transformer (forward pass)

[slavavs82]

Parameters: | x (Tensor) – torch.Tensor of shape (batch_size, K, d_input).

What is K?

[maxjcohen]

Hi, K is the time dimension. In the case of the data challenge (see #2 ), we work on full months with hourly data, so K = 24 * 28 = 672. In the last examples on this repo, I've switch to weekly predictions, so K = 24 * 7 = 168.

[slavavs82]

Thank you so much for the answer!
I'll clarify, as my English prevents me from understanding correctly. K is the window size? d_input is the data size inside the window?

[maxjcohen]

No, K is the time dimension, for example the number of hours in X. d_input is the dimension of the input X for each time step. For instance, if you had 2 inputs (say outdoor temperature and ac schedule) for an entire week (168 hours), X shape would be (1, 168, 2).

We refer to the window size as "attention_size", see here.

[slavavs82]

Fantastic, but I still don't get it :)
My data set is very simple (for the test).
series = np.sin(np.arange(0, 1000))
I'm breaking this list down into 30 size windows.
I want to pass a window sized 30 to the network and predict the next 5 values. Let the Batch size be 1.
What size will x have? What would equal a K?

[maxjcohen]

In your case:

• batch_size=1 : you only have one sample
• K=1000 : 1000 time steps
• d_input=1 : your sin is one dimensional

You don't have to break the list into size windows, the transformer will do it for you using the score, see this line of the MHA block.

[slavavs82]

Dude, I really appreciate it.
Give me one last thing. How do I get a 30 size window online and predict the next 5?
I'm thinking logically. I have a data set. I want to take some data and teach the network to predict the next data that the network can't see.

[slavavs82]

I think I misled you myself.
series = np.sin(np.arange(0, 1000))
It's not a sample. It's a 1,000 size dataset.
I want to break this dataset down into samples. Size sample = 30. That's what I called a window. Then I can combine these samples into batch=4 (for example).
So, I have batch = 4, K = 30, d_input = 1.
Is that so?

[maxjcohen]

Ok I think I see where you're going. In that case, your values for batch_size, K and d_input should be good. Unfortunately, the Transformer isn't well fit to predict future states, as it predicts one single output for each input. We discuss this in #5 .

[slavavs82]

In this case, there is a question.
I submit sample=30(values) to the encoder input. If I want to predict the next 10 values, I submit these 10 values to the decoder input. I have seen this scheme in this document https://arxiv.org/pdf/2001.08317.pdf. Look at figure 1.
Encoder input = T1,T2,T3,T4; decoder input T4,T5. In my case I want to feed Encoder input = T1,T2...T30; decoder input T30,T31...T40.
Can I do that?

[pqy000]

Of course, you can try it ?

[Lisa-FFY]

Excuse me,could you please tell me is there any relevant paper about this code?I want to study it in depth.

[shathaa1983]

Hi, I am trying to use a univariate time series dataset. I got this error:

KeyError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3079 try:
-> 3080 return self._engine.get_loc(casted_key)
3081 except KeyError as err:

pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 521170

The above exception was the direct cause of the following exception:

KeyError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_17036/184118730.py in
9 running_loss = 0
10 with tqdm(total=len(dataloader_train.dataset), desc=f"[Epoch {idx_epoch+1:3d}/{EPOCHS}]") as pbar:
---> 11 for idx_batch, (x, y) in enumerate(dataloader_train):
12 try:
13 print(f'idx_batch {dataloader_train} is {enumerate[dataloader_train]}')

~\anaconda3\lib\site-packages\torch\utils\data\dataloader.py in next(self)
519 if self._sampler_iter is None:
520 self._reset()
--> 521 data = self._next_data()
522 self._num_yielded += 1
523 if self._dataset_kind == _DatasetKind.Iterable and \

~\anaconda3\lib\site-packages\torch\utils\data\dataloader.py in _next_data(self)
559 def _next_data(self):
560 index = self._next_index() # may raise StopIteration
--> 561 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
562 if self._pin_memory:
563 data = _utils.pin_memory.pin_memory(data)

~\anaconda3\lib\site-packages\torch\utils\data_utils\fetch.py in fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]

~\anaconda3\lib\site-packages\torch\utils\data_utils\fetch.py in (.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]

~\anaconda3\lib\site-packages\torch\utils\data\dataset.py in getitem(self, idx)
309
310 def getitem(self, idx):
--> 311 return self.dataset[self.indices[idx]]
312
313 def len(self):

~\anaconda3\lib\site-packages\pandas\core\frame.py in getitem(self, key)
3022 if self.columns.nlevels > 1:
3023 return self._getitem_multilevel(key)
-> 3024 indexer = self.columns.get_loc(key)
3025 if is_integer(indexer):
3026 indexer = [indexer]

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3080 return self._engine.get_loc(casted_key)
3081 except KeyError as err:
-> 3082 raise KeyError(key) from err
3083
3084 if tolerance is not None:

KeyError: 521170

I'd appreciate it if you let me know if your code is suitable for the univariate time series. And how to solve this error?
I used the code in this link:
https://github.com/maxjcohen/transformer

Thanks

[maxjcohen]

Hi, this seems to be an issue with Pandas, as you can see in the last stack of the Traceback. Did you try to feed a pandas Dataframe directly to the trainer ? You most likely need to modify the dataloader class to match your dataset.

In the future, please open a new issue when discussing new/different problems. Thanks !

[shathaa1983]

Sorry, I am new to GitHub.
Do you have any idea how to modify the data loader class?

[maxjcohen]

I'll answer on the new issue :p closing this one as there is no longer any activity.