def create_cell_mlp(input_nodes):
"""Create a cell with convolution.
Args:
input_nodes (list(Node)): a list of input_nodes for this cell.
Returns:
Cell: the corresponding cell.
"""
cell = Cell(input_nodes)
n1 = ConstantNode(name='N1')
cell.graph.add_edge(input_nodes[0], n1) # fixed input connection
n1.set_op(op=Flatten())
n2 = VariableNode('N2')
n2.add_op(Identity())
n2.add_op(Dense(units=10))
n2.add_op(Dense(units=50))
n2.add_op(Dense(units=100))
n2.add_op(Dense(units=200))
n2.add_op(Dense(units=250))
n2.add_op(Dense(units=500))
n2.add_op(Dense(units=750))
n2.add_op(Dense(units=1000))
n3 = VariableNode('N3')
n3.add_op(Identity())
n3.add_op(Activation(activation='relu'))
n3.add_op(Activation(activation='tanh'))
n3.add_op(Activation(activation='sigmoid'))
n4 = VariableNode('N4')
n4.add_op(Identity())
n4.add_op(Dropout(rate=0.5))
n4.add_op(Dropout(rate=0.4))
n4.add_op(Dropout(rate=0.3))
n4.add_op(Dropout(rate=0.2))
n4.add_op(Dropout(rate=0.1))
n4.add_op(Dropout(rate=0.05))
block = Block()
block.add_node(n1)
block.add_node(n2)
block.add_node(n3)
block.add_node(n4)
block.add_edge(n1, n2)
block.add_edge(n2, n3)
block.add_edge(n3, n4)
cell.add_block(block)
cell.set_outputs()
return cell
def create_cell_conv(input_nodes):
"""Create a cell with convolution.
Args:
input_nodes (list(Node)): a list of input_nodes for this cell.
Returns:
Cell: the corresponding cell.
"""
cell = Cell(input_nodes)
n1 = VariableNode('N1')
cell.graph.add_edge(input_nodes[0], n1) # fixed input connection
n1.add_op(Identity())
# n1.add_op(Conv1D(filter_size=2, num_filters=8))
n1.add_op(Conv1D(filter_size=3, num_filters=8))
n1.add_op(Conv1D(filter_size=4, num_filters=8))
n1.add_op(Conv1D(filter_size=5, num_filters=8))
n1.add_op(Conv1D(filter_size=6, num_filters=8))
# n1.add_op(Conv1D(filter_size=7, num_filters=8))
# n1.add_op(Conv1D(filter_size=8, num_filters=8))
# n1.add_op(Conv1D(filter_size=9, num_filters=8))
# n1.add_op(Conv1D(filter_size=10, num_filters=8))
n2 = VariableNode('N2')
n2.add_op(Identity())
n2.add_op(Activation(activation='relu'))
n2.add_op(Activation(activation='tanh'))
n2.add_op(Activation(activation='sigmoid'))
n3 = VariableNode('N3')
n3.add_op(Identity())
# n3.add_op(MaxPooling1D(pool_size=2, padding='same'))
n3.add_op(MaxPooling1D(pool_size=3, padding='same'))
n3.add_op(MaxPooling1D(pool_size=4, padding='same'))
n3.add_op(MaxPooling1D(pool_size=5, padding='same'))
n3.add_op(MaxPooling1D(pool_size=6, padding='same'))
# n3.add_op(MaxPooling1D(pool_size=7, padding='same'))
# n3.add_op(MaxPooling1D(pool_size=8, padding='same'))
# n3.add_op(MaxPooling1D(pool_size=9, padding='same'))
# n3.add_op(MaxPooling1D(pool_size=10, padding='same'))
block = Block()
block.add_node(n1)
block.add_node(n2)
block.add_node(n3)
block.add_edge(n1, n2)
block.add_edge(n2, n3)
cell.add_block(block)
cell.set_outputs()
return cell
def create_block():
# first node of block
n1 = VariableNode('N1')
for inpt in input_nodes:
n1.add_op(Connect(cell.graph, inpt, n1))
# second node of block
mlp_op_list = list()
mlp_op_list.append(Identity())
mlp_op_list.append(Dense(5, tf.nn.relu))
mlp_op_list.append(Dense(5, tf.nn.tanh))
mlp_op_list.append(Dense(10, tf.nn.relu))
mlp_op_list.append(Dense(10, tf.nn.tanh))
mlp_op_list.append(Dense(20, tf.nn.relu))
mlp_op_list.append(Dense(20, tf.nn.tanh))
n2 = VariableNode('N2')
for op in mlp_op_list:
n2.add_op(op)
# third node of block
n3 = VariableNode('N3')
for op in dropout_ops:
n3.add_op(op)
block = Block()
block.add_node(n1)
block.add_node(n2)
block.add_node(n3)
block.add_edge(n1, n2)
block.add_edge(n2, n3)
return block
def set_ops(self, indexes):
"""
Set the operations for each node of each cell of the structure.
Args:
indexes (list): element of list can be float in [0, 1] or int.
output_node (ConstantNode): the output node of the Structure.
"""
cursor = 0
for c in self.struct:
num_nodes = c.num_nodes
c.set_ops(indexes[cursor:cursor+num_nodes])
cursor += num_nodes
self.graph.add_nodes_from(c.graph.nodes())
self.graph.add_edges_from(c.graph.edges())
output_nodes = get_output_nodes(self.graph)
if len(output_nodes) == 1:
node = ConstantNode(op=Identity(), name='Structure_Output')
self.graph.add_node(node)
self.graph.add_edge(output_nodes[0], node)
else:
node = ConstantNode(name='Structure_Output')
node.set_op(self.output_op(self.graph, node, output_nodes))
self.output_node = node
def create_conv_node(name):
n = VariableNode(name)
n.add_op(Identity())
n.add_op(Conv1D(filter_size=5, num_filters=2))
n.add_op(Conv1D(filter_size=5, num_filters=3))
n.add_op(MaxPooling1D(pool_size=3, padding='same'))
n.add_op(MaxPooling1D(pool_size=5, padding='same'))
return n
def create_dense_cell_type2(input_nodes):
"""MLP type 2
Args:
input_nodes (list(Node)): possible inputs of the current cell.
Returns:
Cell: a Cell instance.
"""
cell = Cell(input_nodes)
# first node of block
n1 = VariableNode('N_0')
for inpt in input_nodes:
n1.add_op(Connect(cell.graph, inpt, n1))
# second node of block
mlp_op_list = list()
mlp_op_list.append(Identity())
mlp_op_list.append(Dense(5, tf.nn.relu))
mlp_op_list.append(Dense(10, tf.nn.relu))
mlp_op_list.append(Dense(20, tf.nn.relu))
mlp_op_list.append(Dense(40, tf.nn.relu))
mlp_op_list.append(Dense(80, tf.nn.relu))
mlp_op_list.append(Dense(160, tf.nn.relu))
mlp_op_list.append(Dense(320, tf.nn.relu))
n2 = VariableNode('N_1')
for op in mlp_op_list:
n2.add_op(op)
# third
n3 = VariableNode('N_2')
drop_ops = []
drop_ops.extend(dropout_ops)
for op in drop_ops:
n3.add_op(op)
# 1 Blocks
block1 = Block()
block1.add_node(n1)
block1.add_node(n2)
block1.add_node(n3)
block1.add_edge(n1, n2)
block1.add_edge(n2, n3)
cell.add_block(block1)
cell.set_outputs()
return cell
def create_mlp_node(node):
node.add_op(Identity())
node.add_op(Dense(100, tf.nn.relu))
node.add_op(Dense(100, tf.nn.tanh))
node.add_op(Dense(100, tf.nn.sigmoid))
node.add_op(Dropout(0.3))
node.add_op(Dense(500, tf.nn.relu))
node.add_op(Dense(500, tf.nn.tanh))
node.add_op(Dense(500, tf.nn.sigmoid))
node.add_op(Dropout(0.4))
node.add_op(Dense(1000, tf.nn.relu))
node.add_op(Dense(1000, tf.nn.tanh))
node.add_op(Dense(1000, tf.nn.sigmoid))
node.add_op(Dropout(0.5))
def create_cell_1(input_nodes):
"""Create a cell with convolution.
Args:
input_nodes (list(Node)): a list of input_nodes for this cell.
Returns:
Cell: the corresponding cell.
"""
cell = Cell(input_nodes)
input_dose1 = input_nodes[0]
input_rnaseq = input_nodes[1]
input_drug1descriptor = input_nodes[2]
input_drug1fingerprints = input_nodes[3]
def create_block_3_nodes(input_node):
# first node of block
n1 = VariableNode('N1')
create_mlp_node(n1)
cell.graph.add_edge(input_node, n1) # fixed input of current block
# second node of block
n2 = VariableNode('N2')
create_mlp_node(n2)
# third node of the block
n3 = VariableNode('N3')
create_mlp_node(n3)
block = Block()
block.add_node(n1)
block.add_node(n2)
block.add_node(n3)
block.add_edge(n1, n2)
block.add_edge(n2, n3)
return block, (n1, n2, n3)
# BLOCK FOR: dose1
n = ConstantNode(op=Identity(), name='N1', )
cell.graph.add_edge(input_dose1, n)
block0 = Block()
block0.add_node(n)
cell.add_block(block0)
# BLOCK FOR: rnaseq
block3, _ = create_block_3_nodes(input_rnaseq)
cell.add_block(block3)
# BLOCK FOR: drug1.descriptor
block4, _ = create_block_3_nodes(input_drug1descriptor)
cell.add_block(block4)
# BLOCK FOR: drug1.fingerprints
block5, _ = create_block_3_nodes(input_drug1fingerprints)
cell.add_block(block5)
# set_cell_output_add(cell)
cell.set_outputs()
return cell