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Diffstat (limited to 'benchmark/agbenchmark/reports/processing/graphs.py')
| -rw-r--r-- | benchmark/agbenchmark/reports/processing/graphs.py | 205 |
1 files changed, 205 insertions, 0 deletions
diff --git a/benchmark/agbenchmark/reports/processing/graphs.py b/benchmark/agbenchmark/reports/processing/graphs.py new file mode 100644 index 000000000..8abe9d01a --- /dev/null +++ b/benchmark/agbenchmark/reports/processing/graphs.py @@ -0,0 +1,205 @@ +from pathlib import Path +from typing import Any + +import matplotlib.patches as mpatches +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from matplotlib.colors import Normalize + + +def save_combined_radar_chart( + categories: dict[str, Any], save_path: str | Path +) -> None: + categories = {k: v for k, v in categories.items() if v} + if not all(categories.values()): + raise Exception("No data to plot") + labels = np.array( + list(next(iter(categories.values())).keys()) + ) # We use the first category to get the keys + num_vars = len(labels) + angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist() + angles += angles[ + :1 + ] # Add the first angle to the end of the list to ensure the polygon is closed + + # Create radar chart + fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True)) + ax.set_theta_offset(np.pi / 2) # type: ignore + ax.set_theta_direction(-1) # type: ignore + ax.spines["polar"].set_visible(False) # Remove border + + # Define a custom normalization to start the color from the middle + norm = Normalize( + vmin=0, vmax=max([max(val.values()) for val in categories.values()]) + ) # We use the maximum of all categories for normalization + + cmap = plt.cm.get_cmap("nipy_spectral", len(categories)) # type: ignore + + colors = [cmap(i) for i in range(len(categories))] + + for i, (cat_name, cat_values) in enumerate( + categories.items() + ): # Iterating through each category (series) + values = np.array(list(cat_values.values())) + values = np.concatenate((values, values[:1])) # Ensure the polygon is closed + + ax.fill(angles, values, color=colors[i], alpha=0.25) # Draw the filled polygon + ax.plot(angles, values, color=colors[i], linewidth=2) # Draw polygon + ax.plot( + angles, + values, + "o", + color="white", + markersize=7, + markeredgecolor=colors[i], + markeredgewidth=2, + ) # Draw points + + # Draw legend + legend = ax.legend( + handles=[ + mpatches.Patch(color=color, label=cat_name, alpha=0.25) + for cat_name, color in zip(categories.keys(), colors) + ], + loc="upper left", + bbox_to_anchor=(0.7, 1.3), + ) + + # Adjust layout to make room for the legend + plt.tight_layout() + + lines, labels = plt.thetagrids( + np.degrees(angles[:-1]), (list(next(iter(categories.values())).keys())) + ) # We use the first category to get the keys + + highest_score = 7 + + # Set y-axis limit to 7 + ax.set_ylim(top=highest_score) + + # Move labels away from the plot + for label in labels: + label.set_position( + (label.get_position()[0], label.get_position()[1] + -0.05) + ) # adjust 0.1 as needed + + # Move radial labels away from the plot + ax.set_rlabel_position(180) # type: ignore + + ax.set_yticks([]) # Remove default yticks + + # Manually create gridlines + for y in np.arange(0, highest_score + 1, 1): + if y != highest_score: + ax.plot( + angles, [y] * len(angles), color="gray", linewidth=0.5, linestyle=":" + ) + # Add labels for manually created gridlines + ax.text( + angles[0], + y + 0.2, + str(int(y)), + color="black", + size=9, + horizontalalignment="center", + verticalalignment="center", + ) + + plt.savefig(save_path, dpi=300) # Save the figure as a PNG file + plt.close() # Close the figure to free up memory + + +def save_single_radar_chart( + category_dict: dict[str, int], save_path: str | Path +) -> None: + labels = np.array(list(category_dict.keys())) + values = np.array(list(category_dict.values())) + + num_vars = len(labels) + + angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist() + + angles += angles[:1] + values = np.concatenate((values, values[:1])) + + colors = ["#1f77b4"] + + fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True)) + ax.set_theta_offset(np.pi / 2) # type: ignore + ax.set_theta_direction(-1) # type: ignore + + ax.spines["polar"].set_visible(False) + + lines, labels = plt.thetagrids( + np.degrees(angles[:-1]), (list(category_dict.keys())) + ) + + highest_score = 7 + + # Set y-axis limit to 7 + ax.set_ylim(top=highest_score) + + for label in labels: + label.set_position((label.get_position()[0], label.get_position()[1] + -0.05)) + + ax.fill(angles, values, color=colors[0], alpha=0.25) + ax.plot(angles, values, color=colors[0], linewidth=2) + + for i, (angle, value) in enumerate(zip(angles, values)): + ha = "left" + if angle in {0, np.pi}: + ha = "center" + elif np.pi < angle < 2 * np.pi: + ha = "right" + ax.text( + angle, + value - 0.5, + f"{value}", + size=10, + horizontalalignment=ha, + verticalalignment="center", + color="black", + ) + + ax.set_yticklabels([]) + + ax.set_yticks([]) + + if values.size == 0: + return + + for y in np.arange(0, highest_score, 1): + ax.plot(angles, [y] * len(angles), color="gray", linewidth=0.5, linestyle=":") + + for angle, value in zip(angles, values): + ax.plot( + angle, + value, + "o", + color="white", + markersize=7, + markeredgecolor=colors[0], + markeredgewidth=2, + ) + + plt.savefig(save_path, dpi=300) # Save the figure as a PNG file + plt.close() # Close the figure to free up memory + + +def save_combined_bar_chart(categories: dict[str, Any], save_path: str | Path) -> None: + if not all(categories.values()): + raise Exception("No data to plot") + + # Convert dictionary to DataFrame + df = pd.DataFrame(categories) + + # Create a grouped bar chart + df.plot(kind="bar", figsize=(10, 7)) + + plt.title("Performance by Category for Each Agent") + plt.xlabel("Category") + plt.ylabel("Performance") + + plt.savefig(save_path, dpi=300) # Save the figure as a PNG file + plt.close() # Close the figure to free up memory |