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visual_playnet.py
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visual_playnet.py
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from gentab.evaluators import KNN, LightGBM, XGBoost, MLP
from gentab.generators import (
SMOTE,
ADASYN,
TVAE,
CTGAN,
GaussianCopula,
CopulaGAN,
CTABGAN,
CTABGANPlus,
AutoDiffusion,
ForestDiffusion,
Tabula,
GReaT,
)
from gentab.data import Config, Dataset
from gentab.utils import console
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, Circle, Ellipse, Arc
import numpy as np
def preproc_playnet(dataset):
dataset.reduce_size(
{
"left_attack": 0.97,
"right_attack": 0.97,
"right_transition": 0.9,
"left_transition": 0.9,
"time_out": 0.8,
"left_penal": 0.5,
"right_penal": 0.5,
}
)
dataset.merge_classes(
{
"attack": ["left_attack", "right_attack"],
"transition": ["left_transition", "right_transition"],
"penalty": ["left_penal", "right_penal"],
}
)
dataset.reduce_mem()
return dataset
gens = [
(None, "Baseline"),
(TVAE, "TVAE"),
(CTGAN, "CTGAN"),
(GaussianCopula, "Gaussian Copula"),
(CopulaGAN, "Copula GAN"),
(CTABGAN, "CTAB-GAN"),
(CTABGANPlus, "CTAB-GAN+"),
(AutoDiffusion, "AutoDiffusion"),
(ForestDiffusion, "ForestDiffusion"),
(GReaT, "GReaT"),
(Tabula, "Tabula"),
]
config = Config("configs/playnet_cr.json")
dataset = preproc_playnet(Dataset(config))
gs = gridspec.GridSpec(len(gens), dataset.num_classes())
fig = plt.figure(figsize=(40, 30))
fig.subplots_adjust(wspace=0.25, hspace=0.125)
axs, ims = [], []
i = 0
for g in gens:
if g[0] is not None:
generator = g[0](dataset)
generator.load_from_disk()
j = 0
for c in dataset.class_names():
ax = fig.add_subplot(gs[i, j])
# Set labels
if i == 0:
ax.set_title("timeout" if c == "time_out" else c, fontsize=19)
if j == 0:
if g[0] is not None:
ax.set_ylabel(g[1], fontsize=18)
else:
ax.set_ylabel(g[1], fontsize=18)
# Remove chart borders and ticks
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
# Get positions
if g[0] is not None:
row = generator.dataset.get_random_gen_class_rows(c, 1)
else:
row = dataset.get_random_class_rows(c, 1)
xpoints = row.filter(regex="^#x").values.flatten()
ypoints = row.filter(regex="^#y").values.flatten()
vxpoints = row.filter(regex="^#vx").values.flatten()
vypoints = row.filter(regex="^#vy").values.flatten()
ballx = row.filter(regex="^#ball_x").values.flatten()
bally = row.filter(regex="^#ball_y").values.flatten()
tolerance = 0.05
xpoints[np.isclose(xpoints, 0, atol=tolerance)] = 0
ypoints[np.isclose(ypoints, 0, atol=tolerance)] = 0
vxpoints[np.isclose(vxpoints, 0, atol=tolerance)] = 0
vypoints[np.isclose(vypoints, 0, atol=tolerance)] = 0
ballx[np.isclose(ballx, 0, atol=tolerance)] = 0
bally[np.isclose(bally, 0, atol=tolerance)] = 0
ids = ~(
np.array(xpoints == 0)
& np.array(ypoints == 0)
& np.array(vxpoints == 0)
& np.array(vxpoints == 0)
)
xpoints = xpoints[ids]
ypoints = ypoints[ids]
vxpoints = vxpoints[ids]
vypoints = vypoints[ids]
ids = ~(np.array(ballx == 0) & np.array(bally == 0))
ballx = ballx[ids]
bally = bally[ids]
# First array horiz. coords., second vertical
# Middle line
ax.plot([0.5, 0.5], [0.0, 1.0], color="black")
# Center circle
ax.add_patch(Ellipse((0.5, 0.5), 0.2, 0.7, facecolor="none", ec="k", lw=2))
# Areas
ax.add_patch(
Arc(
(0.0, 0.5),
0.2,
0.9,
angle=180,
theta1=90,
theta2=270,
facecolor="none",
ec="k",
lw=2,
)
)
ax.add_patch(
Arc(
(1.0, 0.5),
0.2,
0.9,
theta1=90,
theta2=270,
facecolor="none",
ec="k",
lw=2,
)
)
# Player positions
ax.plot(xpoints, ypoints, "o")
# Ball
# ax.plot(ballx, bally, "o", color="magenta")
# Speeds
ax.quiver(
xpoints,
ypoints,
vxpoints,
vypoints,
color="orange",
angles="uv",
scale=4,
headaxislength=3,
headlength=3,
)
# Field
ims.append(
ax.add_patch(Rectangle((0, 0), 1, 1, facecolor="none", ec="k", lw=2))
)
axs.append(ax)
j += 1
i += 1
plt.savefig("figures/VisualPlaynet.pdf", format="pdf", bbox_inches="tight")