# Copyright (c) 2023 Jakub Więckowski
import matplotlib.pyplot as plt
import numpy as np
[docs]
def ifs_criteria_bar_plot(criteria_weights, criteria_names=None, ax=None):
"""
Visualize a list of Intuitionistic Fuzzy Sets (IFS) as stacked bars.
Parameters:
- criteria_weights (list): A list of lists, where each inner list represents an IFS (membership, non-membership, uncertainty).
- criteria_names (list, optional): A list of names for each criteria. If not provided, default names will be used.
- ax (Axes or None): Axes object to draw on. If None, then the current axes are used.
Returns:
ax
Example:
```
# Example Usage:
criteria_weights_example = [[0.6, 0.2, 0.2], [0.8, 0.1, 0.1], [0.5, 0.3, 0.2]]
criteria_names_example = ['Criterion 1', 'Criterion 2', 'Criterion 3']
ifs_criteria_bar_plot(criteria_weights_example, criteria_names_example)
```
The stacked bar plot displays the degree of belief for membership, non-membership, and uncertainty of multiple criteria.
"""
num_criteria = len(criteria_weights)
num_components = len(criteria_weights[0])
if criteria_names is None:
criteria_names = [f'$C_{{{i + 1}}}$' for i in range(num_criteria)]
if ax is None:
ax = plt.gca()
# colors
colors = ['dodgerblue', 'red', 'gray']
# Create stacked bar plot
bottom_values = np.zeros(num_components)
for i in range(num_criteria):
values = np.array(criteria_weights[i])
bottom_values = 0
for idx, val in enumerate(values):
ax.bar(criteria_names[i], val, bottom=bottom_values, alpha=0.7, color=colors[idx])
bottom_values += val
# Add labels and title
ax.set_ylabel('Degree of Belief')
# Add legend
ax.legend(['Membership', 'Non-membership', 'Uncertainty'], bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower left",
mode="expand", borderaxespad=0, ncol=3)
# Add grid
ax.grid(axis='y', linestyle='--', alpha=0.7)
ax.set_axisbelow(True)
# Adjust ylim
ax.set_ylim([0, 1.05])
plt.tight_layout()
return ax