I encountered this error when running mofapy2 (v 0.7.0) via muon.
- Automatic Relevance Determination prior on the factors: True
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (ADT): gaussian
- View 1 (RNA): gaussian
GPU mode is activated
But then the error kicks in.
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/scipy/__init__.py:137, in __getattr__(name)
136 try:
--> 137 return globals()[name]
138 except KeyError:
KeyError: 'shape'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
Cell In[84], line 1
----> 1 mu.tl.mofa(mdata, gpu_mode=True, use_var="highly_variable")
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/muon/_core/tools.py:586, in mofa(data, groups_label, use_raw, use_layer, use_var, use_obs, likelihoods, n_factors, scale_views, scale_groups, center_groups, ard_weights, ard_factors, spikeslab_weights, spikeslab_factors, n_iterations, convergence_mode, use_float32, gpu_mode, gpu_device, svi_mode, svi_batch_size, svi_learning_rate, svi_forgetting_rate, svi_start_stochastic, smooth_covariate, smooth_warping, smooth_kwargs, save_parameters, save_data, save_metadata, seed, outfile, expectations, save_interrupted, verbose, quiet, copy)
570 ent.set_smooth_options(
571 scale_cov=smooth_kwargs["scale_cov"],
572 start_opt=smooth_kwargs["start_opt"],
(...)
582 frac_inducing=smooth_kwargs["frac_inducing"],
583 )
585 logging.info(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Building the model...")
--> 586 ent.build()
587 logging.info(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Running the model...")
588 ent.run()
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/run/entry_point.py:1405, in entry_point.build(self)
1397 else:
1398 tmp = buildBiofam(
1399 self.data,
1400 self.dimensionalities,
(...)
1403 self.train_opts,
1404 )
-> 1405 tmp.main()
1407 # Create BayesNet class
1408 if self.train_opts["stochastic"]:
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/build_model/build_model.py:56, in buildBiofam.main(self)
47 self.init_model = initModel(
48 dim=self.dim,
49 data=self.data,
(...)
52 seed=self.train_opts["seed"],
53 )
55 # Build all nodes
---> 56 self.build_nodes()
58 # Define markov blankets
59 self.createMarkovBlankets()
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/build_model/build_model.py:65, in buildBiofam.build_nodes(self)
62 """Method to build all nodes"""
64 # Build general nodes
---> 65 self.build_Z()
66 self.build_W()
67 self.build_Tau()
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/build_model/build_model.py:104, in buildBiofam.build_Z(self)
95 self.init_model.initSZ(
96 qmean_T1="pca",
97 Y=self.data,
98 impute=True,
99 weight_views=self.train_opts["weight_views"],
100 )
101 else:
102 # self.init_model.initZ(qmean=0)
103 # self.init_model.initZ(qmean="random")
--> 104 self.init_model.initZ(
105 qmean="pca",
106 Y=self.data,
107 impute=True,
108 weight_views=self.train_opts["weight_views"],
109 )
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/build_model/init_model.py:145, in initModel.initZ(self, pmean, pvar, qmean, qvar, qE, qE2, Y, impute, weight_views)
142 exit()
144 # Initialise the node
--> 145 self.nodes["Z"] = Z_Node(
146 dim=(self.N, self.K),
147 pmean=pmean,
148 pvar=pvar,
149 qmean=qmean,
150 qvar=qvar,
151 qE=qE,
152 qE2=qE2,
153 weight_views=weight_views,
154 )
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/core/nodes/Z_nodes.py:20, in Z_Node.__init__(self, dim, pmean, pvar, qmean, qvar, qE, qE2, weight_views)
17 def __init__(
18 self, dim, pmean, pvar, qmean, qvar, qE=None, qE2=None, weight_views=False
19 ):
---> 20 super().__init__(
21 dim=dim, pmean=pmean, pvar=pvar, qmean=qmean, qvar=qvar, qE=qE, qE2=qE2
22 )
24 self.mini_batch = None
25 self.factors_axis = 1
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/core/nodes/variational_nodes.py:148, in UnivariateGaussian_Unobserved_Variational_Node.__init__(self, dim, pmean, pvar, qmean, qvar, qE, qE2)
146 Unobserved_Variational_Node.__init__(self, dim)
147 # Initialise the P and Q distributions
--> 148 self.P = UnivariateGaussian(dim=dim, mean=pmean, var=pvar)
149 self.Q = UnivariateGaussian(dim=dim, mean=qmean, var=qvar, E=qE, E2=qE2)
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/core/distributions/univariate_gaussian.py:46, in UnivariateGaussian.__init__(self, dim, mean, var, E, E2)
43 self.to_float32()
45 # Check that dimensionalities match
---> 46 self.CheckDimensionalities()
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/mofapy2/core/distributions/basic_distributions.py:68, in Distribution.CheckDimensionalities(self)
66 """General method to do a sanity check on the dimensionalities"""
67 # p_dim = set(map(s.shape, self.params.values()))
---> 68 e_dim = set(map(s.shape, self.expectations.values()))
69 # assert len(p_dim) == 1, "Parameters have different dimensionalities"
70 assert len(e_dim) == 1, "Expectations have different dimensionalities"
File /exports/archive/hg-funcgenom-research/mdmanurung/conda/envs/totalvi/lib/python3.9/site-packages/scipy/__init__.py:139, in __getattr__(name)
137 return globals()[name]
138 except KeyError:
--> 139 raise AttributeError(
140 f"Module 'scipy' has no attribute '{name}'"
141 )
Do you know how to solve this? My scipy version is 1.12.0. Thanks in advance.
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