adding archive_plot file

code-preprocessing/archive-update/archive_plot.py

@@ -0,0 +1,160 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from collections import defaultdict
+from archive_load_data import get_file_name_list, create_path, parse_archive_file_name #might need to be changed to 'from cocoprep.archive_load_data', but the cocoprep module could not be located so we adapted it to this.
+
+def read_adat_file(file_path):
+    """
+    Reads a .adat file and returns the function evaluations and non-dominated points.
+
+    Parameters:
+    file_path (str): Path to the .adat file.
+
+    Returns:
+    tuple: (function_evals, archive_sizes), where:
+        - function_evals (list): Cumulative function evaluations at each logging point.
+        - archive_sizes (list): Archive size (number of non-dominated points) at each logging point.
+    """
+    function_evals = []
+    archive_sizes = []
+    archive_size = 0  # Track the number of non-dominated points
+
+    with open(file_path, 'r') as file:
+        for line in file:
+            if not line.startswith('%'):
+                data = line.strip().split()
+                eval_count = int(data[0])  # Function evaluations
+                archive_size += 1  # Increment for each line, as each line represents a new non-dominated point
+                function_evals.append(eval_count)
+                archive_sizes.append(archive_size)
+
+    return function_evals, archive_sizes
+
+def plot_archive_size_by_dimension(output_dir="plots"):
+    """
+    Generates separate plots of archive size over time for each dimension,
+    combining all functions and instances for that dimension.
+
+    Parameters:
+    output_dir (str): Directory to save the plots.
+    """
+    create_path(output_dir)
+
+    # Collect all .adat files in the current directory
+    file_paths = get_file_name_list(".", ending=".adat")
+
+    # Data storage by dimension
+    data_by_dimension = defaultdict(lambda: defaultdict(list))
+
+    # Read data from each file
+    for file_path in file_paths:
+        try:
+            suite_name, function, instance, dimension = parse_archive_file_name(file_path)
+        except Exception as e:
+            print(f"Skipping file {file_path}: {e}")
+            continue
+        function_evals, archive_sizes = read_adat_file(file_path)
+
+        # Store data by dimension and function
+        data_by_dimension[dimension][function].append((function_evals, archive_sizes))
+
+    # Generate a plot for each dimension
+    for dimension, functions in data_by_dimension.items():
+        plt.figure(figsize=(12, 8))
+        plt.title(f"Archive Size vs Function Evaluations for Dimension {dimension}")
+        plt.xlabel("Function Evaluations")
+        plt.ylabel("Archive Size")
+
+        colors = plt.cm.tab20(np.linspace(0, 1, len(functions)))
+
+        for i, (function_name, instances) in enumerate(functions.items()):
+            color = colors[i]
+            for evals, sizes in instances:
+                plt.plot(evals, sizes, color=color, linewidth=0.7, alpha=0.5)
+
+            plt.plot([], [], color=color, label=f"{function_name} instances")
+
+        plt.legend(title="Function", loc="center left", bbox_to_anchor=(1.0, 0.5), fontsize="small", framealpha=0.5)
+        plt.grid(True, which="both", linestyle="--", linewidth=0.5)
+
+        plot_filename = f"archive_size_{dimension}.png"
+        plt.savefig(os.path.join(output_dir, plot_filename), bbox_inches="tight")
+        plt.close()
+        print(f"Plot for dimension {dimension} saved as {plot_filename}")
+
+def plot_average_archive_size_by_dimension(output_dir="plots"):
+    """
+    Generates separate plots of average archive size over time for each dimension,
+    combining all functions and averaging over instances for that function.
+
+    Parameters:
+    output_dir (str): Directory to save the plots.
+    """
+    create_path(output_dir)
+
+    # Collect all .adat files in the current directory
+    file_paths = get_file_name_list(".", ending=".adat")
+
+    # Data storage by dimension
+    data_by_dimension = defaultdict(lambda: defaultdict(list))
+
+    # Read data from each file
+    for file_path in file_paths:
+        try:
+            suite_name, function, instance, dimension = parse_archive_file_name(file_path)
+        except Exception as e:
+            print(f"Skipping file {file_path}: {e}")
+            continue
+        function_evals, archive_sizes = read_adat_file(file_path)
+
+        # Store data by dimension and function
+        data_by_dimension[dimension][function].append((function_evals, archive_sizes))
+
+    # Generate a plot for each dimension
+    for dimension, functions in data_by_dimension.items():
+        plt.figure(figsize=(12, 8))
+        plt.title(f"Average Archive Size vs Function Evaluations for Dimension {dimension}")
+        plt.xlabel("Function Evaluations")
+        plt.ylabel("Average Archive Size")
+
+        colors = plt.cm.tab20(np.linspace(0, 1, len(functions)))
+
+        for i, (function_name, instances) in enumerate(functions.items()):
+            color = colors[i]
+
+            max_eval_count = max(max(evals) for evals, _ in instances)
+            archive_sizes_sum = np.zeros(max_eval_count + 1)
+            count_per_eval = np.zeros(max_eval_count + 1)
+
+            for evals, sizes in instances:
+                current_archive_size = 0
+                eval_index = 0
+
+                for eval_count in range(1, max_eval_count + 1):
+                    if eval_index < len(evals) and evals[eval_index] == eval_count:
+                        current_archive_size = sizes[eval_index]
+                        eval_index += 1
+
+                    archive_sizes_sum[eval_count] += current_archive_size
+                    count_per_eval[eval_count] += 1
+
+            with np.errstate(divide='ignore', invalid='ignore'):
+                average_archive_size = np.divide(archive_sizes_sum, count_per_eval, where=count_per_eval > 0)
+                average_archive_size = np.nan_to_num(average_archive_size)
+
+            eval_points = np.arange(1, len(average_archive_size))
+
+            plt.plot(eval_points, average_archive_size[1:], color=color, label=function_name)
+
+        plt.legend(title="Function", loc="center left", bbox_to_anchor=(1.0, 0.5), fontsize="small", framealpha=0.5)
+        plt.grid(True, which="both", linestyle="--", linewidth=0.5)
+
+        plot_filename = f"average_archive_size_{dimension}.png"
+        plt.savefig(os.path.join(output_dir, plot_filename), bbox_inches="tight")
+        plt.close()
+        print(f"Average plot for dimension {dimension} saved as {plot_filename}")
+
+if __name__ == "__main__":
+    plot_archive_size_by_dimension()
+    plot_average_archive_size_by_dimension()

code-preprocessing/archive-update/python/archive_load_data.py

@@ -11,7 +11,7 @@
 from itertools import groupby
 from operator import itemgetter
 
-from .archive_exceptions import PreprocessingWarning, PreprocessingException
+from archive_exceptions import PreprocessingWarning, PreprocessingException
 
 
 def get_file_name_list(paths, ending=None):