refactor ctr model

ctr/README.md

 # 点击率预估
 
+以下是本例目录包含的文件以及对应说明:
+
+```
+├── README.md          # 本教程markdown 文档
+├── dataset.md         # 数据集处理教程
+├── images             # 本教程图片目录
+│   ├── lr_vs_dnn.jpg
+│   └── wide_deep.png
+├── infer.py           # 预测脚本
+├── network_conf.py    # 模型网络配置
+├── reader.py          # data provider
+├── train.py           # 训练脚本
+└── utils.py           # helper functions
+```
+
 ## 背景介绍
 
 CTR(Click-Through Rate，点击率预估)\[[1](https://en.wikipedia.org/wiki/Click-through_rate)\] 是用来表示用户点击一个特定链接的概率，
@@ -61,8 +76,40 @@ LR 对于 DNN 模型的优势是对大规模稀疏特征的容纳能力，包括
 
 我们使用 Kaggle 上 `Click-through rate prediction` 任务的数据集\[[2](https://www.kaggle.com/c/avazu-ctr-prediction/data)\] 来演示模型。
 
-具体的特征处理方法参看 [data process](./dataset.md)
+具体的特征处理方法参看 [data process](./dataset.md)。
+
+本教程中演示模型的输入格式如下：
+
+```
+# <dnn input ids> \t <lr input sparse values> \t click
+1 23 190 \t 230:0.12 3421:0.9 23451:0.12 \t 0
+23 231 \t 1230:0.12 13421:0.9 \t 1
+```
+
+演示数据集\[[2](#参考文档)\] 可以使用 `avazu_data_processor.py` 脚本处理，具体使用方法参考如下说明：
+
+```
+usage: avazu_data_processer.py [-h] --data_path DATA_PATH --output_dir
+                               OUTPUT_DIR
+                               [--num_lines_to_detect NUM_LINES_TO_DETECT]
+                               [--test_set_size TEST_SET_SIZE]
+                               [--train_size TRAIN_SIZE]
+
+PaddlePaddle CTR example
 
+optional arguments:
+  -h, --help            show this help message and exit
+  --data_path DATA_PATH
+                        path of the Avazu dataset
+  --output_dir OUTPUT_DIR
+                        directory to output
+  --num_lines_to_detect NUM_LINES_TO_DETECT
+                        number of records to detect dataset's meta info
+  --test_set_size TEST_SET_SIZE
+                        size of the validation dataset(default: 10000)
+  --train_size TRAIN_SIZE
+                        size of the trainset (default: 100000)
+```
 
 ## Wide & Deep Learning Model
 
@@ -204,15 +251,17 @@ trainer.train(
 1. 下载训练数据，可以使用 Kaggle 上 CTR 比赛的数据\[[2](#参考文献)\]
     1. 从 [Kaggle CTR](https://www.kaggle.com/c/avazu-ctr-prediction/data) 下载 train.gz
     2. 解压 train.gz 得到 train.txt
-2. 执行 `python train.py --train_data_path train.txt` ，开始训练
+    3. `mkdir -p output; python avazu_data_processer.py --data_path train.txt --output_dir output --num_lines_to_detect 1000 --test_set_size 100` 生成演示数据
+2. 执行 `python train.py --train_data_path ./output/train.txt --test_data_path ./output/test.txt --data_meta_file ./output/data.meta.txt --model_type=0` 开始训练
 
 上面第2个步骤可以为 `train.py` 填充命令行参数来定制模型的训练过程，具体的命令行参数及用法如下
 
 ```
 usage: train.py [-h] --train_data_path TRAIN_DATA_PATH
-                [--batch_size BATCH_SIZE] [--test_set_size TEST_SET_SIZE]
+                [--test_data_path TEST_DATA_PATH] [--batch_size BATCH_SIZE]
                 [--num_passes NUM_PASSES]
-                [--num_lines_to_detact NUM_LINES_TO_DETACT]
+                [--model_output_prefix MODEL_OUTPUT_PREFIX] --data_meta_file
+                DATA_META_FILE --model_type MODEL_TYPE
 
 PaddlePaddle CTR example
 
@@ -220,16 +269,63 @@ optional arguments:
   -h, --help            show this help message and exit
   --train_data_path TRAIN_DATA_PATH
                         path of training dataset
+  --test_data_path TEST_DATA_PATH
+                        path of testing dataset
   --batch_size BATCH_SIZE
                         size of mini-batch (default:10000)
-  --test_set_size TEST_SET_SIZE
-                        size of the validation dataset(default: 10000)
   --num_passes NUM_PASSES
                         number of passes to train
-  --num_lines_to_detact NUM_LINES_TO_DETACT
-                        number of records to detect dataset's meta info
+  --model_output_prefix MODEL_OUTPUT_PREFIX
+                        prefix of path for model to store (default:
+                        ./ctr_models)
+  --data_meta_file DATA_META_FILE
+                        path of data meta info file
+  --model_type MODEL_TYPE
+                        model type, classification: 0, regression 1 (default
+                        classification)
+```
+
+## 用训好的模型做预测
+训好的模型可以用来预测新的数据， 预测数据的格式为
+
+```
+# <dnn input ids> \t <lr input sparse values>
+1 23 190 \t 230:0.12 3421:0.9 23451:0.12
+23 231 \t 1230:0.12 13421:0.9
+```
+
+`infer.py` 的使用方法如下
+
+```
+usage: infer.py [-h] --model_gz_path MODEL_GZ_PATH --data_path DATA_PATH
+                --prediction_output_path PREDICTION_OUTPUT_PATH
+                [--data_meta_path DATA_META_PATH] --model_type MODEL_TYPE
+
+PaddlePaddle CTR example
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model_gz_path MODEL_GZ_PATH
+                        path of model parameters gz file
+  --data_path DATA_PATH
+                        path of the dataset to infer
+  --prediction_output_path PREDICTION_OUTPUT_PATH
+                        path to output the prediction
+  --data_meta_path DATA_META_PATH
+                        path of trainset's meta info, default is ./data.meta
+  --model_type MODEL_TYPE
+                        model type, classification: 0, regression 1 (default
+                        classification)
 ```
 
+示例数据可以用如下命令预测
+
+```
+python infer.py --model_gz_path <model_path> --data_path output/infer.txt --prediction_output_path predictions.txt --data_meta_path data.meta.txt
+```
+
+最终的预测结果位于 `predictions.txt`。
+
 ## 参考文献
 1. <https://en.wikipedia.org/wiki/Click-through_rate>
 2. <https://www.kaggle.com/c/avazu-ctr-prediction/data>

ctr/data_provider.py → ctr/avazu_data_processer.py

+import os
 import sys
 import csv
+import cPickle
+import argparse
 import numpy as np
+
+from utils import logger, TaskMode
+
+parser = argparse.ArgumentParser(description="PaddlePaddle CTR example")
+parser.add_argument(
+    '--data_path', type=str, required=True, help="path of the Avazu dataset")
+parser.add_argument(
+    '--output_dir', type=str, required=True, help="directory to output")
+parser.add_argument(
+    '--num_lines_to_detect',
+    type=int,
+    default=500000,
+    help="number of records to detect dataset's meta info")
+parser.add_argument(
+    '--test_set_size',
+    type=int,
+    default=10000,
+    help="size of the validation dataset(default: 10000)")
+parser.add_argument(
+    '--train_size',
+    type=int,
+    default=100000,
+    help="size of the trainset (default: 100000)")
+args = parser.parse_args()
 '''
 The fields of the dataset are:
 
@@ -40,6 +67,14 @@ and some other features as id features:
 
 The `hour` field will be treated as a continuous feature and will be transformed
 to one-hot representation which has 24 bits.
+
+This script will output 3 files:
+
+1. train.txt
+2. test.txt
+3. infer.txt
+
+all the files are for demo.
 '''
 
 feature_dims = {}
@@ -161,6 +196,7 @@ def detect_dataset(path, topn, id_fea_space=10000):
     NOTE the records should be randomly shuffled first.
     '''
     # create categorical statis objects.
+    logger.warning('detecting dataset')
 
     with open(path, 'rb') as csvfile:
         reader = csv.DictReader(csvfile)
@@ -174,9 +210,6 @@ def detect_dataset(path, topn, id_fea_space=10000):
     for key, item in fields.items():
         feature_dims[key] = item.size()
 
-    #for key in id_features:
-    #feature_dims[key] = id_fea_space
-
     feature_dims['hour'] = 24
     feature_dims['click'] = 1
 
@@ -184,10 +217,20 @@ def detect_dataset(path, topn, id_fea_space=10000):
         feature_dims[key] for key in categorial_features + ['hour']) + 1
     feature_dims['lr_input'] = np.sum(feature_dims[key]
                                       for key in id_features) + 1
+    # logger.warning("dump dataset's meta info to %s" % meta_out_path)
+    # cPickle.dump([feature_dims, fields], open(meta_out_path, 'wb'))
 
     return feature_dims
 
 
+def load_data_meta(meta_path):
+    '''
+    Load dataset's meta infomation.
+    '''
+    feature_dims, fields = cPickle.load(open(meta_path, 'rb'))
+    return feature_dims, fields
+
+
 def concat_sparse_vectors(inputs, dims):
     '''
     Concaterate more than one sparse vectors into one.
@@ -211,67 +254,162 @@ class AvazuDataset(object):
     '''
     Load AVAZU dataset as train set.
     '''
-    TRAIN_MODE = 0
-    TEST_MODE = 1
 
-    def __init__(self, train_path, n_records_as_test=-1):
+    def __init__(self,
+                 train_path,
+                 n_records_as_test=-1,
+                 fields=None,
+                 feature_dims=None):
         self.train_path = train_path
         self.n_records_as_test = n_records_as_test
-        # task model: 0 train, 1 test
-        self.mode = 0
+        self.fields = fields
+        # default is train mode.
+        self.mode = TaskMode.create_train()
+
+        self.categorial_dims = [
+            feature_dims[key] for key in categorial_features + ['hour']
+        ]
+        self.id_dims = [feature_dims[key] for key in id_features]
 
     def train(self):
-        self.mode = self.TRAIN_MODE
-        return self._parse(self.train_path, skip_n_lines=self.n_records_as_test)
+        '''
+        Load trainset.
+        '''
+        logger.info("load trainset from %s" % self.train_path)
+        self.mode = TaskMode.create_train()
+        with open(self.train_path) as f:
+            reader = csv.DictReader(f)
 
-    def test(self):
-        self.mode = self.TEST_MODE
-        return self._parse(self.train_path, top_n_lines=self.n_records_as_test)
+            for row_id, row in enumerate(reader):
+                # skip top n lines
+                if self.n_records_as_test > 0 and row_id < self.n_records_as_test:
+                    continue
 
-    def _parse(self, path, skip_n_lines=-1, top_n_lines=-1):
-        with open(path, 'rb') as csvfile:
-            reader = csv.DictReader(csvfile)
+                rcd = self._parse_record(row)
+                if rcd:
+                    yield rcd
 
-            categorial_dims = [
-                feature_dims[key] for key in categorial_features + ['hour']
-            ]
-            id_dims = [feature_dims[key] for key in id_features]
+    def test(self):
+        '''
+        Load testset.
+        '''
+        logger.info("load testset from %s" % self.train_path)
+        self.mode = TaskMode.create_test()
+        with open(self.train_path) as f:
+            reader = csv.DictReader(f)
 
             for row_id, row in enumerate(reader):
-                if skip_n_lines > 0 and row_id < skip_n_lines:
-                    continue
-                if top_n_lines > 0 and row_id > top_n_lines:
+                # skip top n lines
+                if self.n_records_as_test > 0 and row_id > self.n_records_as_test:
                     break
 
+                rcd = self._parse_record(row)
+                if rcd:
+                    yield rcd
+
+    def infer(self):
+        '''
+        Load inferset.
+        '''
+        logger.info("load inferset from %s" % self.train_path)
+        self.mode = TaskMode.create_infer()
+        with open(self.train_path) as f:
+            reader = csv.DictReader(f)
+
+            for row_id, row in enumerate(reader):
+                rcd = self._parse_record(row)
+                if rcd:
+                    yield rcd
+
+    def _parse_record(self, row):
+        '''
+        Parse a CSV row and get a record.
+        '''
         record = []
         for key in categorial_features:
-                    record.append(fields[key].gen(row[key]))
+            record.append(self.fields[key].gen(row[key]))
         record.append([int(row['hour'][-2:])])
-                dense_input = concat_sparse_vectors(record, categorial_dims)
+        dense_input = concat_sparse_vectors(record, self.categorial_dims)
 
         record = []
         for key in id_features:
             if 'cross' not in key:
-                        record.append(fields[key].gen(row[key]))
+                record.append(self.fields[key].gen(row[key]))
             else:
-                        fea0 = fields[key].cross_fea0
-                        fea1 = fields[key].cross_fea1
+                fea0 = self.fields[key].cross_fea0
+                fea1 = self.fields[key].cross_fea1
                 record.append(
-                            fields[key].gen_cross_fea(row[fea0], row[fea1]))
+                    self.fields[key].gen_cross_fea(row[fea0], row[fea1]))
 
-                sparse_input = concat_sparse_vectors(record, id_dims)
+        sparse_input = concat_sparse_vectors(record, self.id_dims)
 
         record = [dense_input, sparse_input]
 
+        if not self.mode.is_infer():
             record.append(list((int(row['click']), )))
-                yield record
+        return record
+
+
+def ids2dense(vec, dim):
+    return vec
+
+
+def ids2sparse(vec):
+    return ["%d:1" % x for x in vec]
 
 
-if __name__ == '__main__':
-    path = 'train.txt'
-    print detect_dataset(path, 400000)
+detect_dataset(args.data_path, args.num_lines_to_detect)
+dataset = AvazuDataset(
+    args.data_path,
+    args.test_set_size,
+    fields=fields,
+    feature_dims=feature_dims)
 
-    filereader = AvazuDataset(path)
-    for no, rcd in enumerate(filereader.train()):
-        print no, rcd
-        if no > 1000: break
+output_trainset_path = os.path.join(args.output_dir, 'train.txt')
+output_testset_path = os.path.join(args.output_dir, 'test.txt')
+output_infer_path = os.path.join(args.output_dir, 'infer.txt')
+output_meta_path = os.path.join(args.output_dir, 'data.meta.txt')
+
+with open(output_trainset_path, 'w') as f:
+    for id, record in enumerate(dataset.train()):
+        if id and id % 10000 == 0:
+            logger.info("load %d records" % id)
+        if id > args.train_size:
+            break
+        dnn_input, lr_input, click = record
+        dnn_input = ids2dense(dnn_input, feature_dims['dnn_input'])
+        lr_input = ids2sparse(lr_input)
+        line = "%s\t%s\t%d\n" % (' '.join(map(str, dnn_input)),
+                                 ' '.join(map(str, lr_input)), click[0])
+        f.write(line)
+    logger.info('write to %s' % output_trainset_path)
+
+with open(output_testset_path, 'w') as f:
+    for id, record in enumerate(dataset.test()):
+        dnn_input, lr_input, click = record
+        dnn_input = ids2dense(dnn_input, feature_dims['dnn_input'])
+        lr_input = ids2sparse(lr_input)
+        line = "%s\t%s\t%d\n" % (' '.join(map(str, dnn_input)),
+                                 ' '.join(map(str, lr_input)), click[0])
+        f.write(line)
+    logger.info('write to %s' % output_testset_path)
+
+with open(output_infer_path, 'w') as f:
+    for id, record in enumerate(dataset.infer()):
+        dnn_input, lr_input = record
+        dnn_input = ids2dense(dnn_input, feature_dims['dnn_input'])
+        lr_input = ids2sparse(lr_input)
+        line = "%s\t%s\n" % (' '.join(map(str, dnn_input)),
+                             ' '.join(map(str, lr_input)), )
+        f.write(line)
+        if id > args.test_set_size:
+            break
+    logger.info('write to %s' % output_infer_path)
+
+with open(output_meta_path, 'w') as f:
+    lines = [
+        "dnn_input_dim: %d" % feature_dims['dnn_input'],
+        "lr_input_dim: %d" % feature_dims['lr_input']
+    ]
+    f.write('\n'.join(lines))
+    logger.info('write data meta into %s' % output_meta_path)

ctr/dataset.md

 # 数据及处理
 ## 数据集介绍
 
+本教程演示使用Kaggle上CTR任务的数据集\[[3](#参考文献)\]的预处理方法，最终产生本模型需要的格式，详细的数据格式参考[README.md](./README.md)。
+
+Wide && Deep Model\[[2](#参考文献)\]的优势是融合稠密特征和大规模稀疏特征，
+因此特征处理方面也针对稠密和稀疏两种特征作处理，
+其中Deep部分的稠密值全部转化为ID类特征，
+通过embedding 来转化为稠密的向量输入；Wide部分主要通过ID的叉乘提升维度。
+
 数据集使用 `csv` 格式存储，其中各个字段内容如下：
 
 -   `id` : ad identifier

ctr/index.html

@@ -42,6 +42,21 @@
 <div id="markdown" style='display:none'>
 # 点击率预估
 
+以下是本例目录包含的文件以及对应说明:
+
+```
+├── README.md          # 本教程markdown 文档
+├── dataset.md         # 数据集处理教程
+├── images             # 本教程图片目录
+│   ├── lr_vs_dnn.jpg
+│   └── wide_deep.png
+├── infer.py           # 预测脚本
+├── network_conf.py    # 模型网络配置
+├── reader.py          # data provider
+├── train.py           # 训练脚本
+└── utils.py           # helper functions
+```
+
 ## 背景介绍
 
 CTR(Click-Through Rate，点击率预估)\[[1](https://en.wikipedia.org/wiki/Click-through_rate)\] 是用来表示用户点击一个特定链接的概率，
@@ -103,8 +118,40 @@ LR 对于 DNN 模型的优势是对大规模稀疏特征的容纳能力，包括
 
 我们使用 Kaggle 上 `Click-through rate prediction` 任务的数据集\[[2](https://www.kaggle.com/c/avazu-ctr-prediction/data)\] 来演示模型。
 
-具体的特征处理方法参看 [data process](./dataset.md)
+具体的特征处理方法参看 [data process](./dataset.md)。
+
+本教程中演示模型的输入格式如下：
+
+```
+# <dnn input ids> \t <lr input sparse values> \t click
+1 23 190 \t 230:0.12 3421:0.9 23451:0.12 \t 0
+23 231 \t 1230:0.12 13421:0.9 \t 1
+```
+
+演示数据集\[[2](#参考文档)\] 可以使用 `avazu_data_processor.py` 脚本处理，具体使用方法参考如下说明：
+
+```
+usage: avazu_data_processer.py [-h] --data_path DATA_PATH --output_dir
+                               OUTPUT_DIR
+                               [--num_lines_to_detect NUM_LINES_TO_DETECT]
+                               [--test_set_size TEST_SET_SIZE]
+                               [--train_size TRAIN_SIZE]
+
+PaddlePaddle CTR example
 
+optional arguments:
+  -h, --help            show this help message and exit
+  --data_path DATA_PATH
+                        path of the Avazu dataset
+  --output_dir OUTPUT_DIR
+                        directory to output
+  --num_lines_to_detect NUM_LINES_TO_DETECT
+                        number of records to detect dataset's meta info
+  --test_set_size TEST_SET_SIZE
+                        size of the validation dataset(default: 10000)
+  --train_size TRAIN_SIZE
+                        size of the trainset (default: 100000)
+```
 
 ## Wide & Deep Learning Model
 
@@ -246,15 +293,17 @@ trainer.train(
 1. 下载训练数据，可以使用 Kaggle 上 CTR 比赛的数据\[[2](#参考文献)\]
     1. 从 [Kaggle CTR](https://www.kaggle.com/c/avazu-ctr-prediction/data) 下载 train.gz
     2. 解压 train.gz 得到 train.txt
-2. 执行 `python train.py --train_data_path train.txt` ，开始训练
+    3. `mkdir -p output; python avazu_data_processer.py --data_path train.txt --output_dir output --num_lines_to_detect 1000 --test_set_size 100` 生成演示数据
+2. 执行 `python train.py --train_data_path ./output/train.txt --test_data_path ./output/test.txt --data_meta_file ./output/data.meta.txt --model_type=0` 开始训练
 
 上面第2个步骤可以为 `train.py` 填充命令行参数来定制模型的训练过程，具体的命令行参数及用法如下
 
 ```
 usage: train.py [-h] --train_data_path TRAIN_DATA_PATH
-                [--batch_size BATCH_SIZE] [--test_set_size TEST_SET_SIZE]
+                [--test_data_path TEST_DATA_PATH] [--batch_size BATCH_SIZE]
                 [--num_passes NUM_PASSES]
-                [--num_lines_to_detact NUM_LINES_TO_DETACT]
+                [--model_output_prefix MODEL_OUTPUT_PREFIX] --data_meta_file
+                DATA_META_FILE --model_type MODEL_TYPE
 
 PaddlePaddle CTR example
 
@@ -262,16 +311,63 @@ optional arguments:
   -h, --help            show this help message and exit
   --train_data_path TRAIN_DATA_PATH
                         path of training dataset
+  --test_data_path TEST_DATA_PATH
+                        path of testing dataset
   --batch_size BATCH_SIZE
                         size of mini-batch (default:10000)
-  --test_set_size TEST_SET_SIZE
-                        size of the validation dataset(default: 10000)
   --num_passes NUM_PASSES
                         number of passes to train
-  --num_lines_to_detact NUM_LINES_TO_DETACT
-                        number of records to detect dataset's meta info
+  --model_output_prefix MODEL_OUTPUT_PREFIX
+                        prefix of path for model to store (default:
+                        ./ctr_models)
+  --data_meta_file DATA_META_FILE
+                        path of data meta info file
+  --model_type MODEL_TYPE
+                        model type, classification: 0, regression 1 (default
+                        classification)
+```
+
+## 用训好的模型做预测
+训好的模型可以用来预测新的数据， 预测数据的格式为
+
+```
+# <dnn input ids> \t <lr input sparse values>
+1 23 190 \t 230:0.12 3421:0.9 23451:0.12
+23 231 \t 1230:0.12 13421:0.9
+```
+
+`infer.py` 的使用方法如下
+
+```
+usage: infer.py [-h] --model_gz_path MODEL_GZ_PATH --data_path DATA_PATH
+                --prediction_output_path PREDICTION_OUTPUT_PATH
+                [--data_meta_path DATA_META_PATH] --model_type MODEL_TYPE
+
+PaddlePaddle CTR example
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model_gz_path MODEL_GZ_PATH
+                        path of model parameters gz file
+  --data_path DATA_PATH
+                        path of the dataset to infer
+  --prediction_output_path PREDICTION_OUTPUT_PATH
+                        path to output the prediction
+  --data_meta_path DATA_META_PATH
+                        path of trainset's meta info, default is ./data.meta
+  --model_type MODEL_TYPE
+                        model type, classification: 0, regression 1 (default
+                        classification)
 ```
 
+示例数据可以用如下命令预测
+
+```
+python infer.py --model_gz_path <model_path> --data_path output/infer.txt --prediction_output_path predictions.txt --data_meta_path data.meta.txt
+```
+
+最终的预测结果位于 `predictions.txt`。
+
 ## 参考文献
 1. <https://en.wikipedia.org/wiki/Click-through_rate>
 2. <https://www.kaggle.com/c/avazu-ctr-prediction/data>

ctr/infer.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import gzip
+import argparse
+import itertools
+
+import paddle.v2 as paddle
+import network_conf
+from train import dnn_layer_dims
+import reader
+from utils import logger, ModelType
+
+parser = argparse.ArgumentParser(description="PaddlePaddle CTR example")
+parser.add_argument(
+    '--model_gz_path',
+    type=str,
+    required=True,
+    help="path of model parameters gz file")
+parser.add_argument(
+    '--data_path', type=str, required=True, help="path of the dataset to infer")
+parser.add_argument(
+    '--prediction_output_path',
+    type=str,
+    required=True,
+    help="path to output the prediction")
+parser.add_argument(
+    '--data_meta_path',
+    type=str,
+    default="./data.meta",
+    help="path of trainset's meta info, default is ./data.meta")
+parser.add_argument(
+    '--model_type',
+    type=int,
+    required=True,
+    default=ModelType.CLASSIFICATION,
+    help='model type, classification: %d, regression %d (default classification)'
+    % (ModelType.CLASSIFICATION, ModelType.REGRESSION))
+
+args = parser.parse_args()
+
+paddle.init(use_gpu=False, trainer_count=1)
+
+
+class CTRInferer(object):
+    def __init__(self, param_path):
+        logger.info("create CTR model")
+        dnn_input_dim, lr_input_dim = reader.load_data_meta(args.data_meta_path)
+        # create the mdoel
+        self.ctr_model = network_conf.CTRmodel(
+            dnn_layer_dims,
+            dnn_input_dim,
+            lr_input_dim,
+            model_type=args.model_type,
+            is_infer=True)
+        # load parameter
+        logger.info("load model parameters from %s" % param_path)
+        self.parameters = paddle.parameters.Parameters.from_tar(
+            gzip.open(param_path, 'r'))
+        self.inferer = paddle.inference.Inference(
+            output_layer=self.ctr_model.model,
+            parameters=self.parameters, )
+
+    def infer(self, data_path):
+        logger.info("infer data...")
+        dataset = reader.Dataset()
+        infer_reader = paddle.batch(
+            dataset.infer(args.data_path), batch_size=1000)
+        logger.warning('write predictions to %s' % args.prediction_output_path)
+        output_f = open(args.prediction_output_path, 'w')
+        for id, batch in enumerate(infer_reader()):
+            res = self.inferer.infer(input=batch)
+            predictions = [x for x in itertools.chain.from_iterable(res)]
+            assert len(batch) == len(
+                predictions), "predict error, %d inputs, but %d predictions" % (
+                    len(batch), len(predictions))
+            output_f.write('\n'.join(map(str, predictions)) + '\n')
+
+
+if __name__ == '__main__':
+    ctr_inferer = CTRInferer(args.model_gz_path)
+    ctr_inferer.infer(args.data_path)

ctr/network_conf.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import paddle.v2 as paddle
+from paddle.v2 import layer
+from paddle.v2 import data_type as dtype
+from utils import logger, ModelType
+
+
+class CTRmodel(object):
+    '''
+    A CTR model which implements wide && deep learning model.
+    '''
+
+    def __init__(self,
+                 dnn_layer_dims,
+                 dnn_input_dim,
+                 lr_input_dim,
+                 model_type=ModelType.create_classification(),
+                 is_infer=False):
+        '''
+        @dnn_layer_dims: list of integer
+            dims of each layer in dnn
+        @dnn_input_dim: int
+            size of dnn's input layer
+        @lr_input_dim: int
+            size of lr's input layer
+        @is_infer: bool
+            whether to build a infer model
+        '''
+        self.dnn_layer_dims = dnn_layer_dims
+        self.dnn_input_dim = dnn_input_dim
+        self.lr_input_dim = lr_input_dim
+        self.model_type = model_type
+        self.is_infer = is_infer
+
+        self._declare_input_layers()
+
+        self.dnn = self._build_dnn_submodel_(self.dnn_layer_dims)
+        self.lr = self._build_lr_submodel_()
+
+        # model's prediction
+        # TODO(superjom) rename it to prediction
+        if self.model_type.is_classification():
+            self.model = self._build_classification_model(self.dnn, self.lr)
+        if self.model_type.is_regression():
+            self.model = self._build_regression_model(self.dnn, self.lr)
+
+    def _declare_input_layers(self):
+        self.dnn_merged_input = layer.data(
+            name='dnn_input',
+            type=paddle.data_type.sparse_binary_vector(self.dnn_input_dim))
+
+        self.lr_merged_input = layer.data(
+            name='lr_input',
+            type=paddle.data_type.sparse_vector(self.lr_input_dim))
+
+        if not self.is_infer:
+            self.click = paddle.layer.data(
+                name='click', type=dtype.dense_vector(1))
+
+    def _build_dnn_submodel_(self, dnn_layer_dims):
+        '''
+        build DNN submodel.
+        '''
+        dnn_embedding = layer.fc(
+            input=self.dnn_merged_input, size=dnn_layer_dims[0])
+        _input_layer = dnn_embedding
+        for i, dim in enumerate(dnn_layer_dims[1:]):
+            fc = layer.fc(
+                input=_input_layer,
+                size=dim,
+                act=paddle.activation.Relu(),
+                name='dnn-fc-%d' % i)
+            _input_layer = fc
+        return _input_layer
+
+    def _build_lr_submodel_(self):
+        '''
+        config LR submodel
+        '''
+        fc = layer.fc(
+            input=self.lr_merged_input,
+            size=1,
+            name='lr',
+            act=paddle.activation.Relu())
+        return fc
+
+    def _build_classification_model(self, dnn, lr):
+        merge_layer = layer.concat(input=[dnn, lr])
+        self.output = layer.fc(
+            input=merge_layer,
+            size=1,
+            name='output',
+            # use sigmoid function to approximate ctr rate, a float value between 0 and 1.
+            act=paddle.activation.Sigmoid())
+
+        self.train_cost = paddle.layer.multi_binary_label_cross_entropy_cost(
+            input=self.output, label=self.click)
+        return self.output
+
+    def _build_regression_model(self, dnn, lr):
+        merge_layer = layer.concat(input=[dnn, lr])
+        self.output = layer.fc(
+            input=merge_layer,
+            size=1,
+            name='output',
+            act=paddle.activation.Sigmoid())
+        if not self.is_infer:
+            self.train_cost = paddle.layer.mse_cost(
+                input=self.output, label=self.click)
+        return self.output

ctr/reader.py

+from utils import logger, TaskMode, load_dnn_input_record, load_lr_input_record
+
+feeding_index = {'dnn_input': 0, 'lr_input': 1, 'click': 2}
+
+
+class Dataset(object):
+    def __init__(self):
+        self.mode = TaskMode.create_train()
+
+    def train(self, path):
+        '''
+        Load trainset.
+        '''
+        logger.info("load trainset from %s" % path)
+        self.mode = TaskMode.create_train()
+        self.path = path
+        return self._parse
+
+    def test(self, path):
+        '''
+        Load testset.
+        '''
+        logger.info("load testset from %s" % path)
+        self.path = path
+        self.mode = TaskMode.create_test()
+        return self._parse
+
+    def infer(self, path):
+        '''
+        Load infer set.
+        '''
+        logger.info("load inferset from %s" % path)
+        self.path = path
+        self.mode = TaskMode.create_infer()
+        return self._parse
+
+    def _parse(self):
+        '''
+        Parse dataset.
+        '''
+        with open(self.path) as f:
+            for line_id, line in enumerate(f):
+                fs = line.strip().split('\t')
+                dnn_input = load_dnn_input_record(fs[0])
+                lr_input = load_lr_input_record(fs[1])
+                if not self.mode.is_infer():
+                    click = [int(fs[2])]
+                    yield dnn_input, lr_input, click
+                else:
+                    yield dnn_input, lr_input
+
+
+def load_data_meta(path):
+    '''
+    load data meta info from path, return (dnn_input_dim, lr_input_dim)
+    '''
+    with open(path) as f:
+        lines = f.read().split('\n')
+        err_info = "wrong meta format"
+        assert len(lines) == 2, err_info
+        assert 'dnn_input_dim:' in lines[0] and 'lr_input_dim:' in lines[
+            1], err_info
+        res = map(int, [_.split(':')[1] for _ in lines])
+        logger.info('dnn input dim: %d' % res[0])
+        logger.info('lr input dim: %d' % res[1])
+        return res

ctr/train.py

-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
 import argparse
-import logging
+import gzip
+
+import reader
 import paddle.v2 as paddle
-from paddle.v2 import layer
-from paddle.v2 import data_type as dtype
-from data_provider import field_index, detect_dataset, AvazuDataset
+from utils import logger, ModelType
+from network_conf import CTRmodel
 
-parser = argparse.ArgumentParser(description="PaddlePaddle CTR example")
-parser.add_argument(
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="PaddlePaddle CTR example")
+    parser.add_argument(
         '--train_data_path',
         type=str,
         required=True,
         help="path of training dataset")
-parser.add_argument(
+    parser.add_argument(
+        '--test_data_path', type=str, help='path of testing dataset')
+    parser.add_argument(
         '--batch_size',
         type=int,
         default=10000,
         help="size of mini-batch (default:10000)")
-parser.add_argument(
-    '--test_set_size',
-    type=int,
-    default=10000,
-    help="size of the validation dataset(default: 10000)")
-parser.add_argument(
+    parser.add_argument(
         '--num_passes', type=int, default=10, help="number of passes to train")
-parser.add_argument(
-    '--num_lines_to_detact',
+    parser.add_argument(
+        '--model_output_prefix',
+        type=str,
+        default='./ctr_models',
+        help='prefix of path for model to store (default: ./ctr_models)')
+    parser.add_argument(
+        '--data_meta_file',
+        type=str,
+        required=True,
+        help='path of data meta info file', )
+    parser.add_argument(
+        '--model_type',
         type=int,
-    default=500000,
-    help="number of records to detect dataset's meta info")
-
-args = parser.parse_args()
+        required=True,
+        default=ModelType.CLASSIFICATION,
+        help='model type, classification: %d, regression %d (default classification)'
+        % (ModelType.CLASSIFICATION, ModelType.REGRESSION))
 
-dnn_layer_dims = [128, 64, 32, 1]
-data_meta_info = detect_dataset(args.train_data_path, args.num_lines_to_detact)
+    return parser.parse_args()
 
-logging.warning('detect categorical fields in dataset %s' %
-                args.train_data_path)
-for key, item in data_meta_info.items():
-    logging.warning('    - {}\t{}'.format(key, item))
 
-paddle.init(use_gpu=False, trainer_count=1)
+dnn_layer_dims = [128, 64, 32, 1]
 
 # ==============================================================================
-#                    input layers
+#                   cost and train period
 # ==============================================================================
-dnn_merged_input = layer.data(
-    name='dnn_input',
-    type=paddle.data_type.sparse_binary_vector(data_meta_info['dnn_input']))
 
-lr_merged_input = layer.data(
-    name='lr_input',
-    type=paddle.data_type.sparse_binary_vector(data_meta_info['lr_input']))
 
-click = paddle.layer.data(name='click', type=dtype.dense_vector(1))
+def train():
+    args = parse_args()
+    args.model_type = ModelType(args.model_type)
+    paddle.init(use_gpu=False, trainer_count=1)
+    dnn_input_dim, lr_input_dim = reader.load_data_meta(args.data_meta_file)
 
+    # create ctr model.
+    model = CTRmodel(
+        dnn_layer_dims,
+        dnn_input_dim,
+        lr_input_dim,
+        model_type=args.model_type,
+        is_infer=False)
 
-# ==============================================================================
-#                    network structure
-# ==============================================================================
-def build_dnn_submodel(dnn_layer_dims):
-    dnn_embedding = layer.fc(input=dnn_merged_input, size=dnn_layer_dims[0])
-    _input_layer = dnn_embedding
-    for i, dim in enumerate(dnn_layer_dims[1:]):
-        fc = layer.fc(
-            input=_input_layer,
-            size=dim,
-            act=paddle.activation.Relu(),
-            name='dnn-fc-%d' % i)
-        _input_layer = fc
-    return _input_layer
-
-
-# config LR submodel
-def build_lr_submodel():
-    fc = layer.fc(
-        input=lr_merged_input, size=1, name='lr', act=paddle.activation.Relu())
-    return fc
-
-
-# conbine DNN and LR submodels
-def combine_submodels(dnn, lr):
-    merge_layer = layer.concat(input=[dnn, lr])
-    fc = layer.fc(
-        input=merge_layer,
-        size=1,
-        name='output',
-        # use sigmoid function to approximate ctr rate, a float value between 0 and 1.
-        act=paddle.activation.Sigmoid())
-    return fc
-
-
-dnn = build_dnn_submodel(dnn_layer_dims)
-lr = build_lr_submodel()
-output = combine_submodels(dnn, lr)
-
-# ==============================================================================
-#                   cost and train period
-# ==============================================================================
-classification_cost = paddle.layer.multi_binary_label_cross_entropy_cost(
-    input=output, label=click)
-
-params = paddle.parameters.create(classification_cost)
-
-optimizer = paddle.optimizer.Momentum(momentum=0.01)
+    params = paddle.parameters.create(model.train_cost)
+    optimizer = paddle.optimizer.AdaGrad()
 
-trainer = paddle.trainer.SGD(
-    cost=classification_cost, parameters=params, update_equation=optimizer)
+    trainer = paddle.trainer.SGD(
+        cost=model.train_cost, parameters=params, update_equation=optimizer)
 
-dataset = AvazuDataset(
-    args.train_data_path, n_records_as_test=args.test_set_size)
+    # dataset = reader.AvazuDataset(
+    #     args.train_data_path,
+    #     n_records_as_test=args.test_set_size,
+    #     fields=reader.fields,
+    #     feature_dims=reader.feature_dims)
 
+    dataset = reader.Dataset()
 
-def event_handler(event):
+    def __event_handler__(event):
         if isinstance(event, paddle.event.EndIteration):
             num_samples = event.batch_id * args.batch_size
             if event.batch_id % 100 == 0:
-            logging.warning("Pass %d, Samples %d, Cost %f" %
-                            (event.pass_id, num_samples, event.cost))
+                logger.warning("Pass %d, Samples %d, Cost %f, %s" % (
+                    event.pass_id, num_samples, event.cost, event.metrics))
 
             if event.batch_id % 1000 == 0:
+                if args.test_data_path:
                     result = trainer.test(
-                reader=paddle.batch(dataset.test, batch_size=args.batch_size),
-                feeding=field_index)
-            logging.warning("Test %d-%d, Cost %f" %
-                            (event.pass_id, event.batch_id, result.cost))
-
-
-trainer.train(
                         reader=paddle.batch(
-        paddle.reader.shuffle(dataset.train, buf_size=500),
+                            dataset.test(args.test_data_path),
+                            batch_size=args.batch_size),
+                        feeding=reader.feeding_index)
+                    logger.warning("Test %d-%d, Cost %f, %s" %
+                                   (event.pass_id, event.batch_id, result.cost,
+                                    result.metrics))
+
+                path = "{}-pass-{}-batch-{}-test-{}.tar.gz".format(
+                    args.model_output_prefix, event.pass_id, event.batch_id,
+                    result.cost)
+                with gzip.open(path, 'w') as f:
+                    params.to_tar(f)
+
+    trainer.train(
+        reader=paddle.batch(
+            paddle.reader.shuffle(
+                dataset.train(args.train_data_path), buf_size=500),
             batch_size=args.batch_size),
-    feeding=field_index,
-    event_handler=event_handler,
+        feeding=reader.feeding_index,
+        event_handler=__event_handler__,
         num_passes=args.num_passes)
+
+
+if __name__ == '__main__':
+    train()

ctr/utils.py

+import logging
+
+logging.basicConfig()
+logger = logging.getLogger("logger")
+logger.setLevel(logging.INFO)
+
+
+class TaskMode:
+    TRAIN_MODE = 0
+    TEST_MODE = 1
+    INFER_MODE = 2
+
+    def __init__(self, mode):
+        self.mode = mode
+
+    def is_train(self):
+        return self.mode == self.TRAIN_MODE
+
+    def is_test(self):
+        return self.mode == self.TEST_MODE
+
+    def is_infer(self):
+        return self.mode == self.INFER_MODE
+
+    @staticmethod
+    def create_train():
+        return TaskMode(TaskMode.TRAIN_MODE)
+
+    @staticmethod
+    def create_test():
+        return TaskMode(TaskMode.TEST_MODE)
+
+    @staticmethod
+    def create_infer():
+        return TaskMode(TaskMode.INFER_MODE)
+
+
+class ModelType:
+    CLASSIFICATION = 0
+    REGRESSION = 1
+
+    def __init__(self, mode):
+        self.mode = mode
+
+    def is_classification(self):
+        return self.mode == self.CLASSIFICATION
+
+    def is_regression(self):
+        return self.mode == self.REGRESSION
+
+    @staticmethod
+    def create_classification():
+        return ModelType(ModelType.CLASSIFICATION)
+
+    @staticmethod
+    def create_regression():
+        return ModelType(ModelType.REGRESSION)
+
+
+def load_dnn_input_record(sent):
+    return map(int, sent.split())
+
+
+def load_lr_input_record(sent):
+    res = []
+    for _ in [x.split(':') for x in sent.split()]:
+        res.append((int(_[0]), float(_[1]), ))
+    return res