Util weight step2 by henrydingliu · Pull Request #995 · casact/chainladder-python

henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

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cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

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henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

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cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
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henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

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cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

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henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

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❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

henrydingliu · 2026-06-15T23:08:56Z

Summary of Changes

Related GitHub Issue(s)

Additional Context for Reviewers

I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

Note

Medium Risk
Refactors how LDF and ML fitting masks cells across three estimators; IncrementalAdditive now exposes tie-breaking via sample_weight, so weights could differ from the previous _set_weight_func calls if logic is not perfectly aligned.

Overview
Development, IncrementalAdditive, and DevelopmentML now build pattern-selection weights via the shared TriangleWeight helper instead of calling DevelopmentBase._set_weight_func (or ad hoc drop helpers in ML).

In Development.fit, w_v2_ is set with TriangleWeight(...).fit(...).w_ on the age-to-age triangle (optionally multiplied by X.w_v2_). Regression still uses the existing self.w_ path (_assign_n_periods_weight × _drop_adjustment).

IncrementalAdditive constructs the same TriangleWeight from its drop/n_periods params and sets w_tri_ via fit, passing sample_weight=X_trended so high/low drops can use secondary tie-breaking.

DevelopmentML._prep_w_ml applies drop / drop_valuation through TriangleWeight.fit(X).w_ rather than inline _drop_func / _drop_valuation_func.

TriangleWeight.fit always delegates to _set_weight_func(X=..., secondary_rank=sample_weight), dropping the old branch that treated X.w_ specially inside fit.

^{Reviewed by Cursor Bugbot for commit 512d3fc. Bugbot is set up for automated code reviews on this repo. Configure here.}

moving internal API to TriangleWeight

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

cursor · 2026-06-15T23:10:01Z

- if self.drop_valuation is not None:
- weight = weight * self._drop_valuation_func(X)
+ weight = weight_base.copy()
+ weight1 = weight_base.copy()


Unused weight1 assignment
Low Severity
_prep_w_ml assigns weight1 = weight_base.copy() but never reads weight1. That line looks like leftover scaffolding from the refactor to TriangleWeight and adds noise without affecting behavior.
^{Reviewed by Cursor Bugbot for commit 512d3fc. Configure here.}

codecov · 2026-06-15T23:14:12Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.58%. Comparing base (60cabb2) to head (512d3fc).

Additional details and impacted files

@@ Coverage Diff @@## #932_triangle_weight #995 +/- ##
========================================================
- Coverage 88.53% 87.58% -0.95% 
========================================================
Files 89 89 Lines 5189 5188 -1 Branches 658 655 -3 ========================================================
- Hits 4594 4544 -50 - Misses 442 451 +9 - Partials 153 193 +40

Flag	Coverage Δ
unittests	`87.58% <100.00%> (-0.95%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
8 checks passed

cursorBot reviewed Jun 15, 2026
View reviewed changes

henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
8 checks passed

cursorBot reviewed Jun 15, 2026
View reviewed changes

henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
8 checks passed

cursorBot reviewed Jun 15, 2026
View reviewed changes

henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
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henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
8 checks passed

cursorBot reviewed Jun 15, 2026
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henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
8 checks passed

cursorBot reviewed Jun 15, 2026
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henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
8 checks passed

cursorBot reviewed Jun 15, 2026
View reviewed changes

henrydingliuand others added 2 commits June 8, 2026 06:54

moving internal API to TriangleWeight
a50393f

Merge pull request#11from henrydingliu/weight_cleanup
512d3fc
moving internal API to TriangleWeight

henrydingliu requested review from genedan, jbogaardt and kennethshsu as code owners June 15, 2026 23:08

henrydingliu merged commit e705d39 into casact:#932_triangle_weightJun 15, 2026
8 checks passed

cursorBot reviewed Jun 15, 2026
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Unused weight1 assignment

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Unused weight1 assignment

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henrydingliu commented Jun 15, 2026• edited by cursor BotLoading Uh oh! There was an error while loading. Please reload this page.

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Unused weight1 assignment

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codecovBot commented Jun 15, 2026

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henrydingliu commented Jun 15, 2026•
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