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I am trying to install IBSpy on my institute HPC cluster, on a local conda env, however it failed during the test run, reporting this error:
======================================================================
ERROR: test_run_affinity_propagation (test_result_set.TestResultSet)
----------------------------------------------------------------------
multiprocess.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/local/src/IBSpy-0.4.4/IBSpy/IBSpy_result_set.py", line 156, in <lambda>
wrapped_function = lambda x: self._function_window_wrapper_tabix(x, function, chromosome, assembly = assembly)
File "/usr/local/src/IBSpy-0.4.4/IBSpy/IBSpy_result_set.py", line 116, in _function_window_wrapper_tabix
return function(m), chromosome, start, end
File "/usr/local/src/IBSpy-0.4.4/IBSpy/IBSpy_result_set.py", line 195, in run_single_run
raise ex
File "/usr/local/src/IBSpy-0.4.4/IBSpy/IBSpy_result_set.py", line 189, in run_single_run
runs = cluster_by_haplotype(gr, seed=seed, iterations=iterations, dampings=dampings, max_missing=max_missing)
File "/usr/local/src/IBSpy-0.4.4/IBSpy/window_affinity_propagation.py", line 163, in cluster_by_haplotype
desc_stats = descriptive_stats(gr)
File "/usr/local/src/IBSpy-0.4.4/IBSpy/window_affinity_propagation.py", line 116, in descriptive_stats
ret['skew'] = skew(melted['value'])
File "/usr/local/lib/python3.9/site-packages/scipy/stats/_axis_nan_policy.py", line 523, in axis_nan_policy_wrapper
res = hypotest_fun_out(*samples, **kwds)
File "/usr/local/lib/python3.9/site-packages/scipy/stats/_stats_py.py", line 1194, in skew
m2 = _moment(a, 2, axis, mean=mean)
File "/usr/local/lib/python3.9/site-packages/scipy/stats/_stats_py.py", line 1072, in _moment
eps = np.finfo(a_zero_mean.dtype).resolution * 10
File "/usr/local/lib/python3.9/site-packages/numpy/core/getlimits.py", line 516, in __new__
raise ValueError("data type %r not inexact" % (dtype))
ValueError: data type <class 'numpy.object_'> not inexact
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/src/IBSpy-0.4.4/tests/test_result_set.py", line 165, in test_run_affinity_propagation
ret = ibspy_results.run_affinity_propagation()
File "/usr/local/src/IBSpy-0.4.4/IBSpy/IBSpy_result_set.py", line 213, in run_affinity_propagation
for best, chromosome, start, end in self.map_window_iterator_tabix(function= run_single_run, chromosome=chr):
File "/usr/local/src/IBSpy-0.4.4/IBSpy/IBSpy_result_set.py", line 160, in map_window_iterator_tabix
newlist = [x for x in res]
File "/usr/local/src/IBSpy-0.4.4/IBSpy/IBSpy_result_set.py", line 160, in <listcomp>
newlist = [x for x in res]
File "/usr/local/lib/python3.9/site-packages/multiprocess/pool.py", line 870, in next
raise value
ValueError: data type <class 'numpy.object_'> not inexact
======================================================================
FAIL: test_cluster_by_haplotype_long (test_affinity_propagation.TestAffinityPropagation)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/src/IBSpy-0.4.4/tests/test_affinity_propagation.py", line 90, in test_cluster_by_haplotype_long
self.assertAlmostEqual(best.random_state, 2906402158)
AssertionError: 3181143732 != 2906402158 within 7 places (274741574 difference).
I also tried to use singularity with the def file available (I added a line that allowed to install sklearn via scikit-learn) but outputed the same error.
Best,
Iacopo
The text was updated successfully, but these errors were encountered:
Hi,
I am trying to install IBSpy on my institute HPC cluster, on a local conda env, however it failed during the test run, reporting this error:
I also tried to use singularity with the def file available (I added a line that allowed to install sklearn via scikit-learn) but outputed the same error.
Best,
Iacopo
The text was updated successfully, but these errors were encountered: