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# fit in data
model.get_data(adata=data,                   # count or expression matrix, (dense or sparse) numpy array 
               labels = data.obs['cluster_label'],       # (optional) labels, which will be converted to string
               gene_names = data.var['features'], # (optional) gene names, which will be converted to string
               cell_names = data.obs['sample_name']    # (optional) cell names, which will be converted to string
              )


# preprocess data
model.preprocess_data(gene_num = 2000,        # (optional) maximum number of influential genes to keep (the default is 2000)
                     data_type = 'Gaussian', # (optional) data_type can be 'UMI', 'non-UMI' or 'Gaussian' (the default is 'UMI')
                      npc = 64,         # (optional) number of PCs to keep if data_type='Gaussian' (the default is 64)
                     processed=True)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-2da55840b803> in <module>
      3                 batch_size=256,              # (Optional) the batch size for pre-training (the default is 32).
      4                 alpha=0.10,                  # (Optional) the value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.
----> 5                 num_epoch = 300,             # (Optional) the maximum number of epoches (the default is 300).
      6                 ) 

~/anaconda3/lib/python3.7/site-packages/VITAE/VITAE.py in pre_train(self, stratify, test_size, random_state, learning_rate, batch_size, L, alpha, num_epoch, num_step_per_epoch, early_stopping_patience, early_stopping_tolerance, path_to_weights)
    274                                                 batch_size,
    275                                                 self.X[id_train].astype(tf.keras.backend.floatx()),
--> 276                                                 self.scale_factor[id_train].astype(tf.keras.backend.floatx()))
    277         self.test_dataset = train.warp_dataset(self.X_normalized[id_test], 
    278                                                 None if self.c_score is None else self.c_score[id_test].astype(tf.keras.backend.floatx()),

TypeError: 'NoneType' object is not subscriptable

# fit in data
model.get_data(adata=data,                   # count or expression matrix, (dense or sparse) numpy array 
               labels = data.obs['cluster_label'],       # (optional) labels, which will be converted to string
               gene_names = data.var['features'], # (optional) gene names, which will be converted to string
               cell_names = data.obs['sample_name']    # (optional) cell names, which will be converted to string
              )

# preprocess data
model.preprocess_data(gene_num = 2000,        # (optional) maximum number of influential genes to keep (the default is 2000)
                      data_type = 'UMI', # (optional) data_type can be 'UMI', 'non-UMI' or 'Gaussian' (the default is 'UMI')
                      npc = 64              # (optional) number of PCs to keep if data_type='Gaussian' (the default is 64)#)
                      )
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-7-aa66b286b1ac> in <module>()
     35 model.preprocess_data(gene_num = 2000,        # (optional) maximum number of influential genes to keep (the default is 2000)
     36                       data_type = 'UMI', # (optional) data_type can be 'UMI', 'non-UMI' or 'Gaussian' (the default is 'UMI')
---> 37                       npc = 64              # (optional) number of PCs to keep if data_type='Gaussian' (the default is 64)#)
     38                       )

2 frames
/usr/local/lib/python3.7/dist-packages/VITAE/preprocess.py in _recipe_seurat(adata, gene_num)
    238     This uses a particular preprocessing
    239     """
--> 240     cell_mask = sc.pp.filter_cells(adata, min_genes=200, inplace=False)[0]
    241     adata = adata[cell_mask,:]
    242     gene_mask = sc.pp.filter_genes(adata, min_cells=3, inplace=False)[0]

NameError: name 'sc' is not defined

# initialize inference
model.init_inference(batch_size=128, 
                     L=150,            # L is the number of MC samples
                     dimred='umap',    # dimension reduction methods
                     #**kwargs         # extra key-value arguments for dimension reduction algorithms.    
                     random_state=seed
                    ) 
# after initialization, we can access some variables by model.pc_x, model.w, model.w_tilde, etc..

Computing posterior estimations over mini-batches.

---------------------------------------------------------------------------

ResourceExhaustedError                    Traceback (most recent call last)

<ipython-input-27-91c48b13b6e4> in <module>()
      4                      dimred='umap',    # dimension reduction methods
      5                      #**kwargs         # extra key-value arguments for dimension reduction algorithms.
----> 6                      random_state=seed
      7                     ) 
      8 # after initialization, we can access some variables by model.pc_x, model.w, model.w_tilde, etc..

10 frames

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

ResourceExhaustedError:  OOM when allocating tensor with shape[128,150,1653,57] and type double on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[node Tile_1 (defined at /usr/local/lib/python3.7/dist-packages/VITAE/model.py:367) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
 [Op:__inference__get_inference_4681280]

Function call stack:
_get_inference