Source code for evalml.pipelines.components.transformers.samplers.undersampler

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): """Random undersampler component. This component is only run during training and not during predict.""" name = "Undersampler" hyperparameter_ranges = {}
[docs] def __init__(self, sampling_ratio=0.25, sampling_ratio_dict=None, min_samples=100, min_percentage=0.1, random_seed=0, **kwargs): """Initializes an undersampling transformer to downsample the majority classes in the dataset. 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. """ 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_undersampler(self, 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) sampler = BalancedClassificationSampler(**param_dic, random_seed=self.random_seed) self._component_obj = sampler
[docs] def fit_transform(self, X, y): """Fit and transform the data using the undersampler. Used during training of the pipeline Arguments: X (ww.DataFrame): Training features y (ww.DataColumn): Target features Returns: ww.DataTable, ww.DataColumn: Undersampled X and y data """ X, y, X_pd, y_pd = self._prepare_data(X, y) self._initialize_undersampler(y_pd) index_df = pd.Series(y_pd.index) indices = self._component_obj.fit_resample(X_pd, y_pd) train_indices = index_df[index_df.isin(indices)].index.values.tolist() return X.iloc[train_indices], y.iloc[train_indices]