import pandas as pd
from evalml.pipelines.components.transformers.samplers.base_sampler import (
BaseSampler,
)
from evalml.preprocessing.data_splitters.balanced_classification_sampler import (
BalancedClassificationSampler,
)
[docs]class Undersampler(BaseSampler):
"""
Initializes an undersampling transformer to downsample the majority classes in the dataset.
This component is only run during training and not during predict.
Arguments:
sampling_ratio (float): The smallest minority:majority ratio that is accepted as 'balanced'. For instance, a 1:4 ratio would be
represented as 0.25, while a 1:1 ratio is 1.0. Must be between 0 and 1, inclusive. 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.
min_samples (int): The minimum number of samples that we must have for any class, pre or post sampling. If a class must be downsampled, it will not be downsampled past this value.
To determine severe imbalance, the minority class must occur less often than this and must have a class ratio below min_percentage.
Must be greater than 0. Defaults to 100.
min_percentage (float): The minimum percentage of the minimum class to total dataset that we tolerate, as long as it is above min_samples.
If min_percentage and min_samples are not met, treat this as severely imbalanced, and we will not resample the data.
Must be between 0 and 0.5, inclusive. Defaults to 0.1.
random_seed (int): The seed to use for random sampling. Defaults to 0.
"""
name = "Undersampler"
hyperparameter_ranges = {}
"""{}"""
def __init__(
self,
sampling_ratio=0.25,
sampling_ratio_dict=None,
min_samples=100,
min_percentage=0.1,
random_seed=0,
**kwargs,
):
parameters = {
"sampling_ratio": sampling_ratio,
"min_samples": min_samples,
"min_percentage": min_percentage,
"sampling_ratio_dict": sampling_ratio_dict,
}
parameters.update(kwargs)
super().__init__(
parameters=parameters, component_obj=None, random_seed=random_seed
)
def _initialize_sampler(self, X, y):
"""Helper function to initialize the undersampler component object.
Arguments:
y (pd.Series): The target data
"""
param_dic = self._dictionary_to_params(
self.parameters["sampling_ratio_dict"], y
)
param_dic.pop("n_jobs", None)
sampler = BalancedClassificationSampler(
**param_dic, random_seed=self.random_seed
)
self._component_obj = sampler