add PNN and update rank models list

models/rank/pnn/__init__.py

+# 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.

models/rank/pnn/config.yaml

+# 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.
+
+
+# global settings 
+debug: false
+workspace: "paddlerec.models.rank.nfm"
+
+dataset:
+  - name: train_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/../dataset/Criteo_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}/../dataset/Criteo_data/sample_data/train"
+    sparse_slots: "label feat_idx"
+    dense_slots: "feat_value:39"
+
+hyper_parameters:
+    # 用户自定义配置
+    optimizer:
+        class: Adam
+        learning_rate: 0.0001
+    sparse_feature_number: 1086460
+    sparse_feature_dim: 9
+    is_sparse: False
+    deep_input_size: 50
+    use_inner_product: True
+    num_field: 39
+    fc_sizes: [32, 32]
+    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: 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

models/rank/pnn/model.py

+# 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.
+
+#
+# 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 collections import OrderedDict
+
+import paddle
+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.is_distributed = True if envs.get_trainer(
+        ) == "CtrTrainer" else False
+        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.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
+                                             False)
+        self.deep_input_size = envs.get_global_env(
+            "hyper_parameters.deep_input_size", 50)
+        self.use_inner_product = envs.get_global_env(
+            "hyper_parameters.use_inner_product", None)
+        self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes",
+                                               None)
+        self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
+        self.num_field = envs.get_global_env("hyper_parameters.num_field",
+                                             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]  # (batch_size * num_field) * 1
+        raw_feat_value = self._dense_data_var[0]  # batch_size * num_field
+        self.label = self._sparse_data_var[0]  # batch_size * 1
+
+        init_value_ = 0.1
+
+        feat_idx = raw_feat_idx
+        feat_value = fluid.layers.reshape(
+            raw_feat_value,
+            [-1, self.num_field, 1])  # batch_size * num_field * 1
+
+        # ------------------------- first order term --------------------------
+
+        first_weights_re = fluid.embedding(
+            input=feat_idx,
+            is_sparse=self.is_sparse,
+            is_distributed=self.is_distributed,
+            dtype='float32',
+            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(self.reg))
+        )  # (batch_size * num_field) * 1 * 1(embedding_size)
+        first_weights = fluid.layers.reshape(
+            first_weights_re,
+            shape=[-1, self.num_field, 1])  # batch_size * num_field * 1
+        y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
+                                                1)  # batch_size * 1
+        # ------------------------- Embedding --------------------------
+
+        feat_embeddings_re = fluid.embedding(
+            input=feat_idx,
+            is_sparse=self.is_sparse,
+            is_distributed=self.is_distributed,
+            dtype='float32',
+            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(self.sparse_feature_dim))))
+        )  # (batch_size * num_field) * 1 * embedding_size
+        feat_embeddings = fluid.layers.reshape(
+            feat_embeddings_re,
+            shape=[-1, self.num_field, self.sparse_feature_dim
+                   ])  # batch_size * num_field * embedding_size
+        feat_embeddings = feat_embeddings * feat_value  # batch_size * num_field * embedding_size
+
+        # ------------------------- Linear Signal --------------------------
+
+        linear_input_size = self.num_field * self.sparse_feature_dim
+        flaten_feat_embedding = fluid.layers.reshape(
+            x=feat_embeddings, shape=[-1, linear_input_size])
+
+        w_z_linear_weights = fluid.layers.create_parameter(
+            shape=[linear_input_size, self.deep_input_size], dtype="float32")
+        linear_signal = fluid.layers.matmul(
+            flaten_feat_embedding,
+            w_z_linear_weights)  # batch_size * deep_input_size
+
+        # ------------------------- Quardatic Singal --------------------------
+
+        quadratic_output = []
+        if self.use_inner_product:
+            w_p_quardatic_weights = fluid.layers.create_parameter(
+                shape=[self.deep_input_size, self.num_field], dtype="float32")
+            for i in range(self.deep_input_size):
+                transpose_embedding = fluid.layers.transpose(
+                    feat_embeddings, perm=[0, 2, 1])
+                theta = fluid.layers.elementwise_mul(
+                    transpose_embedding, w_p_quardatic_weights[i], axis=-1)
+                quadratic_output.append(
+                    paddle.norm(
+                        fluid.layers.reduce_sum(
+                            theta, dim=1),
+                        p=2,
+                        axis=1,
+                        keepdim=True))
+        else:
+            w_p_quardatic_weights_outer = fluid.layers.create_parameter(
+                shape=[
+                    self.deep_input_size, self.sparse_feature_dim,
+                    self.sparse_feature_dim
+                ],
+                dtype="float32")
+            embedding_sum = fluid.layers.reduce_sum(feat_embeddings, dim=1)
+            p = fluid.layers.matmul(
+                fluid.layers.reshape(
+                    embedding_sum, shape=[0, -1, 1]),
+                fluid.layers.reshape(
+                    embedding_sum, shape=[0, 1, -1]))
+            for i in range(self.deep_input_size):
+                theta = fluid.layers.elementwise_mul(
+                    p, w_p_quardatic_weights_outer[i, :, :], axis=-1)
+                quadratic_output.append(
+                    fluid.layers.reshape(
+                        fluid.layers.reduce_sum(
+                            theta, dim=[1, 2]),
+                        shape=[-1, 1]))
+
+        quadratic_signal = fluid.layers.concat(quadratic_output, axis=1)
+
+        y_dnn = linear_signal + quadratic_signal
+
+        y_dnn = fluid.layers.relu6(y_dnn, threshold=10000000.0)
+
+        for s in self.layer_sizes:
+            y_dnn = fluid.layers.fc(
+                input=y_dnn,
+                size=s,
+                act=self.act,
+                param_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.TruncatedNormalInitializer(
+                        loc=0.0, scale=init_value_)),
+                bias_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.TruncatedNormalInitializer(
+                        loc=0.0, scale=init_value_)))
+
+        y_dnn = fluid.layers.fc(
+            input=y_dnn,
+            size=1,
+            act=None,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0, scale=init_value_)),
+            bias_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0, scale=init_value_)))
+
+        # ------------------------- Predict --------------------------
+
+        self.predict = fluid.layers.sigmoid(y_first_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
+
+        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)
+        self._metrics["AUC"] = auc_var
+        self._metrics["BATCH_AUC"] = batch_auc_var
+        if is_infer:
+            self._infer_results["AUC"] = auc_var

models/rank/pnn/readme.md

+# 注意
+本模型默认实现要求paddle版本 >= 2.0, 后续提供基于paddle1.7版本
+```
+pip install paddlepaddle==2.0.0a0
+```

models/rank/readme.md

@@ -23,9 +23,14 @@
 |       模型        |       简介        |       论文        |
 | :------------------: | :--------------------: | :---------: |
 | DNN | 多层神经网络 | -- |
+| Logistic Regression | 逻辑回归 | -- |
+| FM | 因子分解机 | [Factorization Machine](https://ieeexplore.ieee.org/abstract/document/5694074)(2010) |
+| PNN | Product Network | [Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf)(2016) |
 | 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) |
+| NFM | Neural Factorization Machines | [Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777)(2017) |
+| AFM | Attentional Factorization Machines | [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf)(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) |