⚡️ Speed up method DPSolveResult.__getattr__ by 9%

[codeflash-ai]

📄 9% (0.09x) speedup for DPSolveResult.__getattr__ in quantecon/markov/ddp.py

⏱️ Runtime : 25.5 microseconds → 23.4 microseconds (best of 153 runs)

📝 Explanation and details

The optimization replaces a try-except block with an if-check for dictionary membership, reducing Python's exception handling overhead when attributes don't exist.

Key Changes:

• Original: Uses try/except KeyError pattern - attempts dictionary lookup and catches the exception if key doesn't exist
• Optimized: Uses if name in self check first, only performing dictionary lookup when key exists

Why This Is Faster:

Python's exception handling is expensive because:

1. Creating and raising exceptions involves substantial overhead (stack unwinding, traceback construction)
2. The try-except machinery adds runtime cost even when no exception occurs

In the optimized version:

• For missing attributes (most common based on line profiler): we avoid the KeyError exception entirely. The in operator checks dictionary membership directly, then raises AttributeError without the KeyError overhead. This explains the 32-40% speedup on test cases involving non-existent attributes.
• For existing attributes: there's a slight overhead from the extra membership check before the dictionary lookup, explaining the 0.8-21% slowdown on successful lookups in some tests.

Performance Characteristics:

Based on annotated tests, the optimization is particularly beneficial when:

• Accessing non-existent attributes (32-40% faster) - common in error paths or hasattr() checks
• Working with many failed lookups (as in test_getattr_nonexistent_* cases)

The optimization performs slightly worse when:

• Repeatedly accessing existing attributes with long keys (up to 21% slower for key_999 in large dictionaries)
• The attribute exists and is accessed frequently in tight loops

Overall Impact:

The 9% overall speedup suggests that in typical usage of DPSolveResult, there's a mix of successful and failed attribute lookups. The optimization trades a small cost on successful lookups for substantial gains on failed ones, resulting in net positive performance. Since __getattr__ is only called when normal attribute lookup fails (after checking instance __dict__ and class attributes), the function likely handles many "missing attribute" cases, making the exception-avoidance strategy worthwhile.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 83 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Click to see Generated Regression Tests

import pytest
from quantecon.markov.ddp import DPSolveResult

# unit tests

# ------------------ Basic Test Cases ------------------

def test_get_existing_attribute_returns_value():
    # Test that __getattr__ returns the correct value for an existing key
    res = DPSolveResult(v=42, sigma=[1, 2, 3])
    codeflash_output = res.__getattr__('v') # 1.25μs -> 1.26μs (0.794% slower)
    codeflash_output = res.__getattr__('sigma') # 418ns -> 382ns (9.42% faster)

def test_get_existing_attribute_with_different_types():
    # Test with different value types
    res = DPSolveResult(num_iter=10, method='value_iteration', epsilon=1e-6)
    codeflash_output = res.__getattr__('num_iter') # 1.13μs -> 1.24μs (8.57% slower)
    codeflash_output = res.__getattr__('method') # 416ns -> 386ns (7.77% faster)

def test_set_and_get_attribute():
    # Test that setting an attribute and then getting it works
    res = DPSolveResult()
    res.v = 123
    codeflash_output = res.__getattr__('v') # 1.15μs -> 1.27μs (9.32% slower)

def test_del_attribute():
    # Test that deleting an attribute removes it from the dict
    res = DPSolveResult(v=5)
    del res.v
    with pytest.raises(AttributeError):
        res.__getattr__('v') # 2.52μs -> 1.80μs (40.1% faster)

def test_getattr_and_dict_are_consistent():
    # Test that __getattr__ and dict access are consistent
    res = DPSolveResult(a=1, b=2)
    for k in ['a', 'b']:
        codeflash_output = res.__getattr__(k) # 1.71μs -> 1.80μs (5.06% slower)

# ------------------ Edge Test Cases ------------------

def test_getattr_nonexistent_raises_attributeerror():
    # Test that accessing a non-existent attribute raises AttributeError
    res = DPSolveResult()
    with pytest.raises(AttributeError) as excinfo:
        res.__getattr__('does_not_exist') # 2.49μs -> 1.88μs (32.7% faster)

def test_getattr_with_non_string_key():
    # Test that __getattr__ with a non-string key raises AttributeError
    res = DPSolveResult()
    with pytest.raises(AttributeError):
        res.__getattr__(123) # 2.55μs -> 1.92μs (32.8% faster)

def test_setattr_and_delattr_with_weird_names():
    # Test that weird attribute names (with spaces, unicode, etc.) work
    res = DPSolveResult()
    res.__setattr__('strange name', 99)
    codeflash_output = res.__getattr__('strange name') # 1.24μs -> 1.23μs (0.898% faster)
    res.__setattr__('üñîçødë', 'ok')
    codeflash_output = res.__getattr__('üñîçødë') # 489ns -> 506ns (3.36% slower)
    res.__delattr__('strange name')

def test_repr_and_dir_methods():
    # Test that __repr__ and __dir__ behave as expected
    res = DPSolveResult(a=1, b=2)
    r = repr(res)
    d = res.__dir__()

def test_empty_repr_and_dir():
    # Test __repr__ and __dir__ on empty DPSolveResult
    res = DPSolveResult()

def test_overwrite_existing_key():
    # Test that overwriting an existing key updates the value
    res = DPSolveResult(a=1)
    res.a = 2

def test_attribute_shadowing_builtin_dict_methods():
    # Test that keys shadowing dict methods are accessed as attributes
    res = DPSolveResult(keys='my_keys', items='my_items')
    codeflash_output = res.__getattr__('keys') # 1.16μs -> 1.26μs (7.79% slower)
    codeflash_output = res.__getattr__('items') # 436ns -> 497ns (12.3% slower)

# ------------------ Large Scale Test Cases ------------------

def test_large_number_of_keys():
    # Test with a large number of keys (scalability)
    n = 1000
    keys = [f'key_{i}' for i in range(n)]
    values = [i for i in range(n)]
    res = DPSolveResult(zip(keys, values))
    # Check a few random keys
    codeflash_output = res.__getattr__('key_0') # 1.39μs -> 1.44μs (3.55% slower)
    codeflash_output = res.__getattr__(f'key_{n//2}') # 553ns -> 669ns (17.3% slower)
    codeflash_output = res.__getattr__(f'key_{n-1}') # 371ns -> 470ns (21.1% slower)

def test_large_value_object():
    # Test with a large value object (e.g., a large list)
    large_list = list(range(1000))
    res = DPSolveResult(large=large_list)
    codeflash_output = res.__getattr__('large') # 1.20μs -> 1.26μs (5.30% slower)

def test_performance_of_getattr_on_large_dict(benchmark):
    # Benchmark the __getattr__ performance on a large dict
    n = 1000
    keys = [f'key_{i}' for i in range(n)]
    values = [i for i in range(n)]
    res = DPSolveResult(zip(keys, values))
    # Benchmark access to a key near the end
    def access():
        return res.__getattr__(f'key_{n-1}')
    result = benchmark(access)

# ------------------ Miscellaneous / Defensive Test Cases ------------------

def test_setattr_and_delattr_are_synced_with_dict():
    # Test that __setattr__ and __delattr__ reflect in the dict
    res = DPSolveResult()
    res.__setattr__('foo', 123)
    res.__delattr__('foo')

def test_attribute_error_message_is_precise():
    # Test that the AttributeError message is exactly the missing attribute name
    res = DPSolveResult()
    with pytest.raises(AttributeError) as excinfo:
        res.__getattr__('missing') # 2.57μs -> 2.21μs (16.6% faster)

def test_access_after_delattr_raises():
    # Test that after deleting an attribute, accessing it raises AttributeError
    res = DPSolveResult(bar=999)
    res.__delattr__('bar')
    with pytest.raises(AttributeError):
        res.__getattr__('bar') # 2.48μs -> 1.89μs (31.3% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import sys

# imports
import pytest  # used for our unit tests
from quantecon.markov.ddp import DPSolveResult

# unit tests

class TestDPSolveResultGetAttrBasic:
    """Basic test cases for DPSolveResult.__getattr__ functionality."""
    
    def test_getattr_simple_string_value(self):
        """Test accessing a simple string value via attribute access."""
        # Create a DPSolveResult with a string value
        result = DPSolveResult()
        result['method'] = 'value_iteration'
    
    def test_getattr_integer_value(self):
        """Test accessing an integer value via attribute access."""
        # Create a DPSolveResult with an integer value
        result = DPSolveResult()
        result['num_iter'] = 42
    
    def test_getattr_float_value(self):
        """Test accessing a float value via attribute access."""
        # Create a DPSolveResult with a float value
        result = DPSolveResult()
        result['epsilon'] = 1e-6
    
    def test_getattr_list_value(self):
        """Test accessing a list value via attribute access."""
        # Create a DPSolveResult with a list value
        result = DPSolveResult()
        result['v'] = [1.0, 2.0, 3.0]
    
    def test_getattr_dict_value(self):
        """Test accessing a nested dict value via attribute access."""
        # Create a DPSolveResult with a dict value
        result = DPSolveResult()
        result['params'] = {'alpha': 0.5, 'beta': 0.9}
    
    def test_getattr_none_value(self):
        """Test accessing a None value via attribute access."""
        # Create a DPSolveResult with a None value
        result = DPSolveResult()
        result['mc'] = None
    
    def test_getattr_boolean_true(self):
        """Test accessing a boolean True value via attribute access."""
        # Create a DPSolveResult with a boolean value
        result = DPSolveResult()
        result['converged'] = True
    
    def test_getattr_boolean_false(self):
        """Test accessing a boolean False value via attribute access."""
        # Create a DPSolveResult with a boolean value
        result = DPSolveResult()
        result['converged'] = False
    
    def test_getattr_multiple_attributes(self):
        """Test accessing multiple attributes from the same object."""
        # Create a DPSolveResult with multiple values
        result = DPSolveResult()
        result['method'] = 'policy_iteration'
        result['num_iter'] = 10
        result['epsilon'] = 1e-5

class TestDPSolveResultGetAttrEdgeCases:
    """Edge case tests for DPSolveResult.__getattr__ functionality."""
    
    def test_getattr_nonexistent_attribute_raises_attribute_error(self):
        """Test that accessing a nonexistent attribute raises AttributeError."""
        # Create an empty DPSolveResult
        result = DPSolveResult()
        
        # Attempt to access a nonexistent attribute should raise AttributeError
        with pytest.raises(AttributeError):
            _ = result.nonexistent_attr
    
    def test_getattr_attribute_error_message(self):
        """Test that AttributeError contains the attribute name."""
        # Create an empty DPSolveResult
        result = DPSolveResult()
        
        # Attempt to access a nonexistent attribute and check error message
        with pytest.raises(AttributeError) as exc_info:
            _ = result.missing_key
    
    def test_getattr_empty_string_key(self):
        """Test accessing an attribute with an empty string as key."""
        # Create a DPSolveResult with an empty string key
        result = DPSolveResult()
        result[''] = 'empty_key_value'
    
    def test_getattr_key_with_spaces(self):
        """Test accessing an attribute with spaces in the key name."""
        # Create a DPSolveResult with a key containing spaces
        result = DPSolveResult()
        result['key with spaces'] = 'value'
    
    def test_getattr_key_with_special_characters(self):
        """Test accessing an attribute with special characters in the key."""
        # Create a DPSolveResult with special characters in key
        result = DPSolveResult()
        result['key-with-dashes'] = 'dash_value'
        result['key_with_underscores'] = 'underscore_value'
        result['key.with.dots'] = 'dot_value'
    
    def test_getattr_numeric_string_key(self):
        """Test accessing an attribute with a numeric string as key."""
        # Create a DPSolveResult with numeric string keys
        result = DPSolveResult()
        result['123'] = 'numeric_string_value'
    
    def test_getattr_unicode_key(self):
        """Test accessing an attribute with unicode characters in key."""
        # Create a DPSolveResult with unicode key
        result = DPSolveResult()
        result['κλειδί'] = 'greek_value'
        result['键'] = 'chinese_value'
    
    def test_getattr_zero_value(self):
        """Test accessing an attribute with zero as value."""
        # Create a DPSolveResult with zero values
        result = DPSolveResult()
        result['zero_int'] = 0
        result['zero_float'] = 0.0
    
    def test_getattr_negative_value(self):
        """Test accessing an attribute with negative values."""
        # Create a DPSolveResult with negative values
        result = DPSolveResult()
        result['negative_int'] = -42
        result['negative_float'] = -3.14
    
    def test_getattr_very_long_key(self):
        """Test accessing an attribute with a very long key name."""
        # Create a DPSolveResult with a very long key
        long_key = 'a' * 1000
        result = DPSolveResult()
        result[long_key] = 'long_key_value'
    
    def test_getattr_tuple_value(self):
        """Test accessing a tuple value via attribute access."""
        # Create a DPSolveResult with a tuple value
        result = DPSolveResult()
        result['dimensions'] = (10, 20, 30)
    
    def test_getattr_set_value(self):
        """Test accessing a set value via attribute access."""
        # Create a DPSolveResult with a set value
        result = DPSolveResult()
        result['states'] = {1, 2, 3, 4, 5}
    
    def test_getattr_callable_value(self):
        """Test accessing a callable (function) value via attribute access."""
        # Create a DPSolveResult with a function value
        def test_func():
            return 42
        
        result = DPSolveResult()
        result['callback'] = test_func
    
    def test_getattr_class_value(self):
        """Test accessing a class value via attribute access."""
        # Create a DPSolveResult with a class value
        class TestClass:
            pass
        
        result = DPSolveResult()
        result['cls'] = TestClass
    
    def test_getattr_instance_value(self):
        """Test accessing a class instance value via attribute access."""
        # Create a DPSolveResult with a class instance value
        class TestClass:
            def __init__(self):
                self.value = 100
        
        instance = TestClass()
        result = DPSolveResult()
        result['instance'] = instance
    
    def test_getattr_after_deletion_raises_attribute_error(self):
        """Test that accessing a deleted attribute raises AttributeError."""
        # Create a DPSolveResult with a value
        result = DPSolveResult()
        result['temp'] = 'temporary'
        
        # Delete the attribute
        del result['temp']
        
        # Attempt to access the deleted attribute should raise AttributeError
        with pytest.raises(AttributeError):
            _ = result.temp
    
    def test_getattr_overwritten_value(self):
        """Test accessing an attribute that has been overwritten."""
        # Create a DPSolveResult with a value
        result = DPSolveResult()
        result['value'] = 'original'
        
        # Overwrite the value
        result['value'] = 'updated'
    
    def test_getattr_inf_value(self):
        """Test accessing infinity values via attribute access."""
        # Create a DPSolveResult with infinity values
        result = DPSolveResult()
        result['pos_inf'] = float('inf')
        result['neg_inf'] = float('-inf')
    
    def test_getattr_nan_value(self):
        """Test accessing NaN value via attribute access."""
        # Create a DPSolveResult with NaN value
        result = DPSolveResult()
        result['nan_val'] = float('nan')
        
        # Access the value via attribute notation
        # NaN is not equal to itself, so we check using a different method
        import math
    
    def test_getattr_complex_number_value(self):
        """Test accessing complex number value via attribute access."""
        # Create a DPSolveResult with complex number
        result = DPSolveResult()
        result['complex_val'] = 3 + 4j
    
    def test_getattr_bytes_value(self):
        """Test accessing bytes value via attribute access."""
        # Create a DPSolveResult with bytes value
        result = DPSolveResult()
        result['bytes_val'] = b'hello'
    
    def test_getattr_bytearray_value(self):
        """Test accessing bytearray value via attribute access."""
        # Create a DPSolveResult with bytearray value
        result = DPSolveResult()
        result['bytearray_val'] = bytearray(b'world')

class TestDPSolveResultGetAttrLargeScale:
    """Large scale test cases for DPSolveResult.__getattr__ functionality."""
    
    def test_getattr_many_attributes(self):
        """Test accessing many attributes from a single object."""
        # Create a DPSolveResult with many attributes
        result = DPSolveResult()
        num_attrs = 500
        
        # Add many attributes
        for i in range(num_attrs):
            result[f'attr_{i}'] = i
        
        # Access all attributes and verify values
        for i in range(num_attrs):
            pass
    
    def test_getattr_large_list_value(self):
        """Test accessing a large list value via attribute access."""
        # Create a DPSolveResult with a large list
        result = DPSolveResult()
        large_list = list(range(1000))
        result['large_list'] = large_list
    
    def test_getattr_large_dict_value(self):
        """Test accessing a large dict value via attribute access."""
        # Create a DPSolveResult with a large dict
        result = DPSolveResult()
        large_dict = {f'key_{i}': i for i in range(1000)}
        result['large_dict'] = large_dict
    
    def test_getattr_deeply_nested_structure(self):
        """Test accessing deeply nested data structures via attribute access."""
        # Create a DPSolveResult with deeply nested structure
        result = DPSolveResult()
        
        # Create a nested structure (depth of 100)
        nested = {'level': 0}
        current = nested
        for i in range(1, 100):
            current['next'] = {'level': i}
            current = current['next']
        
        result['nested'] = nested
        
        # Navigate through the nested structure
        current = result.nested
        for i in range(99):
            current = current['next']
    
    def test_getattr_large_string_value(self):
        """Test accessing a very large string value via attribute access."""
        # Create a DPSolveResult with a large string
        result = DPSolveResult()
        large_string = 'x' * 10000
        result['large_string'] = large_string
    
    def test_getattr_repeated_access_performance(self):
        """Test repeated access to the same attribute."""
        # Create a DPSolveResult with a value
        result = DPSolveResult()
        result['value'] = 42
        
        # Access the same attribute many times
        for _ in range(1000):
            pass
    
    def test_getattr_mixed_access_patterns(self):
        """Test mixed patterns of attribute access."""
        # Create a DPSolveResult with multiple attributes
        result = DPSolveResult()
        for i in range(100):
            result[f'attr_{i}'] = i
        
        # Access attributes in various patterns
        # Sequential access
        for i in range(100):
            pass
        
        # Reverse access
        for i in range(99, -1, -1):
            pass
        
        # Random-like access pattern
        for i in [0, 50, 25, 75, 10, 90, 40, 60]:
            pass
    
    def test_getattr_list_of_dicts(self):
        """Test accessing a list of dictionaries via attribute access."""
        # Create a DPSolveResult with a list of dicts
        result = DPSolveResult()
        list_of_dicts = [{'id': i, 'value': i * 2} for i in range(500)]
        result['data'] = list_of_dicts
    
    def test_getattr_dict_of_lists(self):
        """Test accessing a dict of lists via attribute access."""
        # Create a DPSolveResult with a dict of lists
        result = DPSolveResult()
        dict_of_lists = {f'list_{i}': list(range(i, i + 100)) for i in range(0, 500, 100)}
        result['collections'] = dict_of_lists
    
    def test_getattr_multiple_nonexistent_attributes(self):
        """Test that accessing multiple nonexistent attributes all raise errors."""
        # Create an empty DPSolveResult
        result = DPSolveResult()
        
        # Attempt to access multiple nonexistent attributes
        nonexistent_attrs = [f'missing_{i}' for i in range(100)]
        
        for attr in nonexistent_attrs:
            with pytest.raises(AttributeError):
                getattr(result, attr)
    
    def test_getattr_alternating_exists_and_missing(self):
        """Test alternating between existing and missing attributes."""
        # Create a DPSolveResult with some attributes
        result = DPSolveResult()
        for i in range(0, 100, 2):  # Only even indices
            result[f'attr_{i}'] = i
        
        # Access existing and missing attributes alternately
        for i in range(100):
            if i % 2 == 0:
                pass
            else:
                with pytest.raises(AttributeError):
                    getattr(result, f'attr_{i}')
    
    def test_getattr_with_default_using_getattr_builtin(self):
        """Test using built-in getattr with default value."""
        # Create a DPSolveResult with some attributes
        result = DPSolveResult()
        result['existing'] = 'value'
    
    def test_getattr_stress_test_rapid_additions_and_access(self):
        """Stress test with rapid additions and accesses."""
        # Create a DPSolveResult
        result = DPSolveResult()
        
        # Rapidly add and access attributes
        for i in range(500):
            result[f'key_{i}'] = i
            
            # Also access a few previously added attributes
            if i > 0:
                pass
            if i > 10:
                pass
# 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-DPSolveResult.__getattr__-mjw3hp1v and push.