# Configuration Reference

The `config.yml` file is used to configure the ConversionFlow model pipeline. This reference document provides detailed explanations of each parameter and its options.

## General Configuration

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `project_name` | str | A descriptive name for identification and organization purposes | None |

## Preprocessing Configuration (`preprocess`)

### Scaling (`preprocess.scaling`)

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `enabled` | bool | Flag to enable or disable feature scaling | `true` |
| `method` | str | Scaling method to be used if scaling is enabled | `standard` |
| `exclude_from_scaling` | list | Feature names to exclude from the scaling process | `[]` |

Available scaling methods:
- `standard`: Standardization (z-score normalization)
- `minmax`: Min-Max scaling
- `robust`: Robust scaling using quartiles

### Missing Values (`preprocess.missing_values`)

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `strategy` | str | Strategy for handling missing values | `fill` |
| `fill_value` | any | Value to use for filling missing values | `0` |

Available strategies:
- `fill`: Fill missing values with the specified `fill_value`
- `drop`: Drop rows containing missing values

## Model Configuration (`model`)

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `name` | str | Name for the model, used for identification | None |
| `description` | str | Brief description of the model | None |
| `nodes` | dict | Nodes (variables) in the Bayesian Network | None |
| `edges` | list | Directed edges (relationships) in the Bayesian Network | None |

Example node definition:
```yaml
nodes:
  stage1:
    - car_configuration
    - finance_calculator
  stage2:
    - brochure_request
    - contact_request
  final:
    - test_drive
```

Example edge definition:
```yaml
edges:
  - [car_configuration, brochure_request]
  - [car_configuration, contact_request]
  - [finance_calculator, brochure_request]
  - [brochure_request, test_drive]
  - [contact_request, test_drive]
```

## Priors Configuration (`priors`)

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `sigma_node` | float | Standard deviation for the prior distribution of node intercepts | `5.0` |
| `sigma_beta_global` | float | Global standard deviation for prior distributions of regression coefficients | `5.0` |
| `beta_distributions` | dict | Prior distribution for each directed edge | None |

Example beta distribution definition:
```yaml
beta_distributions:
  car_configuration_brochure_request:
    dist: HalfCauchy
    parameters:
      loc: 0
      scale: 5
```

## Inference Configuration (`inference`)

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `draws` | int | Number of MCMC samples to draw after tuning | `1000` |
| `tune` | int | Number of MCMC samples to discard as warm-up | `500` |
| `chains` | int | Number of independent MCMC chains to run | `4` |
| `random_seed` | int | Seed for the random number generator | `42` |

## Output Configuration (`output`)

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `directory` | str | Directory where all output files will be saved | `output/` |
| `figures.extension` | str | File extension for output figures | `png` |
| `figures.dpi` | int | DPI for output figures | `300` |

## Logging Configuration (`logging`)

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `level` | str | Logging level, determining the verbosity of logs | `INFO` |
| `file` | str | Path to the log file where messages will be written | `logs/pipeline.log` |

## Complete Example

```yaml
project_name: "Toyota Conversion Analysis"

preprocess:
  scaling:
    enabled: true
    method: standard
    exclude_from_scaling: [date, user_id]
  missing_values:
    strategy: fill
    fill_value: 0

model:
  name: "toyota_conversion_model"
  description: "Bayesian Network model for Toyota conversion flow"
  nodes:
    stage1:
      - car_configuration
      - finance_calculator
    stage2:
      - brochure_request
      - contact_request
    final:
      - test_drive
  edges:
    - [car_configuration, brochure_request]
    - [car_configuration, contact_request]
    - [finance_calculator, brochure_request]
    - [brochure_request, test_drive]
    - [contact_request, test_drive]

priors:
  sigma_node: 5.0
  sigma_beta_global: 5.0
  beta_distributions:
    car_configuration_brochure_request:
      dist: HalfCauchy
      parameters:
        loc: 0
        scale: 5

inference:
  draws: 1000
  tune: 500
  chains: 4
  random_seed: 42

output:
  directory: "output/"
  figures:
    extension: "png"
    dpi: 300

logging:
  level: "INFO"
  file: "logs/pipeline.log"
```