⚡ Bolt: Replace 2D dataframe assignments with 1D vector indexing - #201
⚡ Bolt: Replace 2D dataframe assignments with 1D vector indexing#201seonghobae wants to merge 4 commits into
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💡 What: Changed expressions like `NewScaleParms[idx, "est"] <- FALSE` to `NewScaleParms$est[idx] <- FALSE` across `R/aFIPC.R`. 🎯 Why: Modifying a dataframe via 2D matrix subsetting in R dispatches to the expensive `[<-.data.frame` method, which involves dimensionality checks, attributes preservation, and factor checking. 1D vector subsetting accesses the list element directly and assigns it at the C level, running drastically faster while achieving exactly the same outcome. 📊 Impact: Speeds up parameter assignment and modification loops during heavy item linking tasks. Expected reduction in execution time for large iterative operations. 🔬 Measurement: Run the test suite and observe no functional regressions with standard datasets. Compare `microbenchmark::microbenchmark(df[1, "x"] <- 1, df$x[1] <- 1)` for direct measurement.
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✨ Finishing Touches🧪 Generate unit tests (beta)
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💡 What: Changed expressions like `NewScaleParms[idx, "est"] <- FALSE` to `NewScaleParms$est[idx] <- FALSE` across `R/aFIPC.R`. 🎯 Why: Modifying a dataframe via 2D matrix subsetting in R dispatches to the expensive `[<-.data.frame` method, which involves dimensionality checks, attributes preservation, and factor checking. 1D vector subsetting accesses the list element directly and assigns it at the C level, running drastically faster while achieving exactly the same outcome. 📊 Impact: Speeds up parameter assignment and modification loops during heavy item linking tasks. Expected reduction in execution time for large iterative operations. 🔬 Measurement: Run the test suite and observe no functional regressions with standard datasets. Compare `microbenchmark::microbenchmark(df[1, "x"] <- 1, df$x[1] <- 1)` for direct measurement.
💡 What: Changed expressions like `NewScaleParms[idx, "est"] <- FALSE` to `NewScaleParms$est[idx] <- FALSE` across `R/aFIPC.R`. Added `.semgrepignore` to `.Rbuildignore`. 🎯 Why: Modifying a dataframe via 2D matrix subsetting in R dispatches to the expensive `[<-.data.frame` method, which involves dimensionality checks, attributes preservation, and factor checking. 1D vector subsetting accesses the list element directly and assigns it at the C level, running drastically faster while achieving exactly the same outcome. Including `.semgrepignore` in `.Rbuildignore` fixes the R CMD check failing due to a hidden file error. 📊 Impact: Speeds up parameter assignment and modification loops during heavy item linking tasks. Expected reduction in execution time for large iterative operations. Fixes CI failures. 🔬 Measurement: Run the test suite and observe no functional regressions with standard datasets. Compare `microbenchmark::microbenchmark(df[1, "x"] <- 1, df$x[1] <- 1)` for direct measurement.
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Pull request overview
OpenCode could not approve from deterministic current-head evidence because GitHub Checks have failed.
Findings
1. HIGH Current-head GitHub Checks - Fix failed required checks before approval
- Problem: Failed same-head checks remain for
3f4bebb38eafd8f8ef1c2edff58823f2e25b1423. - Root cause: The model-unavailable evidence fallback is allowed only when peer GitHub Checks are complete and clean.
- Fix: Read and fix the failed check logs below, then rerun the current-head checks.
- Regression test: Keep the model-unavailable fallback gated on an empty failed-check rollup.
Failed checks:
- R CMD check/check: FAILURE (https://github.com/ContextualWisdomLab/aFIPC/actions/runs/30757269493/job/91521492020)
- check check run: failure (https://github.com/ContextualWisdomLab/aFIPC/actions/runs/30757269493/job/91521492020)
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file: .Rbuildignore"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file: .Rbuildignore"]
R1 --> V1["required checks"]
OpenCode Review Overview
Pull request overviewOpenCode cannot approve yet because required coverage evidence did not pass. Review outcome1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
Coverage evidenceCoverage Decision
Changed-File Evidence Mapflowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (3 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (3 files)"]
R1 --> V1["required checks"]
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💡 What: Changed expressions like `NewScaleParms[idx, "est"] <- FALSE` to `NewScaleParms$est[idx] <- FALSE` across `R/aFIPC.R`. Added `.semgrepignore`, `actionlint`, and `gitleaks` patterns to `.Rbuildignore`. 🎯 Why: Modifying a dataframe via 2D matrix subsetting in R dispatches to the expensive `[<-.data.frame` method, which involves dimensionality checks, attributes preservation, and factor checking. 1D vector subsetting accesses the list element directly and assigns it at the C level, running drastically faster while achieving exactly the same outcome. Including `.semgrepignore`, and actionlint/gitleaks tool downloaded binaries in `.Rbuildignore` fixes the R CMD check failing due to a hidden file and unexpected executable files error, which happens during Github CI Check. 📊 Impact: Speeds up parameter assignment and modification loops during heavy item linking tasks. Expected reduction in execution time for large iterative operations. Fixes CI failures. 🔬 Measurement: Run the test suite and observe no functional regressions with standard datasets. Compare `microbenchmark::microbenchmark(df[1, "x"] <- 1, df$x[1] <- 1)` for direct measurement.
There was a problem hiding this comment.
Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
-
Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
-
Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
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Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current head0cff315be7938c30b5cc93efd7fb438079e6ee9e. -
Head SHA:
0cff315be7938c30b5cc93efd7fb438079e6ee9e -
Workflow run: 30759602439
-
Workflow attempt: 1
Coverage evidence
Coverage Decision
- Result: FAIL
- Test evidence: not proven passing
- Docstring evidence: not proven passing when configured
- Failure count: 1
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (3 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (3 files)"]
R1 --> V1["required checks"]
1 participant
⚡ Bolt: Replace 2D dataframe assignments with 1D vector indexing
💡 What: Changed expressions like
NewScaleParms[idx, "est"] <- FALSEtoNewScaleParms$est[idx] <- FALSEacrossR/aFIPC.R.🎯 Why: Modifying a dataframe via 2D matrix subsetting in R dispatches to the expensive
[<-.data.framemethod, which involves dimensionality checks, attributes preservation, and factor checking. 1D vector subsetting accesses the list element directly and assigns it at the C level, running drastically faster while achieving exactly the same outcome.📊 Impact: Speeds up parameter assignment and modification loops during heavy item linking tasks. Expected reduction in execution time for large iterative operations.
🔬 Measurement: Run the test suite and observe no functional regressions with standard datasets. Compare
microbenchmark::microbenchmark(df[1, "x"] <- 1, df$x[1] <- 1)for direct measurement.PR created automatically by Jules for task 18338967958149624134 started by @seonghobae
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