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import plotly.express as px | |
import pandas as pd | |
import re | |
# Define columns for all relevant predictions | |
pred_columns = ['pred_dependencies', 'pred_training', | |
'pred_evaluation', 'pred_weights', 'pred_readme', | |
'pred_license'] | |
# Define the real and predicted column pairs | |
real_pred_columns = { | |
'dependencies': 'pred_dependencies', | |
'training': 'pred_training', | |
'evaluation': 'pred_evaluation', | |
'weights': 'pred_weights', | |
'readme': 'pred_readme', | |
'license': 'pred_license' | |
} | |
df = pd.read_csv('data/results.csv', sep="\t") | |
# Cleanup | |
df['year'] = pd.to_numeric(df['year'], errors='coerce') | |
df = df.dropna(subset=['year']) | |
df['year'] = df['year'].astype(int) | |
custom_order = ["MICCAI", "MIDL", "Nature", "arXiv"] | |
# Group by venue | |
df_filtered = df[df['pred_live'] == "Yes"].copy() | |
df_filtered['license'] = df_filtered['license'].apply(lambda row: row if ((row == "No") | (pd.isna(row))) else "Yes") | |
# Add matching counts for each category | |
for real, pred in real_pred_columns.items(): | |
df_filtered[f'matching_{real}'] = df_filtered[real] == df_filtered[pred] | |
for real, pred in real_pred_columns.items(): | |
print(f"Evaluations for {real}:") | |
for idx, row in df_filtered.iterrows(): | |
if ((row['year'] == 2024) | pd.isna(row["url"]) | (row["url"] == "") | (pd.isna(row[real]))): | |
continue | |
if not(row[f'matching_{real}']): | |
print(f"Automated test for {real} failed for link: {row['url']} [{row[real]} - {row[pred]}]") | |