Merge pull request #162 from 123malin/gnn

Gnn

core/metric.py

@@ -69,7 +69,7 @@ class Metric(object):
         global_metrics = dict()
         for key in self._global_metric_state_vars:
             varname, dtype = self._global_metric_state_vars[key]
-            global_metrics[key] = self.get_global_metric_state(fleet, scope,
+            global_metrics[key] = self._get_global_metric_state(fleet, scope,
                                                                 varname)
 
         return self._calculate(global_metrics)

core/trainers/framework/runner.py

@@ -520,7 +520,6 @@ class SingleInferRunner(RunnerBase):
     def run(self, context):
         self._dir_check(context)
 
-        self.epoch_model_name_list.sort()
         for index, epoch_name in enumerate(self.epoch_model_name_list):
             for model_dict in context["phases"]:
                 model_class = context["model"][model_dict["name"]]["model"]

models/recall/gnn/config.yaml

@@ -42,30 +42,32 @@ hyper_parameters:
   gnn_propogation_steps: 1
 
 # select runner by name
-mode: train_runner
+mode: [single_cpu_train, single_cpu_infer]
 # config of each runner.
 # runner is a kind of paddle training class, which wraps the train/infer process.
 runner:
-- name: train_runner
+- name: single_cpu_train
   class: train
   # num of epochs
-  epochs: 2
+  epochs: 5
   # device to run training or infer
   device: cpu
   save_checkpoint_interval: 1 # save model interval of epochs
   save_inference_interval: 1 # save inference
-  save_checkpoint_path: "increment" # save checkpoint path
-  save_inference_path: "inference" # save inference path
+  save_checkpoint_path: "increment_gnn" # save checkpoint path
+  save_inference_path: "inference_gnn" # 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: 1
-- name: infer_runner
+  phases: [phase1]
+- name: single_cpu_infer
   class: infer
   # device to run training or infer
   device: cpu
   print_interval: 1
-  init_model_path: "increment/0" # load model path
+  init_model_path: "increment_gnn" # load model path
+  phases: [phase2]
 
 # runner will run all the phase in each epoch
 phase:
@@ -73,7 +75,7 @@ phase:
   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
+- name: phase2
+  model: "{workspace}/model.py" # user-defined model
+  dataset_name: dataset_infer # select dataset by name
+  thread_num: 1

models/recall/gnn/data/download.py

@@ -57,5 +57,10 @@ def _download_file(url, savepath, print_progress):
             progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
 
 
-_download_file("https://sr-gnn.bj.bcebos.com/train-item-views.csv",
+if sys.argv[1] == "diginetica":
+    _download_file("https://sr-gnn.bj.bcebos.com/train-item-views.csv",
                    "./train-item-views.csv", True)
+elif sys.argv[1] == "yoochoose":
+    _download_file(
+        "https://paddlerec.bj.bcebos.com/gnn%2Fyoochoose-clicks.dat",
+        "./yoochoose-clicks.dat", True)

models/recall/gnn/data/preprocess.py

@@ -41,39 +41,29 @@ with open(dataset, "r") as f:
     curdate = None
     for data in reader:
         sessid = data['session_id']
-        if curdate and not curid == sessid:
         date = ''
-            if opt.dataset == 'yoochoose':
-                date = time.mktime(
-                    time.strptime(curdate[:19], '%Y-%m-%dT%H:%M:%S'))
-            else:
-                date = time.mktime(time.strptime(curdate, '%Y-%m-%d'))
-            sess_date[curid] = date
-        curid = sessid
         if opt.dataset == 'yoochoose':
             item = data['item_id']
+            date = time.mktime(
+                time.strptime(data['timestamp'][:19], '%Y-%m-%dT%H:%M:%S'))
         else:
             item = data['item_id'], int(data['timeframe'])
-        curdate = ''
-        if opt.dataset == 'yoochoose':
-            curdate = data['timestamp']
-        else:
-            curdate = data['eventdate']
+            date = time.mktime(time.strptime(data['eventdate'], '%Y-%m-%d'))
+
+        if sessid not in sess_date:
+            sess_date[sessid] = date
+        elif date > sess_date[sessid]:
+            sess_date[sessid] = date
 
         if sessid in sess_clicks:
             sess_clicks[sessid] += [item]
         else:
             sess_clicks[sessid] = [item]
         ctr += 1
-    date = ''
-    if opt.dataset == 'yoochoose':
-        date = time.mktime(time.strptime(curdate[:19], '%Y-%m-%dT%H:%M:%S'))
-    else:
-        date = time.mktime(time.strptime(curdate, '%Y-%m-%d'))
+    if opt.dataset != 'yoochoose':
         for i in list(sess_clicks):
             sorted_clicks = sorted(sess_clicks[i], key=operator.itemgetter(1))
             sess_clicks[i] = [c[0] for c in sorted_clicks]
-    sess_date[curid] = date
 print("-- Reading data @ %ss" % datetime.datetime.now())
 
 # Filter out length 1 sessions
@@ -160,7 +150,7 @@ def obtian_tra():
         train_dates += [date]
         train_seqs += [outseq]
     print(item_ctr)  # 43098, 37484
-    with open("./diginetica/config.txt", "w") as fout:
+    with open("./config.txt", "w") as fout:
         fout.write(str(item_ctr) + "\n")
     return train_ids, train_dates, train_seqs
 

models/recall/gnn/data_prepare.sh

@@ -15,21 +15,31 @@
 # limitations under the License.
 
 set -e
-echo "begin to download data"
 
-cd data && python download.py
-mkdir diginetica
-python preprocess.py --dataset diginetica
+dataset=$1
+src=$1
+
+if [[ $src == "yoochoose1_4" || $src == "yoochoose1_64" ]];then
+    src="yoochoose"
+elif [[ $src == "diginetica" ]];then
+    src="diginetica"
+else
+    echo "Usage: sh data_prepare.sh [diginetica|yoochoose1_4|yoochoose1_64]"
+    exit 1
+fi
+
+echo "begin to download data"
+cd data && python download.py $src
+mkdir $dataset
+python preprocess.py --dataset $src
 
 echo "begin to convert data (binary -> txt)"
-python convert_data.py --data_dir diginetica
+python convert_data.py --data_dir $dataset
 
-cat diginetica/train.txt | wc -l >> diginetica/config.txt
+cat ${dataset}/train.txt | wc -l >> config.txt
 
 rm -rf train && mkdir train
-mv diginetica/train.txt train
+mv ${dataset}/train.txt train
 
 rm -rf test && mkdir test
-mv diginetica/test.txt test
-
-mv diginetica/config.txt ./config.txt
+mv ${dataset}/test.txt test

models/recall/gnn/model.py

@@ -20,6 +20,7 @@ import paddle.fluid.layers as layers
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import ModelBase
+from paddlerec.core.metrics import RecallK
 
 
 class Model(ModelBase):
@@ -235,16 +236,16 @@ class Model(ModelBase):
         softmax = layers.softmax_with_cross_entropy(
             logits=logits, label=inputs[6])  # [batch_size, 1]
         self.loss = layers.reduce_mean(softmax)  # [1]
-        self.acc = layers.accuracy(input=logits, label=inputs[6], k=20)
-
+        acc = RecallK(input=logits, label=inputs[6], k=20)
         self._cost = self.loss
+
         if is_infer:
-            self._infer_results['acc'] = self.acc
-            self._infer_results['loss'] = self.loss
+            self._infer_results['P@20'] = acc
+            self._infer_results['LOSS'] = self.loss
             return
 
         self._metrics["LOSS"] = self.loss
-        self._metrics["train_acc"] = self.acc
+        self._metrics["Train_P@20"] = acc
 
     def optimizer(self):
         step_per_epoch = self.corpus_size // self.train_batch_size

models/recall/gnn/readme.md

+# GNN
+
+以下是本例的简要目录结构及说明： 
+
+```
+├── data #样例数据
+	├── train
+		├── train.txt
+    ├── test
+		├── test.txt
+	├── download.py
+	├── convert_data.py
+	├── preprocess.py
+├── __init__.py
+├── README.md # 文档
+├── model.py #模型文件
+├── config.yaml #配置文件
+├── data_prepare.sh #一键数据处理脚本
+├── reader.py #训练数据reader
+├── evaluate_reader.py # 预测数据reader
+```
+
+注：在阅读该示例前，建议您先了解以下内容：
+
+[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md)
+
+
+---
+## 内容
+
+- [模型简介](#模型简介)
+- [数据准备](#数据准备)
+- [运行环境](#运行环境)
+- [快速开始](#快速开始)
+- [论文复现](#论文复现)
+- [进阶使用](#进阶使用)
+- [FAQ](#FAQ)
+
+## 模型简介
+SR-GNN模型的介绍可以参阅论文[Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855)。
+
+本文解决的是Session-based Recommendation这一问题,过程大致分为以下四步：
+
+1. 首先对所有的session序列通过有向图进行建模。
+
+2. 然后通过GNN，学习每个node（item）的隐向量表示
+
+3. 通过一个attention架构模型得到每个session的embedding
+
+4. 最后通过一个softmax层进行全表预测
+
+本示例中，我们复现了论文效果，在DIGINETICA数据集上P@20可以达到50.7。
+
+同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/124382)
+
+本模型配置默认使用demo数据集，若进行精度验证，请参考[论文复现](#论文复现)部分。
+
+本项目支持功能
+
+训练：单机CPU、单机单卡GPU、单机多卡GPU、本地模拟参数服务器训练、增量训练，配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md)   
+
+预测：单机CPU、单机单卡GPU ；配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md) 
+
+## 数据处理
+本示例中数据处理共包含三步：
+- Step1: 原始数据数据集下载，本示例提供了两个开源数据集：DIGINETICA和Yoochoose，可选其中任意一个训练本模型。数据下载命令及原始数据格式如下所示。若采用diginetica数据集，执行完该命令之后，会在data目录下得到原始数据文件train-item-views.csv。若采用yoochoose数据集，执行完该命令之后，会在data目录下得到原始数据文件yoochoose-clicks.dat。
+    ```
+    cd data && python download.py diginetica     # or yoochoose
+    ```
+    > [Yoochooses](https://2015.recsyschallenge.com/challenge.html)数据集来源于RecSys Challenge 2015，原始数据包含如下字段：
+    1. Session ID – the id of the session. In one session there are one or many clicks.
+    2. Timestamp – the time when the click occurred.
+    3. Item ID – the unique identifier of the item.
+    4. Category – the category of the item.
+
+    > [DIGINETICA](https://competitions.codalab.org/competitions/11161#learn_the_details-data2)数据集来源于CIKM Cup 2016 _Personalized E-Commerce Search Challenge_项目。原始数据包含如下字段：
+    1. sessionId - the id of the session. In one session there are one or many clicks.
+    2. userId - the id of the user, with anonymized user ids.
+    3. itemId - the unique identifier of the item.
+    4. timeframe - time since the first query in a session, in milliseconds.
+    5. eventdate - calendar date.
+
+- Step2: 数据预处理。
+    1. 以session_id为key合并原始数据集，得到每个session的日期，及顺序点击列表。
+    2. 过滤掉长度为1的session；过滤掉点击次数小于5的items。
+    3. 训练集、测试集划分。原始数据集里最新日期七天内的作为训练集，更早之前的数据作为测试集。
+    ```
+    cd data && python preprocess.py --dataset diginetica   # or yoochoose
+    ```
+- Step3: 数据整理。 将训练文件统一放在data/train目录下，测试文件统一放在data/test目录下。
+    ```
+    cat data/diginetica/train.txt | wc -l >> data/config.txt    # or yoochoose1_4 or yoochoose1_64
+    rm -rf data/train/*
+    rm -rf data/test/*
+    mv data/diginetica/train.txt data/train
+    mv data/diginetica/test.txt data/test
+    ```
+数据处理完成后，data/train目录存放训练数据，data/test目录下存放测试数据，数据格式如下:
+```
+#session\tlabel
+10,11,12,12,13,14\t15
+```
+data/config.txt中存放数据统计信息，第一行代表训练集中item总数，用以配置模型词表大小，第二行代表训练集大小。
+
+方便起见， 我们提供了一键式数据处理脚本：
+```
+sh data_prepare.sh diginetica      # or yoochoose1_4 or yoochoose1_64
+```
+
+## 运行环境
+
+PaddlePaddle>=1.7.2 
+
+python 2.7/3.5/3.6/3.7
+
+PaddleRec >=0.1
+
+os : windows/linux/macos
+
+## 快速开始
+
+### 单机训练
+
+CPU环境
+
+在config.yaml文件中设置好设备，epochs等。
+
+```
+# select runner by name
+mode: [single_cpu_train, single_cpu_infer]
+# config of each runner.
+# runner is a kind of paddle training class, which wraps the train/infer process.
+runner:
+- name: single_cpu_train
+  class: train
+  # num of epochs
+  epochs: 2
+  # device to run training or infer
+  device: cpu
+  save_checkpoint_interval: 1 # save model interval of epochs
+  save_inference_interval: 1 # save inference
+  save_checkpoint_path: "increment_gnn" # save checkpoint path
+  save_inference_path: "inference_gnn" # 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: 1
+  phases: [phase1]
+```
+### 单机预测
+
+CPU环境
+
+在config.yaml文件中设置好epochs、device等参数。
+
+```
+- name: single_cpu_infer
+  class: infer
+  # device to run training or infer
+  device: cpu
+  print_interval: 1
+  init_model_path: "increment_gnn" # load model path
+  phases: [phase2]
+```
+
+### 运行
+```
+python -m paddlerec.run -m paddlerec.models.recall.gnn
+```
+
+### 结果展示
+
+样例数据训练结果展示：
+
+```
+Running SingleStartup.
+Running SingleRunner.
+batch: 1, LOSS: [10.67443], InsCnt: [200.], RecallCnt: [0.], Acc(Recall@20): [0.]
+batch: 2, LOSS: [10.672471], InsCnt: [300.], RecallCnt: [0.], Acc(Recall@20): [0.]
+batch: 3, LOSS: [10.672463], InsCnt: [400.], RecallCnt: [1.], Acc(Recall@20): [0.0025]
+batch: 4, LOSS: [10.670724], InsCnt: [500.], RecallCnt: [2.], Acc(Recall@20): [0.004]
+batch: 5, LOSS: [10.66949], InsCnt: [600.], RecallCnt: [2.], Acc(Recall@20): [0.00333333]
+batch: 6, LOSS: [10.670102], InsCnt: [700.], RecallCnt: [2.], Acc(Recall@20): [0.00285714]
+batch: 7, LOSS: [10.671348], InsCnt: [800.], RecallCnt: [2.], Acc(Recall@20): [0.0025]
+...
+epoch 0 done, use time: 2926.6897077560425, global metrics: LOSS=[6.0788856], InsCnt=719400.0 RecallCnt=224033.0 Acc(Recall@20)=0.3114164581595774
+...
+epoch 4 done, use time: 3083.101449728012, global metrics: LOSS=[4.249889], InsCnt=3597000.0 RecallCnt=2070666.0 Acc(Recall@20)=0.5756647206005004
+```
+样例数据预测结果展示:
+```
+Running SingleInferStartup.
+Running SingleInferRunner.
+load persistables from increment_gnn/2
+batch: 1, InsCnt: [200.], RecallCnt: [96.], Acc(Recall@20): [0.48], LOSS: [5.7198644]
+batch: 2, InsCnt: [300.], RecallCnt: [153.], Acc(Recall@20): [0.51], LOSS: [5.4096317]
+batch: 3, InsCnt: [400.], RecallCnt: [210.], Acc(Recall@20): [0.525], LOSS: [5.300991]
+batch: 4, InsCnt: [500.], RecallCnt: [258.], Acc(Recall@20): [0.516], LOSS: [5.6269655]
+batch: 5, InsCnt: [600.], RecallCnt: [311.], Acc(Recall@20): [0.5183333], LOSS: [5.39276]
+batch: 6, InsCnt: [700.], RecallCnt: [352.], Acc(Recall@20): [0.50285715], LOSS: [5.633842]
+batch: 7, InsCnt: [800.], RecallCnt: [406.], Acc(Recall@20): [0.5075], LOSS: [5.342844]
+batch: 8, InsCnt: [900.], RecallCnt: [465.], Acc(Recall@20): [0.51666665], LOSS: [4.918761]
+...
+Infer phase2 of epoch 0 done, use time: 549.1640813350677, global metrics: InsCnt=60800.0 RecallCnt=31083.0 Acc(Recall@20)=0.511233552631579, LOSS=[5.8957024]
+```
+
+## 论文复现
+
+用原论文的完整数据复现论文效果需要在config.yaml修改超参：
+- batch_size: 修改config.yaml中dataset_train数据集的batch_size为100。
+- epochs: 修改config.yaml中runner的epochs为5。
+- sparse_feature_number: 不同训练数据集(diginetica or yoochoose)配置不一致，diginetica数据集配置为43098，yoochoose数据集配置为37484。具体见数据处理后得到的data/config.txt文件中第一行。
+- corpus_size: 不同训练数据集配置不一致，diginetica数据集配置为719470，yoochoose数据集配置为5917745。具体见数据处理后得到的data/config.txt文件中第二行。
+
+使用cpu训练 5轮 测试Recall@20:0.51367
+
+修改后运行方案：修改config.yaml中的'workspace'为config.yaml的目录位置，执行
+```
+python -m paddlerec.run -m /home/your/dir/config.yaml #调试模式 直接指定本地config的绝对路径
+```
+
+## 进阶使用
+
+## FAQ