"""

sampler.py

=======================

Balanced sampling based on one of the columns of the patch information.

"""

import torch

import torch.utils.data

import torchvision

import numpy as np

class ImbalancedDatasetSampler(torch.utils.data.sampler.Sampler):

    """Samples elements randomly from a given list of indices for imbalanced dataset

    https://raw.githubusercontent.com/ufoym/imbalanced-dataset-sampler/master/sampler.py

    Arguments:

        indices (list, optional): a list of indices

        num_samples (int, optional): number of samples to draw

    """

    def __init__(self, dataset, indices=None, num_samples=None):

        # if indices is not provided,

        # all elements in the dataset will be considered

        self.indices = list(range(len(dataset))) \

            if indices is None else indices

        self.n_targets=len(dataset.targets)

        # if num_samples is not provided,

        # draw `len(indices)` samples in each iteration

        self.num_samples = len(self.indices) \

            if num_samples is None else num_samples

        # distribution of classes in the dataset

        label_to_count = {}

        for idx in self.indices:

            label = self._get_label(dataset, idx)

            if label in label_to_count:

                label_to_count[label] += 1

            else:

                label_to_count[label] = 1

        # weight for each sample

        weights = [1.0 / label_to_count[self._get_label(dataset, idx)]

                   for idx in self.indices]

        self.weights = torch.DoubleTensor(weights)

    def _get_label(self, dataset, idx):

        dataset_type = type(dataset)

        if dataset_type is torchvision.datasets.MNIST:

            return dataset.train_labels[idx].item()

        elif dataset_type is torchvision.datasets.ImageFolder:

            return dataset.imgs[idx][1]

        else:

            y=dataset.patch_info.iloc[idx][dataset.targets]

            if not isinstance(y,np.float):

                y=y.values

            if self.n_targets>1:

                y=np.argmax(y)

            elif isinstance(y,(list,np.ndarray)):

                y=y[0]

            #print(y)

            return int(y)

    def __iter__(self):

        return (self.indices[i] for i in torch.multinomial(

            self.weights, self.num_samples, replacement=True))

    def __len__(self):

        return self.num_samples

"""MIT License

Copyright (c) 2018 Ming

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE."""