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Plotter cleanup

master
Mark 2023-02-26 15:26:45 -08:00
parent 05d745cc07
commit 3d25d63efe
Signed by: Mark
GPG Key ID: AD62BB059C2AAEE4
3 changed files with 11 additions and 128 deletions
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1
celeste/celeste_ai/plotting/__init__.py
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@ -1,4 +1,3 @@
from .plot_actual_reward import actual_reward
from .plot_predicted_reward import predicted_reward from .plot_predicted_reward import predicted_reward
from .plot_best_action import best_action from .plot_best_action import best_action

81
celeste/celeste_ai/plotting/plot_actual_reward.py
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@ -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()

49
celeste/plot.py
View File

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