feat(ai): add draw_net

src/ai/training.py

@@ -9,7 +9,7 @@ from utils import BASE_PATH, CONFIG
 from .evaluations import eval_genome
 from .io import get_config, save_genome
 from .log import log
-from .visualize import plot_progress, plot_species, plot_stats
+from .visualize import draw_net, plot_species, plot_stats
 
 
 def train(
@@ -52,6 +52,14 @@ def train(
         filename=CONFIG.ai.plot_path / "avg_fitness.png",
     )
     plot_species(stats, view=False, filename=CONFIG.ai.plot_path / "speciation.png")
+    draw_net(config, winner, view=False, filename=CONFIG.ai.plot_path / "network.gv")
+    draw_net(
+        config,
+        winner,
+        view=False,
+        filename=CONFIG.ai.plot_path / "network-pruned.gv",
+        prune_unused=True,
+    )
 
     log.info("Saving best genome")
     save_genome(winner)

src/ai/visualize.py

@@ -4,8 +4,6 @@ import matplotlib.pyplot as plt
 import neat
 import numpy as np
 
-from .log import log
-
 
 def plot_stats(
     statistics: neat.StatisticsReporter,
@@ -14,11 +12,6 @@ def plot_stats(
     filename: str | Path = "avg_fitness.svg",
 ):
     """Plots the population's average and best fitness."""
-    if plt is None:
-        log.warning(
-            "This display is not available due to a missing optional dependency (matplotlib)"
-        )
-        return
 
     generation = range(len(statistics.most_fit_genomes))
     best_fitness = [c.fitness for c in statistics.most_fit_genomes]
@@ -51,11 +44,6 @@ def plot_species(
     filename: str | Path = "speciation.svg",
 ):
     """Visualizes speciation throughout evolution."""
-    if plt is None:
-        log.warning(
-            "This display is not available due to a missing optional dependency (matplotlib)"
-        )
-        return
 
     species_sizes = statistics.get_species_sizes()
     num_generations = len(species_sizes)
@@ -76,28 +64,78 @@ def plot_species(
     plt.close()
 
 
-def plot_progress(
+def draw_net(
-    generations: list[int],
+    config: neat.Config,
-    mean_fitness: list[int],
+    genome: neat.DefaultGenome,
-    max_fitness: list[int],
     view: bool = False,
-    filename: str | Path = "progress.svg",
+    filename: str | Path = None,
+    node_names: dict = None,
+    show_disabled: bool = True,
+    prune_unused: bool = False,
+    node_colors: dict = None,
+    fmt: str = "svg",
 ):
-    if plt is None:
+    """Receives a genome and draws a neural network with arbitrary topology."""
-        log.warning(
-            "This display is not available due to a missing optional dependency (matplotlib)"
-        )
-        return
-    plt.plot(generations, mean_fitness, label="Mean Fitness")
-    plt.plot(generations, max_fitness, label="Max Fitness")
-    plt.xlabel("Generations")
-    plt.ylabel("Fitness")
-    plt.title("NEAT Algorithm Progress")
-    plt.legend()
-    plt.grid(True)
-    plt.savefig(str(filename))
 
-    if view:
+    # If requested, use a copy of the genome which omits all components that won't affect the output.
-        plt.show()
+    if prune_unused:
+        if show_disabled:
+            warnings.warn("show_disabled has no effect when prune_unused is True")
 
-    plt.close()
+        genome = genome.get_pruned_copy(config.genome_config)
+
+    if node_names is None:
+        node_names = {}
+
+    assert type(node_names) is dict
+
+    if node_colors is None:
+        node_colors = {}
+
+    assert type(node_colors) is dict
+
+    node_attrs = {"shape": "circle", "fontsize": "9", "height": "0.2", "width": "0.2"}
+
+    dot = graphviz.Digraph(format=fmt, node_attr=node_attrs)
+
+    inputs = set()
+    for k in config.genome_config.input_keys:
+        inputs.add(k)
+        name = node_names.get(k, str(k))
+        input_attrs = {
+            "style": "filled",
+            "shape": "box",
+            "fillcolor": node_colors.get(k, "lightgray"),
+        }
+        dot.node(name, _attributes=input_attrs)
+
+    outputs = set()
+    for k in config.genome_config.output_keys:
+        outputs.add(k)
+        name = node_names.get(k, str(k))
+        node_attrs = {"style": "filled", "fillcolor": node_colors.get(k, "lightblue")}
+
+        dot.node(name, _attributes=node_attrs)
+
+    for n in genome.nodes.keys():
+        if n in inputs or n in outputs:
+            continue
+
+        attrs = {"style": "filled", "fillcolor": node_colors.get(n, "white")}
+        dot.node(str(n), _attributes=attrs)
+
+    for cg in genome.connections.values():
+        if cg.enabled or show_disabled:
+            input, output = cg.key
+            a = node_names.get(input, str(input))
+            b = node_names.get(output, str(output))
+            style = "solid" if cg.enabled else "dotted"
+            color = "green" if cg.weight > 0 else "red"
+            width = str(0.1 + abs(cg.weight / 5.0))
+            dot.edge(
+                a, b, _attributes={"style": style, "color": color, "penwidth": width}
+            )
+
+    dot.render(filename, view=view)
+
+    return dot