expand dims bug with da.test.wald

[giovp]

I get this error when running:

test = de.test.wald(
    data=adata.layers['counts'],
    formula_loc="~ 1 + leiden",
    factor_loc_totest="leiden",
    gene_names=adata.var_names,
    sample_description=adata.obs,
)

Details

---

TypeError Traceback (most recent call last) in 4 factor_loc_totest="leiden", 5 gene_names=adata.var_names, ----> 6 sample_description=adata.obs 7 )

~/Projects/diffxpy/diffxpy/testing/tests.py in wald(data, factor_loc_totest, coef_to_test, formula_loc, formula_scale, as_numeric, init_a, init_b, gene_names, sample_description, dmat_loc, dmat_scale, constraints_loc, constraints_scale, noise_model, size_factors, batch_size, backend, train_args, training_strategy, quick_scale, dtype, **kwargs) 734 quick_scale=quick_scale, 735 dtype=dtype, --> 736 **kwargs, 737 ) 738

~/Projects/diffxpy/diffxpy/testing/tests.py in _fit(noise_model, data, design_loc, design_scale, design_loc_names, design_scale_names, constraints_loc, constraints_scale, init_model, init_a, init_b, gene_names, size_factors, batch_size, backend, training_strategy, quick_scale, train_args, close_session, dtype) 188 chunk_size_genes=chunk_size_genes, 189 as_dask=backend.lower() in ["numpy"], --> 190 cast_dtype=dtype 191 ) 192

~/Projects/batchglm/batchglm/models/base_glm/input.py in init(self, data, design_loc, design_loc_names, design_scale, design_scale_names, constraints_loc, constraints_scale, size_factors, observation_names, feature_names, chunk_size_cells, chunk_size_genes, as_dask, cast_dtype) 89 chunk_size_genes=chunk_size_genes, 90 cast_dtype=cast_dtype, ---> 91 as_dask=as_dask 92 ) 93

~/Projects/batchglm/batchglm/models/base/input.py in init(self, data, observation_names, feature_names, chunk_size_cells, chunk_size_genes, as_dask, cast_dtype) 88 self.x = self.x.astype(cast_dtype) 89 ---> 90 self._feature_allzero = np.sum(self.x, axis=0) == 0 91 self.chunk_size_cells = chunk_size_cells 92 self.chunk_size_genes = chunk_size_genes

<array_function internals> in sum(*args, **kwargs)

~/miniconda3/envs/diffxpy/lib/python3.7/site-packages/dask/array/core.py in array_function(self, func, types, args, kwargs) 1423 if da_func is func: 1424 return handle_nonmatching_names(func, args, kwargs) -> 1425 return da_func(*args, **kwargs) 1426 1427 @property

~/miniconda3/envs/diffxpy/lib/python3.7/site-packages/dask/array/reductions.py in sum(a, axis, dtype, keepdims, split_every, out) 338 dtype=dtype, 339 split_every=split_every, --> 340 out=out, 341 ) 342 return result

~/miniconda3/envs/diffxpy/lib/python3.7/site-packages/dask/array/reductions.py in reduction(x, chunk, aggregate, axis, keepdims, dtype, split_every, combine, name, out, concatenate, output_size, meta) 155 # The dtype of tmp doesn't actually matter, and may be incorrect. 156 tmp = blockwise( --> 157 chunk, inds, x, inds, axis=axis, keepdims=True, token=name, dtype=dtype or float 158 ) 159 tmp._chunks = tuple(

~/miniconda3/envs/diffxpy/lib/python3.7/site-packages/dask/array/blockwise.py in blockwise(func, out_ind, name, token, dtype, adjust_chunks, new_axes, align_arrays, concatenate, meta, *args, **kwargs) 248 from .utils import compute_meta 249 --> 250 meta = compute_meta(func, dtype, *args[::2], **kwargs) 251 if meta is not None: 252 return Array(graph, out, chunks, meta=meta)

~/miniconda3/envs/diffxpy/lib/python3.7/site-packages/dask/array/utils.py in compute_meta(func, _dtype, *args, **kwargs) 125 if has_keyword(func, "computing_meta"): 126 kwargs_meta["computing_meta"] = True --> 127 meta = func(*args_meta, **kwargs_meta) 128 except TypeError as e: 129 if (

<array_function internals> in sum(*args, **kwargs)

~/miniconda3/envs/diffxpy/lib/python3.7/site-packages/numpy/core/fromnumeric.py in sum(a, axis, dtype, out, keepdims, initial, where) 2227 2228 return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims, -> 2229 initial=initial, where=where) 2230 2231

~/miniconda3/envs/diffxpy/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs) 84 # support a dtype. 85 if dtype is not None: ---> 86 return reduction(axis=axis, dtype=dtype, out=out, **passkwargs) 87 else: 88 return reduction(axis=axis, out=out, **passkwargs)

TypeError: sum() got an unexpected keyword argument 'keepdims'

I feel it's some numpy conflict versions but can't nail it down I have numpy 1.18

[giovp]

after trying different combinations, this seems to work: scanpy==1.6.0 anndata==0.7.5 umap==0.4.6 numpy==1.18.2 scipy==1.4.1 pandas==1.1.3 scikit-learn==0.23.2 statsmodels==0.11.1 python-igraph==0.8.3 leidenalg==0.8.3