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Failed on big dataset with struct.error: 'i' format requires -2147483648 <= number <= 2147483647

Hello,

I am trying to run XEM on a dataset. The algorithm works well on BasicMotion, but it failed when I used more than one tree on my dataset which contains about 6278 MTS (6 dimensions) in the train set. I suspect the error to be in the joblib library but I am not sure. Here is the traceback:

Number of MTS in train set: 6278
Number of MTS in test set: 1570
Number of classes: 14
MTS length: 261
Window size: 52
Number of features: 5

Cross-validation - folds: 1, current: 0
Training set size: 1318380
Validation set size: 0
Testing set size: 329700
joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py", line 357, in _sendback_result
    exception=exception))
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/joblib/externals/loky/backend/queues.py", line 247, in put
    self._writer.send_bytes(obj)
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/multiprocessing/connection.py", line 200, in send_bytes
    self._send_bytes(m[offset:offset + size])
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/multiprocessing/connection.py", line 393, in _send_bytes
    header = struct.pack("!i", n)
struct.error: 'i' format requires -2147483648 <= number <= 2147483647
"""

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

Traceback (most recent call last):
  File "./main.py", line 47, in <module>
    clf.fit(X_train[:,2:], y_train)
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/lce/_lce.py", line 424, in fit
    self.estimators_.fit(X, y)
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/sklearn/ensemble/_bagging.py", line 269, in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/sklearn/ensemble/_bagging.py", line 407, in _fit
    for i in range(n_jobs)
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/joblib/parallel.py", line 1054, in __call__
    self.retrieve()
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/joblib/parallel.py", line 933, in retrieve
    self._output.extend(job.get(timeout=self.timeout))
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 542, in wrap_future_result
    return future.result(timeout=timeout)
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/concurrent/futures/_base.py", line 435, in result
    return self.__get_result()
  File "/home/local.isima.fr/mfmbouopda/miniconda3/envs/shapeletnet/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
    raise self._exception
struct.error: 'i' format requires -2147483648 <= number <= 2147483647
srun: error: node36: task 0: Exited with exit code 1

There is no problem when I use a single tree.

Support for different MTS length

How can the algorithm or algorithm input be changed to support a different length for each time series in the MTS dataset? This is mentioned in the paper but not supported by the current code.

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