def compress(individual):
nodes, edges, labels = gp.graph(individual)
from gp_utils import recompute_tree_graph
graph = recompute_tree_graph(nodes, edges)
root = visit_node(graph, 0, labels)
optimized = optimize(root)
return wrap_children(optimized)
def level_data(expr):
"""
This method review each node in the individual
and it indicate the level, arity, the number of node
and the number of node of the father
:param expr: Individual to take the information
:return: a list [node_num, level, arity, father]
"""
nodes, edges, labels = gp.graph(expr)
edge = sorted(edges)
contador = 1
nod = 0
level=list()
if len(expr)<2:
level.append([0, contador, 0, None])
else:
level.append([edge[0][0], contador, expr[0].arity, None]) # This is for the root.
for i in range(max(nodes)):
restart=True
contador=1
nod=i+1
while restart:
restart=False
for j in range(max(nodes)):
if edge[j][1]==nod:
contador+=1
nod=edge[j][0]
if(nod>0 and contador>1):
restart=True
for elem in edge:
if elem[1]==(i+1):
nod_p=elem[0]
level.append([i+1, contador, expr[i+1].arity, nod_p])
return level
def draw_individual(individual, datafile, run_type):
g = pgv.AGraph()
g.graph_attr['label'] = "{}:{}".format(datafile, run_type).upper()
for i, expr in enumerate(individual):
nodes, edges, labels = gp.graph(expr)
node_map = dict()
for j in range(len(nodes)):
current = nodes[j]
new_node = int(str(i + 1) + str(current))
nodes[j] = new_node
node_map[current] = new_node
for j in range(len(edges)):
edges[j] = (node_map[edges[j][0]], node_map[edges[j][1]])
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for j in range(len(nodes)):
n = g.get_node(node_map[j])
# round constants to 4dp for aesthetics :-)
try:
labels[j] = float(labels[j])
labels[j] = str(round(labels[j], 4))
except:
pass
n.attr["label"] = labels[j]
return g
def level_node(expr):
nodes, edges, labels = gp.graph(expr)
#outfile = open('edges.txt', 'a')
edge=sorted(edges)
#outfile.write('\n edges expre %s %s ' % (edge,expr))
#outfile.close()
contador=1
nod=0
level=list()
restart=True
#numnodo #numnivel #aridad
if len(expr)<2:
level.append([0, contador, 0])
else:
level.append([edge[0][0], contador, expr[0].arity])
for i in range(max(nodes)):
restart=True
contador=1
nod=i+1
while restart:
restart=False
for j in range(max(nodes)):
if edge[j][1]==nod:
contador+=1
nod=edge[j][0]
if(nod>0 and contador>1):
restart=True
level.append([i+1, contador, expr[i+1].arity])
return level
def plot_trees(best):
""" create tree plots from best array """
for i in range(len(best)):
nodes, edges, labels = gp.graph(best[i])
matplotlib.rcParams['figure.figsize'] = (10.0, 10.0)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = graphviz_layout(g, prog="dot")
f = plt.figure()
nx.draw_networkx_nodes(g,
pos,
node_size=1500,
font_size=8,
node_color='lightblue')
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels, font_size=8)
if (i < 10):
plotfile = "tree_0" + str(i) + ".pdf"
else:
plotfile = "tree_" + str(i) + ".pdf"
plt.title(str(best[i]))
f.savefig(plotfile)
def evalSymbReg(individual, points):
# Transform the tree expression in a callable function
func = toolbox.compile(expr=individual)
# traversing the syntax tree to discover options and interactions
nodes, edges, labels = gp.graph(expr=individual)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
degrees = g.degree()
primitives = nx.dfs_successors(g)
L = g.size()
#for i in range(L):
# if degrees[i] == 1:
sqerrors = []
for point in points:
x1 = point[0]
x2 = point[1]
sqerrors.append((func(x1,x2) - x1 ** 4 - x1 ** 3 - x2 ** 2 - x2) ** 2)
return math.fsum(sqerrors) / len(points),
def level_node(expr):
"""
This method review each node in the individual
and it indicate the level, arity and number of node.
:param expr: Individual to take the information
:return: a list [node_num, level, arity]
"""
nodes, edges, labels = gp.graph(expr)
edge = sorted(edges)
contador = 1
nod = 0
level = list()
if len(expr) < 2:
level.append([0, contador, 0])
else:
level.append([edge[0][0], contador, expr[0].arity])
for i in range(max(nodes)):
restart = True
contador = 1
nod = i + 1
while restart:
restart = False
for j in range(max(nodes)):
if edge[j][1] == nod:
contador += 1
nod = edge[j][0]
if (nod > 0) and (contador > 1):
restart = True
level.append([i + 1, contador, expr[i + 1].arity])
return level
def level_node(expr):
nodes, edges, labels = gp.graph(expr)
#outfile = open('edges.txt', 'a')
edge = sorted(edges)
#outfile.write('\n edges expre %s %s ' % (edge,expr))
#outfile.close()
contador = 1
nod = 0
level = list()
restart = True
#numnodo #numnivel #aridad
if len(expr) < 2:
level.append([0, contador, 0])
else:
level.append([edge[0][0], contador, expr[0].arity])
for i in range(max(nodes)):
restart = True
contador = 1
nod = i + 1
while restart:
restart = False
for j in range(max(nodes)):
if edge[j][1] == nod:
contador += 1
nod = edge[j][0]
if (nod > 0 and contador > 1):
restart = True
level.append([i + 1, contador, expr[i + 1].arity])
return level
def level_node(expr):
"""
This method review each node in the individual
and it indicate the level, arity and number of node.
:param expr: Individual to take the information
:return: a list [node_num, level, arity]
"""
nodes, edges, labels = gp.graph(expr)
edge = sorted(edges)
contador = 1
nod = 0
level=list()
if len(expr) < 2:
level.append([0, contador, 0])
else:
level.append([edge[0][0], contador, expr[0].arity]) # This is for the root.
for i in range(max(nodes)):
restart = True
contador = 1
nod = i+1
while restart:
restart=False
for j in range(max(nodes)):
if edge[j][1]==nod:
contador += 1
nod = edge[j][0]
if(nod > 0) and (contador > 1):
restart = True
level.append([i+1, contador, expr[i+1].arity])
return level
def fiteval(individual, treeorret):
global k
penalty = 0
nodes, edges, evals = gp.graph(individual)
print(evals)
print(len(evals))
keys = get_key('gt', evals)
keys1 = get_key('ls', evals)
keys2 = get_key('ls1', evals)
keys3 = get_key('gt1', evals)
print(keys)
print(keys1)
f = gp.compile(individual, pset)
def f1(row):
x = np.array(row)
try:
return f(*x)
except Exception as e:
return 0
def pencalc(keys):
penalty = 0
for i in keys:
subject = [evals[i + 1], evals[i + 2]]
print(subject)
cond = (operator.or_(*[isinstance(j, float) for j in subject])
and (any(i in pset.arguments for i in subject)))
print(cond)
if (cond == True):
if ((subject[0] in pset.arguments)
and isinstance(subject[1], float)):
penalty = penalty + 10
penalty = penalty + 10
return penalty
presgt = 'gt' not in evals.values()
presls = 'ls' not in evals.values()
pres = presgt and presls
presgt = 'gt1' not in evals.values()
presls = 'ls1' not in evals.values()
pres1 = presgt and presls
print(pres)
peninc = -100 if pres else 10
peninc1 = -100 if pres1 else 10
penaltyfin = pencalc(keys) + pencalc(keys1) + pencalc(keys2) + pencalc(
keys3) + peninc + peninc1 - len(evals)
if (treeorret):
k['vals'] = k.drop(['targret', 'Symbol', 'ranchoice'],
axis=1).apply(f1, axis=1)
retfit = (k.vals * k.targret).sum() - (k.ranchoice * k.targret).sum()
k = k.drop('vals', axis=1)
return (penaltyfin, retfit)
else:
return (penaltyfin, )
def print_population(pop, hof):
expr = 0
for data in [pop, hof]:
for ind in data:
# print(str(ind))
expr = ind
# plotting tree graph
nodes, edges, labels = gp.graph(expr)
def printIndTree(ind, label):
nodes, edges, labels = gp.graph(ind)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw("%s.pdf" % (label, ))
def printIndTree(ind, label):
nodes, edges, labels = gp.graph(ind)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw("%s.pdf" % (label,))
def plot(individual):
nodes, edges, labels = gp.graph(individual)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = nx.drawing.nx_agraph.graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
def main():
random.seed()
NGEN = 40
MU = 300
LAMBDA = 200
CXPB = 0.5
MUTPB = 0.4
pop = toolbox.population(n=MU)
hof = tools.HallOfFame(1)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", numpy.mean)
mstats.register("std", numpy.std)
mstats.register("min", numpy.min)
mstats.register("max", numpy.max)
#pop, log = algorithms.eaSimple(pop, toolbox, CXPB, MUTPB, NGEN, stats=mstats, halloffame=hof, verbose=True)
pop, log = algorithms.eaMuPlusLambda(pop,
toolbox,
MU,
LAMBDA,
CXPB,
MUTPB,
NGEN,
stats=mstats,
halloffame=hof)
#print graph
print(str(hof[0]))
print(hof[0].height)
print(hof[0].fitness.values)
#####
#plot graph
expr = hof[0]
nodes, edges, labels = gp.graph(expr)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = nx.nx_pydot.graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
#####
# print log
return pop, log, hof
def evalSymbReg(individual):
# Transform the tree expression in a callable function
func = toolbox.compile(expr=individual)
# Evaluate the sum of squared difference between the expression
# and the real function values : x**4 + x**3 + x**2 + x
diff = numpy.sum((func(samples_x, samples_z) - values)**2)
nodes, edges, labels = gp.graph(individual)
if len(nodes) > 16: # 18
diff += 100000000000000.0
return diff,
def individual_pdf(file_path, individual, pset):
nodes, edges, labels = gp.graph(individual)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog='dot')
for i in nodes:
n = g.get_node(i)
n.attr['label'] = labels[i]
g.draw(file_path + '.pdf')
return
def main():
pop, log, hof = gp_algo()
ind = hof.items[0]
print(ind)
# expr = toolbox.individual()
nodes, edges, labels = gp.graph(ind)
dot = create_dot(edges, labels)
plot_tree(dot=dot)
validate(ind)
test(ind)
def printTree(individual):
nodes, edges, labels = gp.graph(individual)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = nx.graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
def visualize_tree(expression, filename):
import pygraphviz as pgv
nodes, edges, labels = gp.graph(expression)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw(f"{filename}.png", "png")
def create_needfield(individual): #输出因子文件中的needfield
dic = gp.graph(individual)[2]
needfield = "["
for key in dic.keys():
if "needData" in dic[key]:
needfield += dic[key][9:-1] + ","
needfield = needfield[0:-1] + "]"
for x in locals().keys(): #清除局部变量
del locals()[x]
gc.collect()
return needfield
def graph(best_individual, filename):
ind = creator.Individual.from_string(best_individual, pset)
# Generate and Store graph
nodes, edges, labels = gp.graph(ind)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw("../gp_trees/best/" + filename + ".png")
def draw(individual, path):
nodes, edges, labels = gp.graph(individual)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw(path)
def draw_tree(tree):
nodes, edges, labels = gp.graph(tree)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = nx.nx_pydot.graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
def show_results(self, gtitle):
'''
Display the results of executing the DEAP GP object; best individual
is displayed.
Params:
gtitle - string of the graph title.
Returns:
N/A
'''
if self.rlist:
# Show the squence required
result = ", ".join(map(str, self.slist))
print("\nRequired sequence: {}".format(result))
# Show the resultant integer sequence
result = ", ".join(map(str, self.rlist))
print("\nCalculated result: {}".format(result))
# Let the user know how it went.
print("-" * 80)
if self.rlist == self.slist:
print("\nSuccessfully calculated the Integer Sequence.")
else:
print("\nUnsuccessfull in calculating the Integer Sequence.")
# Display the individual
print('\nBest individual : ', self.expr)
# Display the resultant equation from the best individual
tree = gp.PrimitiveTree(self.expr)
str(tree)
# Display the best individual => graph and equation.
# Only works reliably on Linux Ubuntu.
if sys.platform == 'linux' or sys.platform == 'linux2':
print("Running on Linux OS.")
nodes, edges, labels = gp.graph(self.expr)
# Create tree diagram
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
# Remove the filename extension
gtitle = os.path.splitext(gtitle)[0]
plt.title(gtitle, None, 'center', None)
plt.savefig(gtitle + '.png')
# plt.show()
else:
print("Graphical output only available on Linux.")
else:
print("\nError: hof variable is emtpy.")
def draw_graph(expr):
plt.figure(figsize=(14, 4))
nodes, edges, labels = gp.graph(expr)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.savefig('output/graph.png')
def plotEquationStructure(individual, output_name):
nodes, edges, labels = gp.graph(individual)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw(output_name)
def getGrapf():
expr = toolbox.individual()
nodes, edges, labels = gp.graph(expr)
g = nx.DiGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = pgv(g, prog="dot")
nx.draw(g, pos)
nx.draw(g, pos)
nx.draw(g, pos, labels)
plt.show()
def plotExpressionTree(toolbox, individual):
nodes, edges, labels = gp.graph(individual)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
def view_results(self, results):
lab_dict = {
'cat_prim': '+',
'q_constr': '?',
'plus_constr': '+',
'or_prim': '|',
'star_constr': 'x',
'looka_constr': '(?=)',
'lookb_constr': '(?<=)',
'nch_constr': '[^ ]',
'ch_constr': '[ ]',
'term_constr': 't()'
}
hof, log = results
avgs = [l['avg'] for l in log]
plt.plot(avgs)
plt.xlabel('Generations')
plt.ylabel('Fitness Averages')
plt.title('Capture Fitness Avgs over Generations (0 is perfect)')
plt.show()
expr = hof.__dict__['items'][0]
tree = gp.PrimitiveTree(expr)
str(tree) + ' = ' + gp.compile(tree, pset=self.pset).s
# for i in range(3):
nodes, edges, labels = gp.graph(expr)
print(labels.values())
new_labs = {}
for i, lab in enumerate(labels.values()):
if self.has_digit(lab):
new_labs[i] = self.pset.context[lab].s
if new_labs[i] == ' ':
new_labs[i] = '\' \''
else:
new_labs[i] = lab_dict[lab]
print(labels)
print(new_labs)
graph = networkx.Graph()
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
pos = graphviz_layout(graph, prog="dot")
plt.figure(figsize=(10, 10))
networkx.draw_networkx_nodes(graph, pos, node_size=400, node_color='w')
networkx.draw_networkx_edges(graph, pos, edge_color="blue")
networkx.draw_networkx_labels(graph, pos, new_labs)
plt.axis("off")
plt.show()
def graphIndividual(self,expr,fname):
""" creates a file that contains the graph of an individual
"""
nodes, edges, labels = gp.graph(expr)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw(fname)
def write_graph(ind, filename):
nodes, edges, labels = gp.graph(ind)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw(filename)
def drawTree(expr):
nodes, edges, labels = gp.graph(expr)
# Plot the tree
g = pgv.AGraph(nodesep=1.0)
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw("tree.pdf")
def plot_tree(individual):
import pygraphviz as pgv
nodes, edges, labels = gp.graph(individual)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw("tree.png")
def plot_tree2(individual): import matplotlib.pyplot as plt import networkx as nx nodes, edges, labels = gp.graph(individual) g = nx.Graph() g.add_nodes_from(nodes) g.add_edges_from(edges) pos = nx.random_layout(g) nx.draw_networkx_nodes(g, pos) nx.draw_networkx_edges(g, pos) nx.draw_networkx_labels(g, pos, labels) plt.show()
def display_individual_graph(expr):
nodes, edges, labels = gp.graph(expr)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
# On recupere la position des noeuds
pos = nx.drawing.nx_agraph.graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
def plot_graph(expr):
nodes, edges, labels = gp.graph(expr)
import matplotlib.pyplot as plt
import networkx as nx
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = nx.drawing.nx_agraph.graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
def main():
random.seed(318)
pop = toolbox.population(n=300)
hof = tools.HallOfFame(1)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", numpy.mean)
mstats.register("std", numpy.std)
mstats.register("min", numpy.min)
mstats.register("max", numpy.max)
pop, log = algorithms.eaSimple(pop, toolbox, 0.5, 0.1, 40, stats=mstats,
halloffame=hof, verbose=True)
# print log
best = tools.selBest(pop, 1)[0]
return pop, log, hof, best, gp.graph(best)
def main():
p = 1
g = 1
c = 0.6
m = 0.2
pop = toolbox.population(n=p)
hof = tools.HallOfFame(1)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", numpy.mean)
mstats.register("std", numpy.std)
mstats.register("min", numpy.min)
mstats.register("max", numpy.max)
pop, log = algorithms.eaSimple(pop, toolbox, c, m, g, stats=mstats,
halloffame=hof, verbose=True)
f = toolbox.compile(expr=hof[0])
l = [f(i) for i in range(len(target))]
print(target, l)
f = evaluate(hof[0])
print(f)
# print log
#return pop, log, hof
nodes, edges, labels = gp.graph(hof[0])
import pygraphviz as pgv
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for i in nodes:
n = g.get_node(i)
n.attr["label"] = labels[i]
g.draw("tree.pdf")
toolbox.register("compile", gp.compile, pset=pset)
def evalCircuit(individual):
func = toolbox.compile(expr=individual)
fitness = sum(func(*in_) == out for in_, out in zip(inputs, outputs)),
height = individual.height
length = len(individual)
if fitness[0] == 2**NO_OF_INPUTS and height != 0:
fitness = list(fitness)
fitness[0] += float(1/height)
fitness = tuple(fitness)
return fitness
toolbox.register("evaluate", evalCircuit)
toolbox.register("select", tools.selDoubleTournament, fitness_size=300, parsimony_size=2, fitness_first=True)
toolbox.register("mate", gp.cxOnePoint)
toolbox.register("expr_mut", gp.genGrow, min_=0, max_=5)
toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
ind = creator.Individual(gp.PrimitiveTree.from_string("m(1,m(B,A,0),i(m((A,B,1)))",pset))
nodes,edges,labels = gp.graph(ind)
g = pgv.AGraph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
g.layout(prog="dot")
for j in nodes:
n = g.get_node(j)
n.attr["label"] = labels[j]
name = "ind.fig"
g.draw(name)