"""SMOTE Oversampler component. Will automatically select whether to use SMOTE, SMOTEN, or SMOTENC based on inputs to the component."""
from evalml.pipelines.components.transformers.samplers.base_sampler import BaseSampler
from evalml.pipelines.components.utils import make_balancing_dictionary
from evalml.utils import import_or_raise
from evalml.utils.woodwork_utils import infer_feature_types
[docs]class Oversampler(BaseSampler):
"""SMOTE Oversampler component. Will automatically select whether to use SMOTE, SMOTEN, or SMOTENC based on inputs to the component.
Args:
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.
sampling_ratio_dict (dict): A dictionary specifying the desired balanced ratio for each target value. For instance, in a binary case where class 1 is the minority, we could specify:
`sampling_ratio_dict={0: 0.5, 1: 1}`, which means we would undersample class 0 to have twice the number of samples as class 1 (minority:majority ratio = 0.5), and don't sample class 1.
Overrides sampling_ratio if provided. Defaults to None.
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 = "Oversampler"
hyperparameter_ranges = {}
_can_be_used_for_fast_partial_dependence = False
# Incompatibility https://github.com/alteryx/evalml/issues/3974
# TODO: Remove when support is added https://github.com/alteryx/evalml/issues/4067
_boolean_nullable_incompatibilities = ["X"]
_integer_nullable_incompatibilities = ["X"]
"""{}"""
def __init__(
self,
sampling_ratio=0.25,
sampling_ratio_dict=None,
k_neighbors_default=5,
n_jobs=-1,
random_seed=0,
**kwargs,
):
error_msg = "imbalanced-learn is not installed. Please install using 'pip install imbalanced-learn'"
im = import_or_raise("imblearn.over_sampling", error_msg=error_msg)
self.sampler = None
self.sampler_options = {
"SMOTE": im.SMOTE,
"SMOTENC": im.SMOTENC,
"SMOTEN": im.SMOTEN,
}
parameters = {
"sampling_ratio": sampling_ratio,
"k_neighbors_default": k_neighbors_default,
"n_jobs": n_jobs,
"sampling_ratio_dict": sampling_ratio_dict,
}
parameters.update(kwargs)
super().__init__(
parameters=parameters,
component_obj=None,
random_seed=random_seed,
)
[docs] def fit(self, X, y):
"""Fits oversampler to data.
Args:
X (pd.DataFrame): The input training data of shape [n_samples, n_features].
y (pd.Series, optional): The target training data of length [n_samples].
Returns:
self
"""
X_ww = infer_feature_types(X)
if y is None:
raise ValueError("y cannot be None")
y_ww = infer_feature_types(y)
sampler_name = self._get_best_oversampler(X_ww)
self.sampler = self.sampler_options[sampler_name]
# get categorical features first, if necessary
if sampler_name == "SMOTENC":
self._get_categorical(X_ww)
super().fit(X_ww, y_ww)
return self
def _get_best_oversampler(self, X):
cat_cols = X.ww.select(
["category", "boolean", "BooleanNullable"],
return_schema=True,
).columns
if len(cat_cols) == X.shape[1]:
return "SMOTEN"
elif not len(cat_cols):
return "SMOTE"
else:
return "SMOTENC"
def _get_categorical(self, X):
X = infer_feature_types(X)
# Grab categorical columns
self.categorical_features = [
i
for i, val in enumerate(X.ww.semantic_tags.items())
if "category" in val[1]
]
# Grab boolean columns, since SMOTE considers these categorical as well
for i, val in enumerate(X.ww.types["Logical Type"].items()):
if str(val[1]) in {"Boolean", "BooleanNullable"}:
self.categorical_features.append(i)
self._parameters["categorical_features"] = self.categorical_features
def _initialize_sampler(self, X, y):
"""Initializes the oversampler with the given sampler_ratio or sampler_ratio_dict.
If a sampler_ratio_dict is provided, we will opt to use that. Otherwise, we use will create the sampler_ratio_dict dictionary.
Args:
X (pd.DataFrame): Input features.
y (pd.Series): Target.
"""
sampler_class = self.sampler
y_pd = infer_feature_types(y)
sampler_params = {
k: v
for k, v in self.parameters.items()
if k not in ["sampling_ratio", "sampling_ratio_dict", "k_neighbors_default"]
}
if self.parameters["sampling_ratio_dict"] is not None:
# make the dictionary
dic = self._convert_dictionary(self.parameters["sampling_ratio_dict"], y_pd)
else:
# create the sampling dictionary
sampling_ratio = self.parameters["sampling_ratio"]
dic = make_balancing_dictionary(y_pd, sampling_ratio)
sampler_params["sampling_strategy"] = dic
# check for k_neighbors value
neighbors = self.parameters["k_neighbors_default"]
min_counts = y_pd.value_counts().values[-1]
if min_counts == 1:
raise ValueError(
f"Minority class needs more than 1 sample to use SMOTE!, received {min_counts} sample",
)
if min_counts <= neighbors:
neighbors = min_counts - 1
sampler_params["k_neighbors"] = neighbors
self._parameters["k_neighbors"] = neighbors
sampler = sampler_class(**sampler_params, random_state=self.random_seed)
self._component_obj = sampler
