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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e78262c8",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"\n",
"def process_and_save_json(file_paths, annotator_names):\n",
" all_data = []\n",
" \n",
" # Load and categorize data\n",
" for path, name in zip(file_paths, annotator_names):\n",
" with open(path, 'r') as f:\n",
" df = pd.DataFrame(json.load(f))\n",
" \n",
" # Map ratings to health literacy categories\n",
" def map_rating(r):\n",
" if r in [1, 2]: return \"low_health_literacy\"\n",
" if r == 3: return \"intermediate_health_literacy\"\n",
" if r in [4, 5]: return \"proficient_health_literacy\"\n",
" return None\n",
"\n",
" df['human_category'] = df['rating'].apply(map_rating)\n",
" df = df[['doc_id', 'label', 'rating', 'human_category']]\n",
" \n",
" # Rename columns to distinguish between annotators\n",
" df = df.rename(columns={\n",
" 'rating': f'rating_{name}',\n",
" 'human_category': f'category_{name}',\n",
" 'label': 'ai_label'\n",
" })\n",
" all_data.append(df)\n",
"\n",
" # Merge all three dataframes\n",
" merged = all_data[0]\n",
" for next_df in all_data[1:]:\n",
" merged = pd.merge(merged, next_df, on=['doc_id', 'ai_label'])\n",
"\n",
" # Determine agreement count\n",
" cat_cols = [f'category_{name}' for name in annotator_names]\n",
" merged['agreement_count'] = merged.apply(\n",
" lambda row: sum(1 for col in cat_cols if row[col] == row['ai_label']), axis=1\n",
" )\n",
"\n",
" # Filter into two groups\n",
" agreement_data = merged[merged['agreement_count'] >= 2]\n",
" correction_needed = merged[merged['agreement_count'] < 2]\n",
"\n",
" # Export to JSON\n",
" agreement_data.to_json(\"/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/code/annotator_agreement.json\", orient=\"records\", indent=4)\n",
" correction_needed.to_json(\"/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/code/needs_correction.json\", orient=\"records\", indent=4)\n",
" \n",
" print(f\"Success! {len(agreement_data)} items agreed, {len(correction_needed)} need correction.\")\n",
"\n",
"# Usage\n",
"paths = [\n",
" \"/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/Plaban Das/annotation_results.json\",\n",
" \"/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/mahi/annotation_results.json\",\n",
" \"/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/Shama/annotation_results.json\"\n",
"]\n",
"names = [\"plaban\", \"mahi\", \"shama\"]\n",
"\n",
"process_and_save_json(paths, names)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab336faf",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"\n",
"def create_correction_evaluation_file(source_path, agreement_results_path, output_path):\n",
" # 1. Load the source full data\n",
" with open(source_path, 'r') as f:\n",
" source_data = json.load(f)\n",
" source_df = pd.DataFrame(source_data)\n",
" \n",
" # 2. Load the \"needs correction\" data generated from previous step\n",
" with open(agreement_results_path, 'r') as f:\n",
" correction_df = pd.DataFrame(json.load(f))\n",
" \n",
" # 3. Merge based on doc_id (annotation) == index (source)\n",
" # We only keep the rows that exist in the correction list\n",
" enriched_correction = pd.merge(\n",
" correction_df, \n",
" source_df[['index', 'fulltext', 'diff_label_texts']], \n",
" left_on='doc_id', \n",
" right_on='index', \n",
" how='left'\n",
" )\n",
" \n",
" # Optional: Clean up by dropping the redundant 'index' column\n",
" if 'index' in enriched_correction.columns:\n",
" enriched_correction = enriched_correction.drop(columns=['index'])\n",
" \n",
" # 4. Save to a new JSON file\n",
" enriched_correction.to_json(output_path, orient=\"records\", indent=4)\n",
" \n",
" print(f\"Evaluation file created: {output_path}\")\n",
" print(f\"Total entries for re-evaluation: {len(enriched_correction)}\")\n",
"\n",
"# Paths\n",
"source_file = '/home/mshahidul/readctrl/data/synthetic_dataset_diff_labels/syn_data_diff_labels_en_0_80_full.json'\n",
"correction_list = '/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/code/needs_correction.json' # The file created from the previous script\n",
"final_output = '/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/code/correction_evaluation_full_text.json'\n",
"\n",
"create_correction_evaluation_file(source_file, correction_list, final_output)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "93f3ae03",
"metadata": {},
"outputs": [],
"source": [
"# /home/mshahidul/readctrl/data/synthetic_dataset_diff_labels/syn_data_diff_labels_en_0_80_full_updated.json\n",
"import json\n",
"with open(\"/home/mshahidul/readctrl/data/synthetic_dataset_diff_labels/syn_data_diff_labels_en_0_80_full_updated.json\", 'r') as f:\n",
" data = json.load(f)\n",
"text_map={}\n",
"for item in data:\n",
" for label in list(item['diff_label_texts'].keys()):\n",
" key=f\"{item['index']}_{label}\"\n",
" text_map[key] = {\n",
" 'fulltext': item['fulltext'],\n",
" \"diff_label_texts\": item['diff_label_texts'][label],\n",
" 'summary': item.get('summary')\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "c8d64fdf",
"metadata": {},
"outputs": [],
"source": [
"# /home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/correction_data/final_corrected_anu.json\n",
"with open(\"/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/correction_data/final_corrected_anu.json\", 'r') as f:\n",
" annotator_corrections = json.load(f)\n",
"new_data = []\n",
"for item in annotator_corrections:\n",
" key = f\"{item['doc_id']}_{item['ai_label']}\"\n",
" final_text=item['final_text']\n",
" new_data.append({\n",
" 'doc_id': item['doc_id'],\n",
" 'label': item['ai_label'],\n",
" 'fulltext': text_map[key]['fulltext'],\n",
" 'diff_label_texts': final_text,\n",
" 'summary': text_map[key]['summary']\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9521411f",
"metadata": {},
"outputs": [],
"source": [
"# /home/mshahidul/readctrl/data/factual_testing/full_details_evaluation_0_80_qwen3-30B_v2.json\n",
"with open(\"/home/mshahidul/readctrl/data/factual_testing/full_details_evaluation_0_80_qwen3-30B_v2.json\", 'r') as f:\n",
" factual_data = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7628ac8",
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "un",
"language": "python",
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"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
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