Example yml

src/semeio/fmudesign/probabilit_idea.md

@@ -102,11 +102,8 @@ A high-level method such as `exp.report()` can generate a default document with
 - **Output Formats**: HTML, PDF, Markdown.  
 - **Customization**: Possibly via YAML or similar config files to tailor the final report.
 
----
-
-With these updates, **Probabilit** retains *lazy evaluation* (generating samples on demand) but drops any notion of caching the generated samples. Each new request for results triggers fresh sampling (unless the user explicitly decides otherwise).
-"""
 
+```python
 import pytest
 import polars as pl
 from probabilit import Probabilit
@@ -319,4 +316,196 @@ def test_comprehensive_project_analysis():
 
     assert report is not None
     exp.save("comprehensive_risk_model.yml")
-
+```
+
+## Example of yml
+
+# comprehensive_risk_model.yml
+
+```yml
+metadata:
+  name: "Comprehensive Project Risk Analysis"
+  description: "Complete risk analysis including direct costs, quality impacts, schedule effects, market variations, and risk adjustments"
+  created_at: "2024-03-20T10:00:00Z"
+  version: "1.0"
+  sample_size: 10000
+  sampling_method: "latin_hypercube"
+
+variables:
+  # Direct Material and Equipment
+  Steel_Cost:
+    type: "Normal"
+    specification: "percentiles"
+    parameters:
+      p10: 800000
+      p90: 1200000
+
+  Maintenance_Cost:
+    type: "LogNormal"
+    specification: "percentiles"
+    parameters:
+      p10: 50000
+      p90: 150000
+
+  Daily_Fuel_Cost:
+    type: "Uniform"
+    parameters:
+      min_val: 1000
+      max_val: 1500
+
+  Equipment_Lifetime:
+    type: "Weibull"
+    parameters:
+      shape: 2.5
+      scale: 5000
+
+  # Labor and Productivity
+  Productivity_Factor:
+    type: "Triangular"
+    specification: "percentiles"
+    parameters:
+      p10: 85
+      p50: 100
+      p90: 110
+
+  Daily_Worker_Absences:
+    type: "DiscreteUniform"
+    parameters:
+      min_val: 0
+      max_val: 5
+
+  # Quality and Inspection
+  Quality_Score:
+    type: "Beta"
+    parameters:
+      alpha: 5
+      beta: 2
+
+  Weekly_Quality_Issues:
+    type: "Poisson"
+    parameters:
+      lambda_param: 3.5
+
+  Successful_Inspections:
+    type: "Binomial"
+    parameters:
+      n: 10
+      p: 0.8
+
+  Certification_Attempts:
+    type: "NegativeBinomial"
+    parameters:
+      r: 3
+      p: 0.6
+
+  # Schedule and Timing
+  Schedule_Duration:
+    type: "BetaPERT"
+    parameters:
+      min_val: 8
+      most_likely: 10
+      max_val: 14
+
+  Repair_Time:
+    type: "Gamma"
+    parameters:
+      shape: 2
+      scale: 1.5
+
+  Time_Between_Failures:
+    type: "Exponential"
+    parameters:
+      rate: 0.1
+
+  Price_Variation:
+    type: "StudentT"
+    parameters:
+      df: 5
+
+  Overhead_Rate:
+    type: "KDE"
+    data: [0.12, 0.15, 0.14, 0.13, 0.16, 0.15, 0.14]
+
+correlations:
+  cost_variables:
+    method: "iman-conover"
+    variables:
+      - Steel_Cost
+      - Maintenance_Cost
+      - Daily_Fuel_Cost
+    matrix:
+      Steel_Cost: [1.0, 0.6, 0.4]
+      Maintenance_Cost: [0.6, 1.0, 0.3]
+      Daily_Fuel_Cost: [0.4, 0.3, 1.0]
+
+  productivity_variables:
+    method: "iman-conover"
+    variables:
+      - Productivity_Factor
+      - Daily_Worker_Absences
+    matrix:
+      Productivity_Factor: [1.0, -0.4]
+      Daily_Worker_Absences: [-0.4, 1.0]
+
+  quality_schedule_variables:
+    method: "iman-conover"
+    variables:
+      - Quality_Score
+      - Schedule_Duration
+      - Repair_Time
+    matrix:
+      Quality_Score: [1.0, -0.5, -0.3]
+      Schedule_Duration: [-0.5, 1.0, 0.4]
+      Repair_Time: [-0.3, 0.4, 1.0]
+
+expressions:
+  equipment_replacement_cost:
+    formula: "100000 * (10000 / Equipment_Lifetime)"
+
+  rework_cost:
+    formula: "Weekly_Quality_Issues * 5000 * (1 - Quality_Score)"
+
+  inspection_cost:
+    formula: "(10 - Successful_Inspections) * 2000"
+
+  certification_cost:
+    formula: "Certification_Attempts * 10000"
+
+  downtime_cost:
+    formula: "(Repair_Time / Time_Between_Failures) * Schedule_Duration * 5000"
+
+  schedule_delay_cost:
+    formula: "Schedule_Duration * Daily_Fuel_Cost"
+
+  market_adjusted_cost:
+    formula: "(1 + Price_Variation * 0.1)"
+
+  direct_cost:
+    formula: "(Steel_Cost + Maintenance_Cost + equipment_replacement_cost + Productivity_Factor * 1000000 * (1 + Daily_Worker_Absences * 0.01)) * market_adjusted_cost"
+
+  indirect_cost:
+    formula: "rework_cost + inspection_cost + certification_cost + downtime_cost + schedule_delay_cost"
+
+  total_cost:
+    formula: "(direct_cost + indirect_cost) * (1 + Overhead_Rate)"
+
+outputs:
+  plots:
+    - type: "histogram"
+      variable: "total_cost"
+      title: "Total Project Cost Distribution"
+    - type: "tornado"
+      variable: "total_cost"
+      title: "Sensitivity Analysis"
+
+  report:
+    format: "html"
+    title: "Comprehensive Project Risk Analysis"
+    description: |
+      Complete risk analysis with all variables contributing to final cost:
+      - Direct costs (materials, equipment, labor)
+      - Quality and inspection impacts
+      - Schedule effects
+      - Market variations
+      - Risk adjustments
+```