def __init__(self,
sampling_ratio=0.25,
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.
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
}
parameters.update(kwargs)
sampler = BalancedClassificationSampler(**parameters,
random_seed=random_seed)
super().__init__(parameters=parameters,
component_obj=sampler,
random_seed=random_seed)
def __init__(self, sampling_ratio=0.25, min_samples=100, min_percentage=0.1, n_splits=3, shuffle=True, random_seed=0):
"""Create Balanced Classification Data CV splitter
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.
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.
n_splits (int): The number of splits to use for cross validation. Defaults to 3.
shuffle (bool): Whether or not to shuffle the data before splitting. Defaults to True.
random_seed (int): The seed to use for random sampling. Defaults to 0.
"""
self.sampler = BalancedClassificationSampler(sampling_ratio=sampling_ratio, min_samples=min_samples, min_percentage=min_percentage, random_seed=random_seed)
super().__init__(sampler=self.sampler, n_splits=n_splits, random_seed=random_seed)
self.shuffle = shuffle
self.sampling_ratio = sampling_ratio
self.min_samples = min_samples
self.min_percentage = min_percentage
self.splitter = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=random_seed)
def __init__(self,
balanced_ratio=4,
min_samples=100,
min_percentage=0.1,
test_size=0.25,
shuffle=True,
random_seed=0):
"""Create Balanced Classification Data TV splitter
Arguments:
balanced_ratio (float): The largest majority:minority ratio that is accepted as 'balanced'. For instance, a 4:1 ratio would be
represented as 4, while a 6:5 ratio is 1.2. Must be greater than or equal to 1 (or 1:1). Defaults to 4.
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.
test_size (float): The size of the test split. Defaults to 0.25.
shuffle (bool): Whether or not to shuffle the data before splitting. Defaults to True.
random_seed (int): The seed to use for random sampling. Defaults to 0.
"""
self.sampler = BalancedClassificationSampler(
balanced_ratio=balanced_ratio,
min_samples=min_samples,
min_percentage=min_percentage,
random_seed=random_seed)
super().__init__(sampler=self.sampler,
n_splits=1,
random_seed=random_seed)
self.shuffle = shuffle
self.test_size = test_size
self.balanced_ratio = balanced_ratio
self.min_samples = min_samples
self.min_percentage = min_percentage
self.splitter = TrainingValidationSplit(test_size=test_size,
shuffle=shuffle,
random_seed=random_seed)