# Dataset Validation & Curation - Implementation Complete

Comprehensive dataset validation, curation, and analysis utilities have been implemented.

## ✅ Implemented Features

### 1. Dataset Validation (`ylff/utils/dataset_validation.py`)

**DatasetValidator Class:**

- ✅ Data integrity checks (images, poses, metadata)
- ✅ Quality validation (NaN/Inf detection, rotation matrix validity)
- ✅ Statistical analysis (error distributions, image counts)
- ✅ Comprehensive reporting

**Functions:**

- ✅ `validate_dataset_file()` - Validate saved dataset files
- ✅ `check_dataset_integrity()` - Check dataset directory integrity

**Validation Checks:**

- Image format validation (numpy arrays, tensors, file paths)
- Pose shape and validity checks
- Metadata validation (weights, errors, sequence IDs)
- NaN/Inf detection
- Rotation matrix determinant checks

### 2. Dataset Curation (`ylff/utils/dataset_curation.py`)

**DatasetCurator Class:**

- ✅ Quality-based filtering (error, weight, image count thresholds)
- ✅ Outlier removal (percentile-based, statistical IQR method)
- ✅ Dataset balancing (error bins, uniform, weighted strategies)
- ✅ Dataset splitting (train/val/test with stratification)
- ✅ Smart sampling (random, weighted, error-based)

**Curation Strategies:**

- **Filtering**: By error range, weight range, image count
- **Outlier Removal**: Percentile-based or statistical IQR
- **Balancing**: Error bins, uniform distribution, weighted sampling
- **Splitting**: Stratified or random train/val/test splits

### 3. Dataset Analysis (`ylff/utils/dataset_analysis.py`)

**DatasetAnalyzer Class:**

- ✅ Statistical analysis (mean, median, quartiles, percentiles)
- ✅ Distribution computation (histograms, binning)
- ✅ Quality metrics (error ratios, weight diversity, completeness)
- ✅ Correlation analysis
- ✅ Report generation (JSON, text, markdown)

**Analysis Features:**

- Error statistics (mean, median, Q25/Q75, Q90/Q95/Q99)
- Weight statistics
- Image count statistics
- Sequence statistics (samples per sequence)
- Quality metrics (low/medium/high error ratios)
- Completeness metrics

## 📋 API Endpoints

### `/api/v1/dataset/validate` (POST)

**Request Model**: `ValidateDatasetRequest`

```json
{
  "dataset_path": "data/training/dataset.pkl",
  "strict": false,
  "check_images": true,
  "check_poses": true,
  "check_metadata": true
}
```

**Response**: `DatasetValidationResponse`

- `validation_passed`: Boolean
- `statistics`: Dataset statistics
- `issues`: List of validation issues
- `summary`: Validation summary

### `/api/v1/dataset/curate` (POST)

**Request Model**: `CurateDatasetRequest`

```json
{
  "dataset_path": "data/training/dataset.pkl",
  "output_path": "data/training/dataset_curated.pkl",
  "min_error": 0.5,
  "max_error": 30.0,
  "remove_outliers": true,
  "outlier_percentile": 95.0,
  "balance": true,
  "balance_strategy": "error_bins",
  "num_bins": 10
}
```

**Response**: `JobResponse` (async job)

### `/api/v1/dataset/analyze` (POST)

**Request Model**: `AnalyzeDatasetRequest`

```json
{
  "dataset_path": "data/training/dataset.pkl",
  "output_path": "data/training/analysis.json",
  "format": "json",
  "compute_distributions": true,
  "compute_correlations": true
}
```

**Response**: `DatasetAnalysisResponse`

- `statistics`: Dataset statistics
- `quality_metrics`: Quality metrics
- `report`: Human-readable report (if text/markdown)

## 🔧 CLI Commands

### `ylff dataset validate`

```bash
ylff dataset validate data/training/dataset.pkl \
    --strict \
    --check-images \
    --check-poses \
    --check-metadata \
    --output validation_report.json
```

### `ylff dataset curate`

```bash
ylff dataset curate \
    data/training/dataset.pkl \
    data/training/dataset_curated.pkl \
    --min-error 0.5 \
    --max-error 30.0 \
    --remove-outliers \
    --outlier-percentile 95.0 \
    --balance \
    --balance-strategy error_bins \
    --num-bins 10
```

### `ylff dataset analyze`

```bash
ylff dataset analyze data/training/dataset.pkl \
    --output analysis_report.json \
    --format json \
    --compute-distributions \
    --compute-correlations
```

## 🔄 Integration

### Data Pipeline Integration

The `BADataPipeline.build_training_set()` method now automatically:

- ✅ Validates built datasets
- ✅ Analyzes dataset statistics
- ✅ Logs validation and analysis results

### Usage in Training

```python
from ylff.utils.dataset_validation import DatasetValidator
from ylff.utils.dataset_curation import DatasetCurator
from ylff.utils.dataset_analysis import DatasetAnalyzer

# Validate
validator = DatasetValidator(strict=False)
report = validator.validate_dataset(samples)

# Curate
curator = DatasetCurator()
curated, stats = curator.filter_by_quality(
    samples,
    min_error=0.5,
    max_error=30.0,
)
curated, _ = curator.remove_outliers(curated, error_percentile=95.0)

# Analyze
analyzer = DatasetAnalyzer()
analysis = analyzer.analyze_dataset(curated)
analyzer.generate_report("analysis_report.json", format="markdown")
```

## 📊 Features

### Validation Features

- ✅ Image format validation (numpy, tensor, file paths)
- ✅ Pose shape and validity checks
- ✅ Metadata validation
- ✅ NaN/Inf detection
- ✅ Rotation matrix validation
- ✅ File integrity checks

### Curation Features

- ✅ Quality filtering (error, weight, image count)
- ✅ Outlier removal (percentile, IQR)
- ✅ Dataset balancing (error bins, uniform, weighted)
- ✅ Train/val/test splitting (stratified, random)
- ✅ Smart sampling strategies

### Analysis Features

- ✅ Statistical analysis (mean, median, quartiles)
- ✅ Distribution computation
- ✅ Quality metrics
- ✅ Correlation analysis
- ✅ Report generation (JSON, text, markdown)

## 🚀 Next Steps

1. **Dataset Versioning** - Track dataset versions and metadata
2. **Visualization** - Generate plots for distributions and statistics
3. **Advanced Filtering** - Scene-based, sequence-based filtering
4. **Data Augmentation** - Integration with augmentation strategies
5. **Dataset Comparison** - Compare multiple datasets

All core functionality is implemented and ready to use! 🎉