ABTEST_IN_PADDLE_SERVING.md

# ABTEST in Paddle Serving

This document will use an example of text classification task based on IMDB dataset to show how to build a A/B Test framework using Paddle Serving. The structure relationship between the client and servers in the example is shown in the figure below.

<img src="abtest.png" style="zoom:33%;" />

Note that:  A/B Test is only applicable to RPC mode, not web mode.

### Download Data and Models

```shell
cd Serving/python/examples/imdb
sh get_data.sh
```

### Processing Data

The following Python code will process the data `test_data/part-0` and write to the `processed.data` file.

``` python
from imdb_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]))
```

### Start Server

Here, we [use docker](https://github.com/PaddlePaddle/Serving/blob/develop/doc/RUN_IN_DOCKER.md) to start the server-side service. 

First, start the BOW server, which enables the `8000` port:

``` shell
docker run -dit -v $PWD/imdb_bow_model:/model -p 8000:8000 --name bow-server hub.baidubce.com/ctr/paddleserving:0.1.3
docker exec -it bow-server bash
pip install paddle-serving-server
python -m paddle_serving_server.serve --model model --port 8000 >std.log 2>err.log &
exit
```

Similarly, start the LSTM server, which enables the `9000` port:

```bash
docker run -dit -v $PWD/imdb_lstm_model:/model -p 9000:9000 --name lstm-server hub.baidubce.com/ctr/paddleserving:0.1.3
docker exec -it lstm-server bash
pip install paddle-serving-server
python -m paddle_serving_server.serve --model model --port 9000 >std.log 2>err.log &
exit
```

### Start Client

Run the following Python code on the host computer to start client. Make sure that the host computer is installed with the `paddle-serving-client` package.

``` go
from paddle_serving_client import Client

client = Client()
client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt')
client.add_variant("bow", ["127.0.0.1:8000"], 10)
client.add_variant("lstm", ["127.0.0.1:9000"], 90)
client.connect()

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}
        fetch = ["acc", "cost", "prediction"]
        [fetch_map, tag] = client.predict(feed=feed, fetch=fetch, need_variant_tag=True)
        if (float(fetch_map["prediction"][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'])))
```

In the code, the function `client.add_variant(tag, clusters, variant_weight)` is to add a variant with label `tag` and flow weight `variant_weight`. In this example, a BOW variant with label of `bow` and flow weight of `10`, and an LSTM variant with label of `lstm` and a flow weight of `90` are added. The flow on the client side will be distributed to two variants according to the ratio of `10:90`.

When making prediction on the client side, if the parameter `need_variant_tag=True` is specified, the response will contains the variant tag corresponding to the distribution flow.

### Expected Results

``` python
[lstm](total: 1867) acc: 0.490091055169
[bow](total: 217) acc: 0.73732718894
```