319 lines
10 KiB
Python
319 lines
10 KiB
Python
import warnings
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import jax
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from jax import vmap, numpy as jnp
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import numpy as np
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import sympy as sp
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from . import BaseGenome
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from ..gene import DefaultNodeGene, DefaultConnGene
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from .operations import DefaultMutation, DefaultCrossover, DefaultDistance
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from .utils import unflatten_conns, extract_node_attrs, extract_conn_attrs
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from tensorneat.common import (
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topological_sort,
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topological_sort_python,
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I_INF,
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attach_with_inf,
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SYMPY_FUNCS_MODULE_NP,
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SYMPY_FUNCS_MODULE_JNP,
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)
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class DefaultGenome(BaseGenome):
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"""Default genome class, with the same behavior as the NEAT-Python"""
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network_type = "feedforward"
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def __init__(
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self,
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num_inputs: int,
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num_outputs: int,
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max_nodes=50,
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max_conns=100,
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node_gene=DefaultNodeGene(),
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conn_gene=DefaultConnGene(),
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mutation=DefaultMutation(),
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crossover=DefaultCrossover(),
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distance=DefaultDistance(),
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output_transform=None,
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input_transform=None,
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init_hidden_layers=(),
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):
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super().__init__(
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num_inputs,
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num_outputs,
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max_nodes,
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max_conns,
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node_gene,
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conn_gene,
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mutation,
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crossover,
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distance,
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output_transform,
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input_transform,
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init_hidden_layers,
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)
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def transform(self, state, nodes, conns):
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u_conns = unflatten_conns(nodes, conns)
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conn_exist = u_conns != I_INF
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seqs = topological_sort(nodes, conn_exist)
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return seqs, nodes, conns, u_conns
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def forward(self, state, transformed, inputs):
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if self.input_transform is not None:
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inputs = self.input_transform(inputs)
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cal_seqs, nodes, conns, u_conns = transformed
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ini_vals = jnp.full((self.max_nodes,), jnp.nan)
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ini_vals = ini_vals.at[self.input_idx].set(inputs)
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nodes_attrs = vmap(extract_node_attrs)(nodes)
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conns_attrs = vmap(extract_conn_attrs)(conns)
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def cond_fun(carry):
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values, idx = carry
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return (idx < self.max_nodes) & (cal_seqs[idx] != I_INF)
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def body_func(carry):
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values, idx = carry
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i = cal_seqs[idx]
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def input_node():
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z = self.node_gene.input_transform(state, nodes_attrs[i], values[i])
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new_values = values.at[i].set(z)
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return new_values
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def otherwise():
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conn_indices = u_conns[:, i]
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hit_attrs = attach_with_inf(conns_attrs, conn_indices)
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ins = vmap(self.conn_gene.forward, in_axes=(None, 0, 0))(
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state, hit_attrs, values
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)
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z = self.node_gene.forward(
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state,
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nodes_attrs[i],
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ins,
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is_output_node=jnp.isin(i, self.output_idx),
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)
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new_values = values.at[i].set(z)
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return new_values
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values = jax.lax.cond(jnp.isin(i, self.input_idx), input_node, otherwise)
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return values, idx + 1
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vals, _ = jax.lax.while_loop(cond_fun, body_func, (ini_vals, 0))
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if self.output_transform is None:
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return vals[self.output_idx]
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else:
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return self.output_transform(vals[self.output_idx])
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def network_dict(self, state, nodes, conns):
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network = super().network_dict(state, nodes, conns)
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topo_order, topo_layers = topological_sort_python(
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set(network["nodes"]), set(network["conns"])
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)
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network["topo_order"] = topo_order
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network["topo_layers"] = topo_layers
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return network
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def sympy_func(
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self,
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state,
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network,
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sympy_input_transform=None,
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sympy_output_transform=None,
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backend="jax",
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):
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assert backend in ["jax", "numpy"], "backend should be 'jax' or 'numpy'"
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module = SYMPY_FUNCS_MODULE_JNP if backend == "jax" else SYMPY_FUNCS_MODULE_NP
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if sympy_input_transform is None and self.input_transform is not None:
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warnings.warn(
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"genome.input_transform is not None but sympy_input_transform is None!"
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)
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if sympy_input_transform is None:
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sympy_input_transform = lambda x: x
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if sympy_input_transform is not None:
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if not isinstance(sympy_input_transform, list):
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sympy_input_transform = [sympy_input_transform] * self.num_inputs
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if sympy_output_transform is None and self.output_transform is not None:
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warnings.warn(
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"genome.output_transform is not None but sympy_output_transform is None!"
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)
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input_idx = self.get_input_idx()
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output_idx = self.get_output_idx()
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order = network["topo_order"]
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hidden_idx = [
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i for i in network["nodes"] if i not in input_idx and i not in output_idx
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]
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symbols = {}
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for i in network["nodes"]:
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if i in input_idx:
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symbols[-i - 1] = sp.Symbol(f"i{i - min(input_idx)}") # origin_i
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symbols[i] = sp.Symbol(f"norm{i - min(input_idx)}")
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elif i in output_idx:
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symbols[i] = sp.Symbol(f"o{i - min(output_idx)}")
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else: # hidden
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symbols[i] = sp.Symbol(f"h{i - min(hidden_idx)}")
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nodes_exprs = {}
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args_symbols = {}
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for i in order:
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if i in input_idx:
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nodes_exprs[symbols[-i - 1]] = symbols[
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-i - 1
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] # origin equal to its symbol
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nodes_exprs[symbols[i]] = sympy_input_transform[i - min(input_idx)](
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symbols[-i - 1]
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) # normed i
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else:
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in_conns = [c for c in network["conns"] if c[1] == i]
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node_inputs = []
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for conn in in_conns:
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val_represent = symbols[conn[0]]
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# a_s -> args_symbols
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val, a_s = self.conn_gene.sympy_func(
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state,
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network["conns"][conn],
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val_represent,
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)
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args_symbols.update(a_s)
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node_inputs.append(val)
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nodes_exprs[symbols[i]], a_s = self.node_gene.sympy_func(
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state,
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network["nodes"][i],
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node_inputs,
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is_output_node=(i in output_idx),
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)
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args_symbols.update(a_s)
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if i in output_idx and sympy_output_transform is not None:
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nodes_exprs[symbols[i]] = sympy_output_transform(
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nodes_exprs[symbols[i]]
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)
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input_symbols = [symbols[-i - 1] for i in input_idx]
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reduced_exprs = nodes_exprs.copy()
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for i in order:
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reduced_exprs[symbols[i]] = reduced_exprs[symbols[i]].subs(reduced_exprs)
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output_exprs = [reduced_exprs[symbols[i]] for i in output_idx]
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lambdify_output_funcs = [
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sp.lambdify(
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input_symbols + list(args_symbols.keys()),
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exprs,
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modules=[backend, module],
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)
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for exprs in output_exprs
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]
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fixed_args_output_funcs = []
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for i in range(len(output_idx)):
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def f(inputs, i=i):
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return lambdify_output_funcs[i](*inputs, *args_symbols.values())
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fixed_args_output_funcs.append(f)
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forward_func = lambda inputs: jnp.array(
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[f(inputs) for f in fixed_args_output_funcs]
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)
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return (
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symbols,
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args_symbols,
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input_symbols,
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nodes_exprs,
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output_exprs,
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forward_func,
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)
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def visualize(
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self,
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network,
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rotate=0,
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reverse_node_order=False,
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size=(300, 300, 300),
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color=("blue", "blue", "blue"),
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save_path="network.svg",
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save_dpi=800,
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**kwargs,
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):
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import networkx as nx
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from matplotlib import pyplot as plt
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nodes_list = list(network["nodes"])
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conns_list = list(network["conns"])
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input_idx = self.get_input_idx()
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output_idx = self.get_output_idx()
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topo_order, topo_layers = network["topo_order"], network["topo_layers"]
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node2layer = {
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node: layer for layer, nodes in enumerate(topo_layers) for node in nodes
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}
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if reverse_node_order:
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topo_order = topo_order[::-1]
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G = nx.DiGraph()
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if not isinstance(size, tuple):
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size = (size, size, size)
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if not isinstance(color, tuple):
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color = (color, color, color)
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for node in topo_order:
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if node in input_idx:
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G.add_node(node, subset=node2layer[node], size=size[0], color=color[0])
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elif node in output_idx:
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G.add_node(node, subset=node2layer[node], size=size[2], color=color[2])
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else:
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G.add_node(node, subset=node2layer[node], size=size[1], color=color[1])
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for conn in conns_list:
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G.add_edge(conn[0], conn[1])
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pos = nx.multipartite_layout(G)
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def rotate_layout(pos, angle):
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angle_rad = np.deg2rad(angle)
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cos_angle, sin_angle = np.cos(angle_rad), np.sin(angle_rad)
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rotated_pos = {}
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for node, (x, y) in pos.items():
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rotated_pos[node] = (
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cos_angle * x - sin_angle * y,
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sin_angle * x + cos_angle * y,
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)
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return rotated_pos
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rotated_pos = rotate_layout(pos, rotate)
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node_sizes = [n["size"] for n in G.nodes.values()]
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node_colors = [n["color"] for n in G.nodes.values()]
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nx.draw(
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G,
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pos=rotated_pos,
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node_size=node_sizes,
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node_color=node_colors,
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**kwargs,
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)
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plt.savefig(save_path, dpi=save_dpi)
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