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fixing pylint errors
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Maha Albashir authored and Maha Albashir committed Sep 26, 2023
1 parent 7f6d833 commit 39b845c
Showing 1 changed file with 116 additions and 91 deletions.
207 changes: 116 additions & 91 deletions acro/acro_tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,93 +134,18 @@ def crosstab( # pylint: disable=too-many-arguments,too-many-locals
normalize,
)

# suppression masks to apply based on the following checks
masks: dict[str, DataFrame] = {}

if agg_func is not None:
# create lists with single entry for when there is only one aggfunc
count_funcs: list[str] = [AGGFUNC["count"]]
neg_funcs: list[Callable] = [agg_negative]
pperc_funcs: list[Callable] = [agg_p_percent]
nk_funcs: list[Callable] = [agg_nk]
missing_funcs: list[Callable] = [agg_missing]
# then expand them to deal with extra columns as needed
if isinstance(agg_func, list):
num = len(agg_func)
count_funcs.extend([AGGFUNC["count"] for i in range(1, num)])
neg_funcs.extend([agg_negative for i in range(1, num)])
pperc_funcs.extend([agg_p_percent for i in range(1, num)])
nk_funcs.extend([agg_nk for i in range(1, num)])
missing_funcs.extend([agg_missing for i in range(1, num)])
# threshold check- doesn't matter what we pass for value

t_values = pd.crosstab( # type: ignore
index,
columns,
values=values,
rownames=rownames,
colnames=colnames,
aggfunc=count_funcs,
margins=margins,
margins_name=margins_name,
dropna=dropna,
normalize=normalize,
)

if dropna or margins:
for col in t_values.columns:
if t_values[col].sum() == 0:
t_values = t_values.drop(col, axis=1)
t_values = t_values < THRESHOLD
masks["threshold"] = t_values
# check for negative values -- currently unsupported
negative = pd.crosstab( # type: ignore
index, columns, values, aggfunc=neg_funcs, margins=margins
)
if negative.to_numpy().sum() > 0:
masks["negative"] = negative
# p-percent check
masks["p-ratio"] = pd.crosstab( # type: ignore
index,
columns,
values,
aggfunc=pperc_funcs,
margins=margins,
dropna=dropna,
)
# nk values check
masks["nk-rule"] = pd.crosstab( # type: ignore
index, columns, values, aggfunc=nk_funcs, margins=margins, dropna=dropna
)
# check for missing values -- currently unsupported
if CHECK_MISSING_VALUES:
masks["missing"] = pd.crosstab( # type: ignore
index, columns, values, aggfunc=missing_funcs, margins=margins
)
else:
# threshold check- doesn't matter what we pass for value
t_values = pd.crosstab( # type: ignore
index,
columns,
values=None,
rownames=rownames,
colnames=colnames,
aggfunc=None,
margins=margins,
margins_name=margins_name,
dropna=dropna,
normalize=normalize,
)
t_values = t_values < THRESHOLD
masks["threshold"] = t_values

# pd.crosstab returns nan for an empty cell
for name, mask in masks.items():
mask.fillna(value=1, inplace=True)
mask = mask.astype(int)
mask.replace({0: False, 1: True}, inplace=True)
masks[name] = mask

masks = create_crosstab_masks(
index,
columns,
values,
rownames,
colnames,
agg_func,
margins,
margins_name,
dropna,
normalize,
)
# build the sdc dictionary
sdc: dict = get_table_sdc(masks, self.suppress)
# get the status and summary
Expand Down Expand Up @@ -556,6 +481,107 @@ def plot( # pylint: disable=too-many-arguments,too-many-locals
return plot


def create_crosstab_masks( # pylint: disable=too-many-arguments,too-many-locals
index,
columns,
values,
rownames,
colnames,
agg_func,
margins,
margins_name,
dropna,
normalize,
):
# suppression masks to apply based on the following checks
masks: dict[str, DataFrame] = {}

if agg_func is not None:
# create lists with single entry for when there is only one aggfunc
count_funcs: list[str] = [AGGFUNC["count"]]
neg_funcs: list[Callable] = [agg_negative]
pperc_funcs: list[Callable] = [agg_p_percent]
nk_funcs: list[Callable] = [agg_nk]
missing_funcs: list[Callable] = [agg_missing]
# then expand them to deal with extra columns as needed
if isinstance(agg_func, list):
num = len(agg_func)
count_funcs.extend([AGGFUNC["count"] for i in range(1, num)])
neg_funcs.extend([agg_negative for i in range(1, num)])
pperc_funcs.extend([agg_p_percent for i in range(1, num)])
nk_funcs.extend([agg_nk for i in range(1, num)])
missing_funcs.extend([agg_missing for i in range(1, num)])

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# threshold check- doesn't matter what we pass for value

t_values = pd.crosstab( # type: ignore
index,
columns,
values=values,
rownames=rownames,
colnames=colnames,
aggfunc=count_funcs,
margins=margins,
margins_name=margins_name,
dropna=dropna,
normalize=normalize,
)

if dropna or margins:
for col in t_values.columns:
if t_values[col].sum() == 0:
t_values = t_values.drop(col, axis=1)

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t_values = t_values < THRESHOLD
masks["threshold"] = t_values
# check for negative values -- currently unsupported
negative = pd.crosstab( # type: ignore
index, columns, values, aggfunc=neg_funcs, margins=margins
)
if negative.to_numpy().sum() > 0:
masks["negative"] = negative
# p-percent check
masks["p-ratio"] = pd.crosstab( # type: ignore
index,
columns,
values,
aggfunc=pperc_funcs,
margins=margins,
dropna=dropna,
)
# nk values check
masks["nk-rule"] = pd.crosstab( # type: ignore
index, columns, values, aggfunc=nk_funcs, margins=margins, dropna=dropna
)
# check for missing values -- currently unsupported
if CHECK_MISSING_VALUES:
masks["missing"] = pd.crosstab( # type: ignore
index, columns, values, aggfunc=missing_funcs, margins=margins
)
else:
# threshold check- doesn't matter what we pass for value
t_values = pd.crosstab( # type: ignore
index,
columns,
values=None,
rownames=rownames,
colnames=colnames,
aggfunc=None,
margins=margins,
margins_name=margins_name,
dropna=dropna,
normalize=normalize,
)
t_values = t_values < THRESHOLD
masks["threshold"] = t_values

# pd.crosstab returns nan for an empty cell
for name, mask in masks.items():
mask.fillna(value=1, inplace=True)
mask = mask.astype(int)
mask.replace({0: False, 1: True}, inplace=True)
masks[name] = mask
return masks


def rounded_survival_table(survival_table):
"""Calculates the rounded surival function."""
death_censored = (
Expand Down Expand Up @@ -719,7 +745,6 @@ def agg_p_percent(vals: Series) -> bool:
whether the p percent rule is violated.
"""
assert isinstance(vals, Series), "vals is not a pandas series"
logger.debug(f"vals is {vals} with size {vals.size}")
sorted_vals = vals.sort_values(ascending=False)
total: float = sorted_vals.sum()
if total <= 0.0 or vals.size <= 1:
Expand Down Expand Up @@ -1041,7 +1066,7 @@ def get_index_columns(index, columns, data) -> tuple[list | Series, list | Serie
return index_new, columns_new


def crosstab_with_totals(
def crosstab_with_totals( # pylint: disable=too-many-arguments,too-many-locals
masks,
aggfunc,
index,
Expand Down Expand Up @@ -1131,7 +1156,7 @@ def crosstab_with_totals(
return table


def manual_crossstab_with_totals(
def manual_crossstab_with_totals( # pylint: disable=too-many-arguments,too-many-locals
table,
aggfunc,
index,
Expand Down Expand Up @@ -1188,7 +1213,7 @@ def manual_crossstab_with_totals(
"Please create a table for each aggregation function"
)
return None

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elif aggfunc is None or aggfunc == "sum" or aggfunc == "count":
if aggfunc is None or aggfunc == "sum" or aggfunc == "count":
table = recalculate_margin(table, margins_name)

elif aggfunc == "mean":
Expand Down

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