Merge pull request #186 from 123malin/gru4rec

Gru4rec

models/recall/gru4rec/README.md

+# GRU4REC
+
+以下是本例的简要目录结构及说明： 
+
+```
+├── data #样例数据及数据处理相关文件
+  ├── train
+    ├── small_train.txt # 样例训练数据
+  ├── test
+    ├── small_test.txt # 样例测试数据
+  ├── convert_format.py # 数据转换脚本
+  ├── download.py # 数据下载脚本
+  ├── preprocess.py # 数据预处理脚本
+  ├── text2paddle.py # paddle训练数据生成脚本
+├── __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
+├── rsc15_reader.py #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
+GRU4REC模型的介绍可以参阅论文[Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939)。
+
+论文的贡献在于首次将RNN（GRU）运用于session-based推荐，相比传统的KNN和矩阵分解，效果有明显的提升。
+
+论文的核心思想是在一个session中，用户点击一系列item的行为看做一个序列，用来训练RNN模型。预测阶段，给定已知的点击序列作为输入，预测下一个可能点击的item。
+
+session-based推荐应用场景非常广泛，比如用户的商品浏览、新闻点击、地点签到等序列数据。
+
+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、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
+```
+cd data/
+python download.py
+```
+- Step2: 数据预处理及格式转换。
+  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。
+```
+python preprocess.py
+python convert_format.py
+```
+这一步之后，会在data/目录下得到两个文件，rsc15_train_tr_paddle.txt为原始训练文件，rsc15_test_paddle.txt为原始测试文件。格式如下所示：
+```
+214536502 214536500 214536506 214577561
+214662742 214662742 214825110 214757390 214757407 214551617
+214716935 214774687 214832672
+214836765 214706482
+214701242 214826623
+214826835 214826715
+214838855 214838855
+214576500 214576500 214576500
+214821275 214821275 214821371 214821371 214821371 214717089 214563337 214706462 214717436 214743335 214826837 214819762
+214717867 21471786
+```
+- Step3: 生成字典并整理数据路径。这一步会根据训练和测试文件生成字典和对应的paddle输入文件，并将训练文件统一放在data/all_train目录下，测试文件统一放在data/all_test目录下。
+```
+mkdir raw_train_data && mkdir raw_test_data
+mv rsc15_train_tr_paddle.txt raw_train_data/ && mv rsc15_test_paddle.txt raw_test_data/
+mkdir all_train && mkdir all_test
+
+python text2paddle.py raw_train_data/ raw_test_data/ all_train all_test vocab.txt
+```
+
+方便起见，我们提供了一键式数据生成脚本：
+```
+sh data_prepare.sh
+```
+
+## 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
+
+在config.yaml文件中设置好设备，epochs等。
+```
+runner:
+- name: cpu_train_runner
+  class: train
+  device: cpu  # gpu
+  epochs: 10
+  save_checkpoint_interval: 1
+  save_inference_interval: 1
+  save_checkpoint_path: "increment_gru4rec"
+  save_inference_path: "inference_gru4rec"
+  save_inference_feed_varnames: ["src_wordseq", "dst_wordseq"] # feed vars of save inference
+  save_inference_fetch_varnames: ["mean_0.tmp_0", "top_k_0.tmp_0"]
+  print_interval: 10
+  phases: [train]
+
+```
+
+### 单机预测
+
+在config.yaml文件中设置好设备，epochs等。
+```
+- name: cpu_infer_runner
+  class: infer
+  init_model_path: "increment_gru4rec"
+  device: cpu  # gpu
+  phases: [infer]
+```
+
+### 运行
+```
+python -m paddlerec.run -m paddlerec.models.recall.gru4rec
+```
+
+### 结果展示
+
+样例数据训练结果展示：
+
+```
+Running SingleStartup.
+Running SingleRunner.
+2020-09-22 03:31:18,167-INFO:   [Train],  epoch: 0,  batch: 10, time_each_interval: 4.34s, RecallCnt: [1669.], cost: [8.366313], InsCnt: [16228.], Acc(Recall@20): [0.10284693]
+2020-09-22 03:31:21,982-INFO:   [Train],  epoch: 0,  batch: 20, time_each_interval: 3.82s, RecallCnt: [3168.], cost: [8.170701], InsCnt: [31943.], Acc(Recall@20): [0.09917666]
+2020-09-22 03:31:25,797-INFO:   [Train],  epoch: 0,  batch: 30, time_each_interval: 3.81s, RecallCnt: [4855.], cost: [8.017181], InsCnt: [47892.], Acc(Recall@20): [0.10137393]
+...
+epoch 0 done, use time: 6003.78719687, global metrics: cost=[4.4394927], InsCnt=23622448.0 RecallCnt=14547467.0 Acc(Recall@20)=0.6158323218660487
+2020-09-22 05:11:17,761-INFO:   save epoch_id:0 model into: "inference_gru4rec/0"
+...
+epoch 9 done, use time: 6009.97707605, global metrics: cost=[4.069373], InsCnt=236237470.0 RecallCnt=162838200.0 Acc(Recall@20)=0.6892988086157644
+2020-09-22 20:17:11,358-INFO:   save epoch_id:9 model into: "inference_gru4rec/9"
+PaddleRec Finish
+```
+
+样例数据预测结果展示:
+```
+Running SingleInferStartup.
+Running SingleInferRunner.
+load persistables from increment_gru4rec/9
+2020-09-23 03:46:21,081-INFO:   [Infer] batch: 20, time_each_interval: 3.68s, RecallCnt: [24875.], InsCnt: [35581.], Acc(Recall@20): [0.6991091]
+Infer infer of epoch 9 done, use time: 5.25408315659, global metrics: InsCnt=52551.0 RecallCnt=36720.0 Acc(Recall@20)=0.698749785922247
+...
+Infer infer of epoch 0 done, use time: 5.20699501038, global metrics: InsCnt=52551.0 RecallCnt=33664.0 Acc(Recall@20)=0.6405967536298073
+PaddleRec Finish
+```
+
+## 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500。
+- epochs: 修改config.yaml中runner的epochs为10。
+- 数据源：修改config.yaml中dataset_train数据集的data_path为"{workspace}/data/all_train"，dataset_test数据集的data_path为"{workspace}/data/all_test"。
+
+使用gpu训练10轮 测试结果为
+
+epoch | 测试recall@20 | 速度(s)
+-- | -- | --
+1 | 0.6406 | 6003
+2 | 0.6727 | 6007
+3 | 0.6831 | 6108
+4 | 0.6885 | 6025
+5 | 0.6913 | 6019
+6 | 0.6931 | 6011
+7 | 0.6952 | 6015
+8 | 0.6968 | 6076
+9 | 0.6972 | 6076
+10 | 0.6987| 6009
+
+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

models/recall/gru4rec/config.yaml

@@ -16,18 +16,19 @@ workspace: "models/recall/gru4rec"
 
 dataset:
 - name: dataset_train
-  batch_size: 5
-  type: QueueDataset
+  batch_size: 500
+  type: DataLoader # QueueDataset
   data_path: "{workspace}/data/train"
   data_converter: "{workspace}/rsc15_reader.py"
 - name: dataset_infer
-  batch_size: 5
-  type: QueueDataset
+  batch_size: 500
+  type: DataLoader #QueueDataset
   data_path: "{workspace}/data/test"
   data_converter: "{workspace}/rsc15_reader.py"
 
 hyper_parameters:
-  vocab_size: 1000
+  recall_k: 20
+  vocab_size: 37483
   hid_size: 100
   emb_lr_x: 10.0
   gru_lr_x: 1.0
@@ -40,30 +41,34 @@ hyper_parameters:
     strategy: async
 
 #use infer_runner mode and modify 'phase' below if infer
-mode: train_runner
+mode: [cpu_train_runner, cpu_infer_runner]
 #mode: infer_runner
 
 runner:
-- name: train_runner
+- name: cpu_train_runner
   class: train
   device: cpu
-  epochs: 3
-  save_checkpoint_interval: 2
-  save_inference_interval: 4
-  save_checkpoint_path: "increment"
-  save_inference_path: "inference"
+  epochs: 10
+  save_checkpoint_interval: 1
+  save_inference_interval: 1
+  save_checkpoint_path: "increment_gru4rec"
+  save_inference_path: "inference_gru4rec"
+  save_inference_feed_varnames: ["src_wordseq", "dst_wordseq"] # feed vars of save inference
+  save_inference_fetch_varnames: ["mean_0.tmp_0", "top_k_0.tmp_0"]
   print_interval: 10
-- name: infer_runner
+  phases: [train]
+- name: cpu_infer_runner
   class: infer
-  init_model_path: "increment/0"
+  init_model_path: "increment_gru4rec"
   device: cpu
+  phases: [infer]
 
 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
+- name: infer
+  model: "{workspace}/model.py"
+  dataset_name: dataset_infer
+  thread_num: 1

models/recall/gru4rec/data/convert_format.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.
+
+import sys
+import codecs
+
+
+def convert_format(input, output):
+    with codecs.open(input, "r", encoding='utf-8') as rf:
+        with codecs.open(output, "w", encoding='utf-8') as wf:
+            last_sess = -1
+            sign = 1
+            i = 0
+            for l in rf:
+                i = i + 1
+                if i == 1:
+                    continue
+                if (i % 1000000 == 1):
+                    print(i)
+                tokens = l.strip().split()
+                if (int(tokens[0]) != last_sess):
+                    if (sign):
+                        sign = 0
+                        wf.write(tokens[1] + " ")
+                    else:
+                        wf.write("\n" + tokens[1] + " ")
+                    last_sess = int(tokens[0])
+                else:
+                    wf.write(tokens[1] + " ")
+
+
+input = "rsc15_train_tr.txt"
+output = "rsc15_train_tr_paddle.txt"
+input2 = "rsc15_test.txt"
+output2 = "rsc15_test_paddle.txt"
+convert_format(input, output)
+convert_format(input2, output2)

models/recall/gru4rec/data/download.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.
+
+import requests
+import sys
+import time
+import os
+
+lasttime = time.time()
+FLUSH_INTERVAL = 0.1
+
+
+def progress(str, end=False):
+    global lasttime
+    if end:
+        str += "\n"
+        lasttime = 0
+    if time.time() - lasttime >= FLUSH_INTERVAL:
+        sys.stdout.write("\r%s" % str)
+        lasttime = time.time()
+        sys.stdout.flush()
+
+
+def _download_file(url, savepath, print_progress):
+    r = requests.get(url, stream=True)
+    total_length = r.headers.get('content-length')
+
+    if total_length is None:
+        with open(savepath, 'wb') as f:
+            shutil.copyfileobj(r.raw, f)
+    else:
+        with open(savepath, 'wb') as f:
+            dl = 0
+            total_length = int(total_length)
+            starttime = time.time()
+            if print_progress:
+                print("Downloading %s" % os.path.basename(savepath))
+            for data in r.iter_content(chunk_size=4096):
+                dl += len(data)
+                f.write(data)
+                if print_progress:
+                    done = int(50 * dl / total_length)
+                    progress("[%-50s] %.2f%%" %
+                             ('=' * done, float(100 * dl) / total_length))
+        if print_progress:
+            progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
+
+
+_download_file("https://paddlerec.bj.bcebos.com/gnn%2Fyoochoose-clicks.dat",
+               "./yoochoose-clicks.dat", True)

models/recall/gru4rec/data/preprocess.py

+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jun 25 16:20:12 2015
+
+@author: Balázs Hidasi
+"""
+
+import numpy as np
+import pandas as pd
+import datetime as dt
+import time
+
+PATH_TO_ORIGINAL_DATA = './'
+PATH_TO_PROCESSED_DATA = './'
+
+data = pd.read_csv(
+    PATH_TO_ORIGINAL_DATA + 'yoochoose-clicks.dat',
+    sep=',',
+    header=0,
+    usecols=[0, 1, 2],
+    dtype={0: np.int32,
+           1: str,
+           2: np.int64})
+data.columns = ['session_id', 'timestamp', 'item_id']
+data['Time'] = data.timestamp.apply(lambda x: time.mktime(dt.datetime.strptime(x, '%Y-%m-%dT%H:%M:%S.%fZ').timetuple())) #This is not UTC. It does not really matter.
+del (data['timestamp'])
+
+session_lengths = data.groupby('session_id').size()
+data = data[np.in1d(data.session_id, session_lengths[session_lengths > 1]
+                    .index)]
+item_supports = data.groupby('item_id').size()
+data = data[np.in1d(data.item_id, item_supports[item_supports >= 5].index)]
+session_lengths = data.groupby('session_id').size()
+data = data[np.in1d(data.session_id, session_lengths[session_lengths >= 2]
+                    .index)]
+
+tmax = data.Time.max()
+session_max_times = data.groupby('session_id').Time.max()
+session_train = session_max_times[session_max_times < tmax - 86400].index
+session_test = session_max_times[session_max_times >= tmax - 86400].index
+train = data[np.in1d(data.session_id, session_train)]
+test = data[np.in1d(data.session_id, session_test)]
+test = test[np.in1d(test.item_id, train.item_id)]
+tslength = test.groupby('session_id').size()
+test = test[np.in1d(test.session_id, tslength[tslength >= 2].index)]
+print('Full train set\n\tEvents: {}\n\tSessions: {}\n\tItems: {}'.format(
+    len(train), train.session_id.nunique(), train.item_id.nunique()))
+train.to_csv(
+    PATH_TO_PROCESSED_DATA + 'rsc15_train_full.txt', sep='\t', index=False)
+print('Test set\n\tEvents: {}\n\tSessions: {}\n\tItems: {}'.format(
+    len(test), test.session_id.nunique(), test.item_id.nunique()))
+test.to_csv(PATH_TO_PROCESSED_DATA + 'rsc15_test.txt', sep='\t', index=False)
+
+tmax = train.Time.max()
+session_max_times = train.groupby('session_id').Time.max()
+session_train = session_max_times[session_max_times < tmax - 86400].index
+session_valid = session_max_times[session_max_times >= tmax - 86400].index
+train_tr = train[np.in1d(train.session_id, session_train)]
+valid = train[np.in1d(train.session_id, session_valid)]
+valid = valid[np.in1d(valid.item_id, train_tr.item_id)]
+tslength = valid.groupby('session_id').size()
+valid = valid[np.in1d(valid.session_id, tslength[tslength >= 2].index)]
+print('Train set\n\tEvents: {}\n\tSessions: {}\n\tItems: {}'.format(
+    len(train_tr), train_tr.session_id.nunique(), train_tr.item_id.nunique()))
+train_tr.to_csv(
+    PATH_TO_PROCESSED_DATA + 'rsc15_train_tr.txt', sep='\t', index=False)
+print('Validation set\n\tEvents: {}\n\tSessions: {}\n\tItems: {}'.format(
+    len(valid), valid.session_id.nunique(), valid.item_id.nunique()))
+valid.to_csv(
+    PATH_TO_PROCESSED_DATA + 'rsc15_train_valid.txt', sep='\t', index=False)

models/recall/gru4rec/data/text2paddle.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.
+
+import sys
+import six
+import collections
+import os
+import sys
+import io
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf-8')
+
+
+def word_count(input_file, word_freq=None):
+    """
+    compute word count from corpus
+    """
+    if word_freq is None:
+        word_freq = collections.defaultdict(int)
+
+    for l in input_file:
+        for w in l.strip().split():
+            word_freq[w] += 1
+
+    return word_freq
+
+
+def build_dict(min_word_freq=0, train_dir="", test_dir=""):
+    """
+    Build a word dictionary from the corpus,  Keys of the dictionary are words,
+    and values are zero-based IDs of these words.
+    """
+    word_freq = collections.defaultdict(int)
+    files = os.listdir(train_dir)
+    for fi in files:
+        with io.open(os.path.join(train_dir, fi), "r") as f:
+            word_freq = word_count(f, word_freq)
+    files = os.listdir(test_dir)
+    for fi in files:
+        with io.open(os.path.join(test_dir, fi), "r") as f:
+            word_freq = word_count(f, word_freq)
+
+    word_freq = [x for x in six.iteritems(word_freq) if x[1] > min_word_freq]
+    word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
+    words, _ = list(zip(*word_freq_sorted))
+    word_idx = dict(list(zip(words, six.moves.range(len(words)))))
+    return word_idx
+
+
+def write_paddle(word_idx, train_dir, test_dir, output_train_dir,
+                 output_test_dir):
+    files = os.listdir(train_dir)
+    if not os.path.exists(output_train_dir):
+        os.mkdir(output_train_dir)
+    for fi in files:
+        with io.open(os.path.join(train_dir, fi), "r") as f:
+            with io.open(os.path.join(output_train_dir, fi), "w") as wf:
+                for l in f:
+                    l = l.strip().split()
+                    l = [word_idx.get(w) for w in l]
+                    for w in l:
+                        wf.write(str2file(str(w) + " "))
+                    wf.write(str2file("\n"))
+
+    files = os.listdir(test_dir)
+    if not os.path.exists(output_test_dir):
+        os.mkdir(output_test_dir)
+    for fi in files:
+        with io.open(os.path.join(test_dir, fi), "r", encoding='utf-8') as f:
+            with io.open(
+                    os.path.join(output_test_dir, fi), "w",
+                    encoding='utf-8') as wf:
+                for l in f:
+                    l = l.strip().split()
+                    l = [word_idx.get(w) for w in l]
+                    for w in l:
+                        wf.write(str2file(str(w) + " "))
+                    wf.write(str2file("\n"))
+
+
+def str2file(str):
+    if six.PY2:
+        return str.decode("utf-8")
+    else:
+        return str
+
+
+def text2paddle(train_dir, test_dir, output_train_dir, output_test_dir,
+                output_vocab):
+    vocab = build_dict(0, train_dir, test_dir)
+    print("vocab size:", str(len(vocab)))
+    with io.open(output_vocab, "w", encoding='utf-8') as wf:
+        wf.write(str2file(str(len(vocab)) + "\n"))
+    write_paddle(vocab, train_dir, test_dir, output_train_dir, output_test_dir)
+
+
+train_dir = sys.argv[1]
+test_dir = sys.argv[2]
+output_train_dir = sys.argv[3]
+output_test_dir = sys.argv[4]
+output_vocab = sys.argv[5]
+text2paddle(train_dir, test_dir, output_train_dir, output_test_dir,
+            output_vocab)

models/recall/gru4rec/data_prepare.sh

+#! /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"
+cd data && python download.py
+
+python preprocess.py
+echo "begin to convert data (binary -> txt)"
+python convert_format.py
+
+mkdir raw_train_data && mkdir raw_test_data
+mv rsc15_train_tr_paddle.txt raw_train_data/ && mv rsc15_test_paddle.txt raw_test_data/
+
+mkdir all_train && mkdir all_test
+python text2paddle.py raw_train_data/ raw_test_data/ all_train all_test vocab.txt

models/recall/gru4rec/model.py

@@ -16,6 +16,7 @@ import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import ModelBase
+from paddlerec.core.metrics import RecallK
 
 
 class Model(ModelBase):
@@ -81,13 +82,13 @@ class Model(ModelBase):
                                      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=self.recall_k)
+        acc = RecallK(input=fc, label=dst_wordseq, k=self.recall_k)
+
         if is_infer:
-            self._infer_results['recall20'] = acc
+            self._infer_results['Recall@20'] = acc
             return
         avg_cost = fluid.layers.mean(x=cost)
 
         self._cost = avg_cost
         self._metrics["cost"] = avg_cost
-        self._metrics["acc"] = acc
+        self._metrics["Recall@20"] = acc