minifix

measurements/loss_vs_time_plot.py

@@ -0,0 +1,74 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def plot_optimization_graph(csv_file_path):
+    # Load the data from a CSV file, splitting columns by the semicolon delimiter
+    data = pd.read_csv(csv_file_path, sep=';')
+
+    # Rename the columns for clarity
+    data.columns = ['source_data', 'config_id', 'out_data', 'time', 'Loss']
+
+    # Ensure the required columns ('time', 'config_id', 'Loss') are present
+    required_columns = {'time', 'config_id', 'Loss'}
+    if not required_columns.issubset(data.columns):
+        raise ValueError(f"CSV file must contain the following columns: {required_columns}")
+
+    # Create a new column 'OptimizationType' by formatting the 'config_id' column
+    # Replace underscores with spaces and capitalize each word
+    data['OptimizationType'] = data['config_id'].str.replace('_', ' ').str.title()
+
+    # Convert 'time' and 'Loss' columns to numeric, coercing errors to NaN
+    data['time'] = pd.to_numeric(data['time'], errors='coerce')
+    data['Loss'] = pd.to_numeric(data['Loss'], errors='coerce')
+
+    # Begin plotting with matplotlib
+    plt.figure(figsize=(10, 6))  # Set the figure size
+
+    # Iterate through each unique optimization type to plot them separately
+    for opt_type, color in zip(data['OptimizationType'].unique(), ['orange', 'blue', 'green']):
+        # Filter the data for the current optimization type
+        subset = data[data['OptimizationType'] == opt_type]
+
+        # Scatter plot for the current optimization type
+        plt.scatter(
+            subset['time'],  # X-axis: time
+            subset['Loss'],  # Y-axis: loss
+            label=opt_type,  # Legend label
+            s=100,  # Marker size
+            color=color
+        )
+
+        # Calculate mean and variance for each optimization type
+        mean_loss = subset['Loss'].mean()
+
+        # Plot the mean as a horizontal line
+        plt.axhline(
+            y=mean_loss,  # Horizontal line at mean loss
+            color=color, linestyle='solid', linewidth=1, label=f"{opt_type} Mean"
+        )
+
+    # Set both axes to a logarithmic scale
+    plt.xscale('log')
+    plt.yscale('log')
+
+    # Add titles and labels to the plot
+    plt.title("Loss vs Time by Optimization Type", fontsize=16)  # Plot title
+    plt.xlabel("Time (log scale)", fontsize=14)  # X-axis label
+    plt.ylabel("Loss (log scale)", fontsize=14)  # Y-axis label
+
+    # Add a legend to distinguish optimization types and their statistics, fixed in the top-right corner
+    plt.legend(fontsize=12, title_fontsize=13, 
+               loc='center left',
+               bbox_to_anchor=(1, 0.5),
+               borderaxespad=0.5)
+
+    # Adjust layout for better spacing
+    plt.tight_layout()
+
+    # Display the plot
+    plt.show()
+
+# Example usage
+# Replace 'data.csv' with the path to your CSV file
+csv_file_path = 'measurements/results_dual_annealing_main.csv'  # Update with your actual file path
+plot_optimization_graph(csv_file_path)

src/config.yaml

@@ -1,5 +1,5 @@
 global:
-  nothing: nothing
+  generate_hulls: True
 
 editor:
   # initial frame size (the data is normalized to -100, 100 on wider axis)
@@ -153,7 +153,7 @@ labels_generator:
       no_local_search: True
 
     global:
-      generate_greedy_x0: True
+      generate_greedy_x0: False
       maxiter: 1500
       visit: 4
       initial_temp: 7230

src/generator/hull_generator.py

@@ -743,6 +743,8 @@ def calc_one_hull(hull_name,
 def parse_solution_to_editor_hull(hulls: dict, state: dict) -> dict:
     state["hulls_data"]["hulls"] = {}
     state["hulls_data"]['self_drawn'] = []
+    state["hulls_data"]["change"] = {}
+    state["hulls_data"]["undraw"] = set()
     for i in hulls.keys():
         # state['hulls_data'][i] = {
         #     'name': hulls[i]['name'],
@@ -759,8 +761,6 @@ def parse_solution_to_editor_hull(hulls: dict, state: dict) -> dict:
             "gathering_radius": hulls[i]["gathering_radius"],
             "artist": None
         }
-        state["hulls_data"]["change"] = {}
-        state["hulls_data"]["undraw"] = set()
         state["hulls_data"][hulls[i]["name"]] = dict()
         state["hulls_data"][hulls[i]["name"]]["hull_line"] = dict()
 

src/main.py

@@ -157,7 +157,10 @@ def draw_maps(raw_data: Union[str, Experiment],
                         domain_expansion=1.5,
                         closest_points_radius=2)
 
-    hulls = calc_hull(normalized_data, 0.1, 20, 20)
+    if Configuration['global']['generate_hulls']:
+        hulls = calc_hull(normalized_data, 0.1, 20, 20)
+    else:
+        hulls = {}
     state_dict = parse_solution_to_editor_hull(hulls, state_dict)
     state_dict["hulls_data"]['line_size'] = 1.0