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Fix formatting #116

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Merged
elbersb merged 1 commit into from
Feb 26, 2026
Merged

Fix formatting #116

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2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ classifiers = [
dependencies = [
"numpy>=1.21.0",
"scipy>=1.9.0",
"pandas>=1.4.0",
"pandas>=1.4.0,<3",
"statsmodels>=0.13.5",
"chartify>=5.0.0",
"ipywidgets>=8.0.0",
Expand Down
17 changes: 12 additions & 5 deletions spotify_confidence/analysis/confidence_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,9 @@ def remove_group_columns(categorical_columns: Iterable, additional_column: Optio
return list(od)


def validate_categorical_columns(categorical_group_columns: Union[str, Iterable]) -> None:
def validate_categorical_columns(
categorical_group_columns: Union[str, Iterable],
) -> None:
if isinstance(categorical_group_columns, str):
pass
elif isinstance(categorical_group_columns, Iterable):
Expand Down Expand Up @@ -113,15 +115,15 @@ def validate_and_rename_columns(df: DataFrame, columns: Iterable[str]) -> DataFr
if (df[column + SFX1].isna() == df[column + SFX2].isna()).all() and (
df[column + SFX1][df[column + SFX1].notna()] == df[column + SFX2][df[column + SFX2].notna()]
).all():
df = df.rename(columns={column + SFX1: column}).drop(columns=[column + SFX2]) # type: ignore[union-attr,unused-ignore]
df = df.rename(columns={column + SFX1: column}).drop(columns=[column + SFX2])
else:
raise ValueError(f"Values of {column} do not agree across levels: {df[[column + SFX1, column + SFX2]]}")
return df


def drop_and_rename_columns(df: DataFrame, columns: Iterable[str]) -> DataFrame:
columns_dict = {col + SFX1: col for col in columns}
return df.rename(columns=columns_dict).drop(columns=[col + SFX2 for col in columns]) # type: ignore[union-attr,unused-ignore]
return df.rename(columns=columns_dict).drop(columns=[col + SFX2 for col in columns])


def level2str(level: Union[str, Tuple]) -> str:
Expand All @@ -132,7 +134,10 @@ def level2str(level: Union[str, Tuple]) -> str:


def validate_data(
df: DataFrame, columns_that_must_exist, group_columns: Iterable, ordinal_group_column: Optional[str]
df: DataFrame,
columns_that_must_exist,
group_columns: Iterable,
ordinal_group_column: Optional[str],
):
"""Integrity check input dataframe."""
for col in columns_that_must_exist:
Expand Down Expand Up @@ -201,7 +206,9 @@ def axis_format_precision(numbers: Series, absolute: bool, extra_zeros: int = 0)


def to_finite(s: Series, lower_limit: float, upper_limit: float) -> Series:
return s.clip(-100 * abs(lower_limit), 100 * abs(upper_limit))
result = s.clip(-100 * abs(lower_limit), 100 * abs(upper_limit))
assert result is not None
return result


def add_color_column(df: DataFrame, cols: Iterable) -> DataFrame:
Expand Down
68 changes: 35 additions & 33 deletions spotify_confidence/analysis/frequentist/confidence_computers/confidence_computer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,43 +212,45 @@ def _sufficient_statistics(self) -> DataFrame:
self._sufficient = (
self._df.groupby(groupby, sort=False, group_keys=True)
.apply(
lambda df: df.assign(
**{
POINT_ESTIMATE: lambda df: confidence_computers[
df[self._method_column].values[0]
].point_estimate(df, **kwargs)
}
)
.assign(
**{
ORIGINAL_POINT_ESTIMATE: lambda df: (
confidence_computers[ZTEST].point_estimate(df, **kwargs)
if df[self._method_column].values[0] == ZTESTLINREG
else confidence_computers[df[self._method_column].values[0]].point_estimate(
lambda df: (
df.assign(
**{
POINT_ESTIMATE: lambda df: confidence_computers[
df[self._method_column].values[0]
].point_estimate(df, **kwargs)
}
)
.assign(
**{
ORIGINAL_POINT_ESTIMATE: lambda df: (
confidence_computers[ZTEST].point_estimate(df, **kwargs)
if df[self._method_column].values[0] == ZTESTLINREG
else confidence_computers[df[self._method_column].values[0]].point_estimate(
df, **kwargs
)
)
}
)
.assign(
**{
VARIANCE: lambda df: confidence_computers[df[self._method_column].values[0]].variance(
df, **kwargs
)
)
}
)
.assign(
**{
VARIANCE: lambda df: confidence_computers[df[self._method_column].values[0]].variance(
}
)
.assign(
**{
ORIGINAL_VARIANCE: lambda df: (
confidence_computers[ZTEST].variance(df, **kwargs)
if df[self._method_column].values[0] == ZTESTLINREG
else confidence_computers[df[self._method_column].values[0]].variance(df, **kwargs)
)
}
)
.pipe(
lambda df: confidence_computers[df[self._method_column].values[0]].add_point_estimate_ci(
df, **kwargs
)
}
)
.assign(
**{
ORIGINAL_VARIANCE: lambda df: (
confidence_computers[ZTEST].variance(df, **kwargs)
if df[self._method_column].values[0] == ZTESTLINREG
else confidence_computers[df[self._method_column].values[0]].variance(df, **kwargs)
)
}
)
.pipe(
lambda df: confidence_computers[df[self._method_column].values[0]].add_point_estimate_ci(
df, **kwargs
)
)
)
Expand Down
10 changes: 6 additions & 4 deletions spotify_confidence/analysis/frequentist/confidence_computers/sample_size_computer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,10 +131,12 @@ def _sufficient_statistics(self) -> DataFrame:
self._sufficient = (
self._df.groupby(groupby, sort=False, group_keys=True)
.apply(
lambda df: df.assign(**{POINT_ESTIMATE: lambda df: df[self._point_estimate_column]})
.assign(**{ORIGINAL_POINT_ESTIMATE: lambda df: df[self._point_estimate_column]})
.assign(**{VARIANCE: lambda df: df[self._var_column]})
.assign(**{ORIGINAL_VARIANCE: lambda df: df[self._var_column]})
lambda df: (
df.assign(**{POINT_ESTIMATE: lambda df: df[self._point_estimate_column]})
.assign(**{ORIGINAL_POINT_ESTIMATE: lambda df: df[self._point_estimate_column]})
.assign(**{VARIANCE: lambda df: df[self._var_column]})
.assign(**{ORIGINAL_VARIANCE: lambda df: df[self._var_column]})
)
)
.pipe(reset_named_indices)
)
Expand Down
6 changes: 3 additions & 3 deletions spotify_confidence/analysis/frequentist/confidence_computers/z_test_computer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,9 +163,9 @@ def adjusted_alphas_for_group(grp: DataFrame) -> Series:
data=(
df.assign(**{comparison_total_column: df[denominator + SFX1] + df[denominator + SFX2]})
.assign(
max_sample_size=lambda df: df[[comparison_total_column, final_expected_sample_size_column]]
.max(axis=1)
.max()
max_sample_size=lambda df: (
df[[comparison_total_column, final_expected_sample_size_column]].max(axis=1).max()
)
)
.assign(sample_size_proportions=lambda df: df[comparison_total_column] / df["max_sample_size"])
.pipe(adjusted_alphas_for_group)[ADJUSTED_ALPHA]
Expand Down
2 changes: 1 addition & 1 deletion spotify_confidence/analysis/frequentist/sequential_bound_solver.py
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Expand Up @@ -123,7 +123,7 @@ def last_fcab(self):

def __eq__(self, other):
if isinstance(other, ComputationState):
return self._df.equals(other._df) and np.array_equal(self._last_fcab, other._last_fcab) # type: ignore[arg-type,unused-ignore]
return self._df.equals(other._df) and np.array_equal(self._last_fcab, other._last_fcab)
return False


Expand Down
2 changes: 1 addition & 1 deletion tox.ini
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Original file line number Diff line number Diff line change
Expand Up @@ -24,5 +24,5 @@ deps =
commands =
ruff check
ruff format --check
ty check
# don't run ty check - the type stubs for 3.9 are not good.
pytest -n auto --no-cov --basetemp={envtmpdir} {posargs}