refactor ctr model #138
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refactor ctr model #138
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lcy-seso
requested changes
Jul 5, 2017
ctr/README.md
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| ├── train.py # 训练脚本 | ||
| └── utils.py # helper functions | ||
| ``` | ||
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| ## 背景介绍 | ||
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| 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， |
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- 第一句话：是用来表示用户点击一个特定链接的概率 ，通常被用来衡量一个在线广告系统的有效性。--> 是对用户点击一个特定链接的概率做出预测，是广告投放过程中的一个重要环节。精准的点击率预估对在线广告系统收益最大化具有重要意义。
- 11 行，from @llxxxll "召回"这个词对基础用户比较陌生，解释或者再用其他方式描述一下。
- 这一篇分段过于细碎，第23，24 行全部合在第一段中。
- 第24行“系统大体上会执行下列步骤来展示广告” --> 粗略来讲，系统会执行下列步骤展示广告：
- 31 行去掉 “很”，很重要 --> 重要。
- 53 ~ 62 行合并为一段。
ctr/README.md
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| 具体的特征处理方法参看 [data process](./dataset.md)。 | ||
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| 本教程中演示模型的输入格式如下： | ||
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- 69 行
click是指点击率吗？在69行时还没有介绍数据，这里还无法和数据中的click字段关联上，说明更清楚一些吧。 - 79 和 81 合为一段。
- 81 行之后，可否用文字再进一步描述解释一下格式。
ctr/README.md
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| @@ -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 | |||
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| 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。 | |||
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| 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)。 | |||
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77 行 我们使用 Kaggle 上 Click-through rate prediction 任务的数据集来演示模型。 --> 我们使用 Kaggle 上 Click-through rate prediction 任务的数据集来运行本例中的模型。
ctr/README.md
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| 23 231 \t 1230:0.12 13421:0.9 \t 1 | ||
| ``` | ||
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| 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： |
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本例目录下的avazu_data_processor.py 脚本可以对下载的原始数据进行处理，具体使用方法参考如下说明：
ctr/README.md
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| ├── network_conf.py # 模型网络配置 | ||
| ├── reader.py # data provider | ||
| ├── train.py # 训练脚本 | ||
| └── utils.py # helper functions |
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- 这里没有
avazu_data_processor.py这个文件，是刻意的吗？这个文件也挺重要的。 - images 目录是可以简单地省略。
ctr/network_conf.py
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| self.output = layer.fc( | ||
| input=merge_layer, | ||
| size=1, | ||
| name='output', |
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| #!/usr/bin/env python | |||
| # -*- coding: utf-8 -*- | |||
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有些脚本加了 shebang，保持一致，或者都删掉，或者都加上。
ctr/utils.py
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| import logging | ||
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| logging.basicConfig() | ||
| logger = logging.getLogger("logger") |
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- 可以直接
logging.getLogger("paddle")获取config_parser.py中的logger。
ctr/train.py
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| # n_records_as_test=args.test_set_size, | ||
| # fields=reader.fields, | ||
| # feature_dims=reader.feature_dims) | ||
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ctr/avazu_data_processer.py
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| 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')) | ||
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运行训练和测试 有问题，需要check |
lcy-seso
approved these changes
Jul 17, 2017
ctr/README.md
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| 1. 召回满足 query 的广告集合 | ||
| 1. 获取满足 query 的广告集合 |
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“满足 query ” 这一句的意义不明白，我不是特别理解。
能否略微再增加一些描述性词汇。
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修改如下：
infer.py