step_4_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "# Create a connection to the database\n",
    "conn = sqlite3.connect('experiment.db')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jv/cyyhkkf51sz8tztbvyfqbgvc0000gn/T/ipykernel_14499/1539245177.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
      "  pivot_data = df.pivot_table(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics:\n",
      "                        count      mean       std  min       25%       50%  \\\n",
      "eval_function_name                                                           \n",
      "ragas-answer-relevancy  187.0  0.932847  0.127348  0.0  0.922838  0.956788   \n",
      "ragas-faithfulness      187.0  0.793628  0.332804  0.0  0.666667  1.000000   \n",
      "ragas-precision         188.0  0.938830  0.188292  0.0  1.000000  1.000000   \n",
      "ragas-recall            187.0  0.869315  0.283188  0.0  1.000000  1.000000   \n",
      "\n",
      "                             75%  max  \n",
      "eval_function_name                     \n",
      "ragas-answer-relevancy  0.986779  1.0  \n",
      "ragas-faithfulness      1.000000  1.0  \n",
      "ragas-precision         1.000000  1.0  \n",
      "ragas-recall            1.000000  1.0  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def get_data(conn):\n",
    "    query = \"\"\"\n",
    "    SELECT \n",
    "        re.test_run_id,\n",
    "        tr.description as test_run_name,\n",
    "        re.question_id,\n",
    "        q.question as question_content,\n",
    "        re.response_id,\n",
    "        r.response as response_content,\n",
    "        re.test_eval_config_id,\n",
    "        re.eval_score,\n",
    "        ef.name as eval_function_name\n",
    "    FROM response_evals re\n",
    "    JOIN test_runs tr ON re.test_run_id = tr.id\n",
    "    JOIN questions q ON re.question_id = q.id\n",
    "    JOIN responses r ON re.response_id = r.id\n",
    "    JOIN test_eval_configs tec ON re.test_eval_config_id = tec.id\n",
    "    JOIN eval_functions ef ON tec.eval_function_id = ef.id\n",
    "    \"\"\"\n",
    "    return pd.read_sql_query(query, conn)\n",
    "\n",
    "def analyze_data2(df):\n",
    "    # Basic statistics\n",
    "    print(\"Basic Statistics:\")\n",
    "    print(df['eval_score'].describe())\n",
    "    \n",
    "    # Average score by test run and evalfunction\n",
    "    avg_by_config = df.groupby(['test_run_name', 'eval_function_name'])['eval_score'].mean().sort_values(ascending=False)\n",
    "    print(\"\\nAverage Score by Eval Config:\")\n",
    "    print(avg_by_config)\n",
    "\n",
    "    # Boxplot of scores by test run and eval function\n",
    "    df.boxplot(column='eval_score', by=['test_run_name', 'eval_function_name'], rot=90)\n",
    "    plt.title('Boxplot of Evaluation Scores by Test Run and Eval Function')\n",
    "    plt.suptitle('')\n",
    "    plt.ylabel('Score')\n",
    "    plt.show()\n",
    "\n",
    "def analyze_data3(df):\n",
    "    # Set the style for all plots\n",
    "    plt.style.use('seaborn')\n",
    "\n",
    "    # 2. Box plot\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    sns.boxplot(x='eval_function_name', y='eval_score', data=df)\n",
    "    plt.title('Box Plot of Scores by Evaluation Function')\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('box_plot.png')\n",
    "    plt.close()\n",
    "\n",
    "    # 3. Kernel Density Estimation (KDE) plot\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    for func in df['eval_function_name'].unique():\n",
    "        sns.kdeplot(df[df['eval_function_name'] == func]['eval_score'], shade=True, label=func)\n",
    "    plt.title('Kernel Density Estimation of Scores by Evaluation Function')\n",
    "    plt.xlabel('Score')\n",
    "    plt.ylabel('Density')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('kde_plot.png')\n",
    "    plt.close()\n",
    "\n",
    "    # 4. Heatmap of average scores\n",
    "    pivot_df = df.pivot_table(values='eval_score', index='test_run_name', \n",
    "                              columns='eval_function_name', aggfunc='mean')\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    sns.heatmap(pivot_df, annot=True, cmap='YlGnBu', fmt='.2f')\n",
    "    plt.title('Heatmap of Average Scores by Test Run and Evaluation Function')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('heatmap.png')\n",
    "    plt.close()\n",
    "\n",
    "    # 5. Summary statistics\n",
    "    summary_stats = df.groupby('eval_function_name')['eval_score'].describe()\n",
    "    summary_stats.to_csv('summary_statistics.csv')\n",
    "    print(\"Summary Statistics:\")\n",
    "    print(summary_stats)\n",
    "\n",
    "def analyze_data(df):\n",
    "    # Set the style for all plots\n",
    "    plt.style.use('ggplot')\n",
    "\n",
    "    \n",
    "    # Heatmap of scores by test function\n",
    "    # Create score bins\n",
    "    decimal_precision = 2\n",
    "    score_bins = np.round(np.linspace(0, 1, 11), decimal_precision)\n",
    "    bin_labels = [f\"{score:.{decimal_precision}f}\" for score in score_bins[:-1]]\n",
    "\n",
    "    df['score_bin'] = pd.cut(df['eval_score'], bins=score_bins, labels=bin_labels, include_lowest=True)\n",
    "\n",
    "    # Create a pivot table\n",
    "    pivot_data = df.pivot_table(\n",
    "        values='eval_score', \n",
    "        index='score_bin', \n",
    "        columns='eval_function_name', \n",
    "        aggfunc='count',\n",
    "        fill_value=0\n",
    "    ).sort_index(ascending=False)\n",
    "\n",
    "    # Normalize the data (optional, but helps in visualizing the distribution)\n",
    "    #pivot_data_normalized = pivot_data.div(pivot_data.sum(axis=0), axis=1)\n",
    "\n",
    "    # Create the heatmap\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    sns.heatmap(pivot_data, cmap=\"Blues\", annot=True, fmt='.2f', cbar_kws={'label': 'Normalized Count'})\n",
    "\n",
    "    plt.title(\"Distribution of Evaluation Scores by Test Function\")\n",
    "    plt.xlabel(\"Test Function Name\")\n",
    "    plt.ylabel(\"Score Range\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 5. Summary statistics\n",
    "    summary_stats = df.groupby('eval_function_name')['eval_score'].describe()\n",
    "    summary_stats.to_csv('summary_statistics.csv')\n",
    "    print(\"Summary Statistics:\")\n",
    "    print(summary_stats)\n",
    "\n",
    "df = get_data(conn)\n",
    "analyze_data(df)"
   ]
  }
 ],
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   "display_name": "venv",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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