research/active_learning/DL/utils.py [307:391]:
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        labels = labels.cpu().data.numpy()
        all_pairs = np.array(list(combinations(range(len(labels)), 2)))
        all_pairs = torch.LongTensor(all_pairs)
        positive_pairs = all_pairs[(labels[all_pairs[:, 0]] == labels[all_pairs[:, 1]]).nonzero()]
        negative_pairs = all_pairs[(labels[all_pairs[:, 0]] != labels[all_pairs[:, 1]]).nonzero()]
        negative_distances = distance_matrix[negative_pairs[:, 0], negative_pairs[:, 1]]
        negative_distances = negative_distances.cpu().data.numpy()
        top_negatives = np.argpartition(negative_distances, len(positive_pairs))[:len(positive_pairs)]
        top_negative_pairs = negative_pairs[torch.LongTensor(top_negatives)]

        return positive_pairs, top_negative_pairs

class TripletSelector:
    """
    Implementation should return indices of anchors, positive and negative samples
    return np array of shape [N_triplets x 3]
    """

    def __init__(self):
        pass

    def get_pairs(self, embeddings, labels):
        raise NotImplementedError


class AllTripletSelector(TripletSelector):
    """
    Returns all possible triplets
    May be impractical in most cases
    """

    def __init__(self):
        super(AllTripletSelector, self).__init__()

    def get_triplets(self, embeddings, labels):
        labels = labels.cpu().data.numpy()
        triplets = []
        for label in set(labels):
            label_mask = (labels == label)
            label_indices = np.where(label_mask)[0]
            if len(label_indices) < 2:
                continue
            negative_indices = np.where(np.logical_not(label_mask))[0]
            anchor_positives = list(combinations(label_indices, 2))  # All anchor-positive pairs

            # Add all negatives for all positive pairs
            temp_triplets = [[anchor_positive[0], anchor_positive[1], neg_ind] for anchor_positive in anchor_positives
                             for neg_ind in negative_indices]
            triplets += temp_triplets

        return torch.LongTensor(np.array(triplets))


def hardest_negative(loss_values):
    hard_negative = np.argmax(loss_values)
    return hard_negative if loss_values[hard_negative] > 0 else None


def random_hard_negative(loss_values):
    hard_negatives = np.where(loss_values > 0)[0]
    return np.random.choice(hard_negatives) if len(hard_negatives) > 0 else None


def semihard_negative(loss_values, margin):
    semihard_negatives = np.where(np.logical_and(loss_values < margin, loss_values > 0))[0]
    return np.random.choice(semihard_negatives) if len(semihard_negatives) > 0 else None


class FunctionNegativeTripletSelector(TripletSelector):
    """
    For each positive pair, takes the hardest negative sample (with the greatest triplet loss value) to create a triplet
    Margin should match the margin used in triplet loss.
    negative_selection_fn should take array of loss_values for a given anchor-positive pair and all negative samples
    and return a negative index for that pair
    """

    def __init__(self, margin, negative_selection_fn, cpu=True):
        super(FunctionNegativeTripletSelector, self).__init__()
        self.cpu = cpu
        self.margin = margin
        self.negative_selection_fn = negative_selection_fn

    def get_triplets(self, embeddings, labels):
        if self.cpu:
            embeddings = embeddings.cpu()
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research/active_learning/deep_learning/utils.py [191:275]:
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        labels = labels.cpu().data.numpy()
        all_pairs = np.array(list(combinations(range(len(labels)), 2)))
        all_pairs = torch.LongTensor(all_pairs)
        positive_pairs = all_pairs[(labels[all_pairs[:, 0]] == labels[all_pairs[:, 1]]).nonzero()]
        negative_pairs = all_pairs[(labels[all_pairs[:, 0]] != labels[all_pairs[:, 1]]).nonzero()]
        negative_distances = distance_matrix[negative_pairs[:, 0], negative_pairs[:, 1]]
        negative_distances = negative_distances.cpu().data.numpy()
        top_negatives = np.argpartition(negative_distances, len(positive_pairs))[:len(positive_pairs)]
        top_negative_pairs = negative_pairs[torch.LongTensor(top_negatives)]

        return positive_pairs, top_negative_pairs

class TripletSelector:
    """
    Implementation should return indices of anchors, positive and negative samples
    return np array of shape [N_triplets x 3]
    """

    def __init__(self):
        pass

    def get_pairs(self, embeddings, labels):
        raise NotImplementedError


class AllTripletSelector(TripletSelector):
    """
    Returns all possible triplets
    May be impractical in most cases
    """

    def __init__(self):
        super(AllTripletSelector, self).__init__()

    def get_triplets(self, embeddings, labels):
        labels = labels.cpu().data.numpy()
        triplets = []
        for label in set(labels):
            label_mask = (labels == label)
            label_indices = np.where(label_mask)[0]
            if len(label_indices) < 2:
                continue
            negative_indices = np.where(np.logical_not(label_mask))[0]
            anchor_positives = list(combinations(label_indices, 2))  # All anchor-positive pairs

            # Add all negatives for all positive pairs
            temp_triplets = [[anchor_positive[0], anchor_positive[1], neg_ind] for anchor_positive in anchor_positives
                             for neg_ind in negative_indices]
            triplets += temp_triplets

        return torch.LongTensor(np.array(triplets))


def hardest_negative(loss_values):
    hard_negative = np.argmax(loss_values)
    return hard_negative if loss_values[hard_negative] > 0 else None


def random_hard_negative(loss_values):
    hard_negatives = np.where(loss_values > 0)[0]
    return np.random.choice(hard_negatives) if len(hard_negatives) > 0 else None


def semihard_negative(loss_values, margin):
    semihard_negatives = np.where(np.logical_and(loss_values < margin, loss_values > 0))[0]
    return np.random.choice(semihard_negatives) if len(semihard_negatives) > 0 else None


class FunctionNegativeTripletSelector(TripletSelector):
    """
    For each positive pair, takes the hardest negative sample (with the greatest triplet loss value) to create a triplet
    Margin should match the margin used in triplet loss.
    negative_selection_fn should take array of loss_values for a given anchor-positive pair and all negative samples
    and return a negative index for that pair
    """

    def __init__(self, margin, negative_selection_fn, cpu=True):
        super(FunctionNegativeTripletSelector, self).__init__()
        self.cpu = cpu
        self.margin = margin
        self.negative_selection_fn = negative_selection_fn

    def get_triplets(self, embeddings, labels):
        if self.cpu:
            embeddings = embeddings.cpu()
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