From c617a1a132b9ece4fced62f264b636fe5e781e66 Mon Sep 17 00:00:00 2001 From: zhoushiyu <31816202+wilhelmzh@users.noreply.github.com> Date: Tue, 10 Sep 2019 14:43:59 +0800 Subject: [PATCH] add xDeepFM, DCN ctr model and DeepFM data download (#3303) add xDeepFM, DCN model in PaddleRec/ctr. change DeepFM data download script. --- PaddleRec/ctr/dcn/README.md | 64 + PaddleRec/ctr/dcn/config.py | 63 + PaddleRec/ctr/dcn/data/download.sh | 23 + PaddleRec/ctr/dcn/data/preprocess.py | 132 + PaddleRec/ctr/dcn/infer.py | 88 + PaddleRec/ctr/dcn/local_train.py | 82 + PaddleRec/ctr/dcn/network.py | 151 + PaddleRec/ctr/dcn/reader.py | 93 + .../deepfm/data/aid_data/feat_dict_10.pkl2 | 3259366 -------------- .../ctr/deepfm/data/download_preprocess.sh | 1 + PaddleRec/ctr/xdeepfm/README.md | 33 + PaddleRec/ctr/xdeepfm/args.py | 73 + PaddleRec/ctr/xdeepfm/criteo_reader.py | 41 + PaddleRec/ctr/xdeepfm/data/download.sh | 4 + PaddleRec/ctr/xdeepfm/infer.py | 75 + PaddleRec/ctr/xdeepfm/local_train.py | 59 + PaddleRec/ctr/xdeepfm/network_conf.py | 125 + 17 files changed, 1107 insertions(+), 3259366 deletions(-) create mode 100644 PaddleRec/ctr/dcn/README.md create mode 100644 PaddleRec/ctr/dcn/config.py create mode 100755 PaddleRec/ctr/dcn/data/download.sh create mode 100644 PaddleRec/ctr/dcn/data/preprocess.py create mode 100644 PaddleRec/ctr/dcn/infer.py create mode 100644 PaddleRec/ctr/dcn/local_train.py create mode 100644 PaddleRec/ctr/dcn/network.py create mode 100644 PaddleRec/ctr/dcn/reader.py delete mode 100644 PaddleRec/ctr/deepfm/data/aid_data/feat_dict_10.pkl2 create mode 100644 PaddleRec/ctr/xdeepfm/README.md create mode 100644 PaddleRec/ctr/xdeepfm/args.py create mode 100644 PaddleRec/ctr/xdeepfm/criteo_reader.py create mode 100644 PaddleRec/ctr/xdeepfm/data/download.sh create mode 100644 PaddleRec/ctr/xdeepfm/infer.py create mode 100644 PaddleRec/ctr/xdeepfm/local_train.py create mode 100644 PaddleRec/ctr/xdeepfm/network_conf.py diff --git a/PaddleRec/ctr/dcn/README.md b/PaddleRec/ctr/dcn/README.md new file mode 100644 index 00000000..062d90ad --- /dev/null +++ b/PaddleRec/ctr/dcn/README.md @@ -0,0 +1,64 @@ +# Deep & Cross Network + +以下是本例的简要目录结构及说明： + +```text +. +├── README.md # 文档 +├── local_train.py # 本地训练脚本 +├── infer.py # 预测脚本 +├── network.py # 网络结构 +├── config.py # 参数配置 +├── reader.py # 读取数据相关的函数 +├── data/ + ├── download.sh # 下载数据脚本 + ├── preprocess.py # 数据预处理脚本 + +``` + +## 介绍 +DCN模型介绍可以参阅论文[Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) + +## 环境 +- PaddlePaddle 1.5.1 + +## 数据下载 + +我们在Criteo数据集训练测试DCN。整个数据集包含约4500万条记录。每一行第一列是label，表示该条广告是否被点击，剩下的是13个整数型特征(I1 - I13)和26个离散型特征(C1 - C26)。 + +数据下载命令 +```bash +cd data && sh download.sh +``` + +## 数据处理 + +- 根据论文，使用前6天的数据进行训练（大约41million），第7天的数据一半做valid一半做test。基本上是将数据集按照9:0.5:0.5切分，需要注意的是train数据是前90%。而如xdeepfm等论文实验中8:1:1，并且是完全打乱的。 +- 论文对整数型特征数据使用了log transform，因为只有I2最小值为-3，其余最小值为0，所以对I2采用log(4 + l2_value)对其余采用log(1 + l*_value)。 +- 统计每个离散型特征（即C1 - C26）出现的不同feature id，存在大量的低频feature id。所以需要对低频feature id进行过滤，缩小embedding matrix大小。代码默认设置的频率是10，去掉了大量低频feature id。 + +数据预处理命令 +```bash +python preprocess.py +``` + +数据预处理后，训练数据在train中，验证和测试数据在test_valid中，vocab存储离散型特征过滤低频后的feature id。并统计了整数型特征的最小/最大值，离散型特征的feature id数量。 + +## 本地训练 + +```bash +nohup python -u local_train.py > train.log & +``` +训练过程中每隔固定的steps（默认为100）输出当前total loss(logloss + 正则), log loss和auc，可以在args.py中调整print_steps。 + +## 本地预测 +```bash +nohup python -u infer.py --test_epoch 2 > test.log & +``` +注意：最后一行的auc是整个预测数据集的auc + +## 结果 +本结果在Linux CPU机器上使用dataset开启20线程训练，batch size为512。经过150000 steps（~1.87 epoch）后，预测实验结果如下： +```text +loss: [0.44703564] auc_val: [0.80654419] +``` diff --git a/PaddleRec/ctr/dcn/config.py b/PaddleRec/ctr/dcn/config.py new file mode 100644 index 00000000..8b961c18 --- /dev/null +++ b/PaddleRec/ctr/dcn/config.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python +# coding: utf-8 +import argparse +""" +global params +""" + + +def parse_args(): + parser = argparse.ArgumentParser(description="PaddleFluid DCN demo") + parser.add_argument( + '--train_data_dir', + type=str, + default='data/train', + help='The path of train data') + parser.add_argument( + '--test_valid_data_dir', + type=str, + default='data/test_valid', + help='The path of test and valid data') + parser.add_argument( + '--batch_size', type=int, default=512, help="Batch size") + parser.add_argument( + '--steps', + type=int, + default=150000, + help="Early stop steps in training. If set, num_epoch will not work") + parser.add_argument('--num_epoch', type=int, default=2, help="train epoch") + parser.add_argument( + '--model_output_dir', + type=str, + default='models', + help='The path for model to store') + parser.add_argument( + '--num_thread', type=int, default=20, help='The number of threads') + parser.add_argument('--test_epoch', type=str, default='1') + parser.add_argument( + '--dnn_hidden_units', + nargs='+', + type=int, + default=[1024, 1024], + help='DNN layers and hidden units') + parser.add_argument( + '--cross_num', + type=int, + default=6, + help='The number of Cross network layers') + parser.add_argument('--lr', type=float, default=1e-4, help='Learning rate') + parser.add_argument( + '--l2_reg_cross', + type=float, + default=1e-5, + help='Cross net l2 regularizer coefficient') + parser.add_argument( + '--use_bn', + type=bool, + default=True, + help='Whether use batch norm in dnn part') + parser.add_argument( + '--clip_by_norm', type=float, default=100.0, help="gradient clip norm") + parser.add_argument('--print_steps', type=int, default=100) + + return parser.parse_args() diff --git a/PaddleRec/ctr/dcn/data/download.sh b/PaddleRec/ctr/dcn/data/download.sh new file mode 100755 index 00000000..1b6c183d --- /dev/null +++ b/PaddleRec/ctr/dcn/data/download.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +workdir=$(cd $(dirname $0); pwd) + +cd $workdir + +trainfile='train.txt' + +echo "data dir:" ${workdir} + +cd $workdir + +echo "download data starting..." +wget --no-check-certificate -c https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz +echo "download finished" + +echo "extracting ..." +tar xzvf dac.tar.gz +wc -l $trainfile | awk '{print $1}' > line_nums.log + +echo "extract finished" +echo "total records: "`cat line_nums.log` +echo "done" diff --git a/PaddleRec/ctr/dcn/data/preprocess.py b/PaddleRec/ctr/dcn/data/preprocess.py new file mode 100644 index 00000000..6ee5044b --- /dev/null +++ b/PaddleRec/ctr/dcn/data/preprocess.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python +# coding: utf-8 +from __future__ import print_function, absolute_import, division + +import os +import sys +from collections import Counter +import numpy as np +""" +preprocess Criteo train data, generate extra statistic files for model input. +""" +# input filename +FILENAME = 'train.txt' + +# global vars +CAT_FEATURE_NUM = 'cat_feature_num.txt' +INT_FEATURE_MINMAX = 'int_feature_minmax.txt' +VOCAB_DIR = 'vocab' +TRAIN_DIR = 'train' +TEST_VALID_DIR = 'test_valid' +SPLIT_RATIO = 0.9 +LINE_NUMS = "line_nums.log" +FREQ_THR = 10 + +INT_COLUMN_NAMES = ['I' + str(i) for i in range(1, 14)] +CAT_COLUMN_NAMES = ['C' + str(i) for i in range(1, 27)] + + +def check_statfiles(): + """ + check if statistic files of Criteo exists + :return: + """ + statsfiles = [CAT_FEATURE_NUM, INT_FEATURE_MINMAX] + [ + os.path.join(VOCAB_DIR, cat_fn + '.txt') for cat_fn in CAT_COLUMN_NAMES + ] + if all([os.path.exists(fn) for fn in statsfiles]): + return True + return False + + +def create_statfiles(): + """ + create statistic files of Criteo, including: + min/max of interger features + counts of categorical features + vocabs of each categorical features + :return: + """ + int_minmax_list = [[sys.maxsize, -sys.maxsize] + for _ in range(13)] # count integer feature min max + cat_ct_list = [Counter() for _ in range(26)] # count categorical features + for idx, line in enumerate(open(FILENAME)): + spls = line.rstrip('\n').split('\t') + assert len(spls) == 40 + + for i in range(13): + if not spls[1 + i]: continue + int_val = int(spls[1 + i]) + int_minmax_list[i][0] = min(int_minmax_list[i][0], int_val) + int_minmax_list[i][1] = max(int_minmax_list[i][1], int_val) + + for i in range(26): + cat_ct_list[i].update([spls[14 + i]]) + + # save min max of integer features + with open(INT_FEATURE_MINMAX, 'w') as f: + for name, minmax in zip(INT_COLUMN_NAMES, int_minmax_list): + print("{} {} {}".format(name, minmax[0], minmax[1]), file=f) + + # remove '' from all cat_set[i] and filter low freq categorical value + cat_set_list = [set() for i in range(len(cat_ct_list))] + for i, ct in enumerate(cat_ct_list): + if '' in ct: del ct[''] + for key in list(ct.keys()): + if ct[key] >= FREQ_THR: + cat_set_list[i].add(key) + + del cat_ct_list + + # create vocab dir + if not os.path.exists(VOCAB_DIR): + os.makedirs(VOCAB_DIR) + + # write vocab file of categorical features + with open(CAT_FEATURE_NUM, 'w') as cat_feat_count_file: + for name, s in zip(CAT_COLUMN_NAMES, cat_set_list): + print('{} {}'.format(name, len(s)), file=cat_feat_count_file) + + vocabfile = os.path.join(VOCAB_DIR, name + '.txt') + + with open(vocabfile, 'w') as f: + for vocab_val in s: + print(vocab_val, file=f) + + +def split_data(): + """ + split train.txt into train and test_valid files. + :return: + """ + if not os.path.exists(TRAIN_DIR): + os.makedirs(TRAIN_DIR) + if not os.path.exists(TEST_VALID_DIR): + os.makedirs(TEST_VALID_DIR) + + fin = open('train.txt', 'r') + data_dir = TRAIN_DIR + fout = open(os.path.join(data_dir, 'part-0'), 'w') + split_idx = int(45840617 * SPLIT_RATIO) + for line_idx, line in enumerate(fin): + if line_idx == split_idx: + fout.close() + data_dir = TEST_VALID_DIR + cur_part_idx = int(line_idx / 200000) + fout = open(data_dir + '/part-' + str(cur_part_idx), 'w') + if line_idx % 200000 == 0 and line_idx != 0: + fout.close() + cur_part_idx = int(line_idx / 200000) + fout = open(data_dir + '/part-' + str(cur_part_idx), 'w') + fout.write(line) + fout.close() + fin.close() + + +if __name__ == '__main__': + if not check_statfiles(): + print('create statstic files of Criteo...') + create_statfiles() + print('split train.txt...') + split_data() + print('done') diff --git a/PaddleRec/ctr/dcn/infer.py b/PaddleRec/ctr/dcn/infer.py new file mode 100644 index 00000000..f3077388 --- /dev/null +++ b/PaddleRec/ctr/dcn/infer.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python +# coding: utf-8 +import logging +import random + +import numpy as np +import pickle + +# disable gpu training for this example +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '' +import paddle +import paddle.fluid as fluid + +from config import parse_args +from reader import CriteoDataset +from network import DCN + +logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger('fluid') +logger.setLevel(logging.INFO) + + +def infer(): + args = parse_args() + print(args) + + place = fluid.CPUPlace() + inference_scope = fluid.Scope() + + test_files = [ + os.path.join(args.test_valid_data_dir, fname) + for fname in next(os.walk(args.test_valid_data_dir))[2] + ] + + test_files = random.sample(test_files, int(len(test_files) * 0.5)) + print('test files num {}'.format(len(test_files))) + + criteo_dataset = CriteoDataset() + criteo_dataset.setup() + test_reader = criteo_dataset.test_reader(test_files, args.batch_size, 100) + + startup_program = fluid.framework.Program() + test_program = fluid.framework.Program() + cur_model_path = args.model_output_dir + '/epoch_' + args.test_epoch + + with fluid.scope_guard(inference_scope): + with fluid.framework.program_guard(test_program, startup_program): + dcn_model = DCN(args.cross_num, args.dnn_hidden_units, + args.l2_reg_cross, args.use_bn) + dcn_model.build_network(is_test=True) + + exe = fluid.Executor(place) + feeder = fluid.DataFeeder( + feed_list=dcn_model.data_list, place=place) + fluid.io.load_persistables( + executor=exe, + dirname=cur_model_path, + main_program=fluid.default_main_program()) + + auc_states_names = ['_generated_var_2', '_generated_var_3'] + for name in auc_states_names: + param = inference_scope.var(name).get_tensor() + param_array = np.zeros(param._get_dims()).astype("int64") + param.set(param_array, place) + + loss_all = 0 + num_ins = 0 + for batch_id, data_test in enumerate(test_reader()): + loss_val, auc_val = exe.run(test_program, + feed=feeder.feed(data_test), + fetch_list=[ + dcn_model.avg_logloss.name, + dcn_model.auc_var.name + ]) + # num_ins += len(data_test) + num_ins += 1 + loss_all += loss_val + logger.info('TEST --> batch: {} loss: {} auc_val: {}'.format( + batch_id, loss_all / num_ins, auc_val)) + + print( + 'The last log info is the total Logloss and AUC for all test data. ' + ) + + +if __name__ == '__main__': + infer() diff --git a/PaddleRec/ctr/dcn/local_train.py b/PaddleRec/ctr/dcn/local_train.py new file mode 100644 index 00000000..2c2747e7 --- /dev/null +++ b/PaddleRec/ctr/dcn/local_train.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# coding: utf-8 +from __future__ import print_function, absolute_import, division +import os +import random +import sys +import time + +import paddle.fluid as fluid + +from config import parse_args +from network import DCN +""" +train DCN model +""" + + +def train(args): + """train and save DCN model + + :param args: hyperparams of model + :return: + """ + dcn_model = DCN(args.cross_num, args.dnn_hidden_units, args.l2_reg_cross, + args.use_bn, args.clip_by_norm) + dcn_model.build_network() + dcn_model.backward(args.lr) + + # config dataset + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_use_var(dcn_model.data_list) + pipe_command = 'python reader.py' + dataset.set_pipe_command(pipe_command) + dataset.set_batch_size(args.batch_size) + dataset.set_thread(args.num_thread) + train_filelist = [ + os.path.join(args.train_data_dir, fname) + for fname in next(os.walk(args.train_data_dir))[2] + ] + dataset.set_filelist(train_filelist) + num_epoch = args.num_epoch + if args.steps: + epoch = args.steps * args.batch_size / 41000000 + full_epoch = int(epoch // 1) + last_epoch = epoch % 1 + train_filelists = [train_filelist for _ in range(full_epoch)] + [ + random.sample(train_filelist, int( + len(train_filelist) * last_epoch)) + ] + num_epoch = full_epoch + 1 + print("train epoch: {}".format(num_epoch)) + + # Executor + exe = fluid.Executor(fluid.CPUPlace()) + exe.run(fluid.default_startup_program()) + + for epoch_id in range(num_epoch): + start = time.time() + sys.stderr.write('\nepoch%d start ...\n' % (epoch_id + 1)) + dataset.set_filelist(train_filelists[epoch_id]) + exe.train_from_dataset( + program=fluid.default_main_program(), + dataset=dataset, + fetch_list=[ + dcn_model.loss, dcn_model.avg_logloss, dcn_model.auc_var + ], + fetch_info=['total_loss', 'avg_logloss', 'auc'], + debug=False, + print_period=args.print_steps) + model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1) + sys.stderr.write('epoch%d is finished and takes %f s\n' % ( + (epoch_id + 1), time.time() - start)) + fluid.io.save_persistables( + executor=exe, + dirname=model_dir, + main_program=fluid.default_main_program()) + + +if __name__ == '__main__': + args = parse_args() + print(args) + train(args) diff --git a/PaddleRec/ctr/dcn/network.py b/PaddleRec/ctr/dcn/network.py new file mode 100644 index 00000000..a82eb914 --- /dev/null +++ b/PaddleRec/ctr/dcn/network.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python +# coding: utf-8 +from __future__ import print_function, absolute_import, division +import paddle.fluid as fluid +from collections import OrderedDict +""" +DCN network +""" + + +class DCN(object): + def __init__(self, + cross_num=2, + dnn_hidden_units=(128, 128), + l2_reg_cross=1e-5, + dnn_use_bn=False, + clip_by_norm=None): + self.cross_num = cross_num + self.dnn_hidden_units = dnn_hidden_units + self.l2_reg_cross = l2_reg_cross + self.dnn_use_bn = dnn_use_bn + self.clip_by_norm = clip_by_norm + + self.cat_feat_dims_dict = OrderedDict() + + 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)] + target = ['label'] + + for line in open('data/cat_feature_num.txt'): + spls = line.strip().split() + assert len(spls) == 2 + self.cat_feat_dims_dict[spls[0]] = int(spls[1]) + + # {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 build_network(self, is_test=False): + # data input + self.target_input = fluid.layers.data( + name='label', shape=[1], dtype='float32') + + data_dict = OrderedDict() + for feat_name in self.feat_dims_dict: + data_dict[feat_name] = fluid.layers.data( + name=feat_name, shape=[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, is_test) + cross_out, l2_reg_cross_loss = self._cross_net(self.net_input, + 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_list = [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) + label_int = fluid.layers.cast(self.target_input, 'int64') + auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=prob_2d, + label=label_int, + slide_steps=0) + self.auc_var = auc_var + + # logloss + logloss = fluid.layers.log_loss(self.prob, self.target_input) + self.avg_logloss = fluid.layers.reduce_mean(logloss) + + # reg_coeff * l2_reg_cross + l2_reg_cross_loss = self.l2_reg_cross * l2_reg_cross_loss + self.loss = self.avg_logloss + l2_reg_cross_loss + + def backward(self, lr): + p_g_clip = fluid.backward.append_backward(loss=self.loss) + fluid.clip.set_gradient_clip( + fluid.clip.GradientClipByGlobalNorm(clip_norm=self.clip_by_norm)) + p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip) + + optimizer = fluid.optimizer.Adam(learning_rate=lr) + # params_grads = optimizer.backward(self.loss) + optimizer.apply_gradients(p_g_clip) + + 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) + if use_bn: + input = fluid.layers.batch_norm(input, is_test=is_test) + input = fluid.layers.relu(input) + return input + + def _cross_layer(self, x0, x, prefix): + input_dim = x0.shape[-1] + w = fluid.layers.create_parameter( + [input_dim], dtype='float32', name=prefix + "_w") + b = fluid.layers.create_parameter( + [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 = [] + for i in range(num_corss_layers): + x, w = self._cross_layer(x0, x, "cross_layer_{}".format(i)) + l2_reg_cross_list.append(self._l2_loss(w)) + l2_reg_cross_loss = fluid.layers.reduce_sum( + 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 _create_embedding_input(self, data_dict): + # sparse embedding + sparse_emb_dict = OrderedDict(( + name, fluid.layers.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)) + ])) + for name in self.sparse_feat_names + ) + + # combine dense and sparse_emb + dense_input_list = [ + data_dict[name] for name in data_dict if name.startswith('I') + ] + sparse_emb_list = list(sparse_emb_dict.values()) + + sparse_input = fluid.layers.concat(sparse_emb_list, axis=-1) + sparse_input = fluid.layers.flatten(sparse_input) + + dense_input = fluid.layers.concat(dense_input_list, axis=-1) + dense_input = fluid.layers.flatten(dense_input) + dense_input = fluid.layers.cast(dense_input, 'float32') + + net_input = fluid.layers.concat([dense_input, sparse_input], axis=-1) + + return net_input diff --git a/PaddleRec/ctr/dcn/reader.py b/PaddleRec/ctr/dcn/reader.py new file mode 100644 index 00000000..5cdd05e0 --- /dev/null +++ b/PaddleRec/ctr/dcn/reader.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python +# coding: utf-8 +""" +dataset and reader +""" +import math +import sys +import paddle.fluid.incubate.data_generator as dg +import pickle +from collections import Counter +import os + + +class CriteoDataset(dg.MultiSlotDataGenerator): + def setup(self): + self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + self.cont_max_ = [ + 5775, 257675, 65535, 969, 23159456, 431037, 56311, 6047, 29019, 11, + 231, 4008, 7393 + ] + self.cont_diff_ = [ + self.cont_max_[i] - self.cont_min_[i] + for i in range(len(self.cont_min_)) + ] + self.cont_idx_ = list(range(1, 14)) + self.cat_idx_ = list(range(14, 40)) + + dense_feat_names = ['I' + str(i) for i in range(1, 14)] + sparse_feat_names = ['C' + str(i) for i in range(1, 27)] + target = ['label'] + + self.label_feat_names = target + dense_feat_names + sparse_feat_names + + self.cat_feat_idx_dict_list = [{} for _ in range(26)] + for i in range(26): + lookup_idx = 1 # remain 0 for default value + for line in open( + os.path.join('data/vocab', 'C' + str(i + 1) + '.txt')): + self.cat_feat_idx_dict_list[i][line.strip()] = lookup_idx + lookup_idx += 1 + + def _process_line(self, line): + features = line.rstrip('\n').split('\t') + label_feat_list = [[] for _ in range(40)] + for idx in self.cont_idx_: + if features[idx] == '': + label_feat_list[idx].append(0) + else: + # 0-1 minmax norm + # label_feat_list[idx].append((float(features[idx]) - self.cont_min_[idx - 1]) / + # self.cont_diff_[idx - 1]) + # log transform + label_feat_list[idx].append( + math.log(4 + float(features[idx])) + if idx == 2 else math.log(1 + float(features[idx]))) + for idx in self.cat_idx_: + if features[idx] == '' or features[ + idx] not in self.cat_feat_idx_dict_list[idx - 14]: + label_feat_list[idx].append(0) + else: + label_feat_list[idx].append(self.cat_feat_idx_dict_list[ + idx - 14][features[idx]]) + label_feat_list[0].append(int(features[0])) + return label_feat_list + + def test_reader(self, filelist, batch, buf_size): + print(filelist) + + def local_iter(): + for fname in filelist: + with open(fname.strip(), 'r') as fin: + for line in fin: + label_feat_list = self._process_line(line) + yield label_feat_list + + import paddle + batch_iter = paddle.batch( + paddle.reader.buffered( + local_iter, size=buf_size), batch_size=batch) + return batch_iter + + def generate_sample(self, line): + def data_iter(): + label_feat_list = self._process_line(line) + yield list(zip(self.label_feat_names, label_feat_list)) + + return data_iter + + +if __name__ == '__main__': + criteo_dataset = CriteoDataset() + criteo_dataset.setup() + criteo_dataset.run_from_stdin() diff --git a/PaddleRec/ctr/deepfm/data/aid_data/feat_dict_10.pkl2 b/PaddleRec/ctr/deepfm/data/aid_data/feat_dict_10.pkl2 deleted file mode 100644 index 5d38b9da..00000000 --- a/PaddleRec/ctr/deepfm/data/aid_data/feat_dict_10.pkl2 +++ /dev/null @@ -1,3259366 +0,0 @@ -(dp0 -S'3b42f196' -p1 -I7021 -sI1 -I1 -sI2 -I2 -sI3 -I3 -sI4 -I4 -sI5 -I5 -sI6 -I6 -sI7 -I7 -sI8 -I8 -sI9 -I9 -sI10 -I10 -sI11 -I11 -sI12 -I12 -sI13 -I13 -sS'3b1e75b9' -p2 -I835274 -sS'303c2d0b' -p3 -I336323 -sS'aa439d99' -p4 -I490233 -sS'20f60d06' -p5 -I468752 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b/PaddleRec/ctr/deepfm/data/download_preprocess.sh index 3ba76408..93ed326b 100644 --- a/PaddleRec/ctr/deepfm/data/download_preprocess.sh +++ b/PaddleRec/ctr/deepfm/data/download_preprocess.sh @@ -1,6 +1,7 @@ #!/bin/bash wget --no-check-certificate https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz +wget --no-check-certificate https://paddlerec.bj.bcebos.com/deepfm%2Ffeat_dict_10.pkl2 -O ./aid_data/feat_dict_10.pkl2 || rm -f ./aid_data/feat_dict_10.pkl2 tar zxf dac.tar.gz rm -f dac.tar.gz diff --git a/PaddleRec/ctr/xdeepfm/README.md b/PaddleRec/ctr/xdeepfm/README.md new file mode 100644 index 00000000..ceb8c9f3 --- /dev/null +++ b/PaddleRec/ctr/xdeepfm/README.md @@ -0,0 +1,33 @@ + +# xDeepFM for CTR Prediction + +## 简介 +使用PaddlePaddle复现论文 "xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems" 。论文[开源代码](https://github.com/Leavingseason/xDeepFM) 。 + +## 数据集 + +demo数据集，在data目录下执行命令，下载数据 +```bash +sh download.sh +``` + +## 环境 +- PaddlePaddle 1.5 + +## 单机训练 +```bash +python local_train.py --model_output_dir models +``` +训练过程中每隔固定的steps（默认为50）输出当前loss和auc，可以在args.py中调整print_steps。 + +## 单机预测 + +```bash +python infer.py --model_output_dir models --test_epoch 10 +``` +test_epoch设置加载第10轮训练的模型。 + +注意：最后的 log info是测试数据集的整体 Logloss 和 AUC。 + +## 结果 +训练集训练10轮后，测试集的LogLoss : `0.48657` 和 AUC : `0.7308`。 diff --git a/PaddleRec/ctr/xdeepfm/args.py b/PaddleRec/ctr/xdeepfm/args.py new file mode 100644 index 00000000..b5562370 --- /dev/null +++ b/PaddleRec/ctr/xdeepfm/args.py @@ -0,0 +1,73 @@ +import argparse + + +def parse_args(): + parser = argparse.ArgumentParser(description="PaddlePaddle CTR example") + parser.add_argument( + '--train_data_dir', + type=str, + default='data/train_data', + help='The path of train data (default: data/train_data)') + parser.add_argument( + '--test_data_dir', + type=str, + default='data/test_data', + help='The path of test data (default: models)') + parser.add_argument( + '--batch_size', + type=int, + default=100, + help="The size of mini-batch (default:100)") + parser.add_argument( + '--embedding_size', + type=int, + default=10, + help="The size for embedding layer (default:10)") + parser.add_argument( + '--num_epoch', + type=int, + default=10, + help="The number of epochs to train (default: 10)") + parser.add_argument( + '--model_output_dir', + type=str, + required=True, + help='The path for model to store (default: models)') + parser.add_argument( + '--num_thread', + type=int, + default=1, + help='The number of threads (default: 10)') + parser.add_argument('--test_epoch', type=str, default='1') + parser.add_argument( + '--layer_sizes_dnn', + nargs='+', + type=int, + default=[10, 10, 10], + help='The size of each layers') + parser.add_argument( + '--layer_sizes_cin', + nargs='+', + type=int, + default=[10, 10], + help='The size of each layers') + parser.add_argument( + '--act', + type=str, + default='relu', + help='The activation of each layers (default: relu)') + parser.add_argument( + '--lr', type=float, default=1e-1, help='Learning rate (default: 1e-4)') + parser.add_argument( + '--reg', type=float, default=1e-4, help=' (default: 1e-4)') + parser.add_argument('--num_field', type=int, default=39) + parser.add_argument('--num_feat', type=int, default=28651) + parser.add_argument( + '--model_name', + type=str, + default='ctr_xdeepfm_model', + help='The name of model (default: ctr_xdeepfm_model)') + parser.add_argument('--use_gpu', type=int, default=1) + parser.add_argument('--print_steps', type=int, default=50) + + return parser.parse_args() diff --git a/PaddleRec/ctr/xdeepfm/criteo_reader.py b/PaddleRec/ctr/xdeepfm/criteo_reader.py new file mode 100644 index 00000000..a123401e --- /dev/null +++ b/PaddleRec/ctr/xdeepfm/criteo_reader.py @@ -0,0 +1,41 @@ +import sys +import paddle.fluid.incubate.data_generator as dg +import pickle +from collections import Counter +import os + + +class CriteoDataset(dg.MultiSlotDataGenerator): + def _process_line(self, line): + features = line.strip('\n').split('\t') + feat_idx = [] + feat_value = [] + for idx in range(1, 40): + feat_idx.append(int(features[idx])) + feat_value.append(1.0) + label = [int(features[0])] + return feat_idx, feat_value, label + + def test(self, filelist): + def local_iter(): + for fname in filelist: + with open(fname.strip(), 'r') as fin: + for line in fin: + feat_idx, feat_value, label = self._process_line(line) + yield [feat_idx, feat_value, label] + + return local_iter + + def generate_sample(self, line): + def data_iter(): + feat_idx, feat_value, label = self._process_line(line) + feature_name = ['feat_idx', 'feat_value', 'label'] + yield [('feat_idx', feat_idx), ('feat_value', feat_value), ('label', + label)] + + return data_iter + + +if __name__ == '__main__': + criteo_dataset = CriteoDataset() + criteo_dataset.run_from_stdin() diff --git a/PaddleRec/ctr/xdeepfm/data/download.sh b/PaddleRec/ctr/xdeepfm/data/download.sh new file mode 100644 index 00000000..a2559f52 --- /dev/null +++ b/PaddleRec/ctr/xdeepfm/data/download.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +wget --no-check-certificate https://paddlerec.bj.bcebos.com/xdeepfm%2Fev -O ./test_data/ev +wget --no-check-certificate https://paddlerec.bj.bcebos.com/xdeepfm%2Ftr -O ./train_data/tr diff --git a/PaddleRec/ctr/xdeepfm/infer.py b/PaddleRec/ctr/xdeepfm/infer.py new file mode 100644 index 00000000..fe2fc8d3 --- /dev/null +++ b/PaddleRec/ctr/xdeepfm/infer.py @@ -0,0 +1,75 @@ +import logging +import numpy as np +import pickle +import os +import paddle +import paddle.fluid as fluid + +from args import parse_args +from criteo_reader import CriteoDataset +import network_conf + +logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger('fluid') +logger.setLevel(logging.INFO) + + +def infer(): + args = parse_args() + print(args) + + if args.use_gpu == 1: + place = fluid.CUDAPlace(0) + else: + place = fluid.CPUPlace() + inference_scope = fluid.Scope() + + test_files = [ + args.test_data_dir + '/' + x for x in os.listdir(args.test_data_dir) + ] + criteo_dataset = CriteoDataset() + test_reader = paddle.batch( + criteo_dataset.test(test_files), batch_size=args.batch_size) + + startup_program = fluid.framework.Program() + test_program = fluid.framework.Program() + cur_model_path = args.model_output_dir + '/epoch_' + args.test_epoch + + with fluid.scope_guard(inference_scope): + with fluid.framework.program_guard(test_program, startup_program): + loss, auc, data_list = eval('network_conf.' + args.model_name)( + args.embedding_size, args.num_field, args.num_feat, + args.layer_sizes_dnn, args.act, args.reg, args.layer_sizes_cin) + + exe = fluid.Executor(place) + feeder = fluid.DataFeeder(feed_list=data_list, place=place) + fluid.io.load_persistables( + executor=exe, + dirname=cur_model_path, + main_program=fluid.default_main_program()) + + auc_states_names = ['_generated_var_2', '_generated_var_3'] + for name in auc_states_names: + param = inference_scope.var(name).get_tensor() + param_array = np.zeros(param._get_dims()).astype("int64") + param.set(param_array, place) + + loss_all = 0 + num_ins = 0 + for batch_id, data_test in enumerate(test_reader()): + loss_val, auc_val = exe.run(test_program, + feed=feeder.feed(data_test), + fetch_list=[loss.name, auc.name]) + + num_ins += len(data_test) + loss_all += loss_val * len(data_test) + logger.info('TEST --> batch: {} loss: {} auc_val: {}'.format( + batch_id, loss_all / num_ins, auc_val)) + + print( + 'The last log info is the total Logloss and AUC for all test data. ' + ) + + +if __name__ == '__main__': + infer() diff --git a/PaddleRec/ctr/xdeepfm/local_train.py b/PaddleRec/ctr/xdeepfm/local_train.py new file mode 100644 index 00000000..8c548d49 --- /dev/null +++ b/PaddleRec/ctr/xdeepfm/local_train.py @@ -0,0 +1,59 @@ +from args import parse_args +import os +import paddle.fluid as fluid +import sys +import network_conf +import time + + +def train(): + args = parse_args() + print(args) + if not os.path.isdir(args.model_output_dir): + os.mkdir(args.model_output_dir) + + loss, auc, data_list = eval('network_conf.' + args.model_name)( + args.embedding_size, args.num_field, args.num_feat, + args.layer_sizes_dnn, args.act, args.reg, args.layer_sizes_cin) + optimizer = fluid.optimizer.SGD( + learning_rate=args.lr, + regularization=fluid.regularizer.L2DecayRegularizer(args.reg)) + optimizer.minimize(loss) + + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_use_var(data_list) + dataset.set_pipe_command('python criteo_reader.py') + dataset.set_batch_size(args.batch_size) + dataset.set_filelist([ + args.train_data_dir + '/' + x for x in os.listdir(args.train_data_dir) + ]) + + if args.use_gpu == 1: + exe = fluid.Executor(fluid.CUDAPlace(0)) + dataset.set_thread(1) + else: + exe = fluid.Executor(fluid.CPUPlace()) + dataset.set_thread(args.num_thread) + exe.run(fluid.default_startup_program()) + + for epoch_id in range(args.num_epoch): + start = time.time() + sys.stderr.write('\nepoch%d start ...\n' % (epoch_id + 1)) + exe.train_from_dataset( + program=fluid.default_main_program(), + dataset=dataset, + fetch_list=[loss, auc], + fetch_info=['loss', 'auc'], + debug=False, + print_period=args.print_steps) + model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1) + sys.stderr.write('epoch%d is finished and takes %f s\n' % ( + (epoch_id + 1), time.time() - start)) + fluid.io.save_persistables( + executor=exe, + dirname=model_dir, + main_program=fluid.default_main_program()) + + +if __name__ == '__main__': + train() diff --git a/PaddleRec/ctr/xdeepfm/network_conf.py b/PaddleRec/ctr/xdeepfm/network_conf.py new file mode 100644 index 00000000..1f6f76db --- /dev/null +++ b/PaddleRec/ctr/xdeepfm/network_conf.py @@ -0,0 +1,125 @@ +import paddle.fluid as fluid +import math + + +def ctr_xdeepfm_model(embedding_size, num_field, num_feat, layer_sizes_dnn, act, + reg, layer_sizes_cin): + init_value_ = 0.1 + initer = fluid.initializer.TruncatedNormalInitializer( + loc=0.0, scale=init_value_) + + raw_feat_idx = fluid.layers.data( + name='feat_idx', shape=[num_field], dtype='int64') + raw_feat_value = fluid.layers.data( + name='feat_value', shape=[num_field], dtype='float32') + label = fluid.layers.data( + name='label', shape=[1], dtype='float32') # None * 1 + feat_idx = fluid.layers.reshape(raw_feat_idx, + [-1, num_field, 1]) # None * num_field * 1 + feat_value = fluid.layers.reshape( + raw_feat_value, [-1, num_field, 1]) # None * num_field * 1 + + feat_embeddings = fluid.layers.embedding( + input=feat_idx, + dtype='float32', + size=[num_feat + 1, embedding_size], + padding_idx=0, + param_attr=fluid.ParamAttr( + initializer=initer)) # None * num_field * embedding_size + feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size + + # -------------------- linear -------------------- + + weights_linear = fluid.layers.embedding( + input=feat_idx, + dtype='float32', + size=[num_feat + 1, 1], + padding_idx=0, + param_attr=fluid.ParamAttr(initializer=initer)) # 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 -------------------- + + Xs = [feat_embeddings] + last_s = num_field + for s in 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, embedding_size, num_field, + 1]) # None, embedding_size, num_field, 1 + X_k = fluid.layers.reshape( + fluid.layers.transpose(Xs[-1], [0, 2, 1]), + [-1, embedding_size, 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, embedding_size, last_s * 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, embedding_size] + ) # None, last_s*num_field, 1, embedding_size (None, channal_in, h, w) + X_k_1 = fluid.layers.conv2d( + Z_k_1, + num_filters=s, + filter_size=(1, 1), + act=None, + bias_attr=False, + param_attr=fluid.ParamAttr( + initializer=initer)) # None, s, 1, embedding_size + X_k_1 = fluid.layers.reshape( + X_k_1, [-1, s, embedding_size]) # None, s, embedding_size + + Xs.append(X_k_1) + last_s = s + + # sum pooling + y_cin = fluid.layers.concat(Xs[1:], + 1) # None, (num_field++), embedding_size + y_cin = fluid.layers.reduce_sum(y_cin, -1) # None, (num_field++) + y_cin = fluid.layers.fc(input=y_cin, + size=1, + act=None, + param_attr=fluid.ParamAttr(initializer=initer), + bias_attr=None) + y_cin = fluid.layers.reduce_sum(y_cin, dim=-1, keep_dim=True) + + # -------------------- DNN -------------------- + + y_dnn = fluid.layers.reshape(feat_embeddings, + [-1, num_field * embedding_size]) + 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.fc(input=y_dnn, + size=1, + act=None, + param_attr=fluid.ParamAttr(initializer=initer), + bias_attr=None) + + # ------------------- xDeepFM ------------------ + + predict = fluid.layers.sigmoid(y_linear + y_cin + y_dnn) + cost = fluid.layers.log_loss(input=predict, label=label, epsilon=0.0000001) + batch_cost = fluid.layers.reduce_mean(cost) + + # for auc + predict_2d = fluid.layers.concat([1 - predict, predict], 1) + label_int = fluid.layers.cast(label, 'int64') + auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict_2d, + label=label_int, + slide_steps=0) + + return batch_cost, auc_var, [raw_feat_idx, raw_feat_value, label] -- GitLab