verify-code-simplifier

name	verify-code-simplifier
description	Checks if code could be simplified — redundant booleans, manual loops, verbose patterns, if-else chains replaceable by dict lookups. Use after adding or modifying Python code.
disable-model-invocation	false
argument-hint	[optional: specific file or directory to check]

Code Simplification Verification

Purpose

Detects code patterns that could be simplified for readability and maintainability:

1. Redundant boolean comparisons — if x is True:, if x == False: instead of if x: / if not x:
2. Redundant length checks — if len(x) == 0: instead of if not x:
3. If-else chains replaceable by dict lookups — Long if/elif/else chains that map values to functions or objects
4. Manual loop accumulations — Empty list + for-loop + append instead of list/dict comprehensions
5. Verbose dict patterns — if d.get(k) is None: d[k] = []; d[k].append(v) instead of d.setdefault(k, []).append(v)
6. Unnecessary ternary verbosity — True if condition else False instead of just condition
7. Verbose single-use variables — Variable assigned and used only once on the very next line

When to Run

• After adding new Python files
• After modifying existing functions or methods
• Before creating a Pull Request
• When reviewing code for readability improvements
• After refactoring sessions

Related Files

File	Purpose
recommender/train.py	Torch training entry point (thin wrapper)
recommender/train_csr.py	CSR training entry point (thin wrapper)
recommender/pipeline/base.py	Base pipeline class — shared training logic
recommender/pipeline/torch.py	Torch pipeline — training loop, negative sampling, model setup
recommender/pipeline/csr.py	CSR pipeline — ALS/user_based training loop, model setup
recommender/model/recommender_base.py	Base model class — contains if-else chains and manual loop accumulations
recommender/model/neighborhood/user_based.py	User-based CF — contains verbose dict patterns, manual loops, redundant length checks
recommender/libs/sampling/negative_sampling.py	Negative sampling — contains if-else chains and manual loop accumulations
recommender/libs/utils/csr.py	CSR utilities — contains verbose single-use variable patterns
recommender/load_data/movielens_1m.py	Data loader — contains redundant boolean comparisons
recommender/load_data/movielens_10m.py	Data loader — contains redundant boolean comparisons
recommender/load_data/pinterest.py	Data loader — contains redundant boolean comparisons and manual loop accumulations
scripts/download/movielens.py	Download script — contains redundant boolean comparisons

Workflow

Step 1: Identify Changed Files

Tool: Bash

Determine which Python files to check. If an argument is provided, use that path. Otherwise, check files changed in the current session:

git diff HEAD --name-only -- '*.py'
git diff main...HEAD --name-only -- '*.py' 2>/dev/null

Merge into a deduplicated list. If no changed files, check all files under recommender/ and tests/.

Step 2: Check Redundant Boolean Comparisons

Tool: Grep

Search for explicit True/False comparisons:

pattern: "is True[:\s]|is False[:\s]|== True[:\s]|== False[:\s]"
path: recommender/
glob: "*.py"

Also check in tests/ and scripts/:

pattern: "is True[:\s]|is False[:\s]|== True[:\s]|== False[:\s]"
path: tests/
glob: "*.py"

pattern: "is True[:\s]|is False[:\s]|== True[:\s]|== False[:\s]"
path: scripts/
glob: "*.py"

PASS: No explicit boolean comparisons found.

FAIL: Lines found with is True, is False, == True, or == False.

Fix: Replace if x is True: with if x: and if x is False: with if not x:.

Step 3: Check Redundant Length Checks

Tool: Grep

Search for len() comparisons that check emptiness:

pattern: "if len\(.+\) == 0|if len\(.+\) > 0|if len\(.+\) != 0|if len\(.+\) >= 1"
path: recommender/
glob: "*.py"

PASS: No redundant length checks found.

FAIL: Lines using len() to check for empty/non-empty collections.

Fix: Replace if len(x) == 0: with if not x: and if len(x) > 0: with if x:.

Step 4: Check Unnecessary Ternary Verbosity

Tool: Grep

Search for True if ... else False or False if ... else True patterns:

pattern: "= True if .+ else False|= False if .+ else True"
path: recommender/
glob: "*.py"

PASS: No verbose ternary expressions found.

FAIL: Lines with True if condition else False.

Fix: Replace x = True if condition else False with x = condition. Replace x = False if condition else True with x = not condition.

Step 5: Check If-Else Chains Replaceable by Dict Lookups

Tool: Read

Read each changed file and look for if-elif chains with 3+ branches that:

• Compare the same variable against different literal values
• Assign to the same target or call similar functions

Count the number of elif statements per if-block. Flag blocks with 3 or more elif branches that follow a value-to-action mapping pattern.

PASS: No long if-elif chains that are simple value mappings.

FAIL: If-elif chains with 3+ branches mapping values to actions.

Fix: Extract the mapping into a dictionary and use dict.get() or dict[key]() for dispatch.

Step 6: Check Manual Loop Accumulations

Tool: Grep

Search for the pattern of empty list initialization followed by loop append:

pattern: "= \[\]\s*$"
path: recommender/
glob: "*.py"
output_mode: content

For each match, read surrounding lines to check if it is followed by a for-loop that only appends to that list. Flag cases where a list comprehension would be a direct replacement (single append in the loop body, no complex side effects).

PASS: No manual loop accumulations that could be list comprehensions.

FAIL: Empty list + for-loop + append patterns found.

Fix: Replace with list comprehension: result = [transform(x) for x in iterable if condition].

Step 7: Check Verbose Dict Patterns

Tool: Grep

Search for dict.get(key) is None followed by initialization:

pattern: "\.get\(.+\) is None"
path: recommender/
glob: "*.py"
output_mode: content

Read surrounding lines to check if it follows the pattern:

if d.get(k) is None:
    d[k] = []
d[k].append(v)

PASS: No verbose dict initialization patterns found.

FAIL: Dict get-check-initialize patterns found.

Fix: Use d.setdefault(k, []).append(v) or collections.defaultdict(list).

Output Format

| # | Check | Status | Details |
|---|-------|--------|---------|
| 1 | Redundant boolean comparisons | PASS/FAIL | N instances found |
| 2 | Redundant length checks | PASS/FAIL | N instances found |
| 3 | Unnecessary ternary verbosity | PASS/FAIL | N instances found |
| 4 | If-else chains (dict lookup) | PASS/FAIL | N chains with 3+ branches |
| 5 | Manual loop accumulations | PASS/FAIL | N replaceable patterns |
| 6 | Verbose dict patterns | PASS/FAIL | N instances found |

Exceptions

1. Boolean comparisons in type-sensitive contexts — if x is True: is valid when x could be a truthy non-boolean value (e.g., 1, "yes") and you strictly need True. This is rare but legitimate.
2. Length checks with specific counts — if len(x) == 1: or if len(x) > 3: are not redundant; only == 0, > 0, != 0, >= 1 are flagged.
3. Loop accumulations with side effects — Loops that do more than just append (e.g., logging, mutation, conditional logic with multiple branches) should not be converted to comprehensions.
4. If-elif chains with complex logic — Chains where branches contain different logic beyond simple assignment/call are not suitable for dict lookup replacement.
5. Existing code not changed in session — Only check files changed in the current session; do not report violations in untouched files.
6. Test files — Test files may use verbose patterns for clarity; treat these as lower-priority suggestions rather than strict violations.