cell2cell
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earmingol
Example not working
Hi, I'm trying to run this example: Tensor-cell2cell-PBMC.ipynb
with c2c.version = '0.7.3'
But at this step:
fig, error = tensor.elbow_rank_selection(upper_rank=25, runs=10, init='svd', # If it outputs a memory error, replace by 'random' automatic_elbow=True, random_state=888, filename=None # Put a path (e.g. ./Elbow.png) to save the figure )
I get this error:
0%| | 0/25 [00:00<?, ?it/s]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[24], line 1
----> 1 fig, error = tensor.elbow_rank_selection(upper_rank=25,
2 runs=10,
3 init='svd', # If it outputs a memory error, replace by 'random'
4 automatic_elbow=True,
5 random_state=888,
6 filename=None # Put a path (e.g. ./Elbow.png) to save the figure
7 )
File /opt/tools/deg/miniforge3/envs/cell2cell/lib/python3.10/site-packages/cell2cell/tensor/tensor.py:518, in BaseTensor.elbow_rank_selection(self, upper_rank, runs, tf_type, init, svd, metric, random_state, n_iter_max, tol, automatic_elbow, manual_elbow, smooth, mask, ci, figsize, fontsize, filename, output_fig, verbose, **kwargs)
516 fig = None
517 elif runs > 1:
--> 518 all_loss = _multiple_runs_elbow_analysis(tensor=self.tensor,
519 upper_rank=upper_rank,
520 runs=runs,
521 tf_type=tf_type,
522 init=init,
523 svd=svd,
524 metric=metric,
525 random_state=random_state,
526 mask=mask,
527 n_iter_max=n_iter_max,
528 tol=tol,
529 verbose=verbose,
530 **kwargs
531 )
533 # Same outputs as runs = 1
534 loss = np.nanmean(all_loss, axis=0).tolist()
File /opt/tools/deg/miniforge3/envs/cell2cell/lib/python3.10/site-packages/cell2cell/tensor/factorization.py:342, in _multiple_runs_elbow_analysis(tensor, upper_rank, runs, tf_type, init, svd, metric, random_state, mask, n_iter_max, tol, verbose, **kwargs)
340 else:
341 rs = None
--> 342 tl_object, errors = _compute_tensor_factorization(tensor=tensor,
343 rank=r,
344 tf_type=tf_type,
345 init=init,
346 svd=svd,
347 random_state=rs,
348 mask=mask,
349 n_iter_max=n_iter_max,
350 tol=tol,
351 verbose=verbose,
352 **kwargs)
354 if metric == 'error':
355 # This helps to obtain proper error when the mask is not None.
356 if mask is None:
File /opt/tools/deg/miniforge3/envs/cell2cell/lib/python3.10/site-packages/cell2cell/tensor/factorization.py:92, in _compute_tensor_factorization(tensor, rank, tf_type, init, svd, random_state, mask, n_iter_max, tol, verbose, **kwargs)
89 return_errors = kwargs['return_errors']
91 if tf_type == 'non_negative_cp':
---> 92 cp_tf = non_negative_parafac(tensor=tensor,
93 rank=rank,
94 init='random' if mask is not None else init,
95 svd=svd,
96 random_state=random_state,
97 mask=mask,
98 n_iter_max=n_iter_max,
99 tol=tol,
100 verbose=verbose,
101 **kwargs)
103 if return_errors:
104 cp_tf, errors = cp_tf
File /opt/tools/deg/miniforge3/envs/cell2cell/lib/python3.10/site-packages/tensorly/decomposition/_nn_cp.py:85, in non_negative_parafac(tensor, rank, n_iter_max, init, svd, tol, random_state, verbose, normalize_factors, return_errors, mask, cvg_criterion, fixed_modes)
82 epsilon = tl.eps(tensor.dtype)
83 rank = validate_cp_rank(tl.shape(tensor), rank=rank)
---> 85 weights, factors = initialize_cp(
86 tensor,
87 rank,
88 init=init,
89 svd=svd,
90 non_negative=True,
91 mask=mask,
92 random_state=random_state,
93 normalize_factors=normalize_factors,
94 )
95 rec_errors = []
96 norm_tensor = tl.norm(tensor, 2)
File /opt/tools/deg/miniforge3/envs/cell2cell/lib/python3.10/site-packages/tensorly/decomposition/_cp.py:76, in initialize_cp(tensor, rank, init, svd, non_negative, random_state, normalize_factors, mask, svd_mask_repeats)
74 for mode in range(tl.ndim(tensor)):
75 mask_unfold = None if mask is None else unfold(mask, mode)
---> 76 U, S, _ = svd_interface(
77 unfold(tensor, mode),
78 n_eigenvecs=rank,
79 method=svd,
80 non_negative=non_negative,
81 mask=mask_unfold,
82 n_iter_mask_imputation=svd_mask_repeats,
83 )
85 # Put SVD initialization on the same scaling as the tensor in case normalize_factors=False
86 if mode == 0:
File /opt/tools/deg/miniforge3/envs/cell2cell/lib/python3.10/site-packages/tensorly/tenalg/svd.py:414, in svd_interface(matrix, method, n_eigenvecs, flip_sign, u_based_flip_sign, non_negative, mask, n_iter_mask_imputation, **kwargs)
412 svd_fun = method
413 else:
--> 414 raise ValueError(
415 f"Got svd={method}. However, the possible choices are {SVD_FUNS} or to pass a callable."
416 )
418 U, S, V = svd_fun(matrix, n_eigenvecs=n_eigenvecs, **kwargs)
420 if mask is not None:
ValueError: Got svd=numpy_svd. However, the possible choices are ['truncated_svd', 'symeig_svd', 'randomized_svd'] or to pass a callable.
Any idea about to solve this?
Hi @imerelli
I don't recommend using that example. Instead follow any other on the readme: https://github.com/earmingol/cell2cell/blob/master/README.md.
Regarding your issue, a quick way to solve is to add to your command the parameter svd and pick any of the options that the error is giving you. For example, add svd='truncated_svd'.
fig, error = tensor.elbow_rank_selection(upper_rank=25,
runs=10,
init='svd', # If it outputs a memory error, replace by 'random'
svd='truncated_svd',
automatic_elbow=True,
random_state=888,
filename=None # Put a path (e.g. ./Elbow.png) to save the figure
)
Alternatively replace init='svd' with init='random'.
fig, error = tensor.elbow_rank_selection(upper_rank=25,
runs=10,
init='random', # If it outputs a memory error, replace by 'random'
automatic_elbow=True,
random_state=888,
filename=None # Put a path (e.g. ./Elbow.png) to save the figure
)
Also, what versions of tensorly and numpy are you using?
I hope this helps.
