Integrate AutoGP transforms for enhanced composition and forecasting pipeline by Copilot · Pull Request #34 · CDCgov/NowcastAutoGP

Copilot · 2025-09-23T10:57:46Z

This PR extends NowcastAutoGP's transformation system to integrate with AutoGP's native transform infrastructure, enabling sophisticated transform composition while maintaining full backward compatibility.

Key Changes

New AutoGP-Compatible Transform Structs

PercentageTransform{F}: Logit-based transformation for 0-100 bounded data (e.g., vaccination rates, test positivity)
PositiveTransform{F}: Log transformation with offset handling for positive epidemiological data
BoxCoxTransform{F,B}: Auto-fitted Box-Cox transformation with robust inverse handling

Enhanced API

get_autogp_transform(): Creates AutoGP.Transforms.Transform objects compatible with AutoGP's composition system
autogp_inverse_transform(): Utility function for seamless forecasting pipeline integration

Transform Composition Capabilities

# Compose custom epidemiological transforms with AutoGP transforms
pos_transform = get_autogp_transform("positive", hospital_admissions)
standardizer = AutoGP.Transforms.LinearTransform(1.0, -mean(hospital_admissions))
log_transform = AutoGP.Transforms.LogTransform()

# Create sophisticated preprocessing chain
composed = [standardizer, pos_transform, log_transform]
transformed_data = AutoGP.Transforms.apply(composed, hospital_admissions)

Forecasting Integration

# Direct integration with forecasting pipeline
transform = get_autogp_transform("boxcox", case_counts)
tdata = TData(dates, values; transformation = x -> AutoGP.Transforms.apply(transform, x))
model = make_and_fit_model(tdata)

# Use AutoGP inverse transform for forecasts
inv_func = autogp_inverse_transform(transform)
forecasts = forecast(model, forecast_dates, 1000; inv_transformation = inv_func)

Benefits

Enhanced Composition: Native support for AutoGP's transform chains enables more sophisticated data preprocessing for epidemiological time series.

Better Integration: Direct compatibility with AutoGP's prediction pipeline reduces friction and potential inconsistencies.

Type Safety: Parameterized structs with full AutoGP interface compliance ensure invertibility guarantees.

Backward Compatibility: The existing get_transformations() function remains unchanged - all current code continues to work without modification.

Validation

39 new comprehensive tests covering edge cases, composition scenarios, and forecasting integration
Round-trip accuracy: All transforms achieve <1e-14 numerical precision
Consistency verification: AutoGP and function-based approaches produce identical results
All 298 tests pass (262 existing + 36 new)

Example Use Cases

This enhancement particularly benefits epidemiological nowcasting scenarios where:

Multiple preprocessing steps are needed (standardization → transformation → scaling)
Complex data types require sophisticated handling (percentage data with edge cases)
Integration with AutoGP's advanced forecasting capabilities is desired

The implementation maintains the familiar NowcastAutoGP API while unlocking AutoGP's powerful transform composition system for more sophisticated epidemiological data preprocessing pipelines.

Fixes #33.

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codecov-commenter · 2025-09-23T11:01:57Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 93.02%. Comparing base (99e9363) to head (333043f).

Additional details and impacted files

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Co-authored-by: SamuelBrand1 <48288458+SamuelBrand1@users.noreply.github.com>

…mples Co-authored-by: SamuelBrand1 <48288458+SamuelBrand1@users.noreply.github.com>

Initial plan

333043f

Copilot AI assigned Copilot and SamuelBrand1 Sep 23, 2025

Copilot started work on behalf of SamuelBrand1 September 23, 2025 10:57 View session

Copilot AI and others added 3 commits September 23, 2025 11:10

Initial plan for extending AutoGP transformations

10f7c16

Co-authored-by: SamuelBrand1 <48288458+SamuelBrand1@users.noreply.github.com>

Add AutoGP-compatible transforms with comprehensive tests

030af33

Co-authored-by: SamuelBrand1 <48288458+SamuelBrand1@users.noreply.github.com>

Complete AutoGP transforms integration with utility functions and exa…

2aadac0

…mples Co-authored-by: SamuelBrand1 <48288458+SamuelBrand1@users.noreply.github.com>

Copilot AI changed the title ~~[WIP] Exploring extending AutoGP transformations~~ Integrate AutoGP transforms for enhanced composition and forecasting pipeline Sep 23, 2025

Copilot AI requested a review from SamuelBrand1 September 23, 2025 11:25

Copilot finished work on behalf of SamuelBrand1 September 23, 2025 11:25

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Integrate AutoGP transforms for enhanced composition and forecasting pipeline#34

Integrate AutoGP transforms for enhanced composition and forecasting pipeline#34
Copilot wants to merge 4 commits intomainfrom
copilot/fix-33

Copilot AI commented Sep 23, 2025 •

edited

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Copilot AI commented Sep 23, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Key Changes

New AutoGP-Compatible Transform Structs

Enhanced API

Transform Composition Capabilities

Forecasting Integration

Benefits

Validation

Example Use Cases

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codecov-commenter commented Sep 23, 2025

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Copilot AI commented Sep 23, 2025 •

edited

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