Improve unit tests - reuse code via test fixtures, test behavoirs

[cdc-mitzimorris]

Summary

Replace coverage-driven tests with test of behavoir and correctness.

• Replace low-value tests (shape-only assertions, private method tests) with tests that validate real-world behavior
• Consolidate shared fixtures in conftest.py, remove 5 dead fixtures, add 4 reusable fixtures (gen_int_rv, hierarchical_normal_noise, hierarchical_normal_noise_tight, hierarchical_infections)
• Extract shared non-fixture test code (ConcreteMeasurements, create_mock_infections) to new test/test_helpers.py module
• Test base class no-ops and validation stubs in file test/test_interface_coverage.py

Changes by file

test/conftest.py

Removed 5 dead fixtures, added 4 shared fixtures.

Dead fixtures removed (never used by any test file):

• medium_delay_pmf
• counts_process_medium_delay
• counts_process_realistic
• simple_shedding_pmf
• medium_shedding_pmf

Dead fixtures removed (replaced by new shared fixtures):

• sensor_mode_rv, sensor_sd_rv
• sensor_mode_rv_tight, sensor_sd_rv_tight

New shared fixtures added:

• gen_int_rv -- COVID-like 7-day generation interval
• hierarchical_normal_noise -- standard HierarchicalNormalNoise with VectorizedRV
• hierarchical_normal_noise_tight -- tight variance version for near-deterministic testing
• hierarchical_infections -- pre-configured HierarchicalInfections instance

test/test_helpers.py (new file)

Shared non-fixture test code extracted here (since test/ is a package, conftest.py cannot be directly imported by test modules):

• ConcreteMeasurements class (concrete implementation of abstract Measurements for testing)
• create_mock_infections() function (generates spike, constant, or decay infection curves)

test/test_temporal_processes.py

Added:

• test_ar1_mean_reversion -- verifies AR1 trajectories revert toward zero
• test_differenced_ar1_trend_persistence -- verifies DifferencedAR1 produces persistent trends

test/test_hierarchical_priors.py

Strengthened:

• Shape-only tests now include value assertions (centering near zero for Normal/StudentT, positivity for Gamma)

test/test_hierarchical_infections.py

Added:

• test_infections_are_positive -- verifies all infection values are positive (epidemiological invariant)
• test_shape_and_positivity_across_subpop_counts -- parametrized over K=1,3,6 with positivity and weighted-sum checks

Now uses conftest fixtures (gen_int_rv, hierarchical_infections) instead of local fixtures.

test/test_observation_measurements.py

Added:

• test_log_scale_correctness -- verifies output equals log(convolved infections) with known inputs
• test_sensor_bias_differences -- verifies hierarchical noise produces sensor-specific biases

Strengthened:

• test_sample_shape now also verifies sensor variation and log-scale output

test/test_observation_counts.py

Added:

• test_convolution_hand_computable -- 1000 infections on day 10 with PMF [0.5, 0.5] should produce 500 on days 10 and 11
• test_observation_passthrough -- providing obs=known_values returns those exact values
• test_poisson_mean_approximation -- mean of 50 Poisson samples approximates expected rate

Strengthened:

• test_zero_infections now asserts predicted == 0 (not just shape)
• test_small_infections now asserts plausible range on predicted values
• test_sample_returns_correct_shape now includes non-negative value assertions

test/test_pyrenew_builder.py

Added:

• test_prior_predictive_multi_signal -- uses numpyro.infer.Predictive to draw prior predictive samples from a builder-constructed model; verifies shapes and positivity

Strengthened:

• test_run_with_population_structure now also asserts all infection samples are positive

test/test_interface_coverage.py (new file)

Tests interface contracts (repr, validate(), infection_resolution(), get_required_lookback()) across implementing classes via parametrized tests

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 97.85%. Comparing base (d260ea5) to head (4f5534a).
⚠️ Report is 2 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #699      +/-   ##
==========================================
+ Coverage   97.73%   97.85%   +0.12%     
==========================================
  Files          51       51              
  Lines        1542     1542              
==========================================
+ Hits         1507     1509       +2     
+ Misses         35       33       -2     

Flag	Coverage Δ
unittests	97.85% <ø> (+0.12%)	⬆️

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[github-actions]

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[cdc-mitzimorris]

per code review from coderabbit (run by claude.ai):

Plan: Implement Valid Code Review Recommendations

Context

A code review identified issues in the PyRenew test suite on branch mem_690_unit_test_improvements. After critical evaluation, a subset of findings are valid and worth implementing. This plan addresses dead code, missing assertions, weak assertions, non-deterministic tests, missing unit tests, and unused fixtures.

Changes

1. Remove dead code in test/test_latent_infections.py

Lines 32-37: Delete the orphaned Infections()() call whose return value is discarded. The obs dict (lines 27-31) and inf1 (line 25) are already used correctly below.

2. Fix unseeded np.random in test/test_observation_negativebinom.py

Line 22: Replace np.random.randint(1, 5, size=10) with a fixed array like np.array([3, 1, 4, 2, 3, 1, 4, 2, 3, 1]) for deterministic test input.

3. Add assertions to test/test_infection_initialization_process.py

Lines 41-43: The loop samples 3 models but asserts nothing. Add assertions for each:

• All results have shape (n_timepoints,) i.e. (10,)
• zero_pad_model result has leading zeros (all but last element are 0)
• exp_model result is all positive (exponential growth from a LogNormal I0)
• vec_model result equals jnp.arange(n_timepoints) (identity passthrough)

4. Fix misleading docstring in test/test_pyrenew_builder.py

Line 347: Change docstring from "Test that validate() method calls _validate()." to "Test that validate() succeeds on a valid model." — the test only checks it doesn't raise.

5. Strengthen NaN-padding test in test/test_observation_counts.py

Lines 253-268 (test_dense_observations_with_nan_padding): Add assertion that the predicted values are non-NaN (the observation process should produce predictions for all
days regardless of NaN observations).

6. Strengthen negative binomial test in test/test_observation_negativebinom.py

Lines 36-58 (test_negativebinom_random_obs): Add assertion that the sample mean is close to the expected rate (5.0), not just that two samples agree with each other.

7. Add unit tests for pad_observations() and shift_times()

File: test/test_pyrenew_builder.py — add a new test class TestMultiSignalModelHelpers:

• test_pad_observations_prepends_nans: verifies correct NaN count, shape, float conversion of int input
• test_shift_times_adds_offset: verifies times are shifted by n_initialization_points

8. Clean up unused fixtures in test/conftest.py

Remove the 3 fixtures that have zero usage across the test suite:

• realistic_delay_pmf (lines 53-63)
• constant_infections (lines 256-266)
• constant_infections_2d (lines 269-279)

Files to Modify

1. test/test_latent_infections.py — remove dead code
2. test/test_observation_negativebinom.py — fix seed, strengthen assertion
3. test/test_infection_initialization_process.py — add assertions
4. test/test_pyrenew_builder.py — fix docstring, add helper tests
5. test/test_observation_counts.py — strengthen NaN test
6. test/conftest.py — remove unused fixtures

Verification

Run python -m pytest test/ -x -q to confirm all tests pass after changes.