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update to test in servers

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This commit is contained in:
wls2002
2023-05-10 22:33:51 +08:00
parent ce35b01896
commit b271a56827
9 changed files with 112 additions and 34 deletions
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53
algorithms/neat/function_factory.py
View File

@@ -114,7 +114,7 @@ class FunctionFactory:
self.compile_mutate(n)
self.compile_distance(n)
self.compile_crossover(n)
self.compile_topological_sort(n)
self.compile_topological_sort_batch(n)
self.compile_pop_batch_forward(n)
n = int(self.expand_coe * n)
@@ -259,9 +259,8 @@ class FunctionFactory:
def compile_topological_sort(self, n):
func = self.topological_sort_with_args
func = vmap(func)
nodes_lower = np.zeros((self.pop_size, n, 5))
connections_lower = np.zeros((self.pop_size, 2, n, n))
nodes_lower = np.zeros((n, 5))
connections_lower = np.zeros((2, n, n))
func = jit(func).lower(nodes_lower, connections_lower).compile()
self.compiled_function[('topological_sort', n)] = func
@@ -271,6 +270,20 @@ class FunctionFactory:
self.compile_topological_sort(n)
return self.compiled_function[key]
def compile_topological_sort_batch(self, n):
func = self.topological_sort_with_args
func = vmap(func)
nodes_lower = np.zeros((self.pop_size, n, 5))
connections_lower = np.zeros((self.pop_size, 2, n, n))
func = jit(func).lower(nodes_lower, connections_lower).compile()
self.compiled_function[('topological_sort_batch', n)] = func
def create_topological_sort_batch(self, n):
key = ('topological_sort_batch', n)
if key not in self.compiled_function:
self.compile_topological_sort_batch(n)
return self.compiled_function[key]
def create_single_forward_with_args(self):
func = partial(
forward_single,
@@ -315,6 +328,18 @@ class FunctionFactory:
func = jit(func).lower(inputs_lower, cal_seqs_lower, nodes_lower, connections_lower).compile()
self.compiled_function[('batch_forward', n)] = func
def create_batch_forward(self, n):
key = ('batch_forward', n)
if key not in self.compiled_function:
self.compile_batch_forward(n)
if self.debug:
def debug_batch_forward(*args):
return self.compiled_function[key](*args).block_until_ready()
return debug_batch_forward
else:
return self.compiled_function[key]
def compile_pop_batch_forward(self, n):
func = self.single_forward_with_args
func = vmap(func, in_axes=(0, None, None, None)) # batch_forward
@@ -340,9 +365,9 @@ class FunctionFactory:
else:
return self.compiled_function[key]
def ask(self, pop_nodes, pop_connections):
def ask_pop_batch_forward(self, pop_nodes, pop_connections):
n = pop_nodes.shape[1]
ts = self.create_topological_sort(n)
ts = self.create_topological_sort_batch(n)
pop_cal_seqs = ts(pop_nodes, pop_connections)
forward_func = self.create_pop_batch_forward(n)
@@ -352,9 +377,13 @@ class FunctionFactory:
return debug_forward
# return partial(
# forward_func,
# cal_seqs=pop_cal_seqs,
# nodes=pop_nodes,
# connections=pop_connections
# )
def ask_batch_forward(self, nodes, connections):
n = nodes.shape[0]
ts = self.create_topological_sort(n)
cal_seqs = ts(nodes, connections)
forward_func = self.create_batch_forward(n)
def debug_forward(inputs):
return forward_func(inputs, cal_seqs, nodes, connections)
return debug_forward

1
algorithms/neat/genome/activations.py
View File

@@ -68,6 +68,7 @@ def clamped_act(z):
@jit
def inv_act(z):
z = jnp.maximum(z, 1e-7)
return 1 / z

17
algorithms/neat/pipeline.py
View File

@@ -7,7 +7,6 @@ import numpy as np
from .species import SpeciesController
from .genome import expand, expand_single
from .function_factory import FunctionFactory
from examples.time_utils import using_cprofile
class Pipeline:
@@ -16,7 +15,9 @@ class Pipeline:
"""
def __init__(self, config, seed=42):
self.time_dict = {}
self.function_factory = FunctionFactory(config, debug=True)
self.randkey = jax.random.PRNGKey(seed)
np.random.seed(seed)
@@ -35,6 +36,7 @@ class Pipeline:
self.species_controller.init_speciate(self.pop_nodes, self.pop_connections)
self.best_fitness = float('-inf')
self.best_genome = None
self.generation_timestamp = time.time()
def ask(self):
@@ -43,7 +45,7 @@ class Pipeline:
:return:
Algorithm gives the population a forward function, then environment gives back the fitnesses.
"""
return self.function_factory.ask(self.pop_nodes, self.pop_connections)
return self.function_factory.ask_pop_batch_forward(self.pop_nodes, self.pop_connections)
def tell(self, fitnesses):
@@ -72,10 +74,14 @@ class Pipeline:
assert callable(analysis), f"What the fuck you passed in? A {analysis}?"
analysis(fitnesses)
if max(fitnesses) >= self.config.neat.population.fitness_threshold:
print("Fitness limit reached!")
return self.best_genome
self.tell(fitnesses)
print("Generation limit reached!")
return self.best_genome
# @using_cprofile
def update_next_generation(self, crossover_pair: List[Union[int, Tuple[int, int]]]) -> None:
"""
create the next generation
@@ -152,5 +158,10 @@ class Pipeline:
cost_time = new_timestamp - self.generation_timestamp
self.generation_timestamp = new_timestamp
max_idx = np.argmax(fitnesses)
if fitnesses[max_idx] > self.best_fitness:
self.best_fitness = fitnesses[max_idx]
self.best_genome = (self.pop_nodes[max_idx], self.pop_connections[max_idx])
print(f"Generation: {self.generation}",
f"fitness: {max_f}, {min_f}, {mean_f}, {std_f}, Species sizes: {species_sizes}, Cost time: {cost_time}")

15
examples/xor.py
View File

@@ -3,7 +3,7 @@ from functools import partial
from utils import Configer
from algorithms.neat import Pipeline
from time_utils import using_cprofile
from problems import Sin, Xor
from problems import Sin, Xor, DIY
# xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
@@ -25,11 +25,16 @@ from problems import Sin, Xor
@partial(using_cprofile, root_abs_path='/mnt/e/neat-jax/', replace_pattern="/mnt/e/neat-jax/")
def main():
config = Configer.load_config()
# problem = Xor()
problem = Sin()
config.neat.population.pop_size = 50
problem = Xor()
# problem = Sin()
# problem = DIY(func=lambda x: (np.sin(x) + np.exp(x) - x ** 2) / (np.cos(x) + np.sqrt(x)) - np.log(x + 1))
problem.refactor_config(config)
pipeline = Pipeline(config, seed=11454)
pipeline.auto_run(problem.evaluate)
pipeline = Pipeline(config, seed=0)
best_nodes, best_connections = pipeline.auto_run(problem.evaluate)
# print(best_nodes, best_connections)
# func = pipeline.function_factory.ask_batch_forward(best_nodes, best_connections)
# problem.print(func)
if __name__ == '__main__':

1
problems/function_fitting/__init__.py
View File

@@ -1,3 +1,4 @@
from .function_fitting_problem import FunctionFittingProblem
from .xor import *
from .sin import *
from .diy import *

14
problems/function_fitting/diy.py Normal file
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@@ -0,0 +1,14 @@
import numpy as np
from . import FunctionFittingProblem
class DIY(FunctionFittingProblem):
def __init__(self, func, size=100):
self.num_inputs = 1
self.num_outputs = 1
self.batch = size
self.inputs = np.linspace(0, 1, self.batch)[:, None]
self.target = func(self.inputs)
print(self.inputs, self.target)
super().__init__(self.num_inputs, self.num_outputs, self.batch, self.inputs, self.target)

25
problems/function_fitting/function_fitting_problem.py
View File

@@ -15,8 +15,25 @@ class FunctionFittingProblem(Problem):
self.loss = loss
super().__init__(self.forward_way, self.num_inputs, self.num_outputs, self.batch)
def evaluate(self, batch_forward_func):
out = batch_forward_func(self.inputs)
out = jax.device_get(out)
fitnesses = 1 - np.mean((self.target - out) ** 2, axis=(1, 2))
def evaluate(self, pop_batch_forward):
outs = pop_batch_forward(self.inputs)
outs = jax.device_get(outs)
fitnesses = -np.mean((self.target - outs) ** 2, axis=(1, 2))
return fitnesses.tolist()
def draw(self, batch_func):
outs = batch_func(self.inputs)
outs = jax.device_get(outs)
print(outs)
from matplotlib import pyplot as plt
plt.xlabel('x')
plt.ylabel('y')
plt.plot(self.inputs, self.target, color='red', label='target')
plt.plot(self.inputs, outs, color='blue', label='predict')
plt.legend()
plt.show()
def print(self, batch_func):
outs = batch_func(self.inputs)
outs = jax.device_get(outs)
print(outs)

2
problems/function_fitting/sin.py
View File

@@ -8,7 +8,7 @@ class Sin(FunctionFittingProblem):
self.num_inputs = 1
self.num_outputs = 1
self.batch = size
self.inputs = np.linspace(0, np.pi, self.batch)[:, None]
self.inputs = np.linspace(0, 2 * np.pi, self.batch)[:, None]
self.target = np.sin(self.inputs)
print(self.inputs, self.target)
super().__init__(self.num_inputs, self.num_outputs, self.batch, self.inputs, self.target)

18
utils/default_config.json
View File

@@ -11,9 +11,9 @@
"neat": {
"population": {
"fitness_criterion": "max",
"fitness_threshold": 76,
"generation_limit": 100,
"pop_size": 1000,
"fitness_threshold": -0.001,
"generation_limit": 1000,
"pop_size": 30,
"reset_on_extinction": "False"
},
"gene": {
@@ -33,12 +33,12 @@
},
"activation": {
"default": "sigmoid",
"options": ["sigmoid"],
"options": "sigmoid",
"mutate_rate": 0.1
},
"aggregation": {
"default": "sum",
"options": ["sum"],
"options": "sum",
"mutate_rate": 0.1
},
"weight": {
@@ -57,12 +57,12 @@
"compatibility_weight_coefficient": 0.5,
"single_structural_mutation": "False",
"conn_add_prob": 0.5,
"conn_delete_prob": 0,
"node_add_prob": 0.1,
"node_delete_prob": 0
"conn_delete_prob": 0.5,
"node_add_prob": 0.2,
"node_delete_prob": 0.2
},
"species": {
"compatibility_threshold": 3,
"compatibility_threshold": 2.5,
"species_fitness_func": "max",
"max_stagnation": 20,
"species_elitism": 2,