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[pre-commit.ci] pre-commit autoupdate #163

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[pre-commit.ci] pre-commit autoupdate #163

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72538cc
[pre-commit.ci] pre-commit autoupdate
pre-commit-ci[bot] Feb 10, 2025
978425b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 10, 2025
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8 changes: 4 additions & 4 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,25 +3,25 @@

repos:
- repo: https://github.com/psf/black
rev: 24.10.0
rev: 25.1.0
hooks:
- id: black
- repo: https://github.com/pycqa/isort
rev: 5.13.2
rev: 6.0.0
hooks:
- id: isort
name: isort (python)
args: ["--profile", "black", "--filter-files", "--skip", "__init__.py"]
files: ^dodiscover/
- repo: https://github.com/pre-commit/mirrors-mypy
rev: "v1.13.0"
rev: "v1.15.0"
hooks:
- id: mypy
name: mypy (dodiscover)
args: ["--config-file", "pyproject.toml"]
files: ^dodiscover/
- repo: https://github.com/pre-commit/mirrors-mypy
rev: "v1.13.0"
rev: "v1.15.0"
hooks:
- id: mypy
name: mypy (tests)
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6 changes: 3 additions & 3 deletions dodiscover/ci/categorical_test.py
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Expand Up @@ -57,7 +57,7 @@ def power_divergence(
Returns a tuple (chi, p_value, dof). `chi` is the
chi-squared test statistic. The `p_value` for the test, i.e. the
probability of observing the computed chi-square statistic (or an even
higher value), given the null hypothesis that X \u27C2 Y | Zs is True.
higher value), given the null hypothesis that X \u27c2 Y | Zs is True.
If boolean = True, returns True if the p_value of the test is greater
than `significance_level` else returns False.

Expand Down Expand Up @@ -112,11 +112,11 @@ def power_divergence(
# If one of the values is 0 in the 2x2 table.
if isinstance(z_state, str):
logging.info(
f"Skipping the test {X} \u27C2 {Y} | {Z[0]}={z_state}. Not enough samples"
f"Skipping the test {X} \u27c2 {Y} | {Z[0]}={z_state}. Not enough samples"
)
else:
z_str = ", ".join([f"{var}={state}" for var, state in zip(Z, z_state)])
logging.info(f"Skipping the test {X} \u27C2 {Y} | {z_str}. Not enough samples")
logging.info(f"Skipping the test {X} \u27c2 {Y} | {z_str}. Not enough samples")

if np.isnan(c):
raise RuntimeError(
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