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import seaborn as sns
import matplotlib.pyplot as plt
from parameters import INDIVIDUALS
def plt_vis(data, x_axis, y_axis1, color_attr, shape_attr, colors, shapes):
# Plot
plt.figure(figsize=(8, 6))
for _, row in data.iterrows():
plt.scatter(row[x_axis], row[y_axis1],
color=colors[row[color_attr]],
marker=shapes[row[shape_attr]],
label=f"{row[color_attr]} - {row[shape_attr]}")
# Custom legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1.05, 1), loc='upper left')
plt.xticks(rotation=20)
plt.xlabel(x_axis)
plt.ylabel(y_axis1)
plt.title('Matplotlib Visualization')
plt.tight_layout()
def sns_vis(data, x_axis, y_axis1, color_attr, shape_attr, colors, shapes, title = "", legend = "out"):
plt.figure(figsize=(8.5, 4)) #8.5, 7
plt.rcParams.update({
'font.size': 14, # Global font size
'axes.titlesize': 18, # Title font size
'axes.labelsize': 16, # Axis labels font size
'xtick.labelsize': 14, # X-axis tick labels
'ytick.labelsize': 14, # Y-axis tick labels
'legend.fontsize': 14 # Legend font size
})
sns.scatterplot(data=data, x=x_axis, y=y_axis1,
hue=color_attr, style=shape_attr,
palette=colors, markers=shapes, s=100)
plt.xlabel(x_axis) # plt.xlabel("Augmentation on the training set")
plt.ylabel(y_axis1)
plt.xticks(rotation=20)
plt.title(title)
if legend == "out":
plt.legend(bbox_to_anchor=(1.15, 1), loc='upper left') #bbox_to_anchor=(1.35, 1)
elif legend == "left":
plt.legend(loc='lower left', frameon=True)
elif legend == "right":
plt.legend(loc='lower right', frameon=True)
plt.tight_layout()
primary_ax = plt.gca()
primary_ax.set_ylim(0.89, 1.0)
# Secondary y-axis
def secondary_y_axis_transform(value):
return INDIVIDUALS * (1 - value)
def secondary_y_axis_inverse(value):
return 1 - (value / INDIVIDUALS)
primary_ax = plt.gca()
secondary_ax = primary_ax.twinx()
# Set the secondary y-axis limits based on the primary y-axis
secondary_ax.set_ylim(
secondary_y_axis_transform(primary_ax.get_ylim()[0]),
secondary_y_axis_transform(primary_ax.get_ylim()[1])
)
secondary_ax.set_ylabel('Misclassified instances')
# Synchronize primary and secondary y-axes
def sync_axes(ax):
primary_ax.set_ylim(
secondary_y_axis_inverse(ax.get_ylim()[0]),
secondary_y_axis_inverse(ax.get_ylim()[1])
)
secondary_ax.callbacks.connect("ylim_changed", lambda ax: sync_axes(ax))