Source code for asreview.models.balance.double

# 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.
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from math import floor
from math import log

import numpy as np

from asreview.models.balance.base import BaseBalance
from asreview.models.balance.simple import SimpleBalance
from asreview.utils import get_random_state

[docs]class DoubleBalance(BaseBalance): """Double balance strategy (``double``). Class to get the two way rebalancing function and arguments. It super samples ones depending on the number of 0's and total number of samples in the training data. Arguments --------- a: float Governs the weight of the 1's. Higher values mean linearly more 1's in your training sample. alpha: float Governs the scaling the weight of the 1's, as a function of the ratio of ones to zeros. A positive value means that the lower the ratio of zeros to ones, the higher the weight of the ones. b: float Governs how strongly we want to sample depending on the total number of samples. A value of 1 means no dependence on the total number of samples, while lower values mean increasingly stronger dependence on the number of samples. beta: float Governs the scaling of the weight of the zeros depending on the number of samples. Higher values means that larger samples are more strongly penalizing zeros. """ name = "double" label = "Dynamic resampling (Double)" def __init__(self, a=2.155, alpha=0.94, b=0.789, beta=1.0, random_state=None): super(DoubleBalance, self).__init__() self.a = a self.alpha = alpha self.b = b self.beta = beta self.fallback_model = SimpleBalance() 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. """ # Get inclusions and exclusions one_idx = train_idx[np.where(y[train_idx] == 1)] zero_idx = train_idx[np.where(y[train_idx] == 0)] # Fall back to simple sampling if we have only ones or zeroes. if len(one_idx) == 0 or len(zero_idx) == 0: self.fallback_model.sample(X, y, train_idx) n_one = len(one_idx) n_zero = len(zero_idx) n_train = n_one + n_zero # Compute sampling weights. one_weight = _one_weight(n_one, n_zero, self.a, self.alpha) zero_weight = _zero_weight(n_one + n_zero, self.b, self.beta) tot_zo_weight = one_weight * n_one + zero_weight * n_zero # Number of inclusions to sample. n_one_train = random_round( one_weight * n_one * n_train / tot_zo_weight, self._random_state) # Should be at least 1, and at least two spots should be for exclusions. n_one_train = max(1, min(n_train - 2, n_one_train)) # Number of exclusions to sample n_zero_train = n_train - n_one_train # Sample records of ones and zeroes one_train_idx = fill_training(one_idx, n_one_train, self._random_state) zero_train_idx = fill_training(zero_idx, n_zero_train, self._random_state) # Merge and shuffle. all_idx = np.concatenate([one_train_idx, zero_train_idx]) self._random_state.shuffle(all_idx) # Return resampled feature matrix and labels. return X[all_idx], y[all_idx]
[docs] def full_hyper_space(self): from hyperopt import hp parameter_space = { "bal_a": hp.lognormal("bal_a", 0, 1), "bal_alpha": hp.uniform("bal_alpha", 0, 2), "bal_b": hp.uniform("bal_b", 0, 1), # "bal_beta": hp.uniform("bal_beta", 0, 2), } return parameter_space, {}
def _one_weight(n_one, n_zero, a, alpha): """Get the weight of the ones.""" weight = a * (n_one / n_zero)**(-alpha) return weight def _zero_weight(n_read, b, beta): """Get the weight of the zeros.""" weight = 1 - (1 - b) * (1 + log(n_read))**(-beta) return weight def random_round(value, random_state): """Round up or down, depending on how far the value is. For example: 8.1 would be rounded to 8, 90% of the time, and rounded to 9, 10% of the time. """ base = int(floor(value)) if random_state.rand() < value - base: base += 1 return base def fill_training(src_idx, n_train, random_state): """Copy/sample until there are n_train indices sampled/copied. """ # Number of copies needed. n_copy = int(n_train / len(src_idx)) # For the remainder, use sampling. n_sample = n_train - n_copy * len(src_idx) # Copy indices dest_idx = np.tile(src_idx, n_copy).reshape(-1) # Add samples dest_idx = np.append(dest_idx, random_state.choice(src_idx, n_sample, replace=False)) return dest_idx