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correlation.py
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correlation.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.pyplot as plt
import matplotlib as mpl
import matplotlib.lines as lines
from mpl_toolkits.axes_grid1 import ImageGrid
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
def preproc_adult(dataset):
dataset.merge_classes({"<=50K": ["<=50K."], ">50K": [">50K."]})
return dataset
def preproc_car(dataset):
return dataset
configs = [
("configs/car_evaluation_cr.json", preproc_car, "Car Evaluation"),
("configs/playnet_cr.json", preproc_playnet, "PlayNet"),
("configs/adult_cr.json", preproc_adult, "Adult"),
]
gens = [
(TVAE, "TVAE"),
(CTGAN, "CTGAN"),
(GaussianCopula, "Gaussian Copula"),
(CopulaGAN, "Copula GAN"),
(CTABGAN, "CTAB-GAN"),
(CTABGANPlus, "CTAB-GAN+"),
(AutoDiffusion, "AutoDiffusion"),
(ForestDiffusion, "ForestDiffusion"),
(GReaT, "GReaT"),
(Tabula, "Tabula"),
]
for c in configs:
config = Config(c[0])
dataset = c[1](Dataset(config))
console.print(dataset.class_counts(), dataset.row_count())
corrs = []
for g in gens:
generator = g[0](dataset)
try:
generator.load_from_disk()
except FileNotFoundError:
corrs.append(None)
continue
corrs.append(dataset.get_pearson_correlation())
max_corr = max(map(lambda x: x.values.max() if x is not None else 0, corrs))
fig = plt.figure(figsize=(20, 10))
grid = ImageGrid(
fig,
111, # similar to subplot(111)
nrows_ncols=(2, len(gens) // 2),
axes_pad=0.8,
direction="row",
share_all=True,
cbar_mode="single",
cbar_location="right",
)
axs, ims = [], []
i = 0
for corr in corrs:
ax = grid.axes_all[i]
ax.set_title(gens[i][1])
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([])
if corr is not None:
axs.append(ax)
ims.append(
ax.imshow(
corr,
cmap="Greens",
interpolation="nearest",
vmin=0.0,
vmax=max_corr,
)
)
else:
axs.append(ax)
# Iterating over the grid returns the Axes.
l1 = lines.Line2D([0, 1], [0, 1], transform=fig.transFigure, figure=fig)
l2 = lines.Line2D([0, 1], [1, 0], transform=fig.transFigure, figure=fig)
# fig.lines.extend([l1, l2])
ax.plot(
[0, dataset.num_features()], [0, dataset.num_features()], color="red"
) # Diagonal from bottom-left to top-right
ims.append(
ax.plot(
[0, dataset.num_features()],
[dataset.num_features(), 0],
color="red",
) # Diagonal from top-left to bottom-right
)
i += 1
cb = fig.colorbar(ims[0], cax=grid.cbar_axes[0], orientation="vertical")
cb.outline.set_visible(False)
plt.savefig(
"figures/Correlation" + c[2] + ".pdf", format="pdf", bbox_inches="tight"
)