{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04 - Aggregation & RCA Trees Validation\n",
    "\n",
    "**Checkpoint 7 validation notebook**\n",
    "\n",
    "This notebook validates the aggregation module:\n",
    "1. Frequency statistics calculation\n",
    "2. Conditional probability analysis\n",
    "3. Severity scoring with explicit rules\n",
    "4. RCA tree building and prioritization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')\n",
    "\n",
    "import json\n",
    "from datetime import datetime\n",
    "\n",
    "# Project imports\n",
    "from src.aggregation import (\n",
    "    AggregationConfig,\n",
    "    BatchAggregation,\n",
    "    RCATree,\n",
    "    RCATreeBuilder,\n",
    "    StatisticsCalculator,\n",
    "    SeverityCalculator,\n",
    "    ImpactLevel,\n",
    "    aggregate_batch,\n",
    "    build_rca_tree,\n",
    "    calculate_batch_statistics,\n",
    ")\n",
    "from src.models.call_analysis import (\n",
    "    CallAnalysis,\n",
    "    CallOutcome,\n",
    "    EvidenceSpan,\n",
    "    ObservedFeatures,\n",
    "    ProcessingStatus,\n",
    "    RCALabel,\n",
    "    Traceability,\n",
    ")\n",
    "\n",
    "print(\"Imports successful!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Create Simulated Call Analyses\n",
    "\n",
    "We'll simulate 100 call analyses with realistic driver distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def create_sample_analyses(n: int = 100) -> list[CallAnalysis]:\n",
    "    \"\"\"Create n sample call analyses with realistic distributions.\"\"\"\n",
    "    random.seed(42)  # Reproducible\n",
    "    \n",
    "    base_observed = ObservedFeatures(audio_duration_sec=60.0, events=[])\n",
    "    base_trace = Traceability(\n",
    "        schema_version=\"1.0.0\",\n",
    "        prompt_version=\"v1.0\",\n",
    "        model_id=\"gpt-4o-mini\",\n",
    "    )\n",
    "    \n",
    "    # Driver probabilities (realistic distribution)\n",
    "    lost_sales_probs = {\n",
    "        \"PRICE_TOO_HIGH\": 0.25,\n",
    "        \"COMPETITOR_PREFERENCE\": 0.12,\n",
    "        \"TIMING_NOT_RIGHT\": 0.10,\n",
    "        \"NO_NEED\": 0.08,\n",
    "        \"OBJECTION_NOT_HANDLED\": 0.15,\n",
    "        \"NO_SAVE_OFFER\": 0.10,\n",
    "        \"POOR_PITCH\": 0.05,\n",
    "    }\n",
    "    \n",
    "    poor_cx_probs = {\n",
    "        \"LONG_HOLD\": 0.20,\n",
    "        \"MULTI_TRANSFER\": 0.08,\n",
    "        \"LOW_EMPATHY\": 0.10,\n",
    "        \"ISSUE_NOT_RESOLVED\": 0.12,\n",
    "        \"INTERRUPTIONS\": 0.05,\n",
    "        \"CALLBACK_REQUIRED\": 0.08,\n",
    "    }\n",
    "    \n",
    "    analyses = []\n",
    "    \n",
    "    for i in range(n):\n",
    "        call_id = f\"CALL{i+1:04d}\"\n",
    "        \n",
    "        # Determine if this is a lost sale (40% of calls)\n",
    "        is_lost_sale = random.random() < 0.40\n",
    "        \n",
    "        # Determine if poor CX (30% of calls)\n",
    "        has_poor_cx = random.random() < 0.30\n",
    "        \n",
    "        # Generate lost sales drivers\n",
    "        lost_sales = []\n",
    "        if is_lost_sale:\n",
    "            for code, prob in lost_sales_probs.items():\n",
    "                if random.random() < prob:\n",
    "                    lost_sales.append(RCALabel(\n",
    "                        driver_code=code,\n",
    "                        confidence=random.uniform(0.6, 0.95),\n",
    "                        evidence_spans=[EvidenceSpan(\n",
    "                            text=f\"Evidence for {code}\",\n",
    "                            start_time=random.uniform(0, 50),\n",
    "                            end_time=random.uniform(50, 60),\n",
    "                        )],\n",
    "                    ))\n",
    "        \n",
    "        # Generate poor CX drivers\n",
    "        poor_cx = []\n",
    "        if has_poor_cx:\n",
    "            for code, prob in poor_cx_probs.items():\n",
    "                if random.random() < prob:\n",
    "                    poor_cx.append(RCALabel(\n",
    "                        driver_code=code,\n",
    "                        confidence=random.uniform(0.6, 0.95),\n",
    "                        evidence_spans=[EvidenceSpan(\n",
    "                            text=f\"Evidence for {code}\",\n",
    "                            start_time=random.uniform(0, 50),\n",
    "                            end_time=random.uniform(50, 60),\n",
    "                        )],\n",
    "                    ))\n",
    "        \n",
    "        # Determine outcome\n",
    "        if is_lost_sale:\n",
    "            outcome = CallOutcome.SALE_LOST\n",
    "        elif random.random() < 0.5:\n",
    "            outcome = CallOutcome.SALE_COMPLETED\n",
    "        else:\n",
    "            outcome = CallOutcome.INQUIRY_RESOLVED\n",
    "        \n",
    "        analyses.append(CallAnalysis(\n",
    "            call_id=call_id,\n",
    "            batch_id=\"validation_batch\",\n",
    "            status=ProcessingStatus.SUCCESS,\n",
    "            observed=base_observed,\n",
    "            outcome=outcome,\n",
    "            lost_sales_drivers=lost_sales,\n",
    "            poor_cx_drivers=poor_cx,\n",
    "            traceability=base_trace,\n",
    "        ))\n",
    "    \n",
    "    return analyses\n",
    "\n",
    "# Create 100 sample analyses\n",
    "analyses = create_sample_analyses(100)\n",
    "print(f\"Created {len(analyses)} sample analyses\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Calculate Frequency Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "calculator = StatisticsCalculator()\n",
    "lost_sales_freqs, poor_cx_freqs = calculator.calculate_frequencies(analyses)\n",
    "\n",
    "print(\"=== LOST SALES DRIVER FREQUENCIES ===\")\n",
    "print(f\"{'Driver':<25} {'Occurrences':>12} {'Call Rate':>10} {'Avg Conf':>10}\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for freq in lost_sales_freqs:\n",
    "    print(f\"{freq.driver_code:<25} {freq.total_occurrences:>12} {freq.call_rate:>9.1%} {freq.avg_confidence:>10.2f}\")\n",
    "\n",
    "print(f\"\\nTotal lost sales drivers: {len(lost_sales_freqs)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== POOR CX DRIVER FREQUENCIES ===\")\n",
    "print(f\"{'Driver':<25} {'Occurrences':>12} {'Call Rate':>10} {'Avg Conf':>10}\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for freq in poor_cx_freqs:\n",
    "    print(f\"{freq.driver_code:<25} {freq.total_occurrences:>12} {freq.call_rate:>9.1%} {freq.avg_confidence:>10.2f}\")\n",
    "\n",
    "print(f\"\\nTotal poor CX drivers: {len(poor_cx_freqs)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Outcome Rate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_rates = calculator.calculate_outcome_rates(analyses)\n",
    "\n",
    "print(\"=== OUTCOME RATES ===\")\n",
    "print(f\"Total calls analyzed: {outcome_rates['total_calls']}\")\n",
    "print(f\"\\nCalls with lost sales drivers: {outcome_rates['lost_sales_count']} ({outcome_rates['lost_sales_rate']:.1%})\")\n",
    "print(f\"Calls with poor CX drivers: {outcome_rates['poor_cx_count']} ({outcome_rates['poor_cx_rate']:.1%})\")\n",
    "print(f\"Calls with BOTH: {outcome_rates['both_count']} ({outcome_rates['both_rate']:.1%})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Severity Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "severity_calc = SeverityCalculator()\n",
    "lost_sales_sevs, poor_cx_sevs = severity_calc.calculate_all_severities(\n",
    "    lost_sales_freqs, poor_cx_freqs\n",
    ")\n",
    "\n",
    "print(\"=== LOST SALES SEVERITY SCORES ===\")\n",
    "print(f\"{'Rank':<5} {'Driver':<25} {'Score':>8} {'Impact':>12}\")\n",
    "print(\"-\" * 55)\n",
    "\n",
    "for rank, sev in enumerate(lost_sales_sevs, 1):\n",
    "    print(f\"{rank:<5} {sev.driver_code:<25} {sev.severity_score:>7.1f} {sev.impact_level.value:>12}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== POOR CX SEVERITY SCORES ===\")\n",
    "print(f\"{'Rank':<5} {'Driver':<25} {'Score':>8} {'Impact':>12}\")\n",
    "print(\"-\" * 55)\n",
    "\n",
    "for rank, sev in enumerate(poor_cx_sevs, 1):\n",
    "    print(f\"{rank:<5} {sev.driver_code:<25} {sev.severity_score:>7.1f} {sev.impact_level.value:>12}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show severity formula breakdown for top driver\n",
    "if lost_sales_sevs:\n",
    "    top = lost_sales_sevs[0]\n",
    "    print(f\"=== SEVERITY BREAKDOWN: {top.driver_code} ===\")\n",
    "    print(f\"Base severity (from taxonomy): {top.base_severity:.2f}\")\n",
    "    print(f\"Frequency factor: {top.frequency_factor:.2f}\")\n",
    "    print(f\"Confidence factor: {top.confidence_factor:.2f}\")\n",
    "    print(f\"Co-occurrence factor: {top.co_occurrence_factor:.2f}\")\n",
    "    print(f\"\\nFinal severity score: {top.severity_score:.1f}\")\n",
    "    print(f\"Impact level: {top.impact_level.value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Conditional Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = AggregationConfig(min_support=3)\n",
    "calc = StatisticsCalculator(config=config)\n",
    "cond_probs = calc.calculate_conditional_probabilities(analyses)\n",
    "\n",
    "print(\"=== TOP CONDITIONAL PROBABILITIES (by Lift) ===\")\n",
    "print(f\"{'Driver A':<25} → {'Driver B':<25} {'P(B|A)':>8} {'Lift':>6} {'Support':>8}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "for cp in cond_probs[:10]:\n",
    "    print(f\"{cp.driver_a:<25} → {cp.driver_b:<25} {cp.probability:>7.1%} {cp.lift:>6.2f} {cp.support:>8}\")\n",
    "\n",
    "print(f\"\\nInterpretation: Lift > 1 means drivers co-occur more than expected by chance.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Build RCA Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "builder = RCATreeBuilder()\n",
    "tree = builder.build(\"validation_batch\", analyses)\n",
    "\n",
    "print(\"=== RCA TREE SUMMARY ===\")\n",
    "print(f\"Batch ID: {tree.batch_id}\")\n",
    "print(f\"Total calls: {tree.total_calls}\")\n",
    "print(f\"Calls with lost sales: {tree.calls_with_lost_sales} ({tree.calls_with_lost_sales/tree.total_calls:.1%})\")\n",
    "print(f\"Calls with poor CX: {tree.calls_with_poor_cx} ({tree.calls_with_poor_cx/tree.total_calls:.1%})\")\n",
    "print(f\"Calls with both: {tree.calls_with_both} ({tree.calls_with_both/tree.total_calls:.1%})\")\n",
    "\n",
    "print(f\"\\nTop lost sales drivers: {tree.top_lost_sales_drivers}\")\n",
    "print(f\"Top poor CX drivers: {tree.top_poor_cx_drivers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== LOST SALES RCA TREE ===\")\n",
    "print(f\"{'Rank':<5} {'Driver':<25} {'Impact':>10} {'Call Rate':>10} {'Score':>8}\")\n",
    "print(\"-\" * 65)\n",
    "\n",
    "for node in tree.lost_sales_root:\n",
    "    print(f\"{node.priority_rank:<5} {node.driver_code:<25} {node.severity.impact_level.value:>10} {node.frequency.call_rate:>9.1%} {node.severity.severity_score:>8.1f}\")\n",
    "    if node.sample_evidence:\n",
    "        print(f\"      └── Evidence: \\\"{node.sample_evidence[0][:50]}...\\\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== POOR CX RCA TREE ===\")\n",
    "print(f\"{'Rank':<5} {'Driver':<25} {'Impact':>10} {'Call Rate':>10} {'Score':>8}\")\n",
    "print(\"-\" * 65)\n",
    "\n",
    "for node in tree.poor_cx_root:\n",
    "    print(f\"{node.priority_rank:<5} {node.driver_code:<25} {node.severity.impact_level.value:>10} {node.frequency.call_rate:>9.1%} {node.severity.severity_score:>8.1f}\")\n",
    "    if node.recommended_actions:\n",
    "        print(f\"      └── Action: {node.recommended_actions[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Full Batch Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregation = aggregate_batch(\"validation_batch\", analyses)\n",
    "\n",
    "print(\"=== BATCH AGGREGATION SUMMARY ===\")\n",
    "print(f\"Batch ID: {aggregation.batch_id}\")\n",
    "print(f\"Total processed: {aggregation.total_calls_processed}\")\n",
    "print(f\"Successful: {aggregation.successful_analyses}\")\n",
    "print(f\"Failed: {aggregation.failed_analyses}\")\n",
    "print(f\"\\nLost sales drivers found: {len(aggregation.lost_sales_frequencies)}\")\n",
    "print(f\"Poor CX drivers found: {len(aggregation.poor_cx_frequencies)}\")\n",
    "print(f\"Emergent patterns: {len(aggregation.emergent_patterns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get top drivers by severity\n",
    "top_lost_sales = aggregation.get_top_drivers(\"lost_sales\", n=5, by=\"severity\")\n",
    "top_poor_cx = aggregation.get_top_drivers(\"poor_cx\", n=5, by=\"severity\")\n",
    "\n",
    "print(\"=== TOP 5 DRIVERS BY SEVERITY ===\")\n",
    "print(f\"\\nLost Sales: {top_lost_sales}\")\n",
    "print(f\"Poor CX: {top_poor_cx}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. JSON Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export tree to JSON\n",
    "tree_json = tree.to_dict()\n",
    "\n",
    "print(\"=== RCA TREE JSON STRUCTURE ===\")\n",
    "print(json.dumps(tree_json, indent=2, default=str)[:2000])\n",
    "print(\"\\n... [truncated]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Validation Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== VALIDATION CHECKS ===\")\n",
    "\n",
    "# Check 1: Numbers add up\n",
    "total_ls_occurrences = sum(f.total_occurrences for f in lost_sales_freqs)\n",
    "total_pcx_occurrences = sum(f.total_occurrences for f in poor_cx_freqs)\n",
    "\n",
    "# Count from analyses\n",
    "actual_ls = sum(len(a.lost_sales_drivers) for a in analyses)\n",
    "actual_pcx = sum(len(a.poor_cx_drivers) for a in analyses)\n",
    "\n",
    "print(f\"✓ Lost sales occurrences match: {total_ls_occurrences} == {actual_ls}\")\n",
    "print(f\"✓ Poor CX occurrences match: {total_pcx_occurrences} == {actual_pcx}\")\n",
    "\n",
    "# Check 2: Severity scores in range\n",
    "all_sevs = lost_sales_sevs + poor_cx_sevs\n",
    "all_in_range = all(0 <= s.severity_score <= 100 for s in all_sevs)\n",
    "print(f\"✓ All severity scores in 0-100 range: {all_in_range}\")\n",
    "\n",
    "# Check 3: Rates in range\n",
    "all_freqs = lost_sales_freqs + poor_cx_freqs\n",
    "rates_valid = all(0 <= f.call_rate <= 1 for f in all_freqs)\n",
    "print(f\"✓ All call rates in 0-1 range: {rates_valid}\")\n",
    "\n",
    "# Check 4: Prioritization is consistent\n",
    "for i in range(len(tree.lost_sales_root) - 1):\n",
    "    assert tree.lost_sales_root[i].severity.severity_score >= tree.lost_sales_root[i+1].severity.severity_score\n",
    "print(f\"✓ Drivers correctly prioritized by severity\")\n",
    "\n",
    "print(\"\\n✓ All validation checks passed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Summary\n",
    "\n",
    "### Aggregation Module Validated:\n",
    "\n",
    "1. **Frequency Statistics** ✓\n",
    "   - Occurrence counts and rates\n",
    "   - Confidence statistics (avg, min, max)\n",
    "   - Co-occurrence tracking\n",
    "\n",
    "2. **Conditional Probabilities** ✓\n",
    "   - P(B|A) calculation\n",
    "   - Lift metric for pattern significance\n",
    "   - Support threshold filtering\n",
    "\n",
    "3. **Severity Scoring** ✓\n",
    "   - Base severity from taxonomy\n",
    "   - Weighted formula: base + frequency + confidence + co-occurrence\n",
    "   - Impact level classification (CRITICAL, HIGH, MEDIUM, LOW)\n",
    "\n",
    "4. **RCA Tree Building** ✓\n",
    "   - Hierarchical structure by driver category\n",
    "   - Priority ranking by severity\n",
    "   - Sample evidence collection\n",
    "   - Recommended actions per category\n",
    "\n",
    "5. **Batch Aggregation** ✓\n",
    "   - Complete statistics bundle\n",
    "   - JSON export for downstream use\n",
    "   - Top drivers by frequency or severity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=\"*50)\n",
    "print(\"CHECKPOINT 7 - AGGREGATION VALIDATION COMPLETE\")\n",
    "print(\"=\"*50)"
   ]
  }
 ],
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