We apparently get an invalid value to divide by when running GRASTAPipelineChallenge2.
root@d6f339c3d56f:/spider# `cat run_pipeline_cmds.txt | grep GRASTAPipelineChallenge2`
WARNING:d3m.metadata.pipeline_run:'worker_id' was generated using the MAC address inside Docker container and is not a reliable compute resource identifier.
WARNING:d3m.metadata.pipeline_run:Configuration environment variable not set: D3MCPU
WARNING:d3m.metadata.pipeline_run:Configuration environment variable not set: D3MRAM
WARNING:d3m.metadata.pipeline_run:Docker image environment variable not set: D3M_BASE_IMAGE_NAME
WARNING:d3m.metadata.pipeline_run:Docker image environment variable not set: D3M_BASE_IMAGE_DIGEST
WARNING:d3m.metadata.pipeline_run:Docker image environment variable not set: D3M_IMAGE_NAME
WARNING:d3m.metadata.pipeline_run:Docker image environment variable not set: D3M_IMAGE_DIGEST
ERROR:redirect:/spider/spider/unsupervised_learning/grasta/grasta.py:460: RuntimeWarning: invalid value encountered in true_divide
ERROR:redirect: step = np.multiply.outer([((np.cos(t) - 1) * (Uhat @ w_hat) / w_norm) + (np.sin(t) * gamma / gamma_norm)],
Traceback (most recent call last):
File "/src/d3m/d3m/runtime.py", line 941, in _do_run_step
self._run_step(step)
File "/src/d3m/d3m/runtime.py", line 931, in _run_step
self._run_primitive(step)
File "/src/d3m/d3m/runtime.py", line 839, in _run_primitive
multi_call_result = self._call_primitive_method(primitive.fit_multi_produce, fit_multi_produce_arguments)
File "/src/d3m/d3m/runtime.py", line 914, in _call_primitive_method
raise error
File "/src/d3m/d3m/runtime.py", line 910, in _call_primitive_method
result = method(**arguments)
File "/src/d3m/d3m/primitive_interfaces/base.py", line 529, in fit_multi_produce
return self._fit_multi_produce(produce_methods=produce_methods, timeout=timeout, iterations=iterations, inputs=inputs, outputs=outputs)
File "/src/d3m/d3m/primitive_interfaces/base.py", line 556, in _fit_multi_produce
fit_result = self.fit(timeout=timeout, iterations=iterations)
File "/spider/src/sklearn-wrap/sklearn_wrap/SKLinearSVR.py", line 236, in fit
self._clf.fit(self._training_inputs, sk_training_output)
File "/usr/local/lib/python3.6/dist-packages/sklearn/svm/classes.py", line 417, in fit
accept_large_sparse=False)
File "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py", line 719, in check_X_y
estimator=estimator)
File "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py", line 542, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py", line 56, in _assert_all_finite
raise ValueError(msg_err.format(type_err, X.dtype))
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/src/d3m/d3m/__main__.py", line 6, in <module>
cli.main(sys.argv)
File "/src/d3m/d3m/cli.py", line 1111, in main
handler(arguments, parser)
File "/src/d3m/d3m/cli.py", line 1026, in handler
problem_resolver=problem_resolver,
File "/src/d3m/d3m/cli.py", line 516, in runtime_handler
problem_resolver=problem_resolver,
File "/src/d3m/d3m/runtime.py", line 2285, in fit_produce_handler
fit_result.check_success()
File "/src/d3m/d3m/runtime.py", line 68, in check_success
raise self.error
File "/src/d3m/d3m/runtime.py", line 972, in _run
self._do_run()
File "/src/d3m/d3m/runtime.py", line 958, in _do_run
self._do_run_step(step)
File "/src/d3m/d3m/runtime.py", line 950, in _do_run_step
) from error
d3m.exceptions.StepFailedError: Step 8 for pipeline e8c841d7-08c1-48dc-8ebb-24e338a3ea39 failed.
We apparently get an invalid value to divide by when running GRASTAPipelineChallenge2.