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