def delete_node():
global G
node_list = list(G.nodes())
n_boxes = count_boxes()
if n_boxes == 1:
return
id = random.choice(node_list)
if is_blacklisted(id):
return
successors = G.successors(id)
for successor in successors:
try:
if "_out" in successor:
G.remove_node(f"{id}_out")
G.remove_node(f"{id}_feedback")
break
except Exception as e:
log(e)
predecessors = G.predecessors(id)
successors = G.successors(id)
for predecessor in predecessors:
for successor in successors:
if predecessor == successor:
continue
G.add_edge(predecessor, successor)
G.remove_node(id)
return id
def screenshot(G, folderpath, filename):
"""Make a png image of a graph."""
imagepath = os.path.join(folderpath, "{filename}.png")
log(f"SCREENSHOT {imagepath} with {G.order()} nodes, {G.size()} edges")
A = nx.nx_agraph.to_agraph(G)
A.graph_attr.update()
A.draw(path=imagepath, prog="dot")
def decimate(p=0.1):
""" reduce the number of nodes and edges """
global G
n_boxes = count_boxes()
if n_boxes < (1 / p) or G.size() < (1 / p):
return
n_boxes *= (1 - p)
victims = []
loops = 0
while count_boxes() > n_boxes:
log("DELETE A NODE")
victim = delete_node()
victims.append(victim)
loops += 1
if loops > 100:
return
n_edges = G.size() * (1 - p)
loops = 0
while G.size() > n_edges:
log("DELETE AN EDGE")
victim = delete_edge()
victims.append(victim)
loops += 1
if loops > 100:
return
[log("KILLED", victim) for victim in victims]
def build_brick(id, inputs=None):
"""Make the keras operation to be executed at a given node."""
global G
node = G.node[id]
log('build brick for', node)
node_type = node["node_type"]
keys = node.keys()
# TODO: SIMPLIFY THE F**K OUT OF THIS
if "force_residual" in keys and "gives_feedback" not in keys:
if node["force_residual"]:
node["d_out"] = inputs.shape[-1]
if node_type == "input":
brick = L.Input(node['input_shape'])
if node_type == "sepconv1d":
brick = L.SeparableConv1D(node['filters'],
node['kernel_size'],
activation=node['activation'])
if node_type in ["deep", "wide_deep"]:
brick = get_kernel(node_type, node["layers"], node["stddev"],
inputs.shape[-1], node["d_out"], node["activation"],
0)
if "k_conv" in node_type:
brick = KConvSet(node["kernel"], node["layers"], node["stddev"],
inputs.shape[-1], node["d_out"], int(node_type[-1]))
if node_type == "transformer":
brick = Transformer(node["d_model"], node["n_heads"])
G.node[id]['brick'] = brick
return brick
def delete_edge():
global G
edge = random.choice(list(G.edges()))
log("delete_edge", edge)
for node in edge:
if is_blacklisted(node):
log("not deleting", edge, "because", node, "is blacklisted")
return
G.remove_edge(*edge)
return edge
def recurse(hp):
"""with probability P, replace nodes with random feedforward regulons"""
global G
nodes = G.nodes(data=True)
p_insert = hp.p_insert if count_boxes() > hp.initial_boxes else 1.
for node in nodes:
try:
if node[1]["shape"] is "square":
if random.random() < p_insert:
insert_motif(hp, id=node[0])
except Exception as e:
log('exception in recurse', e)
def connect(p=0.2):
global G
if count_boxes() < 10:
return
n_edges = G.size() * (1 + p)
loops = 0
while G.size() < n_edges:
log("CONNECT!")
add_edge()
loops += 1
if loops > 100:
return
def clean_up():
global G
for id in list(G.nodes()):
if G.node[id]["shape"] == "square":
predecessors = G.predecessors(id)
if len([*predecessors]) < 1:
log("reconnecting", id, "to input")
G.add_edge("input", id)
successors = G.successors(id)
if len([*successors]) < 1:
log("reconnecting", id, "to output")
G.add_edge(id, "output")
inputs = list(G.successors("source"))
for input in inputs:
successors = list(G.successors(input))
if len(successors) < 1:
G.remove_node(input)
G.remove_node(f"{input[:-9]}_out")
continue
for successor in successors:
if G.node[successor]["shape"] == "triangle":
log("CLEANUP BYPASS", input, successor)
G.remove_edge(input, successor)
predecessors = list(G.predecessors(input))
for predecessor in predecessors:
if G.node[predecessor]["shape"] == "triangle":
log("CLEANUP BYPASS", predecessor, input)
G.remove_edge(predecessor, input)
def add_edge(node1=None, node2=None):
global G
prior_cycles = list(nx.simple_cycles(G))
sample = safe_sample(G.nodes(), 2)
log(f"SAMPLE, {sample}")
for node in sample:
if is_blacklisted(node):
return
node1, node2 = sample
if node2 in G.successors(node1):
return
G.add_edge(node1, node2)
new_cycles = list(nx.simple_cycles(G))
if len(new_cycles) == len(
prior_cycles) and node1 != node2 and node2 != node1:
return G
else:
handle_feedback(node1, node2)
def split_edge(id1=None,
id2=None,
style="filled",
color="black",
shape="square"):
global G
if id1 is None and id2 is None:
id1 = random.choice(list(G.nodes()))
successors = list(G.successors(id1))
if len(successors) < 1:
return
id2 = random.choice(successors)
log("split_edge", id1, id2)
if is_blacklisted(id1) and is_blacklisted(id2) or id1 == id2:
return
new_node_id = f"{id1}--->{id2}"
log(f"{id1}--->{new_node_id}--->{id2}")
G.add_node(new_node_id, label="", style=style, color=color, shape=shape)
G.add_edge(id1, new_node_id)
G.add_edge(new_node_id, id2)
def is_blacklisted(id):
log(f"is {id} blacklisted?")
try:
node = G.node[id]
if node["shape"] != "square":
log("yup!")
return True
else:
log("nope!")
return False
except Exception as e:
log("nope!")
return False
def get_regulon(hp, parent=None, layers=None):
"""Build a subgraph to replace a node."""
num_layers = safe_sample(hp.min_layers, hp.max_layers)
M = nx.MultiDiGraph()
ids = []
for layer_number in range(num_layers):
n_nodes = safe_sample(hp.min_nodes, hp.max_nodes)
log(f"add layer {layer_number} with {n_nodes} nodes")
ids_of_nodes_in_this_layer = []
for node_number in range(n_nodes):
if parent:
node_id = f"{parent}.{layer_number}.{node_number}"
else:
node_id = f"{layer_number}.{node_number}"
log(f"add node {node_id}")
M.add_node(node_id,
label="",
style="filled",
shape="square",
color="black")
ids_of_nodes_in_this_layer.append(node_id)
ids.append(ids_of_nodes_in_this_layer)
for predecessor_layer_number, predecessor_node_ids in enumerate(ids):
for successor_layer_number, successor_node_ids in enumerate(ids):
if predecessor_layer_number >= successor_layer_number:
continue
for predecessor_node_id in predecessor_node_ids:
for successor_node_id in successor_node_ids:
log(f"{predecessor_node_id}--->{successor_node_id}")
M.add_edge(predecessor_node_id, successor_node_id)
return M, ids
def get_output(id):
"""
Get the output of a node in a computation graph.
Pull inputs from predecessors.
"""
global G
node = G.node[id]
keys = node.keys()
node_type = node["node_type"]
log('get output for', node)
if node["output"] is not None:
return node["output"]
else:
parent_ids = list(G.predecessors(id))
if node_type is not "input":
inputs = [get_output(parent_id) for parent_id in parent_ids]
inputs = L.Concatenate()(inputs) if len(inputs) > 1 else inputs[0]
if node_type is "recurrent":
output = inputs
else:
brick = build_brick(id, inputs)
log("got brick", brick)
output = brick(inputs)
if "output_shape" not in keys and "gives_feedback" not in keys:
try:
output = L.Add()([inputs, output])
except Exception as e:
log("error adding inputs to output", e)
try:
output = L.Concatenate()([inputs, output])
except Exception as e:
log("error concatenating inputs to output", e)
else:
output = build_brick(id)
if "output_shape" not in keys:
output = InstanceNormalization()(output)
G.node[id]["output"] = output
log("got output", output)
return output
def mutate(hp):
boxes = count_boxes()
log(f"{boxes} boxes")
if hp.force_recursion == 1 and k is 0:
log("forced recursion!")
recurse(hp)
# elif boxes > ALWAYS_DECIMATE_IF_MORE_THAN:
# log("forced decimation!")
# decimate(0.2)
else:
mutation = np.random.choice([
recurse, decimate, connect, split_edges, insert_motif, add_edge,
delete_edge, delete_node, split_edge, do_nothing
],
1,
p=hp.mutation_distribution).item(0)
if mutation in [recurse, insert_motif]:
mutation(hp)
else:
mutation()
if CLEAN_UP:
clean_up()
def insert_motif(hp, id=None):
"""Replace a node with a subgraph."""
global G
id = random.choice(list(G.nodes())) if id is None else id
if is_blacklisted(id):
return
M, new_ids = get_regulon(hp, parent=id)
G = nx.compose(G, M)
# connect the node's predecessors to all nodes in first layer
predecessors = G.predecessors(id)
successors = new_ids[0]
for predecessor in predecessors:
for successor in successors:
log(f"{predecessor}--->{successor}")
G.add_edge(predecessor, successor)
# connect the last layer of the motif to the node's successors
predecessors = new_ids[-1]
successors = list(G.successors(id))
for predecessor in predecessors:
for successor in successors:
log(f"{predecessor}--->{successor}")
G.add_edge(predecessor, successor)
G.remove_node(id)
def differentiate(hp):
"""Assign ops and arguments to nodes."""
if hp.force_skip:
G.add_edge("input", "output")
for node in G.nodes(data=True):
node_id, node_data = node
log("differentiate", node_id, node_data)
node_data["output"] = None
node_data["op"] = None
if node_data["shape"] is "square" or "output" in node_id:
if node_id == "output":
d_out = node_data["output_shape"][-1]
node_type = hp.last_layer
activation = "tanh"
else:
node_type = str(
np.random.choice([
'sepconv1d', 'transformer', 'k_conv1', 'k_conv2',
'k_conv3', "deep", "wide_deep"
],
1,
p=hp.layer_distribution).item(0))
activation = str(
np.random.choice([
'tanh', 'linear', 'relu', 'selu', 'elu', 'sigmoid',
'hard_sigmoid', 'exponential', 'softmax', 'softplus',
'softsign', 'gaussian', 'sin', 'cos', 'swish'
],
1,
p=hp.activation_distribution).item(0))
d_out = None
node_data["force_residual"] = random.random(
) < hp.p_force_residual
node_data["activation"] = clean_activation(activation)
node_data["node_type"] = node_type
node_data['style'] = ""
if node_type == 'sepconv1d':
if d_out is None:
d_out = safe_sample(hp.min_filters, hp.max_filters)
node_data["filters"] = d_out
node_data["kernel_size"] = 1
if node_type == "transformer":
if d_out is None:
d_out = safe_sample(hp.min_units,
hp.max_units) * hp.attn_heads
node_data["d_model"] = d_out
node_data["n_heads"] = 2 if d_out % 2 == 0 else 1
if "k_conv" in node_type or node_type in ["deep", "wide_deep"]:
layers = design_layers(hp, d_out, activation)
if d_out is None:
d_out = layers[-1][0]
node_data["stddev"] = hp.stddev
node_data['layers'] = layers
node_data["d_out"] = d_out
if node_type in ["deep", "wide_deep"]:
node_data['kernel'] = node_type
else:
node_data['kernel'] = "wide_deep" if random.random(
) < hp.p_wide_deep else "deep"
label = f"{node_type}"
log(f"set {node_id} to {label}")
node_data["label"] = label
node_data["color"] = "green"
# we handle recurrent shapes:
try:
feedback_node_id = f"{node_id}_feedback"
input_shape = (None, d_out)
log(f"attempt to set input_shape for {feedback_node_id} to {input_shape}"
)
feedback_node = G.node[feedback_node_id]
feedback_node["input_shape"] = input_shape
node_data["gives_feedback"] = True
except Exception as e:
log("ERROR HANDLING FEEDBACK SHAPE:", e)
def do_nothing():
log("did nothing")