diffxpy
diffxpy copied to clipboard
theislab
TypeError: full_like() got an unexpected keyword argument 'shape' when running de.test.wald()
Hi David,
After using diffxpy for quite some time now, I am using it on a new machine now and
I'm running into the following the error when running diffxpy's de.test.wald():
de.test.wald(data = adata_raw.X, formula_loc = '~ 1 + treatment', factor_loc_totest = 'treatment', gene_names = adata_raw.var_names, sample_description = adata_raw.obs, noise_model = 'nb', size_factors = 'n_counts_scaled')
TypeError: full_like() got an unexpected keyword argument 'shape'
I'm running diffxpy==0.7.4 and batchglm==0.7.4 as well as scanpy==1.8.1 anndata==0.7.6 umap==0.5.1 numpy==1.20.3 scipy==1.7.1 pandas==1.3.3 scikit-learn==1.0.1 statsmodels==0.12.2 pynndescent==0.5.4.
Any help is greatly appreciated. Thank you so much!
Hi @niklaslang thanks for the issue, could you post a full or slightly longer error trace, please?
Thanks for getting back to me so quickly! Here's the full trace:
> ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_2146984/1501185839.py in <module>
22 ## step 3: perform DGE testing
23 print('Started Diffxpy Calculation:\t%s' %sct.util.timestamp())
---> 24 de_test = de.test.wald(data = adata_raw.X,
25 formula_loc = '~ 1 + treatment',
26 factor_loc_totest = 'treatment',
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/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)
715
716 # Fit model.
--> 717 model = _fit(
718 noise_model=noise_model,
719 data=data,
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/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)
220 raise ValueError('backend="%s" not recognized.' % backend)
221
--> 222 estim = Estimator(
223 input_data=input_data,
224 init_a=init_a,
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/batchglm/train/numpy/glm_nb/estimator.py in __init__(self, input_data, init_a, init_b, batch_size, quick_scale, dtype, **kwargs)
57 :param dtype: Numerical precision.
58 """
---> 59 init_a, init_b, train_loc, train_scale = init_par(
60 input_data=input_data,
61 init_a=init_a,
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/batchglm/models/glm_nb/utils.py in init_par(input_data, init_a, init_b, init_model)
118
119 if init_a.lower() == "closed_form":
--> 120 groupwise_means, init_a, rmsd_a = closedform_nb_glm_logmu(
121 x=input_data.x,
122 design_loc=input_data.design_loc,
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/batchglm/models/glm_nb/utils.py in closedform_nb_glm_logmu(x, design_loc, constraints_loc, size_factors, link_fn, inv_link_fn)
28 :return: tuple: (groupwise_means, mu, rmsd)
29 """
---> 30 return closedform_glm_mean(
31 x=x,
32 dmat=design_loc,
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/batchglm/models/base_glm/utils.py in closedform_glm_mean(x, dmat, constraints, size_factors, link_fn, inv_link_fn)
116 return link_fn(groupwise_means)
117
--> 118 linker_groupwise_means, mu, rmsd, rank, s = groupwise_solve_lm(
119 dmat=dmat,
120 apply_fun=apply_fun,
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/batchglm/utils/linalg.py in groupwise_solve_lm(dmat, apply_fun, constraints)
91 # Get group-wise means in linker space based on group assignments
92 # based on unique rows of design matrix:
---> 93 params = apply_fun(inverse_idx)
94
95 # Use least-squares solver to compute model parameterization
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/batchglm/models/base_glm/utils.py in apply_fun(grouping)
107
108 def apply_fun(grouping):
--> 109 groupwise_means = np.asarray(np.vstack([
110 np.mean(x[np.where(grouping == g)[0], :], axis=0)
111 for g in np.unique(grouping)
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order, like)
100 return _asarray_with_like(a, dtype=dtype, order=order, like=like)
101
--> 102 return array(a, dtype, copy=False, order=order)
103
104
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/array/core.py in __array__(self, dtype, **kwargs)
1539
1540 def __array__(self, dtype=None, **kwargs):
-> 1541 x = self.compute()
1542 if dtype and x.dtype != dtype:
1543 x = x.astype(dtype)
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/base.py in compute(self, **kwargs)
286 dask.base.compute
287 """
--> 288 (result,) = compute(self, traverse=False, **kwargs)
289 return result
290
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/base.py in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
569 postcomputes.append(x.__dask_postcompute__())
570
--> 571 results = schedule(dsk, keys, **kwargs)
572 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
573
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
77 pool = MultiprocessingPoolExecutor(pool)
78
---> 79 results = get_async(
80 pool.submit,
81 pool._max_workers,
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/local.py in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
505 _execute_task(task, data) # Re-execute locally
506 else:
--> 507 raise_exception(exc, tb)
508 res, worker_id = loads(res_info)
509 state["cache"][key] = res
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/local.py in reraise(exc, tb)
313 if exc.__traceback__ is not tb:
314 raise exc.with_traceback(tb)
--> 315 raise exc
316
317
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
218 try:
219 task, data = loads(task_info)
--> 220 result = _execute_task(task, data)
221 id = get_id()
222 result = dumps((result, id))
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/core.py in <genexpr>(.0)
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/core.py in <genexpr>(.0)
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/optimization.py in __call__(self, *args)
967 if not len(args) == len(self.inkeys):
968 raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 969 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
970
971 def __reduce__(self):
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/core.py in get(dsk, out, cache)
147 for key in toposort(dsk):
148 task = dsk[key]
--> 149 result = _execute_task(task, cache)
150 cache[key] = result
151 result = _execute_task(out, cache)
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/utils.py in apply(func, args, kwargs)
35 def apply(func, args, kwargs=None):
36 if kwargs:
---> 37 return func(*args, **kwargs)
38 else:
39 return func(*args)
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/array/reductions.py in mean_chunk(x, sum, numel, dtype, computing_meta, **kwargs)
584 if computing_meta:
585 return x
--> 586 n = numel(x, dtype=dtype, **kwargs)
587
588 total = sum(x, dtype=dtype, **kwargs)
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/dask/array/reductions.py in numel(x, **kwargs)
571 else:
572 new_shape = tuple(shape[dim] for dim in range(len(shape)) if dim not in axis)
--> 573 return np.full_like(x, prod, shape=new_shape, dtype=dtype)
574
575
<__array_function__ internals> in full_like(*args, **kwargs)
~/miniconda3/envs/niche_fibrosis_env_diffxpy/lib/python3.8/site-packages/sparse/_sparse_array.py in __array_function__(self, func, types, args, kwargs)
239 pass
240 else:
--> 241 return sparse_func(*args, **kwargs)
242
243 try:
TypeError: full_like() got an unexpected keyword argument 'shape'
@davidsebfischer this is an environment error that we cannot figure out how to fix.
The very same notebook works with my existing environment, but crashes when @niklaslang and myself create a new environment with only scanpy, diffxpy, sparse==0.9.1, jupyterlab.
Dirty fix: @niklaslang is now using my environment :joy:
Kind of confusing.
Thanks @Zethson I would guess it s about dask vs sparse version then looking at the error trace.
Yeah, that's possible. @niklaslang could try dask==2021.4.0. This was reported to work with sparse 0.9.1. See https://github.com/theislab/diffxpy/issues/194
The latest dask release is dask 2021.11.2. Would fit because it was released on the 19th of November this year.