name: data-visualizer description: | Automated data visualization for EDA, model performance, and business reporting. Activates for "visualize data", "create plots", "EDA", "exploratory analysis", "confusion matrix", "ROC curve", "feature distribution", "correlation heatmap", "plot results", "dashboard". Generates publication-quality visualizations integrated with SpecWeave increments.

Data Visualizer

Overview

Automated visualization generation for exploratory data analysis, model performance reporting, and stakeholder communication. Creates publication-quality plots, interactive dashboards, and business-friendly reports—all integrated with SpecWeave's increment workflow.

Visualization Categories

1. Exploratory Data Analysis (EDA)

Automated EDA Report:

from specweave import EDAVisualizer

visualizer = EDAVisualizer(increment="0042")

# Generates comprehensive EDA report
report = visualizer.generate_eda_report(df)

# Creates:
# - Dataset overview (rows, columns, memory, missing values)
# - Numerical feature distributions (histograms + KDE)
# - Categorical feature counts (bar charts)
# - Correlation heatmap
# - Missing value pattern
# - Outlier detection plots
# - Feature relationships (pairplot for top features)

Individual EDA Plots:

# Distribution plots
visualizer.plot_distribution(
    data=df['age'],
    title="Age Distribution",
    bins=30
)

# Correlation heatmap
visualizer.plot_correlation_heatmap(
    data=df[numerical_columns],
    method='pearson'  # or 'spearman', 'kendall'
)

# Missing value patterns
visualizer.plot_missing_values(df)

# Outlier detection (boxplots)
visualizer.plot_outliers(df[numerical_columns])

2. Model Performance Visualizations

Classification Performance:

from specweave import ClassificationVisualizer

viz = ClassificationVisualizer(increment="0042")

# Confusion matrix
viz.plot_confusion_matrix(
    y_true=y_test,
    y_pred=y_pred,
    classes=['Negative', 'Positive']
)

# ROC curve
viz.plot_roc_curve(
    y_true=y_test,
    y_proba=y_proba
)

# Precision-Recall curve
viz.plot_precision_recall_curve(
    y_true=y_test,
    y_proba=y_proba
)

# Learning curves (train vs val)
viz.plot_learning_curve(
    train_scores=train_scores,
    val_scores=val_scores
)

# Calibration curve (are probabilities well-calibrated?)
viz.plot_calibration_curve(
    y_true=y_test,
    y_proba=y_proba
)

Regression Performance:

from specweave import RegressionVisualizer

viz = RegressionVisualizer(increment="0042")

# Predicted vs Actual
viz.plot_predictions(
    y_true=y_test,
    y_pred=y_pred
)

# Residual plot
viz.plot_residuals(
    y_true=y_test,
    y_pred=y_pred
)

# Residual distribution (should be normal)
viz.plot_residual_distribution(
    residuals=y_test - y_pred
)

# Error by feature value
viz.plot_error_analysis(
    y_true=y_test,
    y_pred=y_pred,
    features=X_test
)

3. Feature Analysis Visualizations

Feature Importance:

from specweave import FeatureVisualizer

viz = FeatureVisualizer(increment="0042")

# Feature importance (bar chart)
viz.plot_feature_importance(
    feature_names=feature_names,
    importances=model.feature_importances_,
    top_n=20
)

# SHAP summary plot
viz.plot_shap_summary(
    shap_values=shap_values,
    features=X_test
)

# Partial dependence plots
viz.plot_partial_dependence(
    model=model,
    features=['age', 'income'],
    X=X_train
)

# Feature interaction
viz.plot_feature_interaction(
    model=model,
    features=('age', 'income'),
    X=X_train
)

4. Time Series Visualizations

Time Series Plots:

from specweave import TimeSeriesVisualizer

viz = TimeSeriesVisualizer(increment="0042")

# Time series with trend
viz.plot_timeseries(
    data=sales_data,
    show_trend=True
)

# Seasonal decomposition
viz.plot_seasonal_decomposition(
    data=sales_data,
    period=12  # Monthly seasonality
)

# Autocorrelation (ACF, PACF)
viz.plot_autocorrelation(data=sales_data)

# Forecast with confidence intervals
viz.plot_forecast(
    actual=test_data,
    forecast=forecast,
    confidence_intervals=(0.80, 0.95)
)

5. Model Comparison Visualizations

Compare Multiple Models:

from specweave import ModelComparisonVisualizer

viz = ModelComparisonVisualizer(increment="0042")

# Compare metrics across models
viz.plot_model_comparison(
    models=['Baseline', 'XGBoost', 'LightGBM', 'Neural Net'],
    metrics={
        'accuracy': [0.65, 0.87, 0.86, 0.85],
        'roc_auc': [0.70, 0.92, 0.91, 0.90],
        'training_time': [1, 45, 32, 320]
    }
)

# ROC curves for multiple models
viz.plot_roc_curves_comparison(
    models_predictions={
        'XGBoost': (y_test, y_proba_xgb),
        'LightGBM': (y_test, y_proba_lgbm),
        'Neural Net': (y_test, y_proba_nn)
    }
)

Interactive Visualizations

Plotly Integration:

from specweave import InteractiveVisualizer

viz = InteractiveVisualizer(increment="0042")

# Interactive scatter plot (zoom, pan, hover)
viz.plot_interactive_scatter(
    x=X_test[:, 0],
    y=X_test[:, 1],
    colors=y_pred,
    hover_data=df[['id', 'amount', 'merchant']]
)

# Interactive confusion matrix (click for details)
viz.plot_interactive_confusion_matrix(
    y_true=y_test,
    y_pred=y_pred
)

# Interactive feature importance (sortable, filterable)
viz.plot_interactive_feature_importance(
    feature_names=feature_names,
    importances=importances
)

Business Reporting

Automated ML Report:

from specweave import MLReportGenerator

generator = MLReportGenerator(increment="0042")

# Generate executive summary report
report = generator.generate_report(
    model=model,
    test_data=(X_test, y_test),
    business_metrics={
        'false_positive_cost': 5,
        'false_negative_cost': 500
    }
)

# Creates:
# - Executive summary (1 page, non-technical)
# - Key metrics (accuracy, precision, recall)
# - Business impact ($$ saved, ROI)
# - Model performance visualizations
# - Recommendations
# - Technical appendix

Report Output (HTML/PDF):

# Fraud Detection Model - Executive Summary

## Key Results
- **Accuracy**: 87% (target: >85%) ✅
- **Fraud Detection Rate**: 62% (catching 310 frauds/day)
- **False Positive Rate**: 38% (190 false alarms/day)

## Business Impact
- **Fraud Prevented**: $155,000/day
- **Review Cost**: $950/day (190 transactions × $5)
- **Net Benefit**: $154,050/day ✅
- **Annual Savings**: $56.2M

## Model Performance
[Confusion Matrix Visualization]
[ROC Curve]
[Feature Importance]

## Recommendations
1. ✅ Deploy to production immediately
2. Monitor fraud patterns weekly
3. Retrain model monthly with new data

Dashboard Creation

Real-Time Dashboard:

from specweave import DashboardCreator

creator = DashboardCreator(increment="0042")

# Create Grafana/Plotly dashboard
dashboard = creator.create_dashboard(
    title="Model Performance Dashboard",
    panels=[
        {'type': 'metric', 'query': 'prediction_latency_p95'},
        {'type': 'metric', 'query': 'predictions_per_second'},
        {'type': 'timeseries', 'query': 'accuracy_over_time'},
        {'type': 'timeseries', 'query': 'error_rate'},
        {'type': 'heatmap', 'query': 'prediction_distribution'},
        {'type': 'table', 'query': 'recent_anomalies'}
    ]
)

# Exports to Grafana JSON or Plotly Dash app
dashboard.export(format='grafana')

Visualization Best Practices

1. Publication-Quality Plots

# Set consistent styling
visualizer.set_style(
    style='seaborn',  # Or 'ggplot', 'fivethirtyeight'
    context='paper',  # Or 'notebook', 'talk', 'poster'
    palette='colorblind'  # Accessible colors
)

# High-resolution exports
visualizer.save_figure(
    filename='model_performance.png',
    dpi=300,  # Publication quality
    bbox_inches='tight'
)

2. Accessible Visualizations

# Colorblind-friendly palettes
visualizer.use_colorblind_palette()

# Add alt text for accessibility
visualizer.add_alt_text(
    plot=fig,
    description="Confusion matrix showing 87% accuracy"
)

# High contrast for presentations
visualizer.set_high_contrast_mode()

3. Annotation and Context

# Add reference lines
viz.add_reference_line(
    y=0.85,  # Target accuracy
    label='Target',
    color='red',
    linestyle='--'
)

# Add annotations
viz.annotate_point(
    x=optimal_threshold,
    y=optimal_f1,
    text='Optimal threshold: 0.47'
)

Integration with SpecWeave

Automated Visualization in Increments

# All visualizations auto-saved to increment folder
visualizer = EDAVisualizer(increment="0042")

# Creates:
# .specweave/increments/0042-fraud-detection/
# ├── visualizations/
# │   ├── eda/
# │   │   ├── distributions.png
# │   │   ├── correlation_heatmap.png
# │   │   └── missing_values.png
# │   ├── model_performance/
# │   │   ├── confusion_matrix.png
# │   │   ├── roc_curve.png
# │   │   ├── precision_recall.png
# │   │   └── learning_curves.png
# │   ├── feature_analysis/
# │   │   ├── feature_importance.png
# │   │   ├── shap_summary.png
# │   │   └── partial_dependence/
# │   └── reports/
# │       ├── executive_summary.html
# │       └── technical_report.pdf

Living Docs Integration

/sw:sync-docs update

Updates:

<!-- .specweave/docs/internal/architecture/ml-model-performance.md -->

## Fraud Detection Model Performance (Increment 0042)

### Model Accuracy
![Confusion Matrix](../../../increments/0042-fraud-detection/visualizations/confusion_matrix.png)

### Key Metrics
- Accuracy: 87%
- Precision: 85%
- Recall: 62%
- ROC AUC: 0.92

### Feature Importance
![Top Features](../../../increments/0042-fraud-detection/visualizations/feature_importance.png)

Top 5 features:
1. amount_vs_user_average (0.18)
2. days_since_last_purchase (0.12)
3. merchant_risk_score (0.10)
4. velocity_24h (0.08)
5. location_distance_from_home (0.07)

Commands

# Generate EDA report
/ml:visualize-eda 0042

# Generate model performance report
/ml:visualize-performance 0042

# Create interactive dashboard
/ml:create-dashboard 0042

# Export all visualizations
/ml:export-visualizations 0042 --format png,pdf,html

Advanced Features

1. Automated Report Generation

# Generate full increment report with all visualizations
generator = IncrementReportGenerator(increment="0042")

report = generator.generate_full_report()

# Includes:
# - EDA visualizations
# - Experiment comparisons
# - Best model performance
# - Feature importance
# - Business impact
# - Deployment readiness

2. Custom Visualization Templates

# Create reusable templates
template = VisualizationTemplate(name="fraud_analysis")

template.add_panel("confusion_matrix")
template.add_panel("roc_curve")
template.add_panel("top_fraud_features")
template.add_panel("fraud_trends_over_time")

# Apply to any increment
template.apply(increment="0042")

3. Version Control for Visualizations

# Track visualization changes across model versions
viz_tracker = VisualizationTracker(increment="0042")

# Compare model v1 vs v2 visualizations
viz_tracker.compare_versions(
    version_1="model-v1",
    version_2="model-v2"
)

# Shows: Confusion matrix improved, ROC curve comparison, etc.

Summary

Data visualization is critical for:

✅ Exploratory data analysis (understand data before modeling)
✅ Model performance communication (stakeholder buy-in)
✅ Feature analysis (understand what drives predictions)
✅ Business reporting (translate metrics to impact)
✅ Model debugging (identify issues visually)

This skill automates visualization generation, ensuring all ML work is visual, accessible, and business-friendly within SpecWeave's increment workflow.

data-visualizer