# Observational Notebook System - Implementation Summary

## Overview

Created an interactive system for generating Jupyter notebooks to explore MLflow experiment artifacts. The system automatically discovers experiments and runs, allowing users to select and explore them through pre-configured notebooks.

## Components Created

### 1. Main Script: `create_observational_notebook.py`
**Location:** `qualia_lab/scripts/create_observational_notebook.py`

**Features:**
- `ArtifactExplorer` class: Discovers and queries MLflow tracking server
  - Lists all active experiments
  - Searches runs with filters
  - Retrieves artifact information
  - Fetches comprehensive run metadata
  
- `NotebookGenerator` class: Creates Jupyter notebooks
  - Generates proper `.ipynb` JSON structure
  - Creates markdown and code cells
  - Populates with run-specific code
  - Includes comparison and exploration sections

- Interactive CLI workflow:
  - User-friendly prompts with emoji indicators
  - Input validation
  - Progress feedback
  - Error handling

**Entry point:** Added to `pyproject.toml` as `create-observation-notebook`

### 2. Demo Script: `create_demo_runs.py`
**Location:** `qualia_lab/scripts/create_demo_runs.py`

**Purpose:** Creates sample MLflow runs for testing and demonstration

**Features:**
- Generates configurable number of runs
- Randomized parameters (learning rate, batch size, steps)
- Simulated training metrics with realistic progressions
- Creates artifacts (text files with run summaries)
- Tags for easy identification

**Entry point:** Added to `pyproject.toml` as `create-demo-runs`

### 3. Documentation

#### Full Documentation
**File:** `docs/observational_notebooks.md`

**Contents:**
- Complete feature overview
- Usage instructions
- Generated notebook structure explanation
- Use cases and examples
- Tips for working with large datasets
- Customization guidance
- Architecture details
- Future enhancement ideas

#### Quick Start Guide
**File:** `docs/quick_start_observational_notebooks.md`

**Contents:**
- Installation steps
- Step-by-step tutorial
- Example session walkthrough
- Tips for different scenarios
- Real experiment workflow

#### Notebooks Directory README
**File:** `notebooks/README.md`

**Contents:**
- Purpose of the directory
- How to create notebooks
- Organizational suggestions
- Git tracking recommendations

### 4. Integration

Updated `README.md` with:
- New "Observational Notebooks" section
- Quick start example
- Links to detailed documentation

Updated `pyproject.toml` with:
- `create-observation-notebook` command
- `create-demo-runs` command

## Workflow

### For Users

1. **Run Training/Experiments**
   ```bash
   python -m qualia_lab.pipeline.train_pipeline
   ```

2. **Generate Observational Notebook**
   ```bash
   create-observation-notebook
   ```
   
3. **Follow Interactive Prompts:**
   - Select MLflow tracking URI
   - Choose experiments
   - Select specific runs
   - Name the notebook

4. **Explore in Jupyter**
   ```bash
   jupyter notebook notebooks/your_notebook.ipynb
   ```

### For Demo/Testing

1. **Create Sample Runs**
   ```bash
   python -m qualia_lab.scripts.create_demo_runs 5
   ```

2. **Generate Notebook**
   ```bash
   create-observation-notebook
   ```

3. **Explore Demo Data**

## Generated Notebook Structure

Every generated notebook includes:

1. **Title and Metadata**
   - Custom title
   - Generation timestamp
   - MLflow tracking URI
   - Number of runs

2. **Setup Section**
   - Required imports (mlflow, pandas, etc.)
   - Display configuration
   - MLflow client initialization

3. **Configuration**
   - Tracking URI
   - Selected run IDs as list
   - Client setup code

4. **Data Loading**
   - Code to load all selected runs
   - Error handling for missing runs
   - Success feedback

5. **Overview Table**
   - DataFrame with all runs
   - Status, timestamps, durations
   - Parameter and metric counts

6. **Individual Run Sections**
   - One section per selected run
   - Full run details
   - Parameter tables
   - Metric tables
   - Artifact listings

7. **Comparison Section** (if multiple runs)
   - Side-by-side parameter comparison
   - Metrics comparison
   - Optional plotting code (commented)

8. **Free Exploration**
   - Empty cell for custom analysis
   - All data already loaded and accessible

## Key Features

### Interactive Discovery
- Automatic experiment detection
- Run status indicators (✅ ✗ ⏳)
- Metadata preview before selection
- Flexible selection (specific or all)

### Smart Generation
- Proper Jupyter notebook format
- Pre-populated, ready-to-run cells
- Error handling in generated code
- Comprehensive metadata loading

### User-Friendly
- Clear prompts and feedback
- Default values for quick workflows
- Input validation
- Helpful error messages

### Extensible
- Modular class design
- Easy to add new cell types
- Template system ready for expansion
- Support for custom tracking URIs

## Technical Details

### MLflow Integration
- Uses `MlflowClient` for tracking server queries
- Supports both filesystem and database backends
- Handles remote tracking URIs
- Graceful degradation on errors

### Notebook Format
- Standard Jupyter `.ipynb` JSON structure
- Cells as list of dictionaries
- Proper metadata for Python 3.12
- Source code as line arrays

### Error Handling
- Try-catch blocks in generated code
- User-friendly error messages
- Continues on individual failures
- Validates user input

## Usage Scenarios

### 1. Post-Training Analysis
After training runs complete:
- Select best performing runs
- Compare hyperparameters
- Document findings

### 2. Experiment Documentation
For record keeping:
- Generate notebooks as artifacts
- Include analysis and notes
- Share with team

### 3. Model Selection
When choosing models:
- Load candidate runs
- Compare metrics
- Examine artifacts

### 4. Debugging
For investigating issues:
- Compare failed and successful runs
- Identify parameter differences
- Track down problems

## Files Modified/Created

### Created
- `qualia_lab/scripts/create_observational_notebook.py` (578 lines)
- `qualia_lab/scripts/create_demo_runs.py` (95 lines)
- `docs/observational_notebooks.md` (comprehensive guide)
- `docs/quick_start_observational_notebooks.md` (tutorial)
- `notebooks/README.md` (directory documentation)

### Modified
- `README.md` (added Observational Notebooks section)
- `pyproject.toml` (added script entry points)

### Directories Created
- `notebooks/` (for generated notebooks)

## Dependencies

All required dependencies already in `pyproject.toml`:
- `mlflow` - MLflow tracking and client
- `pandas` - Data manipulation in notebooks
- `jupyter` - Running generated notebooks
- Built-in libraries: `json`, `pathlib`, `datetime`

## Next Steps / Potential Enhancements

1. **Filtering Options**
   - Date range filters
   - Parameter value filters
   - Metric threshold filters

2. **Visualization**
   - Automatic plot generation
   - Metric history charts
   - Parameter distribution plots

3. **Templates**
   - Custom notebook templates
   - Project-specific layouts
   - Reusable analysis patterns

4. **Export**
   - HTML export for sharing
   - PDF reports
   - Markdown summaries

5. **Collaboration**
   - Team sharing features
   - Comment integration
   - Notebook versioning

## Testing

To test the system:

```bash
# 1. Create demo data
python -m qualia_lab.scripts.create_demo_runs 3

# 2. Generate notebook (select demo experiment, all runs)
create-observation-notebook

# 3. Open and run all cells
jupyter notebook notebooks/[generated_name].ipynb
```

Expected result: All cells run successfully, displaying run information, comparisons, and allowing further exploration.

## Summary

The Observational Notebook System provides a complete, production-ready solution for exploring MLflow experiments through interactive Jupyter notebooks. It features:

- ✅ Automatic artifact discovery
- ✅ Interactive selection workflow
- ✅ Pre-configured exploration notebooks
- ✅ Comprehensive documentation
- ✅ Demo/testing utilities
- ✅ Extensible architecture
- ✅ User-friendly interface
- ✅ Error handling and validation

The system is ready to use and can be extended with additional features as needed.
