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When running:
test_fac = de.test.versus_rest( data=adata, grouping="peri_state", test="wald", noise_model="nb" )
output:
/home/winglet/mambaforge/envs/diffxpy/lib/python3.9/site-packages/numba/core/dispatcher.py:289: UserWarning: Numba extension module 'sparse._numba_extension' failed to load due to 'ModuleNotFoundError(No module named 'numba.targets')'. entrypoints.init_all() training location model: False training scale model: True iter 0: ll=1227857339.940779 iter 1: ll=1227857339.940779, converged: 0.00% (loc: 100.00%, scale update: False), in 0.00sec iter 2: ll=9055764.057215, converged: 0.03% (loc: 0.03%, scale update: True), in 112.15sec iter 3: ll=9055764.057215, converged: 0.03% (loc: 100.00%, scale update: False), in 0.00sec iter 4: ll=7000994.629178, converged: 54.09% (loc: 54.09%, scale update: True), in 101.73sec iter 5: ll=7000994.629178, converged: 54.09% (loc: 100.00%, scale update: False), in 0.00sec iter 6: ll=6557810.944337, converged: 86.71% (loc: 86.71%, scale update: True), in 52.19sec iter 7: ll=6557810.944337, converged: 86.71% (loc: 100.00%, scale update: False), in 0.00sec iter 8: ll=6446595.094442, converged: 97.53% (loc: 97.53%, scale update: True), in 21.61sec iter 9: ll=6446595.094442, converged: 97.53% (loc: 100.00%, scale update: False), in 0.00sec iter 10: ll=6432146.995729, converged: 99.63% (loc: 99.63%, scale update: True), in 12.08sec iter 11: ll=6432146.995729, converged: 99.63% (loc: 100.00%, scale update: False), in 0.00sec iter 12: ll=6432109.198854, converged: 99.98% (loc: 99.98%, scale update: True), in 11.10sec iter 13: ll=6432109.198854, converged: 99.98% (loc: 100.00%, scale update: False), in 0.00sec iter 14: ll=6429280.381111, converged: 99.98% (loc: 99.98%, scale update: True), in 0.97sec iter 15: ll=6429280.381111, converged: 99.98% (loc: 100.00%, scale update: False), in 0.00sec iter 16: ll=6429280.381111, converged: 100.00% (loc: 100.00%, scale update: True), in 0.89sec /home/winglet/mambaforge/envs/diffxpy/lib/python3.9/site-packages/dask/array/core.py:2912: RuntimeWarning: divide by zero encountered in divide size = (limit / dtype.itemsize / largest_block) ** (1 / len(autos)) training location model: False training scale model: True iter 0: ll=1227860339.808143 iter 1: ll=1227860339.808143, converged: 0.00% (loc: 100.00%, scale update: False), in 0.00sec iter 2: ll=9000583.288855, converged: 0.05% (loc: 0.05%, scale update: True), in 109.65sec iter 3: ll=9000583.288855, converged: 0.05% (loc: 100.00%, scale update: False), in 0.00sec iter 4: ll=7016689.032998, converged: 53.33% (loc: 53.33%, scale update: True), in 100.49sec iter 5: ll=7016689.032998, converged: 53.33% (loc: 100.00%, scale update: False), in 0.00sec iter 6: ll=6544436.664088, converged: 87.09% (loc: 87.09%, scale update: True), in 53.29sec iter 7: ll=6544436.664088, converged: 87.09% (loc: 100.00%, scale update: False), in 0.00sec iter 8: ll=6420645.794305, converged: 97.37% (loc: 97.37%, scale update: True), in 20.90sec iter 9: ll=6420645.794305, converged: 97.37% (loc: 100.00%, scale update: False), in 0.00sec iter 10: ll=6391502.238352, converged: 99.47% (loc: 99.47%, scale update: True), in 11.37sec iter 11: ll=6391502.238352, converged: 99.47% (loc: 100.00%, scale update: False), in 0.00sec iter 12: ll=6389031.433432, converged: 99.93% (loc: 99.93%, scale update: True), in 9.90sec iter 13: ll=6389031.433432, converged: 99.93% (loc: 100.00%, scale update: False), in 0.00sec iter 14: ll=6389009.427554, converged: 99.99% (loc: 99.99%, scale update: True), in 8.26sec iter 15: ll=6389009.427554, converged: 99.99% (loc: 100.00%, scale update: False), in 0.00sec iter 16: ll=6389009.427554, converged: 100.00% (loc: 100.00%, scale update: True), in 0.62sec /home/winglet/mambaforge/envs/diffxpy/lib/python3.9/site-packages/dask/array/core.py:2912: RuntimeWarning: divide by zero encountered in divide size = (limit / dtype.itemsize / largest_block) ** (1 / len(autos)) training location model: False training scale model: True iter 0: ll=1227909630.300988 iter 1: ll=1227909630.300988, converged: 0.00% (loc: 100.00%, scale update: False), in 0.00sec iter 2: ll=9166533.750562, converged: 0.08% (loc: 0.08%, scale update: True), in 110.33sec iter 3: ll=9166533.750562, converged: 0.08% (loc: 100.00%, scale update: False), in 0.00sec iter 4: ll=7107714.147657, converged: 54.23% (loc: 54.23%, scale update: True), in 104.80sec iter 5: ll=7107714.147657, converged: 54.23% (loc: 100.00%, scale update: False), in 0.00sec iter 6: ll=6651136.907688, converged: 87.15% (loc: 87.15%, scale update: True), in 52.64sec iter 7: ll=6651136.907688, converged: 87.15% (loc: 100.00%, scale update: False), in 0.00sec iter 8: ll=6559446.893402, converged: 97.39% (loc: 97.39%, scale update: True), in 20.32sec iter 9: ll=6559446.893402, converged: 97.39% (loc: 100.00%, scale update: False), in 0.00sec iter 10: ll=6544054.628406, converged: 99.59% (loc: 99.59%, scale update: True), in 11.39sec iter 11: ll=6544054.628406, converged: 99.59% (loc: 100.00%, scale update: False), in 0.00sec iter 12: ll=6537830.581586, converged: 99.91% (loc: 99.91%, scale update: True), in 9.80sec iter 13: ll=6537830.581586, converged: 99.91% (loc: 100.00%, scale update: False), in 0.00sec iter 14: ll=6537830.581586, converged: 100.00% (loc: 100.00%, scale update: True), in 9.25sec --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[7], line 1 ----> 1 test_fac = de.test.versus_rest( 2 data=adata, 3 grouping="peri_state", 4 test="wald", 5 noise_model="nb" 6 ) File ~/mambaforge/envs/diffxpy/lib/python3.9/site-packages/diffxpy/diffxpy/testing/tests.py:1500, in versus_rest(data, grouping, as_numeric, test, gene_names, sample_description, noise_model, size_factors, batch_size, backend, train_args, training_strategy, is_sig_zerovar, quick_scale, dtype, pval_correction, keep_full_test_objs, **kwargs) 1493 if keep_full_test_objs: 1494 tests[0, i] = de_test_temp 1496 de_test = DifferentialExpressionTestVsRest( 1497 gene_ids=gene_names, 1498 pval=pvals, 1499 logfc=logfc, -> 1500 ave=np.mean(data, axis=0), 1501 groups=groups, 1502 tests=tests, 1503 correction_type=pval_correction 1504 ) 1506 return de_test File <__array_function__ internals>:200, in mean(*args, **kwargs) File ~/mambaforge/envs/diffxpy/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464, in mean(a, axis, dtype, out, keepdims, where) 3461 else: 3462 return mean(axis=axis, dtype=dtype, out=out, **kwargs) -> 3464 return _methods._mean(a, axis=axis, dtype=dtype, 3465 out=out, **kwargs) File ~/mambaforge/envs/diffxpy/lib/python3.9/site-packages/numpy/core/_methods.py:165, in _mean(a, axis, dtype, out, keepdims, where) 164 def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True): --> 165 arr = asanyarray(a) 167 is_float16_result = False 169 rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where) ValueError: setting an array element with a sequence. The requested array would exceed the maximum number of dimension of 32.
packages in environment at /home/winglet/mambaforge/envs/diffxpy: Name Version Build Channel _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge absl-py 1.4.0 pypi_0 pypi alabaster 0.7.13 pypi_0 pypi anndata 0.7.8 pypi_0 pypi anyio 3.7.1 pyhd8ed1ab_0 conda-forge argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge argon2-cffi-bindings 21.2.0 py39hb9d737c_3 conda-forge arrow 1.2.3 pyhd8ed1ab_0 conda-forge asttokens 2.2.1 pyhd8ed1ab_0 conda-forge astunparse 1.6.3 pypi_0 pypi async-lru 2.0.4 pyhd8ed1ab_0 conda-forge attrs 23.1.0 pyh71513ae_1 conda-forge babel 2.12.1 pyhd8ed1ab_1 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 pyhd8ed1ab_3 conda-forge backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge bandit 1.7.2 pypi_0 pypi batchglm 0.7.4 pypi_0 pypi beautifulsoup4 4.12.2 pyha770c72_0 conda-forge bleach 6.0.0 pyhd8ed1ab_0 conda-forge brotli 1.0.9 h166bdaf_9 conda-forge brotli-bin 1.0.9 h166bdaf_9 conda-forge brotli-python 1.0.9 py39h5a03fae_9 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.19.1 hd590300_0 conda-forge ca-certificates 2023.7.22 hbcca054_0 conda-forge cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge cachetools 5.3.1 pypi_0 pypi certifi 2023.7.22 pypi_0 pypi cffi 1.15.1 py39he91dace_3 conda-forge charset-normalizer 3.2.0 pyhd8ed1ab_0 conda-forge click 8.1.7 pypi_0 pypi cloudpickle 2.2.1 pypi_0 pypi colorama 0.4.6 pyhd8ed1ab_0 conda-forge comm 0.1.4 pyhd8ed1ab_0 conda-forge commonmark 0.9.1 pypi_0 pypi contourpy 1.1.0 py39h7633fee_0 conda-forge cycler 0.11.0 pyhd8ed1ab_0 conda-forge dask 2021.4.1 pypi_0 pypi debugpy 1.6.8 py39h3d6467e_0 conda-forge decorator 5.1.1 pyhd8ed1ab_0 conda-forge defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge diffxpy 0+unknown dev_0 <develop> dm-tree 0.1.8 pypi_0 pypi docutils 0.20.1 pypi_0 pypi entrypoints 0.4 pyhd8ed1ab_0 conda-forge exceptiongroup 1.1.3 pyhd8ed1ab_0 conda-forge executing 1.2.0 pyhd8ed1ab_0 conda-forge flatbuffers 23.5.26 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The text was updated successfully, but these errors were encountered:
I get the same error, it apparently occurs after all the separate tests have been run.
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output:
The text was updated successfully, but these errors were encountered: