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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,54 +1,107 @@ | ||
| import collections | ||
| from __future__ import annotations | ||
|
|
||
| import warnings | ||
| from dataclasses import dataclass | ||
| from typing import Any, Callable, Iterator | ||
|
|
||
| import numpy as np | ||
| from dcor._utils import ArrayType | ||
| from joblib import Parallel, delayed | ||
|
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| from ._utils import _random_state_init | ||
|
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| HypothesisTest = collections.namedtuple('HypothesisTest', ['p_value', | ||
| 'statistic']) | ||
|
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||
| @dataclass | ||
| class HypothesisTest(): | ||
| pvalue: float | ||
| statistic: ArrayType | ||
|
|
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| @property | ||
| def p_value(self) -> float: | ||
| """Old name for pvalue.""" | ||
| warnings.warn( | ||
| "Attribute \"p_value\" deprecated, use \"pvalue\" instead.", | ||
| DeprecationWarning, | ||
| stacklevel=2, | ||
| ) | ||
| return self.pvalue | ||
|
|
||
| def __iter__(self) -> Iterator[Any]: | ||
| warnings.warn( | ||
| "HypothesisTest will cease to be iterable.", | ||
| DeprecationWarning, | ||
| ) | ||
| return iter((self.pvalue, self.statistic)) | ||
|
|
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| def __len__(self) -> int: | ||
| warnings.warn( | ||
| "HypothesisTest will cease to be sized.", | ||
| DeprecationWarning, | ||
| ) | ||
| return 2 | ||
|
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|
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| def _permuted_statistic( | ||
| matrix: ArrayType, | ||
| statistic_function: Callable[[ArrayType], ArrayType], | ||
| permutation: np.typing.NDArray[int], | ||
| ) -> ArrayType: | ||
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| permuted_matrix = matrix[np.ix_(permutation, permutation)] | ||
|
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| return statistic_function(permuted_matrix) | ||
|
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| def _permutation_test_with_sym_matrix(matrix, statistic_function, | ||
| num_resamples, random_state,n_jobs=1): | ||
|
|
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| def _permutation_test_with_sym_matrix( | ||
| matrix: ArrayType, | ||
| *, | ||
| statistic_function: Callable[[ArrayType], ArrayType], | ||
| num_resamples: int, | ||
| random_state: np.random.RandomState | np.random.Generator | int | None, | ||
| n_jobs: int | None = None, | ||
| ) -> HypothesisTest: | ||
| """ | ||
| Execute a permutation test in a symmetric matrix. | ||
| Parameters | ||
| ---------- | ||
| matrix: array_like | ||
| Matrix that will perform the permutation test. | ||
| statistic_function: callable | ||
| Function that computes the desired statistic from the matrix. | ||
| num_resamples: int | ||
| Number of permutations resamples to take in the permutation test. | ||
| random_state: {None, int, array_like, numpy.random.RandomState} | ||
| Random state to generate the permutations. | ||
| Returns | ||
| ------- | ||
| HypothesisTest | ||
| Parameters: | ||
| matrix: Matrix that will perform the permutation test. | ||
| statistic_function: Function that computes the desired statistic from | ||
| the matrix. | ||
| num_resamples: Number of permutations resamples to take in the | ||
| permutation test. | ||
| random_state: Random state to generate the permutations. | ||
| n_jobs: Number of jobs executed in parallel by Joblib. | ||
| Returns: | ||
| Results of the hypothesis test. | ||
| """ | ||
| matrix = np.asarray(matrix) | ||
| random_state = _random_state_init(random_state) | ||
|
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| statistic = statistic_function(matrix) | ||
|
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| def bootstrapPerms(mat): | ||
| permuted_index = random_state.permutation(mat.shape[0]) | ||
|
|
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| permuted_matrix = mat[ | ||
| np.ix_(permuted_index, permuted_index)] | ||
|
|
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| return statistic_function(permuted_matrix) | ||
| permutations = ( | ||
| random_state.permutation(matrix.shape[0]) | ||
| for _ in range(num_resamples) | ||
| ) | ||
|
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| bootstrap_statistics = Parallel(n_jobs=n_jobs)(delayed(bootstrapPerms)(matrix) for bootstrap in range(num_resamples)) | ||
| bootstrap_statistics = np.array(bootstrap_statistics, dtype=statistic.dtype) | ||
| bootstrap_statistics = Parallel(n_jobs=n_jobs)( | ||
| delayed(_permuted_statistic)( | ||
| matrix, | ||
| statistic_function, | ||
| permutation, | ||
| ) for permutation in permutations | ||
| ) | ||
| bootstrap_statistics = np.array( | ||
| bootstrap_statistics, | ||
| dtype=statistic.dtype, | ||
| ) | ||
|
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| extreme_results = bootstrap_statistics > statistic | ||
| p_value = (np.sum(extreme_results) + 1.0) / (num_resamples + 1) | ||
| pvalue = (np.sum(extreme_results) + 1.0) / (num_resamples + 1) | ||
|
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| return HypothesisTest( | ||
| p_value=p_value, | ||
| statistic=statistic | ||
| pvalue=pvalue, | ||
| statistic=statistic, | ||
| ) |
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