```，归一化时用到的最大值并不是用全局最大值，而是取排序后95%位置处的特征值作为最大值，同时保留极值。
+
+### CTR网络输入的定义
+
+正如前所述，Criteo数据集中，分为连续数据与离散（稀疏）数据，所以整体而言，CTR-DNN模型的数据输入层包括三个，分别是：`dense_input`用于输入连续数据，维度由超参数`dense_feature_dim`指定，数据类型是归一化后的浮点型数据。`sparse_input_ids`用于记录离散数据，在Criteo数据集中，共有26个slot，所以我们创建了名为`C1~C26`的26个稀疏参数输入，并设置`lod_level=1`，代表其为变长数据，数据类型为整数；最后是每条样本的`label`，代表了是否被点击，数据类型是整数，0代表负样例，1代表正样例。
+
+在Paddle中数据输入的声明使用`paddle.fluid.layers.data()`，会创建指定类型的占位符，数据IO会依据此定义进行数据的输入。
+
+稀疏参数输入的定义:
+```python
+def sparse_inputs():
+ ids = envs.get_global_env("hyper_parameters.sparse_inputs_slots", None, self._namespace)
+
+ sparse_input_ids = [
+ fluid.layers.data(name="S" + str(i),
+ shape=[1],
+ lod_level=1,
+ dtype="int64") for i in range(1, ids)
+ ]
+ return sparse_input_ids
+```
+
+稠密参数输入的定义：
+```python
+def dense_input():
+ dim = envs.get_global_env("hyper_parameters.dense_input_dim", None, self._namespace)
+
+ dense_input_var = fluid.layers.data(name="D",
+ shape=[dim],
+ dtype="float32")
+ return dense_input_var
+```
+
+标签的定义：
+```python
+def label_input():
+ label = fluid.layers.data(name="click", shape=[1], dtype="int64")
+ return label
+```
+
+组合起来，正确的声明他们：
+```python
+self.sparse_inputs = sparse_inputs()
+self.dense_input = dense_input()
+self.label_input = label_input()
+
+self._data_var.append(self.dense_input)
+
+for input in self.sparse_inputs:
+ self._data_var.append(input)
+
+self._data_var.append(self.label_input)
+
+```
+
+
+### Criteo Reader写法
+
+```python
+# 引入PaddleRec的Reader基类
+from paddlerec.core.reader import Reader
+# 引入PaddleRec的读取yaml配置文件的方法
+from paddlerec.core.utils import envs
+
+# 定义TrainReader，需要继承 paddlerec.core.reader.Reader
+class TrainReader(Reader):
+
+ # 数据预处理逻辑，继承自基类
+ # 如果无需处理， 使用pass跳过该函数的执行
+ def init(self):
+ self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+ self.cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+ self.cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+ self.hash_dim_ = envs.get_global_env("hyper_parameters.sparse_feature_number", None, "train.model")
+ self.continuous_range_ = range(1, 14)
+ self.categorical_range_ = range(14, 40)
+
+ # 读取数据方法，继承自基类
+ # 实现可以迭代的reader函数，逐行处理数据
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.rstrip('\n').split('\t')
+
+ dense_feature = []
+ sparse_feature = []
+ for idx in self.continuous_range_:
+ if features[idx] == "":
+ dense_feature.append(0.0)
+ else:
+ dense_feature.append(
+ (float(features[idx]) - self.cont_min_[idx - 1]) /
+ self.cont_diff_[idx - 1])
+
+ for idx in self.categorical_range_:
+ sparse_feature.append(
+ [hash(str(idx) + features[idx]) % self.hash_dim_])
+ label = [int(features[0])]
+ feature_name = ["D"]
+ for idx in self.categorical_range_:
+ feature_name.append("S" + str(idx - 13))
+ feature_name.append("label")
+ yield zip(feature_name, [dense_feature] + sparse_feature + [label])
+
+ return reader
+```
+
+
+### 调试Reader
+
+在Linux下运行时，默认启动`Dataset`模式，在Win/Mac下运行时，默认启动`Dataloader`模式。
+
+通过在`config.yaml`中添加或修改`reader_debug_mode=True`打开debug模式，只会结合组网运行reader的部分，读取10条样本，并print，方便您观察格式是否符合预期或隐藏bug。
+```yaml
+reader:
+ batch_size: 2
+ class: "{workspace}/../criteo_reader.py"
+ train_data_path: "{workspace}/data/train"
+ reader_debug_mode: True
+```
+
+修改后，使用paddlerec.run执行该修改后的yaml文件，可以观察输出。
+```bash
+python -m paddlerec.run -m ./models/rank/dnn/config.yaml -e single
+```
+
+### Dataset调试
+
+dataset输出的数据格式如下：
+` dense_input:size ; dense_input:value ; sparse_input:size ; sparse_input:value ; ... ; sparse_input:size ; sparse_input:value ; label:size ; label:value `
+
+基本规律是对于每个变量，会先输出其维度大小，再输出其具体值。
+
+直接debug `criteo_reader`理想的输出为(截取了一个片段)：
+```bash
+...
+13 0.0 0.00497512437811 0.05 0.08 0.207421875 0.028 0.35 0.08 0.082 0.0 0.4 0.0 0.08 1 737395 1 210498 1 903564 1 286224 1 286835 1 906818 1 90
+6116 1 67180 1 27346 1 51086 1 142177 1 95024 1 157883 1 873363 1 600281 1 812592 1 228085 1 35900 1 880474 1 984402 1 100885 1 26235 1 410878 1 798162 1 499868 1 306163 1 0
+...
+```
+可以看到首先输出的是13维的dense参数，随后是分立的sparse参数，最后一个是1维的label，数值为0，输出符合预期。
+
+>使用Dataset的一些注意事项
+> - Dataset的基本原理：将数据print到缓存，再由C++端的代码实现读取，因此，我们不能在dataset的读取代码中，加入与数据读取无关的print信息，会导致C++端拿到错误的数据信息。
+> - dataset目前只支持在`unbuntu`及`CentOS`等标准Linux环境下使用，在`Windows`及`Mac`下使用时，会产生预料之外的错误，请知悉。
+
+### DataLoader调试
+
+dataloader的输出格式为`list: [ list[var_1], list[var_2], ... , list[var_3]]`，每条样本的数据会被放在一个 **list[list]** 中，list[0]为第一个variable。
+
+直接debug `criteo_reader`理想的输出为(截取了一个片段)：
+```bash
+...
+[[0.0, 0.004975124378109453, 0.05, 0.08, 0.207421875, 0.028, 0.35, 0.08, 0.082, 0.0, 0.4, 0.0, 0.08], [560746], [902436], [262029], [182633], [368411], [735166], [321120], [39572], [185732], [140298], [926671], [81559], [461249], [728372], [915018], [907965], [818961], [850958], [311492], [980340], [254960], [175041], [524857], [764893], [526288], [220126], [0]]
+...
+```
+可以看到首先输出的是13维的dense参数的list，随后是分立的sparse参数，各自在一个list中，最后一个是1维的label的list，数值为0，输出符合预期。
diff --git a/doc/design.md b/doc/design.md
new file mode 100644
index 0000000000000000000000000000000000000000..a442bd16a25301178538f482cd537a4ca23bc395
--- /dev/null
+++ b/doc/design.md
@@ -0,0 +1,282 @@
+# PaddleRec 设计
+
+
+## PaddleRec 整体设计概览
+PaddleRec将推荐模型的训练与预测流程，整体抽象为了五个大模块：
+
+* [Engine 流程执行引擎](#engine)
+* [Trainer 流程具体定义](#trainer)
+* [Model 模型组网定义](#model)
+* [Reader 数据读取定义](#reader)
+* [Metric 精度指标打印](#metric)
+
+层级结构，以及一键启动训练时的调用关系如下图所示：
+
+
+
+
+core的文件结构如下，后续分别对各个模块进行介绍。
+```
+.core
+├── engine/ 运行引擎实现
+├── metrics/ 全局指标实现
+├── modules/ 自定义op实现
+├── trainers/ 运行流程实现
+├── utils/ 辅助工具
+├── factory.py 运行流程的注册
+├── layer.py 自定义op基类定义
+├── metric.py Metric基类定义
+├── model.py Model基类定义
+├── reader.py Reader基类定义
+└── trainer.py Trainer基类定义
+```
+
+
+## Engine
+
+Engine是整体训练的执行引擎，与组网逻辑及数据无关，只与当前运行模式、运行环境及运行设备有关。
+
+运行模式具体是指：
+- 单机运行
+- 分布式运行
+- 本地模拟分布式
+
+运行环境是指：
+- Linux
+- Windows
+- Mac
+
+运行设备是指：
+- CPU
+- GPU
+- AI芯片
+
+在用户调用`python -m paddlerec.run`时，首先会根据`yaml`文件中的配置信息选择合适的执行引擎， 以下代码位于[run.py](../run.py)：
+```python
+engine_registry()
+which_engine = get_engine(args)
+engine = which_engine(args)
+engine.run()
+```
+
+我们以`single engine`为例，概览engine的行为：
+```python
+def single_engine(args):
+ trainer = get_trainer_prefix(args) + "SingleTrainer"
+ single_envs = {}
+ single_envs["train.trainer.trainer"] = trainer
+ single_envs["train.trainer.threads"] = "2"
+ single_envs["train.trainer.engine"] = "single"
+ single_envs["train.trainer.device"] = args.device
+ single_envs["train.trainer.platform"] = envs.get_platform()
+ print("use {} engine to run model: {}".format(trainer, args.model))
+
+ set_runtime_envs(single_envs, args.model)
+ trainer = TrainerFactory.create(args.model)
+ return trainer
+```
+single_engine被调用后，主要进行了以下两个工作：
+
+1. 根据`yaml`配置文件，设置了**当前进程的环境变量**，后续的所有流程都依赖于环境变量。
+2. 根据模型及环境，指定并初始化了运行流程所用的`Trainer`
+
+进一步细化第一步工作
+- 本地模拟分布式引擎会在单机环境变量的基础上，额外设置本地模拟分布式的环境变量，比如：为各个进程设置不同通信端口，分配ID。最后会启动多个`Trainer`完成本地模拟分布式的工作。
+- 分布式引擎会在单机环境变量的基础上，基于运行参数`-b --backend`所指定的脚本或配置文件，完成分布式任务的文件打包，上传，提交等操作。该脚本格式与分布式任务运行的集群有关，如MPI/K8S/PaddleCloud等，用户可以自定义分布式运行逻辑。
+
+Engine的自定义实现，可以参考[local_cluster.py](../core/engine/local_cluster.py)
+
+## Trainer
+
+`Trainer`是训练与预测流程的具体实现，会run模型中定义的各个流程，与model、reader、metric紧密相关。PaddleRec以有限状态机的逻辑定义了训练中的各个阶段，不同的Trainer子类会分别实现阶段中的特殊需求。有限状态机的流程在`def processor_register()`中注册。
+
+我们以SingleTrainer为例，概览Trainer行为：
+
+```python
+class SingleTrainer(TranspileTrainer):
+ def processor_register(self):
+ self.regist_context_processor('uninit', self.instance)
+ self.regist_context_processor('init_pass', self.init)
+ self.regist_context_processor('startup_pass', self.startup)
+ if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader":
+ self.regist_context_processor('train_pass', self.dataset_train)
+ else:
+ self.regist_context_processor('train_pass', self.dataloader_train)
+
+ self.regist_context_processor('infer_pass', self.infer)
+ self.regist_context_processor('terminal_pass', self.terminal)
+```
+
+SingleTrainer首先注册了完成任务所需的步骤，各步骤首先按照注册顺序加入`Trainer`基类中名为`status_processor`的字典，运行的先后顺序，可以在每个执行步骤中改变`context['status']`的值，指定下一步运行哪个步骤。
+
+SingleTrainer指定了以下6个步骤：
+1. uninit：默认排在首位，通过环境变量决定model的对象
+1. init_pass：调用model_的接口，生成模型的组网，初始化fetch及metric的变量
+2. startup_pass：初始化模型组网中的各个参数，run(fluid.default_startup_program)
+3. train_pass：会根据环境分别调用`dataset`与`dataloader`进行训练的流程。
+4. infer_pass：在训练结束后，会对训练保存的模型在测试集上验证效果
+5. terminal_pass：打印全局变量及预测结果等自定义的信息。
+
+Trainer的自定义实现，可以参照[single_trainer.py](../core/trainers/single_trainer.py)
+
+## Model
+
+Model定义了各个模型实现的范式，模型只要继承并实现基类中的函数，并给一些成员赋值，就可以保证模型被Trainer正确调用。
+
+我们首先看一下Model基类中的部分重要定义，对模型的实现流程有初步概念。
+
+```python
+class Model(object):
+ __metaclass__ = abc.ABCMeta
+
+ def __init__(self, config):
+ self._cost = None
+ self._metrics = {}
+ self._data_var = []
+ self._infer_data_var = []
+ self._infer_results = {}
+ self._data_loader = None
+ self._infer_data_loader = None
+ self._fetch_interval = 20
+ self._namespace = "train.model"
+ self._platform = envs.get_platform()
+
+ def get_inputs(self):
+ return self._data_var
+
+ @abc.abstractmethod
+ def train_net(self):
+ pass
+
+ @abc.abstractmethod
+ def infer_net(self):
+ pass
+
+ def get_avg_cost(self):
+ return self._cost
+
+```
+
+每个模型都一定需要继承`def train_net`与`def infer_net`，并且给`self._data_var`与`self._cost`成员赋值，指定模型入口，实现组网的整体逻辑。若有更多或更复杂的需求，可以参照下面的接口，分别继承各个函数，并实现需要的功能：
+
+```python
+def get_infer_inputs(self):
+ return self._infer_data_var
+
+def get_infer_results(self):
+ return self._infer_results
+
+def get_metrics(self):
+ return self._metrics
+
+def get_fetch_period(self):
+ return self._fetch_interval
+```
+
+model的具体实现，可以参考dnn的示例[model.py](../../models/rank/dnn/../../../paddlerec/core/model.py)
+
+
+## Reader
+
+PaddleRec会根据运行环境，分别指定不同的数据IO方式。在Linux下，优先使用`Dataset`，Win及Mac优先使用`Dataloader`。
+
+
+Dataset的使用介绍可以参考[DatasetFactory](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/dataset_cn/DatasetFactory_cn.html)
+
+Dataloader的使用介绍可以参考[异步数据读取](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/data_preparing/use_py_reader.html)
+
+
+考虑到以上两种高效的数据IO方式仍然有很高的学习门槛，PaddleRec将两种数据读取方式进行了更高层次的封装，用户需要实现的仅是每行数据的处理逻辑，剩下的工作交给PaddleRec的Reader基类完成。
+
+首先浏览以下Reader基类的定义，有一个初步的印象：
+
+```python
+class Reader(dg.MultiSlotDataGenerator):
+ __metaclass__ = abc.ABCMeta
+
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
+
+ envs.set_global_envs(_config)
+ envs.update_workspace()
+
+ @abc.abstractmethod
+ def init(self):
+ pass
+
+ @abc.abstractmethod
+ def generate_sample(self, line):
+ pass
+
+```
+
+用户需要关注并实现的是`def init(self)`与`def generate_sample(self,line)`函数，分别执行数据读取中预处理所需变量的初始化，以及每一行string的切分及处理逻辑。
+
+当用户定义好以上两个函数，完成自己的Reader后，PaddleRec分别使用
+- [dataset_instance.py](../core/utils/dataset_instance.py)
+- [dataloader_instance.py](../core/utils/dataloader_instance.py)
+
+完成reader的构建工作。
+
+Reader数据处理的逻辑，可以参考[criteo_reader.py](../../models/rank/../../paddlerec/models/rank/criteo_reader.py)
+
+
+
+## Metric
+
+训练必然伴随着训练指标的打印，当单机运行时，打印相关信息比较简单。但分布式训练时，单机指标与全局指标往往有很大diff，比如`auc`以及正逆序`pn`。PaddleRec面向大规模分布式训练，将指标打印的逻辑抽象出来单独实现，以解决分布式训练时全局指标打印的问题。
+
+Metric基类定义了基本的接口，如下：
+```python
+class Metric(object):
+ __metaclass__ = abc.ABCMeta
+
+ def __init__(self, config):
+ """ init """
+ pass
+
+ @abc.abstractmethod
+ def clear(self, scope, params):
+ """
+ clear current value
+ Args:
+ scope: value container
+ params: extend varilable for clear
+ """
+ pass
+
+ @abc.abstractmethod
+ def calculate(self, scope, params):
+ """
+ calculate result
+ Args:
+ scope: value container
+ params: extend varilable for clear
+ """
+ pass
+
+ @abc.abstractmethod
+ def get_result(self):
+ """
+ Return:
+ result(dict) : calculate result
+ """
+ pass
+
+ @abc.abstractmethod
+ def get_result_to_string(self):
+ """
+ Return:
+ result(string) : calculate result with string format, for output
+ """
+ pass
+```
+
+全局指标的计算及输出，需要分别继承并实现以上四个成员函数。具体实现的例子，可以参考[auc_metric.py](../core/metrics/auc_metrics.py)
diff --git a/doc/development.md b/doc/development.md
new file mode 100644
index 0000000000000000000000000000000000000000..0c0162ba16349c1b7bd15cce97ec211d2bebb6b3
--- /dev/null
+++ b/doc/development.md
@@ -0,0 +1,163 @@
+# 二次开发
+
+## 如何添加自定义模型
+
+当您希望开发自定义模型时，需要继承模型的模板基类，并实现三个必要的方法`init_hyper_parameter`,`intput_data`,`net`
+
+并按照以下规范添加代码。
+
+### 基类的继承
+
+继承`paddlerec.core.model`的ModelBase，命名为`Class Model`
+
+```python
+from paddlerec.core.model import ModelBase
+
+
+class Model(ModelBase):
+
+ # 构造函数无需显式指定
+ # 若继承，务必调用基类的__init__方法
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+ # ModelBase的__init__方法会调用_init_hyper_parameter()
+
+```
+
+### 超参的初始化
+
+继承并实现`_init_hyper_parameter`方法(必要)，可以在该方法中，从`yaml`文件获取超参或进行自定义操作。如下面的示例：
+
+所有的envs调用接口在_init_hyper_parameters()方法中实现，同时类成员也推荐在此做声明及初始化。
+
+```python
+ def _init_hyper_parameters(self):
+ self.feature_size = envs.get_global_env(
+ "hyper_parameters.feature_size")
+ self.expert_num = envs.get_global_env("hyper_parameters.expert_num")
+ self.gate_num = envs.get_global_env("hyper_parameters.gate_num")
+ self.expert_size = envs.get_global_env("hyper_parameters.expert_size")
+ self.tower_size = envs.get_global_env("hyper_parameters.tower_size")
+```
+
+
+### 数据输入的定义
+继承并实现`input_data`方法(非必要)
+
+
+#### 直接使用基类的数据读取方法
+
+`ModelBase`中的input_data默认实现为slot_reader，在`config.yaml`中分别配置`reader.sparse_slot`及`reader.dense_slot`选项实现`slog:feasign`模式的数据读取。
+
+> Slot : Feasign 是什么？
+>
+> Slot直译是槽位，在Rec工程中，是指某一个宽泛的特征类别，比如用户ID、性别、年龄就是Slot，Feasign则是具体值，比如：12345，男，20岁。
+>
+> 在实践过程中，很多特征槽位不是单一属性，或无法量化并且离散稀疏的，比如某用户兴趣爱好有三个：游戏/足球/数码，且每个具体兴趣又有多个特征维度，则在兴趣爱好这个Slot兴趣槽位中，就会有多个Feasign值。
+>
+> PaddleRec在读取数据时，每个Slot ID对应的特征，支持稀疏，且支持变长，可以非常灵活的支持各种场景的推荐模型训练。
+
+使用示例请参考`rank.dnn`模型。
+
+#### 自定义数据输入
+
+
+如果您不想使用`slot:feasign`模式，则需继承并实现`input_data`接口，接口定义：`def input_data(self, is_infer=False, **kwargs)`
+
+使用示例如下：
+
+```python
+def input_data(self, is_infer=False, **kwargs):
+ ser_slot_names = fluid.data(
+ name='user_slot_names',
+ shape=[None, 1],
+ dtype='int64',
+ lod_level=1)
+ item_slot_names = fluid.data(
+ name='item_slot_names',
+ shape=[None, self.item_len],
+ dtype='int64',
+ lod_level=1)
+ lens = fluid.data(name='lens', shape=[None], dtype='int64')
+ labels = fluid.data(
+ name='labels',
+ shape=[None, self.item_len],
+ dtype='int64',
+ lod_level=1)
+
+ train_inputs = [user_slot_names] + [item_slot_names] + [lens] + [labels]
+ infer_inputs = [user_slot_names] + [item_slot_names] + [lens]
+
+ if is_infer:
+ return infer_inputs
+ else:
+ return train_inputs
+```
+
+更多数据读取教程，请参考[自定义数据集及Reader](custom_dataset_reader.md)
+
+
+### 组网的定义
+
+继承并实现`net`方法(必要)
+
+- 接口定义`def net(self, inputs, is_infer=False)`
+- 自定义网络需在该函数中使用paddle组网，实现前向逻辑，定义网络的Loss及Metrics，通过`is_infer`判断是否为infer网络。
+- 我们强烈建议`train`及`infer`尽量复用相同代码，
+- `net`中调用的其他函数以下划线为头进行命名，封装网络中的结构模块，如`_sparse_embedding_layer(self)`。
+- `inputs`为`def input_data()`的输出，若使用`slot_reader`方式，inputs为占位符，无实际意义，通过以下方法拿到dense及sparse的输入
+
+ ```python
+ self.sparse_inputs = self._sparse_data_var[1:]
+ self.dense_input = self._dense_data_var[0]
+ self.label_input = self._sparse_data_var[0]
+ ```
+
+可以参考官方模型的示例学习net的构造方法。
+
+## 如何运行自定义模型
+
+记录`model.py`,`config.yaml`及数据读取`reader.py`的文件路径，建议置于同一文件夹下，如`/home/custom_model`下，更改`config.yaml`中的配置选项
+
+1. 更改 workerspace为模型文件所在文件夹
+```yaml
+workspace: "/home/custom_model"
+```
+
+2. 更改数据地址及读取reader地址
+```yaml
+dataset:
+- name: custom_model_train
+- data_path: "{workspace}/data/train" # or "/home/custom_model/data/train"
+- data_converter: "{workspace}/reader.py" # or "/home/custom_model/reader.py"
+```
+
+3. 更改执行器的路径配置
+```yaml
+mode: train_runner
+
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 10
+ save_checkpoint_interval: 2
+ save_inference_interval: 5
+ save_checkpoint_path: "{workspace}/increment" # or "/home/custom_model/increment"
+ save_inference_path: "{workspace}/inference" # or "/home/custom_model/inference"
+ print_interval: 10
+
+phase:
+- name: train
+ model: "{workspace}/model.py" # or "/home/custom_model/model"
+ dataset_name: custom_model_train
+ thread_num: 1
+```
+
+4. 使用paddlerec.run方法运行自定义模型
+
+```shell
+python -m paddlerec.run -m /home/custom_model/config.yaml
+```
+
+以上~请开始享受你的推荐算法高效开发流程。如有任何问题，欢迎在[issue](https://github.com/PaddlePaddle/PaddleRec/issues)提出，我们会第一时间跟进解决。
diff --git a/doc/distributed_train.md b/doc/distributed_train.md
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+# PaddleRec 分布式训练
+
+## PaddleRec分布式运行
+> 占位
+### 本地模拟分布式
+> 占位
+
+### K8S集群运行分布式
+> 占位
diff --git a/doc/faq.md b/doc/faq.md
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+# 常见问题FAQ
+> 占位
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index 0000000000000000000000000000000000000000..e65255ebf7e14933f52f9977b2ecec48dabbb76e
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+++ b/doc/local_train.md
@@ -0,0 +1,2 @@
+# PaddleRec 单机训练
+> 占位
diff --git a/doc/model_list.md b/doc/model_list.md
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+++ b/doc/model_list.md
@@ -0,0 +1,14 @@
+# 支持模型列表
+| 方向 | 模型 | 单机CPU训练 | 单机GPU训练 | 分布式CPU训练 | 大规模稀疏 | 分布式GPU训练 | 自定义数据集 |
+| :------: | :--------------------: | :---------: | :---------: | :-----------: | :--------: | :-----------: | :----------: |
+| 内容理解 | [Text-Classifcation]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 内容理解 | [TagSpace]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 召回 | [Word2Vec]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 召回 | [TDM]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 排序 | [CTR-Dnn]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 排序 | [DeepFm]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 排序 | [ListWise]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 多任务 | [MMOE]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 多任务 | [ESMM]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 匹配 | [DSSM]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 匹配 | [Multiview-Simnet]() | ✓ | x | ✓ | x | ✓ | ✓ |
diff --git a/doc/optimization_model.md b/doc/optimization_model.md
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index 0000000000000000000000000000000000000000..e63f45b62b50db55f1c6c0d48c7ca23b016b74d3
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+++ b/doc/optimization_model.md
@@ -0,0 +1,2 @@
+# PaddleRec 模型调参
+> 占位
diff --git a/doc/predict.md b/doc/predict.md
new file mode 100644
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+++ b/doc/predict.md
@@ -0,0 +1 @@
+# PaddleRec 离线预测
diff --git a/doc/ps_background.md b/doc/ps_background.md
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index 0000000000000000000000000000000000000000..e5f2e320940763986351fefd21a5e1f1363b6104
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+++ b/doc/ps_background.md
@@ -0,0 +1,7 @@
+## [分布式训练概述](https://www.paddlepaddle.org.cn/tutorials/projectdetail/459124)
+
+
+## [多机多卡训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/459127)
+
+
+## [参数服务器训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/464839)
diff --git a/doc/rec_background.md b/doc/rec_background.md
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index 0000000000000000000000000000000000000000..b6cf8b1c360165033f5640a775f3c8721754f4a3
--- /dev/null
+++ b/doc/rec_background.md
@@ -0,0 +1,64 @@
+# 推荐系统背景知识
+本文来源于[个性化推荐](https://github.com/PaddlePaddle/book/blob/develop/05.recommender_system/README.cn.md)，进行了节选。
+
+本文代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system),初次使用请您参考[Book文档使用说明](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md#运行这本书)。
+
+更多教程及背景知识可以查阅[深度学习实践应用：个性化推荐](https://www.paddlepaddle.org.cn/tutorials/projectdetail/443958)
+
+## 背景介绍
+
+在网络技术不断发展和电子商务规模不断扩大的背景下，商品数量和种类快速增长，用户需要花费大量时间才能找到自己想买的商品，这就是信息超载问题。为了解决这个难题，个性化推荐系统（Recommender System）应运而生。
+
+个性化推荐系统是信息过滤系统（Information Filtering System）的子集，它可以用在很多领域，如电影、音乐、电商和 Feed 流推荐等。个性化推荐系统通过分析、挖掘用户行为，发现用户的个性化需求与兴趣特点，将用户可能感兴趣的信息或商品推荐给用户。与搜索引擎不同，个性化推荐系统不需要用户准确地描述出自己的需求，而是根据用户的历史行为进行建模，主动提供满足用户兴趣和需求的信息。
+
+1994年明尼苏达大学推出的GroupLens系统[[1](#参考文献)]一般被认为是个性化推荐系统成为一个相对独立的研究方向的标志。该系统首次提出了基于协同过滤来完成推荐任务的思想，此后，基于该模型的协同过滤推荐引领了个性化推荐系统十几年的发展方向。
+
+传统的个性化推荐系统方法主要有：
+
+- 协同过滤推荐（Collaborative Filtering Recommendation）：该方法是应用最广泛的技术之一，需要收集和分析用户的历史行为、活动和偏好。它通常可以分为两个子类：基于用户 （User-Based）的推荐[[1](#参考文献)] 和基于物品（Item-Based）的推荐[[2](#参考文献)]。该方法的一个关键优势是它不依赖于机器去分析物品的内容特征，因此它无需理解物品本身也能够准确地推荐诸如电影之类的复杂物品；缺点是对于没有任何行为的新用户存在冷启动的问题，同时也存在用户与商品之间的交互数据不够多造成的稀疏问题。值得一提的是，社交网络[[3](#参考文献)]或地理位置等上下文信息都可以结合到协同过滤中去。
+- 基于内容过滤推荐[[4](#参考文献)]（Content-based Filtering Recommendation）：该方法利用商品的内容描述，抽象出有意义的特征，通过计算用户的兴趣和商品描述之间的相似度，来给用户做推荐。优点是简单直接，不需要依据其他用户对商品的评价，而是通过商品属性进行商品相似度度量，从而推荐给用户所感兴趣商品的相似商品；缺点是对于没有任何行为的新用户同样存在冷启动的问题。
+- 组合推荐[[5](#参考文献)]（Hybrid Recommendation）：运用不同的输入和技术共同进行推荐，以弥补各自推荐技术的缺点。
+
+近些年来，深度学习在很多领域都取得了巨大的成功。学术界和工业界都在尝试将深度学习应用于个性化推荐系统领域中。深度学习具有优秀的自动提取特征的能力，能够学习多层次的抽象特征表示，并对异质或跨域的内容信息进行学习，可以一定程度上处理个性化推荐系统冷启动问题[[6](#参考文献)]。
+
+### YouTube的深度神经网络个性化推荐系统
+
+YouTube是世界上最大的视频上传、分享和发现网站，YouTube个性化推荐系统为超过10亿用户从不断增长的视频库中推荐个性化的内容。整个系统由两个神经网络组成：候选生成网络和排序网络。候选生成网络从百万量级的视频库中生成上百个候选，排序网络对候选进行打分排序，输出排名最高的数十个结果。系统结构如图1所示：
+
+
+
+
+#### 候选生成网络（Candidate Generation Network）
+
+候选生成网络将推荐问题建模为一个类别数极大的多类分类问题：对于一个Youtube用户，使用其观看历史（视频ID）、搜索词记录（search tokens）、人口学信息（如地理位置、用户登录设备）、二值特征（如性别，是否登录）和连续特征（如用户年龄）等，对视频库中所有视频进行多分类，得到每一类别的分类结果（即每一个视频的推荐概率），最终输出概率较高的几百个视频。
+
+首先，将观看历史及搜索词记录这类历史信息，映射为向量后取平均值得到定长表示；同时，输入人口学特征以优化新用户的推荐效果，并将二值特征和连续特征归一化处理到[0, 1]范围。接下来，将所有特征表示拼接为一个向量，并输入给非线形多层感知器（MLP，详见[识别数字](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md)教程）处理。最后，训练时将MLP的输出给softmax做分类，预测时计算用户的综合特征（MLP的输出）与所有视频的相似度，取得分最高的K个作为候选生成网络的筛选结果。
+
+#### 排序网络（Ranking Network）
+排序网络的结构类似于候选生成网络，但是它的目标是对候选进行更细致的打分排序。和传统广告排序中的特征抽取方法类似，这里也构造了大量的用于视频排序的相关特征（如视频 ID、上次观看时间等）。这些特征的处理方式和候选生成网络类似，不同之处是排序网络的顶部是一个加权逻辑回归（weighted logistic regression），它对所有候选视频进行打分，从高到底排序后将分数较高的一些视频返回给用户。
+
+### 融合推荐模型
+本节会使用卷积神经网络（Convolutional Neural Networks）来学习电影名称的表示。下面会依次介绍文本卷积神经网络以及融合推荐模型。
+
+#### 文本卷积神经网络（CNN）
+
+卷积神经网络经常用来处理具有类似网格拓扑结构（grid-like topology）的数据。例如，图像可以视为二维网格的像素点，自然语言可以视为一维的词序列。卷积神经网络可以提取多种局部特征，并对其进行组合抽象得到更高级的特征表示。实验表明，卷积神经网络能高效地对图像及文本问题进行建模处理。
+
+卷积神经网络主要由卷积（convolution）和池化（pooling）操作构成，其应用及组合方式灵活多变，种类繁多。
+
+
+本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议 进行许可。
diff --git a/doc/yaml.md b/doc/yaml.md
new file mode 100644
index 0000000000000000000000000000000000000000..3dc9c4a5c2624acd8f2b1b9828f89183a256840a
--- /dev/null
+++ b/doc/yaml.md
@@ -0,0 +1,77 @@
+```yaml
+# 全局配置
+# Debug 模式开关，Debug模式下，会打印OP的耗时及IO占比
+debug: false
+
+# 工作区目录
+# 使用文件夹路径，则会在该目录下寻找超参配置，组网，数据等必须文件
+workspace: "/home/demo_model/"
+# 若 workspace: paddlerec.models.rank.dnn
+# 则会使用官方默认配置与组网
+
+
+# 用户可以指定多个dataset(数据读取配置)
+# 运行的不同阶段可以使用不同的dataset
+dataset:
+ # dataloader 示例
+ - name: dataset_1
+ type: DataLoader
+ batch_size: 5
+ data_path: "{workspace}/data/train"
+ # 指定自定义的reader.py所在路径
+ data_converter: "{workspace}/rsc15_reader.py"
+
+ # QueueDataset 示例
+ - name: dataset_2
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/train"
+ # 用户可以配置sparse_slots和dense_slots，无需再定义data_converter，使用默认reader
+ sparse_slots: "click ins_weight 6001 6002 6003 6005 6006 6007 6008 6009"
+ dense_slots: "readlist:9"
+
+
+# 自定义超参数，主要涉及网络中的模型超参及优化器
+hyper_parameters:
+ #优化器
+ optimizer:
+ class: Adam # 直接配置Optimizer，目前支持sgd/Adam/AdaGrad
+ learning_rate: 0.001
+ strategy: "{workspace}/conf/config_fleet.py" # 使用大规模稀疏pslib模式的特有配置
+ # 模型超参
+ vocab_size: 1000
+ hid_size: 100
+
+
+# 通过全局参数mode指定当前运行的runner
+mode: runner_1
+
+# runner主要涉及模型的执行环境，如：单机/分布式，CPU/GPU，迭代轮次，模型加载与保存地址
+runner:
+ - name: runner_1 # 配置一个runner，进行单机的训练
+ class: single_train # 配置运行模式的选择，还可以选择：single_infer/local_cluster_train/cluster_train
+ epochs: 10
+ device: cpu
+ init_model_path: ""
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ # 下面是保存模型路径配置
+ save_checkpoint_path: "xxxx"
+ save_inference_path: "xxxx"
+
+ - name: runner_2 # 配置一个runner，进行单机的预测
+ class: single_infer
+ epochs: 1
+ device: cpu
+ init_model_path: "afs:/xxx/xxx"
+
+
+# 模型在训练时，可能存在多个阶段，每个阶段的组网与数据读取都可能不尽相同
+# 每个runner都会完整的运行所有阶段
+# phase指定运行时加载的模型及reader
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: sample_1
+ thread_num: 1
+```
diff --git a/fleet_rec/check.py b/fleet_rec/check.py
deleted file mode 100755
index d4177fd8516e4f210d1897601fb88f4b11773875..0000000000000000000000000000000000000000
--- a/fleet_rec/check.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import argparse
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='fleet-rec check')
- parser.add_argument("--model", type=str)
- parser.add_argument("--engine", type=str)
-
- print("coming soon")
diff --git a/fleet_rec/core/__init__.py b/fleet_rec/core/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/fleet_rec/core/engine/__init__.py b/fleet_rec/core/engine/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/fleet_rec/core/engine/engine.py b/fleet_rec/core/engine/engine.py
deleted file mode 100755
index 66c43bca3f2121531e8477814d87adc36e6c6096..0000000000000000000000000000000000000000
--- a/fleet_rec/core/engine/engine.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import abc
-
-
-class Engine:
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, envs, trainer):
- self.envs = envs
- self.trainer = trainer
-
- @abc.abstractmethod
- def run(self):
- pass
-
diff --git a/fleet_rec/core/layer.py b/fleet_rec/core/layer.py
deleted file mode 100755
index ab06841706400ceae1ab4444ba77bba66ff9924a..0000000000000000000000000000000000000000
--- a/fleet_rec/core/layer.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import abc
-
-
-class Layer(object):
- """R
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, config):
- """R
- """
- pass
-
- def generate(self, mode, param):
- """R
- """
- if mode == 'fluid':
- return self.generate_fluid(param)
- elif mode == 'tensorflow':
- return self.generate_tensorflow(param)
- print('unsupport this mode: ' + mode)
- return None, None
-
- @abc.abstractmethod
- def generate_fluid(self, param):
- """R
- """
- pass
-
- def generate_tensorflow(self, param):
- """ Not implement currently
- """
- pass
diff --git a/fleet_rec/core/model.py b/fleet_rec/core/model.py
deleted file mode 100755
index 141c15777ad15b4837cca1f6f4a43e5696a7b05f..0000000000000000000000000000000000000000
--- a/fleet_rec/core/model.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import abc
-
-import paddle.fluid as fluid
-
-from fleetrec.core.utils import envs
-
-
-class Model(object):
- """R
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, config):
- """R
- """
- self._cost = None
- self._metrics = {}
- self._data_var = []
- self._infer_data_var = []
- self._infer_results = {}
- self._data_loader = None
- self._infer_data_loader = None
- self._fetch_interval = 20
- self._namespace = "train.model"
- self._platform = envs.get_platform()
-
- def get_inputs(self):
- return self._data_var
-
- def get_infer_inputs(self):
- return self._infer_data_var
-
- def get_infer_results(self):
- return self._infer_results
-
- def get_cost_op(self):
- """R
- """
- return self._cost
-
- def get_metrics(self):
- """R
- """
- return self._metrics
-
- def custom_preprocess(self):
- """
- do something after exe.run(stratup_program) and before run()
- """
- pass
-
- def get_fetch_period(self):
- return self._fetch_interval
-
- def _build_optimizer(self, name, lr):
- name = name.upper()
- optimizers = ["SGD", "ADAM", "ADAGRAD"]
- if name not in optimizers:
- raise ValueError(
- "configured optimizer can only supported SGD/Adam/Adagrad")
-
- if name == "SGD":
- reg = envs.get_global_env(
- "hyper_parameters.reg", 0.0001, self._namespace)
- optimizer_i = fluid.optimizer.SGD(
- lr, regularization=fluid.regularizer.L2DecayRegularizer(reg))
- elif name == "ADAM":
- optimizer_i = fluid.optimizer.Adam(lr, lazy_mode=True)
- elif name == "ADAGRAD":
- optimizer_i = fluid.optimizer.Adagrad(lr)
- else:
- raise ValueError(
- "configured optimizer can only supported SGD/Adam/Adagrad")
-
- return optimizer_i
-
- def optimizer(self):
- learning_rate = envs.get_global_env(
- "hyper_parameters.learning_rate", None, self._namespace)
- optimizer = envs.get_global_env(
- "hyper_parameters.optimizer", None, self._namespace)
- print(">>>>>>>>>>>.learnig rate: %s" % learning_rate)
- return self._build_optimizer(optimizer, learning_rate)
-
- @abc.abstractmethod
- def train_net(self):
- """R
- """
- pass
-
- @abc.abstractmethod
- def infer_net(self):
- pass
diff --git a/fleet_rec/core/modules/__init__.py b/fleet_rec/core/modules/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/fleet_rec/core/modules/coding/__init__.py b/fleet_rec/core/modules/coding/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/fleet_rec/core/modules/coding/layers.py b/fleet_rec/core/modules/coding/layers.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/fleet_rec/core/modules/modul/__init__.py b/fleet_rec/core/modules/modul/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/fleet_rec/core/trainers/__init__.py b/fleet_rec/core/trainers/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/fleet_rec/core/trainers/ctr_coding_trainer.py b/fleet_rec/core/trainers/ctr_coding_trainer.py
deleted file mode 100755
index fdfdd1ecb2f40747cc8cb61e3f8ce0e3c1c19d4c..0000000000000000000000000000000000000000
--- a/fleet_rec/core/trainers/ctr_coding_trainer.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-import numpy as np
-
-import paddle.fluid as fluid
-from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
-from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker
-
-from fleetrec.core.utils import envs
-from fleetrec.core.trainer import Trainer
-
-
-class CtrPaddleTrainer(Trainer):
- """R
- """
-
- def __init__(self, config):
- """R
- """
- Trainer.__init__(self, config)
-
- self.global_config = config
- self._metrics = {}
- self.processor_register()
-
- def processor_register(self):
- role = MPISymetricRoleMaker()
- fleet.init(role)
-
- if fleet.is_server():
- self.regist_context_processor('uninit', self.instance)
- self.regist_context_processor('init_pass', self.init)
- self.regist_context_processor('server_pass', self.server)
- else:
- self.regist_context_processor('uninit', self.instance)
- self.regist_context_processor('init_pass', self.init)
- self.regist_context_processor('train_pass', self.train)
- self.regist_context_processor('terminal_pass', self.terminal)
-
- def _get_dataset(self):
- namespace = "train.reader"
-
- inputs = self.model.get_inputs()
- threads = envs.get_global_env("train.threads", None)
- batch_size = envs.get_global_env("batch_size", None, namespace)
- reader_class = envs.get_global_env("class", None, namespace)
- abs_dir = os.path.dirname(os.path.abspath(__file__))
- reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
- pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "TRAIN", self._config_yaml)
- train_data_path = envs.get_global_env("train_data_path", None, namespace)
-
- dataset = fluid.DatasetFactory().create_dataset()
- dataset.set_use_var(inputs)
- dataset.set_pipe_command(pipe_cmd)
- dataset.set_batch_size(batch_size)
- dataset.set_thread(threads)
- file_list = [
- os.path.join(train_data_path, x)
- for x in os.listdir(train_data_path)
- ]
-
- dataset.set_filelist(file_list)
- return dataset
-
- def instance(self, context):
- models = envs.get_global_env("train.model.models")
- model_class = envs.lazy_instance_by_fliename(models, "Model")
- self.model = model_class(None)
- context['status'] = 'init_pass'
-
- def init(self, context):
- """R
- """
- self.model.train_net()
- optimizer = self.model.optimizer()
-
- optimizer = fleet.distributed_optimizer(optimizer, strategy={"use_cvm": False})
- optimizer.minimize(self.model.get_cost_op())
-
- if fleet.is_server():
- context['status'] = 'server_pass'
- else:
- self.fetch_vars = []
- self.fetch_alias = []
- self.fetch_period = self.model.get_fetch_period()
-
- metrics = self.model.get_metrics()
- if metrics:
- self.fetch_vars = metrics.values()
- self.fetch_alias = metrics.keys()
- context['status'] = 'train_pass'
-
- def server(self, context):
- fleet.run_server()
- fleet.stop_worker()
- context['is_exit'] = True
-
- def train(self, context):
- self._exe.run(fluid.default_startup_program())
- fleet.init_worker()
-
- dataset = self._get_dataset()
-
- shuf = np.array([fleet.worker_index()])
- gs = shuf * 0
- fleet._role_maker._node_type_comm.Allreduce(shuf, gs)
-
- print("trainer id: {}, trainers: {}, gs: {}".format(fleet.worker_index(), fleet.worker_num(), gs))
-
- epochs = envs.get_global_env("train.epochs")
-
- for i in range(epochs):
- self._exe.train_from_dataset(program=fluid.default_main_program(),
- dataset=dataset,
- fetch_list=self.fetch_vars,
- fetch_info=self.fetch_alias,
- print_period=self.fetch_period)
-
- context['status'] = 'terminal_pass'
- fleet.stop_worker()
-
- def terminal(self, context):
- print("terminal ended.")
- context['is_exit'] = True
diff --git a/fleet_rec/core/trainers/ctr_modul_trainer.py b/fleet_rec/core/trainers/ctr_modul_trainer.py
deleted file mode 100755
index e72715e74634791c2c90890bc6b8b8f0a0e56c71..0000000000000000000000000000000000000000
--- a/fleet_rec/core/trainers/ctr_modul_trainer.py
+++ /dev/null
@@ -1,460 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import sys
-import time
-import json
-import datetime
-import numpy as np
-
-import paddle.fluid as fluid
-from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
-from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
-
-
-from fleetrec.core.utils import fs as fs
-from fleetrec.core.utils import util as util
-from fleetrec.core.metrics.auc_metrics import AUCMetric
-from fleetrec.core.modules.modul import build as model_basic
-from fleetrec.core.utils import dataset
-from fleetrec.core.trainer import Trainer
-
-
-def wroker_numric_opt(value, env, opt):
- """
- numric count opt for workers
- Args:
- value: value for count
- env: mpi/gloo
- opt: count operator, SUM/MAX/MIN/AVG
- Return:
- count result
- """
- local_value = np.array([value])
- global_value = np.copy(local_value) * 0
- fleet._role_maker.all_reduce_worker(local_value, global_value, opt)
- return global_value[0]
-
-
-def worker_numric_sum(value, env="mpi"):
- """R
- """
- return wroker_numric_opt(value, env, "sum")
-
-
-def worker_numric_avg(value, env="mpi"):
- """R
- """
- return worker_numric_sum(value, env) / fleet.worker_num()
-
-
-def worker_numric_min(value, env="mpi"):
- """R
- """
- return wroker_numric_opt(value, env, "min")
-
-
-def worker_numric_max(value, env="mpi"):
- """R
- """
- return wroker_numric_opt(value, env, "max")
-
-
-class CtrPaddleTrainer(Trainer):
- """R
- """
-
- def __init__(self, config):
- """R
- """
- Trainer.__init__(self, config)
- config['output_path'] = util.get_absolute_path(
- config['output_path'], config['io']['afs'])
-
- self.global_config = config
- self._metrics = {}
-
- self._path_generator = util.PathGenerator({
- 'templates': [
- {'name': 'xbox_base_done', 'template': config['output_path'] + '/xbox_base_done.txt'},
- {'name': 'xbox_delta_done', 'template': config['output_path'] + '/xbox_patch_done.txt'},
- {'name': 'xbox_base', 'template': config['output_path'] + '/xbox/{day}/base/'},
- {'name': 'xbox_delta', 'template': config['output_path'] + '/xbox/{day}/delta-{pass_id}/'},
- {'name': 'batch_model', 'template': config['output_path'] + '/batch_model/{day}/{pass_id}/'}
- ]
- })
- if 'path_generator' in config:
- self._path_generator.add_path_template(config['path_generator'])
-
- self.regist_context_processor('uninit', self.init)
- self.regist_context_processor('startup', self.startup)
- self.regist_context_processor('begin_day', self.begin_day)
- self.regist_context_processor('train_pass', self.train_pass)
- self.regist_context_processor('end_day', self.end_day)
-
- def init(self, context):
- """R
- """
- role_maker = None
- if self.global_config.get('process_mode', 'mpi') == 'brilliant_cpu':
- afs_config = self.global_config['io']['afs']
- role_maker = GeneralRoleMaker(
- hdfs_name=afs_config['fs_name'], hdfs_ugi=afs_config['fs_ugi'],
- path=self.global_config['output_path'] + "/gloo",
- init_timeout_seconds=1200, run_timeout_seconds=1200)
- fleet.init(role_maker)
- data_var_list = []
- data_var_name_dict = {}
- runnnable_scope = []
- runnnable_cost_op = []
- context['status'] = 'startup'
-
- for executor in self.global_config['executor']:
- scope = fluid.Scope()
- self._exector_context[executor['name']] = {}
- self._exector_context[executor['name']]['scope'] = scope
- self._exector_context[executor['name']]['model'] = model_basic.create(executor)
- model = self._exector_context[executor['name']]['model']
- self._metrics.update(model.get_metrics())
- runnnable_scope.append(scope)
- runnnable_cost_op.append(model.get_cost_op())
- for var in model._data_var:
- if var.name in data_var_name_dict:
- continue
- data_var_list.append(var)
- data_var_name_dict[var.name] = var
-
- optimizer = model_basic.YamlModel.build_optimizer({
- 'metrics': self._metrics,
- 'optimizer_conf': self.global_config['optimizer']
- })
- optimizer.minimize(runnnable_cost_op, runnnable_scope)
- for executor in self.global_config['executor']:
- scope = self._exector_context[executor['name']]['scope']
- model = self._exector_context[executor['name']]['model']
- program = model._build_param['model']['train_program']
- if not executor['is_update_sparse']:
- program._fleet_opt["program_configs"][str(id(model.get_cost_op().block.program))]["push_sparse"] = []
- if 'train_thread_num' not in executor:
- executor['train_thread_num'] = self.global_config['train_thread_num']
- with fluid.scope_guard(scope):
- self._exe.run(model._build_param['model']['startup_program'])
- model.dump_model_program('./')
-
- # server init done
- if fleet.is_server():
- return 0
-
- self._dataset = {}
- for dataset_item in self.global_config['dataset']['data_list']:
- dataset_item['data_vars'] = data_var_list
- dataset_item.update(self.global_config['io']['afs'])
- dataset_item["batch_size"] = self.global_config['batch_size']
- self._dataset[dataset_item['name']] = dataset.FluidTimeSplitDataset(dataset_item)
- # if config.need_reqi_changeslot and config.reqi_dnn_plugin_day >= last_day and config.reqi_dnn_plugin_pass >= last_pass:
- # util.reqi_changeslot(config.hdfs_dnn_plugin_path, join_save_params, common_save_params, update_save_params, scope2, scope3)
- fleet.init_worker()
- pass
-
- def print_log(self, log_str, params):
- """R
- """
- params['index'] = fleet.worker_index()
- if params['master']:
- if fleet.worker_index() == 0:
- print(log_str)
- sys.stdout.flush()
- else:
- print(log_str)
- if 'stdout' in params:
- params['stdout'] += str(datetime.datetime.now()) + log_str
-
- def print_global_metrics(self, scope, model, monitor_data, stdout_str):
- """R
- """
- metrics = model.get_metrics()
- metric_calculator = AUCMetric(None)
- for metric in metrics:
- metric_param = {'label': metric, 'metric_dict': metrics[metric]}
- metric_calculator.calculate(scope, metric_param)
- metric_result = metric_calculator.get_result_to_string()
- self.print_log(metric_result, {'master': True, 'stdout': stdout_str})
- monitor_data += metric_result
- metric_calculator.clear(scope, metric_param)
-
- def save_model(self, day, pass_index, base_key):
- """R
- """
- cost_printer = util.CostPrinter(util.print_cost,
- {'master': True, 'log_format': 'save model cost %s sec'})
- model_path = self._path_generator.generate_path('batch_model', {'day': day, 'pass_id': pass_index})
- save_mode = 0 # just save all
- if pass_index < 1: # batch_model
- save_mode = 3 # unseen_day++, save all
- util.rank0_print("going to save_model %s" % model_path)
- fleet.save_persistables(None, model_path, mode=save_mode)
- if fleet._role_maker.is_first_worker():
- self._train_pass.save_train_progress(day, pass_index, base_key, model_path, is_checkpoint=True)
- cost_printer.done()
- return model_path
-
- def save_xbox_model(self, day, pass_index, xbox_base_key, monitor_data):
- """R
- """
- stdout_str = ""
- xbox_patch_id = str(int(time.time()))
- util.rank0_print("begin save delta model")
-
- model_path = ""
- xbox_model_donefile = ""
- cost_printer = util.CostPrinter(util.print_cost, {'master': True, \
- 'log_format': 'save xbox model cost %s sec',
- 'stdout': stdout_str})
- if pass_index < 1:
- save_mode = 2
- xbox_patch_id = xbox_base_key
- model_path = self._path_generator.generate_path('xbox_base', {'day': day})
- xbox_model_donefile = self._path_generator.generate_path('xbox_base_done', {'day': day})
- else:
- save_mode = 1
- model_path = self._path_generator.generate_path('xbox_delta', {'day': day, 'pass_id': pass_index})
- xbox_model_donefile = self._path_generator.generate_path('xbox_delta_done', {'day': day})
- total_save_num = fleet.save_persistables(None, model_path, mode=save_mode)
- cost_printer.done()
-
- cost_printer = util.CostPrinter(util.print_cost, {'master': True,
- 'log_format': 'save cache model cost %s sec',
- 'stdout': stdout_str})
- model_file_handler = fs.FileHandler(self.global_config['io']['afs'])
- if self.global_config['save_cache_model']:
- cache_save_num = fleet.save_cache_model(None, model_path, mode=save_mode)
- model_file_handler.write(
- "file_prefix:part\npart_num:16\nkey_num:%d\n" % cache_save_num,
- model_path + '/000_cache/sparse_cache.meta', 'w')
- cost_printer.done()
- util.rank0_print("save xbox cache model done, key_num=%s" % cache_save_num)
-
- save_env_param = {
- 'executor': self._exe,
- 'save_combine': True
- }
- cost_printer = util.CostPrinter(util.print_cost, {'master': True,
- 'log_format': 'save dense model cost %s sec',
- 'stdout': stdout_str})
- if fleet._role_maker.is_first_worker():
- for executor in self.global_config['executor']:
- if 'layer_for_inference' not in executor:
- continue
- executor_name = executor['name']
- model = self._exector_context[executor_name]['model']
- save_env_param['inference_list'] = executor['layer_for_inference']
- save_env_param['scope'] = self._exector_context[executor_name]['scope']
- model.dump_inference_param(save_env_param)
- for dnn_layer in executor['layer_for_inference']:
- model_file_handler.cp(dnn_layer['save_file_name'],
- model_path + '/dnn_plugin/' + dnn_layer['save_file_name'])
- fleet._role_maker._barrier_worker()
- cost_printer.done()
-
- xbox_done_info = {
- "id": xbox_patch_id,
- "key": xbox_base_key,
- "ins_path": "",
- "ins_tag": "feasign",
- "partition_type": "2",
- "record_count": "111111",
- "monitor_data": monitor_data,
- "mpi_size": str(fleet.worker_num()),
- "input": model_path.rstrip("/") + "/000",
- "job_id": util.get_env_value("JOB_ID"),
- "job_name": util.get_env_value("JOB_NAME")
- }
- if fleet._role_maker.is_first_worker():
- model_file_handler.write(json.dumps(xbox_done_info) + "\n", xbox_model_donefile, 'a')
- if pass_index > 0:
- self._train_pass.save_train_progress(day, pass_index, xbox_base_key, model_path, is_checkpoint=False)
- fleet._role_maker._barrier_worker()
- return stdout_str
-
- def run_executor(self, executor_config, dataset, stdout_str):
- """R
- """
- day = self._train_pass.date()
- pass_id = self._train_pass._pass_id
- xbox_base_key = self._train_pass._base_key
- executor_name = executor_config['name']
- scope = self._exector_context[executor_name]['scope']
- model = self._exector_context[executor_name]['model']
- with fluid.scope_guard(scope):
- util.rank0_print("Begin " + executor_name + " pass")
- begin = time.time()
- program = model._build_param['model']['train_program']
- self._exe.train_from_dataset(program, dataset, scope,
- thread=executor_config['train_thread_num'], debug=self.global_config['debug'])
- end = time.time()
- local_cost = (end - begin) / 60.0
- avg_cost = worker_numric_avg(local_cost)
- min_cost = worker_numric_min(local_cost)
- max_cost = worker_numric_max(local_cost)
- util.rank0_print("avg train time %s mins, min %s mins, max %s mins" % (avg_cost, min_cost, max_cost))
- self._exector_context[executor_name]['cost'] = max_cost
-
- monitor_data = ""
- self.print_global_metrics(scope, model, monitor_data, stdout_str)
- util.rank0_print("End " + executor_name + " pass")
- if self._train_pass.need_dump_inference(pass_id) and executor_config['dump_inference_model']:
- stdout_str += self.save_xbox_model(day, pass_id, xbox_base_key, monitor_data)
- fleet._role_maker._barrier_worker()
-
- def startup(self, context):
- """R
- """
- if fleet.is_server():
- fleet.run_server()
- context['status'] = 'wait'
- return
- stdout_str = ""
- self._train_pass = util.TimeTrainPass(self.global_config)
- if not self.global_config['cold_start']:
- cost_printer = util.CostPrinter(util.print_cost,
- {'master': True, 'log_format': 'load model cost %s sec',
- 'stdout': stdout_str})
- self.print_log("going to load model %s" % self._train_pass._checkpoint_model_path, {'master': True})
- # if config.need_reqi_changeslot and config.reqi_dnn_plugin_day >= self._train_pass.date()
- # and config.reqi_dnn_plugin_pass >= self._pass_id:
- # fleet.load_one_table(0, self._train_pass._checkpoint_model_path)
- # else:
- fleet.init_server(self._train_pass._checkpoint_model_path, mode=0)
- cost_printer.done()
- if self.global_config['save_first_base']:
- self.print_log("save_first_base=True", {'master': True})
- self.print_log("going to save xbox base model", {'master': True, 'stdout': stdout_str})
- self._train_pass._base_key = int(time.time())
- stdout_str += self.save_xbox_model(self._train_pass.date(), 0, self._train_pass._base_key, "")
- context['status'] = 'begin_day'
-
- def begin_day(self, context):
- """R
- """
- stdout_str = ""
- if not self._train_pass.next():
- context['is_exit'] = True
- day = self._train_pass.date()
- pass_id = self._train_pass._pass_id
- self.print_log("======== BEGIN DAY:%s ========" % day, {'master': True, 'stdout': stdout_str})
- if pass_id == self._train_pass.max_pass_num_day():
- context['status'] = 'end_day'
- else:
- context['status'] = 'train_pass'
-
- def end_day(self, context):
- """R
- """
- day = self._train_pass.date()
- pass_id = self._train_pass._pass_id
- xbox_base_key = int(time.time())
- context['status'] = 'begin_day'
-
- util.rank0_print("shrink table")
- cost_printer = util.CostPrinter(util.print_cost,
- {'master': True, 'log_format': 'shrink table done, cost %s sec'})
- fleet.shrink_sparse_table()
- for executor in self._exector_context:
- self._exector_context[executor]['model'].shrink({
- 'scope': self._exector_context[executor]['scope'],
- 'decay': self.global_config['optimizer']['dense_decay_rate']
- })
- cost_printer.done()
-
- next_date = self._train_pass.date(delta_day=1)
- util.rank0_print("going to save xbox base model")
- self.save_xbox_model(next_date, 0, xbox_base_key, "")
- util.rank0_print("going to save batch model")
- self.save_model(next_date, 0, xbox_base_key)
- self._train_pass._base_key = xbox_base_key
- fleet._role_maker._barrier_worker()
-
- def train_pass(self, context):
- """R
- """
- stdout_str = ""
- day = self._train_pass.date()
- pass_id = self._train_pass._pass_id
- base_key = self._train_pass._base_key
- pass_time = self._train_pass._current_train_time.strftime("%Y%m%d%H%M")
- self.print_log(" ==== begin delta:%s ========" % pass_id, {'master': True, 'stdout': stdout_str})
- train_begin_time = time.time()
-
- cost_printer = util.CostPrinter(util.print_cost, \
- {'master': True, 'log_format': 'load into memory done, cost %s sec',
- 'stdout': stdout_str})
- current_dataset = {}
- for name in self._dataset:
- current_dataset[name] = self._dataset[name].load_dataset({
- 'node_num': fleet.worker_num(), 'node_idx': fleet.worker_index(),
- 'begin_time': pass_time, 'time_window_min': self._train_pass._interval_per_pass
- })
- fleet._role_maker._barrier_worker()
- cost_printer.done()
-
- util.rank0_print("going to global shuffle")
- cost_printer = util.CostPrinter(util.print_cost, {
- 'master': True, 'stdout': stdout_str,
- 'log_format': 'global shuffle done, cost %s sec'})
- for name in current_dataset:
- current_dataset[name].global_shuffle(fleet, self.global_config['dataset']['shuffle_thread'])
- cost_printer.done()
- # str(dataset.get_shuffle_data_size(fleet))
- fleet._role_maker._barrier_worker()
-
- if self.global_config['prefetch_data']:
- next_pass_time = (self._train_pass._current_train_time +
- datetime.timedelta(minutes=self._train_pass._interval_per_pass)).strftime("%Y%m%d%H%M")
- for name in self._dataset:
- self._dataset[name].preload_dataset({
- 'node_num': fleet.worker_num(), 'node_idx': fleet.worker_index(),
- 'begin_time': next_pass_time, 'time_window_min': self._train_pass._interval_per_pass
- })
-
- fleet._role_maker._barrier_worker()
- pure_train_begin = time.time()
- for executor in self.global_config['executor']:
- self.run_executor(executor, current_dataset[executor['dataset_name']], stdout_str)
- cost_printer = util.CostPrinter(util.print_cost, \
- {'master': True, 'log_format': 'release_memory cost %s sec'})
- for name in current_dataset:
- current_dataset[name].release_memory()
- pure_train_cost = time.time() - pure_train_begin
-
- if self._train_pass.is_checkpoint_pass(pass_id):
- self.save_model(day, pass_id, base_key)
-
- train_end_time = time.time()
- train_cost = train_end_time - train_begin_time
- other_cost = train_cost - pure_train_cost
- log_str = "finished train day %s pass %s time cost:%s sec job time cost:" % (day, pass_id, train_cost)
- for executor in self._exector_context:
- log_str += '[' + executor + ':' + str(self._exector_context[executor]['cost']) + ']'
- log_str += '[other_cost:' + str(other_cost) + ']'
- util.rank0_print(log_str)
- stdout_str += util.now_time_str() + log_str
- sys.stdout.write(stdout_str)
- fleet._role_maker._barrier_worker()
- stdout_str = ""
- if pass_id == self._train_pass.max_pass_num_day():
- context['status'] = 'end_day'
- return
- elif not self._train_pass.next():
- context['is_exit'] = True
diff --git a/fleet_rec/core/trainers/single_trainer.py b/fleet_rec/core/trainers/single_trainer.py
deleted file mode 100755
index db3398932e4cefbaf5fb5c211db87bc029019b6f..0000000000000000000000000000000000000000
--- a/fleet_rec/core/trainers/single_trainer.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Training use fluid with one node only.
-"""
-
-from __future__ import print_function
-import logging
-import paddle.fluid as fluid
-
-from fleetrec.core.trainers.transpiler_trainer import TranspileTrainer
-from fleetrec.core.utils import envs
-import numpy as np
-
-logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger("fluid")
-logger.setLevel(logging.INFO)
-
-
-class SingleTrainer(TranspileTrainer):
- def processor_register(self):
- self.regist_context_processor('uninit', self.instance)
- self.regist_context_processor('init_pass', self.init)
- self.regist_context_processor('startup_pass', self.startup)
- if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader":
- self.regist_context_processor('train_pass', self.dataset_train)
- else:
- self.regist_context_processor('train_pass', self.dataloader_train)
-
- self.regist_context_processor('infer_pass', self.infer)
- self.regist_context_processor('terminal_pass', self.terminal)
-
- def init(self, context):
- self.model.train_net()
- optimizer = self.model.optimizer()
- optimizer.minimize((self.model.get_cost_op()))
-
- self.fetch_vars = []
- self.fetch_alias = []
- self.fetch_period = self.model.get_fetch_period()
-
- metrics = self.model.get_metrics()
- if metrics:
- self.fetch_vars = metrics.values()
- self.fetch_alias = metrics.keys()
- context['status'] = 'startup_pass'
-
- def startup(self, context):
- self._exe.run(fluid.default_startup_program())
- context['status'] = 'train_pass'
-
- def dataloader_train(self, context):
- reader = self._get_dataloader("TRAIN")
- epochs = envs.get_global_env("train.epochs")
-
- program = fluid.compiler.CompiledProgram(
- fluid.default_main_program()).with_data_parallel(
- loss_name=self.model.get_cost_op().name)
-
- metrics_varnames = []
- metrics_format = []
-
- metrics_format.append("{}: {{}}".format("epoch"))
- metrics_format.append("{}: {{}}".format("batch"))
-
- for name, var in self.model.get_metrics().items():
- metrics_varnames.append(var.name)
- metrics_format.append("{}: {{}}".format(name))
-
- metrics_format = ", ".join(metrics_format)
-
- for epoch in range(epochs):
- reader.start()
- batch_id = 0
- try:
- while True:
- metrics_rets = self._exe.run(
- program=program,
- fetch_list=metrics_varnames)
-
- metrics = [epoch, batch_id]
- metrics.extend(metrics_rets)
-
- if batch_id % self.fetch_period == 0 and batch_id != 0:
- print(metrics_format.format(*metrics))
- batch_id += 1
- except fluid.core.EOFException:
- reader.reset()
- self.save(epoch, "train", is_fleet=False)
-
- context['status'] = 'infer_pass'
-
- def dataset_train(self, context):
- dataset = self._get_dataset("TRAIN")
- epochs = envs.get_global_env("train.epochs")
-
- for i in range(epochs):
- self._exe.train_from_dataset(program=fluid.default_main_program(),
- dataset=dataset,
- fetch_list=self.fetch_vars,
- fetch_info=self.fetch_alias,
- print_period=self.fetch_period)
- self.save(i, "train", is_fleet=False)
- context['status'] = 'infer_pass'
-
- def terminal(self, context):
- for model in self.increment_models:
- print("epoch :{}, dir: {}".format(model[0], model[1]))
- context['is_exit'] = True
diff --git a/fleet_rec/core/utils/dataloader_instance.py b/fleet_rec/core/utils/dataloader_instance.py
deleted file mode 100755
index a16fde803d5fc1467ee5d266215da4938ff8c579..0000000000000000000000000000000000000000
--- a/fleet_rec/core/utils/dataloader_instance.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import print_function
-
-import os
-import sys
-
-from fleetrec.core.utils.envs import lazy_instance_by_fliename
-from fleetrec.core.utils.envs import get_global_env
-from fleetrec.core.utils.envs import get_runtime_environ
-
-
-def dataloader(readerclass, train, yaml_file):
- if train == "TRAIN":
- reader_name = "TrainReader"
- namespace = "train.reader"
- data_path = get_global_env("train_data_path", None, namespace)
- else:
- reader_name = "EvaluateReader"
- namespace = "evaluate.reader"
- data_path = get_global_env("test_data_path", None, namespace)
-
- if data_path.startswith("fleetrec::"):
- package_base = get_runtime_environ("PACKAGE_BASE")
- assert package_base is not None
- data_path = os.path.join(package_base, data_path.split("::")[1])
-
- files = [str(data_path) + "/%s" % x for x in os.listdir(data_path)]
-
- reader_class = lazy_instance_by_fliename(readerclass, reader_name)
- reader = reader_class(yaml_file)
- reader.init()
-
- def gen_reader():
- for file in files:
- with open(file, 'r') as f:
- for line in f:
- line = line.rstrip('\n')
- iter = reader.generate_sample(line)
- for parsed_line in iter():
- if parsed_line is None:
- continue
- else:
- values = []
- for pased in parsed_line:
- values.append(pased[1])
- yield values
-
- def gen_batch_reader():
- return reader.generate_batch_from_trainfiles(files)
-
- if hasattr(reader, 'generate_batch_from_trainfiles'):
- return gen_batch_reader()
- return gen_reader
diff --git a/fleet_rec/core/utils/dataset_instance.py b/fleet_rec/core/utils/dataset_instance.py
deleted file mode 100755
index 89e6e45d2c53bb033e0e5fc6e436b149d76cc7c2..0000000000000000000000000000000000000000
--- a/fleet_rec/core/utils/dataset_instance.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import print_function
-import sys
-
-from fleetrec.core.utils.envs import lazy_instance_by_fliename
-
-if len(sys.argv) != 4:
- raise ValueError("reader only accept 3 argument: 1. reader_class 2.train/evaluate 3.yaml_abs_path")
-
-reader_package = sys.argv[1]
-
-if sys.argv[2] == "TRAIN":
- reader_name = "TrainReader"
-else:
- reader_name = "EvaluateReader"
-
-yaml_abs_path = sys.argv[3]
-reader_class = lazy_instance_by_fliename(reader_package, reader_name)
-reader = reader_class(yaml_abs_path)
-reader.init()
-reader.run_from_stdin()
diff --git a/fleet_rec/run.py b/fleet_rec/run.py
deleted file mode 100755
index d40cb99420cde906c39cbe34eaa64bdf8e74dbd2..0000000000000000000000000000000000000000
--- a/fleet_rec/run.py
+++ /dev/null
@@ -1,220 +0,0 @@
-import argparse
-import os
-import subprocess
-import yaml
-
-from fleetrec.core.factory import TrainerFactory
-from fleetrec.core.utils import envs
-from fleetrec.core.utils import util
-
-engines = {}
-device = ["CPU", "GPU"]
-clusters = ["SINGLE", "LOCAL_CLUSTER", "CLUSTER"]
-custom_model = ['tdm']
-model_name = ""
-
-
-def engine_registry():
- cpu = {"TRANSPILER": {}, "PSLIB": {}}
- cpu["TRANSPILER"]["SINGLE"] = single_engine
- cpu["TRANSPILER"]["LOCAL_CLUSTER"] = local_cluster_engine
- cpu["TRANSPILER"]["CLUSTER"] = cluster_engine
- cpu["PSLIB"]["SINGLE"] = local_mpi_engine
- cpu["PSLIB"]["LOCAL_CLUSTER"] = local_mpi_engine
- cpu["PSLIB"]["CLUSTER"] = cluster_mpi_engine
-
- gpu = {"TRANSPILER": {}, "PSLIB": {}}
- gpu["TRANSPILER"]["SINGLE"] = single_engine
-
- engines["CPU"] = cpu
- engines["GPU"] = gpu
-
-
-def get_engine(args):
- device = args.device
- d_engine = engines[device]
- transpiler = get_transpiler()
-
- engine = args.engine
- run_engine = d_engine[transpiler].get(engine, None)
-
- if run_engine is None:
- raise ValueError(
- "engine {} can not be supported on device: {}".format(engine, device))
- return run_engine
-
-
-def get_transpiler():
- FNULL = open(os.devnull, 'w')
- cmd = ["python", "-c",
- "import paddle.fluid as fluid; fleet_ptr = fluid.core.Fleet(); [fleet_ptr.copy_table_by_feasign(10, 10, [2020, 1010])];"]
- proc = subprocess.Popen(cmd, stdout=FNULL, stderr=FNULL, cwd=os.getcwd())
- ret = proc.wait()
- if ret == -11:
- return "PSLIB"
- else:
- return "TRANSPILER"
-
-
-def set_runtime_envs(cluster_envs, engine_yaml):
- def get_engine_extras():
- with open(engine_yaml, 'r') as rb:
- _envs = yaml.load(rb.read(), Loader=yaml.FullLoader)
-
- flattens = envs.flatten_environs(_envs)
-
- engine_extras = {}
- for k, v in flattens.items():
- if k.startswith("train.trainer."):
- engine_extras[k] = v
- return engine_extras
-
- if cluster_envs is None:
- cluster_envs = {}
-
- engine_extras = get_engine_extras()
- if "train.trainer.threads" in engine_extras and "CPU_NUM" in cluster_envs:
- cluster_envs["CPU_NUM"] = engine_extras["train.trainer.threads"]
- envs.set_runtime_environs(cluster_envs)
- envs.set_runtime_environs(engine_extras)
-
- need_print = {}
- for k, v in os.environ.items():
- if k.startswith("train.trainer."):
- need_print[k] = v
-
- print(envs.pretty_print_envs(need_print, ("Runtime Envs", "Value")))
-
-
-def get_trainer_prefix(args):
- if model_name in custom_model:
- return model_name.upper()
- return ""
-
-
-def single_engine(args):
- trainer = get_trainer_prefix(args) + "SingleTrainer"
- single_envs = {}
- single_envs["train.trainer.trainer"] = trainer
- single_envs["train.trainer.threads"] = "2"
- single_envs["train.trainer.engine"] = "single"
- single_envs["train.trainer.device"] = args.device
- single_envs["train.trainer.platform"] = envs.get_platform()
- print("use {} engine to run model: {}".format(trainer, args.model))
-
- set_runtime_envs(single_envs, args.model)
- trainer = TrainerFactory.create(args.model)
- return trainer
-
-
-def cluster_engine(args):
- trainer = get_trainer_prefix(args) + "ClusterTrainer"
- cluster_envs = {}
- cluster_envs["train.trainer.trainer"] = trainer
- cluster_envs["train.trainer.engine"] = "cluster"
- cluster_envs["train.trainer.device"] = args.device
- cluster_envs["train.trainer.platform"] = envs.get_platform()
- print("launch {} engine with cluster to run model: {}".format(trainer, args.model))
-
- set_runtime_envs(cluster_envs, args.model)
- trainer = TrainerFactory.create(args.model)
- return trainer
-
-
-def cluster_mpi_engine(args):
- print("launch cluster engine with cluster to run model: {}".format(args.model))
-
- cluster_envs = {}
- cluster_envs["train.trainer.trainer"] = "CtrCodingTrainer"
- cluster_envs["train.trainer.device"] = args.device
- cluster_envs["train.trainer.platform"] = envs.get_platform()
-
- set_runtime_envs(cluster_envs, args.model)
-
- trainer = TrainerFactory.create(args.model)
- return trainer
-
-
-def local_cluster_engine(args):
- from fleetrec.core.engine.local_cluster_engine import LocalClusterEngine
-
- trainer = get_trainer_prefix(args) + "ClusterTrainer"
- cluster_envs = {}
- cluster_envs["server_num"] = 1
- cluster_envs["worker_num"] = 1
- cluster_envs["start_port"] = envs.find_free_port()
- cluster_envs["log_dir"] = "logs"
- cluster_envs["train.trainer.trainer"] = trainer
- cluster_envs["train.trainer.strategy"] = "async"
- cluster_envs["train.trainer.threads"] = "2"
- cluster_envs["train.trainer.engine"] = "local_cluster"
-
- cluster_envs["train.trainer.device"] = args.device
- cluster_envs["train.trainer.platform"] = envs.get_platform()
-
- cluster_envs["CPU_NUM"] = "2"
- print("launch {} engine with cluster to run model: {}".format(trainer, args.model))
-
- set_runtime_envs(cluster_envs, args.model)
- launch = LocalClusterEngine(cluster_envs, args.model)
- return launch
-
-
-def local_mpi_engine(args):
- print("launch cluster engine with cluster to run model: {}".format(args.model))
- from fleetrec.core.engine.local_mpi_engine import LocalMPIEngine
-
- print("use 1X1 MPI ClusterTraining at localhost to run model: {}".format(args.model))
-
- mpi = util.run_which("mpirun")
- if not mpi:
- raise RuntimeError("can not find mpirun, please check environment")
- cluster_envs = {}
- cluster_envs["mpirun"] = mpi
- cluster_envs["train.trainer.trainer"] = "CtrCodingTrainer"
- cluster_envs["log_dir"] = "logs"
- cluster_envs["train.trainer.engine"] = "local_cluster"
-
- cluster_envs["train.trainer.device"] = args.device
- cluster_envs["train.trainer.platform"] = envs.get_platform()
-
- set_runtime_envs(cluster_envs, args.model)
- launch = LocalMPIEngine(cluster_envs, args.model)
- return launch
-
-
-def get_abs_model(model):
- if model.startswith("fleetrec."):
- fleet_base = envs.get_runtime_environ("PACKAGE_BASE")
- workspace_dir = model.split("fleetrec.")[1].replace(".", "/")
- path = os.path.join(fleet_base, workspace_dir, "config.yaml")
- else:
- if not os.path.isfile(model):
- raise IOError("model config: {} invalid".format(model))
- path = model
- return path
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='fleet-rec run')
- parser.add_argument("-m", "--model", type=str)
- parser.add_argument("-e", "--engine", type=str,
- choices=["single", "local_cluster", "cluster",
- "tdm_single", "tdm_local_cluster", "tdm_cluster"])
- parser.add_argument("-d", "--device", type=str,
- choices=["cpu", "gpu"], default="cpu")
-
- abs_dir = os.path.dirname(os.path.abspath(__file__))
- envs.set_runtime_environs({"PACKAGE_BASE": abs_dir})
-
- args = parser.parse_args()
- args.engine = args.engine.upper()
- args.device = args.device.upper()
- model_name = args.model.split('.')[-1]
- args.model = get_abs_model(args.model)
- engine_registry()
-
- which_engine = get_engine(args)
-
- engine = which_engine(args)
- engine.run()
diff --git a/fleet_rec/tests/__init__.py b/fleet_rec/tests/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/models/contentunderstanding/__init__.py b/models/contentunderstanding/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/contentunderstanding/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/contentunderstanding/classification/__init__.py b/models/contentunderstanding/classification/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/contentunderstanding/classification/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/contentunderstanding/classification/config.yaml b/models/contentunderstanding/classification/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9e0bdd1e851ada704eb2377efe0a82154fd2b371
--- /dev/null
+++ b/models/contentunderstanding/classification/config.yaml
@@ -0,0 +1,48 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+workspace: "paddlerec.models.contentunderstanding.classification"
+
+dataset:
+- name: data1
+ batch_size: 5
+ type: DataLoader
+ data_path: "{workspace}/data/train_data"
+ data_converter: "{workspace}/reader.py"
+
+hyper_parameters:
+ optimizer:
+ class: Adagrad
+ learning_rate: 0.001
+ is_sparse: False
+
+mode: runner1
+
+runner:
+- name: runner1
+ class: single_train
+ epochs: 10
+ device: cpu
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ save_inference_feed_varnames: []
+ save_inference_fetch_varnames: []
+
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: data1
+ thread_num: 1
diff --git a/models/rank/text_classification/train_data/part-0 b/models/contentunderstanding/classification/data/train_data/part-0.txt
similarity index 100%
rename from models/rank/text_classification/train_data/part-0
rename to models/contentunderstanding/classification/data/train_data/part-0.txt
diff --git a/models/contentunderstanding/classification/model.py b/models/contentunderstanding/classification/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce9caf5bfa6c5bc229a52d09c5c8f3b6093b80c6
--- /dev/null
+++ b/models/contentunderstanding/classification/model.py
@@ -0,0 +1,74 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+ self.dict_dim = 100
+ self.max_len = 10
+ self.cnn_dim = 32
+ self.cnn_filter_size = 128
+ self.emb_dim = 8
+ self.hid_dim = 128
+ self.class_dim = 2
+ self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
+ False)
+
+ def input_data(self, is_infer=False, **kwargs):
+ data = fluid.data(
+ name="input", shape=[None, self.max_len], dtype='int64')
+ label = fluid.data(name="label", shape=[None, 1], dtype='int64')
+ seq_len = fluid.data(name="seq_len", shape=[None], dtype='int64')
+ return [data, label, seq_len]
+
+ def net(self, input, is_infer=False):
+ """ network definition """
+ data = input[0]
+ label = input[1]
+ seq_len = input[2]
+
+ # embedding layer
+ emb = fluid.embedding(
+ input=data,
+ size=[self.dict_dim, self.emb_dim],
+ is_sparse=self.is_sparse)
+ emb = fluid.layers.sequence_unpad(emb, length=seq_len)
+ # convolution layer
+ conv = fluid.nets.sequence_conv_pool(
+ input=emb,
+ num_filters=self.cnn_dim,
+ filter_size=self.cnn_filter_size,
+ act="tanh",
+ pool_type="max")
+
+ # full connect layer
+ fc_1 = fluid.layers.fc(input=[conv], size=self.hid_dim)
+ # softmax layer
+ prediction = fluid.layers.fc(input=[fc_1],
+ size=self.class_dim,
+ act="softmax")
+ cost = fluid.layers.cross_entropy(input=prediction, label=label)
+ avg_cost = fluid.layers.mean(x=cost)
+ acc = fluid.layers.accuracy(input=prediction, label=label)
+
+ self._cost = avg_cost
+ if is_infer:
+ self._infer_results["acc"] = acc
+ else:
+ self._metrics["acc"] = acc
diff --git a/models/contentunderstanding/classification/reader.py b/models/contentunderstanding/classification/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..18a41ee844c35d0a6fa37a835203121868158c4e
--- /dev/null
+++ b/models/contentunderstanding/classification/reader.py
@@ -0,0 +1,42 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+
+from paddlerec.core.reader import Reader
+
+
+class TrainReader(Reader):
+ def init(self):
+ pass
+
+ def _process_line(self, l):
+ l = l.strip().split()
+ data = l[0:10]
+ seq_len = l[10:11]
+ label = l[11:]
+ return data, label, seq_len
+
+ def generate_sample(self, line):
+ def data_iter():
+ data, label, seq_len = self._process_line(line)
+ if data is None:
+ yield None
+ return
+ data = [int(i) for i in data]
+ label = [int(i) for i in label]
+ seq_len = [int(i) for i in seq_len]
+ yield [('data', data), ('label', label), ('seq_len', seq_len)]
+
+ return data_iter
diff --git a/models/contentunderstanding/readme.md b/models/contentunderstanding/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..217d7124d7cdb481ca7aacb418e36148508e42b8
--- /dev/null
+++ b/models/contentunderstanding/readme.md
@@ -0,0 +1,122 @@
+# 内容理解模型库
+
+## 简介
+我们提供了常见的内容理解任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的内容理解模型包括 [Tagspace](tagspace)、[文本分类](classification)等。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [模型列表](#内容理解模型列表)
+* [使用教程](#使用教程)
+ * [数据处理](#数据处理)
+ * [训练](#训练)
+ * [预测](#预测)
+* [效果对比](#效果对比)
+ * [模型效果列表](#模型效果列表)
+* [分布式](#分布式)
+ * [模型性能列表](#模型性能列表)
+
+## 整体介绍
+### 模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| TagSpace | 标签推荐 | [TagSpace: Semantic Embeddings from Hashtags (2014)](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags/) |
+| Classification | 文本分类 | [Convolutional neural networks for sentence classication (2014)](https://www.aclweb.org/anthology/D14-1181.pdf) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+[TagSpace模型](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags)
+
+
+
+[文本分类CNN模型](https://www.aclweb.org/anthology/D14-1181.pdf)
+
+
+
+##使用教程(快速开始)
+```
+python -m paddlerec.run -m paddlerec.models.contentunderstanding.tagspace
+python -m paddlerec.run -m paddlerec.models.contentunderstanding.classification
+```
+
+## 使用教程（复现论文）
+
+###注意
+
+为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据。如果需要复现readme中的效果请使用以下提供的脚本下载对应数据集以及数据预处理。
+
+### 数据处理
+
+**（1）TagSpace**
+
+[数据地址](https://github.com/mhjabreel/CharCNN/tree/master/data/) , [备份数据地址](https://paddle-tagspace.bj.bcebos.com/data.tar)
+
+数据格式如下
+```
+"3","Wall St. Bears Claw Back Into the Black (Reuters)","Reuters - Short-sellers, Wall Street's dwindling\band of ultra-cynics, are seeing green again."
+```
+
+数据解压后，将文本数据转为paddle数据，先将数据放到训练数据目录和测试数据目录
+
+```
+mkdir raw_big_train_data
+mkdir raw_big_test_data
+mv train.csv raw_big_train_data
+mv test.csv raw_big_test_data
+```
+
+运行脚本text2paddle.py 生成paddle输入格式
+
+```
+python text2paddle.py raw_big_train_data/ raw_big_test_data/ train_big_data test_big_data big_vocab_text.txt big_vocab_tag.txt
+```
+
+### 训练
+```
+cd modles/contentunderstanding/tagspace
+python -m paddlerec.run -m ./config.yaml # 自定义修改超参后，指定配置文件，使用自定义配置
+```
+
+### 预测
+```
+# 修改对应模型的config.yaml, workspace配置为当前目录的绝对路径
+# 修改对应模型的config.yaml，mode配置infer_runner
+# 示例: mode: train_runner -> mode: infer_runner
+# infer_runner中 class配置为 class: single_infer
+# 修改phase阶段为infer的配置，参照config注释
+
+# 修改完config.yaml后 执行:
+python -m paddlerec.run -m ./config.yaml
+```
+
+**（2）Classification**
+
+### 训练
+```
+cd modles/contentunderstanding/classification
+python -m paddlerec.run -m ./config.yaml # 自定义修改超参后，指定配置文件，使用自定义配置
+```
+
+### 预测
+```
+# 修改对应模型的config.yaml, workspace配置为当前目录的绝对路径
+# 修改对应模型的config.yaml，mode配置infer_runner
+# 示例: mode: train_runner -> mode: infer_runner
+# infer_runner中 class配置为 class: single_infer
+# 修改phase阶段为infer的配置，参照config注释
+
+# 修改完config.yaml后 执行:
+python -m paddlerec.run -m ./config.yaml
+```
+
+## 效果对比
+### 模型效果 (测试)
+
+| 数据集 | 模型 | loss | auc | acc | mae |
+| :------------------: | :--------------------: | :---------: |:---------: | :---------: |:---------: |
+| ag news dataset | TagSpace | -- | -- | -- | -- |
+| -- | Classification | -- | -- | -- | -- |
diff --git a/models/contentunderstanding/tagspace/__init__.py b/models/contentunderstanding/tagspace/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/contentunderstanding/tagspace/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/contentunderstanding/tagspace/config.yaml b/models/contentunderstanding/tagspace/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8ca28f2977dd4bfd382e250e5c6513b156360404
--- /dev/null
+++ b/models/contentunderstanding/tagspace/config.yaml
@@ -0,0 +1,55 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+workspace: "paddlerec.models.contentunderstanding.tagspace"
+
+dataset:
+- name: sample_1
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/train_data"
+ data_converter: "{workspace}/reader.py"
+
+hyper_parameters:
+ optimizer:
+ class: Adagrad
+ learning_rate: 0.001
+ vocab_text_size: 11447
+ vocab_tag_size: 4
+ emb_dim: 10
+ hid_dim: 1000
+ win_size: 5
+ margin: 0.1
+ neg_size: 3
+ num_devices: 1
+
+mode: runner1
+
+runner:
+- name: runner1
+ class: single_train
+ epochs: 10
+ device: cpu
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ save_inference_feed_varnames: []
+ save_inference_fetch_varnames: []
+
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: sample_1
+ thread_num: 1
diff --git a/models/rank/tagspace/test_data/small_test.csv b/models/contentunderstanding/tagspace/data/test_data/small_test.csv
similarity index 100%
rename from models/rank/tagspace/test_data/small_test.csv
rename to models/contentunderstanding/tagspace/data/test_data/small_test.csv
diff --git a/models/rank/tagspace/train_data/small_train.csv b/models/contentunderstanding/tagspace/data/train_data/small_train.csv
similarity index 100%
rename from models/rank/tagspace/train_data/small_train.csv
rename to models/contentunderstanding/tagspace/data/train_data/small_train.csv
diff --git a/models/contentunderstanding/tagspace/model.py b/models/contentunderstanding/tagspace/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..34e5ebace1c3f4a44def5ad8006c2eb74a40c3c0
--- /dev/null
+++ b/models/contentunderstanding/tagspace/model.py
@@ -0,0 +1,111 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+import paddle.fluid.layers.nn as nn
+import paddle.fluid.layers.tensor as tensor
+import paddle.fluid.layers.control_flow as cf
+
+from paddlerec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+ self.cost = None
+ self.metrics = {}
+ self.vocab_text_size = envs.get_global_env(
+ "hyper_parameters.vocab_text_size")
+ self.vocab_tag_size = envs.get_global_env(
+ "hyper_parameters.vocab_tag_size")
+ self.emb_dim = envs.get_global_env("hyper_parameters.emb_dim")
+ self.hid_dim = envs.get_global_env("hyper_parameters.hid_dim")
+ self.win_size = envs.get_global_env("hyper_parameters.win_size")
+ self.margin = envs.get_global_env("hyper_parameters.margin")
+ self.neg_size = envs.get_global_env("hyper_parameters.neg_size")
+
+ def input_data(self, is_infer=False, **kwargs):
+ text = fluid.data(
+ name="text", shape=[None, 1], lod_level=1, dtype='int64')
+ pos_tag = fluid.data(
+ name="pos_tag", shape=[None, 1], lod_level=1, dtype='int64')
+ neg_tag = fluid.data(
+ name="neg_tag", shape=[None, 1], lod_level=1, dtype='int64')
+ return [text, pos_tag, neg_tag]
+
+ def net(self, input, is_infer=False):
+ """ network"""
+ text = input[0]
+ pos_tag = input[1]
+ neg_tag = input[2]
+
+ text_emb = fluid.embedding(
+ input=text,
+ size=[self.vocab_text_size, self.emb_dim],
+ param_attr="text_emb")
+ text_emb = fluid.layers.squeeze(input=text_emb, axes=[1])
+ pos_tag_emb = fluid.embedding(
+ input=pos_tag,
+ size=[self.vocab_tag_size, self.emb_dim],
+ param_attr="tag_emb")
+ pos_tag_emb = fluid.layers.squeeze(input=pos_tag_emb, axes=[1])
+ neg_tag_emb = fluid.embedding(
+ input=neg_tag,
+ size=[self.vocab_tag_size, self.emb_dim],
+ param_attr="tag_emb")
+ neg_tag_emb = fluid.layers.squeeze(input=neg_tag_emb, axes=[1])
+
+ conv_1d = fluid.nets.sequence_conv_pool(
+ input=text_emb,
+ num_filters=self.hid_dim,
+ filter_size=self.win_size,
+ act="tanh",
+ pool_type="max",
+ param_attr="cnn")
+ text_hid = fluid.layers.fc(input=conv_1d,
+ size=self.emb_dim,
+ param_attr="text_hid")
+ cos_pos = nn.cos_sim(pos_tag_emb, text_hid)
+ mul_text_hid = fluid.layers.sequence_expand_as(
+ x=text_hid, y=neg_tag_emb)
+ mul_cos_neg = nn.cos_sim(neg_tag_emb, mul_text_hid)
+ cos_neg_all = fluid.layers.sequence_reshape(
+ input=mul_cos_neg, new_dim=self.neg_size)
+ #choose max negtive cosine
+ cos_neg = nn.reduce_max(cos_neg_all, dim=1, keep_dim=True)
+ #calculate hinge loss
+ loss_part1 = nn.elementwise_sub(
+ tensor.fill_constant_batch_size_like(
+ input=cos_pos,
+ shape=[-1, 1],
+ value=self.margin,
+ dtype='float32'),
+ cos_pos)
+ loss_part2 = nn.elementwise_add(loss_part1, cos_neg)
+ loss_part3 = nn.elementwise_max(
+ tensor.fill_constant_batch_size_like(
+ input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
+ loss_part2)
+ avg_cost = nn.mean(loss_part3)
+ less = tensor.cast(cf.less_than(cos_neg, cos_pos), dtype='float32')
+ correct = nn.reduce_sum(less)
+ self._cost = avg_cost
+
+ if is_infer:
+ self._infer_results["correct"] = correct
+ self._infer_results["cos_pos"] = cos_pos
+ else:
+ self._metrics["correct"] = correct
+ self._metrics["cos_pos"] = cos_pos
diff --git a/models/rank/tagspace/reader.py b/models/contentunderstanding/tagspace/reader.py
similarity index 59%
rename from models/rank/tagspace/reader.py
rename to models/contentunderstanding/tagspace/reader.py
index 7803d862ed4f55890e810073f5a561e81be4f5e8..3bf704f17adbafc28302ec0b64180ec3fddf6d01 100644
--- a/models/rank/tagspace/reader.py
+++ b/models/contentunderstanding/tagspace/reader.py
@@ -1,23 +1,29 @@
-import re
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import sys
-import collections
-import os
-import six
-import time
+
import numpy as np
-import paddle.fluid as fluid
-import paddle
-import csv
-import io
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+from paddlerec.core.reader import Reader
+
class TrainReader(Reader):
def init(self):
pass
- def _process_line(self, l):
+ def _process_line(self, l):
tag_size = 4
neg_size = 3
l = l.strip().split(",")
@@ -40,10 +46,7 @@ class TrainReader(Reader):
neg_index = rand_i
neg_tag.append(neg_index)
sum_n += 1
- # if n > 0 and len(text) > n:
- # #yield None
- # return None, None, None
- return text, pos_tag, neg_tag
+ return text, pos_tag, neg_tag
def generate_sample(self, line):
def data_iter():
@@ -52,4 +55,5 @@ class TrainReader(Reader):
yield None
return
yield [('text', text), ('pos_tag', pos_tag), ('neg_tag', neg_tag)]
+
return data_iter
diff --git a/models/match/__init__.py b/models/match/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..abf198b97e6e818e1fbe59006f98492640bcee54 100755
--- a/models/match/__init__.py
+++ b/models/match/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/match/dssm/config.yaml b/models/match/dssm/config.yaml
index 95d93d7843e77c407e6dc335301027b99e7c34b4..22881bdf906178a87f4b89091589523533eac934 100755
--- a/models/match/dssm/config.yaml
+++ b/models/match/dssm/config.yaml
@@ -23,7 +23,7 @@ train:
strategy: "async"
epochs: 4
- workspace: "fleetrec.models.match.dssm"
+ workspace: "paddlerec.models.match.dssm"
reader:
batch_size: 4
diff --git a/models/match/dssm/model.py b/models/match/dssm/model.py
index 414270524bc13987a2ad7f724cc6a78d3b19cbcf..05d6f762cb266b4cbe40c9a972aafe1885af5b86 100755
--- a/models/match/dssm/model.py
+++ b/models/match/dssm/model.py
@@ -12,11 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import math
import paddle.fluid as fluid
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
@@ -24,12 +23,26 @@ class Model(ModelBase):
ModelBase.__init__(self, config)
def input(self):
- TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None, self._namespace)
- Neg = envs.get_global_env("hyper_parameters.NEG", None, self._namespace)
-
- self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
- self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
- self.doc_negs = [fluid.data(name="doc_neg_" + str(i), shape=[-1, TRIGRAM_D], dtype="float32", lod_level=0) for i in range(Neg)]
+ TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None,
+ self._namespace)
+
+ Neg = envs.get_global_env("hyper_parameters.NEG", None,
+ self._namespace)
+
+ self.query = fluid.data(
+ name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+ self.doc_pos = fluid.data(
+ name="doc_pos",
+ shape=[-1, TRIGRAM_D],
+ dtype='float32',
+ lod_level=0)
+ self.doc_negs = [
+ fluid.data(
+ name="doc_neg_" + str(i),
+ shape=[-1, TRIGRAM_D],
+ dtype="float32",
+ lod_level=0) for i in range(Neg)
+ ]
self._data_var.append(self.query)
self._data_var.append(self.doc_pos)
for input in self.doc_negs:
@@ -37,41 +50,55 @@ class Model(ModelBase):
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
-
+ feed_list=self._data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
def net(self, is_infer=False):
- hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
- hidden_acts = envs.get_global_env("hyper_parameters.fc_acts", None, self._namespace)
-
+ hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None,
+ self._namespace)
+ hidden_acts = envs.get_global_env("hyper_parameters.fc_acts", None,
+ self._namespace)
+
def fc(data, hidden_layers, hidden_acts, names):
fc_inputs = [data]
- for i in range(len(hidden_layers)):
- xavier=fluid.initializer.Xavier(uniform=True, fan_in=fc_inputs[-1].shape[1], fan_out=hidden_layers[i])
- out = fluid.layers.fc(input=fc_inputs[-1],
- size=hidden_layers[i],
- act=hidden_acts[i],
- param_attr=xavier,
- bias_attr=xavier,
- name=names[i])
- fc_inputs.append(out)
- return fc_inputs[-1]
-
- query_fc = fc(self.query, hidden_layers, hidden_acts, ['query_l1', 'query_l2', 'query_l3'])
- doc_pos_fc = fc(self.doc_pos, hidden_layers, hidden_acts, ['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3'])
- self.R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
+ for i in range(len(hidden_layers)):
+ xavier = fluid.initializer.Xavier(
+ uniform=True,
+ fan_in=fc_inputs[-1].shape[1],
+ fan_out=hidden_layers[i])
+ out = fluid.layers.fc(input=fc_inputs[-1],
+ size=hidden_layers[i],
+ act=hidden_acts[i],
+ param_attr=xavier,
+ bias_attr=xavier,
+ name=names[i])
+ fc_inputs.append(out)
+ return fc_inputs[-1]
+
+ query_fc = fc(self.query, hidden_layers, hidden_acts,
+ ['query_l1', 'query_l2', 'query_l3'])
+ doc_pos_fc = fc(self.doc_pos, hidden_layers, hidden_acts,
+ ['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3'])
+ self.R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
if is_infer:
return
R_Q_D_ns = []
- for i, doc_neg in enumerate(self.doc_negs):
- doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts, ['doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i), 'doc_neg_l3_' + str(i)])
+ for i, doc_neg in enumerate(self.doc_negs):
+ doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts, [
+ 'doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i),
+ 'doc_neg_l3_' + str(i)
+ ])
R_Q_D_ns.append(fluid.layers.cos_sim(query_fc, doc_neg_fc_i))
- concat_Rs = fluid.layers.concat(input=[self.R_Q_D_p] + R_Q_D_ns, axis=-1)
- prob = fluid.layers.softmax(concat_Rs, axis=1)
-
- hit_prob = fluid.layers.slice(prob, axes=[0,1], starts=[0,0], ends=[4, 1])
+ concat_Rs = fluid.layers.concat(
+ input=[self.R_Q_D_p] + R_Q_D_ns, axis=-1)
+ prob = fluid.layers.softmax(concat_Rs, axis=1)
+
+ hit_prob = fluid.layers.slice(
+ prob, axes=[0, 1], starts=[0, 0], ends=[4, 1])
loss = -fluid.layers.reduce_sum(fluid.layers.log(hit_prob))
self.avg_cost = fluid.layers.mean(x=loss)
@@ -91,20 +118,30 @@ class Model(ModelBase):
self.metrics()
def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
+ None, self._namespace)
optimizer = fluid.optimizer.SGD(learning_rate)
return optimizer
def infer_input(self):
- TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None, self._namespace)
- self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
- self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+ TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None,
+ self._namespace)
+ self.query = fluid.data(
+ name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+ self.doc_pos = fluid.data(
+ name="doc_pos",
+ shape=[-1, TRIGRAM_D],
+ dtype='float32',
+ lod_level=0)
self._infer_data_var = [self.query, self.doc_pos]
- self._infer_data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
-
+ self._infer_data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._infer_data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
+
def infer_net(self):
- self.infer_input()
+ self.infer_input()
self.net(is_infer=True)
- self.infer_results()
+ self.infer_results()
diff --git a/models/match/dssm/synthetic_evaluate_reader.py b/models/match/dssm/synthetic_evaluate_reader.py
index de49774084607a4d37406ad8667c8476a040f42b..97f50abf9720060b008b90c7729e93d13701bb3b 100755
--- a/models/match/dssm/synthetic_evaluate_reader.py
+++ b/models/match/dssm/synthetic_evaluate_reader.py
@@ -13,8 +13,7 @@
# limitations under the License.
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
diff --git a/models/match/dssm/synthetic_reader.py b/models/match/dssm/synthetic_reader.py
index 6eca228fd24da3603f69a822b81370f4a49fba13..13f57a6663ca372bc287386dd939214f362b503d 100755
--- a/models/match/dssm/synthetic_reader.py
+++ b/models/match/dssm/synthetic_reader.py
@@ -11,10 +11,10 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+from paddlerec.core.reader import Reader
class TrainReader(Reader):
@@ -37,7 +37,7 @@ class TrainReader(Reader):
neg_docs = []
for i in range(len(features) - 2):
feature_names.append('doc_neg_' + str(i))
- neg_docs.append(map(float, features[i+2].split(',')))
+ neg_docs.append(map(float, features[i + 2].split(',')))
yield zip(feature_names, [query] + [pos_doc] + neg_docs)
diff --git a/models/match/multiview-simnet/__init__.py b/models/match/multiview-simnet/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/match/multiview-simnet/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/recall/multiview-simnet/config.yaml b/models/match/multiview-simnet/config.yaml
similarity index 93%
rename from models/recall/multiview-simnet/config.yaml
rename to models/match/multiview-simnet/config.yaml
index 0fcc1fc2f18ed2588d4f9eb235a1dd619e99f337..53ac4c095c0d347cca8cba1afb9866c66ab85218 100755
--- a/models/recall/multiview-simnet/config.yaml
+++ b/models/match/multiview-simnet/config.yaml
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
- workspace: "fleetrec.models.recall.multiview-simnet"
+ workspace: "paddlerec.models.match.multiview-simnet"
reader:
batch_size: 2
class: "{workspace}/evaluate_reader.py"
@@ -24,7 +24,7 @@ train:
strategy: "async"
epochs: 2
- workspace: "fleetrec.models.recall.multiview-simnet"
+ workspace: "paddlerec.models.match.multiview-simnet"
reader:
batch_size: 2
diff --git a/models/recall/multiview-simnet/data/test/test.txt b/models/match/multiview-simnet/data/test/test.txt
similarity index 100%
rename from models/recall/multiview-simnet/data/test/test.txt
rename to models/match/multiview-simnet/data/test/test.txt
diff --git a/models/recall/multiview-simnet/data/train/train.txt b/models/match/multiview-simnet/data/train/train.txt
similarity index 100%
rename from models/recall/multiview-simnet/data/train/train.txt
rename to models/match/multiview-simnet/data/train/train.txt
diff --git a/models/match/multiview-simnet/data_process.sh b/models/match/multiview-simnet/data_process.sh
new file mode 100755
index 0000000000000000000000000000000000000000..c8633cc7a41f62a29eee1778251b72a6f3b601eb
--- /dev/null
+++ b/models/match/multiview-simnet/data_process.sh
@@ -0,0 +1,24 @@
+#! /bin/bash
+
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+set -e
+echo "begin to prepare data"
+
+mkdir -p data/train
+mkdir -p data/test
+
+python generate_synthetic_data.py
diff --git a/models/recall/multiview-simnet/evaluate_reader.py b/models/match/multiview-simnet/evaluate_reader.py
similarity index 83%
rename from models/recall/multiview-simnet/evaluate_reader.py
rename to models/match/multiview-simnet/evaluate_reader.py
index 63340ccd003589d6e4411f08ed8ffa554ee170fa..d77032f3ca4e07cbbf20874f79023dc4a6fed8b4 100755
--- a/models/recall/multiview-simnet/evaluate_reader.py
+++ b/models/match/multiview-simnet/evaluate_reader.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,18 +11,17 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
-import io
-import copy
-import random
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
class EvaluateReader(Reader):
def init(self):
- self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
- self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
+ self.query_slots = envs.get_global_env("hyper_parameters.query_slots",
+ None, "train.model")
+ self.title_slots = envs.get_global_env("hyper_parameters.title_slots",
+ None, "train.model")
self.all_slots = []
for i in range(self.query_slots):
@@ -52,6 +51,7 @@ class EvaluateReader(Reader):
if visit:
self._all_slots_dict[slot][0] = False
else:
- output[index][1].append(padding)
+ output[index][1].append(padding)
yield output
+
return data_iter
diff --git a/models/recall/multiview-simnet/generate_synthetic_data.py b/models/match/multiview-simnet/generate_synthetic_data.py
similarity index 79%
rename from models/recall/multiview-simnet/generate_synthetic_data.py
rename to models/match/multiview-simnet/generate_synthetic_data.py
index 5ebb3a355f3904dc0a5cccff8e9d0b48b89f18f4..eb60e5c82f9decc2cfcd87da7bc6832ca98ee9d4 100755
--- a/models/recall/multiview-simnet/generate_synthetic_data.py
+++ b/models/match/multiview-simnet/generate_synthetic_data.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -14,12 +14,18 @@
import random
+
class Dataset:
def __init__(self):
pass
+
class SyntheticDataset(Dataset):
- def __init__(self, sparse_feature_dim, query_slot_num, title_slot_num, dataset_size=10000):
+ def __init__(self,
+ sparse_feature_dim,
+ query_slot_num,
+ title_slot_num,
+ dataset_size=10000):
# ids are randomly generated
self.ids_per_slot = 10
self.sparse_feature_dim = sparse_feature_dim
@@ -39,18 +45,25 @@ class SyntheticDataset(Dataset):
for i in range(self.query_slot_num):
qslot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
- qslot = [str(fea) + ':' + str(i) for fea in qslot]
+ qslot = [str(fea) + ':' + str(i) for fea in qslot]
query_slots += qslot
for i in range(self.title_slot_num):
pt_slot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
- pt_slot = [str(fea) + ':' + str(i + self.query_slot_num) for fea in pt_slot]
+ pt_slot = [
+ str(fea) + ':' + str(i + self.query_slot_num)
+ for fea in pt_slot
+ ]
pos_title_slots += pt_slot
if is_train:
for i in range(self.title_slot_num):
nt_slot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
- nt_slot = [str(fea) + ':' + str(i + self.query_slot_num + self.title_slot_num) for fea in nt_slot]
+ nt_slot = [
+ str(fea) + ':' +
+ str(i + self.query_slot_num + self.title_slot_num)
+ for fea in nt_slot
+ ]
neg_title_slots += nt_slot
yield query_slots + pos_title_slots + neg_title_slots
else:
@@ -67,15 +80,17 @@ class SyntheticDataset(Dataset):
def test(self):
return self._reader_creator(False)
+
if __name__ == '__main__':
sparse_feature_dim = 1000001
query_slots = 1
title_slots = 1
dataset_size = 10
- dataset = SyntheticDataset(sparse_feature_dim, query_slots, title_slots, dataset_size)
+ dataset = SyntheticDataset(sparse_feature_dim, query_slots, title_slots,
+ dataset_size)
train_reader = dataset.train()
test_reader = dataset.test()
-
+
with open("data/train/train.txt", 'w') as fout:
for data in train_reader():
fout.write(' '.join(data))
diff --git a/models/recall/multiview-simnet/model.py b/models/match/multiview-simnet/model.py
similarity index 73%
rename from models/recall/multiview-simnet/model.py
rename to models/match/multiview-simnet/model.py
index c33c10033c55a2d95ce14ac7755eceba8a3a7dd1..f80a1cd0390f3c7aafc772ef535eb36b9657b439 100755
--- a/models/recall/multiview-simnet/model.py
+++ b/models/match/multiview-simnet/model.py
@@ -12,15 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
-import math
import paddle.fluid as fluid
-import paddle.fluid.layers as layers
import paddle.fluid.layers.tensor as tensor
import paddle.fluid.layers.control_flow as cf
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
class BowEncoder(object):
""" bow-encoder """
@@ -97,79 +95,98 @@ class SimpleEncoderFactory(object):
rnn_encode = GrnnEncoder(hidden_size=enc_hid_size)
return rnn_encode
+
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.init_config()
-
+
def init_config(self):
- self._fetch_interval = 1
- query_encoder = envs.get_global_env("hyper_parameters.query_encoder", None, self._namespace)
- title_encoder = envs.get_global_env("hyper_parameters.title_encoder", None, self._namespace)
- query_encode_dim = envs.get_global_env("hyper_parameters.query_encode_dim", None, self._namespace)
- title_encode_dim = envs.get_global_env("hyper_parameters.title_encode_dim", None, self._namespace)
- query_slots = envs.get_global_env("hyper_parameters.query_slots", None, self._namespace)
- title_slots = envs.get_global_env("hyper_parameters.title_slots", None, self._namespace)
+ self._fetch_interval = 1
+ query_encoder = envs.get_global_env("hyper_parameters.query_encoder",
+ None, self._namespace)
+ title_encoder = envs.get_global_env("hyper_parameters.title_encoder",
+ None, self._namespace)
+ query_encode_dim = envs.get_global_env(
+ "hyper_parameters.query_encode_dim", None, self._namespace)
+ title_encode_dim = envs.get_global_env(
+ "hyper_parameters.title_encode_dim", None, self._namespace)
+ query_slots = envs.get_global_env("hyper_parameters.query_slots", None,
+ self._namespace)
+ title_slots = envs.get_global_env("hyper_parameters.title_slots", None,
+ self._namespace)
factory = SimpleEncoderFactory()
self.query_encoders = [
factory.create(query_encoder, query_encode_dim)
for i in range(query_slots)
]
- self.title_encoders = [
+ self.title_encoders = [
factory.create(title_encoder, title_encode_dim)
for i in range(title_slots)
]
- self.emb_size = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
- self.emb_dim = envs.get_global_env("hyper_parameters.embedding_dim", None, self._namespace)
- self.emb_shape = [self.emb_size, self.emb_dim]
- self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
- self.margin = 0.1
+ self.emb_size = envs.get_global_env(
+ "hyper_parameters.sparse_feature_dim", None, self._namespace)
+ self.emb_dim = envs.get_global_env("hyper_parameters.embedding_dim",
+ None, self._namespace)
+ self.emb_shape = [self.emb_size, self.emb_dim]
+ self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size",
+ None, self._namespace)
+ self.margin = 0.1
def input(self, is_train=True):
- self.q_slots = [
+ self.q_slots = [
fluid.data(
name="%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
for i in range(len(self.query_encoders))
]
self.pt_slots = [
fluid.data(
- name="%d" % (i + len(self.query_encoders)), shape=[None, 1], lod_level=1, dtype='int64')
- for i in range(len(self.title_encoders))
+ name="%d" % (i + len(self.query_encoders)),
+ shape=[None, 1],
+ lod_level=1,
+ dtype='int64') for i in range(len(self.title_encoders))
]
- if is_train == False:
- return self.q_slots + self.pt_slots
+ if is_train == False:
+ return self.q_slots + self.pt_slots
self.nt_slots = [
fluid.data(
- name="%d" % (i + len(self.query_encoders) + len(self.title_encoders)), shape=[None, 1], lod_level=1, dtype='int64')
- for i in range(len(self.title_encoders))
+ name="%d" %
+ (i + len(self.query_encoders) + len(self.title_encoders)),
+ shape=[None, 1],
+ lod_level=1,
+ dtype='int64') for i in range(len(self.title_encoders))
]
return self.q_slots + self.pt_slots + self.nt_slots
-
+
def train_input(self):
res = self.input()
self._data_var = res
- use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader", False, self._namespace)
+ use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader",
+ False, self._namespace)
if self._platform != "LINUX" or use_dataloader:
self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=256, use_double_buffer=False, iterable=False)
+ feed_list=self._data_var,
+ capacity=256,
+ use_double_buffer=False,
+ iterable=False)
def get_acc(self, x, y):
less = tensor.cast(cf.less_than(x, y), dtype='float32')
- label_ones = fluid.layers.fill_constant_batch_size_like(
+ label_ones = fluid.layers.fill_constant_batch_size_like(
input=x, dtype='float32', shape=[-1, 1], value=1.0)
correct = fluid.layers.reduce_sum(less)
- total = fluid.layers.reduce_sum(label_ones)
+ total = fluid.layers.reduce_sum(label_ones)
acc = fluid.layers.elementwise_div(correct, total)
- return acc
+ return acc
def net(self):
- q_embs = [
+ q_embs = [
fluid.embedding(
input=query, size=self.emb_shape, param_attr="emb")
for query in self.q_slots
@@ -184,16 +201,18 @@ class Model(ModelBase):
input=title, size=self.emb_shape, param_attr="emb")
for title in self.nt_slots
]
-
- # encode each embedding field with encoder
+
+ # encode each embedding field with encoder
q_encodes = [
self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
]
pt_encodes = [
- self.title_encoders[i].forward(emb) for i, emb in enumerate(pt_embs)
+ self.title_encoders[i].forward(emb)
+ for i, emb in enumerate(pt_embs)
]
nt_encodes = [
- self.title_encoders[i].forward(emb) for i, emb in enumerate(nt_embs)
+ self.title_encoders[i].forward(emb)
+ for i, emb in enumerate(nt_embs)
]
# concat multi view for query, pos_title, neg_title
@@ -201,7 +220,7 @@ class Model(ModelBase):
pt_concat = fluid.layers.concat(pt_encodes)
nt_concat = fluid.layers.concat(nt_encodes)
- # projection of hidden layer
+ # projection of hidden layer
q_hid = fluid.layers.fc(q_concat,
size=self.hidden_size,
param_attr='q_fc.w',
@@ -219,7 +238,7 @@ class Model(ModelBase):
cos_pos = fluid.layers.cos_sim(q_hid, pt_hid)
cos_neg = fluid.layers.cos_sim(q_hid, nt_hid)
- # pairwise hinge_loss
+ # pairwise hinge_loss
loss_part1 = fluid.layers.elementwise_sub(
tensor.fill_constant_batch_size_like(
input=cos_pos,
@@ -236,7 +255,7 @@ class Model(ModelBase):
loss_part2)
self.avg_cost = fluid.layers.mean(loss_part3)
- self.acc = self.get_acc(cos_neg, cos_pos)
+ self.acc = self.get_acc(cos_neg, cos_pos)
def avg_loss(self):
self._cost = self.avg_cost
@@ -252,20 +271,24 @@ class Model(ModelBase):
self.metrics()
def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
- optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
- return optimizer
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
+ None, self._namespace)
+ optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
+ return optimizer
def infer_input(self):
res = self.input(is_train=False)
- self._infer_data_var = res
+ self._infer_data_var = res
self._infer_data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
-
+ feed_list=self._infer_data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
+
def infer_net(self):
- self.infer_input()
- # lookup embedding for each slot
+ self.infer_input()
+ # lookup embedding for each slot
q_embs = [
fluid.embedding(
input=query, size=self.emb_shape, param_attr="emb")
@@ -276,14 +299,15 @@ class Model(ModelBase):
input=title, size=self.emb_shape, param_attr="emb")
for title in self.pt_slots
]
- # encode each embedding field with encoder
+ # encode each embedding field with encoder
q_encodes = [
self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
]
pt_encodes = [
- self.title_encoders[i].forward(emb) for i, emb in enumerate(pt_embs)
+ self.title_encoders[i].forward(emb)
+ for i, emb in enumerate(pt_embs)
]
- # concat multi view for query, pos_title, neg_title
+ # concat multi view for query, pos_title, neg_title
q_concat = fluid.layers.concat(q_encodes)
pt_concat = fluid.layers.concat(pt_encodes)
# projection of hidden layer
diff --git a/models/recall/multiview-simnet/reader.py b/models/match/multiview-simnet/reader.py
similarity index 84%
rename from models/recall/multiview-simnet/reader.py
rename to models/match/multiview-simnet/reader.py
index 34cabd415617bbbc5d4cfc942c5a48406e228d3d..4c0e42a44b0ea05272c832d65a6cfbc0d3f6c495 100755
--- a/models/recall/multiview-simnet/reader.py
+++ b/models/match/multiview-simnet/reader.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,18 +11,17 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
-import io
-import copy
-import random
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
- self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
- self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
+ self.query_slots = envs.get_global_env("hyper_parameters.query_slots",
+ None, "train.model")
+ self.title_slots = envs.get_global_env("hyper_parameters.title_slots",
+ None, "train.model")
self.all_slots = []
for i in range(self.query_slots):
@@ -55,6 +54,7 @@ class TrainReader(Reader):
if visit:
self._all_slots_dict[slot][0] = False
else:
- output[index][1].append(padding)
+ output[index][1].append(padding)
yield output
+
return data_iter
diff --git a/models/match/readme.md b/models/match/readme.md
new file mode 100755
index 0000000000000000000000000000000000000000..d9f91b257d81ffde820a04cad49b56edbd903f6a
--- /dev/null
+++ b/models/match/readme.md
@@ -0,0 +1,39 @@
+# 匹配模型库
+
+## 简介
+我们提供了常见的匹配任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的模型包括 [DSSM](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/dssm)、[MultiView-Simnet](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/multiview-simnet)。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [匹配模型列表](#匹配模型列表)
+* [使用教程](#使用教程)
+ * [训练&预测](#训练&预测)
+
+## 整体介绍
+### 匹配模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| DSSM | Deep Structured Semantic Models | [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf)(2013) |
+| MultiView-Simnet | Multi-view Simnet for Personalized recommendation | [A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf)(2015) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+[DSSM](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf):
+
+
+
+[MultiView-Simnet](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf):
+
+
+
+## 使用教程
+### 训练&预测
+```shell
+python -m paddlerec.run -m paddlerec.models.match.dssm # dssm
+python -m paddlerec.run -m paddlerec.models.match.multiview-simnet # multiview-simnet
+```
diff --git a/models/multitask/__init__.py b/models/multitask/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/multitask/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/multitask/esmm/config.yaml b/models/multitask/esmm/config.yaml
index 5cc16a5d14ee077d962db1de39d7f4f405a43649..b1412515d4c751d0980eb128601cb08066562b41 100644
--- a/models/multitask/esmm/config.yaml
+++ b/models/multitask/esmm/config.yaml
@@ -12,33 +12,55 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
- epochs: 3
- workspace: "fleetrec.models.multitask.esmm"
+workspace: "paddlerec.models.multitask.esmm"
+
+dataset:
+- name: dataset_train
+ batch_size: 1
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/esmm_reader.py"
+- name: dataset_infer
+ batch_size: 1
+ type: QueueDataset
+ data_path: "{workspace}/data/test"
+ data_converter: "{workspace}/esmm_reader.py"
+
+hyper_parameters:
+ vocab_size: 10000
+ embed_size: 128
+ optimizer:
+ class: adam
+ learning_rate: 0.001
+ strategy: async
- reader:
- batch_size: 2
- class: "{workspace}/esmm_reader.py"
- train_data_path: "{workspace}/data/train"
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- vocab_size: 10000
- embed_size: 128
- learning_rate: 0.001
- optimizer: adam
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 10
+- name: infer_runner
+ class: single_infer
+ init_model_path: "increment/0"
+ device: cpu
+ epochs: 3
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
+ #- name: infer
+ # model: "{workspace}/model.py"
+ # dataset_name: dataset_infer
+ # thread_num: 1
diff --git a/models/multitask/esmm/data/train/small.csv b/models/multitask/esmm/data/train/small.txt
similarity index 100%
rename from models/multitask/esmm/data/train/small.csv
rename to models/multitask/esmm/data/train/small.txt
diff --git a/models/multitask/esmm/esmm_reader.py b/models/multitask/esmm/esmm_reader.py
index a18702d39ae922ffd881c11966df0dfe8b713f51..5a3f3f916e1395a05b2f59a98132e5220dd224b9 100644
--- a/models/multitask/esmm/esmm_reader.py
+++ b/models/multitask/esmm/esmm_reader.py
@@ -11,21 +11,24 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
from collections import defaultdict
-import numpy as np
+
+from paddlerec.core.reader import Reader
class TrainReader(Reader):
def init(self):
- all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128', '129',
- '205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
+ all_field_id = [
+ '101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124',
+ '125', '126', '127', '128', '129', '205', '206', '207', '210',
+ '216', '508', '509', '702', '853', '301'
+ ]
self.all_field_id_dict = defaultdict(int)
- for i,field_id in enumerate(all_field_id):
- self.all_field_id_dict[field_id] = [False,i]
+ for i, field_id in enumerate(all_field_id):
+ self.all_field_id_dict[field_id] = [False, i]
def generate_sample(self, line):
"""
@@ -37,30 +40,28 @@ class TrainReader(Reader):
This function needs to be implemented by the user, based on data format
"""
features = line.strip().split(',')
- #ctr = list(map(int, features[1]))
- #cvr = list(map(int, features[2]))
ctr = int(features[1])
cvr = int(features[2])
-
+
padding = 0
- output = [(field_id,[]) for field_id in self.all_field_id_dict]
+ output = [(field_id, []) for field_id in self.all_field_id_dict]
for elem in features[4:]:
- field_id,feat_id = elem.strip().split(':')
+ field_id, feat_id = elem.strip().split(':')
if field_id not in self.all_field_id_dict:
continue
self.all_field_id_dict[field_id][0] = True
index = self.all_field_id_dict[field_id][1]
- #feat_id = list(map(int, feat_id))
- output[index][1].append(int(feat_id))
-
+ output[index][1].append(int(feat_id))
+
for field_id in self.all_field_id_dict:
- visited,index = self.all_field_id_dict[field_id]
+ visited, index = self.all_field_id_dict[field_id]
if visited:
self.all_field_id_dict[field_id][0] = False
else:
- output[index][1].append(padding)
+ output[index][1].append(padding)
output.append(('ctr', [ctr]))
output.append(('cvr', [cvr]))
yield output
+
return reader
diff --git a/models/multitask/esmm/model.py b/models/multitask/esmm/model.py
index 0063cdfefd7abebb315750d13fa723207fb5eb08..b4b257ed8a74829d3619c3b07bbb0cfc8e69ddde 100644
--- a/models/multitask/esmm/model.py
+++ b/models/multitask/esmm/model.py
@@ -12,103 +12,119 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import math
+import numpy as np
import paddle.fluid as fluid
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
-import numpy as np
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
- def fc(self,tag, data, out_dim, active='prelu'):
-
- init_stddev = 1.0
- scales = 1.0 / np.sqrt(data.shape[1])
-
- p_attr = fluid.param_attr.ParamAttr(name='%s_weight' % tag,
- initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=init_stddev * scales))
-
- b_attr = fluid.ParamAttr(name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
-
- out = fluid.layers.fc(input=data,
- size=out_dim,
- act=active,
- param_attr=p_attr,
- bias_attr =b_attr,
- name=tag)
- return out
-
- def input_data(self):
+ def _init_hyper_parameters(self):
+ self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+ self.embed_size = envs.get_global_env("hyper_parameters.embed_size")
+
+ def input_data(self, is_infer=False, **kwargs):
sparse_input_ids = [
- fluid.data(name="field_" + str(i), shape=[-1, 1], dtype="int64", lod_level=1) for i in range(0,23)
+ fluid.data(
+ name="field_" + str(i),
+ shape=[-1, 1],
+ dtype="int64",
+ lod_level=1) for i in range(0, 23)
]
label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64")
label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64")
inputs = sparse_input_ids + [label_ctr] + [label_cvr]
- self._data_var.extend(inputs)
-
- return inputs
-
- def net(self, inputs):
-
- vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
- embed_size = envs.get_global_env("hyper_parameters.embed_size", None, self._namespace)
+ if is_infer:
+ return inputs
+ else:
+ return inputs
+
+ def net(self, inputs, is_infer=False):
+
emb = []
+ # input feature data
for data in inputs[0:-2]:
- feat_emb = fluid.embedding(input=data,
- size=[vocab_size, embed_size],
- param_attr=fluid.ParamAttr(name='dis_emb',
- learning_rate=5,
- initializer=fluid.initializer.Xavier(fan_in=embed_size,fan_out=embed_size)
- ),
- is_sparse=True)
- field_emb = fluid.layers.sequence_pool(input=feat_emb,pool_type='sum')
+ feat_emb = fluid.embedding(
+ input=data,
+ size=[self.vocab_size, self.embed_size],
+ param_attr=fluid.ParamAttr(
+ name='dis_emb',
+ learning_rate=5,
+ initializer=fluid.initializer.Xavier(
+ fan_in=self.embed_size, fan_out=self.embed_size)),
+ is_sparse=True)
+ field_emb = fluid.layers.sequence_pool(
+ input=feat_emb, pool_type='sum')
emb.append(field_emb)
concat_emb = fluid.layers.concat(emb, axis=1)
-
+
# ctr
active = 'relu'
- ctr_fc1 = self.fc('ctr_fc1', concat_emb, 200, active)
- ctr_fc2 = self.fc('ctr_fc2', ctr_fc1, 80, active)
- ctr_out = self.fc('ctr_out', ctr_fc2, 2, 'softmax')
-
+ ctr_fc1 = self._fc('ctr_fc1', concat_emb, 200, active)
+ ctr_fc2 = self._fc('ctr_fc2', ctr_fc1, 80, active)
+ ctr_out = self._fc('ctr_out', ctr_fc2, 2, 'softmax')
+
# cvr
- cvr_fc1 = self.fc('cvr_fc1', concat_emb, 200, active)
- cvr_fc2 = self.fc('cvr_fc2', cvr_fc1, 80, active)
- cvr_out = self.fc('cvr_out', cvr_fc2, 2,'softmax')
-
+ cvr_fc1 = self._fc('cvr_fc1', concat_emb, 200, active)
+ cvr_fc2 = self._fc('cvr_fc2', cvr_fc1, 80, active)
+ cvr_out = self._fc('cvr_out', cvr_fc2, 2, 'softmax')
+
ctr_clk = inputs[-2]
ctcvr_buy = inputs[-1]
-
- ctr_prop_one = fluid.layers.slice(ctr_out, axes=[1], starts=[1], ends=[2])
- cvr_prop_one = fluid.layers.slice(cvr_out, axes=[1], starts=[1], ends=[2])
-
- ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one, cvr_prop_one)
- ctcvr_prop = fluid.layers.concat(input=[1-ctcvr_prop_one,ctcvr_prop_one], axis = 1)
-
+
+ ctr_prop_one = fluid.layers.slice(
+ ctr_out, axes=[1], starts=[1], ends=[2])
+ cvr_prop_one = fluid.layers.slice(
+ cvr_out, axes=[1], starts=[1], ends=[2])
+
+ ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one,
+ cvr_prop_one)
+ ctcvr_prop = fluid.layers.concat(
+ input=[1 - ctcvr_prop_one, ctcvr_prop_one], axis=1)
+
+ auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(
+ input=ctr_out, label=ctr_clk)
+ auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(
+ input=ctcvr_prop, label=ctcvr_buy)
+
+ if is_infer:
+ self._infer_results["AUC_ctr"] = auc_ctr
+ self._infer_results["AUC_ctcvr"] = auc_ctcvr
+ return
+
loss_ctr = fluid.layers.cross_entropy(input=ctr_out, label=ctr_clk)
- loss_ctcvr = fluid.layers.cross_entropy(input=ctcvr_prop, label=ctcvr_buy)
+ loss_ctcvr = fluid.layers.cross_entropy(
+ input=ctcvr_prop, label=ctcvr_buy)
cost = loss_ctr + loss_ctcvr
avg_cost = fluid.layers.mean(cost)
- auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
- auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
-
self._cost = avg_cost
self._metrics["AUC_ctr"] = auc_ctr
self._metrics["BATCH_AUC_ctr"] = batch_auc_ctr
self._metrics["AUC_ctcvr"] = auc_ctcvr
self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr
+ def _fc(self, tag, data, out_dim, active='prelu'):
+
+ init_stddev = 1.0
+ scales = 1.0 / np.sqrt(data.shape[1])
- def train_net(self):
- input_data = self.input_data()
- self.net(input_data)
+ p_attr = fluid.param_attr.ParamAttr(
+ name='%s_weight' % tag,
+ initializer=fluid.initializer.NormalInitializer(
+ loc=0.0, scale=init_stddev * scales))
+ b_attr = fluid.ParamAttr(
+ name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
- def infer_net(self):
- pass
+ out = fluid.layers.fc(input=data,
+ size=out_dim,
+ act=active,
+ param_attr=p_attr,
+ bias_attr=b_attr,
+ name=tag)
+ return out
diff --git a/models/multitask/mmoe/census_reader.py b/models/multitask/mmoe/census_reader.py
index 323c15a40a29070285b89351612b1efdf162cd2c..d71133bd91692c8b17e7449aa305e5241db7777a 100644
--- a/models/multitask/mmoe/census_reader.py
+++ b/models/multitask/mmoe/census_reader.py
@@ -11,11 +11,10 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
-import numpy as np
+from paddlerec.core.reader import Reader
class TrainReader(Reader):
@@ -25,6 +24,7 @@ class TrainReader(Reader):
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
+
"""
def reader():
@@ -44,8 +44,8 @@ class TrainReader(Reader):
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
- #label_income = np.array(label_income)
- #label_marital = np.array(label_marital)
+ # label_income = np.array(label_income)
+ # label_marital = np.array(label_marital)
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
diff --git a/models/multitask/mmoe/config.yaml b/models/multitask/mmoe/config.yaml
index ca6552d8045761792b362fa3345e886cde396abb..9f36f84991ea30ffeb1745bc2d769b19a9887ab2 100644
--- a/models/multitask/mmoe/config.yaml
+++ b/models/multitask/mmoe/config.yaml
@@ -12,36 +12,57 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
+workspace: "paddlerec.models.multitask.mmoe"
- epochs: 3
- workspace: "fleetrec.models.multitask.mmoe"
+dataset:
+- name: dataset_train
+ batch_size: 1
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/census_reader.py"
+- name: dataset_infer
+ batch_size: 1
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/census_reader.py"
+
+hyper_parameters:
+ feature_size: 499
+ expert_num: 8
+ gate_num: 2
+ expert_size: 16
+ tower_size: 8
+ optimizer:
+ class: adam
+ learning_rate: 0.001
+ strategy: async
- reader:
- batch_size: 2
- class: "{workspace}/census_reader.py"
- train_data_path: "{workspace}/data/train"
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- feature_size: 499
- expert_num: 8
- gate_num: 2
- expert_size: 16
- tower_size: 8
- learning_rate: 0.001
- optimizer: adam
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 10
+- name: infer_runner
+ class: single_infer
+ init_model_path: "increment/0"
+ device: cpu
+ epochs: 3
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
+ #- name: infer
+ # model: "{workspace}/model.py"
+ # dataset_name: dataset_infer
+ # thread_num: 1
diff --git a/models/multitask/mmoe/data/run.sh b/models/multitask/mmoe/data/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b60d42b37057593b1c16aa5fd91b8217a5a71bbf
--- /dev/null
+++ b/models/multitask/mmoe/data/run.sh
@@ -0,0 +1,16 @@
+mkdir train_data
+mkdir test_data
+mkdir data
+train_path="data/census-income.data"
+test_path="data/census-income.test"
+train_data_path="train_data/"
+test_data_path="test_data/"
+pip install -r requirements.txt
+
+wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census.tar.gz
+tar -zxvf data/census.tar.gz -C data/
+
+python data_preparation.py --train_path ${train_path} \
+ --test_path ${test_path} \
+ --train_data_path ${train_data_path}\
+ --test_data_path ${test_data_path}
diff --git a/models/multitask/mmoe/data/train/train_data.txt b/models/multitask/mmoe/data/train/train_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ba385736663d5efd4321692d1fbafda8bbf585c1
--- /dev/null
+++ b/models/multitask/mmoe/data/train/train_data.txt
@@ -0,0 +1,30 @@
+0,0,73,0,0,0,0,1700.09,0,0,2,0,95,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0
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diff --git a/models/multitask/mmoe/model.py b/models/multitask/mmoe/model.py
index 0b757d0270e7f5159bf4154e66b8e321fb9d9885..309da6a31e8754110fb8c9d50971bc4dc9aff364 100644
--- a/models/multitask/mmoe/model.py
+++ b/models/multitask/mmoe/model.py
@@ -12,93 +12,118 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import math
import paddle.fluid as fluid
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
- def MMOE(self):
+ def _init_hyper_parameters(self):
+ self.feature_size = envs.get_global_env(
+ "hyper_parameters.feature_size")
+ self.expert_num = envs.get_global_env("hyper_parameters.expert_num")
+ self.gate_num = envs.get_global_env("hyper_parameters.gate_num")
+ self.expert_size = envs.get_global_env("hyper_parameters.expert_size")
+ self.tower_size = envs.get_global_env("hyper_parameters.tower_size")
- feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
- expert_num = envs.get_global_env("hyper_parameters.expert_num", None, self._namespace)
- gate_num = envs.get_global_env("hyper_parameters.gate_num", None, self._namespace)
- expert_size = envs.get_global_env("hyper_parameters.expert_size", None, self._namespace)
- tower_size = envs.get_global_env("hyper_parameters.tower_size", None, self._namespace)
+ def input_data(self, is_infer=False, **kwargs):
+ inputs = fluid.data(
+ name="input", shape=[-1, self.feature_size], dtype="float32")
+ label_income = fluid.data(
+ name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
+ label_marital = fluid.data(
+ name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
+ if is_infer:
+ return [inputs, label_income, label_marital]
+ else:
+ return [inputs, label_income, label_marital]
+
+ def net(self, inputs, is_infer=False):
+ input_data = inputs[0]
+ label_income = inputs[1]
+ label_marital = inputs[2]
- input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
- label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
- label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
-
- self._data_var.extend([input_data, label_income, label_marital])
# f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper
expert_outputs = []
- for i in range(0, expert_num):
- expert_output = fluid.layers.fc(input=input_data,
- size=expert_size,
- act='relu',
- bias_attr=fluid.ParamAttr(learning_rate=1.0),
- name='expert_' + str(i))
+ for i in range(0, self.expert_num):
+ expert_output = fluid.layers.fc(
+ input=input_data,
+ size=self.expert_size,
+ act='relu',
+ bias_attr=fluid.ParamAttr(learning_rate=1.0),
+ name='expert_' + str(i))
expert_outputs.append(expert_output)
expert_concat = fluid.layers.concat(expert_outputs, axis=1)
- expert_concat = fluid.layers.reshape(expert_concat,[-1, expert_num, expert_size])
-
-
+ expert_concat = fluid.layers.reshape(
+ expert_concat, [-1, self.expert_num, self.expert_size])
+
# g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
output_layers = []
- for i in range(0, gate_num):
- cur_gate = fluid.layers.fc(input=input_data,
- size=expert_num,
- act='softmax',
- bias_attr=fluid.ParamAttr(learning_rate=1.0),
- name='gate_' + str(i))
+ for i in range(0, self.gate_num):
+ cur_gate = fluid.layers.fc(
+ input=input_data,
+ size=self.expert_num,
+ act='softmax',
+ bias_attr=fluid.ParamAttr(learning_rate=1.0),
+ name='gate_' + str(i))
# f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))
- cur_gate_expert = fluid.layers.elementwise_mul(expert_concat, cur_gate, axis=0)
+ cur_gate_expert = fluid.layers.elementwise_mul(
+ expert_concat, cur_gate, axis=0)
cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1)
# Build tower layer
- cur_tower = fluid.layers.fc(input=cur_gate_expert,
- size=tower_size,
- act='relu',
- name='task_layer_' + str(i))
- out = fluid.layers.fc(input=cur_tower,
- size=2,
- act='softmax',
- name='out_' + str(i))
-
+ cur_tower = fluid.layers.fc(input=cur_gate_expert,
+ size=self.tower_size,
+ act='relu',
+ name='task_layer_' + str(i))
+ out = fluid.layers.fc(input=cur_tower,
+ size=2,
+ act='softmax',
+ name='out_' + str(i))
+
output_layers.append(out)
- pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
- pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
-
- cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
- cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
-
- label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
- label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
-
- auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
- auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
-
+ pred_income = fluid.layers.clip(
+ output_layers[0], min=1e-15, max=1.0 - 1e-15)
+ pred_marital = fluid.layers.clip(
+ output_layers[1], min=1e-15, max=1.0 - 1e-15)
+
+ label_income_1 = fluid.layers.slice(
+ label_income, axes=[1], starts=[1], ends=[2])
+ label_marital_1 = fluid.layers.slice(
+ label_marital, axes=[1], starts=[1], ends=[2])
+
+ auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(
+ input=pred_income,
+ label=fluid.layers.cast(
+ x=label_income_1, dtype='int64'))
+ auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(
+ input=pred_marital,
+ label=fluid.layers.cast(
+ x=label_marital_1, dtype='int64'))
+ if is_infer:
+ self._infer_results["AUC_income"] = auc_income
+ self._infer_results["AUC_marital"] = auc_marital
+ return
+
+ cost_income = fluid.layers.cross_entropy(
+ input=pred_income, label=label_income, soft_label=True)
+ cost_marital = fluid.layers.cross_entropy(
+ input=pred_marital, label=label_marital, soft_label=True)
+
avg_cost_income = fluid.layers.mean(x=cost_income)
avg_cost_marital = fluid.layers.mean(x=cost_marital)
-
- cost = avg_cost_income + avg_cost_marital
-
+
+ cost = avg_cost_income + avg_cost_marital
+
self._cost = cost
self._metrics["AUC_income"] = auc_income
self._metrics["BATCH_AUC_income"] = batch_auc_1
self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2
-
- def train_net(self):
- self.MMOE()
-
-
def infer_net(self):
pass
diff --git a/models/multitask/readme.md b/models/multitask/readme.md
new file mode 100755
index 0000000000000000000000000000000000000000..07a6c01d77b72ed47153c3fad92521429a4769a2
--- /dev/null
+++ b/models/multitask/readme.md
@@ -0,0 +1,95 @@
+# 多任务学习模型库
+
+## 简介
+我们提供了常见的多任务学习中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的多任务模型包括 [MMoE](mmoe)、[Share-Bottom](share-bottom)、[ESMM](esmm)。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [多任务模型列表](#多任务模型列表)
+* [使用教程](#使用教程)
+ * [数据处理](#数据处理)
+ * [训练](#训练)
+ * [预测](#预测)
+* [效果对比](#效果对比)
+ * [模型效果列表](#模型效果列表)
+
+## 整体介绍
+### 多任务模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| Share-Bottom | share-bottom | [Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf)(1998) |
+| ESMM | Entire Space Multi-Task Model | [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)(2018) |
+| MMoE | Multi-gate Mixture-of-Experts | [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)(2018) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+
+[ESMM](https://arxiv.org/abs/1804.07931):
+
+
+
+[Share-Bottom](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf):
+
+
+
+[MMoE](https://dl.acm.org/doi/abs/10.1145/3219819.3220007):
+
+
+
+## 使用教程(快速开始)
+```shell
+python -m paddlerec.run -m paddlerec.models.multitask.mmoe # mmoe
+python -m paddlerec.run -m paddlerec.models.multitask.share-bottom # share-bottom
+python -m paddlerec.run -m paddlerec.models.multitask.esmm # esmm
+```
+
+## 使用教程（复现论文）
+### 注意
+为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。
+
+| 模型 | batch_size | thread_num | epoch_num |
+| :------------------: | :--------------------: | :--------------------: | :--------------------: |
+| Share-Bottom | 32 | 1 | 400 |
+| MMoE | 32 | 1 | 400 |
+| ESMM | 64 | 2 | 100 |
+
+### 数据处理
+参考每个模型目录数据下载&预处理脚本
+
+```
+sh run.sh
+```
+
+### 训练
+```
+cd modles/multitask/mmoe # 进入选定好的排序模型的目录 以MMoE为例
+python -m paddlerec.run -m ./config.yaml # 自定义修改超参后，指定配置文件，使用自定义配置
+```
+
+### 预测
+```
+# 修改对应模型的config.yaml, workspace配置为当前目录的绝对路径
+# 修改对应模型的config.yaml，mode配置infer_runner
+# 示例: mode: train_runner -> mode: infer_runner
+# infer_runner中 class配置为 class: single_infer
+# 修改phase阶段为infer的配置，参照config注释
+
+# 修改完config.yaml后 执行:
+python -m paddlerec.run -m ./config.yaml # 以MMoE为例
+```
+
+
+## 效果对比
+### 模型效果列表
+
+| 数据集 | 模型 | loss | auc |
+| :------------------: | :--------------------: | :---------: |:---------: |
+| Census-income Data | Share-Bottom | -- | 0.93120/0.99256 |
+| Census-income Data | MMoE | -- | 0.94465/0.99324 |
+| Ali-CCP | ESMM | -- | 0.97181/0.49967 |
diff --git a/models/multitask/share-bottom/census_reader.py b/models/multitask/share-bottom/census_reader.py
index 323c15a40a29070285b89351612b1efdf162cd2c..211e566882e5d8a7f50f22b0a1628307777099c8 100644
--- a/models/multitask/share-bottom/census_reader.py
+++ b/models/multitask/share-bottom/census_reader.py
@@ -11,11 +11,10 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
-import numpy as np
+from paddlerec.core.reader import Reader
class TrainReader(Reader):
@@ -44,8 +43,8 @@ class TrainReader(Reader):
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
- #label_income = np.array(label_income)
- #label_marital = np.array(label_marital)
+ # label_income = np.array(label_income)
+ # label_marital = np.array(label_marital)
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
diff --git a/models/multitask/share-bottom/config.yaml b/models/multitask/share-bottom/config.yaml
index 432ebdf331f098580dfad2019c05af3e4e47553d..3a44b8e7b23a545e5daf67a789a0c3537f614c4e 100644
--- a/models/multitask/share-bottom/config.yaml
+++ b/models/multitask/share-bottom/config.yaml
@@ -12,35 +12,56 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
+workspace: "paddlerec.models.multitask.share-bottom"
- epochs: 3
- workspace: "fleetrec.models.multitask.share-bottom"
+dataset:
+- name: dataset_train
+ batch_size: 1
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/census_reader.py"
+- name: dataset_infer
+ batch_size: 1
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/census_reader.py"
+
+hyper_parameters:
+ feature_size: 499
+ bottom_size: 117
+ tower_nums: 2
+ tower_size: 8
+ optimizer:
+ class: adam
+ learning_rate: 0.001
+ strategy: async
- reader:
- batch_size: 2
- class: "{workspace}/census_reader.py"
- train_data_path: "{workspace}/data/train"
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- feature_size: 499
- bottom_size: 117
- tower_nums: 2
- tower_size: 8
- learning_rate: 0.001
- optimizer: adam
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 5
+- name: infer_runner
+ class: single_infer
+ init_model_path: "increment/0"
+ device: cpu
+ epochs: 3
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
+ #- name: infer
+ # model: "{workspace}/model.py"
+ # dataset_name: dataset_infer
+ # thread_num: 1
diff --git a/models/multitask/share-bottom/data/train/train_data b/models/multitask/share-bottom/data/train/train_data
deleted file mode 100644
index 992314e443942c1b3e08a7db88bf2c1d7354c451..0000000000000000000000000000000000000000
--- a/models/multitask/share-bottom/data/train/train_data
+++ /dev/null
@@ -1,10 +0,0 @@
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diff --git a/models/multitask/mmoe/data/train/train_data b/models/multitask/share-bottom/data/train/train_data.txt
similarity index 100%
rename from models/multitask/mmoe/data/train/train_data
rename to models/multitask/share-bottom/data/train/train_data.txt
diff --git a/models/multitask/share-bottom/model.py b/models/multitask/share-bottom/model.py
index a364f1b82b9f4a6849e67cd1ff34fa8d179b475c..0275d3a10b3dd4f35388da10b303d86421228695 100644
--- a/models/multitask/share-bottom/model.py
+++ b/models/multitask/share-bottom/model.py
@@ -12,78 +12,94 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import math
import paddle.fluid as fluid
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
- def train(self):
-
- feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
- bottom_size = envs.get_global_env("hyper_parameters.bottom_size", None, self._namespace)
- tower_size = envs.get_global_env("hyper_parameters.tower_size", None, self._namespace)
- tower_nums = envs.get_global_env("hyper_parameters.tower_nums", None, self._namespace)
-
- input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
- label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
- label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
-
- self._data_var.extend([input_data, label_income, label_marital])
-
- bottom_output = fluid.layers.fc(input=input_data,
- size=bottom_size,
- act='relu',
- bias_attr=fluid.ParamAttr(learning_rate=1.0),
- name='bottom_output')
-
-
+ def _init_hyper_parameters(self):
+ self.feature_size = envs.get_global_env(
+ "hyper_parameters.feature_size")
+ self.bottom_size = envs.get_global_env("hyper_parameters.bottom_size")
+ self.tower_size = envs.get_global_env("hyper_parameters.tower_size")
+ self.tower_nums = envs.get_global_env("hyper_parameters.tower_nums")
+
+ def input_data(self, is_infer=False, **kwargs):
+ inputs = fluid.data(
+ name="input", shape=[-1, self.feature_size], dtype="float32")
+ label_income = fluid.data(
+ name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
+ label_marital = fluid.data(
+ name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
+ if is_infer:
+ return [inputs, label_income, label_marital]
+ else:
+ return [inputs, label_income, label_marital]
+
+ def net(self, inputs, is_infer=False):
+ input_data = inputs[0]
+ label_income = inputs[1]
+ label_marital = inputs[2]
+
+ bottom_output = fluid.layers.fc(
+ input=input_data,
+ size=self.bottom_size,
+ act='relu',
+ bias_attr=fluid.ParamAttr(learning_rate=1.0),
+ name='bottom_output')
+
# Build tower layer from bottom layer
output_layers = []
- for index in range(tower_nums):
+ for index in range(self.tower_nums):
tower_layer = fluid.layers.fc(input=bottom_output,
- size=tower_size,
- act='relu',
- name='task_layer_' + str(index))
+ size=self.tower_size,
+ act='relu',
+ name='task_layer_' + str(index))
output_layer = fluid.layers.fc(input=tower_layer,
- size=2,
- act='softmax',
- name='output_layer_' + str(index))
+ size=2,
+ act='softmax',
+ name='output_layer_' + str(index))
output_layers.append(output_layer)
+ pred_income = fluid.layers.clip(
+ output_layers[0], min=1e-15, max=1.0 - 1e-15)
+ pred_marital = fluid.layers.clip(
+ output_layers[1], min=1e-15, max=1.0 - 1e-15)
+
+ label_income_1 = fluid.layers.slice(
+ label_income, axes=[1], starts=[1], ends=[2])
+ label_marital_1 = fluid.layers.slice(
+ label_marital, axes=[1], starts=[1], ends=[2])
+
+ auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(
+ input=pred_income,
+ label=fluid.layers.cast(
+ x=label_income_1, dtype='int64'))
+ auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(
+ input=pred_marital,
+ label=fluid.layers.cast(
+ x=label_marital_1, dtype='int64'))
+
+ if is_infer:
+ self._infer_results["AUC_income"] = auc_income
+ self._infer_results["AUC_marital"] = auc_marital
+ return
+
+ cost_income = fluid.layers.cross_entropy(
+ input=pred_income, label=label_income, soft_label=True)
+ cost_marital = fluid.layers.cross_entropy(
+ input=pred_marital, label=label_marital, soft_label=True)
+ cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1)
- pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
- pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
-
- cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
- cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
-
-
- label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
- label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
-
- auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
- auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
+ avg_cost = fluid.layers.mean(x=cost)
- cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1)
-
- avg_cost = fluid.layers.mean(x=cost)
-
self._cost = avg_cost
self._metrics["AUC_income"] = auc_income
self._metrics["BATCH_AUC_income"] = batch_auc_1
self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2
-
-
- def train_net(self):
- self.train()
-
-
- def infer_net(self):
- pass
diff --git a/models/rank/__init__.py b/models/rank/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..abf198b97e6e818e1fbe59006f98492640bcee54 100755
--- a/models/rank/__init__.py
+++ b/models/rank/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rank/criteo_reader.py b/models/rank/criteo_reader.py
deleted file mode 100755
index c4930c8ddc628c13f5c958743673bd42c507161f..0000000000000000000000000000000000000000
--- a/models/rank/criteo_reader.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import print_function
-
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
-
-
-class TrainReader(Reader):
- def init(self):
- self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- self.cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
- self.cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
- self.hash_dim_ = envs.get_global_env("hyper_parameters.sparse_feature_number", None, "train.model")
- self.continuous_range_ = range(1, 14)
- self.categorical_range_ = range(14, 40)
-
- def generate_sample(self, line):
- """
- Read the data line by line and process it as a dictionary
- """
-
- def reader():
- """
- This function needs to be implemented by the user, based on data format
- """
- features = line.rstrip('\n').split('\t')
-
- dense_feature = []
- sparse_feature = []
- for idx in self.continuous_range_:
- if features[idx] == "":
- dense_feature.append(0.0)
- else:
- dense_feature.append(
- (float(features[idx]) - self.cont_min_[idx - 1]) /
- self.cont_diff_[idx - 1])
-
- for idx in self.categorical_range_:
- sparse_feature.append(
- [hash(str(idx) + features[idx]) % self.hash_dim_])
- label = [int(features[0])]
- feature_name = ["D"]
- for idx in self.categorical_range_:
- feature_name.append("S" + str(idx - 13))
- feature_name.append("label")
- yield zip(feature_name, [dense_feature] + sparse_feature + [label])
-
- return reader
diff --git a/models/rank/dcn/config.yaml b/models/rank/dcn/config.yaml
index d84adb748ae460512a5c31fa898adfda1a88da56..390b460a84d9e212867d372c6fd542c0f1f2b478 100755
--- a/models/rank/dcn/config.yaml
+++ b/models/rank/dcn/config.yaml
@@ -12,42 +12,66 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
-
- epochs: 10
- workspace: "fleetrec.models.rank.dcn"
-
- reader:
- batch_size: 2
- class: "{workspace}/criteo_reader.py"
- train_data_path: "{workspace}/data/train"
- feat_dict_name: "{workspace}/data/vocab"
-
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- cross_num: 2
- dnn_hidden_units: [128, 128]
- l2_reg_cross: 0.00005
- dnn_use_bn: False
- clip_by_norm: 100.0
- cat_feat_num: "{workspace}/data/cat_feature_num.txt"
- is_sparse: False
- is_test: False
- num_field: 39
- learning_rate: 0.0001
- act: "relu"
- optimizer: adam
-
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+
+# global settings
+debug: false
+workspace: "paddlerec.models.rank.dcn"
+
+dataset:
+ - name: train_sample
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
+ dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
+ - name: infer_sample
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/infer"
+ sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
+ dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
+
+hyper_parameters:
+ optimizer:
+ class: Adam
+ learning_rate: 0.0001
+ # 用户自定义配置
+ cross_num: 2
+ dnn_hidden_units: [128, 128]
+ l2_reg_cross: 0.00005
+ dnn_use_bn: False
+ clip_by_norm: 100.0
+ cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt"
+ is_sparse: False
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+ - name: train_runner
+ trainer_class: single_train
+ epochs: 1
+ device: cpu
+ init_model_path: ""
+ save_checkpoint_interval: 1
+ save_inference_interval: 1
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 1
+ - name: infer_runner
+ trainer_class: single_infer
+ epochs: 1
+ device: cpu
+ init_model_path: "increment/0"
+ print_interval: 1
+
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: train_sample
+ thread_num: 1
+#- name: infer_phase
+# model: "{workspace}/model.py"
+# dataset_name: infer_sample
+# thread_num: 1
diff --git a/models/rank/dcn/data/download.py b/models/rank/dcn/data/download.py
index b862988aef1802d8bfeb2554b7dce812e941f9e9..4203a3868a577757930ae848736c34bb4da376c7 100755
--- a/models/rank/dcn/data/download.py
+++ b/models/rank/dcn/data/download.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import os
import sys
import io
@@ -6,7 +20,7 @@ LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
sys.path.append(TOOLS_PATH)
-from fleetrec.tools.tools import download_file_and_uncompress
+from paddlerec.tools.tools import download_file_and_uncompress
if __name__ == '__main__':
trainfile = 'train.txt'
diff --git a/models/rank/dcn/criteo_reader.py b/models/rank/dcn/data/get_slot_data.py
similarity index 76%
rename from models/rank/dcn/criteo_reader.py
rename to models/rank/dcn/data/get_slot_data.py
index 4b81e1fb66b6fac2ead7583ad93451e4822077d2..96d4448214d6a87092495326646a279657079f45 100755
--- a/models/rank/dcn/criteo_reader.py
+++ b/models/rank/dcn/data/get_slot_data.py
@@ -11,20 +11,32 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-from __future__ import print_function
import math
import sys
-
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+import yaml
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+import math
+import os
try:
import cPickle as pickle
except ImportError:
import pickle
from collections import Counter
import os
+import paddle.fluid.incubate.data_generator as dg
+
+
+class TrainReader(dg.MultiSlotDataGenerator):
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
-class TrainReader(Reader):
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [
@@ -45,15 +57,15 @@ class TrainReader(Reader):
self.label_feat_names = target + dense_feat_names + sparse_feat_names
self.cat_feat_idx_dict_list = [{} for _ in range(26)]
-
+
# TODO: set vocabulary dictionary
- vocab_dir = envs.get_global_env("feat_dict_name", None, "train.reader")
+ vocab_dir = "./vocab/"
for i in range(26):
lookup_idx = 1 # remain 0 for default value
for line in open(
os.path.join(vocab_dir, 'C' + str(i + 1) + '.txt')):
self.cat_feat_idx_dict_list[i][line.strip()] = lookup_idx
- lookup_idx += 1
+ lookup_idx += 1
def _process_line(self, line):
features = line.rstrip('\n').split('\t')
@@ -78,13 +90,26 @@ class TrainReader(Reader):
idx - 14][features[idx]])
label_feat_list[0].append(int(features[0]))
return label_feat_list
-
+
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
+
def data_iter():
label_feat_list = self._process_line(line)
- yield list(zip(self.label_feat_names, label_feat_list))
+ s = ""
+ for i in list(zip(self.label_feat_names, label_feat_list)):
+ k = i[0]
+ v = i[1]
+ for j in v:
+ s += " " + k + ":" + str(j)
+ print s.strip()
+ yield None
+
+ return data_iter
+
- return data_iter
\ No newline at end of file
+reader = TrainReader("../config.yaml")
+reader.init()
+reader.run_from_stdin()
diff --git a/models/rank/dcn/data/preprocess.py b/models/rank/dcn/data/preprocess.py
index b356607729eedd73854a77449ffda3cc3bb8050f..9a89df10ef42dcfa09faad66f409b21439f340a8 100755
--- a/models/rank/dcn/data/preprocess.py
+++ b/models/rank/dcn/data/preprocess.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
from __future__ import print_function, absolute_import, division
import os
diff --git a/models/rank/dcn/data/run.sh b/models/rank/dcn/data/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..32d653dc4113514740a9ff6de9ec1902aea4eeb1
--- /dev/null
+++ b/models/rank/dcn/data/run.sh
@@ -0,0 +1,14 @@
+python download.py
+python preprocess.py
+
+mkdir slot_train
+for i in `ls ./train`
+do
+ cat train/$i | python get_slot_data.py > slot_train/$i
+done
+
+mkdir slot_test_valid
+for i in `ls ./test_valid`
+do
+ cat test_valid/$i | python get_slot_data.py > slot_test_valid/$i
+done
diff --git a/models/rank/dcn/data/sample_data/cat_feature_num.txt b/models/rank/dcn/data/sample_data/cat_feature_num.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0c75fc39b5890c323d1f94d6cc5c60fa151ef2e0
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/cat_feature_num.txt
@@ -0,0 +1,26 @@
+C1 139
+C2 422
+C3 1548
+C4 1965
+C5 54
+C6 10
+C7 3213
+C8 81
+C9 3
+C10 2402
+C11 2246
+C12 1583
+C13 1911
+C14 24
+C15 2011
+C16 1731
+C17 9
+C18 1197
+C19 584
+C20 3
+C21 1652
+C22 8
+C23 14
+C24 1770
+C25 40
+C26 1349
diff --git a/models/rank/dcn/data/sample_data/infer/infer_sample_data b/models/rank/dcn/data/sample_data/infer/infer_sample_data
new file mode 100644
index 0000000000000000000000000000000000000000..4aa6d249feecf542a5ce947f510bded60aa6414f
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/infer/infer_sample_data
@@ -0,0 +1,10 @@
+label:0 I1:0.69314718056 I2:1.60943791243 I3:1.79175946923 I4:0.0 I5:7.23201033166 I6:1.60943791243 I7:2.77258872224 I8:1.09861228867 I9:5.20400668708 I10:0.69314718056 I11:1.09861228867 I12:0 I13:1.09861228867 C1:95 C2:398 C3:0 C4:0 C5:53 C6:1 C7:73 C8:71 C9:3 C10:1974 C11:832 C12:0 C13:875 C14:8 C15:1764 C16:0 C17:5 C18:390 C19:226 C20:1 C21:0 C22:0 C23:8 C24:1759 C25:1 C26:862
+label:0 I1:1.09861228867 I2:1.38629436112 I3:3.80666248977 I4:0.69314718056 I5:4.63472898823 I6:2.19722457734 I7:1.09861228867 I8:1.09861228867 I9:1.60943791243 I10:0.69314718056 I11:0.69314718056 I12:0 I13:1.60943791243 C1:95 C2:200 C3:1184 C4:1929 C5:53 C6:4 C7:1477 C8:2 C9:3 C10:1283 C11:1567 C12:1048 C13:271 C14:6 C15:1551 C16:899 C17:1 C18:162 C19:226 C20:2 C21:575 C22:0 C23:8 C24:1615 C25:1 C26:659
+label:0 I1:1.09861228867 I2:1.38629436112 I3:0.69314718056 I4:2.7080502011 I5:6.64378973315 I6:4.49980967033 I7:1.60943791243 I8:1.09861228867 I9:5.50533153593 I10:0.69314718056 I11:1.38629436112 I12:1.38629436112 I13:3.82864139649 C1:123 C2:378 C3:991 C4:197 C5:53 C6:1 C7:689 C8:2 C9:3 C10:245 C11:623 C12:1482 C13:887 C14:21 C15:106 C16:720 C17:3 C18:768 C19:0 C20:0 C21:1010 C22:1 C23:8 C24:720 C25:0 C26:0
+label:0 I1:0 I2:6.79905586206 I3:0 I4:0 I5:8.38776764398 I6:0 I7:0.0 I8:0.0 I9:0.0 I10:0 I11:0.0 I12:0 I13:0 C1:95 C2:227 C3:0 C4:219 C5:53 C6:4 C7:3174 C8:2 C9:3 C10:569 C11:1963 C12:0 C13:1150 C14:21 C15:1656 C16:0 C17:6 C18:584 C19:0 C20:0 C21:0 C22:0 C23:8 C24:954 C25:0 C26:0
+label:0 I1:1.38629436112 I2:1.09861228867 I3:0 I4:0.0 I5:1.09861228867 I6:0.0 I7:1.38629436112 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:121 C2:147 C3:0 C4:1356 C5:53 C6:7 C7:2120 C8:2 C9:3 C10:703 C11:1678 C12:1210 C13:1455 C14:8 C15:538 C16:1276 C17:6 C18:346 C19:0 C20:0 C21:944 C22:0 C23:10 C24:355 C25:0 C26:0
+label:0 I1:0 I2:1.09861228867 I3:0 I4:0 I5:9.45915167004 I6:0 I7:0.0 I8:0.0 I9:1.94591014906 I10:0 I11:0.0 I12:0 I13:0 C1:14 C2:75 C3:993 C4:480 C5:50 C6:6 C7:1188 C8:2 C9:3 C10:245 C11:1037 C12:1365 C13:1421 C14:21 C15:786 C16:5 C17:2 C18:555 C19:0 C20:0 C21:1408 C22:6 C23:7 C24:753 C25:0 C26:0
+label:0 I1:0 I2:1.60943791243 I3:1.09861228867 I4:0 I5:8.06117135969 I6:0 I7:0.0 I8:0.69314718056 I9:1.09861228867 I10:0 I11:0.0 I12:0 I13:0 C1:139 C2:343 C3:553 C4:828 C5:50 C6:4 C7:0 C8:2 C9:3 C10:245 C11:2081 C12:260 C13:455 C14:21 C15:122 C16:1159 C17:2 C18:612 C19:0 C20:0 C21:1137 C22:0 C23:1 C24:1583 C25:0 C26:0
+label:1 I1:0.69314718056 I2:2.07944154168 I3:1.09861228867 I4:0.0 I5:0.0 I6:0.0 I7:0.69314718056 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:95 C2:227 C3:0 C4:1567 C5:21 C6:7 C7:2496 C8:71 C9:3 C10:1913 C11:2212 C12:0 C13:673 C14:21 C15:1656 C16:0 C17:5 C18:584 C19:0 C20:0 C21:0 C22:0 C23:10 C24:954 C25:0 C26:0
+label:0 I1:0 I2:3.87120101091 I3:1.60943791243 I4:2.19722457734 I5:9.85277303799 I6:5.52146091786 I7:3.36729582999 I8:3.4657359028 I9:4.9558270576 I10:0 I11:0.69314718056 I12:0 I13:2.19722457734 C1:14 C2:14 C3:454 C4:197 C5:53 C6:1 C7:1386 C8:2 C9:3 C10:0 C11:1979 C12:205 C13:214 C14:6 C15:1837 C16:638 C17:5 C18:6 C19:0 C20:0 C21:70 C22:0 C23:10 C24:720 C25:0 C26:0
+label:0 I1:0 I2:3.66356164613 I3:0 I4:0.69314718056 I5:10.4263800775 I6:3.09104245336 I7:0.69314718056 I8:1.09861228867 I9:1.38629436112 I10:0 I11:0.69314718056 I12:0 I13:0.69314718056 C1:14 C2:179 C3:120 C4:746 C5:53 C6:0 C7:1312 C8:2 C9:3 C10:1337 C11:1963 C12:905 C13:1150 C14:21 C15:1820 C16:328 C17:9 C18:77 C19:0 C20:0 C21:311 C22:0 C23:10 C24:89 C25:0 C26:0
diff --git a/models/rank/dcn/data/sample_data/train/sample_train.txt b/models/rank/dcn/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4aa6d249feecf542a5ce947f510bded60aa6414f
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/train/sample_train.txt
@@ -0,0 +1,10 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C1.txt b/models/rank/dcn/data/sample_data/vocab/C1.txt
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index 0000000000000000000000000000000000000000..4597891767100dcd85ac15caa833d119ba49bb05
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C10.txt b/models/rank/dcn/data/sample_data/vocab/C10.txt
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--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C11.txt b/models/rank/dcn/data/sample_data/vocab/C11.txt
new file mode 100644
index 0000000000000000000000000000000000000000..311221d155de8558a93f2f3f2f2ba652616f12fc
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C11.txt
@@ -0,0 +1,2246 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C12.txt b/models/rank/dcn/data/sample_data/vocab/C12.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4553a544414464def71c643b5988b71614d6d338
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C12.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C13.txt b/models/rank/dcn/data/sample_data/vocab/C13.txt
new file mode 100644
index 0000000000000000000000000000000000000000..204761319cc5f7a33fed867a375c6c512247cf53
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C13.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C14.txt b/models/rank/dcn/data/sample_data/vocab/C14.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a4a5a781ba6b052deb66e0dc44bdf98bfb1bbe24
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C14.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C15.txt b/models/rank/dcn/data/sample_data/vocab/C15.txt
new file mode 100644
index 0000000000000000000000000000000000000000..93dfd19da6bf50714d87348bfd97b59857a281c4
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C15.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C16.txt b/models/rank/dcn/data/sample_data/vocab/C16.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8832f7160de90699622a3260b8c2d36467f833f7
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C16.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C17.txt b/models/rank/dcn/data/sample_data/vocab/C17.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95c0fccec81cab5dfc06bf906492566a043fbfe1
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C18.txt b/models/rank/dcn/data/sample_data/vocab/C18.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C19.txt b/models/rank/dcn/data/sample_data/vocab/C19.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fd141ed95d49f963bfd871d6dc3ac4219b57da0a
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C19.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C2.txt b/models/rank/dcn/data/sample_data/vocab/C2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..26f550d3753641756f72f4afbb6bb503e951f32e
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..7e2df90cf93e2c54611e62aef11f134357bdc613
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C20.txt
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new file mode 100644
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--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C23.txt b/models/rank/dcn/data/sample_data/vocab/C23.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C25.txt b/models/rank/dcn/data/sample_data/vocab/C25.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f23c2ee0cdd3de63cba3bd06d77e3042001f113f
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C25.txt
@@ -0,0 +1,40 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C26.txt b/models/rank/dcn/data/sample_data/vocab/C26.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1cb7d644ce8688720752f3a74f7494df109d824e
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C26.txt
@@ -0,0 +1,1349 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C3.txt b/models/rank/dcn/data/sample_data/vocab/C3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a794b343b63ddc4d727a454a1485b5c557fdd679
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C3.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C4.txt b/models/rank/dcn/data/sample_data/vocab/C4.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed778fd9ca3eb6c3c22410e0ef9594bf26359303
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C4.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C5.txt b/models/rank/dcn/data/sample_data/vocab/C5.txt
new file mode 100644
index 0000000000000000000000000000000000000000..93d3888f00ca9d40867074ebf2d529a5c6c456a6
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C5.txt
@@ -0,0 +1,54 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C6.txt b/models/rank/dcn/data/sample_data/vocab/C6.txt
new file mode 100644
index 0000000000000000000000000000000000000000..67c73128c2b885ee37dab1ede607a559edc8512f
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C6.txt
@@ -0,0 +1,10 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C7.txt b/models/rank/dcn/data/sample_data/vocab/C7.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5335eedaaca0f062c3d7e548f0167824284adaf0
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C7.txt
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+1e4be8d7
+41f01e8e
+89b08e39
+380ff345
+0ac20149
+7227c706
+5bd6584c
+d6ee7377
+7e2503a1
+ae1dfa39
+0bc1953a
+aa0d873c
+63e71e46
+7083ddcc
+55fcaa41
+b7ac0822
+6e895c0a
+1ae85837
+fdd404ad
+c52dd224
+17c5d911
+912c6d03
+19114224
+bd6afa2b
+b13433e4
+020941fb
+9163310a
+d2dbdfe6
+fd0b1667
+5ce839a9
diff --git a/models/rank/dcn/data/sample_data/vocab/C8.txt b/models/rank/dcn/data/sample_data/vocab/C8.txt
new file mode 100644
index 0000000000000000000000000000000000000000..61c11ee5c5aabab0f917ffd6e76d39c82ddc90e5
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C8.txt
@@ -0,0 +1,81 @@
+f504a6f4
+0b153874
+7b6fecd5
+235697d5
+64523cfa
+316074ea
+0017bc7c
+afc9ca6a
+a6d156f4
+13037314
+d7c4a8f5
+e350fe4f
+931c3bf7
+361384ce
+6a698541
+43383eb4
+73b7901e
+cb66451f
+f0e5818a
+0fb392dd
+4671807e
+da1ed842
+51d76abe
+5b392875
+e602701d
+f0298c3c
+a25968f2
+67b76963
+cbe011a6
+1296137f
+d3334ebc
+f1aa21a3
+23eefdc2
+322e63df
+a674580f
+d1b66f7a
+a8d6f709
+66c1ef42
+813607cc
+966033bc
+e7945bc1
+77b89023
+73ba467e
+169d7cc1
+8bedcc53
+9d01afb9
+bb170c38
+233428af
+07a0b7e5
+560f93f8
+9c376700
+d1aaef6c
+71f9a260
+449116d5
+c5e75280
+ba7cbdc6
+25611aba
+56563555
+cb69809d
+25239412
+093a9651
+a61cc0ef
+062b5529
+c7453af1
+b621aeb8
+45f7c2dd
+49dd1874
+8ee20c61
+62c159ed
+c8ddd494
+1f89b562
+0b5a4776
+37e4aa92
+985e3fcb
+efaa8b67
+66f29b89
+6c41e35e
+5b9f3341
+271b0642
+a04b3fa3
+e6210023
diff --git a/models/rank/dcn/data/sample_data/vocab/C9.txt b/models/rank/dcn/data/sample_data/vocab/C9.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eddb3ff906a2aa4c5177be414452c9defbb1496b
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C9.txt
@@ -0,0 +1,3 @@
+7cc72ec2
+a18233ea
+a73ee510
diff --git a/models/rank/dcn/model.py b/models/rank/dcn/model.py
index c0395c27318cda335dd8271e28df1f8b0a9193b9..52764c3e2122c408078c65875427af74c4ae83da 100755
--- a/models/rank/dcn/model.py
+++ b/models/rank/dcn/model.py
@@ -1,56 +1,59 @@
-import paddle.fluid as fluid
-import math
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
from collections import OrderedDict
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
-
- def init_network(self):
- self.cross_num = envs.get_global_env("hyper_parameters.cross_num", None, self._namespace)
- self.dnn_hidden_units = envs.get_global_env("hyper_parameters.dnn_hidden_units", None, self._namespace)
- self.l2_reg_cross = envs.get_global_env("hyper_parameters.l2_reg_cross", None, self._namespace)
- self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn", None, self._namespace)
- self.clip_by_norm = envs.get_global_env("hyper_parameters.clip_by_norm", None, self._namespace)
- cat_feat_num = envs.get_global_env("hyper_parameters.cat_feat_num", None, self._namespace)
- cat_feat_dims_dict = OrderedDict()
- for line in open(cat_feat_num):
- spls = line.strip().split()
- assert len(spls) == 2
- cat_feat_dims_dict[spls[0]] = int(spls[1])
- self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict(
- )
- self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", None, self._namespace)
- self.dense_feat_names = ['I' + str(i) for i in range(1, 14)]
- self.sparse_feat_names = ['C' + str(i) for i in range(1, 27)]
-
- # {feat_name: dims}
- self.feat_dims_dict = OrderedDict(
- [(feat_name, 1) for feat_name in self.dense_feat_names])
- self.feat_dims_dict.update(self.cat_feat_dims_dict)
-
- self.net_input = None
- self.loss = None
-
- def _create_embedding_input(self, data_dict):
+ def _init_hyper_parameters(self):
+ self.cross_num = envs.get_global_env("hyper_parameters.cross_num",
+ None)
+ self.dnn_hidden_units = envs.get_global_env(
+ "hyper_parameters.dnn_hidden_units", None)
+ self.l2_reg_cross = envs.get_global_env(
+ "hyper_parameters.l2_reg_cross", None)
+ self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn",
+ None)
+ self.clip_by_norm = envs.get_global_env(
+ "hyper_parameters.clip_by_norm", None)
+ self.cat_feat_num = envs.get_global_env(
+ "hyper_parameters.cat_feat_num", None)
+ self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
+ None)
+
+ def _create_embedding_input(self):
# sparse embedding
- sparse_emb_dict = OrderedDict((name, fluid.embedding(
- input=fluid.layers.cast(
- data_dict[name], dtype='int64'),
- size=[
- self.feat_dims_dict[name] + 1,
- 6 * int(pow(self.feat_dims_dict[name], 0.25))
- ],
- is_sparse=self.is_sparse)) for name in self.sparse_feat_names)
+ sparse_emb_dict = OrderedDict()
+ for var in self.sparse_inputs:
+ sparse_emb_dict[var.name] = fluid.embedding(
+ input=var,
+ size=[
+ self.feat_dims_dict[var.name] + 1,
+ 6 * int(pow(self.feat_dims_dict[var.name], 0.25))
+ ],
+ is_sparse=self.is_sparse)
# combine dense and sparse_emb
- dense_input_list = [
- data_dict[name] for name in data_dict if name.startswith('I')
- ]
+ dense_input_list = self.dense_inputs
sparse_emb_list = list(sparse_emb_dict.values())
sparse_input = fluid.layers.concat(sparse_emb_list, axis=-1)
@@ -63,7 +66,7 @@ class Model(ModelBase):
net_input = fluid.layers.concat([dense_input, sparse_input], axis=-1)
return net_input
-
+
def _deep_net(self, input, hidden_units, use_bn=False, is_test=False):
for units in hidden_units:
input = fluid.layers.fc(input=input, size=units)
@@ -80,7 +83,7 @@ class Model(ModelBase):
[input_dim], dtype='float32', name=prefix + "_b")
xw = fluid.layers.reduce_sum(x * w, dim=1, keep_dim=True) # (N, 1)
return x0 * xw + b + x, w
-
+
def _cross_net(self, input, num_corss_layers):
x = x0 = input
l2_reg_cross_list = []
@@ -91,33 +94,46 @@ class Model(ModelBase):
fluid.layers.concat(
l2_reg_cross_list, axis=-1))
return x, l2_reg_cross_loss
-
+
def _l2_loss(self, w):
return fluid.layers.reduce_sum(fluid.layers.square(w))
-
- def train_net(self):
- self.init_network()
- self.target_input = fluid.data(
- name='label', shape=[None, 1], dtype='float32')
- data_dict = OrderedDict()
- for feat_name in self.feat_dims_dict:
- data_dict[feat_name] = fluid.data(
- name=feat_name, shape=[None, 1], dtype='float32')
-
- self.net_input = self._create_embedding_input(data_dict)
-
- deep_out = self._deep_net(self.net_input, self.dnn_hidden_units, self.dnn_use_bn, False)
+
+ def net(self, inputs, is_infer=False):
+ self.sparse_inputs = self._sparse_data_var[1:]
+ self.dense_inputs = self._dense_data_var
+ self.target_input = self._sparse_data_var[0]
+
+ cat_feat_dims_dict = OrderedDict()
+ for line in open(self.cat_feat_num):
+ spls = line.strip().split()
+ assert len(spls) == 2
+ cat_feat_dims_dict[spls[0]] = int(spls[1])
+ self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict(
+ )
+
+ self.dense_feat_names = [i.name for i in self.dense_inputs]
+ self.sparse_feat_names = [i.name for i in self.sparse_inputs]
+
+ # {feat_name: dims}
+ self.feat_dims_dict = OrderedDict(
+ [(feat_name, 1) for feat_name in self.dense_feat_names])
+ self.feat_dims_dict.update(self.cat_feat_dims_dict)
+
+ self.net_input = None
+ self.loss = None
+
+ self.net_input = self._create_embedding_input()
+
+ deep_out = self._deep_net(self.net_input, self.dnn_hidden_units,
+ self.dnn_use_bn, False)
cross_out, l2_reg_cross_loss = self._cross_net(self.net_input,
- self.cross_num)
-
+ self.cross_num)
+
last_out = fluid.layers.concat([deep_out, cross_out], axis=-1)
logit = fluid.layers.fc(last_out, 1)
self.prob = fluid.layers.sigmoid(logit)
- self._data_var = [self.target_input] + [
- data_dict[dense_name] for dense_name in self.dense_feat_names
- ] + [data_dict[sparse_name] for sparse_name in self.sparse_feat_names]
# auc
prob_2d = fluid.layers.concat([1 - self.prob, self.prob], 1)
@@ -126,10 +142,14 @@ class Model(ModelBase):
input=prob_2d, label=label_int, slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
-
+
+ if is_infer:
+ self._infer_results["AUC"] = auc_var
# logloss
- logloss = fluid.layers.log_loss(self.prob, self.target_input)
+ logloss = fluid.layers.log_loss(
+ self.prob, fluid.layers.cast(
+ self.target_input, dtype='float32'))
self.avg_logloss = fluid.layers.reduce_mean(logloss)
# reg_coeff * l2_reg_cross
@@ -137,10 +157,7 @@ class Model(ModelBase):
self.loss = self.avg_logloss + l2_reg_cross_loss
self._cost = self.loss
- def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
- optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
- return optimizer
-
- def infer_net(self, parameter_list):
- self.deepfm_net()
+ #def optimizer(self):
+ #
+ # optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)
+ # return optimizer
diff --git a/models/rank/deepfm/config.yaml b/models/rank/deepfm/config.yaml
index fd06406db481fdfb8e00aefecda7ca42a3c89353..d1d25c2c4c05c82f8ee0b9554563d2f310c2ac01 100755
--- a/models/rank/deepfm/config.yaml
+++ b/models/rank/deepfm/config.yaml
@@ -12,38 +12,65 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
-
- epochs: 10
- workspace: "fleetrec.models.rank.deepfm"
-
- reader:
- batch_size: 2
- class: "{workspace}/criteo_reader.py"
- train_data_path: "{workspace}/data/train_data"
- feat_dict_name: "{workspace}/data/aid_data/feat_dict_10.pkl2"
-
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- sparse_feature_number: 1086460
- sparse_feature_dim: 9
- num_field: 39
- fc_sizes: [400, 400, 400]
- learning_rate: 0.0001
- reg: 0.001
- act: "relu"
- optimizer: SGD
-
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+# global settings
+debug: false
+workspace: "paddlerec.models.rank.deepfm"
+
+
+dataset:
+ - name: train_sample
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "label feat_idx"
+ dense_slots: "feat_value:39"
+ - name: infer_sample
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "label feat_idx"
+ dense_slots: "feat_value:39"
+
+hyper_parameters:
+ optimizer:
+ class: SGD
+ learning_rate: 0.0001
+ sparse_feature_number: 1086460
+ sparse_feature_dim: 9
+ num_field: 39
+ fc_sizes: [400, 400, 400]
+ reg: 0.001
+ act: "relu"
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+ - name: train_runner
+ trainer_class: single_train
+ epochs: 2
+ device: cpu
+ init_model_path: ""
+ save_checkpoint_interval: 1
+ save_inference_interval: 1
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 1
+ - name: infer_runner
+ trainer_class: single_infer
+ epochs: 1
+ device: cpu
+ init_model_path: "increment/0"
+ print_interval: 1
+
+
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: train_sample
+ thread_num: 1
+#- name: infer_phase
+# model: "{workspace}/model.py"
+# dataset_name: infer_sample
+# thread_num: 1
diff --git a/models/rank/deepfm/data/download_preprocess.py b/models/rank/deepfm/data/download_preprocess.py
index 1f114bcfb7ec7ce5c93e676bb0467026eedf6f33..7a504b4f88e49d8b4f242d4d6b56f6f168464e5c 100755
--- a/models/rank/deepfm/data/download_preprocess.py
+++ b/models/rank/deepfm/data/download_preprocess.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import os
import shutil
import sys
@@ -6,7 +20,7 @@ LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
sys.path.append(TOOLS_PATH)
-from fleetrec.tools.tools import download_file_and_uncompress, download_file
+from paddlerec.tools.tools import download_file_and_uncompress, download_file
if __name__ == '__main__':
url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
diff --git a/models/rank/deepfm/criteo_reader.py b/models/rank/deepfm/data/get_slot_data.py
similarity index 71%
rename from models/rank/deepfm/criteo_reader.py
rename to models/rank/deepfm/data/get_slot_data.py
index a4c2587a507cd1744383c725e5a237950abffd42..6177c990d8ef0c8a1cf922dd9d50c6419cb8c1b7 100755
--- a/models/rank/deepfm/criteo_reader.py
+++ b/models/rank/deepfm/data/get_slot_data.py
@@ -11,16 +11,26 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+import yaml
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
try:
import cPickle as pickle
except ImportError:
import pickle
-class TrainReader(Reader):
+
+class TrainReader(dg.MultiSlotDataGenerator):
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
+
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [
@@ -34,8 +44,8 @@ class TrainReader(Reader):
self.continuous_range_ = range(1, 14)
self.categorical_range_ = range(14, 40)
# load preprocessed feature dict
- self.feat_dict_name = envs.get_global_env("feat_dict_name", None, "train.reader")
- self.feat_dict_ = pickle.load(open(self.feat_dict_name, 'rb'))
+ self.feat_dict_name = "aid_data/feat_dict_10.pkl2"
+ self.feat_dict_ = pickle.load(open(self.feat_dict_name, 'rb'))
def _process_line(self, line):
features = line.rstrip('\n').split('\t')
@@ -59,13 +69,27 @@ class TrainReader(Reader):
feat_value.append(1.0)
label = [int(features[0])]
return feat_idx, feat_value, label
-
+
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
+
def data_iter():
feat_idx, feat_value, label = self._process_line(line)
- yield [('feat_idx', feat_idx), ('feat_value', feat_value), ('label', label)]
+ s = ""
+ for i in [('feat_idx', feat_idx), ('feat_value', feat_value),
+ ('label', label)]:
+ k = i[0]
+ v = i[1]
+ for j in v:
+ s += " " + k + ":" + str(j)
+ print s.strip()
+ yield None
+
+ return data_iter
+
- return data_iter
\ No newline at end of file
+reader = TrainReader("../config.yaml")
+reader.init()
+reader.run_from_stdin()
diff --git a/models/rank/deepfm/data/preprocess.py b/models/rank/deepfm/data/preprocess.py
index 1fa4a5feae17bde64463d2f05beb3d053284dcda..9da3bdc3d93bfcd0dd98fddc64c870d20feddb38 100755
--- a/models/rank/deepfm/data/preprocess.py
+++ b/models/rank/deepfm/data/preprocess.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import os
import numpy
from collections import Counter
diff --git a/models/rank/deepfm/data/run.sh b/models/rank/deepfm/data/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c2bc4ae8ce1d7ad7c89ebd48f993ab920fba0ba2
--- /dev/null
+++ b/models/rank/deepfm/data/run.sh
@@ -0,0 +1,13 @@
+python download_preprocess.py
+
+mkdir slot_train_data
+for i in `ls ./train_data`
+do
+ cat train_data/$i | python get_slot_data.py > slot_train_data/$i
+done
+
+mkdir slot_test_data
+for i in `ls ./test_data`
+do
+ cat test_data/$i | python get_slot_data.py > slot_test_data/$i
+done
diff --git a/models/rank/deepfm/data/sample_data/feat_dict_10.pkl2 b/models/rank/deepfm/data/sample_data/feat_dict_10.pkl2
new file mode 100644
index 0000000000000000000000000000000000000000..962d552ab2a094b01c1223b8359dc594aa5bbef5
Binary files /dev/null and b/models/rank/deepfm/data/sample_data/feat_dict_10.pkl2 differ
diff --git a/models/rank/deepfm/data/sample_data/train/sample_train.txt b/models/rank/deepfm/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b0308e17f74efa4272e1871e86d03c236b1945a
--- /dev/null
+++ b/models/rank/deepfm/data/sample_data/train/sample_train.txt
@@ -0,0 +1,100 @@
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+feat_idx:0 feat_idx:2 feat_idx:0 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:0 feat_idx:11 feat_idx:12 feat_idx:13 feat_idx:506931 feat_idx:123566 feat_idx:961529 feat_idx:810019 feat_idx:314332 feat_idx:615411 feat_idx:475867 feat_idx:795081 feat_idx:148475 feat_idx:697060 feat_idx:1069621 feat_idx:370551 feat_idx:696973 feat_idx:69630 feat_idx:396064 feat_idx:95177 feat_idx:854924 feat_idx:488825 feat_idx:0 feat_idx:0 feat_idx:581782 feat_idx:0 feat_idx:476211 feat_idx:289148 feat_idx:0 feat_idx:0 feat_value:0.0 feat_value:0.0066672358525 feat_value:0.0 feat_value:0.00103199174407 feat_value:0.000325784854359 feat_value:4.40797425743e-05 feat_value:0.000266377794747 feat_value:0.000165371258475 feat_value:0.00299803576967 feat_value:0.0 feat_value:0.030303030303 feat_value:0.0 feat_value:0.000135263086704 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:0
+feat_idx:1 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:268086 feat_idx:704711 feat_idx:995515 feat_idx:139394 feat_idx:943087 feat_idx:0 feat_idx:546815 feat_idx:144963 feat_idx:148475 feat_idx:364765 feat_idx:552750 feat_idx:920037 feat_idx:816538 feat_idx:223357 feat_idx:790588 feat_idx:560935 feat_idx:13161 feat_idx:750233 feat_idx:734534 feat_idx:1047606 feat_idx:361734 feat_idx:0 feat_idx:122096 feat_idx:434883 feat_idx:502861 feat_idx:203213 feat_value:0.0 feat_value:0.000197921436832 feat_value:4.57770656901e-05 feat_value:0.00206398348813 feat_value:0.000625316933178 feat_value:0.000874634892132 feat_value:0.000142068157198 feat_value:0.000330742516951 feat_value:0.00975223129674 feat_value:0.0 feat_value:0.00865800865801 feat_value:0.0 feat_value:0.000270526173407 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 label:0
diff --git a/models/rank/deepfm/model.py b/models/rank/deepfm/model.py
index bd5e80e56de81cce0c92379a05797ca81027ac23..8ac8df134d08550c0db06e9aacbad21dbd74cfe9 100755
--- a/models/rank/deepfm/model.py
+++ b/models/rank/deepfm/model.py
@@ -1,77 +1,95 @@
-import paddle.fluid as fluid
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import math
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
- def deepfm_net(self):
+ def _init_hyper_parameters(self):
+ self.sparse_feature_number = envs.get_global_env(
+ "hyper_parameters.sparse_feature_number", None)
+ self.sparse_feature_dim = envs.get_global_env(
+ "hyper_parameters.sparse_feature_dim", None)
+ self.num_field = envs.get_global_env("hyper_parameters.num_field",
+ None)
+ self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
+ self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes",
+ None)
+ self.act = envs.get_global_env("hyper_parameters.act", None)
+
+ def net(self, inputs, is_infer=False):
init_value_ = 0.1
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
- sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
- sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
-
+
# ------------------------- network input --------------------------
-
- num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace)
- raw_feat_idx = fluid.data(name='feat_idx', shape=[None, num_field], dtype='int64') # None * num_field(defalut:39)
- raw_feat_value = fluid.data(name='feat_value', shape=[None, num_field], dtype='float32') # None * num_field
- self.label = fluid.data(name='label', shape=[None, 1], dtype='float32') # None * 1
- feat_idx = fluid.layers.reshape(raw_feat_idx,[-1, 1]) # (None * num_field) * 1
- feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1]) # None * num_field * 1
-
- # ------------------------- set _data_var --------------------------
-
- self._data_var.append(raw_feat_idx)
- self._data_var.append(raw_feat_value)
- self._data_var.append(self.label)
- if self._platform != "LINUX":
- self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
-
- #------------------------- first order term --------------------------
-
- reg = envs.get_global_env("hyper_parameters.reg", 1e-4, self._namespace)
+
+ raw_feat_idx = self._sparse_data_var[1]
+ raw_feat_value = self._dense_data_var[0]
+ self.label = self._sparse_data_var[0]
+
+ feat_idx = raw_feat_idx
+ feat_value = fluid.layers.reshape(
+ raw_feat_value, [-1, self.num_field, 1]) # None * num_field * 1
+
first_weights_re = fluid.embedding(
input=feat_idx,
is_sparse=True,
is_distributed=is_distributed,
dtype='float32',
- size=[sparse_feature_number + 1, 1],
+ size=[self.sparse_feature_number + 1, 1],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_),
- regularizer=fluid.regularizer.L1DecayRegularizer(reg)))
+ regularizer=fluid.regularizer.L1DecayRegularizer(self.reg)))
first_weights = fluid.layers.reshape(
- first_weights_re, shape=[-1, num_field, 1]) # None * num_field * 1
- y_first_order = fluid.layers.reduce_sum((first_weights * feat_value), 1)
+ first_weights_re,
+ shape=[-1, self.num_field, 1]) # None * num_field * 1
+ y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
+ 1)
- #------------------------- second order term --------------------------
+ # ------------------------- second order term --------------------------
feat_embeddings_re = fluid.embedding(
input=feat_idx,
is_sparse=True,
is_distributed=is_distributed,
dtype='float32',
- size=[sparse_feature_number + 1, sparse_feature_dim],
+ size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
- loc=0.0, scale=init_value_ / math.sqrt(float(sparse_feature_dim)))))
+ loc=0.0,
+ scale=init_value_ /
+ math.sqrt(float(self.sparse_feature_dim)))))
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
- shape=[-1, num_field,
- sparse_feature_dim]) # None * num_field * embedding_size
+ shape=[-1, self.num_field, self.sparse_feature_dim
+ ]) # None * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size
-
+
# sum_square part
- summed_features_emb = fluid.layers.reduce_sum(feat_embeddings,
- 1) # None * embedding_size
+ summed_features_emb = fluid.layers.reduce_sum(
+ feat_embeddings, 1) # None * embedding_size
summed_features_emb_square = fluid.layers.square(
summed_features_emb) # None * embedding_size
@@ -82,21 +100,19 @@ class Model(ModelBase):
squared_features_emb, 1) # None * embedding_size
y_second_order = 0.5 * fluid.layers.reduce_sum(
- summed_features_emb_square - squared_sum_features_emb, 1,
+ summed_features_emb_square - squared_sum_features_emb,
+ 1,
keep_dim=True) # None * 1
+ # ------------------------- DNN --------------------------
- #------------------------- DNN --------------------------
-
- layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
- act = envs.get_global_env("hyper_parameters.act", None, self._namespace)
- y_dnn = fluid.layers.reshape(feat_embeddings,
- [-1, num_field * sparse_feature_dim])
- for s in layer_sizes:
+ y_dnn = fluid.layers.reshape(
+ feat_embeddings, [-1, self.num_field * self.sparse_feature_dim])
+ for s in self.layer_sizes:
y_dnn = fluid.layers.fc(
input=y_dnn,
size=s,
- act=act,
+ act=self.act,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_ / math.sqrt(float(10)))),
@@ -113,35 +129,23 @@ class Model(ModelBase):
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)))
-
- #------------------------- DeepFM --------------------------
- self.predict = fluid.layers.sigmoid(y_first_order + y_second_order + y_dnn)
-
- def train_net(self):
- self.deepfm_net()
-
- #------------------------- Cost(logloss) --------------------------
+ # ------------------------- DeepFM --------------------------
- cost = fluid.layers.log_loss(input=self.predict, label=self.label)
+ self.predict = fluid.layers.sigmoid(y_first_order + y_second_order +
+ y_dnn)
+ cost = fluid.layers.log_loss(
+ input=self.predict, label=fluid.layers.cast(self.label, "float32"))
avg_cost = fluid.layers.reduce_sum(cost)
-
+
self._cost = avg_cost
- #------------------------- Metric(Auc) --------------------------
-
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
- label=label_int,
- slide_steps=0)
+ label=label_int,
+ slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
-
- def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
- optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
- return optimizer
-
- def infer_net(self, parameter_list):
- self.deepfm_net()
\ No newline at end of file
+ if is_infer:
+ self._infer_results["AUC"] = auc_var
diff --git a/models/rank/din/config.yaml b/models/rank/din/config.yaml
index 1244c1639aae1065cde0c9e109a38b728af9ea3e..2885ba7a58083be470d9bc2f8d2d030c2c3207b5 100755
--- a/models/rank/din/config.yaml
+++ b/models/rank/din/config.yaml
@@ -12,40 +12,60 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
+# global settings
+debug: false
+workspace: "paddlerec.models.rank.din"
- epochs: 10
- workspace: "fleetrec.models.rank.din"
+dataset:
+ - name: sample_1
+ type: DataLoader
+ batch_size: 5
+ data_path: "{workspace}/data/train_data"
+ data_converter: "{workspace}/reader.py"
+ - name: infer_sample
+ type: DataLoader
+ batch_size: 5
+ data_path: "{workspace}/data/train_data"
+ data_converter: "{workspace}/reader.py"
- reader:
- batch_size: 2
- class: "{workspace}/reader.py"
- train_data_path: "{workspace}/data/train_data"
- dataset_class: "DataLoader"
+hyper_parameters:
+ optimizer:
+ class: SGD
+ learning_rate: 0.0001
+ use_DataLoader: True
+ item_emb_size: 64
+ cat_emb_size: 64
+ is_sparse: False
+ item_count: 63001
+ cat_count: 801
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- use_DataLoader: True
- item_emb_size: 64
- cat_emb_size: 64
- is_sparse: False
- config_path: "data/config.txt"
- fc_sizes: [400, 400, 400]
- learning_rate: 0.0001
- reg: 0.001
- act: "sigmoid"
- optimizer: SGD
+ act: "sigmoid"
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+
+mode: train_runner
+
+runner:
+ - name: train_runner
+ trainer_class: single_train
+ epochs: 1
+ device: cpu
+ init_model_path: ""
+ save_checkpoint_interval: 1
+ save_inference_interval: 1
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 1
+ - name: infer_runner
+ trainer_class: single_infer
+ epochs: 1
+ device: cpu
+ init_model_path: "increment/0"
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: sample_1
+ thread_num: 1
+#- name: infer_phase
+# model: "{workspace}/model.py"
+# dataset_name: infer_sample
+# thread_num: 1
diff --git a/models/rank/din/data/build_dataset.py b/models/rank/din/data/build_dataset.py
index 34c053ccdb2686c10875740f72f1e0abf3cb4f10..b0ed187800b2f9f44d4dd0d34df204759059ac06 100755
--- a/models/rank/din/data/build_dataset.py
+++ b/models/rank/din/data/build_dataset.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
from __future__ import print_function
import random
import pickle
diff --git a/models/rank/din/data/convert_pd.py b/models/rank/din/data/convert_pd.py
index d7927c7ef1a9da28732cad9c44be24e72095983a..a66290e1561084a10756ab98c3d70b9a5ac5a6ed 100755
--- a/models/rank/din/data/convert_pd.py
+++ b/models/rank/din/data/convert_pd.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
from __future__ import print_function
import pickle
import pandas as pd
diff --git a/models/rank/din/data/remap_id.py b/models/rank/din/data/remap_id.py
index b110dac54de8f8d201ede7248d6a2844ac350c90..ee6983d7f0769a58352f61a0a05bbd81c6ccbc13 100755
--- a/models/rank/din/data/remap_id.py
+++ b/models/rank/din/data/remap_id.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
from __future__ import print_function
import random
import pickle
diff --git a/models/rank/din/model.py b/models/rank/din/model.py
index 1d7bcca35064912ca4596fed9755755413c54c3b..4f6099119fae745b3b0c975ddcef853d3dce35b8 100755
--- a/models/rank/din/model.py
+++ b/models/rank/din/model.py
@@ -1,21 +1,80 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import paddle.fluid as fluid
-import math
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
-
- def config_read(self, config_path):
- with open(config_path, "r") as fin:
- user_count = int(fin.readline().strip())
- item_count = int(fin.readline().strip())
- cat_count = int(fin.readline().strip())
- return user_count, item_count, cat_count
-
+
+ def _init_hyper_parameters(self):
+ self.item_emb_size = envs.get_global_env(
+ "hyper_parameters.item_emb_size", 64)
+ self.cat_emb_size = envs.get_global_env(
+ "hyper_parameters.cat_emb_size", 64)
+ self.act = envs.get_global_env("hyper_parameters.act", "sigmoid")
+ self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
+ False)
+ #significant for speeding up the training process
+ self.use_DataLoader = envs.get_global_env(
+ "hyper_parameters.use_DataLoader", False)
+ self.item_count = envs.get_global_env("hyper_parameters.item_count",
+ 63001)
+ self.cat_count = envs.get_global_env("hyper_parameters.cat_count", 801)
+
+ def input_data(self, is_infer=False, **kwargs):
+ seq_len = -1
+ self.data_var = []
+ hist_item_seq = fluid.data(
+ name="hist_item_seq", shape=[None, seq_len], dtype="int64")
+ self.data_var.append(hist_item_seq)
+
+ hist_cat_seq = fluid.data(
+ name="hist_cat_seq", shape=[None, seq_len], dtype="int64")
+ self.data_var.append(hist_cat_seq)
+
+ target_item = fluid.data(
+ name="target_item", shape=[None], dtype="int64")
+ self.data_var.append(target_item)
+
+ target_cat = fluid.data(name="target_cat", shape=[None], dtype="int64")
+ self.data_var.append(target_cat)
+
+ label = fluid.data(name="label", shape=[None, 1], dtype="float32")
+ self.data_var.append(label)
+
+ mask = fluid.data(
+ name="mask", shape=[None, seq_len, 1], dtype="float32")
+ self.data_var.append(mask)
+
+ target_item_seq = fluid.data(
+ name="target_item_seq", shape=[None, seq_len], dtype="int64")
+ self.data_var.append(target_item_seq)
+
+ target_cat_seq = fluid.data(
+ name="target_cat_seq", shape=[None, seq_len], dtype="int64")
+ self.data_var.append(target_cat_seq)
+
+ train_inputs = [hist_item_seq] + [hist_cat_seq] + [target_item] + [
+ target_cat
+ ] + [label] + [mask] + [target_item_seq] + [target_cat_seq]
+ return train_inputs
+
def din_attention(self, hist, target_expand, mask):
"""activation weight"""
@@ -45,98 +104,63 @@ class Model(ModelBase):
out = fluid.layers.matmul(weight, hist)
out = fluid.layers.reshape(x=out, shape=[0, hidden_size])
return out
-
- def train_net(self):
- seq_len = -1
- self.item_emb_size = envs.get_global_env("hyper_parameters.item_emb_size", 64, self._namespace)
- self.cat_emb_size = envs.get_global_env("hyper_parameters.cat_emb_size", 64, self._namespace)
- self.act = envs.get_global_env("hyper_parameters.act", "sigmoid", self._namespace)
- #item_emb_size = 64
- #cat_emb_size = 64
- self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", False, self._namespace)
- #significant for speeding up the training process
- self.config_path = envs.get_global_env("hyper_parameters.config_path", "data/config.txt", self._namespace)
- self.use_DataLoader = envs.get_global_env("hyper_parameters.use_DataLoader", False, self._namespace)
- user_count, item_count, cat_count = self.config_read(self.config_path)
+ def net(self, inputs, is_infer=False):
+ hist_item_seq = inputs[0]
+ hist_cat_seq = inputs[1]
+ target_item = inputs[2]
+ target_cat = inputs[3]
+ label = inputs[4]
+ mask = inputs[5]
+ target_item_seq = inputs[6]
+ target_cat_seq = inputs[7]
item_emb_attr = fluid.ParamAttr(name="item_emb")
cat_emb_attr = fluid.ParamAttr(name="cat_emb")
- hist_item_seq = fluid.data(
- name="hist_item_seq", shape=[None, seq_len], dtype="int64")
- self._data_var.append(hist_item_seq)
-
- hist_cat_seq = fluid.data(
- name="hist_cat_seq", shape=[None, seq_len], dtype="int64")
- self._data_var.append(hist_cat_seq)
-
- target_item = fluid.data(name="target_item", shape=[None], dtype="int64")
- self._data_var.append(target_item)
-
- target_cat = fluid.data(name="target_cat", shape=[None], dtype="int64")
- self._data_var.append(target_cat)
-
- label = fluid.data(name="label", shape=[None, 1], dtype="float32")
- self._data_var.append(label)
-
- mask = fluid.data(name="mask", shape=[None, seq_len, 1], dtype="float32")
- self._data_var.append(mask)
-
- target_item_seq = fluid.data(
- name="target_item_seq", shape=[None, seq_len], dtype="int64")
- self._data_var.append(target_item_seq)
-
- target_cat_seq = fluid.data(
- name="target_cat_seq", shape=[None, seq_len], dtype="int64")
- self._data_var.append(target_cat_seq)
-
- if self.use_DataLoader:
- self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=10000, use_double_buffer=False, iterable=False)
-
hist_item_emb = fluid.embedding(
input=hist_item_seq,
- size=[item_count, self.item_emb_size],
+ size=[self.item_count, self.item_emb_size],
param_attr=item_emb_attr,
is_sparse=self.is_sparse)
hist_cat_emb = fluid.embedding(
input=hist_cat_seq,
- size=[cat_count, self.cat_emb_size],
+ size=[self.cat_count, self.cat_emb_size],
param_attr=cat_emb_attr,
is_sparse=self.is_sparse)
target_item_emb = fluid.embedding(
input=target_item,
- size=[item_count, self.item_emb_size],
+ size=[self.item_count, self.item_emb_size],
param_attr=item_emb_attr,
is_sparse=self.is_sparse)
target_cat_emb = fluid.embedding(
input=target_cat,
- size=[cat_count, self.cat_emb_size],
+ size=[self.cat_count, self.cat_emb_size],
param_attr=cat_emb_attr,
is_sparse=self.is_sparse)
target_item_seq_emb = fluid.embedding(
input=target_item_seq,
- size=[item_count, self.item_emb_size],
+ size=[self.item_count, self.item_emb_size],
param_attr=item_emb_attr,
is_sparse=self.is_sparse)
target_cat_seq_emb = fluid.embedding(
input=target_cat_seq,
- size=[cat_count, self.cat_emb_size],
+ size=[self.cat_count, self.cat_emb_size],
param_attr=cat_emb_attr,
is_sparse=self.is_sparse)
item_b = fluid.embedding(
input=target_item,
- size=[item_count, 1],
+ size=[self.item_count, 1],
param_attr=fluid.initializer.Constant(value=0.0))
- hist_seq_concat = fluid.layers.concat([hist_item_emb, hist_cat_emb], axis=2)
+ hist_seq_concat = fluid.layers.concat(
+ [hist_item_emb, hist_cat_emb], axis=2)
target_seq_concat = fluid.layers.concat(
[target_item_seq_emb, target_cat_seq_emb], axis=2)
target_concat = fluid.layers.concat(
@@ -144,21 +168,22 @@ class Model(ModelBase):
out = self.din_attention(hist_seq_concat, target_seq_concat, mask)
out_fc = fluid.layers.fc(name="out_fc",
- input=out,
- size=self.item_emb_size + self.cat_emb_size,
- num_flatten_dims=1)
+ input=out,
+ size=self.item_emb_size + self.cat_emb_size,
+ num_flatten_dims=1)
embedding_concat = fluid.layers.concat([out_fc, target_concat], axis=1)
fc1 = fluid.layers.fc(name="fc1",
- input=embedding_concat,
- size=80,
- act=self.act)
+ input=embedding_concat,
+ size=80,
+ act=self.act)
fc2 = fluid.layers.fc(name="fc2", input=fc1, size=40, act=self.act)
fc3 = fluid.layers.fc(name="fc3", input=fc2, size=1)
logit = fc3 + item_b
- loss = fluid.layers.sigmoid_cross_entropy_with_logits(x=logit, label=label)
-
+ loss = fluid.layers.sigmoid_cross_entropy_with_logits(
+ x=logit, label=label)
+
avg_loss = fluid.layers.mean(loss)
self._cost = avg_loss
@@ -166,16 +191,9 @@ class Model(ModelBase):
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
- label=label_int,
- slide_steps=0)
+ label=label_int,
+ slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
-
-
- def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
- optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
- return optimizer
-
- def infer_net(self, parameter_list):
- self.deepfm_net()
+ if is_infer:
+ self._infer_results["AUC"] = auc_var
diff --git a/models/rank/din/reader.py b/models/rank/din/reader.py
index dfaec833986d161b6706e101854afb3da00fa2e2..90d358b9f8122fd396bb1a6eb37cbb4d03b96143 100755
--- a/models/rank/din/reader.py
+++ b/models/rank/din/reader.py
@@ -13,25 +13,31 @@
# limitations under the License.
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
-import numpy as np
import os
import random
+
try:
import cPickle as pickle
except ImportError:
import pickle
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
+
class TrainReader(Reader):
def init(self):
- self.train_data_path = envs.get_global_env("train_data_path", None, "train.reader")
+ self.train_data_path = envs.get_global_env(
+ "dataset.sample_1.data_path", None)
self.res = []
self.max_len = 0
-
+
data_file_list = os.listdir(self.train_data_path)
- for i in range(0, len(data_file_list)):
- train_data_file = os.path.join(self.train_data_path, data_file_list[i])
+ for i in range(0, len(data_file_list)):
+ train_data_file = os.path.join(self.train_data_path,
+ data_file_list[i])
with open(train_data_file, "r") as fin:
for line in fin:
line = line.strip().split(';')
@@ -40,12 +46,10 @@ class TrainReader(Reader):
fo = open("tmp.txt", "w")
fo.write(str(self.max_len))
fo.close()
- self.batch_size = envs.get_global_env("batch_size", 32, "train.reader")
+ self.batch_size = envs.get_global_env("dataset.sample_1.batch_size",
+ 32, "train.reader")
self.group_size = self.batch_size * 20
-
-
-
def _process_line(self, line):
line = line.strip().split(';')
hist = line[0].split()
@@ -54,22 +58,22 @@ class TrainReader(Reader):
cate = [int(i) for i in cate]
return [hist, cate, [int(line[2])], [int(line[3])], [float(line[4])]]
-
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
+
def data_iter():
- #feat_idx, feat_value, label = self._process_line(line)
+ # feat_idx, feat_value, label = self._process_line(line)
yield self._process_line(line)
return data_iter
-
+
def pad_batch_data(self, input, max_len):
res = np.array([x + [0] * (max_len - len(x)) for x in input])
res = res.astype("int64").reshape([-1, max_len])
return res
-
+
def make_data(self, b):
max_len = max(len(x[0]) for x in b)
item = self.pad_batch_data([x[0] for x in b], max_len)
@@ -79,9 +83,11 @@ class TrainReader(Reader):
[[0] * x + [-1e9] * (max_len - x) for x in len_array]).reshape(
[-1, max_len, 1])
target_item_seq = np.array(
- [[x[2]] * max_len for x in b]).astype("int64").reshape([-1, max_len])
+ [[x[2]] * max_len for x in b]).astype("int64").reshape(
+ [-1, max_len])
target_cat_seq = np.array(
- [[x[3]] * max_len for x in b]).astype("int64").reshape([-1, max_len])
+ [[x[3]] * max_len for x in b]).astype("int64").reshape(
+ [-1, max_len])
res = []
for i in range(len(b)):
res.append([
@@ -89,7 +95,7 @@ class TrainReader(Reader):
target_item_seq[i], target_cat_seq[i]
])
return res
-
+
def batch_reader(self, reader, batch_size, group_size):
def batch_reader():
bg = []
@@ -111,7 +117,7 @@ class TrainReader(Reader):
yield self.make_data(b)
return batch_reader
-
+
def base_read(self, file_dir):
res = []
for train_file in file_dir:
@@ -122,10 +128,9 @@ class TrainReader(Reader):
cate = line[1].split()
res.append([hist, cate, line[2], line[3], float(line[4])])
return res
-
+
def generate_batch_from_trainfiles(self, files):
data_set = self.base_read(files)
random.shuffle(data_set)
- return self.batch_reader(data_set, self.batch_size, self.batch_size * 20)
-
-
\ No newline at end of file
+ return self.batch_reader(data_set, self.batch_size,
+ self.batch_size * 20)
diff --git a/models/rank/dnn/README.md b/models/rank/dnn/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c307efcc255bc3d087de0cbcb2aaae39e65ce2f2
--- /dev/null
+++ b/models/rank/dnn/README.md
@@ -0,0 +1,261 @@
+# 基于DNN模型的点击率预估模型
+
+## 介绍
+`CTR(Click Through Rate)`，即点击率，是“推荐系统/计算广告”等领域的重要指标，对其进行预估是商品推送/广告投放等决策的基础。简单来说，CTR预估对每次广告的点击情况做出预测，预测用户是点击还是不点击。CTR预估模型综合考虑各种因素、特征，在大量历史数据上训练，最终对商业决策提供帮助。本模型实现了下述论文中提出的DNN模型：
+
+```text
+@inproceedings{guo2017deepfm,
+ title={DeepFM: A Factorization-Machine based Neural Network for CTR Prediction},
+ author={Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li and Xiuqiang He},
+ booktitle={the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI)},
+ pages={1725--1731},
+ year={2017}
+}
+```
+
+#
+## 数据准备
+### 数据来源
+训练及测试数据集选用[Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge/)所用的Criteo数据集。该数据集包括两部分：训练集和测试集。训练集包含一段时间内Criteo的部分流量，测试集则对应训练数据后一天的广告点击流量。
+每一行数据格式如下所示：
+```bash
+ ... ...
+```
+其中``````表示广告是否被点击，点击用1表示，未点击用0表示。``````代表数值特征（连续特征），共有13个连续特征。``````代表分类特征（离散特征），共有26个离散特征。相邻两个特征用```\t```分隔，缺失特征用空格表示。测试集中``````特征已被移除。
+
+### 数据预处理
+数据预处理共包括两步：
+- 将原始训练集按9:1划分为训练集和验证集
+- 数值特征（连续特征）需进行归一化处理，但需要注意的是，对每一个特征``````，归一化时用到的最大值并不是用全局最大值，而是取排序后95%位置处的特征值作为最大值，同时保留极值。
+
+### 一键下载训练及测试数据
+```bash
+sh download_data.sh
+```
+执行该脚本，会从国内源的服务器上下载Criteo数据集，并解压到指定文件夹。全量训练数据放置于`./train_data_full/`，全量测试数据放置于`./test_data_full/`，用于快速验证的训练数据与测试数据放置于`./train_data/`与`./test_data/`。
+
+执行该脚本的理想输出为：
+```bash
+> sh download_data.sh
+--2019-11-26 06:31:33-- https://fleet.bj.bcebos.com/ctr_data.tar.gz
+Resolving fleet.bj.bcebos.com... 10.180.112.31
+Connecting to fleet.bj.bcebos.com|10.180.112.31|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 4041125592 (3.8G) [application/x-gzip]
+Saving to: “ctr_data.tar.gz”
+
+100%[==================================================================================================================>] 4,041,125,592 120M/s in 32s
+
+2019-11-26 06:32:05 (120 MB/s) - “ctr_data.tar.gz” saved [4041125592/4041125592]
+
+raw_data/
+raw_data/part-55
+raw_data/part-113
+...
+test_data/part-227
+test_data/part-222
+Complete data download.
+Full Train data stored in ./train_data_full
+Full Test data stored in ./test_data_full
+Rapid Verification train data stored in ./train_data
+Rapid Verification test data stored in ./test_data
+```
+至此，我们已完成数据准备的全部工作。
+
+## 数据读取
+为了能高速运行CTR模型的训练，`PaddleRec`封装了`dataset`与`dataloader`API进行高性能的数据读取。
+
+如何在我们的训练中引入dataset读取方式呢？无需变更数据格式，只需在我们的训练代码中加入以下内容，便可达到媲美二进制读取的高效率，以下是一个比较完整的流程：
+
+### 引入dataset
+
+1. 通过工厂类`fluid.DatasetFactory()`创建一个dataset对象。
+2. 将我们定义好的数据输入格式传给dataset，通过`dataset.set_use_var(inputs)`实现。
+3. 指定我们的数据读取方式，由`dataset_generator.py`实现数据读取的规则，后面将会介绍读取规则的实现。
+4. 指定数据读取的batch_size。
+5. 指定数据读取的线程数，该线程数和训练线程应保持一致，两者为耦合的关系。
+6. 指定dataset读取的训练文件的列表。
+
+```python
+def get_dataset(inputs, args)
+ dataset = fluid.DatasetFactory().create_dataset()
+ dataset.set_use_var(inputs)
+ dataset.set_pipe_command("python dataset_generator.py")
+ dataset.set_batch_size(args.batch_size)
+ dataset.set_thread(int(args.cpu_num))
+ file_list = [
+ str(args.train_files_path) + "/%s" % x
+ for x in os.listdir(args.train_files_path)
+ ]
+ logger.info("file list: {}".format(file_list))
+ return dataset, file_list
+```
+
+### 如何指定数据读取规则
+
+在上文我们提到了由`dataset_generator.py`实现具体的数据读取规则，那么，怎样为dataset创建数据读取的规则呢？
+以下是`dataset_generator.py`的全部代码，具体流程如下：
+1. 首先我们需要引入dataset的库，位于`paddle.fluid.incubate.data_generator`。
+2. 声明一些在数据读取中会用到的变量，如示例代码中的`cont_min_`、`categorical_range_`等。
+3. 创建一个子类，继承dataset的基类，基类有多种选择，如果是多种数据类型混合，并且需要转化为数值进行预处理的，建议使用`MultiSlotDataGenerator`；若已经完成了预处理并保存为数据文件，可以直接以`string`的方式进行读取，使用`MultiSlotStringDataGenerator`，能够进一步加速。在示例代码，我们继承并实现了名为`CriteoDataset`的dataset子类，使用`MultiSlotDataGenerator`方法。
+4. 继承并实现基类中的`generate_sample`函数，逐行读取数据。该函数应返回一个可以迭代的reader方法(带有yield的函数不再是一个普通的函数，而是一个生成器generator，成为了可以迭代的对象，等价于一个数组、链表、文件、字符串etc.)
+5. 在这个可以迭代的函数中，如示例代码中的`def reader()`，我们定义数据读取的逻辑。例如对以行为单位的数据进行截取，转换及预处理。
+6. 最后，我们需要将数据整理为特定的格式，才能够被dataset正确读取，并灌入的训练的网络中。简单来说，数据的输出顺序与我们在网络中创建的`inputs`必须是严格一一对应的，并转换为类似字典的形式。在示例代码中，我们使用`zip`的方法将参数名与数值构成的元组组成了一个list，并将其yield输出。如果展开来看，我们输出的数据形如`[('dense_feature',[value]),('C1',[value]),('C2',[value]),...,('C26',[value]),('label',[value])]`
+
+
+```python
+import paddle.fluid.incubate.data_generator as dg
+
+cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+hash_dim_ = 1000001
+continuous_range_ = range(1, 14)
+categorical_range_ = range(14, 40)
+
+class CriteoDataset(dg.MultiSlotDataGenerator):
+
+ def generate_sample(self, line):
+
+ def reader():
+ features = line.rstrip('\n').split('\t')
+ dense_feature = []
+ sparse_feature = []
+ for idx in continuous_range_:
+ if features[idx] == "":
+ dense_feature.append(0.0)
+ else:
+ dense_feature.append(
+ (float(features[idx]) - cont_min_[idx - 1]) /
+ cont_diff_[idx - 1])
+ for idx in categorical_range_:
+ sparse_feature.append(
+ [hash(str(idx) + features[idx]) % hash_dim_])
+ label = [int(features[0])]
+ process_line = dense_feature, sparse_feature, label
+ feature_name = ["dense_feature"]
+ for idx in categorical_range_:
+ feature_name.append("C" + str(idx - 13))
+ feature_name.append("label")
+
+ yield zip(feature_name, [dense_feature] + sparse_feature + [label])
+
+ return reader
+
+d = CriteoDataset()
+d.run_from_stdin()
+```
+### 快速调试Dataset
+我们可以脱离组网架构，单独验证Dataset的输出是否符合我们预期。使用命令
+`cat 数据文件 | python dataset读取python文件`进行dataset代码的调试：
+```bash
+cat train_data/part-0 | python dataset_generator.py
+```
+输出的数据格式如下：
+` dense_input:size ; dense_input:value ; sparse_input:size ; sparse_input:value ; ... ; sparse_input:size ; sparse_input:value ; label:size ; label:value `
+
+理想的输出为(截取了一个片段)：
+```bash
+...
+13 0.05 0.00663349917081 0.05 0.0 0.02159375 0.008 0.15 0.04 0.362 0.1 0.2 0.0 0.04 1 715353 1 817085 1 851010 1 833725 1 286835 1 948614 1 881652 1 507110 1 27346 1 646986 1 643076 1 200960 1 18464 1 202774 1 532679 1 729573 1 342789 1 562805 1 880474 1 984402 1 666449 1 26235 1 700326 1 452909 1 884722 1 787527 1 0
+...
+```
+
+#
+## 模型组网
+### 数据输入声明
+正如数据准备章节所介绍，Criteo数据集中，分为连续数据与离散（稀疏）数据，所以整体而言，CTR-DNN模型的数据输入层包括三个，分别是：`dense_input`用于输入连续数据，维度由超参数`dense_feature_dim`指定，数据类型是归一化后的浮点型数据。`sparse_input_ids`用于记录离散数据，在Criteo数据集中，共有26个slot，所以我们创建了名为`C1~C26`的26个稀疏参数输入，并设置`lod_level=1`，代表其为变长数据，数据类型为整数；最后是每条样本的`label`，代表了是否被点击，数据类型是整数，0代表负样例，1代表正样例。
+
+在Paddle中数据输入的声明使用`paddle.fluid.data()`，会创建指定类型的占位符，数据IO会依据此定义进行数据的输入。
+```python
+dense_input = fluid.data(name="dense_input",
+ shape=[-1, args.dense_feature_dim],
+ dtype="float32")
+
+sparse_input_ids = [
+ fluid.data(name="C" + str(i),
+ shape=[-1, 1],
+ lod_level=1,
+ dtype="int64") for i in range(1, 27)
+]
+
+label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
+inputs = [dense_input] + sparse_input_ids + [label]
+```
+
+### CTR-DNN模型组网
+
+CTR-DNN模型的组网比较直观，本质是一个二分类任务，代码参考`network_conf.py`。模型主要组成是一个`Embedding`层，三个`FC`层，以及相应的分类任务的loss计算和auc计算。
+
+#### Embedding层
+首先介绍Embedding层的搭建方式：`Embedding`层的输入是`sparse_input`，shape由超参的`sparse_feature_dim`和`embedding_size`定义。需要特别解释的是`is_sparse`参数，当我们指定`is_sprase=True`后，计算图会将该参数视为稀疏参数，反向更新以及分布式通信时，都以稀疏的方式进行，会极大的提升运行效率，同时保证效果一致。
+
+各个稀疏的输入通过Embedding层后，将其合并起来，置于一个list内，以方便进行concat的操作。
+
+```python
+def embedding_layer(input):
+ return fluid.layers.embedding(
+ input=input,
+ is_sparse=True,
+ size=[args.sparse_feature_dim,
+ args.embedding_size],
+ param_attr=fluid.ParamAttr(
+ name="SparseFeatFactors",
+ initializer=fluid.initializer.Uniform()),
+ )
+
+sparse_embed_seq = list(map(embedding_layer, inputs[1:-1])) # [C1~C26]
+```
+
+#### FC层
+将离散数据通过embedding查表得到的值，与连续数据的输入进行`concat`操作，合为一个整体输入，作为全链接层的原始输入。我们共设计了3层FC，每层FC的输出维度都为400，每层FC都后接一个`relu`激活函数，每层FC的初始化方式为符合正态分布的随机初始化，标准差与上一层的输出维度的平方根成反比。
+```python
+concated = fluid.layers.concat(sparse_embed_seq + inputs[0:1], axis=1)
+
+fc1 = fluid.layers.fc(
+ input=concated,
+ size=400,
+ act="relu",
+ param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
+ scale=1 / math.sqrt(concated.shape[1]))),
+)
+fc2 = fluid.layers.fc(
+ input=fc1,
+ size=400,
+ act="relu",
+ param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
+ scale=1 / math.sqrt(fc1.shape[1]))),
+)
+fc3 = fluid.layers.fc(
+ input=fc2,
+ size=400,
+ act="relu",
+ param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
+ scale=1 / math.sqrt(fc2.shape[1]))),
+)
+```
+#### Loss及Auc计算
+- 预测的结果通过一个输出shape为2的FC层给出，该FC层的激活函数时softmax，会给出每条样本分属于正负样本的概率。
+- 每条样本的损失由交叉熵给出，交叉熵的输入维度为[batch_size,2]，数据类型为float，label的输入维度为[batch_size,1]，数据类型为int。
+- 该batch的损失`avg_cost`是各条样本的损失之和
+- 我们同时还会计算预测的auc，auc的结果由`fluid.layers.auc()`给出，该层的返回值有三个，分别是全局auc: `auc_var`，当前batch的auc: `batch_auc_var`，以及auc_states: `auc_states`，auc_states包含了`batch_stat_pos, batch_stat_neg, stat_pos, stat_neg`信息。`batch_auc`我们取近20个batch的平均，由参数`slide_steps=20`指定，roc曲线的离散化的临界数值设置为4096，由`num_thresholds=2**12`指定。
+```
+predict = fluid.layers.fc(
+ input=fc3,
+ size=2,
+ act="softmax",
+ param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
+ scale=1 / math.sqrt(fc3.shape[1]))),
+ )
+
+cost = fluid.layers.cross_entropy(input=predict, label=inputs[-1])
+avg_cost = fluid.layers.reduce_sum(cost)
+accuracy = fluid.layers.accuracy(input=predict, label=inputs[-1])
+auc_var, batch_auc_var, auc_states = fluid.layers.auc(
+ input=predict,
+ label=inputs[-1],
+ num_thresholds=2**12,
+ slide_steps=20)
+```
+
+完成上述组网后，我们最终可以通过训练拿到`avg_cost`与`auc`两个重要指标。
diff --git a/models/rank/dnn/config.yaml b/models/rank/dnn/config.yaml
index 66f5053a0f2364a9038e6b9b5f3ccaad6481d6ca..fd64935dd2080291fc13911befc0481604c3464a 100755
--- a/models/rank/dnn/config.yaml
+++ b/models/rank/dnn/config.yaml
@@ -12,36 +12,72 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
+# workspace
+workspace: "paddlerec.models.rank.dnn"
- epochs: 10
- workspace: "fleetrec.models.rank.dnn"
+# list of dataset
+dataset:
+- name: dataset_train # name of dataset to distinguish different datasets
+ batch_size: 2
+ type: DataLoader # or QueueDataset
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
+ dense_slots: "dense_var:13"
+- name: dataset_infer # name
+ batch_size: 2
+ type: DataLoader # or QueueDataset
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
+ dense_slots: "dense_var:13"
- reader:
- batch_size: 2
- class: "{workspace}/../criteo_reader.py"
- train_data_path: "{workspace}/data/train"
+# hyper parameters of user-defined network
+hyper_parameters:
+ # optimizer config
+ optimizer:
+ class: Adam
+ learning_rate: 0.001
+ strategy: async
+ # user-defined pairs
+ sparse_inputs_slots: 27
+ sparse_feature_number: 1000001
+ sparse_feature_dim: 9
+ dense_input_dim: 13
+ fc_sizes: [512, 256, 128, 32]
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- sparse_inputs_slots: 27
- sparse_feature_number: 1000001
- sparse_feature_dim: 9
- dense_input_dim: 13
- fc_sizes: [512, 256, 128, 32]
- learning_rate: 0.001
- optimizer: adam
+# select runner by name
+mode: runner1
+# config of each runner.
+# runner is a kind of paddle training class, which wraps the train/infer process.
+runner:
+- name: runner1
+ class: single_train
+ # num of epochs
+ epochs: 10
+ # device to run training or infer
+ device: cpu
+ save_checkpoint_interval: 2 # save model interval of epochs
+ save_inference_interval: 4 # save inference
+ save_checkpoint_path: "increment" # save checkpoint path
+ save_inference_path: "inference" # save inference path
+ save_inference_feed_varnames: [] # feed vars of save inference
+ save_inference_fetch_varnames: [] # fetch vars of save inference
+ init_model_path: "" # load model path
+ print_interval: 10
+- name: runner2
+ class: single_infer
+ # num of epochs
+ epochs: 10
+ # device to run training or infer
+ device: cpu
+ init_model_path: "increment/0" # load model path
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+# runner will run all the phase in each epoch
+phase:
+- name: phase1
+ model: "{workspace}/model.py" # user-defined model
+ dataset_name: dataset_train # select dataset by name
+ thread_num: 1
+#- name: phase2
+# model: "{workspace}/model.py" # user-defined model
+# dataset_name: dataset_infer # select dataset by name
+# thread_num: 1
diff --git a/models/rank/dnn/data/download.sh b/models/rank/dnn/data/download.sh
new file mode 100644
index 0000000000000000000000000000000000000000..56816f7d6be2227fbddafe49d3e24b1ef585a40c
--- /dev/null
+++ b/models/rank/dnn/data/download.sh
@@ -0,0 +1,13 @@
+wget --no-check-certificate https://fleet.bj.bcebos.com/ctr_data.tar.gz
+tar -zxvf ctr_data.tar.gz
+mv ./raw_data ./train_data_full
+mkdir train_data && cd train_data
+cp ../train_data_full/part-0 ../train_data_full/part-1 ./ && cd ..
+mv ./test_data ./test_data_full
+mkdir test_data && cd test_data
+cp ../test_data_full/part-220 ./ && cd ..
+echo "Complete data download."
+echo "Full Train data stored in ./train_data_full "
+echo "Full Test data stored in ./test_data_full "
+echo "Rapid Verification train data stored in ./train_data "
+echo "Rapid Verification test data stored in ./test_data "
diff --git a/models/rank/dnn/data/get_slot_data.py b/models/rank/dnn/data/get_slot_data.py
new file mode 100755
index 0000000000000000000000000000000000000000..f52447d06c297335685a704f688d71aa871328bc
--- /dev/null
+++ b/models/rank/dnn/data/get_slot_data.py
@@ -0,0 +1,71 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid.incubate.data_generator as dg
+
+cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+hash_dim_ = 1000001
+continuous_range_ = range(1, 14)
+categorical_range_ = range(14, 40)
+
+
+class CriteoDataset(dg.MultiSlotDataGenerator):
+ """
+ DacDataset: inheritance MultiSlotDataGeneratior, Implement data reading
+ Help document: http://wiki.baidu.com/pages/viewpage.action?pageId=728820675
+ """
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.rstrip('\n').split('\t')
+ dense_feature = []
+ sparse_feature = []
+ for idx in continuous_range_:
+ if features[idx] == "":
+ dense_feature.append(0.0)
+ else:
+ dense_feature.append(
+ (float(features[idx]) - cont_min_[idx - 1]) /
+ cont_diff_[idx - 1])
+ for idx in categorical_range_:
+ sparse_feature.append(
+ [hash(str(idx) + features[idx]) % hash_dim_])
+ label = [int(features[0])]
+ process_line = dense_feature, sparse_feature, label
+ feature_name = ["dense_feature"]
+ for idx in categorical_range_:
+ feature_name.append("C" + str(idx - 13))
+ feature_name.append("label")
+ s = "click:" + str(label[0])
+ for i in dense_feature:
+ s += " dense_feature:" + str(i)
+ for i in range(1, 1 + len(categorical_range_)):
+ s += " " + str(i) + ":" + str(sparse_feature[i - 1][0])
+ print s.strip()
+ yield None
+
+ return reader
+
+
+d = CriteoDataset()
+d.run_from_stdin()
diff --git a/models/rank/dnn/data/run.sh b/models/rank/dnn/data/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f2d1fc9210d65e521cb7fa19cadab3ec95d95a31
--- /dev/null
+++ b/models/rank/dnn/data/run.sh
@@ -0,0 +1,25 @@
+sh download.sh
+
+mkdir slot_train_data_full
+for i in `ls ./train_data_full`
+do
+ cat train_data_full/$i | python get_slot_data.py > slot_train_data_full/$i
+done
+
+mkdir slot_test_data_full
+for i in `ls ./test_data_full`
+do
+ cat test_data_full/$i | python get_slot_data.py > slot_test_data_full/$i
+done
+
+mkdir slot_train_data
+for i in `ls ./train_data`
+do
+ cat train_data/$i | python get_slot_data.py > slot_train_data/$i
+done
+
+mkdir slot_test_data
+for i in `ls ./test_data`
+do
+ cat test_data/$i | python get_slot_data.py > slot_test_data/$i
+done
diff --git a/models/rank/dnn/data/sample_data/train/sample_train.txt b/models/rank/dnn/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b2b2e2022a8601d60b6e47ea9665dcc314bd04b6
--- /dev/null
+++ b/models/rank/dnn/data/sample_data/train/sample_train.txt
@@ -0,0 +1,80 @@
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.08 dense_feature:0.207421875 dense_feature:0.028 dense_feature:0.35 dense_feature:0.08 dense_feature:0.082 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.08 1:737395 2:210498 3:903564 4:286224 5:286835 6:906818 7:906116 8:67180 9:27346 10:51086 11:142177 12:95024 13:157883 14:873363 15:600281 16:812592 17:228085 18:35900 19:880474 20:984402 21:100885 22:26235 23:410878 24:798162 25:499868 26:306163
+click:1 dense_feature:0.0 dense_feature:0.932006633499 dense_feature:0.02 dense_feature:0.14 dense_feature:0.0395625 dense_feature:0.328 dense_feature:0.98 dense_feature:0.12 dense_feature:1.886 dense_feature:0.0 dense_feature:1.8 dense_feature:0.0 dense_feature:0.14 1:715353 2:761523 3:432904 4:892267 5:515218 6:948614 7:266726 8:67180 9:27346 10:266081 11:286126 12:789480 13:49621 14:255651 15:47663 16:79797 17:342789 18:616331 19:880474 20:984402 21:242209 22:26235 23:669531 24:26284 25:269955 26:187951
+click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.08 dense_feature:0.06 dense_feature:0.14125 dense_feature:0.076 dense_feature:0.05 dense_feature:0.22 dense_feature:0.208 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.06 1:737395 2:952384 3:511141 4:271077 5:286835 6:948614 7:903547 8:507110 9:27346 10:56047 11:612953 12:747707 13:977426 14:671506 15:158148 16:833738 17:342789 18:427155 19:880474 20:537425 21:916237 22:26235 23:468277 24:676936 25:751788 26:363967
+click:0 dense_feature:0.0 dense_feature:0.124378109453 dense_feature:0.02 dense_feature:0.04 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.08 dense_feature:0.024 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.04 1:210127 2:286436 3:183920 4:507656 5:286835 6:906818 7:199553 8:67180 9:502607 10:708281 11:809876 12:888238 13:375164 14:202774 15:459895 16:475933 17:555571 18:847163 19:26230 20:26229 21:808836 22:191474 23:410878 24:315120 25:26224 26:26223
+click:0 dense_feature:0.1 dense_feature:0.0149253731343 dense_feature:0.34 dense_feature:0.32 dense_feature:0.016421875 dense_feature:0.098 dense_feature:0.04 dense_feature:0.96 dense_feature:0.202 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.32 1:230803 2:817085 3:539110 4:388629 5:286835 6:948614 7:586040 8:67180 9:27346 10:271155 11:176640 12:827381 13:36881 14:202774 15:397299 16:411672 17:342789 18:474060 19:880474 20:984402 21:216871 22:26235 23:761351 24:787115 25:884722 26:904135
+click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.13 dense_feature:0.04 dense_feature:0.246203125 dense_feature:0.108 dense_feature:0.05 dense_feature:0.04 dense_feature:0.03 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:737395 2:64837 3:259267 4:336976 5:515218 6:154084 7:847938 8:67180 9:27346 10:708281 11:776766 12:964800 13:324323 14:873363 15:212708 16:637238 17:681378 18:895034 19:673458 20:984402 21:18600 22:26235 23:410878 24:787115 25:884722 26:355412
+click:0 dense_feature:0.0 dense_feature:0.028192371476 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0245625 dense_feature:0.016 dense_feature:0.04 dense_feature:0.12 dense_feature:0.016 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:554760 3:661483 4:263696 5:938478 6:906818 7:786926 8:67180 9:27346 10:245862 11:668197 12:745676 13:432600 14:413795 15:751427 16:272410 17:342789 18:422136 19:26230 20:26229 21:452501 22:26235 23:51381 24:776636 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:1.95 dense_feature:0.28 dense_feature:0.092828125 dense_feature:0.57 dense_feature:0.06 dense_feature:0.4 dense_feature:0.4 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.4 1:371155 2:817085 3:773609 4:555449 5:938478 6:906818 7:166117 8:507110 9:27346 10:545822 11:316654 12:172765 13:989600 14:255651 15:792372 16:606361 17:342789 18:566554 19:880474 20:984402 21:235256 22:191474 23:700326 24:787115 25:884722 26:569095
+click:0 dense_feature:0.0 dense_feature:0.0912106135987 dense_feature:0.01 dense_feature:0.02 dense_feature:0.06625 dense_feature:0.018 dense_feature:0.05 dense_feature:0.06 dense_feature:0.098 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.04 1:230803 2:531472 3:284417 4:661677 5:938478 6:553107 7:21150 8:49466 9:27346 10:526914 11:164508 12:631773 13:882348 14:873363 15:523948 16:687081 17:342789 18:271301 19:26230 20:26229 21:647160 22:26235 23:410878 24:231695 25:26224 26:26223
+click:1 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.01 dense_feature:0.02 dense_feature:0.02153125 dense_feature:0.092 dense_feature:0.05 dense_feature:0.68 dense_feature:0.472 dense_feature:0.0 dense_feature:0.3 dense_feature:0.0 dense_feature:0.02 1:737395 2:532829 3:320762 4:887282 5:286835 6:25207 7:640357 8:67180 9:27346 10:695831 11:739268 12:835325 13:402539 14:873363 15:125813 16:168896 17:342789 18:374414 19:26230 20:26229 21:850229 22:26235 23:410878 24:480027 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.04 dense_feature:0.086125 dense_feature:0.098 dense_feature:0.15 dense_feature:0.06 dense_feature:0.228 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.04 1:210127 2:999497 3:646348 4:520638 5:938478 6:906818 7:438398 8:67180 9:27346 10:975902 11:532544 12:708828 13:815045 14:255651 15:896230 16:663630 17:342789 18:820094 19:687226 20:537425 21:481536 22:26235 23:761351 24:888170 25:250729 26:381125
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diff --git a/models/rank/dnn/data/test/sample_test.txt b/models/rank/dnn/data/test/sample_test.txt
deleted file mode 100755
index 3957a7ff04df61a450a8907d6f60e4f7d1ac2862..0000000000000000000000000000000000000000
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\ No newline at end of file
diff --git a/models/rank/dnn/data/train/sample_train.txt b/models/rank/dnn/data/train/sample_train.txt
deleted file mode 100755
index e3468bf965e13cc0af29e38ccd4f5c69a8c857f0..0000000000000000000000000000000000000000
--- a/models/rank/dnn/data/train/sample_train.txt
+++ /dev/null
@@ -1,100 +0,0 @@
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-0 0 2 15 12 2504 365 1 10 77 0 1 12 68fd1e64 08d6d899 03942b3f afe92929 25c83c98 7e0ccccf f4b9d7ad 0b153874 a73ee510 663eefea c1ee56d0 e977ae2f ebd756bd 07d13a8f 1a277242 82f06a35 d4bb7bd8 87c6f83c 08119c8b 55dd3565 f96a556f
-1 13 2153 1 25 37 3 13 9 29 2 2 3 68fd1e64 4f25e98b de211a17 a8925441 25c83c98 5e64ce5f 1f89b562 a73ee510 be630248 8b94178b fcaae253 025225f2 b28479f6 8ab5b746 3b58b07a e5ba7672 7ef5affa 9437f62f b1252a9d ce247dc1 32c7478e 3fdb382b 001f3601 0fd820a6
-1 37 72 2 3 4 2 49 42 222 1 5 2 05db9164 3f0d3f28 d73310fa b40012b1 4cf72387 fbad5c96 ad3508b1 0b153874 a73ee510 08658f3b ad757a5a 0e466d8f 93b18cb5 32813e21 3440b690 f4219d4b e5ba7672 7da064fc 0471db05 ad3062eb c7dc6720 e5fca70a
-0 0 5 11541 0 0 7 0 05db9164 89ddfee8 15d7420a ff441594 25c83c98 7e0ccccf bdaf7920 0b153874 a73ee510 fbbf2c95 4c074d2a 5f27bc59 f948ca5d 051219e6 d5223973 e2b64862 1e88c74f 5bb2ec8e 0053530c a458ea53 2f4978df 32c7478e 75c8ca05 f0f449dd d21d0b82
-0 15 2 2 87297 0 3 23 0 3 05db9164 a8b6b751 3e67fbbb 10056215 25c83c98 7e0ccccf d9aa9d97 5b392875 7cc72ec2 3b08e48b c4adf918 d9f32d8d 85dbe138 b28479f6 694e45e3 345db5a2 776ce399 d787f192 21ddcdc9 5840adea 7463465b ad3062eb 32c7478e 3d236c54 001f3601 984e0db0
-0 30 1 12 5 11 5 608 19 286 1 47 1 5 05db9164 89ddfee8 ab2fe4c8 428cff52 43b19349 3bf701e7 407438c8 1f89b562 a73ee510 0a164266 755e4a50 3989acff 5978055e b28479f6 25753fb1 cf445916 8efede7f 5bb2ec8e 21ddcdc9 b1252a9d d64ee25a 78e2e389 32c7478e 0b351a52 e8b83407 b1c17344
-1 5 7 2 2 414 21 83 33 925 1 36 2 68fd1e64 421b43cd 06ded108 29998ed1 43b19349 7e0ccccf 4aa938fc 5b392875 a73ee510 03ed27e7 2b9c7071 6aaba33c 1aa94af3 b28479f6 2d0bb053 b041b04a e5ba7672 2804effd 723b4dfd c9d4222a 3a171ecb b34f3128
-0 1 6 21905 0 15 49 0 6 05db9164 62e9e9bf 91c52fd6 89085a81 43b19349 fe6b92e5 e88f1cec 45f7c2dd a73ee510 3b08e48b 8f410860 5ad710aa b8eec0b1 cfef1c29 9a7936cb 9decb3fe 776ce399 d2651d6e c7d10c5e be7c41b4 6f90ebe1
-0 0 174 5 14718 10 0 5 5a9ed9b0 2fe85f57 b61789da 230aba50 25c83c98 fe6b92e5 3a6d4c08 0b153874 a73ee510 d108fc83 41656eae 24604d0c 66815d59 07d13a8f d8524628 78d9f0d0 e5ba7672 f4373605 ab303097 c9d4222a 32c7478e fab2a151
-0 0 7 1 15780 12 6 1 1 1 1 05db9164 8ab240be cedcacac 7967fcf5 25c83c98 7e0ccccf 5f29da0e 0b153874 a73ee510 f476fbe3 0ad37b4b 553e02c3 f9d99d81 1adce6ef 28883800 91a6eec5 1e88c74f ca533012 21ddcdc9 5840adea a97b62ca 423fab69 727a7cc7 445bbe3b 6935065e
-0 0 2 1 1540 44 4 4 268 0 4 5 05db9164 68b3edbf 77f2f2e5 d16679b9 25c83c98 7e0ccccf fcf0132a 1f89b562 a73ee510 aed3d80e d650f1bd 9f32b866 863f8f8a b28479f6 f511c49f 31ca40b6 e5ba7672 752d8b8a dfcfc3fa c7dc6720 aee52b6f
-0 7 31 1 239 1 8 9 49 1 2 0 1 68fd1e64 8084ee93 d032c263 c18be181 43b19349 fe6b92e5 cee47266 0b153874 a73ee510 14781fa9 87fe3e10 dfbb09fb 3bd6c21d b28479f6 16d2748c 84898b2a 27c07bd6 003d4f4f 0014c32a 32c7478e 3b183c5c
-0 -1 12674 4 26 0 73 2 05db9164 09e68b86 eecaacb9 d268ac84 25c83c98 13718bbd 33cca6fa 0b153874 a73ee510 401ced54 683e14e9 ce76d69d 2b9fb512 b28479f6 52baadf5 7bf10350 e5ba7672 5aed7436 55dd3565 b1252a9d 3d7cfd1b 3a171ecb 3fdb382b 3d2bedd7 49d68486
-0 259 4 103468 0 0 14 0 05db9164 8947f767 d8ec4c68 ac1667dd 4cf72387 7e0ccccf 3527bb7c 0b153874 7cc72ec2 3b08e48b 2b9f131d 2a63b3ee aca10c14 07d13a8f 2c14c412 11b43c2e 8efede7f bd17c3da 21ddcdc9 a458ea53 79a05ba5 32c7478e 4fb9fee0 010f6491 004f1180
-1 3 145 4 108 6 4 4 31 1 2 4 8cf07265 6c2cbbdc a42bd759 8b3b6b2e 25c83c98 f00bddf8 062b5529 a73ee510 0d538fca 55795b33 6bb7b021 39795005 64c94865 af094307 c3815fe3 e5ba7672 fb299884 987d0b7a 32c7478e 145ae095
-1 147 1 159966 0 1 1 0 1 68fd1e64 38d50e09 c86b2d8d 657dc3b9 25c83c98 7e0ccccf bc324536 1f89b562 7cc72ec2 474773a7 2bcfb78f 1ca7a526 e6fc496d b28479f6 06373944 ba46c3a1 e5ba7672 fffe2a63 21ddcdc9 b1252a9d eb0fc6f8 ad3062eb 32c7478e df487a73 001f3601 c27f155b
diff --git a/models/rank/dnn/model.py b/models/rank/dnn/model.py
index 71f88d627213d8ddab2f3ecaa97e105553d4a99a..f4425e3d9853b7f7decbc45ff607c4173901d0cf 100755
--- a/models/rank/dnn/model.py
+++ b/models/rank/dnn/model.py
@@ -13,90 +13,60 @@
# limitations under the License.
import math
+
import paddle.fluid as fluid
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
- def input(self):
- def sparse_inputs():
- ids = envs.get_global_env("hyper_parameters.sparse_inputs_slots", None, self._namespace)
-
- sparse_input_ids = [
- fluid.layers.data(name="S" + str(i),
- shape=[1],
- lod_level=1,
- dtype="int64") for i in range(1, ids)
- ]
- return sparse_input_ids
-
- def dense_input():
- dim = envs.get_global_env("hyper_parameters.dense_input_dim", None, self._namespace)
-
- dense_input_var = fluid.layers.data(name="D",
- shape=[dim],
- dtype="float32")
- return dense_input_var
-
- def label_input():
- label = fluid.layers.data(name="click", shape=[1], dtype="int64")
- return label
-
- self.sparse_inputs = sparse_inputs()
- self.dense_input = dense_input()
- self.label_input = label_input()
-
- self._data_var.append(self.dense_input)
-
- for input in self.sparse_inputs:
- self._data_var.append(input)
-
- self._data_var.append(self.label_input)
-
- if self._platform != "LINUX":
- self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
-
- def net(self):
- is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
- sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
- sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
+ def _init_hyper_parameters(self):
+ self.is_distributed = True if envs.get_trainer(
+ ) == "CtrTrainer" else False
+ self.sparse_feature_number = envs.get_global_env(
+ "hyper_parameters.sparse_feature_number")
+ self.sparse_feature_dim = envs.get_global_env(
+ "hyper_parameters.sparse_feature_dim")
+ self.learning_rate = envs.get_global_env(
+ "hyper_parameters.learning_rate")
+
+ def net(self, input, is_infer=False):
+ self.sparse_inputs = self._sparse_data_var[1:]
+ self.dense_input = [] #self._dense_data_var[0]
+ self.label_input = self._sparse_data_var[0]
def embedding_layer(input):
emb = fluid.layers.embedding(
input=input,
is_sparse=True,
- is_distributed=is_distributed,
- size=[sparse_feature_number, sparse_feature_dim],
+ is_distributed=self.is_distributed,
+ size=[self.sparse_feature_number, self.sparse_feature_dim],
param_attr=fluid.ParamAttr(
name="SparseFeatFactors",
- initializer=fluid.initializer.Uniform()),
- )
- emb_sum = fluid.layers.sequence_pool(
- input=emb, pool_type='sum')
+ initializer=fluid.initializer.Uniform()), )
+ emb_sum = fluid.layers.sequence_pool(input=emb, pool_type='sum')
return emb_sum
- def fc(input, output_size):
- output = fluid.layers.fc(
- input=input, size=output_size,
- act='relu', param_attr=fluid.ParamAttr(
- initializer=fluid.initializer.Normal(
- scale=1.0 / math.sqrt(input.shape[1]))))
- return output
-
sparse_embed_seq = list(map(embedding_layer, self.sparse_inputs))
- concated = fluid.layers.concat(sparse_embed_seq + [self.dense_input], axis=1)
+ concated = fluid.layers.concat(sparse_embed_seq, axis=1)
+ #sparse_embed_seq + [self.dense_input], axis=1)
fcs = [concated]
- hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
+ hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes")
for size in hidden_layers:
- fcs.append(fc(fcs[-1], size))
+ output = fluid.layers.fc(
+ input=fcs[-1],
+ size=size,
+ act='relu',
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Normal(
+ scale=1.0 / math.sqrt(fcs[-1].shape[1]))))
+ fcs.append(output)
predict = fluid.layers.fc(
input=fcs[-1],
@@ -107,30 +77,25 @@ class Model(ModelBase):
self.predict = predict
- def avg_loss(self):
- cost = fluid.layers.cross_entropy(input=self.predict, label=self.label_input)
- avg_cost = fluid.layers.reduce_mean(cost)
- self._cost = avg_cost
-
- def metrics(self):
auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
label=self.label_input,
- num_thresholds=2 ** 12,
+ num_thresholds=2**12,
slide_steps=20)
+ if is_infer:
+ self._infer_results["AUC"] = auc
+ self._infer_results["BATCH_AUC"] = batch_auc
+ return
+
self._metrics["AUC"] = auc
self._metrics["BATCH_AUC"] = batch_auc
-
- def train_net(self):
- self.input()
- self.net()
- self.avg_loss()
- self.metrics()
+ cost = fluid.layers.cross_entropy(
+ input=self.predict, label=self.label_input)
+ avg_cost = fluid.layers.reduce_mean(cost)
+ self._cost = avg_cost
def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
- optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
+ optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)
return optimizer
def infer_net(self):
- self.input()
- self.net()
+ pass
diff --git a/models/rank/readme.md b/models/rank/readme.md
index e458bc04e43a20a4b802a705a55e4c490632c340..91e165b4edcedd72344bf1a57e134b0d037686e7 100755
--- a/models/rank/readme.md
+++ b/models/rank/readme.md
@@ -1,13 +1,13 @@
# 排序模型库
## 简介
-我们提供了常见的排序任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的排序模型包括 [多层神经网络](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/rank/dnn)、[Deep Cross Network](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/rank/dcn)、[DeepFM](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/rank/deepfm)、 [xDeepFM](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/rank/xdeepfm)、[Deep Interest Network](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/rank/din)、[Wide&Deep](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/rank/wide_deep)。
+我们提供了常见的排序任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的排序模型包括 [多层神经网络](dnn)、[Deep Cross Network](dcn)、[DeepFM](deepfm)、 [xDeepFM](xdeepfm)、[Deep Interest Network](din)、[Wide&Deep](wide_deep)。
模型算法库在持续添加中，欢迎关注。
## 目录
* [整体介绍](#整体介绍)
- * [排序模型列表](#排序模型列表)
+ * [模型列表](#模型列表)
* [使用教程](#使用教程)
* [数据处理](#数据处理)
* [训练](#训练)
@@ -18,41 +18,115 @@
* [模型性能列表](#模型性能列表)
## 整体介绍
-### 排序模型列表
+### 模型列表
| 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: |
| DNN | 多层神经网络 | -- |
-| wide&deep | Deep + wide(LR) | [Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/abs/10.1145/2988450.2988454)(2016) |
-| DeepFM | DeepFM | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/abs/1703.04247)(2017) |
-| xDeepFM | xDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/abs/10.1145/3219819.3220023)(2018) |
-| DCN | Deep Cross Network | [Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/abs/10.1145/3124749.3124754)(2017) |
-| DIN | Deep Interest Network | [Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/abs/10.1145/3219819.3219823)(2018) |
+| wide&deep | Deep + wide(LR) | [Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)(2016) |
+| DeepFM | DeepFM | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf)(2017) |
+| DCN | Deep Cross Network | [Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754)(2017) |
+| xDeepFM | xDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)(2018) |
+| DIN | Deep Interest Network | [Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)(2018) |
-## 使用教程
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+[wide&deep](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454):
+
+
+
+[DeepFM](https://arxiv.org/pdf/1703.04247.pdf):
+
+
+
+[XDeepFM](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023):
+
+
+
+[DCN](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754):
+
+
+
+[DIN](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823):
+
+
+
+## 使用教程(快速开始)
+使用样例数据快速开始，参考[训练](###训练) & [预测](###预测)
+## 使用教程(复现论文)
+为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。
+| 模型 | batch_size | thread_num | epoch_num |
+| :------------------: | :--------------------: | :--------------------: | :--------------------: |
+| DNN | 1000 | 10 | 1 |
+| DCN | 512 | 20 | 2 |
+| DeepFM | 100 | 10 | 30 |
+| DIN | 32 | 10 | 100 |
+| Wide&Deep | 40 | 1 | 40 |
+| xDeepFM | 100 | 1 | 10 |
### 数据处理
+参考每个模型目录数据下载&预处理脚本
+
+```
+sh run.sh
+```
+
+数据读取默认使用core/reader.py
+
### 训练
+```
+cd modles/rank/dnn # 进入选定好的排序模型的目录 以DNN为例
+python -m paddlerec.run -m paddlerec.models.rank.dnn # 使用内置配置
+# 如果需要使用自定义配置，config.yaml中workspace需要使用改模型目录的绝对路径
+# 自定义修改超参后，指定配置文件，使用自定义配置
+python -m paddlerec.run -m ./config.yaml
+```
### 预测
+```
+# 修改对应模型的config.yaml，mode配置infer_runner
+# 示例: mode: runner1 -> mode: infer_runner
+# infer_runner中 class配置为 class: single_infer
+# 如果训练阶段和预测阶段的模型输入一致，phase不需要改动，复用train的即可
+
+# 修改完config.yaml后 执行:
+python -m paddlerec.run -m ./config.yaml # 以DNN为例
+```
## 效果对比
-### 模型效果列表
+### 模型效果 (测试)
-| 数据集 | 模型 | loss | 测试auc | acc | mae |
+| 数据集 | 模型 | loss | auc | acc | mae |
| :------------------: | :--------------------: | :---------: |:---------: | :---------: |:---------: |
| Criteo | DNN | -- | 0.79395 | -- | -- |
-| Criteo | DeepFM | 0.44797 | 0.8046 | -- | -- |
-| Criteo | DCN | 0.44703564 | 0.80654419 | -- | -- |
-| Criteo | xDeepFM | -- | -- | 0.48657 | -- |
-| Census-income Data | Wide&Deep | 0.76195(mean) | 0.90577(mean) | -- | -- |
-| Amazon Product | DIN | 0.47005194 | 0.863794952818 | -- | -- |
+| Criteo | DeepFM | 0.44797 | 0.80460 | -- | -- |
+| Criteo | DCN | 0.44704 | 0.80654 | -- | -- |
+| Criteo | xDeepFM | 0.48657 | -- | -- | -- |
+| Census-income Data | Wide&Deep | 0.76195 | 0.90577 | -- | -- |
+| Amazon Product | DIN | 0.47005 | 0.86379 | -- | -- |
+
## 分布式
-### 模型性能列表
-| 数据集 | 模型 | 单机 | 多机（同步） | 多机（异步） | GPU |
-| :------------------: | :--------------------: | :---------: |:---------: |:---------: |:---------: |
-| Criteo | DNN | -- | -- | -- | -- |
-| Criteo | DeepFM | -- | -- | -- | -- |
-| Criteo | DCN | -- | -- | -- | -- |
-| Criteo | xDeepFM | -- | -- | -- | -- |
-| Census-income Data | Wide&Deep | -- | -- | -- | -- |
-| Amazon Product | DIN | -- | -- | -- | -- |
+### 模型训练性能 (样本/s)
+| 数据集 | 模型 | 单机 | 同步 (4节点) | 同步 (8节点) | 同步 (16节点) | 同步 (32节点) |
+| :------------------: | :--------------------: | :---------: |:---------: |:---------: |:---------: |:---------: |
+| Criteo | DNN | 99821 | 148788 | 148788 | 507936 | 856032 |
+| Criteo | DeepFM | -- | -- | -- | -- | -- |
+| Criteo | DCN | -- | -- | -- | -- | -- |
+| Criteo | xDeepFM | -- | -- | -- | -- | -- |
+| Census-income Data | Wide&Deep | -- | -- | -- | -- | -- |
+| Amazon Product | DIN | -- | -- | -- | -- | -- |
+
+----
+
+| 数据集 | 模型 | 单机 | 异步 (4节点) | 异步 (8节点) | 异步 (16节点) | 异步 (32节点) |
+| :------------------: | :--------------------: | :---------: |:---------: |:---------: |:---------: |:---------: |
+| Criteo | DNN | 99821 | 316918 | 602536 | 1130557 | 2048384 |
+| Criteo | DeepFM | -- | -- | -- | -- | -- |
+| Criteo | DCN | -- | -- | -- | -- | -- |
+| Criteo | xDeepFM | -- | -- | -- | -- | -- |
+| Census-income Data | Wide&Deep | -- | -- | -- | -- | -- |
+| Amazon Product | DIN | -- | -- | -- | -- | -- |
diff --git a/models/rank/tagspace/config.yaml b/models/rank/tagspace/config.yaml
deleted file mode 100644
index ce0283e24e0298d20f6d1b5de7200009eb9c9f35..0000000000000000000000000000000000000000
--- a/models/rank/tagspace/config.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-train:
- trainer:
- # for cluster training
- strategy: "async"
-
- epochs: 10
- workspace: "fleetrec.models.rank.tagspace"
-
- reader:
- batch_size: 5
- class: "{workspace}/reader.py"
- train_data_path: "{workspace}/train_data"
-
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- vocab_text_size: 11447
- vocab_tag_size: 4
- emb_dim: 10
- hid_dim: 1000
- win_size: 5
- margin: 0.1
- neg_size: 3
- num_devices: 1
-
-
- save:
- increment:
- dirname: "increment"
- epoch_interval: 1
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 100
- save_last: True
-
diff --git a/models/rank/tagspace/model.py b/models/rank/tagspace/model.py
deleted file mode 100644
index 46d3d7529db7a689d2769642014e25ea6e3b8142..0000000000000000000000000000000000000000
--- a/models/rank/tagspace/model.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import paddle.fluid as fluid
-import math
-
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
-
-import paddle.fluid as fluid
-import paddle.fluid.layers.nn as nn
-import paddle.fluid.layers.tensor as tensor
-import paddle.fluid.layers.control_flow as cf
-
-class Model(ModelBase):
- def __init__(self, config):
- ModelBase.__init__(self, config)
- self.cost = None
- self.metrics = {}
- self.vocab_text_size = 11447#envs.get_global_env("vocab_text_size", None, self._namespace)
- self.vocab_tag_size = 4#envs.get_global_env("vocab_tag_size", None, self._namespace)
- self.emb_dim = 10#envs.get_global_env("emb_dim", None, self._namespace)
- self.hid_dim = 1000#envs.get_global_env("hid_dim", None, self._namespace)
- self.win_size = 5#envs.get_global_env("win_size", None, self._namespace)
- self.margin = 0.1#envs.get_global_env("margin", None, self._namespace)
- self.neg_size = 3#envs.get_global_env("neg_size", None, self._namespace)
- print self.emb_dim
-
- def train_net(self):
- """ network definition """
- text = fluid.data(name="text", shape=[None, 1], lod_level=1, dtype='int64')
- pos_tag = fluid.data(
- name="pos_tag", shape=[None, 1], lod_level=1, dtype='int64')
- neg_tag = fluid.data(
- name="neg_tag", shape=[None, 1], lod_level=1, dtype='int64')
-
- self._data_var = [text, pos_tag, neg_tag]
-
- text_emb = fluid.embedding(
- input=text, size=[self.vocab_text_size, self.emb_dim], param_attr="text_emb")
- text_emb = fluid.layers.squeeze(input=text_emb, axes=[1])
- pos_tag_emb = fluid.embedding(
- input=pos_tag, size=[self.vocab_tag_size, self.emb_dim], param_attr="tag_emb")
- pos_tag_emb = fluid.layers.squeeze(input=pos_tag_emb, axes=[1])
- neg_tag_emb = fluid.embedding(
- input=neg_tag, size=[self.vocab_tag_size, self.emb_dim], param_attr="tag_emb")
- neg_tag_emb = fluid.layers.squeeze(input=neg_tag_emb, axes=[1])
-
- conv_1d = fluid.nets.sequence_conv_pool(
- input=text_emb,
- num_filters=self.hid_dim,
- filter_size=self.win_size,
- act="tanh",
- pool_type="max",
- param_attr="cnn")
- text_hid = fluid.layers.fc(input=conv_1d,
- size=self.emb_dim,
- param_attr="text_hid")
- cos_pos = nn.cos_sim(pos_tag_emb, text_hid)
- mul_text_hid = fluid.layers.sequence_expand_as(x=text_hid, y=neg_tag_emb)
- mul_cos_neg = nn.cos_sim(neg_tag_emb, mul_text_hid)
- cos_neg_all = fluid.layers.sequence_reshape(
- input=mul_cos_neg, new_dim=self.neg_size)
- #choose max negtive cosine
- cos_neg = nn.reduce_max(cos_neg_all, dim=1, keep_dim=True)
- #calculate hinge loss
- loss_part1 = nn.elementwise_sub(
- tensor.fill_constant_batch_size_like(
- input=cos_pos, shape=[-1, 1], value=self.margin, dtype='float32'),
- cos_pos)
- loss_part2 = nn.elementwise_add(loss_part1, cos_neg)
- loss_part3 = nn.elementwise_max(
- tensor.fill_constant_batch_size_like(
- input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
- loss_part2)
- avg_cost = nn.mean(loss_part3)
- less = tensor.cast(cf.less_than(cos_neg, cos_pos), dtype='float32')
- correct = nn.reduce_sum(less)
- self.cost = avg_cost
-
- self.metrics["correct"] = correct
- self.metrics["cos_pos"] = cos_pos
-
- def get_cost_op(self):
- return self.cost
-
- def get_metrics(self):
- return self.metrics
-
- def optimizer(self):
- learning_rate = 0.01#envs.get_global_env("hyper_parameters.base_lr", None, self._namespace)
- sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=learning_rate)
- #sgd_optimizer.minimize(avg_cost)
- return sgd_optimizer
-
-
- def infer_net(self, parameter_list):
- self.train_net()
diff --git a/models/rank/text_classification/config.yaml b/models/rank/text_classification/config.yaml
deleted file mode 100644
index 2104a6131523fe20540e243e1b42d82ae81a800f..0000000000000000000000000000000000000000
--- a/models/rank/text_classification/config.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-train:
- trainer:
- # for cluster training
- strategy: "async"
-
- epochs: 10
- workspace: "fleetrec.models.rank.text_classification"
-
- reader:
- batch_size: 5
- class: "{workspace}/reader.py"
- train_data_path: "{workspace}/train_data"
-
- model:
- models: "{workspace}/model.py"
-
- save:
- increment:
- dirname: "increment"
- epoch_interval: 1
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 100
- save_last: True
-
diff --git a/models/rank/text_classification/model.py b/models/rank/text_classification/model.py
deleted file mode 100644
index 0218ab8c48c1508db623a27a73e611ba20257a36..0000000000000000000000000000000000000000
--- a/models/rank/text_classification/model.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import paddle.fluid as fluid
-import math
-
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
-
-import paddle.fluid as fluid
-import paddle.fluid.layers.nn as nn
-import paddle.fluid.layers.tensor as tensor
-import paddle.fluid.layers.control_flow as cf
-
-class Model(ModelBase):
- def __init__(self, config):
- ModelBase.__init__(self, config)
-
- def train_net(self):
- """ network definition """
-
- data = fluid.data(name="input", shape=[None, max_len], dtype='int64')
- label = fluid.data(name="label", shape=[None, 1], dtype='int64')
- seq_len = fluid.data(name="seq_len", shape=[None], dtype='int64')
- # embedding layer
- emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
- emb = fluid.layers.sequence_unpad(emb, length=seq_len)
- # convolution layer
- conv = fluid.nets.sequence_conv_pool(
- input=emb,
- num_filters=cnn_dim,
- filter_size=cnn_filter_size,
- act="tanh",
- pool_type="max")
-
- # full connect layer
- fc_1 = fluid.layers.fc(input=[conv], size=hid_dim)
- # softmax layer
- prediction = fluid.layers.fc(input=[fc_1], size=class_dim, act="softmax")
- #if is_prediction:
- # return prediction
- cost = fluid.layers.cross_entropy(input=prediction, label=label)
- avg_cost = fluid.layers.mean(x=cost)
- acc = fluid.layers.accuracy(input=prediction, label=label)
-
- self.cost = avg_cost
- self.metrics["acc"] = cos_pos
-
- def get_cost_op(self):
- return self.cost
-
- def get_metrics(self):
- return self.metrics
-
- def optimizer(self):
- learning_rate = 0.01#envs.get_global_env("hyper_parameters.base_lr", None, self._namespace)
- sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=learning_rate)
- #sgd_optimizer.minimize(avg_cost)
- return sgd_optimizer
-
-
- def infer_net(self, parameter_list):
- self.train_net()
diff --git a/models/rank/text_classification/reader.py b/models/rank/text_classification/reader.py
deleted file mode 100644
index 36d07b2c553e452a23b8d53ce4553a8f5245b84a..0000000000000000000000000000000000000000
--- a/models/rank/text_classification/reader.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import re
-import sys
-import collections
-import os
-import six
-import time
-import numpy as np
-import paddle.fluid as fluid
-import paddle
-import csv
-import io
-
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
-
-class TrainReader(Reader):
- def init(self):
- pass
-
- def _process_line(self, l):
- l = l.strip().split(" ")
- data = l[0:10]
- seq_len = l[10:11]
- label = l[11:]
- return data, label, seq_len
-
- def generate_sample(self, line):
- def data_iter():
- data, label, seq_len = self._process_line(line)
- if data is None:
- yield None
- return
- yield [('data', data), ('label', label), ('seq_len', seq_len)]
- return data_iter
diff --git a/models/rank/wide_deep/config.yaml b/models/rank/wide_deep/config.yaml
index 4608715acc8ea8e1f02c2b623edd129c2453dd31..af9e106e24a6c9a6e985f671fabf0e60c4f8608f 100755
--- a/models/rank/wide_deep/config.yaml
+++ b/models/rank/wide_deep/config.yaml
@@ -12,36 +12,59 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
-
- epochs: 10
- workspace: "fleetrec.models.rank.wide_deep"
-
- reader:
- batch_size: 2
- class: "{workspace}/reader.py"
- train_data_path: "{workspace}/data/train_data"
-
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- hidden1_units: 75
- hidden2_units: 50
- hidden3_units: 25
- learning_rate: 0.0001
- reg: 0.001
- act: "relu"
- optimizer: SGD
-
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+# global settings
+debug: false
+workspace: "paddlerec.models.rank.wide_deep"
+
+
+dataset:
+ - name: sample_1
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "label"
+ dense_slots: "wide_input:8 deep_input:58"
+ - name: infer_sample
+ type: QueueDataset
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "label"
+ dense_slots: "wide_input:8 deep_input:58"
+
+hyper_parameters:
+ optimizer:
+ class: SGD
+ learning_rate: 0.0001
+ hidden1_units: 75
+ hidden2_units: 50
+ hidden3_units: 25
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+ - name: train_runner
+ trainer_class: single_train
+ epochs: 1
+ device: cpu
+ init_model_path: ""
+ save_checkpoint_interval: 1
+ save_inference_interval: 1
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ - name: infer_runner
+ trainer_class: single_infer
+ epochs: 1
+ device: cpu
+ init_model_path: "increment/0"
+
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: sample_1
+ thread_num: 1
+#- name: infer_phase
+# model: "{workspace}/model.py"
+# dataset_name: infer_sample
+# thread_num: 1
diff --git a/models/rank/wide_deep/create_data.sh b/models/rank/wide_deep/create_data.sh
deleted file mode 100755
index 3e5e2f4ef3ea38652302d81ef3441ce5e6f0e838..0000000000000000000000000000000000000000
--- a/models/rank/wide_deep/create_data.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-mkdir train_data
-mkdir test_data
-mkdir data
-train_path="/home/yaoxuefeng/repos/models/models/PaddleRec/ctr/wide_deep/data/adult.data"
-test_path="/home/yaoxuefeng/repos/models/models/PaddleRec/ctr/wide_deep/data/adult.test"
-train_data_path="/home/yaoxuefeng/repos/models/models/PaddleRec/ctr/wide_deep/train_data/train_data.csv"
-test_data_path="/home/yaoxuefeng/repos/models/models/PaddleRec/ctr/wide_deep/test_data/test_data.csv"
-
-#pip install -r requirements.txt
-
-#wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data
-#wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test
-
-python data_preparation.py --train_path ${train_path} \
- --test_path ${test_path} \
- --train_data_path ${train_data_path}\
- --test_data_path ${test_data_path}
diff --git a/models/rank/wide_deep/data/create_data.sh b/models/rank/wide_deep/data/create_data.sh
new file mode 100755
index 0000000000000000000000000000000000000000..daf60cea46562a3d910177f509d51d261d69cf1d
--- /dev/null
+++ b/models/rank/wide_deep/data/create_data.sh
@@ -0,0 +1,16 @@
+mkdir train_data
+mkdir test_data
+train_path="adult.data"
+test_path="adult.test"
+train_data_path="./train_data/train_data.csv"
+test_data_path="./test_data/test_data.csv"
+
+pip install -r requirements.txt
+
+wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data
+wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test
+
+python data_preparation.py --train_path ${train_path} \
+ --test_path ${test_path} \
+ --train_data_path ${train_data_path}\
+ --test_data_path ${test_data_path}
diff --git a/models/rank/wide_deep/data/data_preparation.py b/models/rank/wide_deep/data/data_preparation.py
new file mode 100644
index 0000000000000000000000000000000000000000..885070096cd3fd084e9695919121f782505b9e77
--- /dev/null
+++ b/models/rank/wide_deep/data/data_preparation.py
@@ -0,0 +1,151 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import io
+import args
+import pandas as pd
+from sklearn import preprocessing
+
+
+def _clean_file(source_path, target_path):
+ """makes changes to match the CSV format."""
+ with io.open(source_path, 'r') as temp_eval_file:
+ with io.open(target_path, 'w') as eval_file:
+ for line in temp_eval_file:
+ line = line.strip()
+ line = line.replace(', ', ',')
+ if not line or ',' not in line:
+ continue
+ if line[-1] == '.':
+ line = line[:-1]
+ line += '\n'
+ eval_file.write(line)
+
+
+def build_model_columns(train_data_path, test_data_path):
+ # The column names are from
+ # https://www2.1010data.com/documentationcenter/prod/Tutorials/MachineLearningExamples/CensusIncomeDataSet.html
+ column_names = [
+ 'age', 'workclass', 'fnlwgt', 'education', 'education_num',
+ 'marital_status', 'occupation', 'relationship', 'race', 'gender',
+ 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',
+ 'income_bracket'
+ ]
+
+ # Load the dataset in Pandas
+ train_df = pd.read_csv(
+ train_data_path,
+ delimiter=',',
+ header=None,
+ index_col=None,
+ names=column_names)
+ test_df = pd.read_csv(
+ test_data_path,
+ delimiter=',',
+ header=None,
+ index_col=None,
+ names=column_names)
+
+ # First group of tasks according to the paper
+ #label_columns = ['income_50k', 'marital_stat']
+ categorical_columns = [
+ 'education', 'marital_status', 'relationship', 'workclass',
+ 'occupation'
+ ]
+ for col in categorical_columns:
+ label_train = preprocessing.LabelEncoder()
+ train_df[col] = label_train.fit_transform(train_df[col])
+ label_test = preprocessing.LabelEncoder()
+ test_df[col] = label_test.fit_transform(test_df[col])
+
+ bins = [18, 25, 30, 35, 40, 45, 50, 55, 60, 65]
+ train_df['age_buckets'] = pd.cut(train_df['age'].values.tolist(),
+ bins,
+ labels=False)
+ test_df['age_buckets'] = pd.cut(test_df['age'].values.tolist(),
+ bins,
+ labels=False)
+
+ base_columns = [
+ 'education', 'marital_status', 'relationship', 'workclass',
+ 'occupation', 'age_buckets'
+ ]
+
+ train_df['education_occupation'] = train_df['education'].astype(
+ str) + '_' + train_df['occupation'].astype(str)
+ test_df['education_occupation'] = test_df['education'].astype(
+ str) + '_' + test_df['occupation'].astype(str)
+ train_df['age_buckets_education_occupation'] = train_df[
+ 'age_buckets'].astype(str) + '_' + train_df['education'].astype(
+ str) + '_' + train_df['occupation'].astype(str)
+ test_df['age_buckets_education_occupation'] = test_df[
+ 'age_buckets'].astype(str) + '_' + test_df['education'].astype(
+ str) + '_' + test_df['occupation'].astype(str)
+ crossed_columns = [
+ 'education_occupation', 'age_buckets_education_occupation'
+ ]
+
+ for col in crossed_columns:
+ label_train = preprocessing.LabelEncoder()
+ train_df[col] = label_train.fit_transform(train_df[col])
+ label_test = preprocessing.LabelEncoder()
+ test_df[col] = label_test.fit_transform(test_df[col])
+
+ wide_columns = base_columns + crossed_columns
+
+ train_df_temp = pd.get_dummies(
+ train_df[categorical_columns], columns=categorical_columns)
+ test_df_temp = pd.get_dummies(
+ test_df[categorical_columns], columns=categorical_columns)
+ train_df = train_df.join(train_df_temp)
+ test_df = test_df.join(test_df_temp)
+
+ deep_columns = list(train_df_temp.columns) + [
+ 'age', 'education_num', 'capital_gain', 'capital_loss',
+ 'hours_per_week'
+ ]
+
+ train_df['label'] = train_df['income_bracket'].apply(
+ lambda x: 1 if x == '>50K' else 0)
+ test_df['label'] = test_df['income_bracket'].apply(
+ lambda x: 1 if x == '>50K' else 0)
+
+ with io.open('train_data/columns.txt', 'w') as f:
+ write_str = str(len(wide_columns)) + '\n' + str(len(
+ deep_columns)) + '\n'
+ f.write(write_str)
+ f.close()
+ with io.open('test_data/columns.txt', 'w') as f:
+ write_str = str(len(wide_columns)) + '\n' + str(len(
+ deep_columns)) + '\n'
+ f.write(write_str)
+ f.close()
+
+ train_df[wide_columns + deep_columns + ['label']].fillna(0).to_csv(
+ train_data_path, index=False)
+ test_df[wide_columns + deep_columns + ['label']].fillna(0).to_csv(
+ test_data_path, index=False)
+
+
+def clean_file(train_path, test_path, train_data_path, test_data_path):
+ _clean_file(train_path, train_data_path)
+ _clean_file(test_path, test_data_path)
+
+
+if __name__ == '__main__':
+ args = args.parse_args()
+ clean_file(args.train_path, args.test_path, args.train_data_path,
+ args.test_data_path)
+ build_model_columns(args.train_data_path, args.test_data_path)
diff --git a/models/rank/wide_deep/data/get_slot_data.py b/models/rank/wide_deep/data/get_slot_data.py
new file mode 100755
index 0000000000000000000000000000000000000000..831d05665b01649f22a3270ec949ebda2941928d
--- /dev/null
+++ b/models/rank/wide_deep/data/get_slot_data.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import yaml
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+try:
+ import cPickle as pickle
+except ImportError:
+ import pickle
+import paddle.fluid.incubate.data_generator as dg
+
+
+class TrainReader(dg.MultiSlotDataGenerator):
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
+
+ def init(self):
+ pass
+
+ def _process_line(self, line):
+ line = line.strip().split(',')
+ features = list(map(float, line))
+ wide_feat = features[0:8]
+ deep_feat = features[8:58 + 8]
+ label = features[-1]
+ return wide_feat, deep_feat, [label]
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def data_iter():
+ wide_feat, deep_deat, label = self._process_line(line)
+
+ s = ""
+ for i in [('wide_input', wide_feat), ('deep_input', deep_deat),
+ ('label', label)]:
+ k = i[0]
+ v = i[1]
+ for j in v:
+ s += " " + k + ":" + str(j)
+ print s.strip()
+ yield None
+
+ return data_iter
+
+
+reader = TrainReader("../config.yaml")
+reader.init()
+reader.run_from_stdin()
diff --git a/models/rank/wide_deep/data/run.sh b/models/rank/wide_deep/data/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7b4fb8492a05769377f388faece9e0dc0a82c6c0
--- /dev/null
+++ b/models/rank/wide_deep/data/run.sh
@@ -0,0 +1,13 @@
+sh create_data.sh
+
+mkdir slot_train_data
+for i in `ls ./train_data`
+do
+ cat train_data/$i | python get_slot_data.py > slot_train_data/$i
+done
+
+mkdir slot_test_data
+for i in `ls ./test_data`
+do
+ cat test_data/$i | python get_slot_data.py > slot_test_data/$i
+done
diff --git a/models/rank/wide_deep/data/sample_data/train/train_data.txt b/models/rank/wide_deep/data/sample_data/train/train_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..967b975d703d5aaa0a6e73e6fca74384c6e289dc
--- /dev/null
+++ b/models/rank/wide_deep/data/sample_data/train/train_data.txt
@@ -0,0 +1,500 @@
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+wide_input:15.0 wide_input:4.0 wide_input:3.0 wide_input:4.0 wide_input:7.0 wide_input:0.0 wide_input:88.0 wide_input:57.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:20.0 deep_input:10.0 deep_input:0.0 deep_input:0.0 deep_input:40.0 label:0
+wide_input:4.0 wide_input:4.0 wide_input:3.0 wide_input:4.0 wide_input:8.0 wide_input:0.0 wide_input:144.0 wide_input:100.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:23.0 deep_input:3.0 deep_input:0.0 deep_input:0.0 deep_input:40.0 label:0
+wide_input:15.0 wide_input:4.0 wide_input:1.0 wide_input:4.0 wide_input:10.0 wide_input:1.0 wide_input:78.0 wide_input:217.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:26.0 deep_input:10.0 deep_input:0.0 deep_input:0.0 deep_input:35.0 label:0
+wide_input:11.0 wide_input:2.0 wide_input:0.0 wide_input:0.0 wide_input:0.0 wide_input:0.0 wide_input:25.0 wide_input:1471.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:72.0 deep_input:9.0 deep_input:0.0 deep_input:0.0 deep_input:20.0 label:0
diff --git a/models/rank/wide_deep/model.py b/models/rank/wide_deep/model.py
index c260c90180b017eb777198203b6552d84730a038..e9d4da603e5abf6b44ce86873795695c7cfe150b 100755
--- a/models/rank/wide_deep/model.py
+++ b/models/rank/wide_deep/model.py
@@ -1,84 +1,121 @@
-import paddle.fluid as fluid
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import math
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
-
+
+ def _init_hyper_parameters(self):
+ self.hidden1_units = envs.get_global_env(
+ "hyper_parameters.hidden1_units", 75)
+ self.hidden2_units = envs.get_global_env(
+ "hyper_parameters.hidden2_units", 50)
+ self.hidden3_units = envs.get_global_env(
+ "hyper_parameters.hidden3_units", 25)
+
def wide_part(self, data):
- out = fluid.layers.fc(input=data,
- size=1,
- param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1])),
- regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=1e-4)),
- act=None,
- name='wide')
+ out = fluid.layers.fc(
+ input=data,
+ size=1,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=1.0 / math.sqrt(data.shape[1])),
+ regularizer=fluid.regularizer.L2DecayRegularizer(
+ regularization_coeff=1e-4)),
+ act=None,
+ name='wide')
return out
-
+
def fc(self, data, hidden_units, active, tag):
- output = fluid.layers.fc(input=data,
- size=hidden_units,
- param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1]))),
- act=active,
- name=tag)
-
+ output = fluid.layers.fc(
+ input=data,
+ size=hidden_units,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=1.0 / math.sqrt(data.shape[1]))),
+ act=active,
+ name=tag)
+
return output
-
+
def deep_part(self, data, hidden1_units, hidden2_units, hidden3_units):
l1 = self.fc(data, hidden1_units, 'relu', 'l1')
l2 = self.fc(l1, hidden2_units, 'relu', 'l2')
l3 = self.fc(l2, hidden3_units, 'relu', 'l3')
-
+
return l3
-
- def train_net(self):
- wide_input = fluid.data(name='wide_input', shape=[None, 8], dtype='float32')
- deep_input = fluid.data(name='deep_input', shape=[None, 58], dtype='float32')
- label = fluid.data(name='label', shape=[None, 1], dtype='float32')
- self._data_var.append(wide_input)
- self._data_var.append(deep_input)
- self._data_var.append(label)
-
- hidden1_units = envs.get_global_env("hyper_parameters.hidden1_units", 75, self._namespace)
- hidden2_units = envs.get_global_env("hyper_parameters.hidden2_units", 50, self._namespace)
- hidden3_units = envs.get_global_env("hyper_parameters.hidden3_units", 25, self._namespace)
+
+ def net(self, inputs, is_infer=False):
+ wide_input = self._dense_data_var[0]
+ deep_input = self._dense_data_var[1]
+ label = self._sparse_data_var[0]
+
wide_output = self.wide_part(wide_input)
- deep_output = self.deep_part(deep_input, hidden1_units, hidden2_units, hidden3_units)
-
- wide_model = fluid.layers.fc(input=wide_output,
- size=1,
- param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
- act=None,
- name='w_wide')
-
- deep_model = fluid.layers.fc(input=deep_output,
- size=1,
- param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
- act=None,
- name='w_deep')
-
+ deep_output = self.deep_part(deep_input, self.hidden1_units,
+ self.hidden2_units, self.hidden3_units)
+
+ wide_model = fluid.layers.fc(
+ input=wide_output,
+ size=1,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=1.0)),
+ act=None,
+ name='w_wide')
+
+ deep_model = fluid.layers.fc(
+ input=deep_output,
+ size=1,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=1.0)),
+ act=None,
+ name='w_deep')
+
prediction = fluid.layers.elementwise_add(wide_model, deep_model)
- pred = fluid.layers.sigmoid(fluid.layers.clip(prediction, min=-15.0, max=15.0), name="prediction")
+ pred = fluid.layers.sigmoid(
+ fluid.layers.clip(
+ prediction, min=-15.0, max=15.0),
+ name="prediction")
num_seqs = fluid.layers.create_tensor(dtype='int64')
- acc = fluid.layers.accuracy(input=pred, label=fluid.layers.cast(x=label, dtype='int64'), total=num_seqs)
- auc_var, batch_auc, auc_states = fluid.layers.auc(input=pred, label=fluid.layers.cast(x=label, dtype='int64'))
-
+ acc = fluid.layers.accuracy(
+ input=pred,
+ label=fluid.layers.cast(
+ x=label, dtype='int64'),
+ total=num_seqs)
+ auc_var, batch_auc, auc_states = fluid.layers.auc(
+ input=pred, label=fluid.layers.cast(
+ x=label, dtype='int64'))
+
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc
self._metrics["ACC"] = acc
+ if is_infer:
+ self._infer_results["AUC"] = auc_var
+ self._infer_results["ACC"] = acc
- cost = fluid.layers.sigmoid_cross_entropy_with_logits(x=prediction, label=label)
+ cost = fluid.layers.sigmoid_cross_entropy_with_logits(
+ x=prediction, label=fluid.layers.cast(
+ label, dtype='float32'))
avg_cost = fluid.layers.mean(cost)
self._cost = avg_cost
-
- def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
- optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
- return optimizer
-
- def infer_net(self, parameter_list):
- self.deepfm_net()
\ No newline at end of file
diff --git a/models/rank/xdeepfm/config.yaml b/models/rank/xdeepfm/config.yaml
index a93b61fbce0d835b478c86f0e572bc5c88ab6138..6274d58559f6eaf54549a8cc82b00c2c50684032 100755
--- a/models/rank/xdeepfm/config.yaml
+++ b/models/rank/xdeepfm/config.yaml
@@ -11,40 +11,61 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+debug: false
+workspace: "paddlerec.models.rank.xdeepfm"
-train:
- trainer:
- # for cluster training
- strategy: "async"
+dataset:
+ - name: sample_1
+ type: QueueDataset #或者DataLoader
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "label feat_idx"
+ dense_slots: "feat_value:39"
+ - name: infer_sample
+ type: QueueDataset #或者DataLoader
+ batch_size: 5
+ data_path: "{workspace}/data/sample_data/train"
+ sparse_slots: "label feat_idx"
+ dense_slots: "feat_value:39"
- epochs: 10
- workspace: "fleetrec.models.rank.xdeepfm"
+hyper_parameters:
+ optimizer:
+ class: SGD
+ learning_rate: 0.0001
+ layer_sizes_dnn: [10, 10, 10]
+ layer_sizes_cin: [10, 10]
+ sparse_feature_number: 1086460
+ sparse_feature_dim: 9
+ num_field: 39
+ fc_sizes: [400, 400, 400]
+ act: "relu"
- reader:
- batch_size: 2
- class: "{workspace}/criteo_reader.py"
- train_data_path: "{workspace}/data/train_data"
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- layer_sizes_dnn: [10, 10, 10]
- layer_sizes_cin: [10, 10]
- sparse_feature_number: 1086460
- sparse_feature_dim: 9
- num_field: 39
- fc_sizes: [400, 400, 400]
- learning_rate: 0.0001
- reg: 0.0001
- act: "relu"
- optimizer: SGD
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+runner:
+ - name: train_runner
+ trainer_class: single_train
+ epochs: 1
+ device: cpu
+ init_model_path: ""
+ save_checkpoint_interval: 1
+ save_inference_interval: 1
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ - name: infer_runner
+ trainer_class: single_infer
+ epochs: 1
+ device: cpu
+ init_model_path: "increment/0"
+
+phase:
+- name: phase1
+ model: "{workspace}/model.py"
+ dataset_name: sample_1
+ thread_num: 1
+#- name: infer_phase
+# model: "{workspace}/model.py"
+# dataset_name: infer_sample
+# thread_num: 1
diff --git a/models/rank/xdeepfm/data/download.py b/models/rank/xdeepfm/data/download.py
index d0483ea3f0d5ddfeb1ad5123bd91cf2d5b6e1331..e46a9ced4a69339f5c5f6c45067d34bbbfa39469 100755
--- a/models/rank/xdeepfm/data/download.py
+++ b/models/rank/xdeepfm/data/download.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import os
import shutil
import sys
@@ -6,7 +20,7 @@ LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
sys.path.append(TOOLS_PATH)
-from fleetrec.tools.tools import download_file_and_uncompress, download_file
+from paddlerec.tools.tools import download_file_and_uncompress, download_file
if __name__ == '__main__':
url_train = "https://paddlerec.bj.bcebos.com/xdeepfm%2Ftr"
diff --git a/models/rank/xdeepfm/criteo_reader.py b/models/rank/xdeepfm/data/get_slot_data.py
similarity index 56%
rename from models/rank/xdeepfm/criteo_reader.py
rename to models/rank/xdeepfm/data/get_slot_data.py
index 1b5e4041625228f5ebaa0fefe9f2ada566a5cecb..4426e9647c080dce5debdcdbc3e039ac69a69935 100755
--- a/models/rank/xdeepfm/criteo_reader.py
+++ b/models/rank/xdeepfm/data/get_slot_data.py
@@ -11,19 +11,29 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+import yaml
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
try:
import cPickle as pickle
except ImportError:
import pickle
+import paddle.fluid.incubate.data_generator as dg
+
+
+class TrainReader(dg.MultiSlotDataGenerator):
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
-class TrainReader(Reader):
def init(self):
pass
-
+
def _process_line(self, line):
features = line.strip('\n').split('\t')
feat_idx = []
@@ -33,11 +43,24 @@ class TrainReader(Reader):
feat_value.append(1.0)
label = [int(features[0])]
return feat_idx, feat_value, label
-
+
def generate_sample(self, line):
def data_iter():
feat_idx, feat_value, label = self._process_line(line)
- yield [('feat_idx', feat_idx), ('feat_value', feat_value), ('label',
- label)]
- return data_iter
\ No newline at end of file
+ s = ""
+ for i in [('feat_idx', feat_idx), ('feat_value', feat_value),
+ ('label', label)]:
+ k = i[0]
+ v = i[1]
+ for j in v:
+ s += " " + k + ":" + str(j)
+ print s.strip()
+ yield None
+
+ return data_iter
+
+
+reader = TrainReader("../config.yaml")
+reader.init()
+reader.run_from_stdin()
diff --git a/models/rank/xdeepfm/data/run.sh b/models/rank/xdeepfm/data/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e0e6780632153cd53b4de329b5500f944035b70a
--- /dev/null
+++ b/models/rank/xdeepfm/data/run.sh
@@ -0,0 +1,13 @@
+python download.py
+
+mkdir -p slot_train_data/tr
+for i in `ls ./train_data/tr`
+do
+ cat train_data/tr/$i | python get_slot_data.py > slot_train_data/tr/$i
+done
+
+mkdir slot_test_data/ev
+for i in `ls ./test_data/ev`
+do
+ cat test_data/ev/$i | python get_slot_data.py > slot_test_data/ev/$i
+done
diff --git a/models/rank/xdeepfm/data/sample_data/train/sample_train.txt b/models/rank/xdeepfm/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b0308e17f74efa4272e1871e86d03c236b1945a
--- /dev/null
+++ b/models/rank/xdeepfm/data/sample_data/train/sample_train.txt
@@ -0,0 +1,100 @@
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+feat_idx:0 feat_idx:2 feat_idx:0 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:0 feat_idx:11 feat_idx:12 feat_idx:13 feat_idx:506931 feat_idx:123566 feat_idx:961529 feat_idx:810019 feat_idx:314332 feat_idx:615411 feat_idx:475867 feat_idx:795081 feat_idx:148475 feat_idx:697060 feat_idx:1069621 feat_idx:370551 feat_idx:696973 feat_idx:69630 feat_idx:396064 feat_idx:95177 feat_idx:854924 feat_idx:488825 feat_idx:0 feat_idx:0 feat_idx:581782 feat_idx:0 feat_idx:476211 feat_idx:289148 feat_idx:0 feat_idx:0 feat_value:0.0 feat_value:0.0066672358525 feat_value:0.0 feat_value:0.00103199174407 feat_value:0.000325784854359 feat_value:4.40797425743e-05 feat_value:0.000266377794747 feat_value:0.000165371258475 feat_value:0.00299803576967 feat_value:0.0 feat_value:0.030303030303 feat_value:0.0 feat_value:0.000135263086704 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:0
+feat_idx:1 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:268086 feat_idx:704711 feat_idx:995515 feat_idx:139394 feat_idx:943087 feat_idx:0 feat_idx:546815 feat_idx:144963 feat_idx:148475 feat_idx:364765 feat_idx:552750 feat_idx:920037 feat_idx:816538 feat_idx:223357 feat_idx:790588 feat_idx:560935 feat_idx:13161 feat_idx:750233 feat_idx:734534 feat_idx:1047606 feat_idx:361734 feat_idx:0 feat_idx:122096 feat_idx:434883 feat_idx:502861 feat_idx:203213 feat_value:0.0 feat_value:0.000197921436832 feat_value:4.57770656901e-05 feat_value:0.00206398348813 feat_value:0.000625316933178 feat_value:0.000874634892132 feat_value:0.000142068157198 feat_value:0.000330742516951 feat_value:0.00975223129674 feat_value:0.0 feat_value:0.00865800865801 feat_value:0.0 feat_value:0.000270526173407 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 label:0
diff --git a/models/rank/xdeepfm/model.py b/models/rank/xdeepfm/model.py
index 6619c78bc718674f0efd12cf841efbe85f3cd729..4ca057bdcf9b858b7423b3fbaaa8e1e51d12ae86 100755
--- a/models/rank/xdeepfm/model.py
+++ b/models/rank/xdeepfm/model.py
@@ -1,96 +1,113 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import paddle.fluid as fluid
-import math
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
- def xdeepfm_net(self):
+ def _init_hyper_parameters(self):
+ self.sparse_feature_number = envs.get_global_env(
+ "hyper_parameters.sparse_feature_number", None)
+ self.sparse_feature_dim = envs.get_global_env(
+ "hyper_parameters.sparse_feature_dim", None)
+ self.num_field = envs.get_global_env("hyper_parameters.num_field",
+ None)
+ self.layer_sizes_cin = envs.get_global_env(
+ "hyper_parameters.layer_sizes_cin", None)
+ self.layer_sizes_dnn = envs.get_global_env(
+ "hyper_parameters.layer_sizes_dnn", None)
+ self.act = envs.get_global_env("hyper_parameters.act", None)
+
+ def net(self, inputs, is_infer=False):
+ raw_feat_idx = self._sparse_data_var[1]
+ raw_feat_value = self._dense_data_var[0]
+ self.label = self._sparse_data_var[0]
+
init_value_ = 0.1
initer = fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)
-
+
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
- sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
- sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
-
+
# ------------------------- network input --------------------------
-
- num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace)
- raw_feat_idx = fluid.data(name='feat_idx', shape=[None, num_field], dtype='int64')
- raw_feat_value = fluid.data(name='feat_value', shape=[None, num_field], dtype='float32')
- self.label = fluid.data(name='label', shape=[None, 1], dtype='float32') # None * 1
- feat_idx = fluid.layers.reshape(raw_feat_idx, [-1, 1]) # (None * num_field) * 1
- feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1]) # None * num_field * 1
+
+ feat_idx = raw_feat_idx
+ feat_value = fluid.layers.reshape(
+ raw_feat_value, [-1, self.num_field, 1]) # None * num_field * 1
feat_embeddings = fluid.embedding(
input=feat_idx,
is_sparse=True,
dtype='float32',
- size=[sparse_feature_number + 1, sparse_feature_dim],
+ size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0,
param_attr=fluid.ParamAttr(initializer=initer))
- feat_embeddings = fluid.layers.reshape(
- feat_embeddings,
- [-1, num_field, sparse_feature_dim]) # None * num_field * embedding_size
+ feat_embeddings = fluid.layers.reshape(feat_embeddings, [
+ -1, self.num_field, self.sparse_feature_dim
+ ]) # None * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size
-
- # ------------------------- set _data_var --------------------------
-
- self._data_var.append(raw_feat_idx)
- self._data_var.append(raw_feat_value)
- self._data_var.append(self.label)
- if self._platform != "LINUX":
- self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
-
+
# -------------------- linear --------------------
weights_linear = fluid.embedding(
input=feat_idx,
is_sparse=True,
dtype='float32',
- size=[sparse_feature_number + 1, 1],
+ size=[self.sparse_feature_number + 1, 1],
padding_idx=0,
param_attr=fluid.ParamAttr(initializer=initer))
weights_linear = fluid.layers.reshape(
- weights_linear, [-1, num_field, 1]) # None * num_field * 1
+ weights_linear, [-1, self.num_field, 1]) # None * num_field * 1
b_linear = fluid.layers.create_parameter(
shape=[1],
dtype='float32',
default_initializer=fluid.initializer.ConstantInitializer(value=0))
y_linear = fluid.layers.reduce_sum(
(weights_linear * feat_value), 1) + b_linear
-
+
# -------------------- CIN --------------------
- layer_sizes_cin = envs.get_global_env("hyper_parameters.layer_sizes_cin", None, self._namespace)
Xs = [feat_embeddings]
- last_s = num_field
- for s in layer_sizes_cin:
+ last_s = self.num_field
+ for s in self.layer_sizes_cin:
# calculate Z^(k+1) with X^k and X^0
X_0 = fluid.layers.reshape(
fluid.layers.transpose(Xs[0], [0, 2, 1]),
- [-1, sparse_feature_dim, num_field,
- 1]) # None, embedding_size, num_field, 1
+ [-1, self.sparse_feature_dim, self.num_field,
+ 1]) # None, embedding_size, num_field, 1
X_k = fluid.layers.reshape(
fluid.layers.transpose(Xs[-1], [0, 2, 1]),
- [-1, sparse_feature_dim, 1, last_s]) # None, embedding_size, 1, last_s
+ [-1, self.sparse_feature_dim, 1,
+ last_s]) # None, embedding_size, 1, last_s
Z_k_1 = fluid.layers.matmul(
X_0, X_k) # None, embedding_size, num_field, last_s
# compresses Z^(k+1) to X^(k+1)
Z_k_1 = fluid.layers.reshape(Z_k_1, [
- -1, sparse_feature_dim, last_s * num_field
+ -1, self.sparse_feature_dim, last_s * self.num_field
]) # None, embedding_size, last_s*num_field
Z_k_1 = fluid.layers.transpose(
Z_k_1, [0, 2, 1]) # None, s*num_field, embedding_size
Z_k_1 = fluid.layers.reshape(
- Z_k_1, [-1, last_s * num_field, 1, sparse_feature_dim]
+ Z_k_1,
+ [-1, last_s * self.num_field, 1, self.sparse_feature_dim]
) # None, last_s*num_field, 1, embedding_size (None, channal_in, h, w)
X_k_1 = fluid.layers.conv2d(
Z_k_1,
@@ -101,7 +118,8 @@ class Model(ModelBase):
param_attr=fluid.ParamAttr(
initializer=initer)) # None, s, 1, embedding_size
X_k_1 = fluid.layers.reshape(
- X_k_1, [-1, s, sparse_feature_dim]) # None, s, embedding_size
+ X_k_1,
+ [-1, s, self.sparse_feature_dim]) # None, s, embedding_size
Xs.append(X_k_1)
last_s = s
@@ -119,16 +137,15 @@ class Model(ModelBase):
# -------------------- DNN --------------------
- layer_sizes_dnn = envs.get_global_env("hyper_parameters.layer_sizes_dnn", None, self._namespace)
- act = envs.get_global_env("hyper_parameters.act", None, self._namespace)
- y_dnn = fluid.layers.reshape(feat_embeddings,
- [-1, num_field * sparse_feature_dim])
- for s in layer_sizes_dnn:
- y_dnn = fluid.layers.fc(input=y_dnn,
- size=s,
- act=act,
- param_attr=fluid.ParamAttr(initializer=initer),
- bias_attr=None)
+ y_dnn = fluid.layers.reshape(
+ feat_embeddings, [-1, self.num_field * self.sparse_feature_dim])
+ for s in self.layer_sizes_dnn:
+ y_dnn = fluid.layers.fc(
+ input=y_dnn,
+ size=s,
+ act=self.act,
+ param_attr=fluid.ParamAttr(initializer=initer),
+ bias_attr=None)
y_dnn = fluid.layers.fc(input=y_dnn,
size=1,
act=None,
@@ -138,11 +155,10 @@ class Model(ModelBase):
# ------------------- xDeepFM ------------------
self.predict = fluid.layers.sigmoid(y_linear + y_cin + y_dnn)
-
- def train_net(self):
- self.xdeepfm_net()
-
- cost = fluid.layers.log_loss(input=self.predict, label=self.label, epsilon=0.0000001)
+ cost = fluid.layers.log_loss(
+ input=self.predict,
+ label=fluid.layers.cast(self.label, "float32"),
+ epsilon=0.0000001)
batch_cost = fluid.layers.reduce_mean(cost)
self._cost = batch_cost
@@ -150,15 +166,9 @@ class Model(ModelBase):
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
- label=label_int,
- slide_steps=0)
+ label=label_int,
+ slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
-
- def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
- optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
- return optimizer
-
- def infer_net(self, parameter_list):
- self.xdeepfm_net()
\ No newline at end of file
+ if is_infer:
+ self._infer_results["AUC"] = auc_var
diff --git a/models/recall/__init__.py b/models/recall/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..abf198b97e6e818e1fbe59006f98492640bcee54 100755
--- a/models/recall/__init__.py
+++ b/models/recall/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/recall/gnn/config.yaml b/models/recall/gnn/config.yaml
index 19eeb9e4f8fcbf473e2ef399801fe4d2f85a468d..50c6d401153a607a88d5eba713fc439303aad868 100755
--- a/models/recall/gnn/config.yaml
+++ b/models/recall/gnn/config.yaml
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
- workspace: "fleetrec.models.recall.gnn"
+ workspace: "paddlerec.models.recall.gnn"
reader:
batch_size: 50
class: "{workspace}/evaluate_reader.py"
@@ -24,7 +24,7 @@ train:
strategy: "async"
epochs: 2
- workspace: "fleetrec.models.recall.gnn"
+ workspace: "paddlerec.models.recall.gnn"
reader:
batch_size: 100
diff --git a/models/recall/gnn/data_process.sh b/models/recall/gnn/data_process.sh
index 9aa009f03cf2595ea0cb54e691800486f26a21bf..fc7ed827e0368c59cab8134d22f78e2200980f18 100755
--- a/models/recall/gnn/data_process.sh
+++ b/models/recall/gnn/data_process.sh
@@ -1,5 +1,19 @@
#! /bin/bash
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
set -e
echo "begin to download data"
@@ -17,5 +31,3 @@ mv diginetica/train.txt train_data
mkdir test_data
mv diginetica/test.txt test_data
-
-
diff --git a/models/recall/gnn/evaluate_reader.py b/models/recall/gnn/evaluate_reader.py
index 113433986b79530aad1c8da8aae84e5d7ad3d60d..b26ea8fa9fc347ce402575104dcfa6de23aa80fc 100755
--- a/models/recall/gnn/evaluate_reader.py
+++ b/models/recall/gnn/evaluate_reader.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,28 +11,32 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
-import io
+
import copy
import random
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
class EvaluateReader(Reader):
def init(self):
- self.batch_size = envs.get_global_env("batch_size", None, "evaluate.reader")
-
+ self.batch_size = envs.get_global_env("batch_size", None,
+ "evaluate.reader")
+
self.input = []
self.length = None
def base_read(self, files):
res = []
for f in files:
- with open(f, "r") as fin:
+ with open(f, "r") as fin:
for line in fin:
- line = line.strip().split('\t')
- res.append(tuple([map(int, line[0].split(',')), int(line[1])]))
+ line = line.strip().split('\t')
+ res.append(
+ tuple([map(int, line[0].split(',')), int(line[1])]))
return res
def make_data(self, cur_batch, batch_size):
@@ -73,10 +77,8 @@ class EvaluateReader(Reader):
u_deg_out[np.where(u_deg_out == 0)] = 1
adj_out.append(np.divide(adj.transpose(), u_deg_out).transpose())
- seq_index.append(
- [[id, np.where(node == i)[0][0]] for i in e[0]])
- last_index.append(
- [id, np.where(node == e[0][last_id[id]])[0][0]])
+ seq_index.append([[id, np.where(node == i)[0][0]] for i in e[0]])
+ last_index.append([id, np.where(node == e[0][last_id[id]])[0][0]])
label.append(e[1] - 1)
mask.append([[1] * (last_id[id] + 1) + [0] *
(max_seq_len - last_id[id] - 1)])
@@ -99,10 +101,13 @@ class EvaluateReader(Reader):
def _reader():
random.shuffle(self.input)
group_remain = self.length % batch_group_size
- for bg_id in range(0, self.length - group_remain, batch_group_size):
- cur_bg = copy.deepcopy(self.input[bg_id:bg_id + batch_group_size])
+ for bg_id in range(0, self.length - group_remain,
+ batch_group_size):
+ cur_bg = copy.deepcopy(self.input[bg_id:bg_id +
+ batch_group_size])
if train:
- cur_bg = sorted(cur_bg, key=lambda x: len(x[0]), reverse=True)
+ cur_bg = sorted(
+ cur_bg, key=lambda x: len(x[0]), reverse=True)
for i in range(0, batch_group_size, batch_size):
cur_batch = cur_bg[i:i + batch_size]
yield self.make_data(cur_batch, batch_size)
@@ -120,10 +125,11 @@ class EvaluateReader(Reader):
else:
# Due to fixed batch_size, discard the remaining ins
return
- #cur_batch = remain_data[i:]
- #yield self.make_data(cur_batch, group_remain % batch_size)
+ # cur_batch = remain_data[i:]
+ # yield self.make_data(cur_batch, group_remain % batch_size)
+
return _reader
-
+
def generate_batch_from_trainfiles(self, files):
self.input = self.base_read(files)
self.length = len(self.input)
@@ -132,4 +138,5 @@ class EvaluateReader(Reader):
def generate_sample(self, line):
def data_iter():
yield []
+
return data_iter
diff --git a/models/recall/gnn/model.py b/models/recall/gnn/model.py
index e63945e8a424257e742c911e2ae2444d234a0805..027fbb721131e203ed22485b4d8f9bd96b8ed3a3 100755
--- a/models/recall/gnn/model.py
+++ b/models/recall/gnn/model.py
@@ -12,32 +12,39 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
import math
+import numpy as np
+
import paddle.fluid as fluid
import paddle.fluid.layers as layers
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.init_config()
-
+
def init_config(self):
self._fetch_interval = 1
- self.items_num, self.ins_num = self.config_read(envs.get_global_env("hyper_parameters.config_path", None, self._namespace))
- self.train_batch_size = envs.get_global_env("batch_size", None, "train.reader")
- self.evaluate_batch_size = envs.get_global_env("batch_size", None, "evaluate.reader")
- self.hidden_size = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
- self.step = envs.get_global_env("hyper_parameters.gnn_propogation_steps", None, self._namespace)
-
+ self.items_num, self.ins_num = self.config_read(
+ envs.get_global_env("hyper_parameters.config_path", None,
+ self._namespace))
+ self.train_batch_size = envs.get_global_env("batch_size", None,
+ "train.reader")
+ self.evaluate_batch_size = envs.get_global_env("batch_size", None,
+ "evaluate.reader")
+ self.hidden_size = envs.get_global_env(
+ "hyper_parameters.sparse_feature_dim", None, self._namespace)
+ self.step = envs.get_global_env(
+ "hyper_parameters.gnn_propogation_steps", None, self._namespace)
def config_read(self, config_path=None):
- if config_path is None:
- raise ValueError("please set train.model.hyper_parameters.config_path at first")
+ if config_path is None:
+ raise ValueError(
+ "please set train.model.hyper_parameters.config_path at first")
with open(config_path, "r") as fin:
item_nums = int(fin.readline().strip())
ins_nums = int(fin.readline().strip())
@@ -45,106 +52,114 @@ class Model(ModelBase):
def input(self, bs):
self.items = fluid.data(
- name="items",
- shape=[bs, -1],
- dtype="int64") #[batch_size, uniq_max]
+ name="items", shape=[bs, -1],
+ dtype="int64") # [batch_size, uniq_max]
self.seq_index = fluid.data(
- name="seq_index",
- shape=[bs, -1, 2],
- dtype="int32") #[batch_size, seq_max, 2]
+ name="seq_index", shape=[bs, -1, 2],
+ dtype="int32") # [batch_size, seq_max, 2]
self.last_index = fluid.data(
- name="last_index",
- shape=[bs, 2],
- dtype="int32") #[batch_size, 2]
+ name="last_index", shape=[bs, 2], dtype="int32") # [batch_size, 2]
self.adj_in = fluid.data(
- name="adj_in",
- shape=[bs, -1, -1],
- dtype="float32") #[batch_size, seq_max, seq_max]
+ name="adj_in", shape=[bs, -1, -1],
+ dtype="float32") # [batch_size, seq_max, seq_max]
self.adj_out = fluid.data(
- name="adj_out",
- shape=[bs, -1, -1],
- dtype="float32") #[batch_size, seq_max, seq_max]
+ name="adj_out", shape=[bs, -1, -1],
+ dtype="float32") # [batch_size, seq_max, seq_max]
self.mask = fluid.data(
- name="mask",
- shape=[bs, -1, 1],
- dtype="float32") #[batch_size, seq_max, 1]
+ name="mask", shape=[bs, -1, 1],
+ dtype="float32") # [batch_size, seq_max, 1]
self.label = fluid.data(
- name="label",
- shape=[bs, 1],
- dtype="int64") #[batch_size, 1]
+ name="label", shape=[bs, 1], dtype="int64") # [batch_size, 1]
- res = [self.items, self.seq_index, self.last_index, self.adj_in, self.adj_out, self.mask, self.label]
+ res = [
+ self.items, self.seq_index, self.last_index, self.adj_in,
+ self.adj_out, self.mask, self.label
+ ]
return res
-
+
def train_input(self):
res = self.input(self.train_batch_size)
self._data_var = res
- use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader", False, self._namespace)
+ use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader",
+ False, self._namespace)
if self._platform != "LINUX" or use_dataloader:
self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=256, use_double_buffer=False, iterable=False)
+ feed_list=self._data_var,
+ capacity=256,
+ use_double_buffer=False,
+ iterable=False)
def net(self, items_num, hidden_size, step, bs):
- stdv = 1.0 / math.sqrt(hidden_size)
+ stdv = 1.0 / math.sqrt(hidden_size)
- def embedding_layer(input, table_name, emb_dim, initializer_instance=None):
+ def embedding_layer(input,
+ table_name,
+ emb_dim,
+ initializer_instance=None):
emb = fluid.embedding(
input=input,
size=[items_num, emb_dim],
param_attr=fluid.ParamAttr(
- name=table_name,
- initializer=initializer_instance),
- )
- return emb
-
- sparse_initializer = fluid.initializer.Uniform(low=-stdv, high=stdv)
- items_emb = embedding_layer(self.items, "emb", hidden_size, sparse_initializer)
+ name=table_name, initializer=initializer_instance), )
+ return emb
+
+ sparse_initializer = fluid.initializer.Uniform(low=-stdv, high=stdv)
+ items_emb = embedding_layer(self.items, "emb", hidden_size,
+ sparse_initializer)
pre_state = items_emb
for i in range(step):
- pre_state = layers.reshape(x=pre_state, shape=[bs, -1, hidden_size])
+ pre_state = layers.reshape(
+ x=pre_state, shape=[bs, -1, hidden_size])
state_in = layers.fc(
input=pre_state,
name="state_in",
size=hidden_size,
act=None,
num_flatten_dims=2,
- param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv)),
- bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv))) #[batch_size, uniq_max, h]
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Uniform(
+ low=-stdv, high=stdv)),
+ bias_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Uniform(
+ low=-stdv, high=stdv))) # [batch_size, uniq_max, h]
state_out = layers.fc(
input=pre_state,
name="state_out",
size=hidden_size,
act=None,
num_flatten_dims=2,
- param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv)),
- bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv))) #[batch_size, uniq_max, h]
-
- state_adj_in = layers.matmul(self.adj_in, state_in) #[batch_size, uniq_max, h]
- state_adj_out = layers.matmul(self.adj_out, state_out) #[batch_size, uniq_max, h]
-
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Uniform(
+ low=-stdv, high=stdv)),
+ bias_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Uniform(
+ low=-stdv, high=stdv))) # [batch_size, uniq_max, h]
+
+ state_adj_in = layers.matmul(self.adj_in,
+ state_in) # [batch_size, uniq_max, h]
+ state_adj_out = layers.matmul(
+ self.adj_out, state_out) # [batch_size, uniq_max, h]
+
gru_input = layers.concat([state_adj_in, state_adj_out], axis=2)
-
- gru_input = layers.reshape(x=gru_input, shape=[-1, hidden_size * 2])
- gru_fc = layers.fc(
- input=gru_input,
- name="gru_fc",
- size=3 * hidden_size,
- bias_attr=False)
+
+ gru_input = layers.reshape(
+ x=gru_input, shape=[-1, hidden_size * 2])
+ gru_fc = layers.fc(input=gru_input,
+ name="gru_fc",
+ size=3 * hidden_size,
+ bias_attr=False)
pre_state, _, _ = fluid.layers.gru_unit(
input=gru_fc,
- hidden=layers.reshape(x=pre_state, shape=[-1, hidden_size]),
+ hidden=layers.reshape(
+ x=pre_state, shape=[-1, hidden_size]),
size=3 * hidden_size)
-
+
final_state = layers.reshape(pre_state, shape=[bs, -1, hidden_size])
seq = layers.gather_nd(final_state, self.seq_index)
last = layers.gather_nd(final_state, self.last_index)
-
+
seq_fc = layers.fc(
input=seq,
name="seq_fc",
@@ -152,34 +167,32 @@ class Model(ModelBase):
bias_attr=False,
act=None,
num_flatten_dims=2,
- param_attr=fluid.ParamAttr(
- initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv))) #[batch_size, seq_max, h]
- last_fc = layers.fc(
- input=last,
- name="last_fc",
- size=hidden_size,
- bias_attr=False,
- act=None,
- num_flatten_dims=1,
- param_attr=fluid.ParamAttr(
- initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv))) #[bathc_size, h]
-
+ param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
+ low=-stdv, high=stdv))) # [batch_size, seq_max, h]
+ last_fc = layers.fc(input=last,
+ name="last_fc",
+ size=hidden_size,
+ bias_attr=False,
+ act=None,
+ num_flatten_dims=1,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Uniform(
+ low=-stdv, high=stdv))) # [bathc_size, h]
+
seq_fc_t = layers.transpose(
- seq_fc, perm=[1, 0, 2]) #[seq_max, batch_size, h]
- add = layers.elementwise_add(
- seq_fc_t, last_fc) #[seq_max, batch_size, h]
+ seq_fc, perm=[1, 0, 2]) # [seq_max, batch_size, h]
+ add = layers.elementwise_add(seq_fc_t,
+ last_fc) # [seq_max, batch_size, h]
b = layers.create_parameter(
shape=[hidden_size],
dtype='float32',
- default_initializer=fluid.initializer.Constant(value=0.0)) #[h]
- add = layers.elementwise_add(add, b) #[seq_max, batch_size, h]
-
- add_sigmoid = layers.sigmoid(add) #[seq_max, batch_size, h]
+ default_initializer=fluid.initializer.Constant(value=0.0)) # [h]
+ add = layers.elementwise_add(add, b) # [seq_max, batch_size, h]
+
+ add_sigmoid = layers.sigmoid(add) # [seq_max, batch_size, h]
add_sigmoid = layers.transpose(
- add_sigmoid, perm=[1, 0, 2]) #[batch_size, seq_max, h]
-
+ add_sigmoid, perm=[1, 0, 2]) # [batch_size, seq_max, h]
+
weight = layers.fc(
input=add_sigmoid,
name="weight_fc",
@@ -187,15 +200,16 @@ class Model(ModelBase):
act=None,
num_flatten_dims=2,
bias_attr=False,
- param_attr=fluid.ParamAttr(
- initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv))) #[batch_size, seq_max, 1]
+ param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
+ low=-stdv, high=stdv))) # [batch_size, seq_max, 1]
weight *= self.mask
- weight_mask = layers.elementwise_mul(seq, weight, axis=0) #[batch_size, seq_max, h]
- global_attention = layers.reduce_sum(weight_mask, dim=1) #[batch_size, h]
-
+ weight_mask = layers.elementwise_mul(
+ seq, weight, axis=0) # [batch_size, seq_max, h]
+ global_attention = layers.reduce_sum(
+ weight_mask, dim=1) # [batch_size, h]
+
final_attention = layers.concat(
- [global_attention, last], axis=1) #[batch_size, 2*h]
+ [global_attention, last], axis=1) # [batch_size, 2*h]
final_attention_fc = layers.fc(
input=final_attention,
name="final_attention_fc",
@@ -203,16 +217,17 @@ class Model(ModelBase):
bias_attr=False,
act=None,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
- low=-stdv, high=stdv))) #[batch_size, h]
-
- # all_vocab = layers.create_global_var(
- # shape=[items_num - 1],
- # value=0,
- # dtype="int64",
- # persistable=True,
- # name="all_vocab")
+ low=-stdv, high=stdv))) # [batch_size, h]
+
+ # all_vocab = layers.create_global_var(
+ # shape=[items_num - 1],
+ # value=0,
+ # dtype="int64",
+ # persistable=True,
+ # name="all_vocab")
all_vocab = np.arange(1, items_num).reshape((-1)).astype('int32')
- all_vocab = fluid.layers.cast(x=fluid.layers.assign(all_vocab), dtype='int64')
+ all_vocab = fluid.layers.cast(
+ x=fluid.layers.assign(all_vocab), dtype='int64')
all_emb = fluid.embedding(
input=all_vocab,
@@ -220,13 +235,13 @@ class Model(ModelBase):
name="emb",
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv)),
- size=[items_num, hidden_size]) #[all_vocab, h]
-
+ size=[items_num, hidden_size]) # [all_vocab, h]
+
logits = layers.matmul(
x=final_attention_fc, y=all_emb,
- transpose_y=True) #[batch_size, all_vocab]
+ transpose_y=True) # [batch_size, all_vocab]
softmax = layers.softmax_with_cross_entropy(
- logits=logits, label=self.label) #[batch_size, 1]
+ logits=logits, label=self.label) # [batch_size, 1]
self.loss = layers.reduce_mean(softmax) # [1]
self.acc = layers.accuracy(input=logits, label=self.label, k=20)
@@ -239,17 +254,21 @@ class Model(ModelBase):
def train_net(self):
self.train_input()
- self.net(self.items_num, self.hidden_size, self.step, self.train_batch_size)
+ self.net(self.items_num, self.hidden_size, self.step,
+ self.train_batch_size)
self.avg_loss()
self.metrics()
def optimizer(self):
- learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
+ None, self._namespace)
step_per_epoch = self.ins_num // self.train_batch_size
- decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None, self._namespace)
- decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None, self._namespace)
+ decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None,
+ self._namespace)
+ decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None,
+ self._namespace)
l2 = envs.get_global_env("hyper_parameters.l2", None, self._namespace)
- optimizer = fluid.optimizer.Adam(
+ optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.exponential_decay(
learning_rate=learning_rate,
decay_steps=decay_steps * step_per_epoch,
@@ -257,18 +276,22 @@ class Model(ModelBase):
regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=l2))
- return optimizer
+ return optimizer
def infer_input(self):
self._reader_namespace = "evaluate.reader"
res = self.input(self.evaluate_batch_size)
- self._infer_data_var = res
+ self._infer_data_var = res
self._infer_data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
-
+ feed_list=self._infer_data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
+
def infer_net(self):
- self.infer_input()
- self.net(self.items_num, self.hidden_size, self.step, self.evaluate_batch_size)
+ self.infer_input()
+ self.net(self.items_num, self.hidden_size, self.step,
+ self.evaluate_batch_size)
self._infer_results['acc'] = self.acc
- self._infer_results['loss'] = self.loss
+ self._infer_results['loss'] = self.loss
diff --git a/models/recall/gnn/raw_data/convert_data.py b/models/recall/gnn/raw_data/convert_data.py
index 2e0e57f1f781f7210c46ef265e1189e99a6f7a96..dfe6bc49fcfca0b98ed5cb0ee9d41832dc5c2205 100755
--- a/models/recall/gnn/raw_data/convert_data.py
+++ b/models/recall/gnn/raw_data/convert_data.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import argparse
import time
import pickle
@@ -10,6 +24,7 @@ parser.add_argument(
help='dataset dir: diginetica/yoochoose1_4/yoochoose1_64/sample')
opt = parser.parse_args()
+
def process_data(file_type):
path = os.path.join(opt.data_dir, file_type)
output_path = os.path.splitext(path)[0] + ".txt"
@@ -23,6 +38,7 @@ def process_data(file_type):
fout.write(str(data[i][1]))
fout.write("\n")
+
process_data("train")
process_data("test")
diff --git a/models/recall/gnn/raw_data/download.py b/models/recall/gnn/raw_data/download.py
index 69a1ee20b2d634e9eca47c621dce82ac2d98b5f2..9bebdf1b37e2cd45369c14bb7446c206de8017a0 100755
--- a/models/recall/gnn/raw_data/download.py
+++ b/models/recall/gnn/raw_data/download.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
import requests
import sys
import time
diff --git a/models/recall/gnn/reader.py b/models/recall/gnn/reader.py
index 89150d5d2faf241f132614ef825a409d600c7ec3..68170f09a7a7c84547a67f970b6e127de40b0ccc 100755
--- a/models/recall/gnn/reader.py
+++ b/models/recall/gnn/reader.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,28 +11,32 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
-import io
+
import copy
import random
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
- self.batch_size = envs.get_global_env("batch_size", None, "train.reader")
-
+ self.batch_size = envs.get_global_env("batch_size", None,
+ "train.reader")
+
self.input = []
self.length = None
def base_read(self, files):
res = []
for f in files:
- with open(f, "r") as fin:
+ with open(f, "r") as fin:
for line in fin:
- line = line.strip().split('\t')
- res.append(tuple([map(int, line[0].split(',')), int(line[1])]))
+ line = line.strip().split('\t')
+ res.append(
+ tuple([map(int, line[0].split(',')), int(line[1])]))
return res
def make_data(self, cur_batch, batch_size):
@@ -73,10 +77,8 @@ class TrainReader(Reader):
u_deg_out[np.where(u_deg_out == 0)] = 1
adj_out.append(np.divide(adj.transpose(), u_deg_out).transpose())
- seq_index.append(
- [[id, np.where(node == i)[0][0]] for i in e[0]])
- last_index.append(
- [id, np.where(node == e[0][last_id[id]])[0][0]])
+ seq_index.append([[id, np.where(node == i)[0][0]] for i in e[0]])
+ last_index.append([id, np.where(node == e[0][last_id[id]])[0][0]])
label.append(e[1] - 1)
mask.append([[1] * (last_id[id] + 1) + [0] *
(max_seq_len - last_id[id] - 1)])
@@ -99,10 +101,13 @@ class TrainReader(Reader):
def _reader():
random.shuffle(self.input)
group_remain = self.length % batch_group_size
- for bg_id in range(0, self.length - group_remain, batch_group_size):
- cur_bg = copy.deepcopy(self.input[bg_id:bg_id + batch_group_size])
+ for bg_id in range(0, self.length - group_remain,
+ batch_group_size):
+ cur_bg = copy.deepcopy(self.input[bg_id:bg_id +
+ batch_group_size])
if train:
- cur_bg = sorted(cur_bg, key=lambda x: len(x[0]), reverse=True)
+ cur_bg = sorted(
+ cur_bg, key=lambda x: len(x[0]), reverse=True)
for i in range(0, batch_group_size, batch_size):
cur_batch = cur_bg[i:i + batch_size]
yield self.make_data(cur_batch, batch_size)
@@ -120,10 +125,11 @@ class TrainReader(Reader):
else:
# Due to fixed batch_size, discard the remaining ins
return
- #cur_batch = remain_data[i:]
- #yield self.make_data(cur_batch, group_remain % batch_size)
+ # cur_batch = remain_data[i:]
+ # yield self.make_data(cur_batch, group_remain % batch_size)
+
return _reader
-
+
def generate_batch_from_trainfiles(self, files):
self.input = self.base_read(files)
self.length = len(self.input)
@@ -132,4 +138,5 @@ class TrainReader(Reader):
def generate_sample(self, line):
def data_iter():
yield []
+
return data_iter
diff --git a/models/recall/gru4rec/config.yaml b/models/recall/gru4rec/config.yaml
index 35e57e01fddf030905a4ca0ae3a94ac782ea1bb3..90cc2d2debca27a0a5e5e7c2fba512c2796a1b14 100644
--- a/models/recall/gru4rec/config.yaml
+++ b/models/recall/gru4rec/config.yaml
@@ -12,38 +12,59 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
+workspace: "paddlerec.models.recall.gru4rec"
- epochs: 3
- workspace: "fleetrec.models.recall.gru4rec"
+dataset:
+- name: dataset_train
+ batch_size: 5
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/rsc15_reader.py"
+- name: dataset_infer
+ batch_size: 5
+ type: QueueDataset
+ data_path: "{workspace}/data/test"
+ data_converter: "{workspace}/rsc15_reader.py"
+
+hyper_parameters:
+ vocab_size: 1000
+ hid_size: 100
+ emb_lr_x: 10.0
+ gru_lr_x: 1.0
+ fc_lr_x: 1.0
+ init_low_bound: -0.04
+ init_high_bound: 0.04
+ optimizer:
+ class: adagrad
+ learning_rate: 0.01
+ strategy: async
- reader:
- batch_size: 5
- class: "{workspace}/rsc15_reader.py"
- train_data_path: "{workspace}/data/train"
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- vocab_size: 1000
- hid_size: 100
- emb_lr_x: 10.0
- gru_lr_x: 1.0
- fc_lr_x: 1.0
- init_low_bound: -0.04
- init_high_bound: 0.04
- learning_rate: 0.01
- optimizer: adagrad
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 10
+- name: infer_runner
+ class: single_infer
+ init_model_path: "increment/0"
+ device: cpu
+ epochs: 3
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
+ #- name: infer
+ # model: "{workspace}/model.py"
+ # dataset_name: dataset_infer
+ # thread_num: 1
diff --git a/models/recall/gru4rec/hdfs.log b/models/recall/gru4rec/hdfs.log
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/models/recall/gru4rec/model.py b/models/recall/gru4rec/model.py
index 5b461d76b17bae5535bc945c1d4c9e988eb7e1a8..571deadf7d97c1010a03590d5360337528b25685 100644
--- a/models/recall/gru4rec/model.py
+++ b/models/recall/gru4rec/model.py
@@ -12,76 +12,82 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import math
import paddle.fluid as fluid
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
- def all_vocab_network(self):
- """ network definition """
- recall_k = envs.get_global_env("hyper_parameters.recall_k", None, self._namespace)
- vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
- hid_size = envs.get_global_env("hyper_parameters.hid_size", None, self._namespace)
- init_low_bound = envs.get_global_env("hyper_parameters.init_low_bound", None, self._namespace)
- init_high_bound = envs.get_global_env("hyper_parameters.init_high_bound", None, self._namespace)
- emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x", None, self._namespace)
- gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x", None, self._namespace)
- fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x", None, self._namespace)
+ def _init_hyper_parameters(self):
+ self.recall_k = envs.get_global_env("hyper_parameters.recall_k")
+ self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+ self.hid_size = envs.get_global_env("hyper_parameters.hid_size")
+ self.init_low_bound = envs.get_global_env(
+ "hyper_parameters.init_low_bound")
+ self.init_high_bound = envs.get_global_env(
+ "hyper_parameters.init_high_bound")
+ self.emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x")
+ self.gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x")
+ self.fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x")
+
+ def input_data(self, is_infer=False, **kwargs):
+
# Input data
src_wordseq = fluid.data(
name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
dst_wordseq = fluid.data(
name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
+ return [src_wordseq, dst_wordseq]
+
+ def net(self, inputs, is_infer=False):
+ src_wordseq = inputs[0]
+ dst_wordseq = inputs[1]
+
emb = fluid.embedding(
input=src_wordseq,
- size=[vocab_size, hid_size],
+ size=[self.vocab_size, self.hid_size],
param_attr=fluid.ParamAttr(
+ name="emb",
initializer=fluid.initializer.Uniform(
- low=init_low_bound, high=init_high_bound),
- learning_rate=emb_lr_x),
+ low=self.init_low_bound, high=self.init_high_bound),
+ learning_rate=self.emb_lr_x),
is_sparse=True)
fc0 = fluid.layers.fc(input=emb,
- size=hid_size * 3,
+ size=self.hid_size * 3,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
- low=init_low_bound, high=init_high_bound),
- learning_rate=gru_lr_x))
+ low=self.init_low_bound,
+ high=self.init_high_bound),
+ learning_rate=self.gru_lr_x))
gru_h0 = fluid.layers.dynamic_gru(
input=fc0,
- size=hid_size,
+ size=self.hid_size,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
- low=init_low_bound, high=init_high_bound),
- learning_rate=gru_lr_x))
+ low=self.init_low_bound, high=self.init_high_bound),
+ learning_rate=self.gru_lr_x))
fc = fluid.layers.fc(input=gru_h0,
- size=vocab_size,
+ size=self.vocab_size,
act='softmax',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
- low=init_low_bound, high=init_high_bound),
- learning_rate=fc_lr_x))
+ low=self.init_low_bound,
+ high=self.init_high_bound),
+ learning_rate=self.fc_lr_x))
cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq)
- acc = fluid.layers.accuracy(input=fc, label=dst_wordseq, k=recall_k)
+ acc = fluid.layers.accuracy(
+ input=fc, label=dst_wordseq, k=self.recall_k)
+ if is_infer:
+ self._infer_results['recall20'] = acc
+ return
avg_cost = fluid.layers.mean(x=cost)
- self._data_var.append(src_wordseq)
- self._data_var.append(dst_wordseq)
self._cost = avg_cost
self._metrics["cost"] = avg_cost
self._metrics["acc"] = acc
-
-
- def train_net(self):
- self.all_vocab_network()
-
-
- def infer_net(self):
- pass
diff --git a/models/recall/gru4rec/rsc15_reader.py b/models/recall/gru4rec/rsc15_reader.py
index fbfc4b91c749eb0c603ac335932103e2dabe83b0..4fe9433a65e1ceec508891eecfaaa5e464bc9e24 100644
--- a/models/recall/gru4rec/rsc15_reader.py
+++ b/models/recall/gru4rec/rsc15_reader.py
@@ -11,10 +11,10 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+from paddlerec.core.reader import Reader
class TrainReader(Reader):
diff --git a/models/recall/multiview-simnet/data_process.sh b/models/recall/multiview-simnet/data_process.sh
deleted file mode 100755
index 15c6c908477cd3ba6a72a65bad039bb10295bd9c..0000000000000000000000000000000000000000
--- a/models/recall/multiview-simnet/data_process.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#! /bin/bash
-
-set -e
-echo "begin to prepare data"
-
-mkdir -p data/train
-mkdir -p data/test
-
-python generate_synthetic_data.py
-
diff --git a/models/recall/ncf/__init__.py b/models/recall/ncf/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/recall/ncf/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/recall/ncf/config.yaml b/models/recall/ncf/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..16d298b12fd551bd8421b44bc12d536fdc962e8b
--- /dev/null
+++ b/models/recall/ncf/config.yaml
@@ -0,0 +1,67 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+workspace: "paddlerec.models.recall.ncf"
+
+dataset:
+- name: dataset_train
+ batch_size: 5
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/movielens_reader.py"
+- name: dataset_infer
+ batch_size: 5
+ type: QueueDataset
+ data_path: "{workspace}/data/test"
+ data_converter: "{workspace}/movielens_infer_reader.py"
+
+hyper_parameters:
+ num_users: 6040
+ num_items: 3706
+ latent_dim: 8
+ fc_layers: [64, 32, 16, 8]
+ optimizer:
+ class: adam
+ learning_rate: 0.001
+ strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 10
+- name: infer_runner
+ class: single_infer
+ init_model_path: "increment/0"
+ device: cpu
+ epochs: 3
+
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
+ #- name: infer
+ # model: "{workspace}/model.py"
+ # dataset_name: dataset_infer
+ # thread_num: 1
diff --git a/models/recall/ncf/data/test/small_data.txt b/models/recall/ncf/data/test/small_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/recall/ncf/data/test/small_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
+3933,3568,0
+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
+1757,3633,0
+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
+2664,1711,0
+2664,3386,0
+2664,1668,0
+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
+1738,2187,0
+1738,228,0
+1738,650,0
+1738,3101,0
+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
+638,1880,0
+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
+4283,405,0
+4283,1505,0
+5111,200,1
+5111,3488,0
+5111,477,0
+5111,2790,0
+5111,40,0
+3964,515,1
+3964,1528,0
+3964,2173,0
+3964,1701,0
+3964,2832,0
diff --git a/models/recall/ncf/data/train/small_data.txt b/models/recall/ncf/data/train/small_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/recall/ncf/data/train/small_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
+3933,3568,0
+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
+1757,3633,0
+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
+2664,1711,0
+2664,3386,0
+2664,1668,0
+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
+1738,2187,0
+1738,228,0
+1738,650,0
+1738,3101,0
+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
+638,1880,0
+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
+4283,405,0
+4283,1505,0
+5111,200,1
+5111,3488,0
+5111,477,0
+5111,2790,0
+5111,40,0
+3964,515,1
+3964,1528,0
+3964,2173,0
+3964,1701,0
+3964,2832,0
diff --git a/models/recall/ncf/model.py b/models/recall/ncf/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc8b71cd85af647e054dda38048da68703859c88
--- /dev/null
+++ b/models/recall/ncf/model.py
@@ -0,0 +1,124 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+import numpy as np
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def _init_hyper_parameters(self):
+ self.num_users = envs.get_global_env("hyper_parameters.num_users")
+ self.num_items = envs.get_global_env("hyper_parameters.num_items")
+ self.latent_dim = envs.get_global_env("hyper_parameters.latent_dim")
+ self.layers = envs.get_global_env("hyper_parameters.fc_layers")
+
+ def input_data(self, is_infer=False, **kwargs):
+ user_input = fluid.data(
+ name="user_input", shape=[-1, 1], dtype="int64", lod_level=0)
+ item_input = fluid.data(
+ name="item_input", shape=[-1, 1], dtype="int64", lod_level=0)
+ label = fluid.data(
+ name="label", shape=[-1, 1], dtype="int64", lod_level=0)
+ if is_infer:
+ inputs = [user_input] + [item_input]
+ else:
+ inputs = [user_input] + [item_input] + [label]
+
+ return inputs
+
+ def net(self, inputs, is_infer=False):
+
+ num_layer = len(self.layers) #Number of layers in the MLP
+
+ MF_Embedding_User = fluid.embedding(
+ input=inputs[0],
+ size=[self.num_users, self.latent_dim],
+ param_attr=fluid.initializer.Normal(
+ loc=0.0, scale=0.01),
+ is_sparse=True)
+ MF_Embedding_Item = fluid.embedding(
+ input=inputs[1],
+ size=[self.num_items, self.latent_dim],
+ param_attr=fluid.initializer.Normal(
+ loc=0.0, scale=0.01),
+ is_sparse=True)
+
+ MLP_Embedding_User = fluid.embedding(
+ input=inputs[0],
+ size=[self.num_users, int(self.layers[0] / 2)],
+ param_attr=fluid.initializer.Normal(
+ loc=0.0, scale=0.01),
+ is_sparse=True)
+ MLP_Embedding_Item = fluid.embedding(
+ input=inputs[1],
+ size=[self.num_items, int(self.layers[0] / 2)],
+ param_attr=fluid.initializer.Normal(
+ loc=0.0, scale=0.01),
+ is_sparse=True)
+
+ # MF part
+ mf_user_latent = fluid.layers.flatten(x=MF_Embedding_User, axis=1)
+ mf_item_latent = fluid.layers.flatten(x=MF_Embedding_Item, axis=1)
+ mf_vector = fluid.layers.elementwise_mul(mf_user_latent,
+ mf_item_latent)
+
+ # MLP part
+ # The 0-th layer is the concatenation of embedding layers
+ mlp_user_latent = fluid.layers.flatten(x=MLP_Embedding_User, axis=1)
+ mlp_item_latent = fluid.layers.flatten(x=MLP_Embedding_Item, axis=1)
+ mlp_vector = fluid.layers.concat(
+ input=[mlp_user_latent, mlp_item_latent], axis=-1)
+
+ for i in range(1, num_layer):
+ mlp_vector = fluid.layers.fc(
+ input=mlp_vector,
+ size=self.layers[i],
+ act='relu',
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=1.0 / math.sqrt(mlp_vector.shape[1])),
+ regularizer=fluid.regularizer.L2DecayRegularizer(
+ regularization_coeff=1e-4)),
+ name='layer_' + str(i))
+
+ # Concatenate MF and MLP parts
+ predict_vector = fluid.layers.concat(
+ input=[mf_vector, mlp_vector], axis=-1)
+
+ # Final prediction layer
+ prediction = fluid.layers.fc(
+ input=predict_vector,
+ size=1,
+ act='sigmoid',
+ param_attr=fluid.initializer.MSRAInitializer(uniform=True),
+ name='prediction')
+ if is_infer:
+ self._infer_results["prediction"] = prediction
+ return
+
+ cost = fluid.layers.log_loss(
+ input=prediction,
+ label=fluid.layers.cast(
+ x=inputs[2], dtype='float32'))
+ avg_cost = fluid.layers.mean(cost)
+
+ self._cost = avg_cost
+ self._metrics["cost"] = avg_cost
diff --git a/models/rank/wide_deep/reader.py b/models/recall/ncf/movielens_infer_reader.py
old mode 100755
new mode 100644
similarity index 57%
rename from models/rank/wide_deep/reader.py
rename to models/recall/ncf/movielens_infer_reader.py
index acb6d3cc21ce553f4f876622b3f0a3f749f619c0..148c8008eb058ee3a126b1ec3253f2893d2e7150
--- a/models/rank/wide_deep/reader.py
+++ b/models/recall/ncf/movielens_infer_reader.py
@@ -13,31 +13,29 @@
# limitations under the License.
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
-try:
- import cPickle as pickle
-except ImportError:
- import pickle
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+from collections import defaultdict
+import numpy as np
+
class TrainReader(Reader):
def init(self):
pass
- def _process_line(self, line):
- line = line.strip().split(',')
- features = list(map(float, line))
- wide_feat = features[0:8]
- deep_feat = features[8:58+8]
- label = features[-1]
- return wide_feat, deep_feat, [label]
-
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
- def data_iter():
- wide_feat, deep_deat, label = self._process_line(line)
- yield [('wide_input', wide_feat), ('deep_input', deep_deat), ('label', label)]
- return data_iter
\ No newline at end of file
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.strip().split(',')
+
+ feature_name = ["user_input", "item_input"]
+ yield zip(feature_name,
+ [[int(features[0])]] + [[int(features[1])]])
+
+ return reader
diff --git a/fleet_rec/core/reader.py b/models/recall/ncf/movielens_reader.py
old mode 100755
new mode 100644
similarity index 52%
rename from fleet_rec/core/reader.py
rename to models/recall/ncf/movielens_reader.py
index 81da9ebf82f5bfa9d409a9a17169f19b13c7716b..add9b6397cef93f3a8f416f19c6847c41537fb5f
--- a/fleet_rec/core/reader.py
+++ b/models/recall/ncf/movielens_reader.py
@@ -13,34 +13,29 @@
# limitations under the License.
from __future__ import print_function
-import abc
-import os
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+from collections import defaultdict
+import numpy as np
-import paddle.fluid.incubate.data_generator as dg
-import yaml
-from fleetrec.core.utils import envs
-
-
-class Reader(dg.MultiSlotDataGenerator):
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, config):
- dg.MultiSlotDataGenerator.__init__(self)
-
- if os.path.isfile(config):
- with open(config, 'r') as rb:
- _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
- else:
- raise ValueError("reader config only support yaml")
-
- envs.set_global_envs(_config)
- envs.update_workspace()
-
- @abc.abstractmethod
+class TrainReader(Reader):
def init(self):
pass
- @abc.abstractmethod
def generate_sample(self, line):
- pass
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.strip().split(',')
+
+ feature_name = ["user_input", "item_input", "label"]
+ yield zip(feature_name, [[int(features[0])]] +
+ [[int(features[1])]] + [[int(features[2])]])
+
+ return reader
diff --git a/models/recall/readme.md b/models/recall/readme.md
new file mode 100755
index 0000000000000000000000000000000000000000..421df1315dc22396f2ff3bb5aec99508435e2c8d
--- /dev/null
+++ b/models/recall/readme.md
@@ -0,0 +1,80 @@
+# 召回模型库
+
+## 简介
+我们提供了常见的召回任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的召回模型包括 [SR-GNN](gnn)、[GRU4REC](gru4rec)、[Sequence Semantic Retrieval Model](ssr)、[Word2Vector](word2vec)、[Youtube_DNN](youtube_dnn)、[ncf](ncf)。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [召回模型列表](#召回模型列表)
+* [使用教程](#使用教程)
+ * [训练 预测](#训练 预测)
+* [效果对比](#效果对比)
+ * [模型效果列表](#模型效果列表)
+
+## 整体介绍
+### 召回模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| Word2Vec | word2vector | [Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)(2013) |
+| GRU4REC | SR-GRU | [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939)(2015) |
+| Youtube_DNN | Youtube_DNN | [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)(2016) |
+| SSR | Sequence Semantic Retrieval Model | [Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf)(2016) |
+| NCF | Neural Collaborative Filtering | [Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf)(2017) |
+| GNN | SR-GNN | [Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855)(2018) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+[Word2Vec](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf):
+
+
+
+[GRU4REC](https://arxiv.org/abs/1511.06939):
+
+
+
+[Youtube_DNN](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf):
+
+
+
+[SSR](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf):
+
+
+
+[NCF](https://arxiv.org/pdf/1708.05031.pdf):
+
+
+
+[GNN](https://arxiv.org/abs/1811.00855):
+
+
+
+## 使用教程
+### 训练 预测
+```shell
+python -m paddlerec.run -m paddlerec.models.recall.word2vec # word2vec
+python -m paddlerec.run -m paddlerec.models.recall.ssr # ssr
+python -m paddlerec.run -m paddlerec.models.recall.gru4rec # gru4rec
+python -m paddlerec.run -m paddlerec.models.recall.gnn # gnn
+python -m paddlerec.run -m paddlerec.models.recall.ncf # ncf
+python -m paddlerec.run -m paddlerec.models.recall.youtube_dnn # youtube_dnn
+```
+## 效果对比
+### 模型效果列表
+
+| 数据集 | 模型 | HR@10 | Recall@20 |
+| :------------------: | :--------------------: | :---------: |:---------: |
+| DIGINETICA | GNN | -- | 0.507 |
+| RSC15 | GRU4REC | -- | 0.670 |
+| RSC15 | SSR | -- | 0.590 |
+| MOVIELENS | NCF | 0.688 | -- |
+| -- | Youtube | -- | -- |
+| 1 Billion Word Language Model Benchmark | Word2Vec | -- | 0.54 |
diff --git a/models/recall/ssr/config.yaml b/models/recall/ssr/config.yaml
index 640c93b9dfa9f10cb2de4092bc7d94c45ba45b85..7dcecde84d6119501dea9c84047b705e2a9ba410 100644
--- a/models/recall/ssr/config.yaml
+++ b/models/recall/ssr/config.yaml
@@ -12,34 +12,55 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-train:
- trainer:
- # for cluster training
- strategy: "async"
+workspace: "paddlerec.models.recall.ssr"
- epochs: 3
- workspace: "fleetrec.models.recall.ssr"
+dataset:
+- name: dataset_train
+ batch_size: 5
+ type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/ssr_reader.py"
+- name: dataset_infer
+ batch_size: 5
+ type: QueueDataset
+ data_path: "{workspace}/data/test"
+ data_converter: "{workspace}/ssr_infer_reader.py"
+
+hyper_parameters:
+ vocab_size: 1000
+ emb_dim: 128
+ hidden_size: 100
+ optimizer:
+ class: adagrad
+ learning_rate: 0.01
+ strategy: async
- reader:
- batch_size: 5
- class: "{workspace}/ssr_reader.py"
- train_data_path: "{workspace}/data/train"
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
- model:
- models: "{workspace}/model.py"
- hyper_parameters:
- vocab_size: 1000
- emb_dim: 128
- hidden_size: 100
- learning_rate: 0.01
- optimizer: adagrad
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 10
+- name: infer_runner
+ class: single_infer
+ init_model_path: "increment/0"
+ device: cpu
+ epochs: 3
- save:
- increment:
- dirname: "increment"
- epoch_interval: 2
- save_last: True
- inference:
- dirname: "inference"
- epoch_interval: 4
- save_last: True
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
+ #- name: infer
+ # model: "{workspace}/model.py"
+ # dataset_name: dataset_infer
+ # thread_num: 1
diff --git a/models/recall/ssr/model.py b/models/recall/ssr/model.py
index 5fd94cf8763d79e4cc7edd92019aae1e8bf716a2..b97a5927f736e97c763fec177882f40097650011 100644
--- a/models/recall/ssr/model.py
+++ b/models/recall/ssr/model.py
@@ -12,15 +12,126 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import math
import paddle.fluid as fluid
-
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
import paddle.fluid.layers.tensor as tensor
-import paddle.fluid.layers.io as io
import paddle.fluid.layers.control_flow as cf
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def _init_hyper_parameters(self):
+ self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+ self.emb_dim = envs.get_global_env("hyper_parameters.emb_dim")
+ self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
+
+ def input_data(self, is_infer=False, **kwargs):
+ if is_infer:
+ user_data = fluid.data(
+ name="user", shape=[None, 1], dtype="int64", lod_level=1)
+ all_item_data = fluid.data(
+ name="all_item", shape=[None, self.vocab_size], dtype="int64")
+ pos_label = fluid.data(
+ name="pos_label", shape=[None, 1], dtype="int64")
+ return [user_data, all_item_data, pos_label]
+ else:
+ user_data = fluid.data(
+ name="user", shape=[None, 1], dtype="int64", lod_level=1)
+ pos_item_data = fluid.data(
+ name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
+ neg_item_data = fluid.data(
+ name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
+ return [user_data, pos_item_data, neg_item_data]
+
+ def net(self, inputs, is_infer=False):
+ if is_infer:
+ self._infer_net(inputs)
+ return
+ user_data = inputs[0]
+ pos_item_data = inputs[1]
+ neg_item_data = inputs[2]
+ emb_shape = [self.vocab_size, self.emb_dim]
+ self.user_encoder = GrnnEncoder()
+ self.item_encoder = BowEncoder()
+ self.pairwise_hinge_loss = PairwiseHingeLoss()
+
+ user_emb = fluid.embedding(
+ input=user_data, size=emb_shape, param_attr="emb.item")
+ pos_item_emb = fluid.embedding(
+ input=pos_item_data, size=emb_shape, param_attr="emb.item")
+ neg_item_emb = fluid.embedding(
+ input=neg_item_data, size=emb_shape, param_attr="emb.item")
+ user_enc = self.user_encoder.forward(user_emb)
+ pos_item_enc = self.item_encoder.forward(pos_item_emb)
+ neg_item_enc = self.item_encoder.forward(neg_item_emb)
+ user_hid = fluid.layers.fc(input=user_enc,
+ size=self.hidden_size,
+ param_attr='user.w',
+ bias_attr="user.b")
+ pos_item_hid = fluid.layers.fc(input=pos_item_enc,
+ size=self.hidden_size,
+ param_attr='item.w',
+ bias_attr="item.b")
+ neg_item_hid = fluid.layers.fc(input=neg_item_enc,
+ size=self.hidden_size,
+ param_attr='item.w',
+ bias_attr="item.b")
+ cos_pos = fluid.layers.cos_sim(user_hid, pos_item_hid)
+ cos_neg = fluid.layers.cos_sim(user_hid, neg_item_hid)
+ hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
+ avg_cost = fluid.layers.mean(hinge_loss)
+ correct = self._get_correct(cos_neg, cos_pos)
+
+ self._cost = avg_cost
+ self._metrics["correct"] = correct
+ self._metrics["hinge_loss"] = hinge_loss
+
+ def _infer_net(self, inputs):
+ user_data = inputs[0]
+ all_item_data = inputs[1]
+ pos_label = inputs[2]
+
+ user_emb = fluid.embedding(
+ input=user_data,
+ size=[self.vocab_size, self.emb_dim],
+ param_attr="emb.item")
+ all_item_emb = fluid.embedding(
+ input=all_item_data,
+ size=[self.vocab_size, self.emb_dim],
+ param_attr="emb.item")
+ all_item_emb_re = fluid.layers.reshape(
+ x=all_item_emb, shape=[-1, self.emb_dim])
+
+ user_encoder = GrnnEncoder()
+ user_enc = user_encoder.forward(user_emb)
+ user_hid = fluid.layers.fc(input=user_enc,
+ size=self.hidden_size,
+ param_attr='user.w',
+ bias_attr="user.b")
+ user_exp = fluid.layers.expand(
+ x=user_hid, expand_times=[1, self.vocab_size])
+ user_re = fluid.layers.reshape(
+ x=user_exp, shape=[-1, self.hidden_size])
+
+ all_item_hid = fluid.layers.fc(input=all_item_emb_re,
+ size=self.hidden_size,
+ param_attr='item.w',
+ bias_attr="item.b")
+ cos_item = fluid.layers.cos_sim(X=all_item_hid, Y=user_re)
+ all_pre_ = fluid.layers.reshape(
+ x=cos_item, shape=[-1, self.vocab_size])
+ acc = fluid.layers.accuracy(input=all_pre_, label=pos_label, k=20)
+
+ self._infer_results['recall20'] = acc
+
+ def _get_correct(self, x, y):
+ less = tensor.cast(cf.less_than(x, y), dtype='float32')
+ correct = fluid.layers.reduce_sum(less)
+ return correct
class BowEncoder(object):
@@ -54,6 +165,7 @@ class GrnnEncoder(object):
bias_attr=self.param_name + ".bias")
return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
+
class PairwiseHingeLoss(object):
def __init__(self, margin=0.8):
self.margin = margin
@@ -69,70 +181,3 @@ class PairwiseHingeLoss(object):
input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_part2)
return loss_part3
-
-class Model(ModelBase):
- def __init__(self, config):
- ModelBase.__init__(self, config)
-
- def get_correct(self, x, y):
- less = tensor.cast(cf.less_than(x, y), dtype='float32')
- correct = fluid.layers.reduce_sum(less)
- return correct
-
- def train(self):
-
- vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
- emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None, self._namespace)
- hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
- emb_shape = [vocab_size, emb_dim]
-
- self.user_encoder = GrnnEncoder()
- self.item_encoder = BowEncoder()
- self.pairwise_hinge_loss = PairwiseHingeLoss()
-
- user_data = fluid.data(
- name="user", shape=[None, 1], dtype="int64", lod_level=1)
- pos_item_data = fluid.data(
- name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
- neg_item_data = fluid.data(
- name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
- self._data_var.extend([user_data, pos_item_data, neg_item_data])
-
- user_emb = fluid.embedding(
- input=user_data, size=emb_shape, param_attr="emb.item")
- pos_item_emb = fluid.embedding(
- input=pos_item_data, size=emb_shape, param_attr="emb.item")
- neg_item_emb = fluid.embedding(
- input=neg_item_data, size=emb_shape, param_attr="emb.item")
- user_enc = self.user_encoder.forward(user_emb)
- pos_item_enc = self.item_encoder.forward(pos_item_emb)
- neg_item_enc = self.item_encoder.forward(neg_item_emb)
- user_hid = fluid.layers.fc(input=user_enc,
- size=hidden_size,
- param_attr='user.w',
- bias_attr="user.b")
- pos_item_hid = fluid.layers.fc(input=pos_item_enc,
- size=hidden_size,
- param_attr='item.w',
- bias_attr="item.b")
- neg_item_hid = fluid.layers.fc(input=neg_item_enc,
- size=hidden_size,
- param_attr='item.w',
- bias_attr="item.b")
- cos_pos = fluid.layers.cos_sim(user_hid, pos_item_hid)
- cos_neg = fluid.layers.cos_sim(user_hid, neg_item_hid)
- hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
- avg_cost = fluid.layers.mean(hinge_loss)
- correct = self.get_correct(cos_neg, cos_pos)
-
- self._cost = avg_cost
- self._metrics["correct"] = correct
- self._metrics["hinge_loss"] = hinge_loss
-
-
- def train_net(self):
- self.train()
-
-
- def infer_net(self):
- pass
diff --git a/models/recall/ssr/ssr_infer_reader.py b/models/recall/ssr/ssr_infer_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f94b1d21fbd428282d3e9faecd09a590588fbc9
--- /dev/null
+++ b/models/recall/ssr/ssr_infer_reader.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
+
+class EvaluateReader(Reader):
+ def init(self):
+ self.vocab_size = envs.get_global_env("vocab_size", 10,
+ "train.model.hyper_parameters")
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ ids = line.strip().split()
+ conv_ids = [int(i) for i in ids]
+ boundary = len(ids) - 1
+ src = conv_ids[:boundary]
+ pos_tgt = [conv_ids[boundary]]
+ feature_name = ["user", "all_item", "p_item"]
+ yield zip(
+ feature_name,
+ [src] + [np.arange(self.vocab_size).astype("int64").tolist()] +
+ [pos_tgt])
+
+ return reader
diff --git a/models/recall/ssr/ssr_reader.py b/models/recall/ssr/ssr_reader.py
index ba81e01f7b84a462938383a141eeb2bfe3f816d1..d2d35458d867bd560e7e0b751f61de83d0f822b6 100644
--- a/models/recall/ssr/ssr_reader.py
+++ b/models/recall/ssr/ssr_reader.py
@@ -11,12 +11,13 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+
from __future__ import print_function
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
import random
+from paddlerec.core.reader import Reader
+
class TrainReader(Reader):
def init(self):
@@ -25,7 +26,6 @@ class TrainReader(Reader):
def sample_neg_from_seq(self, seq):
return seq[random.randint(0, len(seq) - 1)]
-
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
diff --git a/models/recall/tdm/__init__.py b/models/recall/tdm/__init__.py
deleted file mode 100755
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/models/recall/tdm/tree/layer_list.txt b/models/recall/tdm/tree/layer_list.txt
deleted file mode 100755
index d8606bc601202390bd9aa54197fac8f34e3c5b59..0000000000000000000000000000000000000000
--- a/models/recall/tdm/tree/layer_list.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-1,2
-3,4,5,6
-7,8,9,10,11,12,13
-14,15,16,17,18,19,20,21,22,23,24,25
\ No newline at end of file
diff --git a/models/recall/word2vec/config.yaml b/models/recall/word2vec/config.yaml
index a3591e73d447a1a51286fba295bc2eea26a097d0..9bb5c4d3fe42bc2385ff22ad150924ad5a3a59cd 100755
--- a/models/recall/word2vec/config.yaml
+++ b/models/recall/word2vec/config.yaml
@@ -12,7 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
- workspace: "fleetrec.models.recall.word2vec"
+ workspace: "paddlerec.models.recall.word2vec"
+
+ evaluate_only: False
+ evaluate_model_path: ""
+
reader:
batch_size: 50
class: "{workspace}/w2v_evaluate_reader.py"
@@ -25,7 +29,7 @@ train:
strategy: "async"
epochs: 2
- workspace: "fleetrec.models.recall.word2vec"
+ workspace: "paddlerec.models.recall.word2vec"
reader:
batch_size: 100
diff --git a/models/recall/word2vec/model.py b/models/recall/word2vec/model.py
index 7899710a19740926fc43691276600611b515d1fb..fefc89043c2f926f37318e1094b9cdf98dd6235a 100755
--- a/models/recall/word2vec/model.py
+++ b/models/recall/word2vec/model.py
@@ -12,12 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import math
import numpy as np
import paddle.fluid as fluid
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
@@ -25,46 +24,57 @@ class Model(ModelBase):
ModelBase.__init__(self, config)
def input(self):
- neg_num = int(envs.get_global_env(
- "hyper_parameters.neg_num", None, self._namespace))
- self.input_word = fluid.data(name="input_word", shape=[
- None, 1], dtype='int64')
- self.true_word = fluid.data(name='true_label', shape=[
- None, 1], dtype='int64')
+ neg_num = int(
+ envs.get_global_env("hyper_parameters.neg_num", None,
+ self._namespace))
+ self.input_word = fluid.data(
+ name="input_word", shape=[None, 1], dtype='int64')
+ self.true_word = fluid.data(
+ name='true_label', shape=[None, 1], dtype='int64')
self._data_var.append(self.input_word)
self._data_var.append(self.true_word)
- with_shuffle_batch = bool(int(envs.get_global_env(
- "hyper_parameters.with_shuffle_batch", None, self._namespace)))
+ with_shuffle_batch = bool(
+ int(
+ envs.get_global_env("hyper_parameters.with_shuffle_batch",
+ None, self._namespace)))
if not with_shuffle_batch:
- self.neg_word = fluid.data(name="neg_label", shape=[
- None, neg_num], dtype='int64')
+ self.neg_word = fluid.data(
+ name="neg_label", shape=[None, neg_num], dtype='int64')
self._data_var.append(self.neg_word)
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
+ feed_list=self._data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
def net(self):
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
- neg_num = int(envs.get_global_env(
- "hyper_parameters.neg_num", None, self._namespace))
+ neg_num = int(
+ envs.get_global_env("hyper_parameters.neg_num", None,
+ self._namespace))
sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None, self._namespace)
sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None, self._namespace)
- with_shuffle_batch = bool(int(envs.get_global_env(
- "hyper_parameters.with_shuffle_batch", None, self._namespace)))
+ with_shuffle_batch = bool(
+ int(
+ envs.get_global_env("hyper_parameters.with_shuffle_batch",
+ None, self._namespace)))
- def embedding_layer(input, table_name, emb_dim, initializer_instance=None, squeeze=False):
+ def embedding_layer(input,
+ table_name,
+ emb_dim,
+ initializer_instance=None,
+ squeeze=False):
emb = fluid.embedding(
input=input,
is_sparse=True,
is_distributed=is_distributed,
size=[sparse_feature_number, emb_dim],
param_attr=fluid.ParamAttr(
- name=table_name,
- initializer=initializer_instance),
- )
+ name=table_name, initializer=initializer_instance), )
if squeeze:
return fluid.layers.squeeze(input=emb, axes=[1])
else:
@@ -74,35 +84,38 @@ class Model(ModelBase):
emb_initializer = fluid.initializer.Uniform(-init_width, init_width)
emb_w_initializer = fluid.initializer.Constant(value=0.0)
- input_emb = embedding_layer(
- self.input_word, "emb", sparse_feature_dim, emb_initializer, True)
- true_emb_w = embedding_layer(
- self.true_word, "emb_w", sparse_feature_dim, emb_w_initializer, True)
- true_emb_b = embedding_layer(
- self.true_word, "emb_b", 1, emb_w_initializer, True)
+ input_emb = embedding_layer(self.input_word, "emb", sparse_feature_dim,
+ emb_initializer, True)
+ true_emb_w = embedding_layer(self.true_word, "emb_w",
+ sparse_feature_dim, emb_w_initializer,
+ True)
+ true_emb_b = embedding_layer(self.true_word, "emb_b", 1,
+ emb_w_initializer, True)
if with_shuffle_batch:
neg_emb_w_list = []
for i in range(neg_num):
- neg_emb_w_list.append(fluid.contrib.layers.shuffle_batch(
- true_emb_w)) # shuffle true_word
+ neg_emb_w_list.append(
+ fluid.contrib.layers.shuffle_batch(
+ true_emb_w)) # shuffle true_word
neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0)
neg_emb_w = fluid.layers.reshape(
neg_emb_w_concat, shape=[-1, neg_num, sparse_feature_dim])
neg_emb_b_list = []
for i in range(neg_num):
- neg_emb_b_list.append(fluid.contrib.layers.shuffle_batch(
- true_emb_b)) # shuffle true_word
+ neg_emb_b_list.append(
+ fluid.contrib.layers.shuffle_batch(
+ true_emb_b)) # shuffle true_word
neg_emb_b = fluid.layers.concat(neg_emb_b_list, axis=0)
neg_emb_b_vec = fluid.layers.reshape(
neg_emb_b, shape=[-1, neg_num])
else:
- neg_emb_w = embedding_layer(
- self.neg_word, "emb_w", sparse_feature_dim, emb_w_initializer)
- neg_emb_b = embedding_layer(
- self.neg_word, "emb_b", 1, emb_w_initializer)
+ neg_emb_w = embedding_layer(self.neg_word, "emb_w",
+ sparse_feature_dim, emb_w_initializer)
+ neg_emb_b = embedding_layer(self.neg_word, "emb_b", 1,
+ emb_w_initializer)
neg_emb_b_vec = fluid.layers.reshape(
neg_emb_b, shape=[-1, neg_num])
@@ -118,7 +131,8 @@ class Model(ModelBase):
neg_matmul = fluid.layers.matmul(
input_emb_re, neg_emb_w, transpose_y=True)
neg_logits = fluid.layers.elementwise_add(
- fluid.layers.reshape(neg_matmul, shape=[-1, neg_num]),
+ fluid.layers.reshape(
+ neg_matmul, shape=[-1, neg_num]),
neg_emb_b_vec)
label_ones = fluid.layers.fill_constant_batch_size_like(
@@ -137,9 +151,17 @@ class Model(ModelBase):
neg_xent, dim=1))
self.avg_cost = fluid.layers.reduce_mean(cost)
global_right_cnt = fluid.layers.create_global_var(
- name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+ name="global_right_cnt",
+ persistable=True,
+ dtype='float32',
+ shape=[1],
+ value=0)
global_total_cnt = fluid.layers.create_global_var(
- name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+ name="global_total_cnt",
+ persistable=True,
+ dtype='float32',
+ shape=[1],
+ value=0)
global_right_cnt.stop_gradient = True
global_total_cnt.stop_gradient = True
@@ -156,12 +178,12 @@ class Model(ModelBase):
self.metrics()
def optimizer(self):
- learning_rate = envs.get_global_env(
- "hyper_parameters.learning_rate", None, self._namespace)
- decay_steps = envs.get_global_env(
- "hyper_parameters.decay_steps", None, self._namespace)
- decay_rate = envs.get_global_env(
- "hyper_parameters.decay_rate", None, self._namespace)
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
+ None, self._namespace)
+ decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None,
+ self._namespace)
+ decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None,
+ self._namespace)
optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=learning_rate,
@@ -181,11 +203,15 @@ class Model(ModelBase):
name="analogy_c", shape=[None], dtype='int64')
self.analogy_d = fluid.data(
name="analogy_d", shape=[None], dtype='int64')
- self._infer_data_var = [self.analogy_a,
- self.analogy_b, self.analogy_c, self.analogy_d]
+ self._infer_data_var = [
+ self.analogy_a, self.analogy_b, self.analogy_c, self.analogy_d
+ ]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+ feed_list=self._infer_data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
def infer_net(self):
sparse_feature_dim = envs.get_global_env(
@@ -217,18 +243,28 @@ class Model(ModelBase):
dist = fluid.layers.matmul(
x=target, y=emb_all_label_l2, transpose_y=True)
values, pred_idx = fluid.layers.topk(input=dist, k=4)
- label = fluid.layers.expand(fluid.layers.unsqueeze(
- self.analogy_d, axes=[1]), expand_times=[1, 4])
+ label = fluid.layers.expand(
+ fluid.layers.unsqueeze(
+ self.analogy_d, axes=[1]),
+ expand_times=[1, 4])
label_ones = fluid.layers.fill_constant_batch_size_like(
label, shape=[-1, 1], value=1.0, dtype='float32')
- right_cnt = fluid.layers.reduce_sum(
- input=fluid.layers.cast(fluid.layers.equal(pred_idx, label), dtype='float32'))
+ right_cnt = fluid.layers.reduce_sum(input=fluid.layers.cast(
+ fluid.layers.equal(pred_idx, label), dtype='float32'))
total_cnt = fluid.layers.reduce_sum(label_ones)
global_right_cnt = fluid.layers.create_global_var(
- name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+ name="global_right_cnt",
+ persistable=True,
+ dtype='float32',
+ shape=[1],
+ value=0)
global_total_cnt = fluid.layers.create_global_var(
- name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+ name="global_total_cnt",
+ persistable=True,
+ dtype='float32',
+ shape=[1],
+ value=0)
global_right_cnt.stop_gradient = True
global_total_cnt.stop_gradient = True
diff --git a/models/recall/word2vec/prepare_data.sh b/models/recall/word2vec/prepare_data.sh
index 743ae99871ba1cc2d7309cda11117446613eb0e5..cfd067350ce1d33112806ab72ca78222381a86f4 100755
--- a/models/recall/word2vec/prepare_data.sh
+++ b/models/recall/word2vec/prepare_data.sh
@@ -1,5 +1,20 @@
#! /bin/bash
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
# download train_data
mkdir raw_data
wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
@@ -20,6 +35,3 @@ wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_dir.ta
tar xzvf test_dir.tar -C raw_data
mv raw_data/data/test_dir test_data/
rm -rf raw_data
-
-
-
diff --git a/models/recall/word2vec/preprocess.py b/models/recall/word2vec/preprocess.py
index 31088efb90498e44ffd5fe4fe24942e1d52c2e6b..6c9ee16cd2d136006dc10e7ce0c970974e8bf2b5 100755
--- a/models/recall/word2vec/preprocess.py
+++ b/models/recall/word2vec/preprocess.py
@@ -1,11 +1,27 @@
# -*- coding: utf-8 -*
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import io
+import math
import os
import random
import re
import six
+
import argparse
-import io
-import math
+
prog = re.compile("[^a-z ]", flags=0)
@@ -33,8 +49,7 @@ def parse_args():
'--file_nums',
type=int,
default=1024,
- help="re-split input corpus file nums"
- )
+ help="re-split input corpus file nums")
parser.add_argument(
'--downsample',
type=float,
@@ -59,7 +74,7 @@ def parse_args():
def text_strip(text):
- #English Preprocess Rule
+ # English Preprocess Rule
return prog.sub("", text.lower())
@@ -101,7 +116,7 @@ def filter_corpus(args):
word_all_count = 0
id_counts = []
word_id = 0
- #read dict
+ # read dict
with io.open(args.dict_path, 'r', encoding='utf-8') as f:
for line in f:
word, count = line.split()[0], int(line.split()[1])
@@ -111,19 +126,21 @@ def filter_corpus(args):
id_counts.append(count)
word_all_count += count
- #write word2id file
+ # write word2id file
print("write word2id file to : " + args.dict_path + "_word_to_id_")
with io.open(
args.dict_path + "_word_to_id_", 'w+', encoding='utf-8') as fid:
for k, v in word_to_id_.items():
fid.write(k + " " + str(v) + '\n')
- #filter corpus and convert id
+ # filter corpus and convert id
if not os.path.exists(args.output_corpus_dir):
os.makedirs(args.output_corpus_dir)
for file in os.listdir(args.input_corpus_dir):
- with io.open(args.output_corpus_dir + '/convert_' + file + '.csv', "w") as wf:
+ with io.open(args.output_corpus_dir + '/convert_' + file + '.csv',
+ "w") as wf:
with io.open(
- args.input_corpus_dir + '/' + file, encoding='utf-8') as rf:
+ args.input_corpus_dir + '/' + file,
+ encoding='utf-8') as rf:
print(args.input_corpus_dir + '/' + file)
for line in rf:
signal = False
@@ -166,7 +183,8 @@ def build_dict(args):
for file in os.listdir(args.build_dict_corpus_dir):
with io.open(
- args.build_dict_corpus_dir + "/" + file, encoding='utf-8') as f:
+ args.build_dict_corpus_dir + "/" + file,
+ encoding='utf-8') as f:
print("build dict : ", args.build_dict_corpus_dir + "/" + file)
for line in f:
line = text_strip(line)
@@ -186,7 +204,7 @@ def build_dict(args):
for item in item_to_remove:
unk_sum += word_count[item]
del word_count[item]
- #sort by count
+ # sort by count
word_count[native_to_unicode('')] = unk_sum
word_count = sorted(
word_count.items(), key=lambda word_count: -word_count[1])
@@ -208,17 +226,19 @@ def data_split(args):
for file_ in files:
with open(os.path.join(raw_data_dir, file_), 'r') as f:
contents.extend(f.readlines())
-
+
num = int(args.file_nums)
lines_per_file = len(contents) / num
print("contents: ", str(len(contents)))
print("lines_per_file: ", str(lines_per_file))
-
- for i in range(1, num+1):
+
+ for i in range(1, num + 1):
with open(os.path.join(new_data_dir, "part_" + str(i)), 'w') as fout:
- data = contents[(i-1)*lines_per_file:min(i*lines_per_file,len(contents))]
+ data = contents[(i - 1) * lines_per_file:min(i * lines_per_file,
+ len(contents))]
for line in data:
- fout.write(line)
+ fout.write(line)
+
if __name__ == "__main__":
args = parse_args()
diff --git a/models/recall/word2vec/w2v_evaluate_reader.py b/models/recall/word2vec/w2v_evaluate_reader.py
index df6de9315360c1db0e6a2e4b3534a950390b6659..6350c960e61d8ef3580cc4cc605ba24cb5623b0b 100755
--- a/models/recall/word2vec/w2v_evaluate_reader.py
+++ b/models/recall/word2vec/w2v_evaluate_reader.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,16 +11,19 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
+
import io
+
import six
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
class EvaluateReader(Reader):
def init(self):
- dict_path = envs.get_global_env("word_id_dict_path", None, "evaluate.reader")
+ dict_path = envs.get_global_env("word_id_dict_path", None,
+ "evaluate.reader")
self.word_to_id = dict()
self.id_to_word = dict()
with io.open(dict_path, 'r', encoding='utf-8') as f:
@@ -46,19 +49,16 @@ class EvaluateReader(Reader):
if isinstance(s, str):
return True
return False
-
-
+
def _to_unicode(self, s, ignore_errors=False):
if self._is_unicode(s):
return s
error_mode = "ignore" if ignore_errors else "strict"
return s.decode("utf-8", errors=error_mode)
-
-
+
def strip_lines(self, line, vocab):
return self._replace_oov(vocab, self.native_to_unicode(line))
-
-
+
def _replace_oov(self, original_vocab, line):
"""Replace out-of-vocab words with "".
This maintains compatibility with published results.
@@ -69,12 +69,17 @@ class EvaluateReader(Reader):
a unicode string - a space-delimited sequence of words.
"""
return u" ".join([
- word if word in original_vocab else u"" for word in line.split()
+ word if word in original_vocab else u""
+ for word in line.split()
])
def generate_sample(self, line):
def reader():
features = self.strip_lines(line.lower(), self.word_to_id)
features = features.split()
- yield [('analogy_a', [self.word_to_id[features[0]]]), ('analogy_b', [self.word_to_id[features[1]]]), ('analogy_c', [self.word_to_id[features[2]]]), ('analogy_d', [self.word_to_id[features[3]]])]
+ yield [('analogy_a', [self.word_to_id[features[0]]]),
+ ('analogy_b', [self.word_to_id[features[1]]]),
+ ('analogy_c', [self.word_to_id[features[2]]]),
+ ('analogy_d', [self.word_to_id[features[3]]])]
+
return reader
diff --git a/models/recall/word2vec/w2v_reader.py b/models/recall/word2vec/w2v_reader.py
index 4857a6b06e78f05503349ebaeee17f6d44d423a4..9b3e69127055118bbc16b30eaac63f9a282bd1eb 100755
--- a/models/recall/word2vec/w2v_reader.py
+++ b/models/recall/word2vec/w2v_reader.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,10 +11,13 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
+
import io
-from fleetrec.core.reader import Reader
-from fleetrec.core.utils import envs
+
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
class NumpyRandomInt(object):
@@ -37,10 +40,14 @@ class NumpyRandomInt(object):
class TrainReader(Reader):
def init(self):
- dict_path = envs.get_global_env("word_count_dict_path", None, "train.reader")
- self.window_size = envs.get_global_env("hyper_parameters.window_size", None, "train.model")
- self.neg_num = envs.get_global_env("hyper_parameters.neg_num", None, "train.model")
- self.with_shuffle_batch = envs.get_global_env("hyper_parameters.with_shuffle_batch", None, "train.model")
+ dict_path = envs.get_global_env("word_count_dict_path", None,
+ "train.reader")
+ self.window_size = envs.get_global_env("hyper_parameters.window_size",
+ None, "train.model")
+ self.neg_num = envs.get_global_env("hyper_parameters.neg_num", None,
+ "train.model")
+ self.with_shuffle_batch = envs.get_global_env(
+ "hyper_parameters.with_shuffle_batch", None, "train.model")
self.random_generator = NumpyRandomInt(1, self.window_size + 1)
self.cs = None
@@ -72,19 +79,21 @@ class TrainReader(Reader):
start_point = 0
end_point = idx + target_window
targets = words[start_point:idx] + words[idx + 1:end_point + 1]
- return targets
+ return targets
def generate_sample(self, line):
def reader():
word_ids = [w for w in line.split()]
for idx, target_id in enumerate(word_ids):
- context_word_ids = self.get_context_words(
- word_ids, idx)
+ context_word_ids = self.get_context_words(word_ids, idx)
for context_id in context_word_ids:
- output = [('input_word', [int(target_id)]), ('true_label', [int(context_id)])]
+ output = [('input_word', [int(target_id)]),
+ ('true_label', [int(context_id)])]
if not self.with_shuffle_batch:
- neg_array = self.cs.searchsorted(np.random.sample(self.neg_num))
- output += [('neg_label', [int(str(i)) for i in neg_array ])]
+ neg_array = self.cs.searchsorted(
+ np.random.sample(self.neg_num))
+ output += [('neg_label',
+ [int(str(i)) for i in neg_array])]
yield output
- return reader
+ return reader
diff --git a/models/recall/youtube_dnn/__init__.py b/models/recall/youtube_dnn/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/recall/youtube_dnn/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/recall/youtube_dnn/config.yaml b/models/recall/youtube_dnn/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5bbc41a9e850044101fa844fca256db358dc1754
--- /dev/null
+++ b/models/recall/youtube_dnn/config.yaml
@@ -0,0 +1,54 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+workspace: "paddlerec.models.recall.youtube_dnn"
+
+dataset:
+- name: dataset_train
+ batch_size: 5
+ type: DataLoader
+ #type: QueueDataset
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/random_reader.py"
+
+hyper_parameters:
+ watch_vec_size: 64
+ search_vec_size: 64
+ other_feat_size: 64
+ output_size: 100
+ layers: [128, 64, 32]
+ optimizer:
+ class: adam
+ learning_rate: 0.001
+ strategy: async
+
+mode: train_runner
+
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+ print_interval: 10
+
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
diff --git a/models/recall/youtube_dnn/data/test/small_data.txt b/models/recall/youtube_dnn/data/test/small_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/recall/youtube_dnn/data/test/small_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
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+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
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+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
+2664,1711,0
+2664,3386,0
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+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
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+1738,228,0
+1738,650,0
+1738,3101,0
+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
+638,1880,0
+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
+4283,405,0
+4283,1505,0
+5111,200,1
+5111,3488,0
+5111,477,0
+5111,2790,0
+5111,40,0
+3964,515,1
+3964,1528,0
+3964,2173,0
+3964,1701,0
+3964,2832,0
diff --git a/models/recall/youtube_dnn/data/train/samll_data.txt b/models/recall/youtube_dnn/data/train/samll_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/recall/youtube_dnn/data/train/samll_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
+3933,3568,0
+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
+1757,3633,0
+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
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+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
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+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
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+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
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+5111,200,1
+5111,3488,0
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+3964,515,1
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diff --git a/models/recall/youtube_dnn/model.py b/models/recall/youtube_dnn/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1203447c6a66404f270a8f65215eea5cd9e82c7
--- /dev/null
+++ b/models/recall/youtube_dnn/model.py
@@ -0,0 +1,94 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def _init_hyper_parameters(self):
+ self.watch_vec_size = envs.get_global_env(
+ "hyper_parameters.watch_vec_size")
+ self.search_vec_size = envs.get_global_env(
+ "hyper_parameters.search_vec_size")
+ self.other_feat_size = envs.get_global_env(
+ "hyper_parameters.other_feat_size")
+ self.output_size = envs.get_global_env("hyper_parameters.output_size")
+ self.layers = envs.get_global_env("hyper_parameters.layers")
+
+ def input_data(self, is_infer=False, **kwargs):
+
+ watch_vec = fluid.data(
+ name="watch_vec",
+ shape=[None, self.watch_vec_size],
+ dtype="float32")
+ search_vec = fluid.data(
+ name="search_vec",
+ shape=[None, self.search_vec_size],
+ dtype="float32")
+ other_feat = fluid.data(
+ name="other_feat",
+ shape=[None, self.other_feat_size],
+ dtype="float32")
+ label = fluid.data(name="label", shape=[None, 1], dtype="int64")
+ inputs = [watch_vec] + [search_vec] + [other_feat] + [label]
+
+ return inputs
+
+ def net(self, inputs, is_infer=False):
+ concat_feats = fluid.layers.concat(input=inputs[:-1], axis=-1)
+
+ l1 = self._fc('l1', concat_feats, self.layers[0], 'relu')
+ l2 = self._fc('l2', l1, self.layers[1], 'relu')
+ l3 = self._fc('l3', l2, self.layers[2], 'relu')
+ l4 = self._fc('l4', l3, self.output_size, 'softmax')
+
+ num_seqs = fluid.layers.create_tensor(dtype='int64')
+ acc = fluid.layers.accuracy(input=l4, label=inputs[-1], total=num_seqs)
+
+ cost = fluid.layers.cross_entropy(input=l4, label=inputs[-1])
+ avg_cost = fluid.layers.mean(cost)
+
+ self._cost = avg_cost
+ self._metrics["acc"] = acc
+
+ def _fc(self, tag, data, out_dim, active='relu'):
+ init_stddev = 1.0
+ scales = 1.0 / np.sqrt(data.shape[1])
+
+ if tag == 'l4':
+ p_attr = fluid.param_attr.ParamAttr(
+ name='%s_weight' % tag,
+ initializer=fluid.initializer.NormalInitializer(
+ loc=0.0, scale=init_stddev * scales))
+ else:
+ p_attr = None
+
+ b_attr = fluid.ParamAttr(
+ name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
+
+ out = fluid.layers.fc(input=data,
+ size=out_dim,
+ act=active,
+ param_attr=p_attr,
+ bias_attr=b_attr,
+ name=tag)
+ return out
diff --git a/models/recall/youtube_dnn/random_reader.py b/models/recall/youtube_dnn/random_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdb0add6dbb358dba52ba9c933c060fec3ddf516
--- /dev/null
+++ b/models/recall/youtube_dnn/random_reader.py
@@ -0,0 +1,50 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+from collections import defaultdict
+
+
+class TrainReader(Reader):
+ def init(self):
+ self.watch_vec_size = envs.get_global_env(
+ "hyper_parameters.watch_vec_size")
+ self.search_vec_size = envs.get_global_env(
+ "hyper_parameters.search_vec_size")
+ self.other_feat_size = envs.get_global_env(
+ "hyper_parameters.other_feat_size")
+ self.output_size = envs.get_global_env("hyper_parameters.output_size")
+
+ def generate_sample(self, line):
+ """
+ the file is not used
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+
+ feature_name = ["watch_vec", "search_vec", "other_feat", "label"]
+ yield zip(feature_name,
+ [np.random.rand(self.watch_vec_size).tolist()] +
+ [np.random.rand(self.search_vec_size).tolist()] +
+ [np.random.rand(self.other_feat_size).tolist()] +
+ [[np.random.randint(self.output_size)]])
+
+ return reader
diff --git a/models/rerank/__init__.py b/models/rerank/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/rerank/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rerank/listwise/__init__.py b/models/rerank/listwise/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/rerank/listwise/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rerank/listwise/config.yaml b/models/rerank/listwise/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2ddfa32fe08aa8bece00727aefc46bb893b4d090
--- /dev/null
+++ b/models/rerank/listwise/config.yaml
@@ -0,0 +1,67 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+workspace: "paddlerec.models.rerank.listwise"
+
+dataset:
+- name: dataset_train
+ type: DataLoader
+ data_path: "{workspace}/data/train"
+ data_converter: "{workspace}/random_reader.py"
+- name: dataset_infer
+ type: DataLoader
+ data_path: "{workspace}/data/test"
+ data_converter: "{workspace}/random_reader.py"
+
+hyper_parameters:
+ hidden_size: 128
+ user_vocab: 200
+ item_vocab: 1000
+ item_len: 5
+ embed_size: 16
+ batch_size: 1
+ optimizer:
+ class: sgd
+ learning_rate: 0.01
+ strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+ class: single_train
+ device: cpu
+ epochs: 3
+ save_checkpoint_interval: 2
+ save_inference_interval: 4
+ save_checkpoint_path: "increment"
+ save_inference_path: "inference"
+- name: infer_runner
+ class: single_infer
+ init_model_path: "increment/0"
+ device: cpu
+ epochs: 3
+
+phase:
+- name: train
+ model: "{workspace}/model.py"
+ dataset_name: dataset_train
+ thread_num: 1
+ #- name: infer
+ # model: "{workspace}/model.py"
+ # dataset_name: dataset_infer
+ # thread_num: 1
diff --git a/models/rerank/listwise/data/test/small_data.txt b/models/rerank/listwise/data/test/small_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/rerank/listwise/data/test/small_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
+3933,3568,0
+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
+1757,3633,0
+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
+2664,1711,0
+2664,3386,0
+2664,1668,0
+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
+1738,2187,0
+1738,228,0
+1738,650,0
+1738,3101,0
+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
+638,1880,0
+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
+4283,405,0
+4283,1505,0
+5111,200,1
+5111,3488,0
+5111,477,0
+5111,2790,0
+5111,40,0
+3964,515,1
+3964,1528,0
+3964,2173,0
+3964,1701,0
+3964,2832,0
diff --git a/models/rerank/listwise/data/train/small_data.txt b/models/rerank/listwise/data/train/small_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/rerank/listwise/data/train/small_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
+3933,3568,0
+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
+1757,3633,0
+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
+2664,1711,0
+2664,3386,0
+2664,1668,0
+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
+1738,2187,0
+1738,228,0
+1738,650,0
+1738,3101,0
+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
+638,1880,0
+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
+4283,405,0
+4283,1505,0
+5111,200,1
+5111,3488,0
+5111,477,0
+5111,2790,0
+5111,40,0
+3964,515,1
+3964,1528,0
+3964,2173,0
+3964,1701,0
+3964,2832,0
diff --git a/models/rerank/listwise/model.py b/models/rerank/listwise/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d588db0629439eec9396ec9b1f81f1988e99d51e
--- /dev/null
+++ b/models/rerank/listwise/model.py
@@ -0,0 +1,218 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def _init_hyper_parameters(self):
+ self.item_len = envs.get_global_env("hyper_parameters.self.item_len")
+ self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
+ self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
+ self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
+ self.embed_size = envs.get_global_env("hyper_parameters.embed_size")
+
+ def input_data(self, is_infer=False, **kwargs):
+ user_slot_names = fluid.data(
+ name='user_slot_names',
+ shape=[None, 1],
+ dtype='int64',
+ lod_level=1)
+ item_slot_names = fluid.data(
+ name='item_slot_names',
+ shape=[None, self.item_len],
+ dtype='int64',
+ lod_level=1)
+ lens = fluid.data(name='lens', shape=[None], dtype='int64')
+ labels = fluid.data(
+ name='labels',
+ shape=[None, self.item_len],
+ dtype='int64',
+ lod_level=1)
+
+ inputs = [user_slot_names] + [item_slot_names] + [lens] + [labels]
+
+ # demo: hot to use is_infer:
+ if is_infer:
+ return inputs
+ else:
+ return inputs
+
+ def net(self, inputs, is_infer=False):
+ # user encode
+ user_embedding = fluid.embedding(
+ input=inputs[0],
+ size=[self.user_vocab, self.embed_size],
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Xavier(),
+ regularizer=fluid.regularizer.L2Decay(1e-5)),
+ is_sparse=True)
+
+ user_feature = fluid.layers.fc(
+ input=user_embedding,
+ size=self.hidden_size,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=np.sqrt(1.0 / self.hidden_size))),
+ bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
+ value=0.0)),
+ act='relu',
+ name='user_feature_fc')
+ # item encode
+ item_embedding = fluid.embedding(
+ input=inputs[1],
+ size=[self.item_vocab, self.embed_size],
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Xavier(),
+ regularizer=fluid.regularizer.L2Decay(1e-5)),
+ is_sparse=True)
+
+ item_embedding = fluid.layers.sequence_unpad(
+ x=item_embedding, length=inputs[2])
+
+ item_fc = fluid.layers.fc(
+ input=item_embedding,
+ size=self.hidden_size,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=np.sqrt(1.0 / self.hidden_size))),
+ bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
+ value=0.0)),
+ act='relu',
+ name='item_fc')
+
+ pos = self._fluid_sequence_get_pos(item_fc)
+ pos_embed = fluid.embedding(
+ input=pos,
+ size=[self.user_vocab, self.embed_size],
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Xavier(),
+ regularizer=fluid.regularizer.L2Decay(1e-5)),
+ is_sparse=True)
+
+ pos_embed = fluid.layers.squeeze(pos_embed, [1])
+
+ # item gru
+ gru_input = fluid.layers.fc(
+ input=fluid.layers.concat([item_fc, pos_embed], 1),
+ size=self.hidden_size * 3,
+ name='item_gru_fc')
+
+ # forward gru
+ item_gru_forward = fluid.layers.dynamic_gru(
+ input=gru_input,
+ size=self.hidden_size,
+ is_reverse=False,
+ h_0=user_feature)
+ # backward gru
+ item_gru_backward = fluid.layers.dynamic_gru(
+ input=gru_input,
+ size=self.hidden_size,
+ is_reverse=True,
+ h_0=user_feature)
+
+ item_gru = fluid.layers.concat(
+ [item_gru_forward, item_gru_backward], axis=1)
+
+ out_click_fc1 = fluid.layers.fc(
+ input=item_gru,
+ size=self.hidden_size,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormal(
+ loc=0.0, scale=np.sqrt(1.0 / self.hidden_size))),
+ bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
+ value=0.0)),
+ act='relu',
+ name='out_click_fc1')
+
+ click_prob = fluid.layers.fc(input=out_click_fc1,
+ size=2,
+ act='softmax',
+ name='out_click_fc2')
+
+ labels = fluid.layers.sequence_unpad(x=inputs[3], length=inputs[2])
+
+ auc_val, batch_auc, auc_states = fluid.layers.auc(input=click_prob,
+ label=labels)
+
+ if is_infer:
+ self._infer_results["AUC"] = auc_val
+ return
+
+ loss = fluid.layers.reduce_mean(
+ fluid.layers.cross_entropy(
+ input=click_prob, label=labels))
+ self._cost = loss
+ self._metrics['auc'] = auc_val
+
+ def _fluid_sequence_pad(self, input, pad_value, maxlen=None):
+ """
+ args:
+ input: (batch*seq_len, dim)
+ returns:
+ (batch, max_seq_len, dim)
+ """
+ pad_value = fluid.layers.cast(
+ fluid.layers.assign(input=np.array([pad_value], 'float32')),
+ input.dtype)
+ input_padded, _ = fluid.layers.sequence_pad(
+ input, pad_value,
+ maxlen=maxlen) # (batch, max_seq_len, 1), (batch, 1)
+ # TODO, maxlen=300, used to solve issues: https://github.com/PaddlePaddle/Paddle/issues/14164
+ return input_padded
+
+ def _fluid_sequence_get_pos(self, lodtensor):
+ """
+ args:
+ lodtensor: lod = [[0,4,7]]
+ return:
+ pos: lod = [[0,4,7]]
+ data = [0,1,2,3,0,1,3]
+ shape = [-1, 1]
+ """
+ lodtensor = fluid.layers.reduce_sum(lodtensor, dim=1, keep_dim=True)
+ assert lodtensor.shape == (-1, 1), (lodtensor.shape())
+ ones = fluid.layers.cast(lodtensor * 0 + 1,
+ 'float32') # (batch*seq_len, 1)
+ ones_padded = self._fluid_sequence_pad(ones,
+ 0) # (batch, max_seq_len, 1)
+ ones_padded = fluid.layers.squeeze(ones_padded,
+ [2]) # (batch, max_seq_len)
+ seq_len = fluid.layers.cast(
+ fluid.layers.reduce_sum(
+ ones_padded, 1, keep_dim=True), 'int64') # (batch, 1)
+ seq_len = fluid.layers.squeeze(seq_len, [1])
+
+ pos = fluid.layers.cast(
+ fluid.layers.cumsum(
+ ones_padded, 1, exclusive=True), 'int64')
+ pos = fluid.layers.sequence_unpad(pos, seq_len) # (batch*seq_len, 1)
+ pos.stop_gradient = True
+ return pos
+
+ #def train_net(self):
+ # input_data = self.input_data()
+ # self.net(input_data)
+
+ #def infer_net(self):
+ # input_data = self.input_data(is_infer=True)
+ # self.net(input_data, is_infer=True)
diff --git a/models/rerank/listwise/random_infer_reader.py b/models/rerank/listwise/random_infer_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f93688e59dd2dc142a0ab79201278b25e18b468
--- /dev/null
+++ b/models/rerank/listwise/random_infer_reader.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+from collections import defaultdict
+
+
+class EvaluateReader(Reader):
+ def init(self):
+ self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
+ None, "train.model")
+ self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
+ None, "train.model")
+ self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
+ "train.model")
+ self.batch_size = envs.get_global_env("batch_size", None,
+ "train.reader")
+
+ def reader_creator(self):
+ def reader():
+ user_slot_name = []
+ for j in range(self.batch_size):
+ user_slot_name.append(
+ [int(np.random.randint(self.user_vocab))])
+ item_slot_name = np.random.randint(
+ self.item_vocab, size=(self.batch_size,
+ self.item_len)).tolist()
+ length = [self.item_len] * self.batch_size
+ label = np.random.randint(
+ 2, size=(self.batch_size, self.item_len)).tolist()
+ output = [user_slot_name, item_slot_name, length, label]
+
+ yield output
+
+ return reader
+
+ def generate_batch_from_trainfiles(self, files):
+ return fluid.io.batch(
+ self.reader_creator(), batch_size=self.batch_size)
+
+ def generate_sample(self, line):
+ """
+ the file is not used
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ pass
+
+ return reader
diff --git a/models/rerank/listwise/random_reader.py b/models/rerank/listwise/random_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa7af3f083c720d35e9f11f5f5ec1bddd107cabc
--- /dev/null
+++ b/models/rerank/listwise/random_reader.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+from collections import defaultdict
+
+
+class TrainReader(Reader):
+ def init(self):
+ self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
+ self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
+ self.item_len = envs.get_global_env("hyper_parameters.item_len")
+ self.batch_size = envs.get_global_env("hyper_parameters.batch_size")
+
+ def reader_creator(self):
+ def reader():
+ user_slot_name = []
+ for j in range(self.batch_size):
+ user_slot_name.append(
+ [int(np.random.randint(self.user_vocab))])
+ item_slot_name = np.random.randint(
+ self.item_vocab, size=(self.batch_size,
+ self.item_len)).tolist()
+ length = [self.item_len] * self.batch_size
+ label = np.random.randint(
+ 2, size=(self.batch_size, self.item_len)).tolist()
+ output = [user_slot_name, item_slot_name, length, label]
+
+ yield output
+
+ return reader
+
+ def generate_batch_from_trainfiles(self, files):
+ return fluid.io.batch(
+ self.reader_creator(), batch_size=self.batch_size)
+
+ def generate_sample(self, line):
+ """
+ the file is not used
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ pass
+
+ return reader
diff --git a/models/rerank/readme.md b/models/rerank/readme.md
new file mode 100755
index 0000000000000000000000000000000000000000..6f698daf9f9a7529abcb8d18010965988838a940
--- /dev/null
+++ b/models/rerank/readme.md
@@ -0,0 +1,36 @@
+# 重排序模型库
+
+## 简介
+我们提供了常见的重排序使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。目前实现的模型是 [Listwise](listwise)。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [重排序模型列表](#重排序模型列表)
+* [使用教程](#使用教程)
+
+## 整体介绍
+### 融合模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| Listwise | Listwise | [Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf)(2019) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+
+[Listwise](https://arxiv.org/pdf/1902.00245.pdf):
+
+
+
+
+## 使用教程(快速开始)
+```shell
+python -m paddlerec.run -m paddlerec.models.rerank.listwise # listwise
+```
+
+## 使用教程（复现论文）
+
+listwise原论文没有给出训练数据，我们使用了随机的数据，可参考快速开始
diff --git a/models/treebased/README.md b/models/treebased/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3ceb13b62eba8127aa0394397d141b2abe343a32
--- /dev/null
+++ b/models/treebased/README.md
@@ -0,0 +1,27 @@
+# Paddle-TDM
+本代码库提供了基于PaddlePaddle实现的TreeBased推荐搜索算法，主要包含以下组成：
+
+- 基于fake数据集，适用于快速调试的paddle-tdm模型。主要用于理解paddle-tdm的设计原理，高效上手设计适合您的使用场景的模型。
+
+以上内容将随paddle版本迭代不断更新，欢迎您关注该代码库。
+
+## TDM设计思路
+
+### 基本概念
+TDM是为大规模推荐系统设计的、能承载任意先进模型来高效检索用户兴趣的推荐算法解决方案。TDM基于树结构，提出了一套对用户兴趣度量进行层次化建模与检索的方法论，使得系统能直接利高级深度学习模型在全库范围内检索用户兴趣。其基本原理是使用树结构对全库item进行索引，然后训练深度模型以支持树上的逐层检索，从而将大规模推荐中全库检索的复杂度由O(n)（n为所有item的量级）下降至O(log n)。
+
+### 核心问题
+
+1. 如何构建树结构？
+2. 如何基于树结构做深度学习模型的训练？
+3. 如何基于树及模型进行高效检索？
+
+### PaddlePaddle的TDM方案
+
+1. 树结构的数据，来源于各个业务的实际场景，构造方式各有不同，paddle-TDM一期暂不提供统一的树的构造流程，但会统一树构造好之后，输入paddle网络的数据组织形式。业务方可以使用任意工具构造自己的树，生成指定的数据格式，参与tdm网络训练。
+2. 网络训练中，有三个核心问题:
+
+ - 如何组网？答：paddle封装了大量的深度学习OP，用户可以根据需求设计自己的网络结构。
+ - 训练数据如何组织？答：tdm的训练数据主要为：`user/query emb` 加 `item`的正样本，`item`需要映射到树的某个叶子节点。用户只需准备符合该构成的数据即可。负样本的生成，会基于用户提供的树结构，以及paddle提供的`tdm-sampler op`完成高效的负采样，并自动添加相应的label，参与tdm中深度学习模型的训练。
+ - 大规模的数据与模型训练如何实现？答：基于paddle优秀的大规模参数服务器分布式能力，可以实现高效的分布式训练。基于paddle-fleet api，学习门槛极低，且可以灵活的支持增量训练，流式训练等业务需求。
+3. 训练好模型后，可以基于paddle，将检索与打分等流程都融入paddle的组网中，生成inference_model与参数文件，基于PaddlePaddle的预测库或者PaddleLite进行快速部署与高效检索。
diff --git a/models/treebased/__init__.py b/models/treebased/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/treebased/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/treebased/tdm/README.md b/models/treebased/tdm/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..220b90481962a157aa1bb348c040d35fbcda6fa7
--- /dev/null
+++ b/models/treebased/tdm/README.md
@@ -0,0 +1,532 @@
+# Paddle-TDM-DEMO
+
+本代码仅作tdm组网示例，使用fake数据集，用于快速调研paddle-tdm。
+
+
+## 树结构的准备
+### 名词概念
+
+为防止概念混淆，让我们明确tdm中名词的概念：
+
+
+
+
+- **item**：具有实际物理含义，是我们希望通过tdm检索最后得到的结果，如一个商品，一篇文章，一个关键词等等，在tdm模型中，item位于树的叶子节点。item有其自身的ID，我们姑且称之为 `item_id`。
+- **节点(node)**：tdm树的任意节点。我们完成聚类后，会生成一颗树，树的叶子节点对应着item。而非叶子节点，则是一种类别上的概括，从大方向的兴趣，不断细分到小方向的兴趣。我们希望这棵树的结构满足最大堆树的性质。同样，节点也有其自身的ID，我们称之为node_id。如上图，最左下方的节点，它的node_id是14，而对应的item_id是0.
+- **Node-Embedding**：注意，此处的Embedding，并非我们已有的item-embedding，而是构建完成的树的节点对应的Embedding，由item-embedding通过规则生成，是我们的网络主要训练的目标。ID范围为所有0->节点数-1。我们同时也需准备一个映射表，来告诉模型，item_id到node_id的映射关系。
+- **Travel**：是指叶子节点从root开始直到其自身的遍历路径，如上图，14号节点的Travel：0->1->3->7->14
+- **Layer**：指树的层，如上图，共有4层。
+
+> Paddle-TDM在训练时，不会改动树的结构，只会改动Node-Embedding。
+
+
+### 树的准备流程
+
+让我们以上图给出的简单树结构为例，来介绍TDM的模型准备流程。假设我们已经完成了树的聚类，并得到了如上图所示的树结构：
+
+- 问题一：叶子节点的Embedding值是多少？答：叶子节点的node-embedding与对应的item的embedding值一致
+- 问题二：非叶子节点的Embedding值是多少？答：非叶子节点的Embedding值初始化目前有两种方案：1、随机初始化。2、使用其子节点的emb均值。
+- 问题三：树一定要求二叉树吗？答：没有这样的硬性要求。
+- 问题四：若有item不在最后一层，树不平衡怎么办？答：树尽量平衡，不在最后一层也没有关系，我们可以通过其他方式让网络正常训练。
+- 问题五：是每个用户都有一棵树，还是所有用户共享一颗树？答：只有一棵树，通过每一层的模型牵引节点的emb，使其尽量满足最大堆性质。
+
+完成以上步骤，我们已经得到了树的结构，与每个节点的全部信息，现在让我们将其转换为Paddle-TDM训练所需的格式。我们需要产出四个数据：
+#### Layer_list
+
+记录了每一层都有哪些节点。训练用
+```bash
+# Layer list
+1,2
+3,4,5,6
+7,8,9,10,11,12,13
+14,15,16,17,18,19,20,21,22,23,24,25
+```
+#### Travel_list
+
+记录每个叶子节点的Travel路径。训练用
+```bash
+# Travel list
+1,3,7,14
+1,3,7,15
+1,3,8,16
+...
+2,5,12,25
+2,6,13,0
+```
+
+#### Tree_Info
+
+记录了每个节点的信息，主要为：是否是item/item_id，所在层级，父节点，子节点。检索用
+```bash
+# Tree info
+0,0,0,1,2
+0,1,0,3,4
+0,1,0,5,6
+0,2,1,7,8
+...
+10,4,12,0,0
+11,4,12,0,0
+```
+
+#### Tree_Embedding
+
+记录所有节点的Embedding表，格式如正常表。训练及检索用
+
+以上数据设计的初衷是为了高效，在paddle网络中以Tensor的形式参与训练，运行时，不再需要进行频繁的树的结构的遍历，直接根据已有信息进行快速查找与训练。以上数据可以明文保存，但最终都需要转成ndarray，参与网络的初始化。
+结合示例树，数据可以组织如右，下面介绍一些细节：
+
+- Layer list从第2（index=1）层开始即可，因为0号节点不参与训练也不参与检索；
+- Travel list的按照item_id的顺序组织，如第一行对应着item_id=0的遍历信息，同样，也不需要包含0号节点；
+- Travel_list每行的长度必须相等，遇到不在最后一层的item，需要padding 0 直至长度和其他item一致；
+- Tree_info包含了0号节点的信息，主要考量是，当我们拿到node_id查找其信息时，可以根据id在该数据中寻找第id行；
+- Tree_info各列的含义是：itme_id（若无则为0），层级Layer，父节点node_id（无则为0），子节点node_id（若无则为0，若子节点数量不满，则需要paddding 0）
+
+## 数据准备
+如前所述，若我们关心的是输入一个user emb，得到他所感兴趣的item id，那我们就准备user_emb + 正样本item的格式的数据，负采样会通过paddle的tdm_sampler op得到。数据的准备不涉及树的结构，因而可以快速复用其他任务的训练数据来验证TDM效果。
+
+```bash
+# emb(float) \t item_id (int)
+-0.9480544328689575 0.8702829480171204 -0.5691063404083252 ...... -0.04391402751207352 -0.5352795124053955 -0.9972627758979797 0.9397293329238892 4690780
+```
+
+## TDM网络设计
+假设输入数据是 Emb + item_id，下面让我们开始介绍一个最简单的网络设计。
+
+
+
+
+上图给出了一个非常简单的TDM示例网络，没有添加任何复杂的逻辑，纯用DNN实现。
+TDM的组网，宏观上，可以概括为三个部分
+- 第一部分，输入侧的组网，如果想要对user/query进行一些预处理，或者添加Attention结构，通常都是在这一层次实现。
+- 第二部分，每层的输入与节点信息交互的组网，这一部分是将user/query的信息与node信息结合，在树的不同层下，进行不同粒度兴趣的学习。通常而言，第一部分与第二部分具有紧密的联系，可以统一为一个部分。
+- 第三部分，最终的判别组网，将每层交互得到的信息进行最终的概率判决。但这一层也不是必须的，并不要求所有层的信息都经过一个统一的分类器，可以各层拥有独立的概率判决器。为了逻辑划分更加清晰，我们在示例中添加了这个层次的组网，方便您更加直观的理解tdm网络。
+再次强调，该示例组网仅为展示tdm的基本运行逻辑，请基于这个框架，升级改进您自己的网络。
+
+## TDM组网细节
+
+### 训练组网
+
+训练组网中需要重点关注五个部分：
+1. 网络输入的定义
+2. 网络中输入侧的处理逻辑
+3. node的负采样组网
+4. input与node的交互网络
+5. 判别及loss计算组网
+
+
+#### 输入的定义
+
+首先简要介绍输入的定义：
+
+**demo模型，假设输入为两个元素：**
+> 一、user/query的emb表示，该emb应该来源于特征的组合在某个空间的映射（比如若干特征取emb后concat到一起），或其他预训练模型的处理结果（比如将明文query通过nlp预处理得到emb表示）
+
+> 二、item的正样本，是发生了实际点击/购买/浏览等行为的item_id，与输入的user/query emb强相关，是我们之后通过预测想得到的结果。
+
+在paddle组网中，我们这样定义上面两个变量：
+```python
+def input_data(self):
+ """
+ 指定tdm训练网络的输入变量
+ """
+ input_emb = fluid.data(
+ name="input_emb",
+ shape=[None, self.input_embed_size],
+ dtype="float32",
+ )
+
+ item_label = fluid.data(
+ name="item_label",
+ shape=[None, 1],
+ dtype="int64",
+ )
+
+ inputs = [input_emb] + [item_label]
+ return inputs
+```
+
+#### 输入侧的组网
+
+**输入侧组网由FC层组成**
+> 一、`input_fc`，主要功能是input_emb维度的压缩，只需一个fc即可。
+
+> 二、`layer_fc`，主要功能是将input_emb映射到不同的兴趣层空间，和当层的node学习兴趣关系。有多少层，就添加多少个fc。
+
+
+
+
+在paddle组网中，我们这样快速实现输入侧组网：
+```python
+def input_trans_layer(self, input_emb):
+ """
+ 输入侧训练组网
+ """
+ # 将input压缩到与node相同的维度
+ input_fc_out = fluid.layers.fc(
+ input=input_emb,
+ size=self.node_emb_size,
+ act=None,
+ param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
+ bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"),
+ )
+
+ # 将input_emb映射到各个不同层次的向量表示空间
+ input_layer_fc_out = [
+ fluid.layers.fc(
+ input=input_fc_out,
+ size=self.node_emb_size,
+ act="tanh",
+ param_attr=fluid.ParamAttr(
+ name="trans.layer_fc.weight." + str(i)),
+ bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias."+str(i)),
+ ) for i in range(self.max_layers)
+ ]
+
+ return input_layer_fc_out
+```
+
+#### node的负采样组网
+**tdm 负采样的核心是tdm_sampler OP**
+
+tdm_sampler的运行逻辑如下：
+
+1. 输入item_id，读travel_list，查表，得到该item_id对应的遍历路径（从靠近根节点的第一层一直往下直到存放该item的node）
+2. 读layer_list，查表，得到每层都有哪些node
+3. 循环：i = 0, 从第i层开始进行负采样
+ - 在item遍历路径上的node视为正样本，`positive_node_id`由`travel_list[item_id][i]`给出，其他同层的兄弟节点视为负样本，该层节点列表由`layer_list[i]`给出，如果`positive_node_id`不在`layer_list[i]`中，会提示错误。
+
+ - 在兄弟节点中进行随机采样，采样N个node，N由`neg_sampling_list[i]`的值决定，如果该值大于兄弟节点的数量，会提示错误。 采样结果不会重复，且不会采样到正样本。
+
+ - 如果`output_positive=True`，则会同时输出正负样本，否则只输出负采样的结果
+
+ - 生成该层`label`，shape与采样结果一致，正样本对应的label=1，负样本的label=0
+
+ - 生成该层`mask`，如果树是不平衡的，则有些item不会位于树的最后一层，所以遍历路径的实际长度会比其他item少，为了tensor维度一致，travel_list中padding了0。当遇到了padding的0时，tdm_sampler也会输出正常维度的采样结果，采样结果与label都为0。为了区分这部分虚拟的采样结果与真实采样结果，会给虚拟采样结果额外设置mask=0，如果是真实采样结果mask=1
+ - i += 1, 若i > layer_nums, break
+4. 对输出的采样结果、label、mask进行整理：
+ - 如果`output_list=False`，则会输出三个tensor(samping_result, label, mask)，shape形如`[batch_size, all_layer_sampling_nums, 1]`；
+ - 若`output_list=True`，则会输出三个`list[tensor,...,tensor]`，`sampling_result_list/label_list/mask_list`，`len(list)`等于层数，将采样结果按照分属哪一层进行拆分，每个tensor的shape形如`[batch_size, layer_i_sampling_nums,1]`
+
+```python
+# 根据输入的item的正样本在给定的树上进行负采样
+# sample_nodes 是采样的node_id的结果，包含正负样本
+# sample_label 是采样的node_id对应的正负标签
+# sample_mask 是为了保持tensor维度一致，padding部分的标签，若为0，则是padding的虚拟node_id
+sample_nodes, sample_label, sample_mask = fluid.contrib.layers.tdm_sampler(
+ x=item_label,
+ neg_samples_num_list=self.neg_sampling_list,
+ layer_node_num_list=self.layer_node_num_list,
+ leaf_node_num=self.leaf_node_num,
+ tree_travel_attr=fluid.ParamAttr(name="TDM_Tree_Travel"),
+ tree_layer_attr=fluid.ParamAttr(name="TDM_Tree_Layer"),
+ output_positive=self.output_positive,
+ output_list=True,
+ seed=0,
+ tree_dtype='int64',
+ dtype='int64'
+)
+
+# 查表得到每个节点的Embedding
+sample_nodes_emb = [
+ fluid.embedding(
+ input=sample_nodes[i],
+ is_sparse=True,
+ size=[self.node_nums, self.node_emb_size],
+ param_attr=fluid.ParamAttr(
+ name="TDM_Tree_Emb")
+ ) for i in range(self.max_layers)
+]
+
+# 此处进行reshape是为了之后层次化的分类器训练
+sample_nodes_emb = [
+ fluid.layers.reshape(sample_nodes_emb[i],
+ [-1, self.neg_sampling_list[i] +
+ self.output_positive, self.node_emb_size]
+ ) for i in range(self.max_layers)
+]
+
+```
+
+#### input与node的交互网络
+**交互网络由FC层组成**
+
+主要包含两个流程：
+> 一、将输入进行维度上的`expand`，与采样得到的noed数量一致（当然也可以使用其他`broadcast`的网络结构）
+
+> 二、input_emb与node_emb进行`concat`，过FC，计算兴趣上的匹配关系
+
+
+
+
+在paddle的组网中，我们这样实现这一部分的逻辑：
+
+```python
+def _expand_layer(self, input_layer, node, layer_idx):
+ # 扩展input的输入，使数量与node一致，
+ # 也可以以其他broadcast的操作进行代替
+ # 同时兼容了训练组网与预测组网
+ input_layer_unsequeeze = fluid.layers.unsqueeze(
+ input=input_layer, axes=[1])
+ if self.is_test:
+ input_layer_expand = fluid.layers.expand(
+ input_layer_unsequeeze, expand_times=[1, node.shape[1], 1])
+ else:
+ input_layer_expand = fluid.layers.expand(
+ input_layer_unsequeeze, expand_times=[1, node[layer_idx].shape[1], 1])
+ return input_layer_expand
+
+def classifier_layer(self, input, node):
+ # 扩展input，使维度与node匹配
+ input_expand = [
+ self._expand_layer(input[i], node, i) for i in range(self.max_layers)
+ ]
+
+ # 将input_emb与node_emb concat到一起过分类器FC
+ input_node_concat = [
+ fluid.layers.concat(
+ input=[input_expand[i], node[i]],
+ axis=2) for i in range(self.max_layers)
+ ]
+
+ hidden_states_fc = [
+ fluid.layers.fc(
+ input=input_node_concat[i],
+ size=self.node_emb_size,
+ num_flatten_dims=2,
+ act="tanh",
+ param_attr=fluid.ParamAttr(
+ name="cls.concat_fc.weight."+str(i)),
+ bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(i))
+ ) for i in range(self.max_layers)
+ ]
+
+ # 如果将所有层次的node放到一起计算loss，则需要在此处concat
+ # 将分类器结果以batch为准绳concat到一起，而不是layer
+ # 维度形如[batch_size, total_node_num, node_emb_size]
+ hidden_states_concat = fluid.layers.concat(hidden_states_fc, axis=1)
+ return hidden_states_concat
+```
+
+#### 判别及loss计算组网
+
+最终的判别组网会将所有层的输出打平放到一起，过`tdm.cls_fc`，再过`softmax_with_cross_entropy`层，计算cost，同时得到softmax的中间结果，计算acc或者auc。
+
+```python
+tdm_fc = fluid.layers.fc(input=layer_classifier_res,
+ size=self.label_nums,
+ act=None,
+ num_flatten_dims=2,
+ param_attr=fluid.ParamAttr(
+ name="tdm.cls_fc.weight"),
+ bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+
+# 将loss打平，放到一起计算整体网络的loss
+tdm_fc_re = fluid.layers.reshape(tdm_fc, [-1, 2])
+
+# 若想对各个层次的loss辅以不同的权重，则在此处无需concat
+# 支持各个层次分别计算loss，再乘相应的权重
+sample_label = fluid.layers.concat(sample_label, axis=1)
+sample_label.stop_gradient = True
+labels_reshape = fluid.layers.reshape(sample_label, [-1, 1])
+
+# 计算整体的loss并得到softmax的输出
+cost, softmax_prob = fluid.layers.softmax_with_cross_entropy(
+ logits=tdm_fc_re, label=labels_reshape, return_softmax=True)
+
+# 通过mask过滤掉虚拟节点的loss
+sample_mask = fluid.layers.concat(sample_mask, axis=1)
+sample_mask.stop_gradient = True
+mask_reshape = fluid.layers.reshape(sample_mask, [-1, 1])
+
+mask_index = fluid.layers.where(mask_reshape != 0)
+mask_cost = fluid.layers.gather_nd(cost, mask_index)
+
+# 计算该batch的均值loss，同时计算acc, 亦可在这里计算auc
+avg_cost = fluid.layers.reduce_mean(mask_cost)
+acc = fluid.layers.accuracy(input=softmax_prob, label=labels_reshape)
+```
+
+### 预测组网
+
+预测的整体流程类似于beamsearch。预测组网中需要重点关注三个部分：
+1. 网络输入的定义，及初始检索层的确定
+2. 串行的通过各层分类器的流程
+3. 每层选出topK及最终选出topK的方法
+
+#### 网络输入
+
+首先考虑这样一个问题，假设树是一颗十层的完全二叉树，我们要取topK=1024的召回结果，那么我们需不需要从树的第一层开始逐层计算fc与topK？答案显然是否定的，我们只需要计算最后一层的1024个节点即可。在开发的实验中，我们得到的结论是，tdm检索时，计算复杂度并不高，时间耗费中OP的调度占据了主要矛盾，通过模型裁剪，树剪枝，模型量化等都可以加快预测速度。
+
+因此infer组网中首先考虑了跳层与剪枝的实现：我们定义了三个变量，`input_emb`,`first_layer_node`,`first_layer_node_mask`作为网络的输入。
+- `input_emb`：预测时输入的user/query向量。
+- `first_layer_node`：检索时的起始node_id，是变长类型。这样设置的好处是：1、可以输入某一层所有节点node_id，从而在某一层开始进行检索。2、也可以设置为特定node_id，从指定的树分枝开始检索。输入的node_id应该位于同一层。
+- `first_layer_node_mask`：维度与`first_layer_node`相同，值一一对应。若node是叶子节点，则mask=1，否则设置mask=0。
+
+在demo网络中，我们设置为从某一层的所有节点开始进行检索。paddle组网对输入定义的实现如下：
+```python
+def input_data(self):
+ input_emb = fluid.layers.data(
+ name="input_emb",
+ shape=[self.input_embed_size],
+ dtype="float32",
+ )
+
+ # first_layer 与 first_layer_mask 对应着infer起始的节点
+ first_layer = fluid.layers.data(
+ name="first_layer_node",
+ shape=[1],
+ dtype="int64",
+ lod_level=1, #支持变长
+ )
+
+ first_layer_mask = fluid.layers.data(
+ name="first_layer_node_mask",
+ shape=[1],
+ dtype="int64",
+ lod_level=1,
+ )
+
+ inputs = [input_emb] + [first_layer] + [first_layer_mask]
+ return inputs
+```
+
+确定起始层的方式比较简单，比较topK的大小与当层节点数，选取第一个节点数大于等于topK的层作为起始层，取它的节点作为起始节点。代码如下：
+```python
+def create_first_layer(self, args):
+ """decide which layer to start infer"""
+ first_layer_id = 0
+ for idx, layer_node in args.layer_node_num_list:
+ if layer_node >= self.topK:
+ first_layer_id = idx
+ break
+ first_layer_node = self.layer_list[first_layer_id]
+ self.first_layer_idx = first_layer_id
+ return first_layer_node
+```
+
+#### 通过各层分类器的流程
+
+tdm的检索逻辑类似beamsearch，简单来说：在每一层计算打分，得到topK的节点，将这些节点的孩子节点作为下一层的输入，如此循环，得到最终的topK。但仍然有一些需要注意的细节，下面将详细介绍。
+
+- 问题一：怎么处理`input_emb`？
+
+ - input_emb过`input_fc`，检索中，只需过一次即可:
+ ```python
+ nput_trans_emb = self.input_trans_net.input_fc_infer(input_emb)
+ ```
+ - 在通过每一层的分类器之前，过`layer_fc`，指定`layer_idx`，加载对应层的分类器，将输入映射到不同的兴趣粒度空间
+ ```python
+ input_fc_out = self.input_trans_net.layer_fc_infer(
+ input_trans_emb, layer_idx)
+ ```
+
+- 问题二：怎样实现beamsearch？
+
+ 我们通过在每一层计算打分，计算topK并拿到对应的孩子节点，for循环这个过程实现beamsearch。
+ ```python
+ for layer_idx in range(self.first_layer_idx, self.max_layers):
+ # 确定当前层的需要计算的节点数
+ if layer_idx == self.first_layer_idx:
+ current_layer_node_num = len(self.first_layer_node)
+ else:
+ current_layer_node_num = current_layer_node.shape[1] * \
+ current_layer_node.shape[2]
+
+ current_layer_node = fluid.layers.reshape(
+ current_layer_node, [self.batch_size, current_layer_node_num])
+ current_layer_child_mask = fluid.layers.reshape(
+ current_layer_child_mask, [self.batch_size, current_layer_node_num])
+
+ # 查当前层node的emb
+ node_emb = fluid.embedding(
+ input=current_layer_node,
+ size=[self.node_nums, self.node_embed_size],
+ param_attr=fluid.ParamAttr(name="TDM_Tree_Emb"))
+
+ input_fc_out = self.input_trans_net.layer_fc_infer(
+ input_trans_emb, layer_idx)
+
+ # 过每一层的分类器
+ layer_classifier_res = self.layer_classifier.classifier_layer_infer(input_fc_out, node_emb, layer_idx)
+
+ # 过最终的判别分类器
+ tdm_fc = fluid.layers.fc(input=layer_classifier_res,
+ size=self.label_nums,
+ act=None,
+ num_flatten_dims=2,
+ param_attr=fluid.ParamAttr(
+ name="tdm.cls_fc.weight"),
+ bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+
+ prob = fluid.layers.softmax(tdm_fc)
+ positive_prob = fluid.layers.slice(
+ prob, axes=[2], starts=[1], ends=[2])
+ prob_re = fluid.layers.reshape(
+ positive_prob, [self.batch_size, current_layer_node_num])
+
+ # 过滤掉padding产生的无效节点（node_id=0）
+ node_zero_mask = fluid.layers.cast(current_layer_node, 'bool')
+ node_zero_mask = fluid.layers.cast(node_zero_mask, 'float')
+ prob_re = prob_re * node_zero_mask
+
+ # 在当前层的分类结果中取topK，并将对应的score及node_id保存下来
+ k = self.topK
+ if current_layer_node_num < self.topK:
+ k = current_layer_node_num
+ _, topk_i = fluid.layers.topk(prob_re, k)
+
+ # index_sample op根据下标索引tensor对应位置的值
+ # 若paddle版本>2.0，调用方式为paddle.index_sample
+ top_node = fluid.contrib.layers.index_sample(
+ current_layer_node, topk_i)
+ prob_re_mask = prob_re * current_layer_child_mask # 过滤掉非叶子节点
+ topk_value = fluid.contrib.layers.index_sample(
+ prob_re_mask, topk_i)
+ node_score.append(topk_value)
+ node_list.append(top_node)
+
+ # 取当前层topK结果的孩子节点，作为下一层的输入
+ if layer_idx < self.max_layers - 1:
+ # tdm_child op 根据输入返回其 child 及 child_mask
+ # 若child是叶子节点，则child_mask=1，否则为0
+ current_layer_node, current_layer_child_mask = \
+ fluid.contrib.layers.tdm_child(x=top_node,
+ node_nums=self.node_nums,
+ child_nums=self.child_nums,
+ param_attr=fluid.ParamAttr(
+ name="TDM_Tree_Info"),
+ dtype='int64')
+ ```
+
+- 问题三：怎样得到最终的topK个叶子节点？
+
+ 在过每层分类器的过程中，我们保存了每层的topk节点，并将非叶子节点的打分置为了0，保存在`node_score`与`node_list`中。显然，我们需要召回的是topk个叶子节点，对所有层的叶子节点打分再计算一次topk，拿到结果。
+
+ ```python
+ total_node_score = fluid.layers.concat(node_score, axis=1)
+ total_node = fluid.layers.concat(node_list, axis=1)
+
+ # 考虑到树可能是不平衡的，计算所有层的叶子节点的topK
+ res_score, res_i = fluid.layers.topk(total_node_score, self.topK)
+ res_layer_node = fluid.contrib.layers.index_sample(total_node, res_i)
+ res_node = fluid.layers.reshape(res_layer_node, [-1, self.topK, 1])
+ ```
+
+- 问题四：现在拿到的是node_id，怎么转成item_id？
+
+ 如果有额外的映射表，可以将node_id转为item_id，但也有更方便的方法。在生成`tree_info`时，我们保存了每个node_id相应的item_id信息，直接使用它，将node_id对应的tree_info行的数据的第零维的item_id切出来。
+
+ ```python
+ # 利用Tree_info信息，将node_id转换为item_id
+ tree_info = fluid.default_main_program().global_block().var("TDM_Tree_Info")
+ res_node_emb = fluid.layers.gather_nd(tree_info, res_node)
+
+ res_item = fluid.layers.slice(
+ res_node_emb, axes=[2], starts=[0], ends=[1])
+ res_item_re = fluid.layers.reshape(res_item, [-1, self.topK])
+ ```
+以上，我们便完成了训练及预测组网的全部部分。
diff --git a/models/treebased/tdm/__init__.py b/models/treebased/tdm/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/treebased/tdm/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/recall/tdm/config.yaml b/models/treebased/tdm/config.yaml
similarity index 94%
rename from models/recall/tdm/config.yaml
rename to models/treebased/tdm/config.yaml
index bd870490f79646e7e250a65bda302d726ff61473..ec79017496c971d40d62f9f8a64769f2f39ccd9e 100755
--- a/models/recall/tdm/config.yaml
+++ b/models/treebased/tdm/config.yaml
@@ -18,13 +18,14 @@ train:
strategy: "async"
epochs: 2
- workspace: "fleetrec.models.recall.tdm"
+ workspace: "paddlerec.models.treebased.tdm"
reader:
batch_size: 32
class: "{workspace}/tdm_reader.py"
train_data_path: "{workspace}/data/train"
test_data_path: "{workspace}/data/test"
+ reader_debug_mode: False
model:
models: "{workspace}/model.py"
@@ -73,7 +74,7 @@ train:
save_last: True
evaluate:
- workspace: "fleetrec.models.recall.tdm"
+ workspace: "paddlerec.models.recall.tdm"
reader:
batch_size: 1
class: "{workspace}/tdm_evaluate_reader.py"
diff --git a/models/recall/tdm/data/test/demo_fake_test.txt b/models/treebased/tdm/data/test/demo_fake_test.txt
similarity index 100%
rename from models/recall/tdm/data/test/demo_fake_test.txt
rename to models/treebased/tdm/data/test/demo_fake_test.txt
diff --git a/models/recall/tdm/data/train/demo_fake_input.txt b/models/treebased/tdm/data/train/demo_fake_input.txt
similarity index 100%
rename from models/recall/tdm/data/train/demo_fake_input.txt
rename to models/treebased/tdm/data/train/demo_fake_input.txt
diff --git a/models/treebased/tdm/img/demo_network.png b/models/treebased/tdm/img/demo_network.png
new file mode 100644
index 0000000000000000000000000000000000000000..3a9981f360ffaaaa337d0c87b2fd6135969bc2a3
Binary files /dev/null and b/models/treebased/tdm/img/demo_network.png differ
diff --git a/models/treebased/tdm/img/demo_tree.png b/models/treebased/tdm/img/demo_tree.png
new file mode 100644
index 0000000000000000000000000000000000000000..5e50d34ab7e53ce4bf505b5affc5c2e1d9ebfa73
Binary files /dev/null and b/models/treebased/tdm/img/demo_tree.png differ
diff --git a/models/treebased/tdm/img/dnn-net.png b/models/treebased/tdm/img/dnn-net.png
new file mode 100644
index 0000000000000000000000000000000000000000..3974bdc10e74ff80f738b4372947c7acc669a507
Binary files /dev/null and b/models/treebased/tdm/img/dnn-net.png differ
diff --git a/models/treebased/tdm/img/input-net.png b/models/treebased/tdm/img/input-net.png
new file mode 100644
index 0000000000000000000000000000000000000000..ca0633cc120367de1a7834a9987764ca4a092951
Binary files /dev/null and b/models/treebased/tdm/img/input-net.png differ
diff --git a/models/recall/tdm/model.py b/models/treebased/tdm/model.py
similarity index 77%
rename from models/recall/tdm/model.py
rename to models/treebased/tdm/model.py
index ee96b260c10e85901242ac527f653e40573fefb9..319a7b4f9a3695537b43c8f1078dc4e1b73549fb 100755
--- a/models/recall/tdm/model.py
+++ b/models/treebased/tdm/model.py
@@ -14,49 +14,49 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
+
import paddle.fluid as fluid
-import math
-from fleetrec.core.utils import envs
-from fleetrec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
# tree meta hyper parameters
- self.max_layers = envs.get_global_env(
- "tree_parameters.max_layers", 4, self._namespace)
- self.node_nums = envs.get_global_env(
- "tree_parameters.node_nums", 26, self._namespace)
+ self.max_layers = envs.get_global_env("tree_parameters.max_layers", 4,
+ self._namespace)
+ self.node_nums = envs.get_global_env("tree_parameters.node_nums", 26,
+ self._namespace)
self.leaf_node_nums = envs.get_global_env(
"tree_parameters.leaf_node_nums", 13, self._namespace)
self.output_positive = envs.get_global_env(
"tree_parameters.output_positive", True, self._namespace)
self.layer_node_num_list = envs.get_global_env(
- "tree_parameters.layer_node_num_list", [
- 2, 4, 7, 12], self._namespace)
- self.child_nums = envs.get_global_env(
- "tree_parameters.child_nums", 2, self._namespace)
- self.tree_layer_path = envs.get_global_env(
- "tree.tree_layer_path", None, "train.startup")
+ "tree_parameters.layer_node_num_list", [2, 4, 7,
+ 12], self._namespace)
+ self.child_nums = envs.get_global_env("tree_parameters.child_nums", 2,
+ self._namespace)
+ self.tree_layer_path = envs.get_global_env("tree.tree_layer_path",
+ None, "train.startup")
# model training hyper parameter
self.node_emb_size = envs.get_global_env(
"hyper_parameters.node_emb_size", 64, self._namespace)
self.input_emb_size = envs.get_global_env(
"hyper_parameters.input_emb_size", 768, self._namespace)
- self.act = envs.get_global_env(
- "hyper_parameters.act", "tanh", self._namespace)
+ self.act = envs.get_global_env("hyper_parameters.act", "tanh",
+ self._namespace)
self.neg_sampling_list = envs.get_global_env(
- "hyper_parameters.neg_sampling_list", [
- 1, 2, 3, 4], self._namespace)
+ "hyper_parameters.neg_sampling_list", [1, 2, 3,
+ 4], self._namespace)
# model infer hyper parameter
- self.topK = envs.get_global_env(
- "hyper_parameters.node_nums", 1, self._namespace)
- self.batch_size = envs.get_global_env(
- "batch_size", 1, "evaluate.reader")
+ self.topK = envs.get_global_env("hyper_parameters.node_nums", 1,
+ self._namespace)
+ self.batch_size = envs.get_global_env("batch_size", 1,
+ "evaluate.reader")
def train_net(self):
self.train_input()
@@ -76,21 +76,22 @@ class Model(ModelBase):
input_emb = fluid.data(
name="input_emb",
shape=[None, self.input_emb_size],
- dtype="float32",
- )
+ dtype="float32", )
self._data_var.append(input_emb)
item_label = fluid.data(
name="item_label",
shape=[None, 1],
- dtype="int64",
- )
+ dtype="int64", )
self._data_var.append(item_label)
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
+ feed_list=self._data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
def tdm_net(self):
"""
@@ -116,8 +117,7 @@ class Model(ModelBase):
output_list=True,
seed=0,
tree_dtype='int64',
- dtype='int64'
- )
+ dtype='int64')
# 查表得到每个节点的Embedding
sample_nodes_emb = [
@@ -125,35 +125,34 @@ class Model(ModelBase):
input=sample_nodes[i],
is_sparse=True,
size=[self.node_nums, self.node_emb_size],
- param_attr=fluid.ParamAttr(
- name="TDM_Tree_Emb")
- ) for i in range(self.max_layers)
+ param_attr=fluid.ParamAttr(name="TDM_Tree_Emb"))
+ for i in range(self.max_layers)
]
# 此处进行Reshape是为了之后层次化的分类器训练
sample_nodes_emb = [
- fluid.layers.reshape(sample_nodes_emb[i],
- [-1, self.neg_sampling_list[i] +
- self.output_positive, self.node_emb_size]
- ) for i in range(self.max_layers)
+ fluid.layers.reshape(sample_nodes_emb[i], [
+ -1, self.neg_sampling_list[i] + self.output_positive,
+ self.node_emb_size
+ ]) for i in range(self.max_layers)
]
# 对输入的input_emb进行转换，使其维度与node_emb维度一致
input_trans_emb = self.input_trans_layer(input_emb)
# 分类器的主体网络，分别训练不同层次的分类器
- layer_classifier_res = self.classifier_layer(
- input_trans_emb, sample_nodes_emb)
+ layer_classifier_res = self.classifier_layer(input_trans_emb,
+ sample_nodes_emb)
# 最后的概率判别FC，将所有层次的node分类结果放到一起以相同的标准进行判别
# 考虑到树极大可能不平衡，有些item不在最后一层，所以需要这样的机制保证每个item都有机会被召回
- tdm_fc = fluid.layers.fc(input=layer_classifier_res,
- size=2,
- act=None,
- num_flatten_dims=2,
- param_attr=fluid.ParamAttr(
- name="tdm.cls_fc.weight"),
- bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+ tdm_fc = fluid.layers.fc(
+ input=layer_classifier_res,
+ size=2,
+ act=None,
+ num_flatten_dims=2,
+ param_attr=fluid.ParamAttr(name="tdm.cls_fc.weight"),
+ bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
# 将loss打平，放到一起计算整体网络的loss
tdm_fc_re = fluid.layers.reshape(tdm_fc, [-1, 2])
@@ -202,7 +201,7 @@ class Model(ModelBase):
def metrics(self):
auc, batch_auc, _ = fluid.layers.auc(input=self._predict,
label=self.mask_label,
- num_thresholds=2 ** 12,
+ num_thresholds=2**12,
slide_steps=20)
self._metrics["AUC"] = auc
self._metrics["BATCH_AUC"] = batch_auc
@@ -218,8 +217,7 @@ class Model(ModelBase):
size=self.node_emb_size,
act=None,
param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
- bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"),
- )
+ bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"), )
# 将input_emb映射到各个不同层次的向量表示空间
input_layer_fc_out = [
@@ -229,8 +227,9 @@ class Model(ModelBase):
act=self.act,
param_attr=fluid.ParamAttr(
name="trans.layer_fc.weight." + str(i)),
- bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias."+str(i)),
- ) for i in range(self.max_layers)
+ bias_attr=fluid.ParamAttr(
+ name="trans.layer_fc.bias." + str(i)), )
+ for i in range(self.max_layers)
]
return input_layer_fc_out
@@ -246,20 +245,22 @@ class Model(ModelBase):
input_layer_unsequeeze, expand_times=[1, node.shape[1], 1])
else:
input_layer_expand = fluid.layers.expand(
- input_layer_unsequeeze, expand_times=[1, node[layer_idx].shape[1], 1])
+ input_layer_unsequeeze,
+ expand_times=[1, node[layer_idx].shape[1], 1])
return input_layer_expand
def classifier_layer(self, input, node):
# 扩展input，使维度与node匹配
input_expand = [
- self._expand_layer(input[i], node, i) for i in range(self.max_layers)
+ self._expand_layer(input[i], node, i)
+ for i in range(self.max_layers)
]
# 将input_emb与node_emb concat到一起过分类器FC
input_node_concat = [
fluid.layers.concat(
- input=[input_expand[i], node[i]],
- axis=2) for i in range(self.max_layers)
+ input=[input_expand[i], node[i]], axis=2)
+ for i in range(self.max_layers)
]
hidden_states_fc = [
fluid.layers.fc(
@@ -268,9 +269,9 @@ class Model(ModelBase):
num_flatten_dims=2,
act=self.act,
param_attr=fluid.ParamAttr(
- name="cls.concat_fc.weight."+str(i)),
- bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(i))
- ) for i in range(self.max_layers)
+ name="cls.concat_fc.weight." + str(i)),
+ bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias." + str(i)))
+ for i in range(self.max_layers)
]
# 如果将所有层次的node放到一起计算loss，则需要在此处concat
@@ -285,12 +286,14 @@ class Model(ModelBase):
input_emb = fluid.layers.data(
name="input_emb",
shape=[self.input_emb_size],
- dtype="float32",
- )
+ dtype="float32", )
self._infer_data_var.append(input_emb)
self._infer_data_loader = fluid.io.DataLoader.from_generator(
- feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+ feed_list=self._infer_data_var,
+ capacity=64,
+ use_double_buffer=False,
+ iterable=False)
def get_layer_list(self):
"""get layer list from layer_list.txt"""
@@ -318,10 +321,12 @@ class Model(ModelBase):
node_list = []
mask_list = []
for id in first_layer_node:
- node_list.append(fluid.layers.fill_constant(
- [self.batch_size, 1], value=int(id), dtype='int64'))
- mask_list.append(fluid.layers.fill_constant(
- [self.batch_size, 1], value=0, dtype='int64'))
+ node_list.append(
+ fluid.layers.fill_constant(
+ [self.batch_size, 1], value=int(id), dtype='int64'))
+ mask_list.append(
+ fluid.layers.fill_constant(
+ [self.batch_size, 1], value=0, dtype='int64'))
self.first_layer_node = fluid.layers.concat(node_list, axis=1)
self.first_layer_node_mask = fluid.layers.concat(mask_list, axis=1)
@@ -348,7 +353,7 @@ class Model(ModelBase):
current_layer_node_num = self.first_layer_node.shape[1]
else:
current_layer_node_num = current_layer_node.shape[1] * \
- current_layer_node.shape[2]
+ current_layer_node.shape[2]
current_layer_node = fluid.layers.reshape(
current_layer_node, [-1, current_layer_node_num])
@@ -359,28 +364,26 @@ class Model(ModelBase):
size=[self.node_nums, self.node_emb_size],
param_attr=fluid.ParamAttr(name="TDM_Tree_Emb"))
- input_fc_out = self.layer_fc_infer(
- input_trans_emb, layer_idx)
+ input_fc_out = self.layer_fc_infer(input_trans_emb, layer_idx)
# 过每一层的分类器
- layer_classifier_res = self.classifier_layer_infer(input_fc_out,
- node_emb,
- layer_idx)
+ layer_classifier_res = self.classifier_layer_infer(
+ input_fc_out, node_emb, layer_idx)
# 过最终的判别分类器
- tdm_fc = fluid.layers.fc(input=layer_classifier_res,
- size=2,
- act=None,
- num_flatten_dims=2,
- param_attr=fluid.ParamAttr(
- name="tdm.cls_fc.weight"),
- bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+ tdm_fc = fluid.layers.fc(
+ input=layer_classifier_res,
+ size=2,
+ act=None,
+ num_flatten_dims=2,
+ param_attr=fluid.ParamAttr(name="tdm.cls_fc.weight"),
+ bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
prob = fluid.layers.softmax(tdm_fc)
positive_prob = fluid.layers.slice(
prob, axes=[2], starts=[1], ends=[2])
- prob_re = fluid.layers.reshape(
- positive_prob, [-1, current_layer_node_num])
+ prob_re = fluid.layers.reshape(positive_prob,
+ [-1, current_layer_node_num])
# 过滤掉padding产生的无效节点（node_id=0）
node_zero_mask = fluid.layers.cast(current_layer_node, 'bool')
@@ -395,11 +398,11 @@ class Model(ModelBase):
# index_sample op根据下标索引tensor对应位置的值
# 若paddle版本>2.0，调用方式为paddle.index_sample
- top_node = fluid.contrib.layers.index_sample(
- current_layer_node, topk_i)
+ top_node = fluid.contrib.layers.index_sample(current_layer_node,
+ topk_i)
prob_re_mask = prob_re * current_layer_node_mask # 过滤掉非叶子节点
- topk_value = fluid.contrib.layers.index_sample(
- prob_re_mask, topk_i)
+ topk_value = fluid.contrib.layers.index_sample(prob_re_mask,
+ topk_i)
node_score.append(topk_value)
node_list.append(top_node)
@@ -424,7 +427,8 @@ class Model(ModelBase):
res_node = fluid.layers.reshape(res_layer_node, [-1, self.topK, 1])
# 利用Tree_info信息，将node_id转换为item_id
- tree_info = fluid.default_main_program().global_block().var("TDM_Tree_Info")
+ tree_info = fluid.default_main_program().global_block().var(
+ "TDM_Tree_Info")
res_node_emb = fluid.layers.gather_nd(tree_info, res_node)
res_item = fluid.layers.slice(
@@ -442,8 +446,7 @@ class Model(ModelBase):
size=self.node_emb_size,
act=None,
param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
- bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"),
- )
+ bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"), )
return input_fc_out
def layer_fc_infer(self, input_fc_out, layer_idx):
@@ -458,8 +461,7 @@ class Model(ModelBase):
param_attr=fluid.ParamAttr(
name="trans.layer_fc.weight." + str(layer_idx)),
bias_attr=fluid.ParamAttr(
- name="trans.layer_fc.bias."+str(layer_idx)),
- )
+ name="trans.layer_fc.bias." + str(layer_idx)), )
return input_layer_fc_out
def classifier_layer_infer(self, input, node, layer_idx):
@@ -479,6 +481,7 @@ class Model(ModelBase):
num_flatten_dims=2,
act=self.act,
param_attr=fluid.ParamAttr(
- name="cls.concat_fc.weight."+str(layer_idx)),
- bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(layer_idx)))
+ name="cls.concat_fc.weight." + str(layer_idx)),
+ bias_attr=fluid.ParamAttr(
+ name="cls.concat_fc.bias." + str(layer_idx)))
return hidden_states_fc
diff --git a/models/recall/tdm/tdm_evaluate_reader.py b/models/treebased/tdm/tdm_evaluate_reader.py
similarity index 89%
rename from models/recall/tdm/tdm_evaluate_reader.py
rename to models/treebased/tdm/tdm_evaluate_reader.py
index 915389ba3328e3e0ba60052532f4f93eca8faa4f..4e3b64770b42c05726dd3c90466d77e422e00902 100644
--- a/models/recall/tdm/tdm_evaluate_reader.py
+++ b/models/treebased/tdm/tdm_evaluate_reader.py
@@ -17,7 +17,7 @@
from __future__ import print_function
-from fleetrec.core.reader import Reader
+from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
@@ -28,12 +28,14 @@ class EvaluateReader(Reader):
"""
Read the data line by line and process it as a dictionary
"""
+
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = (line.strip('\n')).split('\t')
- input_emb = map(float, features[0].split(' '))
+ input_emb = features[0].split(' ')
+ input_emb = [float(i) for i in input_emb]
feature_name = ["input_emb"]
yield zip(feature_name, [input_emb])
diff --git a/models/recall/tdm/tdm_reader.py b/models/treebased/tdm/tdm_reader.py
similarity index 89%
rename from models/recall/tdm/tdm_reader.py
rename to models/treebased/tdm/tdm_reader.py
index 17413249c2adc54d450129c76ac0761e27ba27a4..709900649a03c3439cbf474781a5c0ae7b087dd7 100755
--- a/models/recall/tdm/tdm_reader.py
+++ b/models/treebased/tdm/tdm_reader.py
@@ -17,7 +17,7 @@
from __future__ import print_function
-from fleetrec.core.reader import Reader
+from paddlerec.core.reader import Reader
class TrainReader(Reader):
@@ -28,12 +28,14 @@ class TrainReader(Reader):
"""
Read the data line by line and process it as a dictionary
"""
+
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = (line.strip('\n')).split('\t')
- input_emb = map(float, features[0].split(' '))
+ input_emb = features[0].split(' ')
+ input_emb = [float(i) for i in input_emb]
item_label = [int(features[1])]
feature_name = ["input_emb", "item_label"]
diff --git a/models/treebased/tdm/tree/layer_list.txt b/models/treebased/tdm/tree/layer_list.txt
new file mode 100755
index 0000000000000000000000000000000000000000..d1c6c50a10f1b40aa1fbdef7d57bdd600549fb11
--- /dev/null
+++ b/models/treebased/tdm/tree/layer_list.txt
@@ -0,0 +1,4 @@
+1,2
+3,4,5,6
+7,8,9,10,11,12,13
+14,15,16,17,18,19,20,21,22,23,24,25
diff --git a/models/recall/tdm/tree/travel_list.npy b/models/treebased/tdm/tree/travel_list.npy
similarity index 100%
rename from models/recall/tdm/tree/travel_list.npy
rename to models/treebased/tdm/tree/travel_list.npy
diff --git a/models/recall/tdm/tree/tree_emb.npy b/models/treebased/tdm/tree/tree_emb.npy
similarity index 100%
rename from models/recall/tdm/tree/tree_emb.npy
rename to models/treebased/tdm/tree/tree_emb.npy
diff --git a/models/recall/tdm/tree/tree_info.npy b/models/treebased/tdm/tree/tree_info.npy
similarity index 100%
rename from models/recall/tdm/tree/tree_info.npy
rename to models/treebased/tdm/tree/tree_info.npy
diff --git a/readme.md b/readme.md
deleted file mode 100644
index 6a37e1d30c5582a8ff6aa6236ec961b1ae8fa403..0000000000000000000000000000000000000000
--- a/readme.md
+++ /dev/null
@@ -1,155 +0,0 @@
-
-
-
-[](LICENSE)
-[](https://github.com/PaddlePaddle/PaddleRec/releases)
-
-PaddleRec是源于飞桨生态的搜索推荐模型一站式开箱即用工具，无论您是初学者，开发者，研究者均可便捷的使用PaddleRec完成调研，训练到预测部署的全流程工作。PaddleRec提供了搜索推荐任务中语义理解、召回、粗排、精排、多任务学习的全流程解决方案。
-
-PadlleRec以预置模型为核心,具备以下特点：
-- [易于上手，开箱即用](https://www.paddlepaddle.org.cn)
-- [灵活配置，个性调参](https://www.paddlepaddle.org.cn)
-- [分布式训练，大规模稀疏](https://www.paddlepaddle.org.cn)
-- [快速部署，一键上线](https://www.paddlepaddle.org.cn)
-
-
-
-
-# 目录
-* [特性](#特性)
-* [支持模型列表](#支持模型列表)
-* [文档教程](#文档教程)
- * [入门教程](#入门教程)
- * [环境要求](#环境要求)
- * [安装命令](#安装命令)
- * [快速开始](#快速开始)
- * [常见问题FAQ](#常见问题faq)
- * [进阶教程](#进阶教程)
- * [自定义数据集及Reader](#自定义数据集及reader)
- * [模型调参](#模型调参)
- * [单机训练](#单机训练)
- * [分布式训练](#分布式训练)
- * [预测部署](#预测部署)
-* [版本历史](#版本历史)
- * [版本更新](#版本更新)
- * [Benchamrk](#benchamrk)
-* [许可证书](#许可证书)
-* [如何贡献代码](#如何贡献代码)
- * [优化PaddleRec框架](#优化paddlerec框架)
- * [新增模型到PaddleRec](#新增模型到paddlerec)
-
-
-
-# 特性
-- 易于上手，开箱即用
-- 灵活配置，个性调参
-- 分布式训练，大规模稀疏
-- 快速部署，一键上线
-
-# 支持模型列表
-| 方向 | 模型 | 单机CPU训练 | 单机GPU训练 | 分布式CPU训练 | 分布式GPU训练 | 自定义数据集 | 服务器部署 |
-| :------------------: | :--------------------: | :---------: | :---------: | :-----------: | :-----------: | :----------: | :--------: |
-| ContentUnderstanding | [Text-Classifcation]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| ContentUnderstanding | [TagSpace]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| Recall | [Word2Vec]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| Recall | [TDM]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| Rank | [CTR-Dnn]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| Rank | [DeepFm]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| Rerank | [ListWise]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| MultiTask | [MMOE]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| MultiTask | [ESSM]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| Match | [DSSM]() | ✓ | x | ✓ | x | ✓ | ✓ |
-| Match | [Multiview-Simnet]() | ✓ | x | ✓ | x | ✓ | ✓ |
-
-# 文档教程
-## 入门教程
-### 环境要求
-* Python >= 2.7
-* PaddlePaddle >= 1.7.2
-* 操作系统: Windows/Mac/Linux
-
-### 安装命令
-
-- 安装方法一：
- ```bash
- python -m pip install fleet-rec
- ```
-
-- 安装方法二
-
- * 安装飞桨 **注：需要用户安装最新版本的飞桨<当前只支持Linux系统>。**
-
- ```bash
- python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
- ```
-
- * 源码安装Fleet-Rec
-
- ```
- git clone https://github.com/seiriosPlus/FleetRec/
- cd FleetRec
- python setup.py install
- ```
-
-### 快速开始
-#### ctr-dnn示例使用
-目前框架内置了多个模型，简单的命令即可使用内置模型开始单机训练和本地1*1模拟训练
-
-##### 单机训练
-```bash
-cd FleetRec
-
-python -m fleetrec.run \
- -m fleetrec.models.rank.dnn \
- -d cpu \
- -e single
-
-# 使用GPU资源进行训练
-python -m fleetrec.run \
- -m fleetrec.models.rank.dnn \
- -d gpu \
- -e single
-```
-
-##### 本地模拟分布式训练
-
-```bash
-cd FleetRec
-# 使用CPU资源进行训练
-python -m fleetrec.run \
- -m fleetrec.models.rank.dnn \
- -d cpu \
- -e local_cluster
-```
-
-##### 集群提交分布式训练<需要用户预先配置好集群环境，本提交命令不包含提交客户端>
-
-```bash
-cd FleetRec
-
-python -m fleetrec.run \
- -m fleetrec.models.rank.dnn \
- -d cpu \
- -e cluster
-```
-
-### 常见问题FAQ
-
-## 进阶教程
-### 自定义数据集及Reader
-### 模型调参
-### 单机训练
-### 分布式训练
-### 预测部署
-
-# 版本历史
-## 版本更新
-## Benchamrk
-
-# 许可证书
-本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
-# 如何贡献代码
-## 优化PaddleRec框架
-## 新增模型到PaddleRec
diff --git a/run.py b/run.py
new file mode 100755
index 0000000000000000000000000000000000000000..594801fcdd5edb1821799ef53994674aec6a934d
--- /dev/null
+++ b/run.py
@@ -0,0 +1,329 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import subprocess
+
+import argparse
+import tempfile
+import yaml
+import copy
+from paddlerec.core.factory import TrainerFactory
+from paddlerec.core.utils import envs
+from paddlerec.core.utils import util
+
+engines = {}
+device = ["CPU", "GPU"]
+clusters = ["SINGLE", "LOCAL_CLUSTER", "CLUSTER"]
+engine_choices = [
+ "SINGLE_TRAIN", "LOCAL_CLUSTER", "CLUSTER", "TDM_SINGLE",
+ "TDM_LOCAL_CLUSTER", "TDM_CLUSTER", "SINGLE_INFER"
+]
+custom_model = ['TDM']
+model_name = ""
+
+
+def engine_registry():
+ engines["TRANSPILER"] = {}
+ engines["PSLIB"] = {}
+
+ engines["TRANSPILER"]["SINGLE_TRAIN"] = single_train_engine
+ engines["TRANSPILER"]["SINGLE_INFER"] = single_infer_engine
+ engines["TRANSPILER"]["LOCAL_CLUSTER"] = local_cluster_engine
+ engines["TRANSPILER"]["CLUSTER"] = cluster_engine
+ engines["PSLIB"]["SINGLE"] = local_mpi_engine
+ engines["PSLIB"]["LOCAL_CLUSTER"] = local_mpi_engine
+ engines["PSLIB"]["CLUSTER"] = cluster_mpi_engine
+
+
+def get_inters_from_yaml(file, filters):
+ with open(file, 'r') as rb:
+ _envs = yaml.load(rb.read(), Loader=yaml.FullLoader)
+
+ flattens = envs.flatten_environs(_envs)
+ inters = {}
+ for k, v in flattens.items():
+ for f in filters:
+ if k.startswith(f):
+ inters[k] = v
+ return inters
+
+
+def get_all_inters_from_yaml(file, filters):
+ with open(file, 'r') as rb:
+ _envs = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ all_flattens = {}
+
+ def fatten_env_namespace(namespace_nests, local_envs):
+ for k, v in local_envs.items():
+ if isinstance(v, dict):
+ nests = copy.deepcopy(namespace_nests)
+ nests.append(k)
+ fatten_env_namespace(nests, v)
+ elif (k == "dataset" or k == "phase" or
+ k == "runner") and isinstance(v, list):
+ for i in v:
+ if i.get("name") is None:
+ raise ValueError("name must be in dataset list ", v)
+ nests = copy.deepcopy(namespace_nests)
+ nests.append(k)
+ nests.append(i["name"])
+ fatten_env_namespace(nests, i)
+ else:
+ global_k = ".".join(namespace_nests + [k])
+ all_flattens[global_k] = v
+
+ fatten_env_namespace([], _envs)
+ ret = {}
+ for k, v in all_flattens.items():
+ for f in filters:
+ if k.startswith(f):
+ ret[k] = v
+ return ret
+
+
+def get_engine(args):
+ transpiler = get_transpiler()
+ with open(args.model, 'r') as rb:
+ envs = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ run_extras = get_all_inters_from_yaml(args.model, ["train.", "runner."])
+
+ engine = run_extras.get("train.engine", None)
+ if engine is None:
+ engine = run_extras.get("runner." + envs["mode"] + ".class", None)
+ if engine is None:
+ engine = "single_train"
+ engine = engine.upper()
+ if engine not in engine_choices:
+ raise ValueError("train.engin can not be chosen in {}".format(
+ engine_choices))
+
+ print("engines: \n{}".format(engines))
+
+ run_engine = engines[transpiler].get(engine, None)
+
+ return run_engine
+
+
+def get_transpiler():
+ FNULL = open(os.devnull, 'w')
+ cmd = [
+ "python", "-c",
+ "import paddle.fluid as fluid; fleet_ptr = fluid.core.Fleet(); [fleet_ptr.copy_table_by_feasign(10, 10, [2020, 1010])];"
+ ]
+ proc = subprocess.Popen(cmd, stdout=FNULL, stderr=FNULL, cwd=os.getcwd())
+ ret = proc.wait()
+ if ret == -11:
+ return "PSLIB"
+ else:
+ return "TRANSPILER"
+
+
+def set_runtime_envs(cluster_envs, engine_yaml):
+ if cluster_envs is None:
+ cluster_envs = {}
+
+ engine_extras = get_inters_from_yaml(engine_yaml, "train.trainer.")
+ if "train.trainer.threads" in engine_extras and "CPU_NUM" in cluster_envs:
+ cluster_envs["CPU_NUM"] = engine_extras["train.trainer.threads"]
+
+ envs.set_runtime_environs(cluster_envs)
+ envs.set_runtime_environs(engine_extras)
+
+ need_print = {}
+ for k, v in os.environ.items():
+ if k.startswith("train.trainer."):
+ need_print[k] = v
+
+ print(envs.pretty_print_envs(need_print, ("Runtime Envs", "Value")))
+
+
+def get_trainer_prefix(args):
+ if model_name in custom_model:
+ return model_name.upper()
+ return ""
+
+
+def single_train_engine(args):
+ trainer = get_trainer_prefix(args) + "SingleTrainer"
+ single_envs = {}
+ single_envs["train.trainer.trainer"] = trainer
+ single_envs["train.trainer.threads"] = "2"
+ single_envs["train.trainer.engine"] = "single_train"
+ single_envs["train.trainer.platform"] = envs.get_platform()
+ print("use {} engine to run model: {}".format(trainer, args.model))
+ set_runtime_envs(single_envs, args.model)
+ trainer = TrainerFactory.create(args.model)
+ return trainer
+
+
+def single_infer_engine(args):
+ trainer = get_trainer_prefix(args) + "SingleInfer"
+ single_envs = {}
+ single_envs["train.trainer.trainer"] = trainer
+ single_envs["train.trainer.threads"] = "2"
+ single_envs["train.trainer.engine"] = "single_infer"
+ single_envs["train.trainer.platform"] = envs.get_platform()
+ print("use {} engine to run model: {}".format(trainer, args.model))
+ set_runtime_envs(single_envs, args.model)
+ trainer = TrainerFactory.create(args.model)
+ return trainer
+
+
+def cluster_engine(args):
+ def update_workspace(cluster_envs):
+ workspace = cluster_envs.get("engine_workspace", None)
+
+ if not workspace:
+ return
+ path = envs.path_adapter(workspace)
+ for name, value in cluster_envs.items():
+ if isinstance(value, str):
+ value = value.replace("{workspace}", path)
+ value = envs.windows_path_converter(value)
+ cluster_envs[name] = value
+
+ def master():
+ role = "MASTER"
+ from paddlerec.core.engine.cluster.cluster import ClusterEngine
+ with open(args.backend, 'r') as rb:
+ _envs = yaml.load(rb.read(), Loader=yaml.FullLoader)
+
+ flattens = envs.flatten_environs(_envs, "_")
+ flattens["engine_role"] = role
+ flattens["engine_run_config"] = args.model
+ flattens["engine_temp_path"] = tempfile.mkdtemp()
+ update_workspace(flattens)
+
+ envs.set_runtime_environs(flattens)
+ print(envs.pretty_print_envs(flattens, ("Submit Runtime Envs", "Value"
+ )))
+
+ launch = ClusterEngine(None, args.model)
+ return launch
+
+ def worker():
+ role = "WORKER"
+ trainer = get_trainer_prefix(args) + "ClusterTrainer"
+ cluster_envs = {}
+ cluster_envs["train.trainer.trainer"] = trainer
+ cluster_envs["train.trainer.engine"] = "cluster"
+ cluster_envs["train.trainer.threads"] = envs.get_runtime_environ(
+ "CPU_NUM")
+ cluster_envs["train.trainer.platform"] = envs.get_platform()
+ print("launch {} engine with cluster to with model: {}".format(
+ trainer, args.model))
+ set_runtime_envs(cluster_envs, args.model)
+
+ trainer = TrainerFactory.create(args.model)
+ return trainer
+
+ role = os.getenv("PADDLE_PADDLEREC_ROLE", "MASTER")
+
+ if role == "WORKER":
+ return worker()
+ else:
+ return master()
+
+
+def cluster_mpi_engine(args):
+ print("launch cluster engine with cluster to run model: {}".format(
+ args.model))
+
+ cluster_envs = {}
+ cluster_envs["train.trainer.trainer"] = "CtrCodingTrainer"
+ cluster_envs["train.trainer.platform"] = envs.get_platform()
+
+ set_runtime_envs(cluster_envs, args.model)
+
+ trainer = TrainerFactory.create(args.model)
+ return trainer
+
+
+def local_cluster_engine(args):
+ from paddlerec.core.engine.local_cluster import LocalClusterEngine
+
+ trainer = get_trainer_prefix(args) + "ClusterTrainer"
+ cluster_envs = {}
+ cluster_envs["server_num"] = 1
+ cluster_envs["worker_num"] = 1
+ cluster_envs["start_port"] = envs.find_free_port()
+ cluster_envs["log_dir"] = "logs"
+ cluster_envs["train.trainer.trainer"] = trainer
+ cluster_envs["train.trainer.strategy"] = "async"
+ cluster_envs["train.trainer.threads"] = "2"
+ cluster_envs["train.trainer.engine"] = "local_cluster"
+ cluster_envs["train.trainer.platform"] = envs.get_platform()
+
+ cluster_envs["CPU_NUM"] = "2"
+ print("launch {} engine with cluster to run model: {}".format(trainer,
+ args.model))
+
+ set_runtime_envs(cluster_envs, args.model)
+ launch = LocalClusterEngine(cluster_envs, args.model)
+ return launch
+
+
+def local_mpi_engine(args):
+ print("launch cluster engine with cluster to run model: {}".format(
+ args.model))
+ from paddlerec.core.engine.local_mpi import LocalMPIEngine
+
+ print("use 1X1 MPI ClusterTraining at localhost to run model: {}".format(
+ args.model))
+
+ mpi = util.run_which("mpirun")
+ if not mpi:
+ raise RuntimeError("can not find mpirun, please check environment")
+ cluster_envs = {}
+ cluster_envs["mpirun"] = mpi
+ cluster_envs["train.trainer.trainer"] = "CtrCodingTrainer"
+ cluster_envs["log_dir"] = "logs"
+ cluster_envs["train.trainer.engine"] = "local_cluster"
+
+ cluster_envs["train.trainer.platform"] = envs.get_platform()
+
+ set_runtime_envs(cluster_envs, args.model)
+ launch = LocalMPIEngine(cluster_envs, args.model)
+ return launch
+
+
+def get_abs_model(model):
+ if model.startswith("paddlerec."):
+ dir = envs.path_adapter(model)
+ path = os.path.join(dir, "config.yaml")
+ else:
+ if not os.path.isfile(model):
+ raise IOError("model config: {} invalid".format(model))
+ path = model
+ return path
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description='paddle-rec run')
+ parser.add_argument("-m", "--model", type=str)
+ parser.add_argument("-b", "--backend", type=str, default=None)
+
+ abs_dir = os.path.dirname(os.path.abspath(__file__))
+ envs.set_runtime_environs({"PACKAGE_BASE": abs_dir})
+
+ args = parser.parse_args()
+
+ model_name = args.model.split('.')[-1]
+ args.model = get_abs_model(args.model)
+ engine_registry()
+
+ which_engine = get_engine(args)
+ engine = which_engine(args)
+ engine.run()
diff --git a/setup.py b/setup.py
index 80e39b5533e050e2307a436865ce14e44d797773..8ad1cc742434aa39513a1c618b56649c3530686a 100644
--- a/setup.py
+++ b/setup.py
@@ -1,25 +1,37 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
"""
-setup for fleet-rec.
+setup for paddle-rec.
"""
+
import os
+
from setuptools import setup, find_packages
-import tempfile
import shutil
+import tempfile
-requires = [
- "paddlepaddle == 1.7.2",
- "pyyaml >= 5.1.1"
-]
+requires = ["paddlepaddle == 1.7.2", "pyyaml >= 5.1.1"]
about = {}
-about["__title__"] = "fleet-rec"
+about["__title__"] = "paddle-rec"
about["__version__"] = "0.0.2"
-about["__description__"] = "fleet-rec"
-about["__author__"] = "seiriosPlus"
-about["__author_email__"] = "tangwei12@baidu.com"
-about["__url__"] = "https://github.com/seiriosPlus/FleetRec"
+about["__description__"] = "paddle-rec"
+about["__author__"] = "paddle-dev"
+about["__author_email__"] = "paddle-dev@baidu.com"
+about["__url__"] = "https://github.com/PaddlePaddle/PaddleRec"
-readme = "..."
+readme = ""
def run_cmd(command):
@@ -30,19 +42,36 @@ def run_cmd(command):
def build(dirname):
package_dir = os.path.dirname(os.path.abspath(__file__))
run_cmd("cp -r {}/* {}".format(package_dir, dirname))
- run_cmd("mkdir {}".format(os.path.join(dirname, "fleetrec")))
- run_cmd("mv {}/* {}".format(os.path.join(dirname, "fleet_rec"), os.path.join(dirname, "fleetrec")))
- run_cmd("mv {} {}".format(os.path.join(dirname, "doc"), os.path.join(dirname, "fleetrec")))
- run_cmd("mv {} {}".format(os.path.join(dirname, "models"), os.path.join(dirname, "fleetrec")))
- run_cmd("mv {} {}".format(os.path.join(dirname, "tools"), os.path.join(dirname, "fleetrec")))
+ run_cmd("mkdir {}".format(os.path.join(dirname, "paddlerec")))
+ run_cmd("mv {} {}".format(
+ os.path.join(dirname, "core"), os.path.join(dirname, "paddlerec")))
+ run_cmd("mv {} {}".format(
+ os.path.join(dirname, "doc"), os.path.join(dirname, "paddlerec")))
+ run_cmd("mv {} {}".format(
+ os.path.join(dirname, "models"), os.path.join(dirname, "paddlerec")))
+ run_cmd("mv {} {}".format(
+ os.path.join(dirname, "tests"), os.path.join(dirname, "paddlerec")))
+ run_cmd("mv {} {}".format(
+ os.path.join(dirname, "tools"), os.path.join(dirname, "paddlerec")))
+ run_cmd("mv {} {}".format(
+ os.path.join(dirname, "*.py"), os.path.join(dirname, "paddlerec")))
- packages = find_packages(dirname, include=('fleetrec.*'))
+ packages = find_packages(dirname, include=('paddlerec.*'))
package_dir = {'': dirname}
package_data = {}
- need_copy = ['data/*.txt', 'data/*/*.txt', '*.yaml', 'tree/*.npy','tree/*.txt']
+
+ models_copy = [
+ 'data/*.txt', 'data/*/*.txt', '*.yaml', '*.sh', 'tree/*.npy',
+ 'tree/*.txt', 'data/sample_data/*', 'data/sample_data/train/*',
+ 'data/sample_data/infer/*', 'data/*/*.csv'
+ ]
+
+ engine_copy = ['*/*.sh']
for package in packages:
- if package.startswith("fleetrec.models."):
- package_data[package] = need_copy
+ if package.startswith("paddlerec.models."):
+ package_data[package] = models_copy
+ if package.startswith("paddlerec.core.engine"):
+ package_data[package] = engine_copy
setup(
name=about["__title__"],
@@ -57,8 +86,7 @@ def build(dirname):
package_data=package_data,
python_requires=">=2.7",
install_requires=requires,
- zip_safe=False
- )
+ zip_safe=False)
dirname = tempfile.mkdtemp()
@@ -67,13 +95,13 @@ shutil.rmtree(dirname)
print('''
\033[32m
- _ _ _ _ _ _ _ _ _
- / \ / \ / \ / \ / \ / \ / \ / \ / \
-( F | L | E | E | T | - | R | E | C )
- \_/ \_/ \_/ \_/ \_/ \_/ \_/ \_/ \_/
+ _ _ _ _ _ _ _ _ _
+ / \ / \ / \ / \ / \ / \ / \ / \ / \
+( P | A | D | D | L | E | - | R | E | C )
+ \_/ \_/ \_/ \_/ \_/ \_/ \_/ \_/ \_/
\033[0m
\033[34m
Installation Complete. Congratulations!
-How to use it ? Please visit our webside: https://github.com/seiriosPlus/FleetRec
+How to use it ? Please visit our webside: https://github.com/PaddlePaddle/PaddleRec
\033[0m
''')
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/tools/__init__.py b/tools/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..abf198b97e6e818e1fbe59006f98492640bcee54 100644
--- a/tools/__init__.py
+++ b/tools/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/tools/build_script.sh b/tools/build_script.sh
new file mode 100755
index 0000000000000000000000000000000000000000..6fa779fac7b7e99f203d64fe69d339469f19d3bf
--- /dev/null
+++ b/tools/build_script.sh
@@ -0,0 +1,61 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#=================================================
+# Utils
+#=================================================
+
+set -ex
+
+function init() {
+ RED='\033[0;31m'
+ BLUE='\033[0;34m'
+ BOLD='\033[1m'
+ NONE='\033[0m'
+
+ ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}")/../../" && pwd )"
+}
+
+function check_style() {
+ set -e
+
+ export PATH=/usr/bin:$PATH
+ pre-commit install
+
+ if ! pre-commit run -a; then
+ git diff
+ exit 1
+ fi
+
+ exit 0
+}
+
+function main() {
+ local CMD=$1
+ init
+ case $CMD in
+ check_style)
+ check_style
+ ;;
+ *)
+ echo "build failed"
+ exit 1
+ ;;
+ esac
+ echo "check_style finished as expected"
+}
+
+main $@
diff --git a/tools/codestyle/copyright.hook b/tools/codestyle/copyright.hook
new file mode 100644
index 0000000000000000000000000000000000000000..23aaf38f6f9b97220a55b29c7d0e800fb1e86105
--- /dev/null
+++ b/tools/codestyle/copyright.hook
@@ -0,0 +1,121 @@
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import io, re
+import sys, os
+import subprocess
+import platform
+
+COPYRIGHT = '''
+Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+'''
+
+LANG_COMMENT_MARK = None
+
+NEW_LINE_MARK = None
+
+COPYRIGHT_HEADER = None
+
+if platform.system() == "Windows":
+ NEW_LINE_MARK = "\r\n"
+else:
+ NEW_LINE_MARK = '\n'
+ COPYRIGHT_HEADER = COPYRIGHT.split(NEW_LINE_MARK)[1]
+ p = re.search('(\d{4})', COPYRIGHT_HEADER).group(0)
+ process = subprocess.Popen(["date", "+%Y"], stdout=subprocess.PIPE)
+ date, err = process.communicate()
+ date = date.decode("utf-8").rstrip("\n")
+ COPYRIGHT_HEADER = COPYRIGHT_HEADER.replace(p, date)
+
+
+def generate_copyright(template, lang='C'):
+ if lang == 'Python':
+ LANG_COMMENT_MARK = '#'
+ else:
+ LANG_COMMENT_MARK = "//"
+
+ lines = template.split(NEW_LINE_MARK)
+ BLANK = " "
+ ans = LANG_COMMENT_MARK + BLANK + COPYRIGHT_HEADER + NEW_LINE_MARK
+ for lino, line in enumerate(lines):
+ if lino == 0 or lino == 1 or lino == len(lines) - 1: continue
+ if len(line) == 0:
+ BLANK = ""
+ else:
+ BLANK = " "
+ ans += LANG_COMMENT_MARK + BLANK + line + NEW_LINE_MARK
+
+ return ans + "\n"
+
+
+def lang_type(filename):
+ if filename.endswith(".py"):
+ return "Python"
+ elif filename.endswith(".h"):
+ return "C"
+ elif filename.endswith(".c"):
+ return "C"
+ elif filename.endswith(".hpp"):
+ return "C"
+ elif filename.endswith(".cc"):
+ return "C"
+ elif filename.endswith(".cpp"):
+ return "C"
+ elif filename.endswith(".cu"):
+ return "C"
+ elif filename.endswith(".cuh"):
+ return "C"
+ elif filename.endswith(".go"):
+ return "C"
+ elif filename.endswith(".proto"):
+ return "C"
+ else:
+ print("Unsupported filetype %s", filename)
+ exit(0)
+
+
+PYTHON_ENCODE = re.compile("^[ \t\v]*#.*?coding[:=][ \t]*([-_.a-zA-Z0-9]+)")
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(
+ description='Checker for copyright declaration.')
+ parser.add_argument('filenames', nargs='*', help='Filenames to check')
+ args = parser.parse_args(argv)
+
+ retv = 0
+ for filename in args.filenames:
+ fd = io.open(filename, encoding="utf-8")
+ first_line = fd.readline()
+ second_line = fd.readline()
+ if "COPYRIGHT (C)" in first_line.upper(): continue
+ if first_line.startswith("#!") or PYTHON_ENCODE.match(
+ second_line) != None or PYTHON_ENCODE.match(first_line) != None:
+ continue
+ original_contents = io.open(filename, encoding="utf-8").read()
+ new_contents = generate_copyright(
+ COPYRIGHT, lang_type(filename)) + original_contents
+ print('Auto Insert Copyright Header {}'.format(filename))
+ retv = 1
+ with io.open(filename, 'w') as output_file:
+ output_file.write(new_contents)
+
+ return retv
+
+
+if __name__ == '__main__':
+ exit(main())
diff --git a/tools/codestyle/pylint_pre_commit.hook b/tools/codestyle/pylint_pre_commit.hook
new file mode 100644
index 0000000000000000000000000000000000000000..150a3f5666bd39d30b7e6518e58a14fb5fe2f14b
--- /dev/null
+++ b/tools/codestyle/pylint_pre_commit.hook
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+TOTAL_ERRORS=0
+
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+export PYTHONPATH=$DIR:$PYTHONPATH
+
+# The trick to remove deleted files: https://stackoverflow.com/a/2413151
+for file in $(git diff --name-status | awk '$1 != "D" {print $2}'); do
+ pylint --disable=all --load-plugins=docstring_checker \
+ --enable=doc-string-one-line,doc-string-end-with,doc-string-with-all-args,doc-string-triple-quotes,doc-string-missing,doc-string-indent-error,doc-string-with-returns,doc-string-with-raises $file;
+ TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?);
+done
+
+exit $TOTAL_ERRORS
+#For now, just warning:
+#exit 0
+
diff --git a/tools/tools.py b/tools/tools.py
index da34a027c027d6809603869f946499ec45edf8e6..8508a790bc8012954b53cc43b088d2e50655647d 100644
--- a/tools/tools.py
+++ b/tools/tools.py
@@ -1,12 +1,27 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import functools
import os
-import time
+import platform
+import sys
import shutil
+import time
+
import requests
-import sys
import tarfile
import zipfile
-import platform
-import functools
lasttime = time.time()
FLUSH_INTERVAL = 0.1
+
+
+
+