def __init__(self, reduction=tf.losses.Reduction.AUTO, name=None, lambda_weight=None): super(ApproxMRRLoss, self).__init__(reduction, name) self._loss = losses_impl.ApproxMRRLoss(name='{}_impl'.format(name), lambda_weight=lambda_weight)
def _approx_mrr_loss(labels, logits, weights=None, reduction=tf.compat.v1.losses.Reduction.SUM, name=None, alpha=10.): """Computes ApproxMRR loss. ApproxMRR ["A general approximation framework for direct optimization of information retrieval measures" by Qin et al.] is a smooth approximation to MRR. Args: labels: A `Tensor` of the same shape as `logits` representing graded relevance. logits: A `Tensor` with shape [batch_size, list_size]. Each value is the ranking score of the corresponding item. weights: A scalar, a `Tensor` with shape [batch_size, 1] for list-wise weights, or a `Tensor` with shape [batch_size, list_size] for item-wise weights. If None, the weight of a list in the mini-batch is set to the sum of the labels of the items in that list. reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. name: A string used as the name for this loss. alpha: The exponent in the generalized sigmoid function. Returns: An op for the ApproxMRR loss. """ loss = losses_impl.ApproxMRRLoss(name, params={'alpha': alpha}) with tf.compat.v1.name_scope(loss.name, 'approx_mrr_loss', (labels, logits, weights)): return loss.compute(labels, logits, weights, reduction)
def __init__(self, reduction=tf.losses.Reduction.AUTO, name=None, lambda_weight=None, temperature=0.1, ragged=False): super().__init__(reduction, name, lambda_weight, temperature, ragged) self._loss = losses_impl.ApproxMRRLoss( name='{}_impl'.format(name) if name else None, lambda_weight=lambda_weight, temperature=temperature, ragged=ragged)
def test_approx_mrr_loss(self): with tf.Graph().as_default(): scores = [[1.4, -2.8, -0.4], [0., 1.8, 10.2], [1., 1.2, -3.2]] labels = [[0., 0., 1.], [1., 0., 1.], [0., 0., 0.]] weights = [[2.], [1.], [1.]] reduction = tf.compat.v1.losses.Reduction.SUM with self.cached_session(): loss_fn = losses_impl.ApproxMRRLoss(name=None) self.assertAlmostEqual( loss_fn.compute(labels, scores, None, reduction).eval(), -((1 / 2.) + 1 / 2. * (1 / 3. + 1 / 1.)), places=5) self.assertAlmostEqual( loss_fn.compute(labels, scores, weights, reduction).eval(), -(2 * 1 / 2. + 1 * 1 / 2. * (1 / 3. + 1 / 1.)), places=5)
def make_loss_metric_fn(loss_key, weights_feature_name=None, lambda_weight=None, name=None): """Factory method to create a metric based on a loss. Args: loss_key: A key in `RankingLossKey`. weights_feature_name: A `string` specifying the name of the weights feature in `features` dict. lambda_weight: A `_LambdaWeight` object. name: A `string` used as the name for this metric. Returns: A metric fn with the following Args: * `labels`: A `Tensor` of the same shape as `predictions` representing graded relevance. * `predictions`: A `Tensor` with shape [batch_size, list_size]. Each value is the ranking score of the corresponding example. * `features`: A dict of `Tensor`s that contains all features. """ metric_dict = { RankingLossKey.PAIRWISE_HINGE_LOSS: losses_impl.PairwiseHingeLoss(name, lambda_weight=lambda_weight), RankingLossKey.PAIRWISE_LOGISTIC_LOSS: losses_impl.PairwiseLogisticLoss(name, lambda_weight=lambda_weight), RankingLossKey.PAIRWISE_SOFT_ZERO_ONE_LOSS: losses_impl.PairwiseSoftZeroOneLoss( name, lambda_weight=lambda_weight), RankingLossKey.SOFTMAX_LOSS: losses_impl.SoftmaxLoss(name, lambda_weight=lambda_weight), RankingLossKey.SIGMOID_CROSS_ENTROPY_LOSS: losses_impl.SigmoidCrossEntropyLoss(name), RankingLossKey.MEAN_SQUARED_LOSS: losses_impl.MeanSquaredLoss(name), RankingLossKey.LIST_MLE_LOSS: losses_impl.ListMLELoss(name, lambda_weight=lambda_weight), RankingLossKey.APPROX_NDCG_LOSS: losses_impl.ApproxNDCGLoss(name), RankingLossKey.APPROX_MRR_LOSS: losses_impl.ApproxMRRLoss(name), RankingLossKey.GUMBEL_APPROX_NDCG_LOSS: losses_impl.ApproxNDCGLoss(name), } def _get_weights(features): """Get weights tensor from features and reshape it to 2-D if necessary.""" weights = None if weights_feature_name: weights = tf.convert_to_tensor(value=features[weights_feature_name]) # Convert weights to a 2-D Tensor. weights = utils.reshape_to_2d(weights) return weights def metric_fn(labels, predictions, features): """Defines the metric fn.""" weights = _get_weights(features) loss = metric_dict.get(loss_key, None) if loss is None: raise ValueError('loss_key {} not supported.'.format(loss_key)) return loss.eval_metric(labels, predictions, weights) return metric_fn