# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ["UndersampleBalance"]
from math import ceil
import numpy as np
from asreview.models.balance.base import BaseBalance
from asreview.utils import get_random_state
[docs]
class UndersampleBalance(BaseBalance):
"""Undersampling balance strategy (``undersample``).
This undersamples the data, leaving out excluded papers so that the
included and excluded papers are in some particular ratio (closer to one).
Arguments
---------
ratio: double
Undersampling ratio of the zero's. If for example we set a ratio of
0.25, we would sample only a quarter of the zeros and all the ones.
"""
name = "undersample"
label = "Undersampling"
def __init__(self, ratio=1.0, random_state=None):
"""Initialize the undersampling balance strategy."""
super(UndersampleBalance, self).__init__()
self.ratio = ratio
self._random_state = get_random_state(random_state)
[docs]
def sample(self, X, y, train_idx):
"""Resample the training data.
Arguments
---------
X: numpy.ndarray
Complete feature matrix.
y: numpy.ndarray
Labels for all papers.
train_idx: numpy.ndarray
Training indices, that is all papers that have been reviewed.
Returns
-------
numpy.ndarray,numpy.ndarray:
X_train, y_train: the resampled matrix, labels.
"""
one_ind = train_idx[np.where(y[train_idx] == 1)]
zero_ind = train_idx[np.where(y[train_idx] == 0)]
n_one = len(one_ind)
n_zero = len(zero_ind)
# If we don't have an excess of 0's, give back all training_samples.
if n_one / n_zero >= self.ratio:
shuf_ind = np.append(one_ind, zero_ind)
else:
n_zero_epoch = ceil(n_one / self.ratio)
zero_under = self._random_state.choice(
np.arange(n_zero), n_zero_epoch, replace=False
)
shuf_ind = np.append(one_ind, zero_ind[zero_under])
self._random_state.shuffle(shuf_ind)
return X[shuf_ind], y[shuf_ind]
[docs]
def full_hyper_space(self):
from hyperopt import hp
parameter_space = {
"bal_ratio": hp.lognormal("bal_ratio", 0, 1),
}
return parameter_space, {}