Sample hypothesis testing. #64
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WalkthroughThe recent changes enhance the project's testing capabilities by adding the Changes
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Codecov ReportAll modified and coverable lines are covered by tests ✅ |
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Actionable comments posted: 2
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Files selected for processing (2)
- pyproject.toml (1 hunks)
- tests/test_aggregate_hypothesis.py (1 hunks)
Files skipped from review due to trivial changes (1)
- pyproject.toml
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tests/test_aggregate_hypothesis.py
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Additional comments not posted (3)
tests/test_aggregate_hypothesis.py (3)
17-28: Good use of Hypothesis strategies for datetime generation.The strategy for generating datetime values is well-defined, using a sensible range. This is important for ensuring that your tests cover a realistic span of time and can handle edge cases effectively.
19-28: Efficient setup of test dataframes using dynamic column generation.The use of list comprehension to dynamically generate columns for predicates is efficient and makes the code scalable if the number of predicates changes.
31-41: Review the test function signature and decorators.The test function is well-decorated with Hypothesis strategies, which is appropriate for property-based testing. However, ensure that the
max_examplessetting in the@settingsdecorator aligns with your testing goals, especially in terms of balancing thoroughness against test execution time.Verification successful
The
max_examplessetting in the@settingsdecorator is appropriate.The test execution time for
test_aggregate_temporal_windowis extremely short (approximately 0.003 seconds), indicating that themax_examplessetting of 50 does not cause any significant delay.
- Location:
tests/test_aggregate_hypothesis.pylines 31-41Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify if the max_examples setting is appropriate by checking test execution times. echo "Check test execution logs for the duration of test_aggregate_temporal_window to ensure it's within acceptable limits."Length of output: 247
Script:
#!/bin/bash # Locate the test_aggregate_temporal_window function in the codebase rg "def test_aggregate_temporal_window" --glob "*.py"Length of output: 126
Script:
#!/bin/bash # Execute the test suite and capture the duration of the test_aggregate_temporal_window test function pytest --durations=10 tests/test_aggregate_hypothesis.pyLength of output: 132
Script:
#!/bin/bash # Measure the duration of the test script execution using the time command { time python3 -m unittest tests/test_aggregate_hypothesis.py; } 2>&1 | grep realLength of output: 25
| import rootutils | ||
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| root = rootutils.setup_root(__file__, dotenv=True, pythonpath=True, cwd=True) | ||
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| from datetime import datetime, timedelta | ||
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| import polars as pl | ||
| import polars.selectors as cs | ||
| from hypothesis import given, settings | ||
| from hypothesis import strategies as st | ||
| from polars.testing import assert_series_equal | ||
| from polars.testing.parametric import column, dataframes | ||
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| from aces.aggregate import aggregate_temporal_window | ||
| from aces.types import TemporalWindowBounds | ||
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There was a problem hiding this comment.
Reorder imports to adhere to Python's convention.
The imports should be placed at the top of the file before any other code, including the setup of root utilities. This follows Python's convention for better readability and to avoid potential issues with import shadowing.
- import rootutils
- root = rootutils.setup_root(__file__, dotenv=True, pythonpath=True, cwd=True)
+ import rootutils
+ from datetime import datetime, timedelta
+ import polars as pl
+ import polars.selectors as cs
+ from hypothesis import given, settings
+ from hypothesis import strategies as st
+ from polars.testing import assert_series_equal
+ from polars.testing.parametric import column, dataframes
+ from aces.aggregate import aggregate_temporal_window
+ from aces.types import TemporalWindowBounds
+ root = rootutils.setup_root(__file__, dotenv=True, pythonpath=True, cwd=True)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import rootutils | |
| root = rootutils.setup_root(__file__, dotenv=True, pythonpath=True, cwd=True) | |
| from datetime import datetime, timedelta | |
| import polars as pl | |
| import polars.selectors as cs | |
| from hypothesis import given, settings | |
| from hypothesis import strategies as st | |
| from polars.testing import assert_series_equal | |
| from polars.testing.parametric import column, dataframes | |
| from aces.aggregate import aggregate_temporal_window | |
| from aces.types import TemporalWindowBounds | |
| import rootutils | |
| from datetime import datetime, timedelta | |
| import polars as pl | |
| import polars.selectors as cs | |
| from hypothesis import given, settings | |
| from hypothesis import strategies as st | |
| from polars.testing import assert_series_equal | |
| from polars.testing.parametric import column, dataframes | |
| from aces.aggregate import aggregate_temporal_window | |
| from aces.types import TemporalWindowBounds | |
| root = rootutils.setup_root(__file__, dotenv=True, pythonpath=True, cwd=True) |
Tools
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5-5: Module level import not at top of file (E402)
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Partly addresses #62 |
Summary by CodeRabbit
Tests
aggregate_temporal_windowfunction to ensure reliability and accuracy in handling temporal windows and predicates within dataframes.Chores
"hypothesis"as an optional development dependency to facilitate advanced property-based testing.