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environment.yml

@@ -12,7 +12,7 @@ dependencies:
   - pandas
   - pip
   - scanpy
-  - squidpy>=1.2.0
+  - squidpy
   - tensorflow=2.13
   - tensorflow-probability=0.21
   - pip:

mNSF/process_multiSample.py

@@ -108,7 +108,7 @@ def get_D_fromAnnData(ad):
 	return D
 
 
-def get_listD_chunked(list_D_,list_nchunk=[1]*len(list_D_)):
+def get_listDtrain(list_D_,nbatch=1):
 	"""
 	Prepare the training data by creating TensorFlow Datasets.
     
@@ -124,66 +124,12 @@ def get_listD_chunked(list_D_,list_nchunk=[1]*len(list_D_)):
 	"""
 	list_Dtrain=list()
 	nsample=len(list_D_)
-	# data chunking
-	nsample_splitted = sum(list_nchunk)
-	list_D_chunk = list()
 	for ksample in range(0,nsample):
 		D=list_D_[ksample]
-		nchunk = list_nchunk[ksample]
-		X = D['X']
-		Y = D['Y']
-		nspot = X.shape[1]
-		nspot_perChunk = int(nspot/nchunk)
-		for kchunk in range(0,nchunk):
-			st = (kchunk-1)*nspot_perChunk
-			end_ = kchunk*nspot_perChunk
-			if (kchunk==nchunk-1):end_=nspot
-			X_chunk=X[st:end_,]
-			Y_chunk=Y[st:end_,]
-			D_chunk = get_D(X,Y)
-			list_D_chunk.append(D_chunk)
-	return list_D_chunk
-
-def get_listDtrain(list_D_,nbatch=1,list_nchunk=[False]*len(list_D_)):
-	"""
-	Prepare the training data by creating TensorFlow Datasets.
-    
-    This function converts the data dictionaries into TensorFlow Dataset objects,
-    which are efficient for training machine learning models.
-    
-    Args:
-    list_D_: List of data dictionaries, one for each sample
-    nbatch: Number of batches to split the data into (default is 1)
-    
-    Returns:
-    list_Dtrain: List of TensorFlow Datasets for training
-	"""
-	list_Dtrain=list()
-	nsample=len(list_D_)
-	# data chunking
-	nsample_splitted = sum(list_nchunk)
-	list_D_chunk = list()
-	for ksample in range(0,nsample):
-		D=list_D_[ksample]
-		nchunk = list_nchunk[ksample]
-		X = D['X']
-		Y = D['Y']
-		nspot = X.shape[1]
-		nspot_perChunk = int(nspot/nchunk)
-		for kchunk in range(0,nchunk):
-			st = (kchunk-1)*nspot_perChunk
-			end_ = kchunk*nspot_perChunk
-			if (kchunk==nchunk-1):end_=nspot
-			X_chunk=X[st:end_,]
-			Y_chunk=Y[st:end_,]
-			D_chunk = get_D(X,Y)
-			list_D_chunk.append(D_chunk)
-	for ksample_splitted in range(0,nsample_splitted):
-		D_chunk=list_D_chunk[ksample]
-		Ntr = D_chunk["Y"].shape[0] # Number of observations in this sample
+		Ntr = D["Y"].shape[0] # Number of observations in this sample
 
 		# Convert dictionary to TensorFlow Dataset
-		Dtrain = Dataset.from_tensor_slices(D_chunk)
+		Dtrain = Dataset.from_tensor_slices(D)
 
 		# Batch the data
 		if (nbatch==1): D_train = Dtrain.batch(round(Ntr)+1)

tests/test_small_run.py

@@ -28,22 +28,21 @@ def _run(
 
     # step 0  Data loading
     D, X = load_data(data_dir, n_sample)
-    ninduced
     listDtrain = process_multiSample.get_listDtrain(D)
-    list_D_chunked = process_multiSample.get_listD_chunked(D)
-    for ksample in range(0, len(list_D_chunked)):
+
+    for ksample in range(0, len(D)):
         random.seed(10)
-        ninduced = round(list_D_chunked[ksample]["X"].shape[0] * 0.35)
+        ninduced = round(D[ksample]["X"].shape[0] * 0.35)
         D_tmp = D[ksample]
         D[ksample]["Z"] = D_tmp["X"][random.sample(range(0, D_tmp["X"].shape[0] - 1), ninduced), :]
 
     # step 1 initialize model
-    fit = process_multiSample.ini_multiSample(list_D_chunked, n_loadings, "nb", chol=False)
-    
+    fit = process_multiSample.ini_multiSample(D, n_loadings, "nb", chol=False)
+
     # step 2 fit model
 
     (pp := (output_dir / "models" / "pp")).mkdir(parents=True, exist_ok=True)
-    fit = training_multiSample.train_model_mNSF(fit, pp, listDtrain, list_D_chunked, legacy=legacy, num_epochs=epochs)
+    fit = training_multiSample.train_model_mNSF(fit, pp, listDtrain, D, legacy=legacy, num_epochs=epochs)
     (output_dir / "list_fit_smallData.pkl").write_bytes(pickle.dumps(fit))
 
     # step 3 save results