рисование дерева

lab4/draw_tree.py

@@ -0,0 +1,50 @@
+from graphviz import Digraph
+
+
+def make_pow2_sum_tree(n=8):
+    dot = Digraph("FullTree")
+    dot.attr(rankdir="TB")  # направление сверху вниз
+    dot.attr("node", shape="circle", style="filled", fillcolor="lightgray")
+
+    node_count = 0
+
+    def new_node(label):
+        nonlocal node_count
+        node_id = f"n{node_count}"
+        node_count += 1
+        dot.node(node_id, label)
+        return node_id
+
+    def pow2_node(xi):
+        n1 = new_node("pow2")
+        n2 = new_node(xi)
+        dot.edge(n1, n2)
+        return n1
+
+    def plus(a, b):
+        n = new_node("+")
+        dot.edge(n, a)
+        dot.edge(n, b)
+        return n
+
+    all_terms = []
+    for i in range(1, n + 1):
+        terms = [pow2_node(f"x{j}") for j in range(1, i + 1)]
+        s = terms[0]
+        for t in terms[1:]:
+            s = plus(s, t)
+        all_terms.append(s)
+
+    root = all_terms[0]
+    for t in all_terms[1:]:
+        root = plus(root, t)
+
+    dot.node("root", "f(x)")
+    dot.edge("root", root)
+
+    return dot
+
+
+if __name__ == "__main__":
+    g = make_pow2_sum_tree(8)
+    g.render("original_tree", format="png", cleanup=True)

lab4/gp/__init__.py

@@ -1,3 +0,0 @@
-from .chromosome import Chromosome
-
-__all__ = ["Chromosome"]

lab4/gp/ga.py

@@ -6,11 +6,12 @@ from copy import deepcopy
 from dataclasses import asdict, dataclass
 from typing import Callable
 
+import graphviz
 import numpy as np
 from matplotlib import pyplot as plt
-from numpy.typing import NDArray
 
 from .chromosome import Chromosome
+from .node import Node
 from .types import Fitnesses, Population
 
 type FitnessFn = Callable[[Chromosome], float]
@@ -66,6 +67,7 @@ class Generation:
     number: int
     best: Chromosome
     best_fitness: float
+    avg_fitness: float
     population: Population
     fitnesses: Fitnesses
 
@@ -148,27 +150,45 @@ def eval_population(population: Population, fitness_func: FitnessFn) -> Fitnesse
     return np.array([fitness_func(chrom) for chrom in population])
 
 
+def render_tree_to_graphviz(
+    node: Node, graph: graphviz.Digraph, node_id: str = "0"
+) -> None:
+    """Рекурсивно добавляет узлы дерева в graphviz граф."""
+    graph.node(node_id, label=node.value.name)
+
+    for i, child in enumerate(node.children):
+        child_id = f"{node_id}_{i}"
+        render_tree_to_graphviz(child, graph, child_id)
+        graph.edge(node_id, child_id)
+
+
 def save_generation(
     generation: Generation, history: list[Generation], config: GARunConfig
 ) -> None:
+    """Сохраняет визуализацию лучшей хромосомы поколения в виде дерева."""
     os.makedirs(config.results_dir, exist_ok=True)
 
-    fig = plt.figure(figsize=(7, 7))
+    # Создаем граф для визуализации дерева
-    fig.suptitle(
+    dot = graphviz.Digraph(comment=f"Generation {generation.number}")
-        f"Поколение #{generation.number}. "
+    dot.attr(rankdir="TB")  # Top to Bottom direction
-        f"Лучшая особь: {generation.best_fitness:.0f}. "
+    dot.attr("node", shape="circle", style="filled", fillcolor="lightblue")
-        f"Среднее значение: {np.mean(generation.fitnesses):.0f}",
+
-        fontsize=14,
+    # Добавляем заголовок
-        y=0.95,
+    depth = generation.best.root.get_depth()
+    title = (
+        f"Поколение #{generation.number}\\n"
+        f"Лучшая особь: {generation.best_fitness:.4f}\\n"
+        f"Глубина дерева: {depth}"
     )
+    dot.attr(label=title, labelloc="t", fontsize="14")
 
-    # Рисуем
+    # Рендерим дерево
-    ...
+    render_tree_to_graphviz(generation.best.root, dot)
 
-    filename = f"generation_{generation.number:03d}.png"
+    # Сохраняем
-    path_png = os.path.join(config.results_dir, filename)
+    filename = f"generation_{generation.number:03d}"
-    fig.savefig(path_png, dpi=150, bbox_inches="tight")
+    filepath = os.path.join(config.results_dir, filename)
-    plt.close(fig)
+    dot.render(filepath, format="png", cleanup=True)
 
 
 def genetic_algorithm(config: GARunConfig) -> GARunResult:
@@ -216,6 +236,7 @@ def genetic_algorithm(config: GARunConfig) -> GARunResult:
             number=generation_number,
             best=population[best_index],
             best_fitness=fitnesses[best_index],
+            avg_fitness=float(np.mean(fitnesses)),
             # population=deepcopy(population),
             population=[],
             # fitnesses=deepcopy(fitnesses),
@@ -301,41 +322,61 @@ def genetic_algorithm(config: GARunConfig) -> GARunResult:
     end = time.perf_counter()
 
     assert best is not None, "Best was never set"
-    return GARunResult(
+    result = GARunResult(
         len(history),
         best,
         history,
         (end - start) * 1000.0,
     )
 
+    # Автоматически строим графики истории фитнеса
+    if config.save_generations:
+        plot_fitness_history(result, save_dir=config.results_dir)
 
-def plot_fitness_history(result: GARunResult, save_path: str | None = None) -> None:
+    return result
-    """Рисует график изменения лучших и средних значений фитнеса по поколениям."""
+
+
+def plot_fitness_history(result: GARunResult, save_dir: str | None = None) -> None:
+    """Рисует графики изменения лучших и средних значений фитнеса по поколениям.
+
+    Создает два отдельных графика:
+    - fitness_best.png - график лучших значений
+    - fitness_avg.png - график средних значений
+    """
     generations = [gen.number for gen in result.history]
     best_fitnesses = [gen.best_fitness for gen in result.history]
-    avg_fitnesses = [np.mean(gen.fitnesses) for gen in result.history]
+    avg_fitnesses = [gen.avg_fitness for gen in result.history]
 
-    fig, ax = plt.subplots(figsize=(10, 6))
+    # График лучших значений
+    fig_best, ax_best = plt.subplots(figsize=(10, 6))
+    ax_best.plot(generations, best_fitnesses, linewidth=2, color="blue")
+    ax_best.set_xlabel("Поколение", fontsize=12)
+    ax_best.set_ylabel("Лучшее значение фитнес-функции", fontsize=12)
+    ax_best.set_title("Лучшее значение фитнеса по поколениям", fontsize=14)
+    ax_best.grid(True, alpha=0.3)
 
-    ax.plot(
+    if save_dir:
-        generations, best_fitnesses, label="Лучшее значение", linewidth=2, color="blue"
+        best_path = os.path.join(save_dir, "fitness_best.png")
-    )
+        fig_best.savefig(best_path, dpi=150, bbox_inches="tight")
-    ax.plot(
+        print(f"График лучших значений сохранен в {best_path}")
-        generations,
-        avg_fitnesses,
-        label="Среднее значение",
-        linewidth=2,
-        color="orange",
-    )
-
-    ax.set_xlabel("Поколение", fontsize=12)
-    ax.set_ylabel("Значение фитнес-функции", fontsize=12)
-    ax.legend(fontsize=11)
-    ax.grid(True, alpha=0.3)
-
-    if save_path:
-        fig.savefig(save_path, dpi=150, bbox_inches="tight")
-        print(f"График сохранен в {save_path}")
     else:
         plt.show()
-        plt.close(fig)
+
+    plt.close(fig_best)
+
+    # График средних значений
+    fig_avg, ax_avg = plt.subplots(figsize=(10, 6))
+    ax_avg.plot(generations, avg_fitnesses, linewidth=2, color="orange")
+    ax_avg.set_xlabel("Поколение", fontsize=12)
+    ax_avg.set_ylabel("Среднее значение фитнес-функции", fontsize=12)
+    ax_avg.set_title("Среднее значение фитнеса по поколениям", fontsize=14)
+    ax_avg.grid(True, alpha=0.3)
+
+    if save_dir:
+        avg_path = os.path.join(save_dir, "fitness_avg.png")
+        fig_avg.savefig(avg_path, dpi=150, bbox_inches="tight")
+        print(f"График средних значений сохранен в {avg_path}")
+    else:
+        plt.show()
+
+    plt.close(fig_avg)

lab4/gp/population.py

@@ -1,30 +0,0 @@
-from typing import Sequence
-
-from .chromosome import Chromosome
-from .primitive import Primitive
-
-type Population = list[Chromosome]
-
-
-def ramped_initialization(
-    chromosomes_per_variation: int,
-    depths: list[int],
-    terminals: Sequence[Primitive],
-    operations: Sequence[Primitive],
-) -> Population:
-    """Комбинация методов grow и full инициализации хромосом для инициализации начальной
-    популяции.
-    """
-    population: Population = []
-
-    for depth in depths:
-        population.extend(
-            Chromosome.full_init(terminals, operations, depth)
-            for _ in range(chromosomes_per_variation)
-        )
-        population.extend(
-            Chromosome.grow_init(terminals, operations, depth)
-            for _ in range(chromosomes_per_variation)
-        )
-
-    return population

lab4/main.py

@@ -1,16 +1,20 @@
+"""
+graphviz должен быть доступен в PATH (недостаточно просто установить через pip)
+
+Можно проверить командой
+dot -V
+"""
+
 import random
-from math import log
 
 import numpy as np
 from numpy.typing import NDArray
 
-from gp import Chromosome
 from gp.crossovers import crossover_subtree
 from gp.fitness import (
     MAEFitness,
     MSEFitness,
     NRMSEFitness,
-    PenalizedFitness,
     RMSEFitness,
 )
 from gp.ga import GARunConfig, genetic_algorithm
@@ -21,10 +25,10 @@ from gp.mutations import (
     NodeReplacementMutation,
     ShrinkMutation,
 )
-from gp.ops import ADD, COS, DIV, EXP, MUL, NEG, POW, SIN, SQUARE, SUB
+from gp.ops import ADD, COS, DIV, EXP, MUL, POW, SIN, SQUARE, SUB
 from gp.population import ramped_initialization
-from gp.primitive import Const, Var
+from gp.primitive import Var
-from gp.selection import roulette_selection, tournament_selection
+from gp.selection import tournament_selection
 
 NUM_VARS = 8
 TEST_POINTS = 10000
@@ -34,8 +38,6 @@ SEED = 17
 np.random.seed(SEED)
 random.seed(SEED)
 X = np.random.uniform(-5.536, 5.536, size=(TEST_POINTS, NUM_VARS))
-# axes = [np.linspace(-5.536, 5.536, TEST_POINTS) for _ in range(NUM_VARS)]
-# X = np.array(np.meshgrid(*axes)).T.reshape(-1, NUM_VARS)
 operations = [SQUARE, SIN, COS, EXP, ADD, SUB, MUL, DIV, POW]
 terminals = [Var(f"x{i}") for i in range(1, NUM_VARS + 1)]
 
@@ -74,7 +76,6 @@ config = GARunConfig(
     fitness_func=fitness_function,
     crossover_fn=lambda p1, p2: crossover_subtree(p1, p2, max_depth=MAX_DEPTH),
     mutation_fn=combined_mutation,
-    # selection_fn=roulette_selection,
     selection_fn=lambda p, f: tournament_selection(p, f, k=3),
     init_population=init_population,
     seed=SEED,
@@ -83,6 +84,7 @@ config = GARunConfig(
     elitism=15,
     max_generations=MAX_GENERATIONS,
     log_every_generation=True,
+    save_generations=[1, 10, 20, 30, 40, 50, 100, 150, 200],
 )
 
 result = genetic_algorithm(config)

lab4/pyproject.toml

@@ -3,6 +3,7 @@ name = "lab4"
 version = "0.1.0"
 requires-python = ">=3.14"
 dependencies = [
+    "graphviz>=0.21",
     "matplotlib>=3.10.7",
     "numpy>=2.3.4",
 ]

lab4/uv.lock

@@ -69,6 +69,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/93/0dd45cd283c32dea1545151d8c3637b4b8c53cdb3a625aeb2885b184d74d/fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb", size = 1143175, upload-time = "2025-09-29T21:13:24.134Z" },
 ]
 
+[[package]]
+name = "graphviz"
+version = "0.21"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/b3/3ac91e9be6b761a4b30d66ff165e54439dcd48b83f4e20d644867215f6ca/graphviz-0.21.tar.gz", hash = "sha256:20743e7183be82aaaa8ad6c93f8893c923bd6658a04c32ee115edb3c8a835f78", size = 200434, upload-time = "2025-06-15T09:35:05.824Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/4c/e0ce1ef95d4000ebc1c11801f9b944fa5910ecc15b5e351865763d8657f8/graphviz-0.21-py3-none-any.whl", hash = "sha256:54f33de9f4f911d7e84e4191749cac8cc5653f815b06738c54db9a15ab8b1e42", size = 47300, upload-time = "2025-06-15T09:35:04.433Z" },
+]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.9"
@@ -108,12 +117,14 @@ name = "lab4"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "graphviz" },
     { name = "matplotlib" },
     { name = "numpy" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "graphviz", specifier = ">=0.21" },
     { name = "matplotlib", specifier = ">=3.10.7" },
     { name = "numpy", specifier = ">=2.3.4" },
 ]