fix code style and formatting

Author: zStupan

docs/conf.py

@@ -13,7 +13,7 @@
 import sys
 from pathlib import Path
 
-sys.path.insert(0, str(Path('..', 'src').resolve()))
+sys.path.insert(0, str(Path("..", "src").resolve()))
 
 # -- Project information -----------------------------------------------------
 

examples/visualization_examples/sankey_diagram/abalone_sankey_diagram.py

@@ -29,5 +29,5 @@
 print("\nMetrics:", metrics)
 
 # Visualize sankey diagram
-fig = sankey_diagram(rules=rules, interestingness_measure="support", M=4)
+fig = sankey_diagram(rules=rules, interestingness_measure="support", max_rules=4)
 fig.show()

examples/visualization_examples/sankey_diagram/weather_data_sankey_diagram.py

@@ -36,5 +36,5 @@
 print("\nMetrics:", metrics)
 
 # Visualize sankey diagram
-fig = sankey_diagram(rules=rules, interestingness_measure="support", M=4)
+fig = sankey_diagram(rules=rules, interestingness_measure="support", max_rules=4)
 fig.show()

src/niaarm/visualize.py

@@ -593,15 +593,16 @@ def prepare_data(rules, metrics):
         return plt
 
 
-def sankey_diagram(rules, interestingness_measure, M=4):
+def sankey_diagram(rules, interestingness_measure, max_rules=4):
     """
     Visualize rules as a sankey diagram.
 
     Args:
         rules (Rule): Association rule or rules to visualize.
-        interestingness_measures (str): Interestingness measure Z = {supp, cons,
+        interestingness_measure (str): Interestingness measure Z = {supp, cons,
         lift},reflecting the quality of a particular connection.
-        m (int): Maximum number of rules to be selected for visualization. Default: 4
+        max_rules (int): Maximum number of rules to be selected for visualization.
+         Default: 4
 
     Returns:
         Figure or plot.
@@ -626,22 +627,22 @@ def build_adjacency_matrix(rules):
 
         return adjacency_matrix
 
-    def knapsack_selection(adj_matrix, rules, M):
+    def knapsack_selection(adj_matrix, rules, max_rules):
         fitness_scores = np.array([rule.fitness for rule in rules])
-        N = len(rules)  # number of rules
-        weights = np.ones(N)  # all rules have the same weight
+        n_rules = len(rules)  # number of rules
+        weights = np.ones(n_rules)  # all rules have the same weight
         similarity_weight = 1.0
         fitness_weight = 0.5
         combined_profits = (
             similarity_weight * np.sum(adj_matrix) + fitness_weight * fitness_scores
         )  # combined similarities with fitness for values
 
-        selected = np.zeros(N, dtype=int)
+        selected = np.zeros(n_rules, dtype=int)
 
         # Initialize DP table
-        dp = np.zeros((N + 1, M + 1))
-        for i in range(1, N + 1):
-            for w in range(1, M + 1):
+        dp = np.zeros((n_rules + 1, max_rules + 1))
+        for i in range(1, n_rules + 1):
+            for w in range(1, max_rules + 1):
                 if weights[i - 1] <= w:
                     dp[i, w] = max(
                         dp[i - 1, w], dp[i - 1, w - 1] + combined_profits[i - 1]
@@ -650,22 +651,22 @@ def knapsack_selection(adj_matrix, rules, M):
                     dp[i, w] = dp[i - 1, w]
 
         # Backtrack to find selected rules
-        w = M
-        for i in range(N, 0, -1):
+        w = max_rules
+        for i in range(n_rules, 0, -1):
             if dp[i, w] != dp[i - 1, w]:
                 selected[i - 1] = 1
                 w -= 1
 
-        selected_rules = [rules[i] for i in range(N) if selected[i]]
+        selected_rules = [rules[i] for i in range(n_rules) if selected[i]]
 
         return selected_rules
 
-    def prepare_data(rules, M, interestingness_measure):
+    def prepare_data(rules, max_rules, interestingness_measure):
         if not rules:
             return [], [], [], []
 
         adj_matrix = build_adjacency_matrix(rules)
-        selected_rules = knapsack_selection(adj_matrix, rules, M)
+        selected_rules = knapsack_selection(adj_matrix, rules, max_rules)
 
         sources = []
         targets = []
@@ -696,7 +697,9 @@ def prepare_data(rules, M, interestingness_measure):
 
         return labels, sources, targets, values
 
-    labels, sources, targets, values = prepare_data(rules, M, interestingness_measure)
+    labels, sources, targets, values = prepare_data(
+        rules, max_rules, interestingness_measure
+    )
 
     # Visualization using Plotly
     fig = go.Figure(