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License: MIT License
A simple Auto-Regressive Neural Network for time-series
License: MIT License
I was trying to use the AR-Net for one of my project when I came up with the following error:
`ValueError Traceback (most recent call last)
in
----> 1 m.fit(deltas)
~/opt/anaconda3/lib/python3.8/site-packages/arnet/ar_net.py in fit(self, series, plot)
217 self.make_datasets(series)
218 self.create_learner()
--> 219 self.fit_one_cycle(plot=plot)
220 return self
221
~/opt/anaconda3/lib/python3.8/site-packages/arnet/ar_net.py in fit_one_cycle(self, n_epoch, lr, cycles, plot)
201
202 if lr is None:
--> 203 self.find_lr(plot=plot)
204 lr = self.lr
205 for i in range(0, cycles):
~/opt/anaconda3/lib/python3.8/site-packages/arnet/ar_net.py in find_lr(self, plot)
187 if self.learn is None:
188 raise ValueError("create learner first.")
--> 189 lr_at_min, lr_steep = self.learn.lr_find(start_lr=1e-6, end_lr=10, num_it=300, show_plot=plot)
190 if plot:
191 plt.show()
ValueError: not enough values to unpack (expected 2, got 1)`
Apparently the Fast-AI functionality was changed: https://forums.fast.ai/t/error-in-lr-find/89968
So the code requires a small update so that the AR-Net can work.
Hey, don't know wether this is actually an issue with the module or just user error, but I'm getting a Name Error and I honestly can't figure out why.
Any idea what could cause this?
Cell In[1], [line 1](vscode-notebook-cell:?execution_count=1&line=1)
----> [1](vscode-notebook-cell:?execution_count=1&line=1) import arnet
File .\arnet\__init__.py:24
[21](./arnet/__init__.py:21) from fastai.basics import *
[22](./arnet/__init__.py:22) from fastai.tabular.all import *
---> [24](./arnet/__init__.py:24) from .ar_net import ARNet
[26](.arnet/__init__.py:26) # from .ar_net_legacy import init_ar_learner
[27](./arnet/__init__.py:27) from .utils_data import load_from_file, tabularize_univariate, estimate_noise, split_by_p_valid
File .\arnet\ar_net.py:13
[11](./arnet/ar_net.py:11) from fastai.data.transforms import Normalize
[12](./arnet/ar_net.py:12) from fastai.learner import load_learner
---> [13](./arnet/ar_net.py:13) from fastai.distributed import ParallelTrainer
[15](./arnet/ar_net.py:15) from arnet import utils, utils_data, plotting
[16](./arnet/ar_net.py:16) from arnet.fastai_mods import SparsifyAR, huber, sTPE, get_loss_func
File .\fastai\distributed.py:139
[136](./fastai/distributed.py:136) _hidden_params = ["mixed_precision", "fp16", "log_with", "logging_dir", "step_scheduler_with_optimizer"]
[138](./fastai/distributed.py:138) # %% ../nbs/20a_distributed.ipynb 30
--> [139](./fastai/distributed.py:139) class DistributedTrainer(Callback):
[140](./fastai/distributed.py:140) "Wrap `model` in `DistributedDataParallel` and `dls` in `DistributedDL`"
[141](./fastai/distributed.py:141) order = 11
...
[146](./fastai/distributed.py:146) ):
[147](./fastai/distributed.py:147) store_attr()
[148](./fastai/distributed.py:148) self.accelerator = Accelerator(**kwargs)
NameError: name 'Accelerator' is not defined
more like a questions about the feature instead of issue, I am trying to understand if it is possible to use multivariate TS dataset for forecasting using AR-Net. In case yes, are there some examples?
Also, can it take care of dependencies b.w. variables?
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