fix update_abtest

fix update_abtest

doc/ABTEST_IN_PADDLE_SERVING_CN.md

@@ -16,31 +16,25 @@ sh get_data.sh
 ```
 
 ### 处理数据
-
-下面Python代码将处理`test_data/part-0`的数据，写入`processed.data`文件中。
-
-```python
-from paddle_serving_app.reader import IMDBDataset
-imdb_dataset = IMDBDataset()
-imdb_dataset.load_resource('imdb.vocab')
-
-with open('test_data/part-0') as fin:
-    with open('processed.data', 'w') as fout:
-        for line in fin:
-            word_ids, label = imdb_dataset.get_words_and_label(line)
-            fout.write("{};{}\n".format(','.join([str(x) for x in word_ids]), label[0]))
-```
+由于处理数据需要用到相关库，请使用pip进行安装
+`pip install paddlepaddle`
+`pip install paddle-serving-app`
+`pip install Shapely`
+您可以直接运行
+python [abtest_get_data.py](../python/examples/imdb/abtest_get_data.py)
+文件中的Python代码将处理`test_data/part-0`的数据，并将处理后的数据生成并写入`processed.data`文件中。
 
 ### 启动Server端
 
-这里采用[Docker方式](https://github.com/PaddlePaddle/Serving/blob/develop/doc/RUN_IN_DOCKER_CN.md)启动Server端服务。
+这里采用[Docker方式](RUN_IN_DOCKER_CN.md)启动Server端服务。
 
 首先启动BOW Server，该服务启用`8000`端口：
 
 ```bash
-docker run -dit -v $PWD/imdb_bow_model:/model -p 8000:8000 --name bow-server hub.baidubce.com/paddlepaddle/serving:latest
-docker exec -it bow-server bash
+docker run -dit -v $PWD/imdb_bow_model:/model -p 8000:8000 --name bow-server hub.baidubce.com/paddlepaddle/serving:latest /bin/bash
+docker exec -it bow-server /bin/bash
 pip install paddle-serving-server -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip install paddle-serving-client -i https://pypi.tuna.tsinghua.edu.cn/simple
 python -m paddle_serving_server.serve --model model --port 8000 >std.log 2>err.log &
 exit
 ```
@@ -48,19 +42,22 @@ exit
 同理启动LSTM Server，该服务启用`9000`端口：
 
 ```bash
-docker run -dit -v $PWD/imdb_lstm_model:/model -p 9000:9000 --name lstm-server hub.baidubce.com/paddlepaddle/serving:latest
-docker exec -it lstm-server bash
+docker run -dit -v $PWD/imdb_lstm_model:/model -p 9000:9000 --name lstm-server hub.baidubce.com/paddlepaddle/serving:latest /bin/bash
+docker exec -it lstm-server /bin/bash
 pip install paddle-serving-server -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip install paddle-serving-client -i https://pypi.tuna.tsinghua.edu.cn/simple
 python -m paddle_serving_server.serve --model model --port 9000 >std.log 2>err.log &
 exit
 ```
 
 ### 启动Client端
-
-在宿主机运行下面Python代码启动Client端，需要确保宿主机装好`paddle-serving-client`包。
+为了模拟ABTEST工况，您可以在宿主机运行下面Python代码启动Client端，但需确保宿主机具备相关环境，您也可以在docker环境下运行，运行前使用`pip install paddle-serving-client`安装paddle-serving-client包。
+您可以直接使用下面的命令，进行ABTEST预测。
+python [abtest_client.py](../python/examples/imdb/abtest_client.py)
 
 ```python
 from paddle_serving_client import Client
+import numpy as np
 
 client = Client()
 client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt')
@@ -68,28 +65,32 @@ client.add_variant("bow", ["127.0.0.1:8000"], 10)
 client.add_variant("lstm", ["127.0.0.1:9000"], 90)
 client.connect()
 
+print('please wait for about 10s')
 with open('processed.data') as f:
     cnt = {"bow": {'acc': 0, 'total': 0}, "lstm": {'acc': 0, 'total': 0}}
     for line in f:
         word_ids, label = line.split(';')
         word_ids = [int(x) for x in word_ids.split(',')]
-        feed = {"words": word_ids}
+        word_len = len(word_ids)
+        feed = {
+            "words": np.array(word_ids).reshape(word_len, 1),
+            "words.lod": [0, word_len]
+        }
         fetch = ["acc", "cost", "prediction"]
-        [fetch_map, tag] = client.predict(feed=feed, fetch=fetch, need_variant_tag=True)
+        [fetch_map, tag] = client.predict(feed=feed, fetch=fetch, need_variant_tag=True,batch=True)
         if (float(fetch_map["prediction"][0][1]) - 0.5) * (float(label[0]) - 0.5) > 0:
             cnt[tag]['acc'] += 1
         cnt[tag]['total'] += 1
 
     for tag, data in cnt.items():
-        print('[{}](total: {}) acc: {}'.format(tag, data['total'], float(data['acc']) / float(data['total'])))
+        print('[{}](total: {}) acc: {}'.format(tag, data['total'], float(data['acc'])/float(data['total']) ))
 ```
-
 代码中，`client.add_variant(tag, clusters, variant_weight)`是为了添加一个标签为`tag`、流量权重为`variant_weight`的variant。在这个样例中，添加了一个标签为`bow`、流量权重为`10`的BOW variant，以及一个标签为`lstm`、流量权重为`90`的LSTM variant。Client端的流量会根据`10:90`的比例分发到两个variant。
 
 Client端做预测时，若指定参数`need_variant_tag=True`，返回值则包含分发流量对应的variant标签。
 
 ### 预期结果
-
+由于网络情况的不同，可能每次预测的结果略有差异。
 ``` bash
 [lstm](total: 1867) acc: 0.490091055169
 [bow](total: 217) acc: 0.73732718894