test_auc_output = []
val_auc_output = []
average_time_seed = 0
for s in args.seeds:
init_time = time.time()
print('New seed: ' + str(s))
print(torch.cuda.memory_allocated())
print(torch.cuda.memory_reserved())
dendronet = DendroMatrixLinReg(device, root_weights, parent_path_tensor, edge_tensor_matrix)
best_root_weights = dendronet.root_weights
best_delta_matrix = dendronet.delta_mat
train_idx, test_idx = split_indices(mapping, seed=0)
train_idx, val_idx = split_indices(train_idx, seed=s)
# creating idx dataset objects for batching
train_set = IndicesDataset(train_idx)
val_set = IndicesDataset(val_idx)
test_set = IndicesDataset(test_idx)
# Setting some parameters for shuffle batch
params = {'batch_size': BATCH_SIZE,
'shuffle': True,
'num_workers': 0}
train_batch_gen = torch.utils.data.DataLoader(train_set, **params)
val_batch_gen = torch.utils.data.DataLoader(val_set, **params)
test_batch_gen = torch.utils.data.DataLoader(test_set, **params)
for seed in seeds:
# Multi_CT_conv = MultiCTConvNet(device=device, num_cell_types=len(cell_names), seq_length=501,
# kernel_size=26, number_of_kernels=64, polling_window=7)
convolution = SeqConvModule(device=device,
seq_length=501,
kernel_sizes=(16, 3, 3),
num_of_kernels=(128, 64, 32),
polling_windows=(3, 4),
input_channels=4)
fully_connected = FCModule(device=device,
layer_sizes=(len(tissue_encodings[0]) + 32,
32, 1))
packed_train_idx, packed_test_idx = split_indices(packed_samples,
seed=0)
packed_train_idx, packed_val_idx = split_indices(packed_train_idx,
seed=seed)
# unpacking
train_idx = []
val_idx = []
test_idx = []
for packet in packed_train_idx:
for sample in packet:
train_idx.append(sample)
for packet in packed_val_idx:
for sample in packet:
val_idx.append(sample)
num_nodes = len(node_list)
num_edges = num_nodes - 1
# constructing the parent-child matrix, would be nice to find a faster way to do this
parent_child_mat = np.zeros(shape=(num_nodes, num_nodes),
dtype=np.float32)
for child_idx in range(1,
len(node_list)): # excluding the root
parent_idx = node_list.index(node_list[child_idx].parent)
parent_child_mat[parent_idx, child_idx] = 1.0
pp_mat = build_parent_path_mat(parent_child_mat,
num_edges=num_edges)
# split the leaves into train and test
train_idx, valid_idx = split_indices(range(len(leaves)))
# constructing train and valid x and y matrices
train_col_idx = [leaves[i][0] for i in train_idx]
valid_col_idx = [leaves[i][0] for i in valid_idx]
train_col_idx_tensor = torch.tensor(train_col_idx,
device=device)
valid_col_idx_tensor = torch.tensor(valid_col_idx,
device=device)
train_x = torch.tensor(np.asarray(
[leaves[i][1].x for i in train_idx]),
device=device,
dtype=torch.double)
train_y = torch.tensor(np.asarray(
[leaves[i][1].y for i in train_idx]),
val_auc_output = []
average_time_seed = 0 # to test time performance of the training of this model
for s in args.seeds:
init_time = time.time()
# simple linear model to which a sigmoid will be applied in order to make it a logistic model
# Used for comparison with DendroNet performance
# We use a linear regression in order to be able to use BCEWithLogitsLoss as loss function,
# a more stable version of BCEloss
logistic = LinRegModel(len(X[0]))
logistic.to(device)
logistic = logistic.double()
best_weights = logistic.lin_1
train_idx, test_idx = split_indices(range(len(X)), seed=0)
train_idx, val_idx = split_indices(train_idx, seed=s)
# creating idx dataset objects for batching
train_set = IndicesDataset(train_idx)
val_set = IndicesDataset(val_idx)
test_set = IndicesDataset(test_idx)
# Setting some parameters for shuffle batch
params = {'batch_size': BATCH_SIZE, 'shuffle': True, 'num_workers': 0}
train_batch_gen = torch.utils.data.DataLoader(train_set, **params)
val_batch_gen = torch.utils.data.DataLoader(val_set, **params)
test_batch_gen = torch.utils.data.DataLoader(test_set, **params)
# converting X and y to tensors, and transferring to GPU if the cuda flag is set
# each column holds a trainable delta vector for each edge in the graph
edge_tensor_matrix = np.zeros(shape=(num_features, num_edges))
"""
Now we have all the components, and can create an instance of the DendroNet model specific to our graph architecture
We will use a linear regressor as the base architecture
"""
dendronet = DendroMatrixLinReg(device, root_weights, parent_path_tensor,
edge_tensor_matrix)
"""
We typically want to split into a train and test/validation set. We will do this by assigning indices to either group so
that we can keep the mapping between parent_path column ID and the corresponding sample in X and y
We can then use the IndicesDataset class to perform shuffle-batching during training
"""
train_idx, test_idx = split_indices(range(len(y)))
# creating idx dataset objects for batching
train_set = IndicesDataset(train_idx)
test_set = IndicesDataset(test_idx)
# Setting some parameters for shuffle batch
params = {'batch_size': BATCH_SIZE, 'shuffle': True, 'num_workers': 0}
train_batch_gen = torch.utils.data.DataLoader(train_set, **params)
test_batch_gen = torch.utils.data.DataLoader(test_set, **params)
# converting X and y to tensors, and transferring to GPU if the cuda flag is set
X = torch.tensor(X, dtype=torch.double, device=device)
y = torch.tensor(y, dtype=torch.double, device=device)
