Initialization failed

[dat19-8]

Describe the bug
When fitting the ETSFull and ETSMap models on an hourly time frame, I'm receiving the following error: Initialization failed.
You can find the full error description below in the Additional context

Expected behavior
I want to forecasting the demand according to an hourly data frame ( or 30 mins time frame). This is not even starting by fitting.

Screenshots
If applicable, add screenshots to help explain your problem.

Environment (please complete the following information):

• OS: macOS
• Python Version: Python 3.6. 9
• Versions of Major Dependencies : pandas==1.1.5, scikit-learn==0.24.2, 'matplotlib==3.3.4'

Additional context

RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/lib/python3.7/multiprocessing/pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.7/multiprocessing/pool.py", line 44, in mapstar
return list(map(*args))
File "stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396.pyx", line 373, in stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396._call_sampler_star
File "stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396.pyx", line 406, in stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396._call_sampler
RuntimeError: Initialization failed.
"""

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

RuntimeError Traceback (most recent call last)
in ()
----> 1 dlt.fit(train_df)

7 frames
/usr/lib/python3.7/multiprocessing/pool.py in mapstar()
42
43 def mapstar(args):
---> 44 return list(map(*args))
45
46 def starmapstar(args):

stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396.pyx in stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396._call_sampler_star()

stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396.pyx in stanfit4anon_model_982090c5656030fa038b63e5c383dbff_326254919482697396._call_sampler()

RuntimeError: Initialization failed.

[edwinnglabs]

@dat19-8 could you also provide the version of orbit-ml you are using?
couple of checks suggesting here:

1. could you also check under orbit/stan_compiled/ do you have the ets.pkl file? if so, maybe try remove it and recompile one more time
2. reinstall orbit-ml with latest version
3. check any nulls, invalid values in your data frame

[dat19-8]

This is the version I'm using: Version: 1.0.15
I tried all the suggestions and it didn't work, I'm still receiving the same error for the hourly timeframe forecasting.

[wangzhishi]

can you share a reproducible notebook with the errors (along with the data)?

[dat19-8]

Just a new update, the LGT model did fit after checking if there are some missing hours/days.
But, I received a new error which is MemoryError: Unable to allocate 1.43 GiB for an array with shape (10000, 19191) and data type float64. Knowing that, the shape of the dataset is: 19859 rows × 6 columns, df_train shape as follows: 19139 rows × 6 columns, and df_test shape: 720 rows × 6 columns. The error is showing that we have 10k rows and 19k columns when running the following code predicted_df = lgt.predict(df=test_df)

Additional context

MemoryError Traceback (most recent call last)
in
----> 1 predicted_df = lgt.predict(df=test_df)

/opt/conda/lib/python3.7/site-packages/orbit/models/template.py in predict(self, df, decompose, **kwargs)
400 posterior_samples[k] = np.repeat(v, self.n_bootstrap_draws, axis=0)
401 predicted_dict = self._predict(
--> 402 posterior_estimates=posterior_samples, df=df, include_error=True, **kwargs
403 )
404 percentiles_dict = compute_percentiles(predicted_dict, self._prediction_percentiles)

/opt/conda/lib/python3.7/site-packages/orbit/models/lgt.py in _predict(self, posterior_estimates, df, include_error, **kwargs)
305 # Model Attributes
306 ################################################################
--> 307 model = deepcopy(posterior_estimates)
308 for k, v in model.items():
309 model[k] = torch.from_numpy(v)

/opt/conda/lib/python3.7/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:

/opt/conda/lib/python3.7/copy.py in _deepcopy_dict(x, memo, deepcopy)
239 memo[id(x)] = y
240 for key, value in x.items():
--> 241 y[deepcopy(key, memo)] = deepcopy(value, memo)
242 return y
243 d[dict] = _deepcopy_dict

/opt/conda/lib/python3.7/copy.py in deepcopy(x, memo, _nil)
159 copier = getattr(x, "deepcopy", None)
160 if copier:
--> 161 y = copier(memo)
162 else:
163 reductor = dispatch_table.get(cls)

MemoryError: Unable to allocate 1.43 GiB for an array with shape (10000, 19191) and data type float64

[wangzhishi]

I think this is related to your machine specifics (maybe not enough RAM).