assistance-engine/scratches/pseco/synthetic_dataset/reward_algorithms/n03 HeatMap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "925b048c",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c3215835",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import plotly.subplots as sp\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import yaml\n",
    "from scipy.stats import entropy as scipy_stats_entropy\n",
    "import yaml\n",
    "from scipy.stats import entropy as scipy_stats_entropy, entropy\n",
    "from src.config import settings\n",
    "\n",
    "base_path = Path(\"/home/pseco/VsCodeProjects/assistance-engine/output\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b63d88",
   "metadata": {},
   "source": [
    "# Read and Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d052f1bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "candidates = {}\n",
    "\n",
    "with open(base_path / \"candidate_E_reward_10_coverage_stats.json\") as f:\n",
    "    candidates[\"E\"] = json.load(f)\n",
    "\n",
    "with open(base_path / \"candidate_F_reward_10_coverage_stats.json\") as f:\n",
    "    candidates[\"F\"] = json.load(f)\n",
    "\n",
    "with open(base_path / \"mbpp_avap_v2_reward_stats_A.json\") as f:\n",
    "    candidates[\"A\"] = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "725d9cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {\"A\": \"#1f77b4\", \"E\": \"#ff7f0e\", \"F\": \"#2ca02c\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871a758b",
   "metadata": {},
   "source": [
    "# Cell Fill Rate Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e674f19b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fill Rate Summary for E and F:\n",
      "Candidate  Fill Rate (%)  Filled Cells  Empty Cells  Total Cells  Node Types\n",
      "        E           0.11            10         9129         9139          21\n",
      "        F           0.11            10         9129         9139          18\n",
      "\n",
      "Both candidates have identical dimensions: 9139 total cells\n"
     ]
    }
   ],
   "source": [
    "total_cells = 9139\n",
    "candidates_E_F = [\"E\", \"F\"]\n",
    "\n",
    "fill_rate_data = []\n",
    "for candidate in candidates_E_F:\n",
    "    stats = candidates[candidate]\n",
    "    fill_rate_data.append({\n",
    "        \"Candidate\": candidate,\n",
    "        \"Fill Rate (%)\": stats.get(\"fill_rate\", 0) * 100,\n",
    "        \"Filled Cells\": stats.get(\"filled_cells\", 0),\n",
    "        \"Empty Cells\": stats.get(\"empty_cells\", 0),\n",
    "        \"Total Cells\": stats.get(\"total_cells\", 0),\n",
    "        \"Node Types\": len(stats.get(\"node_type_frequency\", {}))\n",
    "    })\n",
    "\n",
    "fill_rate_df = pd.DataFrame(fill_rate_data)\n",
    "\n",
    "print(\"Fill Rate Summary for E and F:\")\n",
    "print(fill_rate_df.to_string(index=False))\n",
    "print(f\"\\nBoth candidates have identical dimensions: {total_cells} total cells\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1fa5024",
   "metadata": {},
   "outputs": [
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      "application/vnd.plotly.v1+json": {
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        "plotlyServerURL": "https://plot.ly"
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         "hovertemplate": "Row: %{y}<br>Col: %{x}<br>Status: %{z|Filled|Empty}<extra></extra>",
         "name": "E",
         "showscale": false,
         "type": "heatmap",
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         "yaxis": "y",
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    "grid_cols = 100  \n",
    "grid_rows = 92  \n",
    "\n",
    "fig = sp.make_subplots(\n",
    "    rows=1, cols=2,\n",
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    "\n",
    "for idx, candidate in enumerate(candidates_E_F, 1):\n",
    "    grid_matrix = np.zeros((grid_rows, grid_cols))\n",
    "    \n",
    "    stats = candidates[candidate]\n",
    "    filled_count = stats.get(\"filled_cells\", 0)\n",
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    "    if filled_count > 0:\n",
    "        interval = total_cells // filled_count\n",
    "        for i in range(filled_count):\n",
    "            cell_index = i * interval\n",
    "            row = (cell_index // grid_cols) % grid_rows\n",
    "            col = cell_index % grid_cols\n",
    "            if row < grid_rows and col < grid_cols:\n",
    "                grid_matrix[row, col] = 1\n",
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    "    fig.add_trace(\n",
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    "fig.update_xaxes(title_text=\"Cell Column Index\", row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"Cell Row Index\", row=1, col=1)\n",
    "fig.update_xaxes(title_text=\"Cell Column Index\", row=1, col=2)\n",
    "fig.update_yaxes(title_text=\"Cell Row Index\", row=1, col=2)\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text=\"Cell Fill Rate Distribution: Candidates E vs F<br>\",\n",
    "    height=500,\n",
    "    width=1200\n",
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    "\n",
    "fig.show()"
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    "all_node_types_E = list(candidates[\"E\"].get(\"node_type_frequency\", {}).keys())\n",
    "all_node_types_F = list(candidates[\"F\"].get(\"node_type_frequency\", {}).keys())\n",
    "all_node_types = sorted(list(set(all_node_types_E + all_node_types_F)))\n",
    "\n",
    "node_freq_matrix = []\n",
    "for node_type in all_node_types:\n",
    "    row = [\n",
    "        candidates[\"E\"].get(\"node_type_frequency\", {}).get(node_type, 0),\n",
    "        candidates[\"F\"].get(\"node_type_frequency\", {}).get(node_type, 0)\n",
    "    ]\n",
    "    node_freq_matrix.append(row)\n",
    "\n",
    "fig = go.Figure(\n",
    "    data=go.Heatmap(\n",
    "        z=node_freq_matrix,\n",
    "        x=[\"Candidate E\", \"Candidate F\"],\n",
    "        y=all_node_types,\n",
    "        colorscale=\"YlOrRd\",\n",
    "        hovertemplate=\"Node Type: %{y}<br>Candidate: %{x}<br>Frequency: %{z}<extra></extra>\",\n",
    "        text=np.array(node_freq_matrix),\n",
    "        texttemplate=\"%{z}\",\n",
    "        textfont={\"size\": 10}\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Node Type Frequency Heatmap: Candidates E vs F\",\n",
    "    xaxis_title=\"Candidate\",\n",
    "    yaxis_title=\"Node Type\",\n",
    "    height=800,\n",
    "    width=600,\n",
    "    yaxis=dict(autorange=\"reversed\")\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd542a4",
   "metadata": {},
   "outputs": [
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      "\n",
      "================================================================================\n",
      "DETAILED FILL RATE STATISTICS: E vs F\n",
      "================================================================================\n",
      "\n",
      "Candidate E:\n",
      "  Total Cells:      9,139\n",
      "  Filled Cells:     10\n",
      "  Empty Cells:      9,129\n",
      "  Low Quality:      0\n",
      "  Fill Rate (%):    0.1100%\n",
      "  Node Types:       21\n",
      "\n",
      "Candidate F:\n",
      "  Total Cells:      9,139\n",
      "  Filled Cells:     10\n",
      "  Empty Cells:      9,129\n",
      "  Low Quality:      0\n",
      "  Fill Rate (%):    0.1100%\n",
      "  Node Types:       18\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "metrics = {\n",
    "    \"Fill Rate (%)\": [],\n",
    "    \"Filled Cells\": [],\n",
    "    \"Empty Cells\": [],\n",
    "    \"Low Quality\": [],\n",
    "    \"Coverage %\": []\n",
    "}\n",
    "\n",
    "for candidate in [\"E\", \"F\"]:\n",
    "    stats = candidates[candidate]\n",
    "    filled = stats.get(\"filled_cells\", 0)\n",
    "    empty = stats.get(\"empty_cells\", 0)\n",
    "    total = stats.get(\"total_cells\", 9139)\n",
    "    low_quality = stats.get(\"low_quality_cells\", 0)\n",
    "    \n",
    "    metrics[\"Fill Rate (%)\"].append(stats.get(\"fill_rate\", 0) * 100)\n",
    "    metrics[\"Filled Cells\"].append(filled)\n",
    "    metrics[\"Empty Cells\"].append(empty / 100)  # Scale down for visualization\n",
    "    metrics[\"Low Quality\"].append(low_quality)\n",
    "    metrics[\"Coverage %\"].append((filled / total) * 100)\n",
    "\n",
    "\n",
    "metric_names = list(metrics.keys())\n",
    "metric_matrix = [metrics[m] for m in metric_names]\n",
    "\n",
    "fig = go.Figure(\n",
    "    data=go.Heatmap(\n",
    "        z=metric_matrix,\n",
    "        x=[\"Candidate E\", \"Candidate F\"],\n",
    "        y=metric_names,\n",
    "        colorscale=\"Blues\",\n",
    "        hovertemplate=\"Metric: %{y}<br>Candidate: %{x}<br>Value: %{z:.2f}<extra></extra>\",\n",
    "        text=[[f\"{v:.2f}\" for v in row] for row in metric_matrix],\n",
    "        texttemplate=\"%{text}\",\n",
    "        textfont={\"size\": 12}\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Fill Rate Metrics Heatmap: Candidates E vs F<br><sub>Showing fill rate percentages and cell counts</sub>\",\n",
    "    xaxis_title=\"Candidate\",\n",
    "    yaxis_title=\"Metric\",\n",
    "    height=500,\n",
    "    width=700,\n",
    "    yaxis=dict(autorange=\"reversed\")\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"DETAILED FILL RATE STATISTICS: E vs F\")\n",
    "print(\"=\"*80)\n",
    "for candidate in [\"E\", \"F\"]:\n",
    "    stats = candidates[candidate]\n",
    "    print(f\"\\nCandidate {candidate}:\")\n",
    "    print(f\"  Total Cells:      {stats.get('total_cells', 'N/A'):,}\")\n",
    "    print(f\"  Filled Cells:     {stats.get('filled_cells', 'N/A'):,}\")\n",
    "    print(f\"  Empty Cells:      {stats.get('empty_cells', 'N/A'):,}\")\n",
    "    print(f\"  Low Quality:      {stats.get('low_quality_cells', 'N/A'):,}\")\n",
    "    print(f\"  Fill Rate (%):    {stats.get('fill_rate', 0) * 100:.4f}%\")\n",
    "    print(f\"  Node Types:       {len(stats.get('node_type_frequency', {}))}\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506dc5a8",
   "metadata": {},
   "source": [
    "# Co-occurrence Analysis: Pairs and Trios of Node Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b9c5dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded candidate examples:\n",
      "  Candidate E: 10 examples\n",
      "  Candidate F: 10 examples\n"
     ]
    }
   ],
   "source": [
    "# Load the full candidate datasets to extract pairs and trios from examples\n",
    "candidate_full_data = {}\n",
    "\n",
    "with open(base_path / \"candidate_E_reward_10.json\") as f:\n",
    "    candidate_full_data[\"E\"] = json.load(f)\n",
    "\n",
    "with open(base_path / \"candidate_F_reward_10.json\") as f:\n",
    "    candidate_full_data[\"F\"] = json.load(f)\n",
    "\n",
    "print(\"Loaded candidate examples:\")\n",
    "print(f\"  Candidate E: {len(candidate_full_data['E'])} examples\")\n",
    "print(f\"  Candidate F: {len(candidate_full_data['F'])} examples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa86a24c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pairs of node types found:\n",
      "  Candidate E: 28 unique pairs\n",
      "  Candidate F: 24 unique pairs\n",
      "\n",
      "Trios of node types found:\n",
      "  Candidate E: 9 unique trios\n",
      "  Candidate F: 7 unique trios\n",
      "\n",
      "============================================================\n",
      "TOP 10 PAIRS - Candidate E:\n",
      "============================================================\n",
      "  avapConnector + startLoop: 1\n",
      "  gather + getQueryParamList: 1\n",
      "  gather + ormAccessUpdate: 1\n",
      "  getQueryParamList + ormAccessUpdate: 1\n",
      "  RequestPost + getListLen: 1\n",
      "  RequestPost + getTimeStamp: 1\n",
      "  getListLen + getTimeStamp: 1\n",
      "  addVar + end: 1\n",
      "  addVar + getDateTime: 1\n",
      "  end + getDateTime: 1\n",
      "\n",
      "============================================================\n",
      "TOP 10 PAIRS - Candidate F:\n",
      "============================================================\n",
      "  import + randomString: 1\n",
      "  import + startLoop: 1\n",
      "  randomString + startLoop: 1\n",
      "  RequestGet + _status: 1\n",
      "  addVar + itemFromList: 1\n",
      "  addVar + ormAccessUpdate: 1\n",
      "  itemFromList + ormAccessUpdate: 1\n",
      "  addParam + ormAccessSelect: 1\n",
      "  addParam + variableFromJSON: 1\n",
      "  ormAccessSelect + variableFromJSON: 1\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "def extract_pairs_and_trios(examples):\n",
    "    \"\"\"Extract pairs and trios of node types from examples.\"\"\"\n",
    "    pairs_count = {}\n",
    "    trios_count = {}\n",
    "    \n",
    "    for example in examples:\n",
    "        if isinstance(example, dict):\n",
    "            # Get the node types used in this example from the _cell field\n",
    "            nodes_in_example = example.get(\"_cell\", [])\n",
    "            \n",
    "            if isinstance(nodes_in_example, list) and len(nodes_in_example) > 0:\n",
    "                nodes = list(set(nodes_in_example))  # Remove duplicates\n",
    "                \n",
    "                # Extract pairs\n",
    "                if len(nodes) >= 2:\n",
    "                    for pair in combinations(sorted(nodes), 2):\n",
    "                        pair_key = f\"{pair[0]} + {pair[1]}\"\n",
    "                        pairs_count[pair_key] = pairs_count.get(pair_key, 0) + 1\n",
    "                \n",
    "                # Extract trios\n",
    "                if len(nodes) >= 3:\n",
    "                    for trio in combinations(sorted(nodes), 3):\n",
    "                        trio_key = f\"{trio[0]} + {trio[1]} + {trio[2]}\"\n",
    "                        trios_count[trio_key] = trios_count.get(trio_key, 0) + 1\n",
    "    \n",
    "    return pairs_count, trios_count\n",
    "\n",
    "# Extract pairs and trios for both candidates\n",
    "pairs_E, trios_E = extract_pairs_and_trios(candidate_full_data[\"E\"])\n",
    "pairs_F, trios_F = extract_pairs_and_trios(candidate_full_data[\"F\"])\n",
    "\n",
    "print(\"Pairs of node types found:\")\n",
    "print(f\"  Candidate E: {len(pairs_E)} unique pairs\")\n",
    "print(f\"  Candidate F: {len(pairs_F)} unique pairs\")\n",
    "print(f\"\\nTrios of node types found:\")\n",
    "print(f\"  Candidate E: {len(trios_E)} unique trios\")\n",
    "print(f\"  Candidate F: {len(trios_F)} unique trios\")\n",
    "\n",
    "# Show top pairs\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"TOP 10 PAIRS - Candidate E:\")\n",
    "print(\"=\"*60)\n",
    "for pair, count in sorted(pairs_E.items(), key=lambda x: x[1], reverse=True)[:10]:\n",
    "    print(f\"  {pair}: {count}\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"TOP 10 PAIRS - Candidate F:\")\n",
    "print(\"=\"*60)\n",
    "for pair, count in sorted(pairs_F.items(), key=lambda x: x[1], reverse=True)[:10]:\n",
    "    print(f\"  {pair}: {count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "metadata": {},
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   "source": [
    "# Create heatmap for top pairs for E and F\n",
    "fig = sp.make_subplots(\n",
    "    rows=1, cols=2,\n",
    "    subplot_titles=(\n",
    "        f\"Top Pairs - Candidate E ({len(pairs_E)} total pairs)\",\n",
    "        f\"Top Pairs - Candidate F ({len(pairs_F)} total pairs)\"\n",
    "    ),\n",
    "    specs=[[{\"type\": \"bar\"}, {\"type\": \"bar\"}]]\n",
    ")\n",
    "\n",
    "# Get top 15 pairs for each candidate\n",
    "top_pairs_E = sorted(pairs_E.items(), key=lambda x: x[1], reverse=True)[:15]\n",
    "top_pairs_F = sorted(pairs_F.items(), key=lambda x: x[1], reverse=True)[:15]\n",
    "\n",
    "# Create traces for E\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        y=[pair[0] for pair in top_pairs_E],\n",
    "        x=[pair[1] for pair in top_pairs_E],\n",
    "        orientation='h',\n",
    "        marker_color=colors[\"E\"],\n",
    "        text=[pair[1] for pair in top_pairs_E],\n",
    "        textposition=\"auto\",\n",
    "        name=\"Candidate E\"\n",
    "    ),\n",
    "    row=1, col=1\n",
    ")\n",
    "\n",
    "# Create traces for F\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        y=[pair[0] for pair in top_pairs_F],\n",
    "        x=[pair[1] for pair in top_pairs_F],\n",
    "        orientation='h',\n",
    "        marker_color=colors[\"F\"],\n",
    "        text=[pair[1] for pair in top_pairs_F],\n",
    "        textposition=\"auto\",\n",
    "        name=\"Candidate F\"\n",
    "    ),\n",
    "    row=1, col=2\n",
    ")\n",
    "\n",
    "fig.update_xaxes(title_text=\"Frequency\", row=1, col=1)\n",
    "fig.update_xaxes(title_text=\"Frequency\", row=1, col=2)\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text=\"Co-occurrence Heatmap: Top Node Type Pairs\",\n",
    "    height=600,\n",
    "    width=1400,\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48231e70",
   "metadata": {},
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      "\n",
      "================================================================================\n",
      "CO-OCCURRENCE ANALYSIS SUMMARY\n",
      "================================================================================\n",
      "\n",
      "Candidate E:\n",
      "  Total unique pairs: 28\n",
      "  Total unique trios: 9\n",
      "  Most common pair: avapConnector + startLoop (appears 1 time(s))\n",
      "  Most common trio: gather + getQueryParamList + ormAccessUpdate (appears 1 time(s))\n",
      "\n",
      "Candidate F:\n",
      "  Total unique pairs: 24\n",
      "  Total unique trios: 7\n",
      "  Most common pair: import + randomString (appears 1 time(s))\n",
      "  Most common trio: import + randomString + startLoop (appears 1 time(s))\n",
      "================================================================================\n"
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   "source": [
    "# Create heatmap for top trios for E and F\n",
    "fig = sp.make_subplots(\n",
    "    rows=1, cols=2,\n",
    "    subplot_titles=(\n",
    "        f\"Top Trios - Candidate E ({len(trios_E)} total trios)\",\n",
    "        f\"Top Trios - Candidate F ({len(trios_F)} total trios)\"\n",
    "    ),\n",
    "    specs=[[{\"type\": \"bar\"}, {\"type\": \"bar\"}]]\n",
    ")\n",
    "\n",
    "# Get top 12 trios for each candidate\n",
    "top_trios_E = sorted(trios_E.items(), key=lambda x: x[1], reverse=True)[:12]\n",
    "top_trios_F = sorted(trios_F.items(), key=lambda x: x[1], reverse=True)[:12]\n",
    "\n",
    "# Create traces for E\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        y=[trio[0] for trio in top_trios_E],\n",
    "        x=[trio[1] for trio in top_trios_E],\n",
    "        orientation='h',\n",
    "        marker_color=colors[\"E\"],\n",
    "        text=[trio[1] for trio in top_trios_E],\n",
    "        textposition=\"auto\",\n",
    "        name=\"Candidate E\"\n",
    "    ),\n",
    "    row=1, col=1\n",
    ")\n",
    "\n",
    "# Create traces for F\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        y=[trio[0] for trio in top_trios_F],\n",
    "        x=[trio[1] for trio in top_trios_F],\n",
    "        orientation='h',\n",
    "        marker_color=colors[\"F\"],\n",
    "        text=[trio[1] for trio in top_trios_F],\n",
    "        textposition=\"auto\",\n",
    "        name=\"Candidate F\"\n",
    "    ),\n",
    "    row=1, col=2\n",
    ")\n",
    "\n",
    "fig.update_xaxes(title_text=\"Frequency\", row=1, col=1)\n",
    "fig.update_xaxes(title_text=\"Frequency\", row=1, col=2)\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text=\"Co-occurrence Heatmap: Top Node Type Trios\",\n",
    "    height=700,\n",
    "    width=1400,\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "\n",
    "# Summary statistics\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"CO-OCCURRENCE ANALYSIS SUMMARY\")\n",
    "print(\"=\"*80)\n",
    "print(f\"\\nCandidate E:\")\n",
    "print(f\"  Total unique pairs: {len(pairs_E)}\")\n",
    "print(f\"  Total unique trios: {len(trios_E)}\")\n",
    "if top_pairs_E:\n",
    "    print(f\"  Most common pair: {top_pairs_E[0][0]} (appears {top_pairs_E[0][1]} time(s))\")\n",
    "if top_trios_E:\n",
    "    print(f\"  Most common trio: {top_trios_E[0][0]} (appears {top_trios_E[0][1]} time(s))\")\n",
    "\n",
    "print(f\"\\nCandidate F:\")\n",
    "print(f\"  Total unique pairs: {len(pairs_F)}\")\n",
    "print(f\"  Total unique trios: {len(trios_F)}\")\n",
    "if top_pairs_F:\n",
    "    print(f\"  Most common pair: {top_pairs_F[0][0]} (appears {top_pairs_F[0][1]} time(s))\")\n",
    "if top_trios_F:\n",
    "    print(f\"  Most common trio: {top_trios_F[0][0]} (appears {top_trios_F[0][1]} time(s))\")\n",
    "print(\"=\"*80)"
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     "text": [
      "\n",
      "Node types included in co-occurrence matrices (top 12 by frequency):\n",
      "\n",
      "Candidate E: addResult, startLoop, ormAccessUpdate, gather, getListLen, getDateTime, end, avapConnector, getQueryParamList, RequestPost, getTimeStamp, addVar\n",
      "\n",
      "Candidate F: ormAccessSelect, import, randomString, startLoop, _status, RequestGet, itemFromList, addParam, addVar, ormAccessUpdate, variableFromJSON, else\n"
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   "source": [
    "# Create co-occurrence matrix heatmap showing node type relationships\n",
    "def create_cooccurrence_matrix(examples, top_nodes=12):\n",
    "    \"\"\"Create a co-occurrence matrix for node types.\"\"\"\n",
    "    # Extract all nodes from examples\n",
    "    all_nodes_set = set()\n",
    "    examples_node_lists = []\n",
    "    \n",
    "    for example in examples:\n",
    "        if isinstance(example, dict):\n",
    "            nodes_in_example = example.get(\"_cell\", [])\n",
    "            if isinstance(nodes_in_example, list):\n",
    "                nodes = list(set(nodes_in_example))\n",
    "                examples_node_lists.append(nodes)\n",
    "                all_nodes_set.update(nodes)\n",
    "    \n",
    "    # Get top nodes by frequency\n",
    "    node_counts = {}\n",
    "    for nodes_list in examples_node_lists:\n",
    "        for node in nodes_list:\n",
    "            node_counts[node] = node_counts.get(node, 0) + 1\n",
    "    \n",
    "    top_nodes_list = sorted(node_counts.items(), key=lambda x: x[1], reverse=True)[:top_nodes]\n",
    "    top_node_names = [node[0] for node in top_nodes_list]\n",
    "    \n",
    "    # Create co-occurrence matrix\n",
    "    cooccurrence_matrix = np.zeros((len(top_node_names), len(top_node_names)))\n",
    "    \n",
    "    for nodes_list in examples_node_lists:\n",
    "        for i, node1 in enumerate(top_node_names):\n",
    "            for j, node2 in enumerate(top_node_names):\n",
    "                if node1 in nodes_list and node2 in nodes_list:\n",
    "                    cooccurrence_matrix[i][j] += 1\n",
    "    \n",
    "    return cooccurrence_matrix, top_node_names\n",
    "\n",
    "# Create co-occurrence matrices for E and F\n",
    "cooccur_matrix_E, nodes_E = create_cooccurrence_matrix(candidate_full_data[\"E\"], top_nodes=12)\n",
    "cooccur_matrix_F, nodes_F = create_cooccurrence_matrix(candidate_full_data[\"F\"], top_nodes=12)\n",
    "\n",
    "# Create heatmap visualization\n",
    "fig = sp.make_subplots(\n",
    "    rows=1, cols=2,\n",
    "    subplot_titles=(\n",
    "        \"Co-occurrence Matrix - Candidate E\",\n",
    "        \"Co-occurrence Matrix - Candidate F\"\n",
    "    ),\n",
    "    specs=[[{\"type\": \"heatmap\"}, {\"type\": \"heatmap\"}]]\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Heatmap(\n",
    "        z=cooccur_matrix_E,\n",
    "        x=nodes_E,\n",
    "        y=nodes_E,\n",
    "        colorscale=\"YlOrRd\",\n",
    "        hovertemplate=\"Node 1: %{y}<br>Node 2: %{x}<br>Co-occurrences: %{z}<extra></extra>\",\n",
    "        text=cooccur_matrix_E.astype(int),\n",
    "        texttemplate=\"%{text}\",\n",
    "        textfont={\"size\": 9},\n",
    "        colorbar=dict(title=\"Count\", x=0.46)\n",
    "    ),\n",
    "    row=1, col=1\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Heatmap(\n",
    "        z=cooccur_matrix_F,\n",
    "        x=nodes_F,\n",
    "        y=nodes_F,\n",
    "        colorscale=\"YlGn\",\n",
    "        hovertemplate=\"Node 1: %{y}<br>Node 2: %{x}<br>Co-occurrences: %{z}<extra></extra>\",\n",
    "        text=cooccur_matrix_F.astype(int),\n",
    "        texttemplate=\"%{text}\",\n",
    "        textfont={\"size\": 9},\n",
    "        colorbar=dict(title=\"Count\", x=1.02)\n",
    "    ),\n",
    "    row=1, col=2\n",
    ")\n",
    "\n",
    "fig.update_xaxes(tickangle=45, row=1, col=1)\n",
    "fig.update_xaxes(tickangle=45, row=1, col=2)\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text=\"Node Type Co-occurrence Matrix Heatmap<br><sub>Shows how frequently node types appear together in examples</sub>\",\n",
    "    height=700,\n",
    "    width=1400\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "\n",
    "print(\"\\nNode types included in co-occurrence matrices (top 12 by frequency):\")\n",
    "print(f\"\\nCandidate E: {', '.join(nodes_E)}\")\n",
    "print(f\"\\nCandidate F: {', '.join(nodes_F)}\")"
   ]
  }
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