SVG Template Gallery
Explore all 49 SVG visualization templates covering fairness analysis, reporting, experimentation, and workflow integration in the vfairness rendering module. Click any template for a detailed preview with code examples.
The vfairness.rendering module provides polished, card-based SVG dashboards
using Jinja2 templates. Zero dependencies beyond Python stdlib — auto-upgrades to Jinja2 when available.
Preprocessing & Feature Engineering
Bias detection and feature analysis visualizations
Training-Time Interventions
Fairness-aware training analysis and method comparison
Post-Processing Interventions
Threshold optimization and prediction reweighting
Calibration Visualizations
Group-specific calibration analysis and reliability diagrams
Evaluation & Metrics
Fairness metrics visualization and statistical analysis
Monitoring & Operations
Real-time fairness monitoring, drift detection, and alert visualizations
Reporting & Dashboards
Interactive dashboards, automated reports, and metrics storage for stakeholder communication
Experimentation & A/B Testing
Fairness-aware A/B testing, power analysis, Pareto optimization, and causal decomposition
Workflow Integration
MLOps, CI/CD gates, and PR report cards
Complete Template Reference
All 49 SVG templates in vfairness.rendering plus interactive components from vfairness.operations.reporting and vfairness.operations.experimentation:
| Template | Adapter Function | Category | Description |
|---|---|---|---|
bias_audit.svg | bias_audit_to_svg() | Preprocessing | Bias detection dashboard |
correlation_heatmap.svg | correlation_heatmap_to_svg() | Preprocessing | Feature correlation matrix |
correlation_matrix.svg | correlation_matrix_to_svg() | Preprocessing | General multi-method correlation matrix |
proxy_risk.svg | proxy_risk_to_svg() | Preprocessing | Proxy variable detection |
transformation_comparison.svg | transformation_comparison_to_svg() | Preprocessing | Feature transformation impact |
intersectional_analysis.svg | intersectional_analysis_to_svg() | Preprocessing | Subgroup analysis |
fairness_report.svg | fairness_report_to_svg() | Preprocessing | Summary dashboard |
data_validation.svg | data_validation_to_svg() | Preprocessing | Pre-training data validation |
auto_discovery.svg | auto_discovery_to_svg() | Preprocessing | Protected attribute scanner |
training_report.svg | training_report_to_svg() | Training | Compact training report |
training_analysis_report.svg | training_analysis_report_to_svg() | Training | Full training analysis |
method_comparison.svg | method_comparison_to_svg() | Training | Method bar chart |
tradeoff_analysis.svg | tradeoff_analysis_to_svg() | Training | Pareto scatter plot |
threshold_optimization_report.svg | threshold_optimization_to_svg() | Post-Processing | Threshold dashboard |
reweighting_comparison_report.svg | reweighting_comparison_to_svg() | Post-Processing | Reweighting methods |
cicd_pipeline.svg | cicd_pipeline_to_svg() | Post-Processing | Deployment gate |
calibration_report.svg | calibration_report_to_svg() | Calibration | Calibration dashboard |
reliability_diagram.svg | reliability_diagram_to_svg() | Calibration | Classic reliability plot |
group_calibration.svg | group_calibration_to_svg() | Calibration | Per-group calibration |
calibration_disparity.svg | calibration_disparity_to_svg() | Calibration | Calibration gap |
pareto_frontier.svg | pareto_frontier_to_svg() | Calibration | Trade-off frontier |
fairness_detailed_report.svg | fairness_detailed_report_to_svg() | Evaluation | Executive report |
radar_chart.svg | radar_chart_to_svg() | Evaluation | Multi-metric radar |
disparity_heatmap.svg | disparity_heatmap_to_svg() | Evaluation | Pairwise disparities |
metrics_bar_chart.svg | metrics_bar_chart_to_svg() | Evaluation | Metrics comparison |
group_comparison.svg | group_comparison_to_svg() | Evaluation | Group metrics |
effect_sizes.svg | effect_sizes_to_svg() | Evaluation | Effect magnitudes |
confidence_intervals.svg | confidence_intervals_to_svg() | Evaluation | Statistical CIs |
robustness_testing.svg | robustness_testing_to_svg() | Evaluation | Robustness & sensitivity testing |
ranking_fairness.svg | ranking_fairness_to_svg() | Evaluation | Ranking fairness metrics |
regression_fairness.svg | regression_fairness_to_svg() | Evaluation | Regression equity report |
monitoring_dashboard.svg | monitoring_dashboard_to_svg() | Monitoring | Live fairness dashboard |
drift_report.svg | drift_report_to_svg() | Monitoring | Multi-scale drift report |
alert_timeline.svg | alert_timeline_to_svg() | Monitoring | Alert history timeline |
temporal_analysis.svg | temporal_analysis_to_svg() | Monitoring | Trend & pattern analysis |
experiment_results.svg | experiment_results_to_svg() | Experimentation | A/B test results & forest plot |
experiment_recommendation.svg | experiment_recommendation_to_svg() | Experimentation | Deployment recommendation |
power_analysis.svg | power_analysis_to_svg() | Experimentation | Statistical power analysis |
causal_decomposition.svg | causal_decomposition_to_svg() | Experimentation | Causal mediation analysis |
workflow_overview.svg | workflow_overview_to_svg() | Workflow | Development workflow pipeline |
hierarchical_gate.svg | hierarchical_gate_to_svg() | Workflow | Hierarchical fairness gate |
report_card.svg | report_card_to_svg() | Workflow | PR fairness report card |
reporting_dashboard_executive.svg | reporting_dashboard_to_svg(tier="executive") | Reporting | Executive tier — green, board-level |
reporting_dashboard_operational.svg | reporting_dashboard_to_svg(tier="operational") | Reporting | Operational tier — yellow, engineer view |
reporting_dashboard_technical.svg | reporting_dashboard_to_svg(tier="technical") | Reporting | Technical tier — red, full audit |
MetricsStore | MetricsStore() | Reporting | Unified metrics data layer |
FairnessDashboard | FairnessDashboard() | Reporting | Interactive Plotly dashboard |
ReportGenerator | ReportGenerator() | Reporting | Automated multi-format reports |
FairnessExperiment | FairnessExperiment() | Experimentation | Fairness A/B testing |
FairnessPowerAnalyzer | FairnessPowerAnalyzer() | Experimentation | Power analysis & SPRT |
ExperimentAnalysis | ExperimentAnalysis() | Experimentation | Pareto & causal analysis |
Quick Usage
from vfairness.rendering import (
render_svg, list_templates,
# Report dashboards
bias_audit_to_svg, calibration_report_to_svg, fairness_report_to_svg,
# Feature engineering
correlation_matrix_to_svg,
# Training
training_report_to_svg, training_analysis_report_to_svg,
# Post-processing
threshold_optimization_to_svg, reweighting_comparison_to_svg,
# Evaluation
fairness_detailed_report_to_svg, radar_chart_to_svg,
# Monitoring
monitoring_dashboard_to_svg, drift_report_to_svg,
alert_timeline_to_svg, temporal_analysis_to_svg,
# Reporting
reporting_dashboard_to_svg,
# Experimentation
experiment_results_to_svg, experiment_recommendation_to_svg,
power_analysis_to_svg,
# Workflow integration
workflow_overview_to_svg,
hierarchical_gate_to_svg,
report_card_to_svg,
)
# List all available templates
print(list_templates())
# Render using adapter functions
svg = training_analysis_report_to_svg(report, save_path='report.svg')
# Or use render_svg directly
svg = render_svg('radar_chart', {'metrics': [...], 'groups': [...]})
# ── Reporting ──────────────────────────────────────────────
from vfairness.operations.reporting import (
MetricsStore, FairnessDashboard, ReportGenerator, InteractiveDashboard
)
store = MetricsStore()
store.ingest_from_monitor(monitor) # Feed from monitoring
dashboard = FairnessDashboard(store)
fig = dashboard.create_executive_view() # Plotly figure
gen = ReportGenerator(store, dashboard)
report = gen.generate_executive_report() # HTML / PDF / JSON
# ── Experimentation ────────────────────────────────────────
from vfairness.operations.experimentation import (
FairnessExperiment, FairnessPowerAnalyzer, ExperimentAnalysis
)
exp = FairnessExperiment(
control_data=df_ctrl, treatment_data=df_treat,
protected_attributes=['gender', 'race'], outcome_column='approved',
)
result = exp.run_full_analysis()
analysis = ExperimentAnalysis(result, experiment=exp)
rec = analysis.decision_recommendation() # Deploy / hold / revert