from evalml.pipelines.components.transformers.samplers.base_sampler import (
BaseOversampler,
)
from evalml.utils.woodwork_utils import infer_feature_types
[docs]class SMOTEOversampler(BaseOversampler):
"""SMOTE Oversampler component. Works on numerical datasets only. This component is only run during training and not during predict.
Arguments:
sampling_ratio (float): This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio
of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class
and the values responding to the number of samples. Defaults to 0.25.
k_neighbors_default (int): The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller
if there are less samples. Defaults to 5.
n_jobs (int): The number of CPU cores to use. Defaults to -1.
random_seed (int): The seed to use for random sampling. Defaults to 0.
"""
name = "SMOTE Oversampler"
hyperparameter_ranges = {}
"""{}"""
def __init__(
self,
sampling_ratio=0.25,
k_neighbors_default=5,
n_jobs=-1,
random_seed=0,
**kwargs,
):
super().__init__(
"SMOTE",
sampling_ratio=sampling_ratio,
k_neighbors_default=k_neighbors_default,
n_jobs=n_jobs,
random_seed=random_seed,
**kwargs,
)
[docs]class SMOTENCOversampler(BaseOversampler):
"""SMOTENC Oversampler component. Uses SMOTENC to generate synthetic samples. Works on a mix of numerical and categorical columns.
Input data must be Woodwork type, and this component is only run during training and not during predict.
Arguments:
sampling_ratio (float): This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio
of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class
and the values responding to the number of samples. Defaults to 0.25.
k_neighbors_default (int): The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller
if there are less samples. Defaults to 5.
n_jobs (int): The number of CPU cores to use. Defaults to -1.
random_seed (int): The seed to use for random sampling. Defaults to 0.
"""
name = "SMOTENC Oversampler"
hyperparameter_ranges = {}
"""{}"""
def __init__(
self,
sampling_ratio=0.25,
k_neighbors_default=5,
n_jobs=-1,
random_seed=0,
**kwargs,
):
self.categorical_features = None
super().__init__(
"SMOTENC",
sampling_ratio=sampling_ratio,
k_neighbors_default=k_neighbors_default,
n_jobs=n_jobs,
random_seed=random_seed,
**kwargs,
)
def _get_categorical(self, X):
X = infer_feature_types(X)
self.categorical_features = [
i
for i, val in enumerate(X.ww.types["Logical Type"].items())
if str(val[1]) in {"Boolean", "Categorical"}
]
self._parameters["categorical_features"] = self.categorical_features
[docs] def fit(self, X, y):
# get categorical features first
self._get_categorical(X)
super().fit(X, y)
return self
[docs]class SMOTENOversampler(BaseOversampler):
"""
SMOTEN Oversampler component. Uses SMOTEN to generate synthetic samples. Works for purely categorical datasets.
This component is only run during training and not during predict.
Arguments:
sampling_ratio (float): This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio
of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class
and the values responding to the number of samples. Defaults to 0.25.
k_neighbors_default (int): The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller
if there are less samples. Defaults to 5.
n_jobs (int): The number of CPU cores to use. Defaults to -1.
random_seed (int): The seed to use for random sampling. Defaults to 0.
"""
name = "SMOTEN Oversampler"
hyperparameter_ranges = {}
"""{}"""
def __init__(
self,
sampling_ratio=0.25,
k_neighbors_default=5,
n_jobs=-1,
random_seed=0,
**kwargs,
):
super().__init__(
"SMOTEN",
sampling_ratio=sampling_ratio,
k_neighbors_default=k_neighbors_default,
n_jobs=n_jobs,
random_seed=random_seed,
**kwargs,
)