This commit is related to issue: https://github.com/EMI-Group/tensorneat/issues/11
1. Add origin_node and origin_conn. 2. Change the behavior of crossover and mutation. Now, TensorNEAT will use all fix_attrs(include historical marker if it has one) as identifier for gene in crossover and distance calculation. 3. Other slightly change. 4. Add two related examples: xor_origin and hopper_origin 5. Add related test file.
This commit is contained in:
wls2002
247
test/origin_operations_test.py
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247
test/origin_operations_test.py
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import jax, jax.numpy as jnp
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from tensorneat.genome.operations import (
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DefaultMutation,
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DefaultDistance,
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DefaultCrossover,
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)
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from tensorneat.genome import (
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DefaultGenome,
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DefaultNode,
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DefaultConn,
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OriginNode,
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OriginConn,
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)
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from tensorneat.genome.utils import add_node, add_conn
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origin_genome = DefaultGenome(
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node_gene=OriginNode(response_init_std=1),
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conn_gene=OriginConn(),
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mutation=DefaultMutation(conn_add=1, node_add=1, conn_delete=0, node_delete=0),
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crossover=DefaultCrossover(),
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distance=DefaultDistance(),
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num_inputs=3,
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num_outputs=1,
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max_nodes=6,
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max_conns=6,
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)
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default_genome = DefaultGenome(
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node_gene=DefaultNode(response_init_std=1),
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conn_gene=DefaultConn(),
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mutation=DefaultMutation(conn_add=1, node_add=1, conn_delete=0, node_delete=0),
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crossover=DefaultCrossover(),
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distance=DefaultDistance(),
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num_inputs=3,
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num_outputs=1,
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max_nodes=6,
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max_conns=6,
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)
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state = default_genome.setup()
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state = origin_genome.setup(state)
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randkey = jax.random.PRNGKey(42)
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def mutation_default():
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nodes, conns = default_genome.initialize(state, randkey)
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print("old genome:\n", default_genome.repr(state, nodes, conns))
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nodes, conns = default_genome.execute_mutation(
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state,
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randkey,
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nodes,
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conns,
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new_node_key=jnp.asarray(10),
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new_conn_keys=jnp.array([20, 21, 22]),
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)
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# new_conn_keys is not used in default genome
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print("new genome:\n", default_genome.repr(state, nodes, conns))
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def mutation_origin():
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nodes, conns = origin_genome.initialize(state, randkey)
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print(conns)
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print("old genome:\n", origin_genome.repr(state, nodes, conns))
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nodes, conns = origin_genome.execute_mutation(
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state,
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randkey,
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nodes,
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conns,
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new_node_key=jnp.asarray(10),
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new_conn_keys=jnp.array([20, 21, 22]),
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)
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print(conns)
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# new_conn_keys is used in origin genome
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print("new genome:\n", origin_genome.repr(state, nodes, conns))
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def distance_default():
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nodes, conns = default_genome.initialize(state, randkey)
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nodes = add_node(
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nodes,
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fix_attrs=jnp.asarray([10]),
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custom_attrs=default_genome.node_gene.new_identity_attrs(state)
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)
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conns1 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10]), # in-idx, out-idx
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custom_attrs=default_genome.conn_gene.new_zero_attrs(state)
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)
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conns2 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10]), # in-idx, out-idx
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custom_attrs=default_genome.conn_gene.new_random_attrs(state, randkey)
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)
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print("genome1:\n", default_genome.repr(state, nodes, conns1))
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print("genome2:\n", default_genome.repr(state, nodes, conns2))
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distance = default_genome.execute_distance(state, nodes, conns1, nodes, conns2)
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print("distance: ", distance)
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def distance_origin_case1():
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"""
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distance with different historical marker
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"""
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nodes, conns = origin_genome.initialize(state, randkey)
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nodes = add_node(
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nodes,
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fix_attrs=jnp.asarray([10]),
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custom_attrs=origin_genome.node_gene.new_identity_attrs(state)
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)
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conns1 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 99]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_zero_attrs(state)
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)
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conns2 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 88]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_random_attrs(state, randkey)
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)
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print("genome1:\n", origin_genome.repr(state, nodes, conns1))
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print("genome2:\n", origin_genome.repr(state, nodes, conns2))
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distance = origin_genome.execute_distance(state, nodes, conns1, nodes, conns2)
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print("distance: ", distance)
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def distance_origin_case2():
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"""
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distance with same historical marker
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"""
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nodes, conns = origin_genome.initialize(state, randkey)
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nodes = add_node(
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nodes,
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fix_attrs=jnp.asarray([10]),
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custom_attrs=origin_genome.node_gene.new_identity_attrs(state)
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)
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conns1 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 99]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_zero_attrs(state)
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)
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conns2 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 99]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_random_attrs(state, randkey)
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)
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print("genome1:\n", origin_genome.repr(state, nodes, conns1))
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print("genome2:\n", origin_genome.repr(state, nodes, conns2))
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distance = origin_genome.execute_distance(state, nodes, conns1, nodes, conns2)
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print("distance: ", distance)
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def crossover_origin_case1():
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"""
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crossover with different historical marker
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"""
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nodes, conns = origin_genome.initialize(state, randkey)
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nodes = add_node(
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nodes,
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fix_attrs=jnp.asarray([10]),
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custom_attrs=origin_genome.node_gene.new_identity_attrs(state)
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)
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conns1 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 99]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_zero_attrs(state)
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)
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conns2 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 88]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_random_attrs(state, randkey)
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)
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print("genome1:\n", origin_genome.repr(state, nodes, conns1))
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print("genome2:\n", origin_genome.repr(state, nodes, conns2))
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# (0, 10)'s weight must be 0 (disjoint gene, use fitter)
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child_nodes, child_conns = origin_genome.execute_crossover(state, randkey, nodes, conns1, nodes, conns2)
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print("child:\n", origin_genome.repr(state, child_nodes, child_conns))
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def crossover_origin_case2():
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"""
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crossover with same historical marker
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"""
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nodes, conns = origin_genome.initialize(state, randkey)
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nodes = add_node(
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nodes,
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fix_attrs=jnp.asarray([10]),
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custom_attrs=origin_genome.node_gene.new_identity_attrs(state)
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)
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conns1 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 99]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_zero_attrs(state)
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)
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conns2 = add_conn(
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conns,
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fix_attrs=jnp.array([0, 10, 99]), # in-idx, out-idx, historical mark
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custom_attrs=origin_genome.conn_gene.new_random_attrs(state, randkey)
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)
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print("genome1:\n", origin_genome.repr(state, nodes, conns1))
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print("genome2:\n", origin_genome.repr(state, nodes, conns2))
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# (0, 10)'s weight might be random or zero (homologous gene)
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# zero case:
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child_nodes, child_conns = origin_genome.execute_crossover(state, jax.random.key(99), nodes, conns1, nodes, conns2)
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print("child_zero:\n", origin_genome.repr(state, child_nodes, child_conns))
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# random case:
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child_nodes, child_conns = origin_genome.execute_crossover(state, jax.random.key(0), nodes, conns1, nodes, conns2)
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print("child_random:\n", origin_genome.repr(state, child_nodes, child_conns))
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def crossover_origin_case3():
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"""
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test examine it use random gene rather than attribute exchange
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"""
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nodes, conns = origin_genome.initialize(state, randkey)
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nodes1 = add_node(
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nodes,
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fix_attrs=jnp.asarray([10]),
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custom_attrs=jnp.array([1, 2, 0, 0])
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)
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nodes2 = add_node(
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nodes,
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fix_attrs=jnp.asarray([10]),
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custom_attrs=jnp.array([100, 200, 0, 0])
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)
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# [1, 2] case
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child_nodes, child_conns = origin_genome.execute_crossover(state, jax.random.key(99), nodes1, conns, nodes2, conns)
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print("child1:\n", origin_genome.repr(state, child_nodes, child_conns))
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# [100, 200] case
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child_nodes, child_conns = origin_genome.execute_crossover(state, jax.random.key(1), nodes1, conns, nodes2, conns)
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print("child2:\n", origin_genome.repr(state, child_nodes, child_conns))
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if __name__ == "__main__":
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# mutation_origin()
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# distance_default()
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# distance_origin_case1()
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# distance_origin_case2()
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# crossover_origin_case1()
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# crossover_origin_case2()
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crossover_origin_case3()
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