feat(strings): similar string groups#104
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BrianLusina
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Caution Review failedThe pull request is closed. WalkthroughAdds a Union-Find implementation and re-exports, introduces a new "Similar String Groups" module with two DSU-based implementations, tests and README, and updates package navigation in DIRECTORY.md. Changes
Sequence Diagram(s)sequenceDiagram
participant Caller as num_similar_groups()
participant DSU as DisjointSetUnion
participant Check as are_similar()
Caller->>DSU: __init__(n)
Note right of DSU: parent[], rank[], count initialized
loop for each pair (i, j)
Caller->>Check: are_similar(strs[i], strs[j])
alt similar
Check-->>Caller: True
Caller->>DSU: union(i, j)
Note right of DSU: merge roots, update count
else not similar
Check-->>Caller: False
end
end
Caller->>DSU: get_count()
DSU-->>Caller: group_count
Caller-->>Caller: return group_count
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~30 minutes
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Pre-merge checks and finishing touches❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
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Actionable comments posted: 3
🧹 Nitpick comments (2)
pystrings/similar_string_groups/__init__.py (1)
67-77: Implementation works but uses different counting approach.This implementation correctly groups similar strings. However, there's a subtle difference in how it counts groups compared to
num_similar_groups:
- This version counts unique roots via set comprehension (line 76)
num_similar_groupsusesDisjointSetUnion.get_count()Both approaches are correct, but the set-based counting approach is less efficient as it performs O(n) find operations after the union operations complete.
Consider documenting why two implementations exist or consolidating them:
def num_similar_groups_2(strs: List[str]) -> int: """ Alternative implementation that demonstrates counting groups via unique roots instead of maintaining a count during unions. """ n = len(strs) uf = DisjointSetUnion(n) for i in range(n): for j in range(i + 1, n): if are_similar(strs[i], strs[j]): uf.union(i, j) # Alternative: return uf.get_count() for better performance roots = {uf.find(i) for i in range(n)} return len(roots)datastructures/sets/union_find/__init__.py (1)
25-26: Remove debug comments.Lines 25-26 contain inline comments (
# 0,# 1) that appear to be debug artifacts from development. These should be removed for cleaner code.- root_i = self.find(i) # 0 - root_j = self.find(j) # 1 + root_i = self.find(i) + root_j = self.find(j)
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pystrings/similar_string_groups/images/similar_string_groups_example_1.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_example_2.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_example_3.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_solution_1.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_solution_2.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_solution_3.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_solution_4.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_solution_5.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_solution_6.pngis excluded by!**/*.pngpystrings/similar_string_groups/images/similar_string_groups_solution_7.pngis excluded by!**/*.png
📒 Files selected for processing (7)
DIRECTORY.md(1 hunks)datastructures/__init__.py(1 hunks)datastructures/sets/__init__.py(1 hunks)datastructures/sets/union_find/__init__.py(1 hunks)pystrings/similar_string_groups/README.md(1 hunks)pystrings/similar_string_groups/__init__.py(1 hunks)pystrings/similar_string_groups/test_similar_string_groups.py(1 hunks)
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🧬 Code graph analysis (4)
pystrings/similar_string_groups/test_similar_string_groups.py (1)
pystrings/similar_string_groups/__init__.py (2)
num_similar_groups(5-52)num_similar_groups_2(67-77)
datastructures/sets/__init__.py (1)
datastructures/sets/union_find/__init__.py (2)
DisjointSetUnion(1-45)UnionFind(48-62)
pystrings/similar_string_groups/__init__.py (1)
datastructures/sets/union_find/__init__.py (7)
DisjointSetUnion(1-45)UnionFind(48-62)union(20-41)union(58-62)get_count(43-45)find(12-18)find(52-56)
datastructures/__init__.py (1)
datastructures/sets/union_find/__init__.py (2)
DisjointSetUnion(1-45)UnionFind(48-62)
🪛 LanguageTool
pystrings/similar_string_groups/README.md
[style] ~3-~3: ‘exactly the same’ might be wordy. Consider a shorter alternative.
Context: ...e considered similar if they are either exactly the same or can be made identical by swapping at...
(EN_WORDINESS_PREMIUM_EXACTLY_THE_SAME)
[style] ~38-~38: ‘exactly the same’ might be wordy. Consider a shorter alternative.
Context: ...o strings are similar — that is, either exactly the same or differ at exactly two positions (me...
(EN_WORDINESS_PREMIUM_EXACTLY_THE_SAME)
🪛 markdownlint-cli2 (0.18.1)
DIRECTORY.md
732-732: Unordered list indentation
Expected: 0; Actual: 2
(MD007, ul-indent)
733-733: Unordered list indentation
Expected: 2; Actual: 4
(MD007, ul-indent)
pystrings/similar_string_groups/README.md
54-54: Inconsistent indentation for list items at the same level
Expected: 3; Actual: 4
(MD005, list-indent)
56-56: Reference links and images should use a label that is defined
Missing link or image reference definition: "::-1"
(MD052, reference-links-images)
🔇 Additional comments (6)
DIRECTORY.md (1)
732-733: LGTM! Documentation entry properly placed.The Similar String Groups entry is correctly positioned under Pystrings and follows the existing indentation pattern used throughout the file.
datastructures/__init__.py (1)
1-6: LGTM! Clean package organization.The re-export pattern properly exposes the DSU classes at the package level, making them conveniently accessible via
from datastructures import DisjointSetUnion, UnionFind.pystrings/similar_string_groups/test_similar_string_groups.py (1)
1-60: LGTM! Comprehensive test coverage.The test suite effectively validates both implementations with identical test cases covering:
- Multiple distinct groups (test_1: 3 groups)
- All strings in a single group (test_2: 1 group)
- Partial grouping (test_3, test_4: 2 groups)
- Longer strings for performance validation
The parallel test structure ensures both
num_similar_groupsandnum_similar_groups_2maintain consistent behavior.datastructures/sets/__init__.py (1)
1-3: LGTM! Proper module organization.Clean re-export pattern that maintains the package hierarchy while making the Union-Find classes accessible at the
setsmodule level.pystrings/similar_string_groups/README.md (1)
1-113: Excellent documentation!The README provides comprehensive coverage of the problem with:
- Clear problem statement and constraints
- Visual examples for understanding
- Detailed solution walkthrough
- Accurate time complexity analysis: O(n²·m) dominated by string comparisons
- Space complexity analysis: O(n) for Union-Find and tracking structures
The explanation of the Union-Find approach is well-articulated and helps readers understand both the algorithm and data structure choice.
pystrings/similar_string_groups/__init__.py (1)
55-65: LGTM! Correct similarity check.The
are_similarfunction correctly implements the similarity logic:
- Returns True for identical strings (0 differences)
- Returns True for strings differing at exactly 2 positions where the character pairs are symmetric
- Early exits when more than 2 differences detected
The symmetry check
diff[0] == diff[1][::-1]properly validates that swapping would make the strings equal.
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Actionable comments posted: 1
🧹 Nitpick comments (1)
datastructures/sets/union_find/__init__.py (1)
48-74: Consider consolidating or documenting the purpose of having two Union-Find implementations.Both
UnionFindandDisjointSetUnionserve the same purpose but with different optimizations:
DisjointSetUnion: Uses union by rank + path compression, tracks set count → O(α(n)) amortized per operationUnionFind: Uses only path compression → can degrade to O(log n) per operationThis creates potential confusion about which implementation to use. Consider one of the following:
- Consolidate: Remove
UnionFindand use onlyDisjointSetUnion(recommended for best performance)- Document differences: Add clear docstring guidance on when to use each (e.g., "Use
UnionFindfor simpler cases where minimal memory overhead is preferred; useDisjointSetUnionfor optimal performance")- Enhance UnionFind: Add union by rank and
get_count()to matchDisjointSetUnion's API and performanceBased on learnings from the past review comment, option 1 (consolidation) was suggested. If both are needed, consider adding this to the
UnionFinddocstring:class UnionFind: """ A minimal Union-Find data structure with path compression. Note: This is a simpler alternative to DisjointSetUnion without union by rank optimization. For performance-critical applications, prefer DisjointSetUnion. """
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Actionable comments posted: 1
♻️ Duplicate comments (1)
pystrings/similar_string_groups/__init__.py (1)
17-41: Code duplication between is_similar and are_similar.The nested
is_similarfunction duplicates the logic of the module-levelare_similarfunction (lines 56-66). This was flagged in previous reviews and remains unaddressed.Consider removing the nested
is_similarand usingare_similardirectly:- # All strings have the same length, per constraints - word_len = len(strs[0]) - # Initialize Union-Find with n elements, one for each string. # The initial count is n (each string is its own group). uf = DisjointSetUnion(strs_len) - def is_similar(s1: str, s2: str) -> bool: - """ - Checks if two strings are similar. - Similar means identical (0 diffs) or 1 swap (2 diffs). - """ - diff_count = 0 - positions_that_differ = [] - for k in range(word_len): - if s1[k] != s2[k]: - positions_that_differ.append(k) - diff_count += 1 - - # Optimization: If more than 2 differences, - # they can't be similar. - if diff_count > 2: - return False - - if diff_count == 2: - i = positions_that_differ[0] - j = positions_that_differ[1] - return s1[i] == s2[j] and s1[j] == s2[i] - - # At this point, diff_count is either 0 or 1 - # Only 0 differences (identical strings) are similar - return diff_count == 0 - # Iterate over all unique pairs of strings for i in range(strs_len): for j in range(i + 1, strs_len): # If the strings are similar, merge their groups. # The union() method handles decrementing the count # only if they were in different groups. - if is_similar(strs[i], strs[j]): + if are_similar(strs[i], strs[j]): uf.union(i, j)
🧹 Nitpick comments (3)
pystrings/similar_string_groups/__init__.py (3)
2-2: Remove unused import.
UnionFindis imported but never used in this file.Apply this diff to remove the unused import:
-from datastructures import DisjointSetUnion, UnionFind +from datastructures import DisjointSetUnion
56-56: Add type hints for consistency.The
are_similarfunction is missing type hints for parameters and return type, which is inconsistent with the rest of the codebase.Apply this diff to add type hints:
-def are_similar(s1, s2): +def are_similar(s1: str, s2: str) -> bool:
77-78: Consider using get_count() for consistency.
num_similar_groups_2manually counts unique roots by creating a set, whilenum_similar_groupsuses the more efficientuf.get_count()method. Both approaches are correct, but usingget_count()is more efficient (O(1) vs O(n)) and consistent with the DSU API.Apply this diff to use
get_count():- roots = {uf.find(i) for i in range(n)} - return len(roots) + return uf.get_count()
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pystrings/similar_string_groups/__init__.py(1 hunks)
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pystrings/similar_string_groups/__init__.py (1)
datastructures/sets/union_find/__init__.py (7)
DisjointSetUnion(1-45)UnionFind(48-74)union(20-41)union(64-74)get_count(43-45)find(12-18)find(57-62)
🔇 Additional comments (1)
pystrings/similar_string_groups/__init__.py (1)
39-41: Bug fix verified.The logic issue from the previous review has been correctly addressed. The function now properly rejects strings with exactly 1 difference, returning
Trueonly for identical strings whendiff_count != 2.
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Describe your change:
Algorithm to find similar string groups
Checklist:
Fixes: #{$ISSUE_NO}.Summary by CodeRabbit
New Features
Documentation
Tests