feat: Add Streamlit dashboard with Blueprint compliance (v2.1.0)
Dashboard Features: - 8 navigation sections: Overview, Outcomes, Poor CX, FCR, Churn, Agent, Call Explorer, Export - Beyond Brand Identity styling (colors #6D84E3, Outfit font) - RCA Sankey diagram (Driver → Outcome → Churn Risk flow) - Correlation heatmaps (driver co-occurrence, driver-outcome) - Outcome Deep Dive (root causes, correlation, duration analysis) - Export functionality (Excel, HTML, JSON) Blueprint Compliance: - FCR: 4 categories (Primera Llamada/Rellamada × Sin/Con Riesgo de Fuga) - Churn: Binary view (Sin Riesgo de Fuga / En Riesgo de Fuga) - Agent: Talento Para Replicar / Oportunidades de Mejora - Fixed FCR rate calculation (only FIRST_CALL counts as success) Technical: - Streamlit + Plotly for interactive visualizations - Light theme configuration (.streamlit/config.toml) - Fixed Plotly colorbar titlefont deprecation Documentation: - Updated PROJECT_CONTEXT.md, TODO.md, CHANGELOG.md - Added 4 new technical decisions (TD-014 to TD-017) - Created TROUBLESHOOTING.md with 10 common issues Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
sujucu70
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notebooks/04_aggregation_validation.ipynb
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544
notebooks/04_aggregation_validation.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 04 - Aggregation & RCA Trees Validation\n",
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"\n",
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"**Checkpoint 7 validation notebook**\n",
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"\n",
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"This notebook validates the aggregation module:\n",
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"1. Frequency statistics calculation\n",
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"2. Conditional probability analysis\n",
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"3. Severity scoring with explicit rules\n",
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"4. RCA tree building and prioritization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.insert(0, '..')\n",
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"\n",
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"import json\n",
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"from datetime import datetime\n",
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"\n",
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"# Project imports\n",
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"from src.aggregation import (\n",
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" AggregationConfig,\n",
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" BatchAggregation,\n",
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" RCATree,\n",
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" RCATreeBuilder,\n",
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" StatisticsCalculator,\n",
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" SeverityCalculator,\n",
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" ImpactLevel,\n",
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" aggregate_batch,\n",
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" build_rca_tree,\n",
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" calculate_batch_statistics,\n",
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")\n",
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"from src.models.call_analysis import (\n",
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" CallAnalysis,\n",
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" CallOutcome,\n",
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" EvidenceSpan,\n",
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" ObservedFeatures,\n",
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" ProcessingStatus,\n",
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" RCALabel,\n",
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" Traceability,\n",
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")\n",
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"\n",
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"print(\"Imports successful!\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Create Simulated Call Analyses\n",
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"\n",
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"We'll simulate 100 call analyses with realistic driver distributions."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"\n",
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"def create_sample_analyses(n: int = 100) -> list[CallAnalysis]:\n",
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" \"\"\"Create n sample call analyses with realistic distributions.\"\"\"\n",
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" random.seed(42) # Reproducible\n",
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" \n",
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" base_observed = ObservedFeatures(audio_duration_sec=60.0, events=[])\n",
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" base_trace = Traceability(\n",
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" schema_version=\"1.0.0\",\n",
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" prompt_version=\"v1.0\",\n",
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" model_id=\"gpt-4o-mini\",\n",
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" )\n",
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" \n",
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" # Driver probabilities (realistic distribution)\n",
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" lost_sales_probs = {\n",
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" \"PRICE_TOO_HIGH\": 0.25,\n",
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" \"COMPETITOR_PREFERENCE\": 0.12,\n",
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" \"TIMING_NOT_RIGHT\": 0.10,\n",
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" \"NO_NEED\": 0.08,\n",
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" \"OBJECTION_NOT_HANDLED\": 0.15,\n",
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" \"NO_SAVE_OFFER\": 0.10,\n",
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" \"POOR_PITCH\": 0.05,\n",
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" }\n",
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" \n",
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" poor_cx_probs = {\n",
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" \"LONG_HOLD\": 0.20,\n",
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" \"MULTI_TRANSFER\": 0.08,\n",
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" \"LOW_EMPATHY\": 0.10,\n",
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" \"ISSUE_NOT_RESOLVED\": 0.12,\n",
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" \"INTERRUPTIONS\": 0.05,\n",
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" \"CALLBACK_REQUIRED\": 0.08,\n",
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" }\n",
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" \n",
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" analyses = []\n",
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" \n",
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" for i in range(n):\n",
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" call_id = f\"CALL{i+1:04d}\"\n",
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" \n",
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" # Determine if this is a lost sale (40% of calls)\n",
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" is_lost_sale = random.random() < 0.40\n",
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" \n",
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" # Determine if poor CX (30% of calls)\n",
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" has_poor_cx = random.random() < 0.30\n",
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" \n",
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" # Generate lost sales drivers\n",
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" lost_sales = []\n",
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" if is_lost_sale:\n",
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" for code, prob in lost_sales_probs.items():\n",
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" if random.random() < prob:\n",
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" lost_sales.append(RCALabel(\n",
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" driver_code=code,\n",
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" confidence=random.uniform(0.6, 0.95),\n",
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" evidence_spans=[EvidenceSpan(\n",
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" text=f\"Evidence for {code}\",\n",
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" start_time=random.uniform(0, 50),\n",
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" end_time=random.uniform(50, 60),\n",
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" )],\n",
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" ))\n",
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" \n",
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" # Generate poor CX drivers\n",
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" poor_cx = []\n",
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" if has_poor_cx:\n",
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" for code, prob in poor_cx_probs.items():\n",
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" if random.random() < prob:\n",
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" poor_cx.append(RCALabel(\n",
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" driver_code=code,\n",
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" confidence=random.uniform(0.6, 0.95),\n",
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" evidence_spans=[EvidenceSpan(\n",
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" text=f\"Evidence for {code}\",\n",
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" start_time=random.uniform(0, 50),\n",
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" end_time=random.uniform(50, 60),\n",
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" )],\n",
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" ))\n",
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" \n",
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" # Determine outcome\n",
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" if is_lost_sale:\n",
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" outcome = CallOutcome.SALE_LOST\n",
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" elif random.random() < 0.5:\n",
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" outcome = CallOutcome.SALE_COMPLETED\n",
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" else:\n",
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" outcome = CallOutcome.INQUIRY_RESOLVED\n",
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" \n",
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" analyses.append(CallAnalysis(\n",
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" call_id=call_id,\n",
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" batch_id=\"validation_batch\",\n",
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" status=ProcessingStatus.SUCCESS,\n",
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" observed=base_observed,\n",
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" outcome=outcome,\n",
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" lost_sales_drivers=lost_sales,\n",
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" poor_cx_drivers=poor_cx,\n",
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" traceability=base_trace,\n",
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" ))\n",
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" \n",
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" return analyses\n",
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"\n",
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"# Create 100 sample analyses\n",
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"analyses = create_sample_analyses(100)\n",
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"print(f\"Created {len(analyses)} sample analyses\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Calculate Frequency Statistics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"calculator = StatisticsCalculator()\n",
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"lost_sales_freqs, poor_cx_freqs = calculator.calculate_frequencies(analyses)\n",
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"\n",
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"print(\"=== LOST SALES DRIVER FREQUENCIES ===\")\n",
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"print(f\"{'Driver':<25} {'Occurrences':>12} {'Call Rate':>10} {'Avg Conf':>10}\")\n",
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"print(\"-\" * 60)\n",
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"\n",
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"for freq in lost_sales_freqs:\n",
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" print(f\"{freq.driver_code:<25} {freq.total_occurrences:>12} {freq.call_rate:>9.1%} {freq.avg_confidence:>10.2f}\")\n",
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"\n",
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"print(f\"\\nTotal lost sales drivers: {len(lost_sales_freqs)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"=== POOR CX DRIVER FREQUENCIES ===\")\n",
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"print(f\"{'Driver':<25} {'Occurrences':>12} {'Call Rate':>10} {'Avg Conf':>10}\")\n",
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"print(\"-\" * 60)\n",
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"\n",
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"for freq in poor_cx_freqs:\n",
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" print(f\"{freq.driver_code:<25} {freq.total_occurrences:>12} {freq.call_rate:>9.1%} {freq.avg_confidence:>10.2f}\")\n",
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"\n",
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"print(f\"\\nTotal poor CX drivers: {len(poor_cx_freqs)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Outcome Rate Analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"outcome_rates = calculator.calculate_outcome_rates(analyses)\n",
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"\n",
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"print(\"=== OUTCOME RATES ===\")\n",
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"print(f\"Total calls analyzed: {outcome_rates['total_calls']}\")\n",
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"print(f\"\\nCalls with lost sales drivers: {outcome_rates['lost_sales_count']} ({outcome_rates['lost_sales_rate']:.1%})\")\n",
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"print(f\"Calls with poor CX drivers: {outcome_rates['poor_cx_count']} ({outcome_rates['poor_cx_rate']:.1%})\")\n",
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"print(f\"Calls with BOTH: {outcome_rates['both_count']} ({outcome_rates['both_rate']:.1%})\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Severity Scoring"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"severity_calc = SeverityCalculator()\n",
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"lost_sales_sevs, poor_cx_sevs = severity_calc.calculate_all_severities(\n",
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" lost_sales_freqs, poor_cx_freqs\n",
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")\n",
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"\n",
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"print(\"=== LOST SALES SEVERITY SCORES ===\")\n",
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"print(f\"{'Rank':<5} {'Driver':<25} {'Score':>8} {'Impact':>12}\")\n",
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"print(\"-\" * 55)\n",
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"\n",
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"for rank, sev in enumerate(lost_sales_sevs, 1):\n",
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" print(f\"{rank:<5} {sev.driver_code:<25} {sev.severity_score:>7.1f} {sev.impact_level.value:>12}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"=== POOR CX SEVERITY SCORES ===\")\n",
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"print(f\"{'Rank':<5} {'Driver':<25} {'Score':>8} {'Impact':>12}\")\n",
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"print(\"-\" * 55)\n",
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"\n",
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"for rank, sev in enumerate(poor_cx_sevs, 1):\n",
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" print(f\"{rank:<5} {sev.driver_code:<25} {sev.severity_score:>7.1f} {sev.impact_level.value:>12}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Show severity formula breakdown for top driver\n",
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"if lost_sales_sevs:\n",
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" top = lost_sales_sevs[0]\n",
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" print(f\"=== SEVERITY BREAKDOWN: {top.driver_code} ===\")\n",
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" print(f\"Base severity (from taxonomy): {top.base_severity:.2f}\")\n",
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" print(f\"Frequency factor: {top.frequency_factor:.2f}\")\n",
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" print(f\"Confidence factor: {top.confidence_factor:.2f}\")\n",
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" print(f\"Co-occurrence factor: {top.co_occurrence_factor:.2f}\")\n",
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" print(f\"\\nFinal severity score: {top.severity_score:.1f}\")\n",
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" print(f\"Impact level: {top.impact_level.value}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Conditional Probabilities"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"config = AggregationConfig(min_support=3)\n",
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"calc = StatisticsCalculator(config=config)\n",
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"cond_probs = calc.calculate_conditional_probabilities(analyses)\n",
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"\n",
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"print(\"=== TOP CONDITIONAL PROBABILITIES (by Lift) ===\")\n",
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"print(f\"{'Driver A':<25} → {'Driver B':<25} {'P(B|A)':>8} {'Lift':>6} {'Support':>8}\")\n",
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"print(\"-\" * 80)\n",
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"\n",
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"for cp in cond_probs[:10]:\n",
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" print(f\"{cp.driver_a:<25} → {cp.driver_b:<25} {cp.probability:>7.1%} {cp.lift:>6.2f} {cp.support:>8}\")\n",
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"\n",
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"print(f\"\\nInterpretation: Lift > 1 means drivers co-occur more than expected by chance.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 6. Build RCA Tree"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"builder = RCATreeBuilder()\n",
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"tree = builder.build(\"validation_batch\", analyses)\n",
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"\n",
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"print(\"=== RCA TREE SUMMARY ===\")\n",
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"print(f\"Batch ID: {tree.batch_id}\")\n",
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"print(f\"Total calls: {tree.total_calls}\")\n",
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"print(f\"Calls with lost sales: {tree.calls_with_lost_sales} ({tree.calls_with_lost_sales/tree.total_calls:.1%})\")\n",
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"print(f\"Calls with poor CX: {tree.calls_with_poor_cx} ({tree.calls_with_poor_cx/tree.total_calls:.1%})\")\n",
|
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"print(f\"Calls with both: {tree.calls_with_both} ({tree.calls_with_both/tree.total_calls:.1%})\")\n",
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"\n",
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"print(f\"\\nTop lost sales drivers: {tree.top_lost_sales_drivers}\")\n",
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"print(f\"Top poor CX drivers: {tree.top_poor_cx_drivers}\")"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
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"source": [
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"print(\"=== LOST SALES RCA TREE ===\")\n",
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"print(f\"{'Rank':<5} {'Driver':<25} {'Impact':>10} {'Call Rate':>10} {'Score':>8}\")\n",
|
||||
"print(\"-\" * 65)\n",
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"\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]}...\\\"\")"
|
||||
]
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||||
},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"print(\"=== POOR CX RCA TREE ===\")\n",
|
||||
"print(f\"{'Rank':<5} {'Driver':<25} {'Impact':>10} {'Call Rate':>10} {'Score':>8}\")\n",
|
||||
"print(\"-\" * 65)\n",
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||||
"\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)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.11.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in New Issue
Block a user