from imblearn.over_sampling import SMOTE
from DashAI.back.converters.imbalanced_learn_wrapper import ImbalancedLearnWrapper
from DashAI.back.core.schema_fields import (
enum_field,
float_field,
int_field,
none_type,
schema_field,
union_type,
)
from DashAI.back.core.schema_fields.base_schema import BaseSchema
class SMOTESchema(BaseSchema):
sampling_strategy: schema_field(
union_type(float_field(gt=0.0, le=1.0), enum_field(["auto"])),
"auto",
"Sampling strategy (float or 'auto') to determine minority class size.",
) # type: ignore
random_state: schema_field(
none_type(int_field()),
None,
"Seed for reproducibility.",
) # type: ignore
k_neighbors: schema_field(
int_field(ge=1),
5,
"Number of neighbors to use for generating synthetic samples.",
) # type: ignore
[docs]
class SMOTEConverter(ImbalancedLearnWrapper, SMOTE):
SCHEMA = SMOTESchema
DESCRIPTION = "SMOTE: Synthetic Minority Over-sampling Technique."
def __init___(self, **kwargs):
super(SMOTEConverter, self).__init__(**kwargs)