⚡️ Speed up method TransformsContainer._is_superset by 132%

[codeflash-ai]

📄 132% (1.32x) speedup for TransformsContainer._is_superset in marimo/_plugins/ui/_impl/dataframes/transforms/apply.py

⏱️ Runtime : 444 microseconds → 191 microseconds (best of 250 runs)

📝 Explanation and details

The optimization replaces an element-by-element loop comparison with a direct list slice comparison, achieving a 132% speedup by leveraging Python's optimized C-level list comparison.

Key Changes:

• Eliminated the manual loop: Removed the for i, transform in enumerate(self._transforms): loop that compared each element individually
• Added direct slice comparison: Introduced self._transforms == transforms.transforms[:len(self._transforms)] to compare the entire prefix at once

Why This Is Faster:

1. C-level optimization: Python's list equality operator (==) is implemented in optimized C code, making it significantly faster than Python-level loops
2. Early termination: The C implementation can short-circuit as soon as it finds a mismatch, without the overhead of Python function calls
3. Reduced overhead: Eliminates the per-iteration costs of enumerate(), indexing (transforms.transforms[i]), and Python-level comparisons

Performance Analysis:

• Line profiler shows the loop (for i, transform in enumerate(self._transforms):) took 76% of execution time in the original (2.1ms out of 2.8ms total)
• The optimized slice comparison takes 92.4% of the reduced execution time (0.9ms out of 1.0ms total), but the absolute time is much lower
• Most dramatic improvements occur in large-scale tests: up to 2746% faster for 500-element lists that match completely

Test Case Performance:

• Best improvements: Large exact matches and strict supersets (101-2746% faster)
• Moderate improvements: Small to medium lists with matches or early mismatches (32-65% faster)
• Minimal impact: Very short lists or early exits due to length checks (1-13% faster)

The optimization maintains identical behavior while dramatically improving performance, especially for longer transformation sequences where the prefix comparison would otherwise require many iterations.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 80 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from typing import Generic, TypeVar

# imports
import pytest
from marimo._plugins.ui._impl.dataframes.transforms.apply import \
    TransformsContainer


# Mocks for Transform, TransformHandler, Transformations
class DummyTransform:
    """A simple transform class for equality comparison."""
    def __init__(self, value):
        self.value = value

    def __eq__(self, other):
        if not isinstance(other, DummyTransform):
            return False
        return self.value == other.value

    def __repr__(self):
        return f"DummyTransform({self.value})"

class DummyTransformHandler:
    """A dummy handler, not used in _is_superset tests."""
    pass

class Transformations:
    """A container for a list of transforms."""
    def __init__(self, transforms):
        self.transforms = transforms


T = TypeVar("T")
from marimo._plugins.ui._impl.dataframes.transforms.apply import \
    TransformsContainer

# --- Unit Tests ---

# 1. Basic Test Cases

def test_superset_exact_match():
    """Test when new transforms exactly match the existing ones."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1), DummyTransform(2)]
    new_transforms = Transformations([DummyTransform(1), DummyTransform(2)])
    codeflash_output = tc._is_superset(new_transforms) # 2.48μs -> 1.73μs (43.4% faster)

def test_superset_strict_superset():
    """Test when new transforms are a strict superset (existing transforms are prefix)."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1), DummyTransform(2)]
    new_transforms = Transformations([DummyTransform(1), DummyTransform(2), DummyTransform(3)])
    codeflash_output = tc._is_superset(new_transforms) # 2.00μs -> 1.36μs (47.4% faster)

def test_not_superset_shorter():
    """Test when new transforms are shorter than the existing ones."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1), DummyTransform(2), DummyTransform(3)]
    new_transforms = Transformations([DummyTransform(1), DummyTransform(2)])
    codeflash_output = tc._is_superset(new_transforms) # 677ns -> 658ns (2.89% faster)

def test_not_superset_different_prefix():
    """Test when new transforms have a different prefix."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1), DummyTransform(2)]
    new_transforms = Transformations([DummyTransform(1), DummyTransform(99)])
    codeflash_output = tc._is_superset(new_transforms) # 2.06μs -> 1.34μs (53.5% faster)

def test_not_superset_empty_existing():
    """Test when the container's _transforms is empty."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = []
    new_transforms = Transformations([DummyTransform(1)])
    codeflash_output = tc._is_superset(new_transforms) # 423ns -> 407ns (3.93% faster)

def test_not_superset_empty_new():
    """Test when the new transforms are empty but existing is not."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1)]
    new_transforms = Transformations([])
    codeflash_output = tc._is_superset(new_transforms) # 659ns -> 648ns (1.70% faster)

def test_superset_both_empty():
    """Test when both the container's _transforms and new transforms are empty."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = []
    new_transforms = Transformations([])
    codeflash_output = tc._is_superset(new_transforms) # 404ns -> 397ns (1.76% faster)

# 2. Edge Test Cases

def test_superset_one_element_match():
    """Test with a single element in both lists and they match."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform("a")]
    new_transforms = Transformations([DummyTransform("a")])
    codeflash_output = tc._is_superset(new_transforms) # 1.84μs -> 1.31μs (40.4% faster)

def test_superset_one_element_different():
    """Test with a single element in both lists and they differ."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform("a")]
    new_transforms = Transformations([DummyTransform("b")])
    codeflash_output = tc._is_superset(new_transforms) # 1.74μs -> 1.26μs (38.3% faster)

def test_superset_partial_match():
    """Test where only part of the prefix matches."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1), DummyTransform(2), DummyTransform(3)]
    new_transforms = Transformations([DummyTransform(1), DummyTransform(99), DummyTransform(3), DummyTransform(4)])
    codeflash_output = tc._is_superset(new_transforms) # 2.10μs -> 1.40μs (49.4% faster)

def test_superset_non_transform_objects():
    """Test with objects that are not DummyTransform, should always be False."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1)]
    new_transforms = Transformations([object()])
    codeflash_output = tc._is_superset(new_transforms) # 1.67μs -> 1.14μs (46.2% faster)

def test_superset_with_duplicates():
    """Test with duplicate transforms in the list."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1), DummyTransform(1)]
    new_transforms = Transformations([DummyTransform(1), DummyTransform(1), DummyTransform(2)])
    codeflash_output = tc._is_superset(new_transforms) # 1.99μs -> 1.42μs (40.2% faster)

def test_superset_type_mismatch():
    """Test with transforms of different types."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(1)]
    new_transforms = Transformations([1])  # int, not DummyTransform
    codeflash_output = tc._is_superset(new_transforms) # 1.57μs -> 1.19μs (32.0% faster)

def test_superset_large_prefix_mismatch_at_end():
    """Test where only the last element of the prefix mismatches."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = [DummyTransform(i) for i in range(10)]
    new_transforms = Transformations([DummyTransform(i) for i in range(9)] + [DummyTransform(999)] + [DummyTransform(11)])
    codeflash_output = tc._is_superset(new_transforms) # 3.24μs -> 1.97μs (64.5% faster)

# 3. Large Scale Test Cases

def test_superset_large_exact_match():
    """Test with large lists that exactly match."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    N = 500
    tc._transforms = [DummyTransform(i) for i in range(N)]
    new_transforms = Transformations([DummyTransform(i) for i in range(N)])
    codeflash_output = tc._is_superset(new_transforms) # 63.6μs -> 31.7μs (101% faster)

def test_superset_large_strict_superset():
    """Test with large lists where new transforms are a strict superset."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    N = 500
    tc._transforms = [DummyTransform(i) for i in range(N)]
    new_transforms = Transformations([DummyTransform(i) for i in range(N)] + [DummyTransform(999)])
    codeflash_output = tc._is_superset(new_transforms) # 64.8μs -> 31.6μs (105% faster)

def test_superset_large_prefix_mismatch():
    """Test with large lists where the mismatch is early in the prefix."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    N = 500
    tc._transforms = [DummyTransform(i) for i in range(N)]
    # Change the 10th element to something else
    wrong_prefix = [DummyTransform(i) if i != 10 else DummyTransform(999) for i in range(N+10)]
    new_transforms = Transformations(wrong_prefix)
    codeflash_output = tc._is_superset(new_transforms) # 3.47μs -> 3.31μs (4.95% faster)

def test_superset_large_shorter_new():
    """Test with large existing transforms and shorter new transforms."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    N = 500
    tc._transforms = [DummyTransform(i) for i in range(N)]
    new_transforms = Transformations([DummyTransform(i) for i in range(N-1)])
    codeflash_output = tc._is_superset(new_transforms) # 765ns -> 799ns (4.26% slower)

def test_superset_large_empty_existing():
    """Test with large new transforms but empty existing transforms."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = []
    new_transforms = Transformations([DummyTransform(i) for i in range(500)])
    codeflash_output = tc._is_superset(new_transforms) # 420ns -> 434ns (3.23% slower)

def test_superset_large_both_empty():
    """Test with both existing and new transforms empty (large scale, but empty)."""
    handler = DummyTransformHandler()
    tc = TransformsContainer(df=None, handler=handler)
    tc._transforms = []
    new_transforms = Transformations([])
    codeflash_output = tc._is_superset(new_transforms) # 387ns -> 404ns (4.21% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import Generic, TypeVar

# imports
import pytest
from marimo._plugins.ui._impl.dataframes.transforms.apply import \
    TransformsContainer


# Minimal stubs for Transform, TransformHandler, Transformations for testing
class Transform:
    def __init__(self, name, params=None):
        self.name = name
        self.params = params or {}

    def __eq__(self, other):
        if not isinstance(other, Transform):
            return False
        return self.name == other.name and self.params == other.params

    def __repr__(self):
        return f"Transform({self.name!r}, {self.params!r})"

class TransformHandler:
    pass

class Transformations:
    def __init__(self, transforms):
        self.transforms = transforms


T = TypeVar("T")
from marimo._plugins.ui._impl.dataframes.transforms.apply import \
    TransformsContainer

# ---------------------------
# UNIT TESTS FOR _is_superset
# ---------------------------

# Basic Test Cases

def test_empty_existing_transforms_returns_false():
    """If no existing transforms, always return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    new_transforms = Transformations([Transform("A")])
    codeflash_output = tc._is_superset(new_transforms) # 430ns -> 400ns (7.50% faster)

def test_new_transforms_shorter_than_existing_returns_false():
    """If new transforms is shorter than existing, return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B")]
    new_transforms = Transformations([Transform("A")])
    codeflash_output = tc._is_superset(new_transforms) # 727ns -> 642ns (13.2% faster)

def test_new_transforms_equal_to_existing_returns_true():
    """If new transforms equals existing, return True."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B")]
    new_transforms = Transformations([Transform("A"), Transform("B")])
    codeflash_output = tc._is_superset(new_transforms) # 2.31μs -> 1.71μs (34.9% faster)

def test_new_transforms_longer_and_prefix_matches_returns_true():
    """If new transforms is longer and prefix matches existing, return True."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B")]
    new_transforms = Transformations([Transform("A"), Transform("B"), Transform("C")])
    codeflash_output = tc._is_superset(new_transforms) # 2.08μs -> 1.47μs (41.5% faster)

def test_new_transforms_longer_and_prefix_does_not_match_returns_false():
    """If new transforms is longer but prefix does not match, return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("X")]
    new_transforms = Transformations([Transform("A"), Transform("B"), Transform("C")])
    codeflash_output = tc._is_superset(new_transforms) # 2.20μs -> 1.50μs (46.8% faster)

def test_new_transforms_equal_length_and_different_returns_false():
    """If new transforms is same length but not equal, return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B")]
    new_transforms = Transformations([Transform("A"), Transform("C")])
    codeflash_output = tc._is_superset(new_transforms) # 2.09μs -> 1.46μs (43.1% faster)

# Edge Test Cases

def test_existing_transforms_empty_new_transforms_empty_returns_false():
    """Both existing and new transforms empty, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = []
    new_transforms = Transformations([])
    codeflash_output = tc._is_superset(new_transforms) # 407ns -> 393ns (3.56% faster)

def test_existing_transforms_nonempty_new_transforms_empty_returns_false():
    """Existing non-empty, new empty, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A")]
    new_transforms = Transformations([])
    codeflash_output = tc._is_superset(new_transforms) # 650ns -> 624ns (4.17% faster)

def test_existing_transforms_one_new_transforms_one_different_returns_false():
    """Single transform, but different, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A")]
    new_transforms = Transformations([Transform("B")])
    codeflash_output = tc._is_superset(new_transforms) # 1.77μs -> 1.33μs (33.0% faster)

def test_existing_transforms_one_new_transforms_one_same_returns_true():
    """Single transform, same, should return True."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A")]
    new_transforms = Transformations([Transform("A")])
    codeflash_output = tc._is_superset(new_transforms) # 1.84μs -> 1.31μs (39.8% faster)

def test_existing_transforms_with_params_new_transforms_with_params_returns_true():
    """Transforms with parameters, same params, should return True."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A", {"x": 1}), Transform("B", {"y": 2})]
    new_transforms = Transformations([Transform("A", {"x": 1}), Transform("B", {"y": 2}), Transform("C")])
    codeflash_output = tc._is_superset(new_transforms) # 2.21μs -> 1.62μs (36.8% faster)

def test_existing_transforms_with_params_new_transforms_with_different_params_returns_false():
    """Transforms with parameters, different params, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A", {"x": 1}), Transform("B", {"y": 2})]
    new_transforms = Transformations([Transform("A", {"x": 2}), Transform("B", {"y": 2}), Transform("C")])
    codeflash_output = tc._is_superset(new_transforms) # 1.93μs -> 1.43μs (35.6% faster)

def test_existing_transforms_with_non_transform_object_returns_false():
    """If new transforms contains non-Transform objects, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A")]
    new_transforms = Transformations(["not a transform"])
    codeflash_output = tc._is_superset(new_transforms) # 1.57μs -> 1.18μs (33.1% faster)

def test_existing_transforms_with_duplicate_transforms_returns_true():
    """Duplicate transforms in existing and new, should return True."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("A")]
    new_transforms = Transformations([Transform("A"), Transform("A"), Transform("B")])
    codeflash_output = tc._is_superset(new_transforms) # 2.07μs -> 1.48μs (39.7% faster)

def test_existing_transforms_with_duplicate_transforms_returns_false_on_mismatch():
    """Duplicate transforms but mismatch in new, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("A")]
    new_transforms = Transformations([Transform("A"), Transform("B"), Transform("A")])
    codeflash_output = tc._is_superset(new_transforms) # 2.15μs -> 1.49μs (44.5% faster)

# Large Scale Test Cases

def test_large_scale_superset_true():
    """Large scale: existing is prefix of new, should return True."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    existing = [Transform(f"T{i}") for i in range(500)]
    tc._transforms = existing
    new = existing + [Transform(f"T{i}") for i in range(500, 800)]
    new_transforms = Transformations(new)
    codeflash_output = tc._is_superset(new_transforms) # 74.1μs -> 2.60μs (2746% faster)

def test_large_scale_superset_false_due_to_mismatch():
    """Large scale: mismatch at one position, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    existing = [Transform(f"T{i}") for i in range(500)]
    tc._transforms = existing
    # Introduce a mismatch at position 250
    new = existing.copy()
    new[250] = Transform("WRONG")
    new += [Transform(f"T{i}") for i in range(500, 800)]
    new_transforms = Transformations(new)
    codeflash_output = tc._is_superset(new_transforms) # 37.4μs -> 2.79μs (1239% faster)

def test_large_scale_new_shorter_than_existing_returns_false():
    """Large scale: new transforms shorter than existing, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    existing = [Transform(f"T{i}") for i in range(1000)]
    tc._transforms = existing
    new = [Transform(f"T{i}") for i in range(900)]
    new_transforms = Transformations(new)
    codeflash_output = tc._is_superset(new_transforms) # 835ns -> 819ns (1.95% faster)

def test_large_scale_exact_match_returns_true():
    """Large scale: new transforms exactly same as existing, should return True."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    existing = [Transform(f"T{i}") for i in range(1000)]
    tc._transforms = existing
    new = [Transform(f"T{i}") for i in range(1000)]
    new_transforms = Transformations(new)
    codeflash_output = tc._is_superset(new_transforms) # 150μs -> 82.0μs (83.5% faster)

def test_large_scale_empty_existing_returns_false():
    """Large scale: existing transforms empty, should return False."""
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = []
    new = [Transform(f"T{i}") for i in range(1000)]
    new_transforms = Transformations(new)
    codeflash_output = tc._is_superset(new_transforms) # 414ns -> 469ns (11.7% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-TransformsContainer._is_superset-mhwv3zvr and push.