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Random initialization #107

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merged 2 commits into from
Nov 2, 2023
Merged

Random initialization #107

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336485b
updated estimator_graph_individual_generator to generate random pipel…
jgh9094 Nov 1, 2023
18ce156
fixed range to get correct number of nodes
jgh9094 Nov 1, 2023
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79 changes: 40 additions & 39 deletions tpot2/individual_representations/graph_pipeline_individual/templates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
from tpot2.individual_representations.graph_pipeline_individual.individual import create_node


# will randomly generate individuals (no predefined order)
def estimator_graph_individual_generator(
root_config_dict,
inner_config_dict=None,
Expand All @@ -22,47 +23,47 @@ def estimator_graph_individual_generator(

rng = np.random.default_rng(rng_)

n_nodes = 0
while True:
if n_nodes < max_size:
n_nodes += 1

for k in root_config_dict.keys():

graph = nx.DiGraph()
root = create_node(config_dict={k:root_config_dict[k]}, rng_=rng)
graph.add_node(root)

ind = GraphIndividual( rng_=rng,
inner_config_dict=inner_config_dict,
leaf_config_dict=leaf_config_dict,
root_config_dict=root_config_dict,
initial_graph = graph,

max_size = max_size,
linear_pipeline = linear_pipeline,
hyperparameter_probability = hyperparameter_probability,
hyper_node_probability = hyper_node_probability,
hyperparameter_alpha = hyperparameter_alpha,

**kwargs,
)

starting_ops = []
if inner_config_dict is not None:
starting_ops.append(ind._mutate_insert_inner_node)
if leaf_config_dict is not None:
starting_ops.append(ind._mutate_insert_leaf)

if len(starting_ops) > 0:
if n_nodes > 0:
for _ in range(rng.integers(0,min(n_nodes,3))):
func = rng.choice(starting_ops)
func(rng_=rng)


yield ind

# if user specified limit, grab a random number between that limit
if max_size is not np.inf:
n_nodes = rng.integers(1,max_size+1)
# else, grab random number between 1,11 (theaksaini)
else:
n_nodes = rng.integers(1,11)

graph = nx.DiGraph()
root = create_node(config_dict=root_config_dict, rng_=rng) # grab random root model method
graph.add_node(root)

ind = GraphIndividual( rng_=rng,
inner_config_dict=inner_config_dict,
leaf_config_dict=leaf_config_dict,
root_config_dict=root_config_dict,
initial_graph = graph,

max_size = max_size,
linear_pipeline = linear_pipeline,
hyperparameter_probability = hyperparameter_probability,
hyper_node_probability = hyper_node_probability,
hyperparameter_alpha = hyperparameter_alpha,

**kwargs,
)

starting_ops = []
if inner_config_dict is not None:
starting_ops.append(ind._mutate_insert_inner_node)
if leaf_config_dict is not None:
starting_ops.append(ind._mutate_insert_leaf)
n_nodes -= 1

if len(starting_ops) > 0:
for _ in range(n_nodes-1):
func = rng.choice(starting_ops)
func(rng_=rng)

yield ind


class BaggingCompositeGraphSklearn():
Expand Down
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