def compute_pareto_frontier()

in method_comparison/app.py [0:0]

• 32 lines of code
• 14 McCabe index (conditional complexity)

def compute_pareto_frontier(df, metric_x, metric_y):
    if df.empty:
        return df

    df = df.copy()
    points = df[[metric_x, metric_y]].values
    selected_indices = []

    def dominates(a, b, metric_x, metric_y):
        # Check for each metric whether b is as good or better than a
        if metric_preferences[metric_x] == "higher":
            cond_x = b[0] >= a[0]
            better_x = b[0] > a[0]
        else:
            cond_x = b[0] <= a[0]
            better_x = b[0] < a[0]
        if metric_preferences[metric_y] == "higher":
            cond_y = b[1] >= a[1]
            better_y = b[1] > a[1]
        else:
            cond_y = b[1] <= a[1]
            better_y = b[1] < a[1]
        return cond_x and cond_y and (better_x or better_y)

    for i, point in enumerate(points):
        dominated = False
        for j, other_point in enumerate(points):
            if i == j:
                continue
            if dominates(point, other_point, metric_x, metric_y):
                dominated = True
                break
        if not dominated:
            selected_indices.append(i)
    pareto_df = df.iloc[selected_indices]
    return pareto_df