Closed sgbaird closed 2 years ago
@ardunn tests are failing, seems related to matminer. e.g.
======================================================================
ERROR: test_has_polymorphs (matbench.tests.test_task.TestMatbenchTask)
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Traceback (most recent call last):
File "/home/runner/work/matbench/matbench/matbench/tests/test_task.py", line 464, in test_has_polymorphs
mbt = MatbenchTask("matbench_steels", autoload=True)
File "/home/runner/work/matbench/matbench/matbench/task.py", line 89, in __init__
self.df = load(self.dataset_name) if autoload else None
File "/home/runner/work/matbench/matbench/matbench/data_ops.py", line 66, in load
df = load_dataset(dataset_name)
File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/matminer/datasets/dataset_retrieval.py", line 66, in load_dataset
_validate_dataset(
File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/matminer/datasets/utils.py", line 89, in _validate_dataset
raise UserWarning(
UserWarning: Error, hash of downloaded file does not match that included in metadata, the data may be corrupt or altered
======================================================================
ERROR: test_instantiation (matbench.tests.test_task.TestMatbenchTask)
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Traceback (most recent call last):
File "/home/runner/work/matbench/matbench/matbench/tests/test_task.py", line 35, in test_instantiation
MatbenchTask(ds, autoload=True)
File "/home/runner/work/matbench/matbench/matbench/task.py", line 89, in __init__
self.df = load(self.dataset_name) if autoload else None
File "/home/runner/work/matbench/matbench/matbench/data_ops.py", line 66, in load
df = load_dataset(dataset_name)
File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/matminer/datasets/dataset_retrieval.py", line 66, in load_dataset
_validate_dataset(
File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/matminer/datasets/utils.py", line 89, in _validate_dataset
raise UserWarning(
UserWarning: Error, hash of downloaded file does not match that included in metadata, the data may be corrupt or altered
======================================================================
ERROR: test_record (matbench.tests.test_task.TestMatbenchTask)
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Traceback (most recent call last):
File "/home/runner/work/matbench/matbench/matbench/tests/test_task.py", line 211, in test_record
mbt.load()
File "/home/runner/work/matbench/matbench/matbench/task.py", line 235, in load
self.df = load(self.dataset_name)
File "/home/runner/work/matbench/matbench/matbench/data_ops.py", line 66, in load
df = load_dataset(dataset_name)
File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/matminer/datasets/dataset_retrieval.py", line 66, in load_dataset
_validate_dataset(
File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/matminer/datasets/utils.py", line 89, in _validate_dataset
raise UserWarning(
UserWarning: Error, hash of downloaded file does not match that included in metadata, the data may be corrupt or altered
----------------------------------------------------------------------
Ran 30 tests in 73.[767](https://github.com/materialsproject/matbench/runs/6874143276?check_suite_focus=true#step:4:768)s
@sgbaird Thanks for the PR! Let me see if I can fix this and I'll merge this in.
Thanks!
Merged! Not sure what was going on with the tests, maybe some sort of version issue. I was able to pass all the tests of your branch on my machine so it's probably just some CI problem which I'll debug.
Sweet
eXtreme Gradient Boosting trees (XGBoost) is applied on basic tabular data describing the crystal lattice of each compound: lattice parameter lengths and angles, space group number, and unit cell volume. Fixed XGBoost hyperparameters were used. This serves as part of a baseline to answer the question: how much predictive performance is present in the basic details of a crystal lattice (i.e. no composition, no site information)?
This is designed for use on the
matbench_mp_e_form
task as an alternative perspective in a more established domain (i.e. model accuracy) to that of generative model benchmarking. This is specifically part of a series of baselines and tests related to the xtal2png representation.https://github.com/sparks-baird/xtal2png/issues/51
Authored primarily by @cseeg