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Enhanced get_binned_data Function with Flexible Fill Methods and Dynamic Scaling #1660

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Enhanced get_binned_data Function with Flexible Fill Methods and Dynamic Scaling #1660

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49 changes: 32 additions & 17 deletions src/evidently/legacy/calculations/stattests/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,23 @@ def get_unique_not_nan_values_list_from_series(current_data: pd.Series, referenc


def get_binned_data(
reference_data: pd.Series, current_data: pd.Series, feature_type: ColumnType, n: int, feel_zeroes: bool = True
reference_data: pd.Series,
current_data: pd.Series,
feature_type: ColumnType,
n: int,
feel_zeroes: bool = True,
fill_method: str = "auto",
dynamic_scale: bool = False,
):
"""Split variable into n buckets based on reference quantiles
Args:
reference_data: reference data
current_data: current data
feature_type: feature type
n: number of quantiles
feel_zeroes: whether to fill zero percentages
fill_method: method to calculate fill value ('auto', 'min', 'mean')
dynamic_scale: whether to use dynamic scaling for fill value
Returns:
reference_percents: % of records in each bucket for reference
current_percents: % of records in each bucket for current
Expand All @@ -39,22 +48,28 @@ def get_binned_data(
current_percents = np.array([current_feature_dict[key] / len(current_data) for key in keys])

if feel_zeroes:
np.place(
reference_percents,
reference_percents == 0,
min(reference_percents[reference_percents != 0]) / 10**6
if min(reference_percents[reference_percents != 0]) <= 0.0001
else 0.0001,
)
np.place(
current_percents,
current_percents == 0,
min(current_percents[current_percents != 0]) / 10**6
if min(current_percents[current_percents != 0]) <= 0.0001
else 0.0001,
)

return reference_percents, current_percents
min_non_zero_ref = np.min(reference_percents[reference_percents != 0])
min_non_zero_cur = np.min(current_percents[current_percents != 0])

if fill_method == "auto":
fill_value = min(min_non_zero_ref, min_non_zero_cur) / 10
fill_value = min(fill_value, min(min_non_zero_ref, min_non_zero_cur) / 2)
elif fill_method == "min":
fill_value = min(min_non_zero_ref, min_non_zero_cur)
elif fill_method == "mean":
fill_value = (min_non_zero_ref + min_non_zero_cur) / 2
else:
raise ValueError("Invalid fill_method. Choose 'auto', 'min', or 'mean'.")

if dynamic_scale:
scale_factor = min(min_non_zero_ref, min_non_zero_cur) / max(min_non_zero_ref, min_non_zero_cur)
fill_value *= scale_factor

np.place(reference_percents, reference_percents == 0, fill_value)
np.place(current_percents, current_percents == 0, fill_value)

reference_percents = reference_percents / np.sum(reference_percents)
current_percents = current_percents / np.sum(current_percents)


def permutation_test(reference_data, current_data, observed, test_statistic_func, iterations=100):
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