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import gradio as gr
import pandas as pd
import re
import os
import json
import yaml
import matplotlib.pyplot as plt
import seaborn as sns
import plotnine as p9
import sys
script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append('..')
sys.path.append('.')
from about import *
global data_component, filter_component
def get_method_color(method):
return color_dict.get(method, 'black') # If method is not in color_dict, use black
def draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title):
df = pd.read_csv(CSV_RESULT_PATH)
# Filter the dataframe based on selected methods
filtered_df = df[df['method_name'].isin(methods_selected)]
def get_method_color(method):
return color_dict.get(method.upper(), 'black')
# Add a new column to the dataframe for the color
filtered_df['color'] = filtered_df['method_name'].apply(get_method_color)
adjust_text_dict = {
'expand_text': (1.15, 1.4), 'expand_points': (1.15, 1.25), 'expand_objects': (1.05, 1.5),
'expand_align': (1.05, 1.2), 'autoalign': 'xy', 'va': 'center', 'ha': 'center',
'force_text': (.0, 1.), 'force_objects': (.0, 1.),
'lim': 500000, 'precision': 1., 'avoid_points': True, 'avoid_text': True
}
# Create the scatter plot using plotnine (ggplot)
g = (p9.ggplot(data=filtered_df,
mapping=p9.aes(x=x_metric, # Use the selected x_metric
y=y_metric, # Use the selected y_metric
color='color', # Use the dynamically generated color
label='method_names')) # Label each point by the method name
+ p9.geom_point(size=3) # Add points with no jitter, set point size
+ p9.geom_text(nudge_y=0.02, size=8) # Add method names as labels, nudge slightly above the points
+ p9.labs(title=title, x=f"{x_metric}", y=f"{y_metric}") # Dynamic labels for X and Y axes
+ p9.scale_color_identity() # Use colors directly from the dataframe
+ p9.theme(legend_position='none',
figure_size=(8, 8), # Set figure size
axis_text=p9.element_text(size=10),
axis_title_x=p9.element_text(size=12),
axis_title_y=p9.element_text(size=12))
)
# Save the plot as an image
save_path = "./plot_images" # Ensure this folder exists or adjust the path
os.makedirs(save_path, exist_ok=True) # Create directory if it doesn't exist
filename = os.path.join(save_path, title.replace(" ", "_") + "_Similarity_Scatter.png")
g.save(filename=filename, dpi=400)
return filename
def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
if benchmark_type == 'flexible':
# Use general visualizer logic
return general_visualizer_plot(methods_selected, x_metric=x_metric, y_metric=y_metric)
elif benchmark_type == 'similarity':
title = f"{x_metric} vs {y_metric}"
return draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title)
elif benchmark_type == 'Benchmark 3':
return benchmark_3_plot(x_metric, y_metric)
elif benchmark_type == 'Benchmark 4':
return benchmark_4_plot(x_metric, y_metric)
else:
return "Invalid benchmark type selected."
def get_baseline_df(selected_methods, selected_metrics):
df = pd.read_csv(CSV_RESULT_PATH)
present_columns = ["method_name"] + selected_metrics
df = df[df['method_name'].isin(selected_methods)][present_columns]
return df
def general_visualizer(methods_selected, x_metric, y_metric):
df = pd.read_csv(CSV_RESULT_PATH)
filtered_df = df[df['method_name'].isin(methods_selected)]
# Create a Seaborn lineplot with method as hue
plt.figure(figsize=(10, 8)) # Increase figure size
sns.lineplot(
data=filtered_df,
x=x_metric,
y=y_metric,
hue="method_name", # Different colors for different methods
marker="o", # Add markers to the line plot
)
# Add labels and title
plt.xlabel(x_metric)
plt.ylabel(y_metric)
plt.title(f'{y_metric} vs {x_metric} for selected methods')
plt.grid(True)
# Save the plot to display it in Gradio
plot_path = "plot.png"
plt.savefig(plot_path)
plt.close()
return plot_path |