add ZILN loss for ltv prediction task

docs/source/models/loss.md

@@ -27,6 +27,10 @@ EasyRec支持两种损失函数配置方式：1）使用单个损失函数；2
 | LISTWISE_DISTILL_LOSS                      | 用来蒸馏给定list排序的损失函数，与listwise rank loss 比较类似                 |
 | ZILN_LOSS                                  | LTV预测任务的损失函数（num_class必须设置为3）                              |
 
+- ZILN_LOSS：使用时模型有3个可选的输出（在多目标任务重，输出名有一个目标相关的后缀）
+  - probs: 预估的转化概率
+  - y: 预估的LTV值
+  - logits: Shape为`[batch_size, 3]`的tensor，第一列是`probs`，第二列和第三列是学习到的LogNormal分布的均值与方差
 - 说明：SOFTMAX_CROSS_ENTROPY_WITH_NEGATIVE_MINING
   - 支持参数配置，升级为 [support vector guided softmax loss](https://128.84.21.199/abs/1812.11317) ，
   - 目前只在DropoutNet模型中可用，可参考《 [冷启动推荐模型DropoutNet深度解析与改进](https://zhuanlan.zhihu.com/p/475117993) 》。

easy_rec/python/loss/zero_inflated_lognormal.py

@@ -17,6 +17,7 @@ def zero_inflated_lognormal_pred(logits):
     logits: [batch_size, 3] tensor of logits.
 
   Returns:
+    positive_probs: [batch_size, 1] tensor of positive probability.
     preds: [batch_size, 1] tensor of predicted mean.
   """
   logits = tf.convert_to_tensor(logits, dtype=tf.float32)
@@ -26,7 +27,7 @@ def zero_inflated_lognormal_pred(logits):
   preds = (
       positive_probs *
       tf.keras.backend.exp(loc + 0.5 * tf.keras.backend.square(scale)))
-  return preds
+  return positive_probs, preds
 
 
 def zero_inflated_lognormal_loss(labels, logits, name=''):

easy_rec/python/model/rank_model.py

@@ -82,10 +82,12 @@ def _output_to_prediction_impl(self,
       prediction_dict['probs' + suffix] = probs[:, 1]
     elif loss_type == LossType.ZILN_LOSS:
       assert num_class == 3, 'num_class must be 3 when loss type is ZILN_LOSS'
-      probs = zero_inflated_lognormal_pred(output)
+      probs, preds = zero_inflated_lognormal_pred(output)
       tf.summary.scalar('prediction/probs', tf.reduce_mean(probs))
+      tf.summary.scalar('prediction/y', tf.reduce_mean(preds))
       prediction_dict['logits' + suffix] = output
       prediction_dict['probs' + suffix] = probs
+      prediction_dict['y' + suffix] = preds
     elif loss_type == LossType.CLASSIFICATION:
       if num_class == 1:
         output = tf.squeeze(output, axis=1)
@@ -146,7 +148,7 @@ def build_rtp_output_dict(self):
           LossType.PAIR_WISE_LOSS, LossType.BINARY_FOCAL_LOSS,
           LossType.PAIRWISE_FOCAL_LOSS, LossType.PAIRWISE_LOGISTIC_LOSS,
           LossType.JRC_LOSS, LossType.LISTWISE_DISTILL_LOSS,
-          LossType.LISTWISE_RANK_LOSS, LossType.ZILN_LOSS
+          LossType.LISTWISE_RANK_LOSS
       }
       if loss_types & binary_loss_set:
         if 'probs' in self._prediction_dict:
@@ -156,7 +158,7 @@ def build_rtp_output_dict(self):
               'failed to build RTP rank_predict output: classification model ' +
               "expect 'probs' prediction, which is not found. Please check if" +
               ' build_predict_graph() is called.')
-      elif loss_types & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS}:
+      elif loss_types & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
         if 'y' in self._prediction_dict:
           forwarded = self._prediction_dict['y']
         else:
@@ -377,7 +379,7 @@ def _build_metric_impl(self,
           metric.recall_at_topk.topk)
     elif metric.WhichOneof('metric') == 'mean_absolute_error':
       label = tf.to_float(self._labels[label_name])
-      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS}:
+      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
         metric_dict['mean_absolute_error' +
                     suffix] = metrics_tf.mean_absolute_error(
                         label, self._prediction_dict['y' + suffix])
@@ -389,7 +391,7 @@ def _build_metric_impl(self,
         assert False, 'mean_absolute_error is not supported for this model'
     elif metric.WhichOneof('metric') == 'mean_squared_error':
       label = tf.to_float(self._labels[label_name])
-      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS}:
+      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
         metric_dict['mean_squared_error' +
                     suffix] = metrics_tf.mean_squared_error(
                         label, self._prediction_dict['y' + suffix])
@@ -401,7 +403,7 @@ def _build_metric_impl(self,
         assert False, 'mean_squared_error is not supported for this model'
     elif metric.WhichOneof('metric') == 'root_mean_squared_error':
       label = tf.to_float(self._labels[label_name])
-      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS}:
+      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
         metric_dict['root_mean_squared_error' +
                     suffix] = metrics_tf.root_mean_squared_error(
                         label, self._prediction_dict['y' + suffix])
@@ -437,13 +439,14 @@ def _get_outputs_impl(self, loss_type, num_class=1, suffix=''):
         LossType.F1_REWEIGHTED_LOSS, LossType.PAIR_WISE_LOSS,
         LossType.BINARY_FOCAL_LOSS, LossType.PAIRWISE_FOCAL_LOSS,
         LossType.LISTWISE_RANK_LOSS, LossType.PAIRWISE_HINGE_LOSS,
-        LossType.PAIRWISE_LOGISTIC_LOSS, LossType.LISTWISE_DISTILL_LOSS,
-        LossType.ZILN_LOSS
+        LossType.PAIRWISE_LOGISTIC_LOSS, LossType.LISTWISE_DISTILL_LOSS
     }
     if loss_type in binary_loss_set:
       return ['probs' + suffix, 'logits' + suffix]
     if loss_type == LossType.JRC_LOSS:
       return ['probs' + suffix, 'pos_logits' + suffix]
+    if loss_type == LossType.ZILN_LOSS:
+      return ['probs' + suffix, 'y' + suffix, 'logits' + suffix]
     if loss_type == LossType.CLASSIFICATION:
       if num_class == 1:
         return ['probs' + suffix, 'logits' + suffix]