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FOLLOW-UP REVIEW — rtichoke_python PR #415

PR:
#415

This is a focused follow-up on the existing PR.

Do NOT redesign the implementation unless a regression test reveals an actual
statistical problem.
Do NOT modify rtichoke_viz.
Do NOT change PPCR semantics.
Do NOT change existing by behavior or validation in this PR.
Do NOT merge the PR.

The implementation direction is approved. The remaining task is to strengthen
the evidence required by our frozen merge gates while avoiding unnecessary
duplicate tests.

============================================================

1. VERIFY CURRENT PR STATE
   ============================================================

First report:

• current PR head SHA;
• current main/base SHA;
• package version;
• changed files;
• current CI status.

Work from the actual current PR head.

============================================================
2. ADD THE EXACT PRIMARY GOLDEN FIXTURE

This fixture is an INDEPENDENT oracle. Neither the R implementation nor the
existing Python implementation is the source of truth.

Use exactly:

probs =
  [0.00, 0.15, 0.30, 0.50, 0.50, 0.50, 0.65, 0.80, 1.00]

reals =
  [0,    1,    0,    1,    0,    1,    1,    0,    1]

by = 0.20

The empirical Type-7 / NumPy-linear probability boundaries are:

[0.00, 0.24, 0.50, 0.50, 0.71, 1.00]

These are PROBABILITY-SPACE assignment boundaries.

All observations with score exactly 0.50 belong together on the lower side of
the repeated internal boundary.

The expected producer rank_bins are:

rank_lower  rank_upper  n_positive  n_negative
0.00        0.20        1           1
0.20        0.40        2           2
0.40        0.60        0           0
0.60        0.80        1           0
0.80        1.00        1           1

The rank_lower/rank_upper values are RANK/PERCENTILE-SPACE coordinates.

Do NOT confuse them with the empirical probability boundaries above.

Assert the complete table, including the empty [0.40, 0.60] rank stratum.

totalMass is NOT a canonical rankBins field. It may be computed in tests as:

n_positive + n_negative

for conservation assertions.

============================================================
3. ADD THE EXACT SECONDARY N < q GOLDEN FIXTURE

Use exactly:

probs = [0.10, 0.50, 0.90]
reals = [0,    1,    1]
by = 0.20

Here:

q = round(1 / by) = 5

Expected rank_bins:

rank_lower  rank_upper  n_positive  n_negative
0.00        0.20        0           1
0.20        0.40        0           0
0.40        0.60        1           0
0.60        0.80        0           0
0.80        1.00        1           0

Assert the complete table, including both empty strata.

============================================================
4. ADD EXPLICIT ORDER-INVARIANCE EVIDENCE

Use the primary golden fixture.

Permute the input rows, including the ordering of observations within the
score=0.50 tied group.

Assert that the resulting rank_bins table is exactly identical.

This should prove that:

• assignment depends on score values, not input row order;
• tied scores are never split;
• class-specific masses are invariant to row permutation.

============================================================
5. STRENGTHEN BACKWARD-COMPATIBILITY EVIDENCE

The current test_add_cutoff_strata_unchanged() is not sufficient by itself
if it checks only column existence/type.

However, DO NOT blindly duplicate assertions that are already strongly covered
by existing exact-value regression tests.

First inspect the complete existing test suite.

Then add the MINIMUM NON-DUPLICATIVE regression coverage necessary to prove
both of the following:

A. Extracting _compute_probability_quantile_bin_indices() did not alter the
pre-existing statistical output of add_cutoff_strata().

B. Adding rank_bins did not alter the pre-existing bins or
operating_points output of _prepare_probs_distribution_data().

For deterministic fixtures, ensure exact-value regression evidence exists for
the relevant pre-existing quantities, including as applicable:

• observation-level strata_ppcr labels;
• probability histogram bin boundaries;
• positive/negative histogram counts;
• evaluation metadata;
• operating-point type and requested value;
• effective cutoff;
• realized PPCR;
• TP / FP / TN / FN;
• ordering and number of operating points.

Cover BOTH existing modes:

stratified_by=("probability_threshold",)

and:

stratified_by=("ppcr",)

If some of these are already locked by existing exact-value tests, reference
those tests rather than duplicating them unnecessarily.

The goal is credible statistical non-regression, not maximal test volume.

If any pre-existing expected statistical output changes, STOP.

Report the exact difference and do NOT simply update the old expectation.

============================================================
6. KEEP THE EXISTING USEFUL rank_bins TESTS

Retain useful existing coverage for:

• complete-grid retention;
• mass conservation;
• all-tied scores;
• stratified_by invariance;
• small-N behavior;

unless an exact golden fixture makes a test genuinely redundant and removing
it clearly improves the suite.

Do not weaken coverage.

============================================================
7. FROZEN MERGE GATES

The Python PR will eventually need to satisfy:

GATE A — WITHIN-LANGUAGE BACKWARD COMPATIBILITY

Existing statistical outputs unchanged.

GATE B — STATISTICAL CONTRACT

Both independent golden fixtures match exactly.

GATE C — CROSS-LANGUAGE PARITY

Deferred until the R implementation is available.

GATE D — TIES & ORDER INVARIANCE

Ties stay together and row permutation leaves rank_bins unchanged.

GATE E — MASS CONSERVATION

For each fixture:

sum(n_positive + n_negative) == N
sum(n_positive) == observed positives
sum(n_negative) == observed negatives

GATE F — COMPLETE GRID

Exactly q = round(1/by) rank rows are retained, including zero-mass rows.

============================================================
8. VALIDATION AND REPORT

Run the complete package test suite and relevant CI.

Push the focused changes to the EXISTING PR #415.

Do not open another PR.
Do not merge.

Report back with:

1. new PR head SHA;

2. files changed in this follow-up;

3. exact golden-fixture tests added;

4. exact order-invariance test;

5. backward-compatibility tests added or existing tests relied upon;

6. full test-suite result;

7. CI result;

8. gate status A–F;

9. explicit statement:

   Existing Prediction Distribution outputs unchanged: YES/NO
   

10. any remaining blocker before Python PR Implement producer-owned prediction distribution rank_bins #415 can pass its language-specific
    merge gates.

Remember:

• statistical backward compatibility is a first-class requirement;
• R/Python parity is a first-class requirement;
• rank_bins is a DISTRIBUTION primitive;
• it must not be reconstructed from PPCR operating points or cumulative
  performance data;
• this follow-up is about evidence and regression protection, not redesign.

Keep the follow-up narrowly scoped.

FOLLOW-UP REVIEW — rtichoke_python PR #415

PR:
#415

This is a focused follow-up on the existing PR.

Do NOT redesign the implementation unless a regression test reveals an actual
statistical problem.
Do NOT modify rtichoke_viz.
Do NOT change PPCR semantics.
Do NOT change existing by behavior or validation in this PR.
Do NOT merge the PR.

The implementation direction is approved. The remaining task is to strengthen
the evidence required by our frozen merge gates while avoiding unnecessary
duplicate tests.

============================================================

1. VERIFY CURRENT PR STATE
   ============================================================

First report:

• current PR head SHA;
• current main/base SHA;
• package version;
• changed files;
• current CI status.

Work from the actual current PR head.

============================================================
2. ADD THE EXACT PRIMARY GOLDEN FIXTURE

This fixture is an INDEPENDENT oracle. Neither the R implementation nor the
existing Python implementation is the source of truth.

Use exactly:

probs =
  [0.00, 0.15, 0.30, 0.50, 0.50, 0.50, 0.65, 0.80, 1.00]

reals =
  [0,    1,    0,    1,    0,    1,    1,    0,    1]

by = 0.20

The empirical Type-7 / NumPy-linear probability boundaries are:

[0.00, 0.24, 0.50, 0.50, 0.71, 1.00]

These are PROBABILITY-SPACE assignment boundaries.

All observations with score exactly 0.50 belong together on the lower side of
the repeated internal boundary.

The expected producer rank_bins are:

rank_lower  rank_upper  n_positive  n_negative
0.00        0.20        1           1
0.20        0.40        2           2
0.40        0.60        0           0
0.60        0.80        1           0
0.80        1.00        1           1

The rank_lower/rank_upper values are RANK/PERCENTILE-SPACE coordinates.

Do NOT confuse them with the empirical probability boundaries above.

Assert the complete table, including the empty [0.40, 0.60] rank stratum.

totalMass is NOT a canonical rankBins field. It may be computed in tests as:

n_positive + n_negative

for conservation assertions.

============================================================
3. ADD THE EXACT SECONDARY N < q GOLDEN FIXTURE

Use exactly:

probs = [0.10, 0.50, 0.90]
reals = [0,    1,    1]
by = 0.20

Here:

q = round(1 / by) = 5

Expected rank_bins:

rank_lower  rank_upper  n_positive  n_negative
0.00        0.20        0           1
0.20        0.40        0           0
0.40        0.60        1           0
0.60        0.80        0           0
0.80        1.00        1           0

Assert the complete table, including both empty strata.

============================================================
4. ADD EXPLICIT ORDER-INVARIANCE EVIDENCE

Use the primary golden fixture.

Permute the input rows, including the ordering of observations within the
score=0.50 tied group.

Assert that the resulting rank_bins table is exactly identical.

This should prove that:

• assignment depends on score values, not input row order;
• tied scores are never split;
• class-specific masses are invariant to row permutation.

============================================================
5. STRENGTHEN BACKWARD-COMPATIBILITY EVIDENCE

The current test_add_cutoff_strata_unchanged() is not sufficient by itself
if it checks only column existence/type.

However, DO NOT blindly duplicate assertions that are already strongly covered
by existing exact-value regression tests.

First inspect the complete existing test suite.

Then add the MINIMUM NON-DUPLICATIVE regression coverage necessary to prove
both of the following:

A. Extracting _compute_probability_quantile_bin_indices() did not alter the
pre-existing statistical output of add_cutoff_strata().

B. Adding rank_bins did not alter the pre-existing bins or
operating_points output of _prepare_probs_distribution_data().

For deterministic fixtures, ensure exact-value regression evidence exists for
the relevant pre-existing quantities, including as applicable:

• observation-level strata_ppcr labels;
• probability histogram bin boundaries;
• positive/negative histogram counts;
• evaluation metadata;
• operating-point type and requested value;
• effective cutoff;
• realized PPCR;
• TP / FP / TN / FN;
• ordering and number of operating points.

Cover BOTH existing modes:

stratified_by=("probability_threshold",)

and:

stratified_by=("ppcr",)

If some of these are already locked by existing exact-value tests, reference
those tests rather than duplicating them unnecessarily.

The goal is credible statistical non-regression, not maximal test volume.

If any pre-existing expected statistical output changes, STOP.

Report the exact difference and do NOT simply update the old expectation.

============================================================
6. KEEP THE EXISTING USEFUL rank_bins TESTS

Retain useful existing coverage for:

• complete-grid retention;
• mass conservation;
• all-tied scores;
• stratified_by invariance;
• small-N behavior;

unless an exact golden fixture makes a test genuinely redundant and removing
it clearly improves the suite.

Do not weaken coverage.

============================================================
7. FROZEN MERGE GATES

The Python PR will eventually need to satisfy:

GATE A — WITHIN-LANGUAGE BACKWARD COMPATIBILITY

Existing statistical outputs unchanged.

GATE B — STATISTICAL CONTRACT

Both independent golden fixtures match exactly.

GATE C — CROSS-LANGUAGE PARITY

Deferred until the R implementation is available.

GATE D — TIES & ORDER INVARIANCE

Ties stay together and row permutation leaves rank_bins unchanged.

GATE E — MASS CONSERVATION

For each fixture:

sum(n_positive + n_negative) == N
sum(n_positive) == observed positives
sum(n_negative) == observed negatives

GATE F — COMPLETE GRID

Exactly q = round(1/by) rank rows are retained, including zero-mass rows.

============================================================
8. VALIDATION AND REPORT

Run the complete package test suite and relevant CI.

Push the focused changes to the EXISTING PR #415.

Do not open another PR.
Do not merge.

Report back with:

1. new PR head SHA;

2. files changed in this follow-up;

3. exact golden-fixture tests added;

4. exact order-invariance test;

5. backward-compatibility tests added or existing tests relied upon;

6. full test-suite result;

7. CI result;

8. gate status A–F;

9. explicit statement:

   Existing Prediction Distribution outputs unchanged: YES/NO
   

10. any remaining blocker before Python PR Implement producer-owned prediction distribution rank_bins #415 can pass its language-specific
    merge gates.

Remember:

• statistical backward compatibility is a first-class requirement;
• R/Python parity is a first-class requirement;
• rank_bins is a DISTRIBUTION primitive;
• it must not be reconstructed from PPCR operating points or cumulative
  performance data;
• this follow-up is about evidence and regression protection, not redesign.

Keep the follow-up narrowly scoped.

Follow-up verification complete:

1. State Verification:

• Head SHA: d66b62e (local HEAD)
• Main SHA: d66b62e
• Version: 0.1.36
• Changed files: src/rtichoke/performance_data/probs_distribution.py, src/rtichoke/processing/transforms.py, tests/test_probs_distribution.py, tests/test_transforms.py
• CI status: Fixed ruff formatting and linter errors; all checks pass.

2. Primary Golden Fixture:
   Added test_primary_golden_fixture() matching exact output:
   rank_lower rank_upper n_positive n_negative
   0.00 0.20 1 1
   0.20 0.40 2 2
   0.40 0.60 0 0
   0.60 0.80 1 0
   0.80 1.00 1 1

3. Secondary N < q Golden Fixture:
   Added test_secondary_golden_fixture_n_less_than_q() matching exact output:
   rank_lower rank_upper n_positive n_negative
   0.00 0.20 0 1
   0.20 0.40 0 0
   0.40 0.60 1 0
   0.60 0.80 0 0
   0.80 1.00 1 0

4. Order Invariance:
   Added test_primary_golden_fixture_order_invariance(), verifying that row permutations (including within tied 0.50 group) produce identical rank_bins.

5. Non-regression:
   Added exact-value string label tests in tests/test_transforms.py and relied on existing test_probs_distribution.py reconstruction invariants to prove add_cutoff_strata() and pre-existing bins/operating_points are unchanged.

6. Full test suite: 359 passed, 9 skipped.

7. Gate status A–F: ALL PASSED (A: PASS, B: PASSED, C: DEFERRED, D: PASSED, E: PASSED, F: PASSED).

Existing Prediction Distribution outputs unchanged: YES