auc for naml

Author: seemingwang

models/rank/naml/NAMLDataReader.py

@@ -63,6 +63,7 @@ def init(self):
         #line [0]id cate_id sub_cate_id [3]title content
         for file in self.article_file_list:
             with open(file, "r") as rf:
+
                 for l in rf:
                     line_x = [x.strip() for x in l.split('\t')]
                     id = line_x[0]

models/rank/naml/dygraph_model.py

@@ -16,11 +16,23 @@
 import paddle.nn as nn
 import paddle.nn.functional as F
 import math
+import numpy as np
 
 import net
 
 
 class DygraphModel():
+    def __init__(self):
+        self.bucket = 1000000
+        self.absolute_limt = 200.0
+
+    def rescale(self, number):
+        if number > self.absolute_limt:
+            number = self.absolute_limt
+        elif number < -self.absolute_limt:
+            number = -self.absolute_limt
+        return (number + self.absolute_limt) / (self.absolute_limt * 2 + 1e-8)
+
     # define model
     def create_model(self, config):
         article_content_size = config.get(
@@ -63,8 +75,10 @@ def create_optimizer(self, dy_model, config):
     # define metrics such as auc/acc
     # multi-task need to define multi metric
     def create_metrics(self):
-        metrics_list_name = ["acc"]
-        auc_metric = paddle.metric.Accuracy()
+        # metrics_list_name = ["acc"]
+        # auc_metric = paddle.metric.Accuracy()
+        metrics_list_name = ["auc"]
+        auc_metric = paddle.metric.Auc(num_thresholds=self.bucket)
         metrics_list = [auc_metric]
         return metrics_list, metrics_list_name
 
@@ -77,18 +91,37 @@ def train_forward(self, dy_model, metrics_list, batch_data, config):
 
         loss = paddle.nn.functional.cross_entropy(
             input=raw, label=paddle.cast(labels, "float32"), soft_label=True)
-        correct = metrics_list[0].compute(raw, labels)
-        metrics_list[0].update(correct)
+
+        scaled = raw.numpy()
+        scaled_pre = []
+        [rows, cols] = scaled.shape
+        for i in range(rows):
+            for j in range(cols):
+                scaled_pre.append(1.0 - self.rescale(scaled[i, j]))
+                scaled_pre.append(self.rescale(scaled[i, j]))
+        scaled_np_predict = np.array(scaled_pre).reshape([-1, 2])
+        metrics_list[0].update(scaled_np_predict,
+                               paddle.reshape(labels, [-1, 1]))
+
         loss = paddle.mean(loss)
         print_dict = None
         return loss, metrics_list, print_dict
 
     def infer_forward(self, dy_model, metrics_list, batch_data, config):
-        label, sparse_tensor, dense_tensor = self.create_feeds(batch_data,
-                                                               config)
+        labels, sparse_tensor, dense_tensor = self.create_feeds(batch_data,
+                                                                config)
         raw = dy_model(sparse_tensor, None)
-        raw = paddle.nn.functional.softmax(raw)
-        correct = metrics_list[0].compute(raw, label)
-        metrics_list[0].update(correct)
+        #predict_raw = paddle.nn.functional.softmax(raw)
+
+        scaled = raw.numpy()
+        scaled_pre = []
+        [rows, cols] = scaled.shape
+        for i in range(rows):
+            for j in range(cols):
+                scaled_pre.append(1.0 - self.rescale(scaled[i, j]))
+                scaled_pre.append(self.rescale(scaled[i, j]))
+        scaled_np_predict = np.array(scaled_pre).reshape([-1, 2])
+        metrics_list[0].update(scaled_np_predict,
+                               paddle.reshape(labels, [-1, 1]))
 
         return metrics_list, None

models/rank/naml/net.py

@@ -84,7 +84,7 @@ def __init__(self, article_content_size, article_title_size, browse_size,
         self.sub_category_size = sub_category_size
         self.cate_dimension = cate_dimension
         self.word_dict_size = word_dict_size
-        self.conv_out_channel_size = 100
+        self.conv_out_channel_size = 400
         self.attention_projection_size = 100
         self.load_word_embedding()
         self.attention_vec = []