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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1408eea5",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"with open('/home/mshahidul/readctrl/results/dataset_quality_check/subclaim_verifier_results_100_qwen3-32B.json', 'r') as f:\n",
" data_item = json.load(f)\n",
"data = []\n",
"for item in data_item:\n",
" attribution=item['attribution']['accuracy']\n",
" data.append(attribution)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c706e713",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"\n",
"# Example data list\n",
"# data = [12, 15, 14, 18, 19, 17, 21]\n",
"\n",
"# Convert to a pandas Series for convenience\n",
"s = pd.Series(data)\n",
"\n",
"# --- 1. Basic statistics ---\n",
"summary = s.describe()\n",
"print(\"Basic statistics:\")\n",
"print(summary)\n",
"\n",
"# Extra metrics\n",
"print(\"\\nAdditional info:\")\n",
"print(f\"Variance: {s.var():.2f}\")\n",
"print(f\"Skewness: {s.skew():.2f}\")\n",
"print(f\"Kurtosis: {s.kurt():.2f}\")\n",
"print(f\"Mode: {s.mode().tolist()}\")\n",
"\n",
"# --- 2. Visualization ---\n",
"plt.figure(figsize=(8, 5))\n",
"sns.histplot(s, bins=10, kde=True, color='skyblue', edgecolor='black')\n",
"plt.title(\"Distribution curve of data\")\n",
"plt.xlabel(\"Value\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "860aff4b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"s = pd.Series(data) # sample data with an outlier\n",
"\n",
"# Compute IQR boundaries\n",
"Q1 = s.quantile(0.25)\n",
"Q3 = s.quantile(0.75)\n",
"IQR = Q3 - Q1\n",
"\n",
"lower_lim = Q1 - 1.5 * IQR\n",
"upper_lim = Q3 + 1.5 * IQR\n",
"\n",
"cleaned = s[(s >= lower_lim) & (s <= upper_lim)]\n",
"\n",
"print(\"Cleaned data:\")\n",
"print(len(cleaned.tolist()))\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"sns.boxplot(x=s, color=\"lightblue\")\n",
"plt.title(\"Before cleaning\")\n",
"plt.show()\n",
"\n",
"sns.boxplot(x=cleaned, color=\"lightgreen\")\n",
"plt.title(\"After IQR cleaning\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b1f16b3",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import stats\n",
"\n",
"z_scores = np.abs(stats.zscore(s))\n",
"threshold = 3 # commonly used threshold\n",
"cleaned_z = s[z_scores < threshold]\n",
"print(len(cleaned_z.tolist()))\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"print(\"Cleaned data (Z-score method):\")\n",
"sns.boxplot(x=s, color=\"lightblue\")\n",
"plt.title(\"Before cleaning\")\n",
"plt.show()\n",
"\n",
"sns.boxplot(x=cleaned_z, color=\"lightgreen\")\n",
"plt.title(\"After Z-score cleaning\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4394d44c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e24c8c2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f97f821e",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"def analyze_doclens_results(file_path):\n",
" \"\"\"\n",
" Loads, parses, and analyzes the DOCLENS evaluation results from a JSON file.\n",
"\n",
" Args:\n",
" file_path (str): The path to the JSON results file.\n",
"\n",
" Returns:\n",
" pandas.DataFrame: A DataFrame with the aggregated mean scores.\n",
" \"\"\"\n",
" # Load the entire JSON file\n",
" try:\n",
" with open(file_path, 'r', encoding='utf-8') as f:\n",
" data = json.load(f)\n",
" except FileNotFoundError:\n",
" print(f\"Error: The file '{file_path}' was not found.\")\n",
" return None\n",
" except json.JSONDecodeError:\n",
" print(f\"Error: The file '{file_path}' is not a valid JSON file.\")\n",
" return None\n",
"\n",
" # Parse the nested data into a flat list of dictionaries\n",
" parsed_data = []\n",
" for record in data:\n",
" record_id = record.get(\"id\")\n",
" version = record.get(\"version\")\n",
" \n",
" # Extract accuracy scores safely\n",
" completeness_acc = record.get(\"completeness\", {}).get(\"accuracy\", 0)\n",
" conciseness_acc = record.get(\"conciseness\", {}).get(\"accuracy\", 0)\n",
" attribution_acc = record.get(\"attribution\", {}).get(\"accuracy\", 0)\n",
"\n",
" parsed_data.append({\n",
" \"id\": record_id,\n",
" \"version\": version,\n",
" \"completeness\": completeness_acc,\n",
" \"conciseness\": conciseness_acc,\n",
" \"attribution\": attribution_acc\n",
" })\n",
"\n",
" # Create a pandas DataFrame\n",
" df = pd.DataFrame(parsed_data)\n",
"\n",
" # Calculate the mean scores for each version\n",
" # The order is specified to ensure 'easy', 'intermediate', 'hard' are plotted correctly\n",
" version_order = ['easy', 'intermediate', 'hard']\n",
" df['version'] = pd.Categorical(df['version'], categories=version_order, ordered=True)\n",
" \n",
" agg_results = df.groupby('version')[['completeness', 'conciseness', 'attribution']].mean().reset_index()\n",
"\n",
" print(\"--- Aggregated Mean Scores ---\")\n",
" print(agg_results.to_string(index=False))\n",
" \n",
" return agg_results\n",
"\n",
"def visualize_results(df):\n",
" \"\"\"\n",
" Generates and saves bar charts to visualize the aggregated results.\n",
" \"\"\"\n",
" if df is None or df.empty:\n",
" print(\"Cannot visualize results. DataFrame is empty.\")\n",
" return\n",
"\n",
" sns.set_style(\"whitegrid\")\n",
" fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True)\n",
" fig.suptitle('Average Evaluation Metrics Across Summary Versions', fontsize=16)\n",
"\n",
" # Plot Completeness\n",
" sns.barplot(ax=axes[0], x='version', y='completeness', data=df, palette='Blues_d')\n",
" axes[0].set_title('Completeness (Claim Recall)')\n",
" axes[0].set_xlabel('Summary Version')\n",
" axes[0].set_ylabel('Average Accuracy (%)')\n",
"\n",
" # Plot Conciseness\n",
" sns.barplot(ax=axes[1], x='version', y='conciseness', data=df, palette='Greens_d')\n",
" axes[1].set_title('Conciseness (Claim Precision)')\n",
" axes[1].set_xlabel('Summary Version')\n",
" axes[1].set_ylabel('')\n",
"\n",
" # Plot Attribution\n",
" sns.barplot(ax=axes[2], x='version', y='attribution', data=df, palette='Oranges_d')\n",
" axes[2].set_title('Attribution')\n",
" axes[2].set_xlabel('Summary Version')\n",
" axes[2].set_ylabel('')\n",
" \n",
" # Improve layout and save the figure\n",
" plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
" plt.savefig(\"doclens_evaluation_summary.png\", dpi=300)\n",
" print(\"\\nChart saved as 'doclens_evaluation_summary.png'\")\n",
" plt.show()\n",
"\n",
"\n",
"# --- Main Execution ---\n",
"# Replace 'your_results_file.json' with the actual path to your file\n",
"results_file = '/home/mshahidul/readctrl/results/dataset_quality_check/subclaim_verifier_results_100_qwen3-32B.json' \n",
"aggregated_data = analyze_doclens_results(results_file)\n",
"\n",
"if aggregated_data is not None:\n",
" visualize_results(aggregated_data)"
]
},
{
"cell_type": "markdown",
"id": "b5afb981",
"metadata": {},
"source": [
"## Eliminate dataset"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b29bcf30",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rejected 15 items due to low attribution.\n",
"Rejected 9 additional items due to incorrect completeness trend.\n",
"\n",
"--- Filtering Summary ---\n",
"Total unique items analyzed: 100\n",
"Items kept (High Quality): 76\n",
"Items rejected (Low Quality): 24\n",
"Saved data to '/home/mshahidul/readctrl/results/dataset_quality_check/subclaim_verifier_results_100_qwen3-32B_clean.json'\n",
"Saved data to '/home/mshahidul/readctrl/results/dataset_quality_check/rejected_dataset.json'\n"
]
}
],
"source": [
"import json\n",
"import pandas as pd\n",
"\n",
"def filter_low_quality_data(file_path, attribution_threshold=80.0, completeness_trend_check=True):\n",
" \"\"\"\n",
" Loads DOCLENS results, filters out low-quality data, and returns clean/rejected data.\n",
" \"\"\"\n",
" try:\n",
" with open(file_path, 'r', encoding='utf-8') as f:\n",
" data = json.load(f)\n",
" except (FileNotFoundError, json.JSONDecodeError) as e:\n",
" print(f\"Error loading file: {e}\")\n",
" return None, None\n",
"\n",
" # --- FIX: Parse the nested JSON to extract numeric accuracy scores ---\n",
" # Create a flat list of dictionaries instead of a list of nested objects\n",
" parsed_data = []\n",
" for record in data:\n",
" parsed_data.append({\n",
" \"id\": record.get(\"id\"),\n",
" \"version\": record.get(\"version\"),\n",
" \"completeness\": record.get(\"completeness\", {}).get(\"accuracy\", 0),\n",
" \"conciseness\": record.get(\"conciseness\", {}).get(\"accuracy\", 0),\n",
" \"attribution\": record.get(\"attribution\", {}).get(\"accuracy\", 0)\n",
" })\n",
"\n",
" # Create DataFrame from the *parsed* data\n",
" df = pd.DataFrame(parsed_data)\n",
" # --------------------------------------------------------------------\n",
" \n",
" all_ids = set(df['id'].unique())\n",
" rejected_ids = set()\n",
"\n",
" # --- Pivot data for easier comparison across versions ---\n",
" # This part now works correctly because the columns are numeric\n",
" pivot_df = df.pivot_table(\n",
" index='id',\n",
" columns='version',\n",
" values=['completeness', 'conciseness', 'attribution']\n",
" )\n",
" pivot_df.columns = ['_'.join(map(str, col)).strip() for col in pivot_df.columns.values]\n",
" \n",
" # --- Filter 1: Low Attribution ---\n",
" low_attribution_mask = (pivot_df['attribution_easy'] < attribution_threshold) | \\\n",
" (pivot_df['attribution_intermediate'] < attribution_threshold) | \\\n",
" (pivot_df['attribution_hard'] < attribution_threshold)\n",
" rejected_attribution_ids = pivot_df[low_attribution_mask].index\n",
" rejected_ids.update(rejected_attribution_ids)\n",
" print(f\"Rejected {len(rejected_attribution_ids)} items due to low attribution.\")\n",
"\n",
" # --- Filter 2: Incorrect Completeness Trend ---\n",
" if completeness_trend_check:\n",
" bad_trend_mask = pivot_df['completeness_easy'] > pivot_df['completeness_hard']\n",
" rejected_trend_ids = pivot_df[bad_trend_mask].index\n",
" newly_rejected_count = len(rejected_trend_ids.difference(rejected_ids))\n",
" rejected_ids.update(rejected_trend_ids)\n",
" print(f\"Rejected {newly_rejected_count} additional items due to incorrect completeness trend.\")\n",
"\n",
" # --- Separate the data ---\n",
" clean_ids = all_ids - rejected_ids\n",
" \n",
" # We need to filter the original 'data' list, not the parsed one, to keep the full structure\n",
" original_df = pd.DataFrame(data)\n",
" clean_data = original_df[original_df['id'].isin(clean_ids)].to_dict('records')\n",
" rejected_data = original_df[original_df['id'].isin(rejected_ids)].to_dict('records')\n",
" \n",
" print(\"\\n--- Filtering Summary ---\")\n",
" print(f\"Total unique items analyzed: {len(all_ids)}\")\n",
" print(f\"Items kept (High Quality): {len(clean_ids)}\")\n",
" print(f\"Items rejected (Low Quality): {len(rejected_ids)}\")\n",
" \n",
" return clean_data, rejected_data\n",
"\n",
"def save_json(data, file_path):\n",
" \"\"\"Saves data to a JSON file.\"\"\"\n",
" with open(file_path, 'w', encoding='utf-8') as f:\n",
" json.dump(data, f, indent=4, ensure_ascii=False)\n",
" print(f\"Saved data to '{file_path}'\")\n",
"\n",
"\n",
"# --- Main Execution ---\n",
"# Replace with your file paths and desired thresholds\n",
"RESULTS_FILE = '/home/mshahidul/readctrl/results/dataset_quality_check/subclaim_verifier_results_100_qwen3-32B.json' # Make sure this points to your file\n",
"# CLEAN_FILE_PATH = '/home/mshahidul/readctrl/results/dataset_quality_check/high_quality_dataset.json'\n",
"# REJECTED_FILE_PATH = '/home/mshahidul/readctrl/results/dataset_quality_check/rejected_dataset.json'\n",
"ATTRIBUTION_THRESHOLD = 80.0\n",
"\n",
"clean_dataset, rejected_dataset = filter_low_quality_data(\n",
" RESULTS_FILE,\n",
" attribution_threshold=ATTRIBUTION_THRESHOLD\n",
")\n",
"\n",
"if clean_dataset is not None:\n",
" save_json(clean_dataset, '/home/mshahidul/readctrl/results/dataset_quality_check/subclaim_verifier_results_100_qwen3-32B_clean.json')\n",
" save_json(rejected_dataset, '/home/mshahidul/readctrl/results/dataset_quality_check/rejected_dataset.json')"
]
}
],
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