Power #28
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Power #28
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alexdtat
requested changes
Dec 8, 2024
| @@ -0,0 +1,39 @@ | |||
| from pathlib import Path | |||
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Add here docstring description of class, specifying tested hypotheses.
| @staticmethod | ||
| def __checking_near_point(container: CPContainer): | ||
| """Checks whether at least one change point is about the expected ones.""" | ||
| margin = 50 |
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Imo it'd be better to make it a parameter.
| return False | ||
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| for detected in detected_points: | ||
| if any(expected - margin <= detected <= expected + margin for expected in expected_points): |
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It seems at least this if can be turned into boolean return. You can expand this further if you want, but that would probably degrade readability already. The main thing is to be careful with empty cases.
| return False | ||
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| def calculate_power(self): | ||
| """Calculates the power of the algorithm based on hits about the disorder.""" |
| time_sec=mock_data["time_sec"], | ||
| ) | ||
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| # Инициализация Power и расчет мощности |
| with TemporaryDirectory() as temp_dir: | ||
| root_path = Path(temp_dir) | ||
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| # Создание mock-файлов CSV |
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Added new Power class for calculating the statistical power of change point detection (CPD) algorithms by analyzing CSV results stored in directories. Additionally, two new methods, to_csv and from_csv, are added to the CPContainer class to enable saving and loading of CPD results in a structured CSV format.
Usage: