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/13

feat: add workflow Publish-to-PyPI by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/14

Merge branch 'dev' into main by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/15

Specify Conda channels for the dependencies by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/18

fix the bug of tensors on different devices by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/22

New Contributors

@MaciejSkrabski made their first contribution in https://github.com/WenjieDu/PyPOTS/pull/11

@WenjieDu made their first contribution in https://github.com/WenjieDu/PyPOTS/pull/13

Full Changelog: https://github.com/WenjieDu/PyPOTS/compare/v0.0.7...v0.0.8
Source code(tar.gz)
Source code(zip)
v0.0.7(Jul 12, 2022)

Fix the bug mentioned in #4 and some other issues.

Full Changelog: https://github.com/WenjieDu/PyPOTS/commits/v0.0.7
Source code(tar.gz)
Source code(zip)

Owner

Wenjie Du

"Do one thing, and do it well."

GitHub Repository

Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Trading Tesla with Machine Learning and Sentiment Analysis An interactive program to train a Random Forest Classifier to predict Tesla daily prices us

31 Nov 17, 2022

flexible time-series processing & feature extraction

A corona statistics and information telegram bot.

206 Dec 28, 2022

Summer: compartmental disease modelling in Python

Summer: compartmental disease modelling in Python Summer is a Python-based framework for the creation and execution of compartmental (or "state-based"

6 May 13, 2022

A simple machine learning python sign language detection project.

SST Coursework 2022 About the app A python application that utilises the tensorflow object detection algorithm to achieve automatic detection of ameri

2 Jun 30, 2022

MasTrade is a trading bot in baselines3,pytorch,gym

mastrade MasTrade is a trading bot in baselines3,pytorch,gym idea we have for example 1 btc and we buy a crypto with it with market option to trade in

18 May 24, 2022

Magenta: Music and Art Generation with Machine Intelligence

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new

18.1k Dec 30, 2022

ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.

ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. It has a simple, flexible syntax, is cloud and tool agnostic, and has interfaces/abstraction

2.6k Jan 08, 2023

Tribuo - A Java machine learning library

Tribuo - A Java prediction library (v4.1) Tribuo is a machine learning library in Java that provides multi-class classification, regression, clusterin

1.1k Dec 28, 2022

GAM timeseries modeling with auto-changepoint detection. Inspired by Facebook Prophet and implemented in PyMC3

pm-prophet Pymc3-based universal time series prediction and decomposition library (inspired by Facebook Prophet). However, while Faceook prophet is a

314 Dec 25, 2022

A flexible CTF contest platform for coming PKU GeekGame events

Project Guiding Star: the Backend A flexible CTF contest platform for coming PKU GeekGame events Still in early development Highlights Not configurabl

14 Dec 15, 2022

Project to deploy a machine learning model based on Titanic dataset from Kaggle

kaggle_titanic_deploy Project to deploy a machine learning model based on Titanic dataset from Kaggle In this project we used the Titanic dataset from

8 May 23, 2022

Drug prediction

I have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Dr

1 Jan 28, 2022

ML Kaggle Titanic Problem using LogisticRegrission

-ML-Kaggle-Titanic-Problem-using-LogisticRegrission here you will find the solution for the titanic problem on kaggle with comments and step by step c

3 Oct 23, 2022

Flightfare-Prediction - It is a Flightfare Prediction Web Application Using Machine learning,Python and flask

Flight_fare-Prediction It is a Flight_fare Prediction Web Application Using Machine learning,Python and flask Using Machine leaning i have created a F

1 Dec 06, 2022

Module for statistical learning, with a particular emphasis on time-dependent modelling

Operating system Build Status Linux/Mac Windows tick tick is a Python 3 module for statistical learning, with a particular emphasis on time-dependent

410 Dec 14, 2022

XAI - An eXplainability toolbox for machine learning

XAI - An eXplainability toolbox for machine learning XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contai

875 Dec 27, 2022

🤖 ⚡ scikit-learn tips

🤖 ⚡ scikit-learn tips New tips are posted on LinkedIn, Twitter, and Facebook. 👉 Sign up to receive 2 video tips by email every week! 👈 List of all

1.6k Jan 03, 2023

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

3k Jan 08, 2023

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

sklearn-evaluation Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking, and Jupyter notebook analysis. Suppo

354 Dec 31, 2022

Python based GBDT implementation

Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A

20 Sep 21, 2022

PyPOTS - A Python Toolbox for Data Mining on Partially-Observed Time Series

Related tags