Rhino Health SDK — Metrics Guide

Help configure and run any of the 40+ federated metrics in the rhino-health Python SDK (v2.1.x).

Context Loading

Before responding, read these reference files:

1. Metrics Reference — ../../context/metrics_reference.md All metric classes with parameters, import paths, categories, and the Quick Decision Guide.

2. Patterns & Gotchas — ../../context/patterns_and_gotchas.md Focus on §4 (Per-site vs Aggregated Metrics), §5 (Filtering), §6 (Group By), and §7 (Federated Joins).

Metric Decision Tree

Map the user's question to the right metric class:

If unsure, consult the full Quick Decision Guide in metrics_reference.md.

Execution Mode

Choose the right execution method based on scope:

All metrics are imported from rhino_health.lib.metrics (NOT rhino_health.metrics).

Filtering

Apply filters to narrow the data before computing the metric. Two approaches:

Inline FilterVariable (for simple per-column filters)

from rhino_health.lib.metrics import Mean, FilterType, FilterVariable

config = Mean(
    variable="Height",
    data_filters=[
        FilterVariable(
            data_column="Gender",
            filter_column="Gender",
            filter_value="Female",
            filter_type=FilterType.EQUALS,
        )
    ],
)

Range Filter (BETWEEN)

from rhino_health.lib.metrics import FilterBetweenRange, FilterType, FilterVariable

config = Mean(
    variable="Height",
    data_filters=[
        FilterVariable(
            data_column="Age",
            filter_column="Age",
            filter_value=FilterBetweenRange(min=18, max=65),
            filter_type=FilterType.BETWEEN,
        )
    ],
)

Grouping

Split results by one or more categorical columns:

config = Mean(
    variable="Height",
    group_by={"groupings": ["Gender"]},
)

Grouping and filtering can be combined on any metric.

Response Format

Structure every response as:

1. Recommended metric — which class and why it fits the user's question
2. Configuration — complete metric config object with correct import path
3. Execution call — the right method (get_dataset_metric, aggregate_dataset_metric, or joined_dataset_metric) with correct parameter types
4. Filtering/grouping — if the user specified subsets or breakdowns, add the appropriate data_filters and/or group_by
5. See also — point to a matching example from ../../context/examples/INDEX.md if one exists

Working Examples

Check ../../context/examples/INDEX.md for matching examples. Key ones for metrics:

• eda.py — per-site and aggregated metrics with filtering and grouping
• cox.py — Cox proportional hazard regression
• metrics_examples.py — TwoByTwoTable, OddsRatio, ChiSquare, TTest, ANOVA
• roc_analysis.py — ROC curves and confidence intervals
• aggregate_quantile.py — federated percentile calculations