Plotter cleanup

celeste/celeste_ai/plotting/__init__.py

@@ -1,4 +1,3 @@
-from .plot_actual_reward import actual_reward
 from .plot_predicted_reward import predicted_reward
 from .plot_best_action import best_action
 

celeste/celeste_ai/plotting/plot_actual_reward.py

@@ -1,81 +0,0 @@
-import torch
-import numpy as np
-from pathlib import Path
-import matplotlib.pyplot as plt
-from multiprocessing import Pool
-
-# All of the following are required to load
-# a pickled model.
-from celeste_ai.celeste import Celeste
-from celeste_ai.network import DQN
-from celeste_ai.network import Transition
-
-def actual_reward(
-	model_file: Path,
-	target_point: tuple[int, int],
-	out_filename: Path,
-	*,
-	device = torch.device("cpu")
-):
-	if not model_file.is_file():
-		raise Exception(f"Bad model file {model_file}")
-	out_filename.parent.mkdir(exist_ok = True, parents = True)
-
-
-	checkpoint = torch.load(
-		model_file,
-		map_location = device
-	)
-	memory = checkpoint["memory"]
-
-
-	r = np.zeros((128, 128, 8), dtype=np.float32)
-	for m in memory:
-		x, y, x_target, y_target = list(m.state[0])
-
-		action = m.action[0].item()
-		x = int(x.item())
-		y = int(y.item())
-		x_target = int(x_target.item())
-		y_target = int(y_target.item())
-
-		# Only plot memory related to this point
-		if (x_target, y_target) != target_point:
-			continue
-
-		if m.reward[0].item() == 1:
-			r[y][x][action] += 1
-		else:
-			r[y][x][action] -= 1
-
-
-	fig, axs = plt.subplots(2, 4, figsize = (20, 10))
-
-
-	for a in range(len(axs.ravel())):
-		ax = axs.ravel()[a]
-		ax.set(
-			adjustable = "box",
-			aspect = "equal",
-			title = Celeste.action_space[a]
-		)
-
-		plot = ax.pcolor(
-			r[:,:,a],
-			cmap = "seismic_r",
-			vmin = -10,
-			vmax = 10
-		)
-
-		# Draw target point on plot
-		ax.plot(
-			target_point[0],
-			target_point[1],
-			"k."
-		)
-
-		ax.invert_yaxis()
-		fig.colorbar(plot)
-
-	fig.savefig(out_filename)
-	plt.close()

celeste/plot.py

@@ -5,10 +5,9 @@ import celeste_ai.plotting as plotting
 from multiprocessing import Pool
 
 
-
 m = Path("model_data/current")
 
-# Make "predicted reward" plots
+
 def plot_pred(src_model):
 	plotting.predicted_reward(
 		src_model,
@@ -17,7 +16,6 @@ def plot_pred(src_model):
 		device = torch.device("cpu")
 	)
 
-# Make "best action" plots
 def plot_best(src_model):
 	plotting.best_action(
 		src_model,
@@ -26,47 +24,14 @@ def plot_best(src_model):
 		device = torch.device("cpu")
 	)
 
-# Make "actual reward" plots
-def plot_act(src_model):
-	plotting.actual_reward(
-		src_model,
-		(60, 80),
-		m / f"plots/actual/{src_model.stem}.png",
 
-		device = torch.device("cpu")
-	)
-
-
-# Which plots should we make?
-plots = {
-	"prediction": True,
-	"actual": False,
-	"best": True
-}
-
-
-if __name__ == "__main__":
-
-	if plots["best"]:
-		print("Making best-action plots...")
-		with Pool(5) as p:
-			p.map(
-				plot_best,
-				list((m / "model_archive").iterdir())
-			)
-
-	if plots["prediction"]:
-		print("Making prediction plots...")
-		with Pool(5) as p:
-			p.map(
-				plot_pred,
-				list((m / "model_archive").iterdir())
-			)
-
-	if plots["actual"]:
-		print("Making actual plots...")
-		with Pool(5) as p:
-			p.map(
-				plot_act,
-				list((m / "model_archive").iterdir())
-			)
+for k, v in {
+	#"prediction":	plot_pred,
+	"best_action":	plot_best,
+}.items():
+	print(f"Making {k} plots...")
+	with Pool(5) as p:
+		p.map(
+			v,
+			list((m / "model_archive").iterdir())
+		)