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!5788 [ModelZoo]Add bgcf to model zoo 

Merge pull request !5788 from zhanke/bgcf
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<!--TOC -->
- [Bayesian Graph Collaborative Filtering](#bayesian-graph-collaborative-filtering)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Features](#features)
- [Mixed Precision](#mixed-precision)
- [Environment Requirements](#environment-requirements)
- [Quick Start](#quick-start)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
- [Training Process](#training-process)
- [Training](#training)
- [Model Description](#model-description)
- [Performance](#performance)
- [Description of random situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
<!--TOC -->
# [Bayesian Graph Collaborative Filtering](#contents)
Bayesian Graph Collaborative Filtering(BGCF) was proposed in 2020 by Sun J, Guo W, Zhang D et al. By naturally incorporating the
uncertainty in the user-item interaction graph shows excellent performance on Amazon recommendation dataset.This is an example of
training of BGCF with Amazon-Beauty dataset in MindSpore. More importantly, this is the first open source version for BGCF.
[Paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403254): Sun J, Guo W, Zhang D, et al. A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020: 2030-2039.
# [Model Architecture](#contents)
Specially, BGCF contains two main modules. The first is sampling, which produce sample graphs based in node copying. Another module
aggregate the neighbors sampling from nodes consisting of mean aggregator and attention aggregator.
# [Dataset](#contents)
- Dataset size:
Statistics of dataset used are summarized as below:
| | Amazon-Beauty |
| ------------------ | -----------------------:|
| Task | Recommendation |
| # User | 7068 (1 graph) |
| # Item | 3570 |
| # Interaction | 79506 |
| # Training Data | 60818 |
| # Test Data | 18688 |
| # Density | 0.315% |
- Data Preparation
- Place the dataset to any path you want, the folder should include files as follows(we use Amazon-Beauty dataset as an example)"
```
.
└─data
├─ratings_Beauty.csv
```
- Generate dataset in mindrecord format for Amazon-Beauty.
```builddoutcfg
cd ./scripts
# SRC_PATH is the dataset file path you download.
sh run_process_data_ascend.sh [SRC_PATH]
```
- Launch
```
# Generate dataset in mindrecord format for Amazon-Beauty.
sh ./run_process_data_ascend.sh ./data
# [Features](#contents)
## Mixed Precision
To ultilize the strong computation power of Ascend chip, and accelerate the training process, the mixed training method is used. MindSpore is able to cope with FP32 inputs and FP16 operators. In BGCF example, the model is set to FP16 mode except for the loss calculation part.
# [Environment Requirements](#contents)
- Hardward (Ascend)
- Framework
- [MindSpore](https://www.mindspore.cn/install/en)
- For more information, please check the resources below:
- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
# [Quick Start](#contents)
After installing MindSpore via the official website and Dataset is correctly generated, you can start training and evaluation as follows.
- running on Ascend
```
# run training example with Amazon-Beauty dataset
sh run_train_ascend.sh
```
# [Script Description](#contents)
## [Script and Sample Code](#contents)
```shell
.
└─bgcf
├─README.md
├─scripts
| ├─run_process_data_ascend.sh # Generate dataset in mindrecord format
| └─run_train_ascend.sh # Launch training
|
├─src
| ├─bgcf.py # BGCF model
| ├─callback.py # Callback function
| ├─config.py # Training configurations
| ├─dataset.py # Data preprocessing
| ├─metrics.py # Recommendation metrics
| └─utils.py # Utils for training bgcf
|
└─train.py # Train net
```
## [Script Parameters](#contents)
Parameters for both training and evaluation can be set in config.py.
- config for BGCF dataset
```python
"learning_rate": 0.001, # Learning rate
"num_epochs": 600, # Epoch sizes for training
"num_neg": 10, # Negative sampling rate
"raw_neighs": 40, # Num of sampling neighbors in raw graph
"gnew_neighs": 20, # Num of sampling neighbors in sample graph
"input_dim": 64, # User and item embedding dimension
"l2_coeff": 0.03 # l2 coefficient
"neighbor_dropout": [0.0, 0.2, 0.3]# Dropout ratio for different aggregation layer
"num_graphs":5 # Num of sample graph
```
## [Training Process](#contents)
### Training
- running on Ascend
```python
sh run_train_ascend.sh
```
Training result will be stored in the scripts path, whose folder name begins with "train". You can find the result like the
followings in log.
```python
Epoch 001 iter 12 loss 34696.242
Epoch 002 iter 12 loss 34275.508
Epoch 003 iter 12 loss 30620.635
Epoch 004 iter 12 loss 21628.908
...
Epoch 597 iter 12 loss 3662.3152
Epoch 598 iter 12 loss 3640.7612
Epoch 599 iter 12 loss 3654.9087
Epoch 600 iter 12 loss 3632.4585
epoch:600, recall_@10:0.10393, recall_@20:0.15669, ndcg_@10:0.07564, ndcg_@20:0.09343,
sedp_@10:0.01936, sedp_@20:0.01544, nov_@10:7.58599, nov_@20:7.79782
...
```
# [Model Description](#contents)
## [Performance](#contents)
| Parameter | BGCF |
| ------------------------------------ | ----------------------------------------- |
| Resource | Ascend 910 |
| uploaded Date | 09/04/2020(month/day/year) |
| MindSpore Version | 1.0 |
| Dataset | Amazon-Beauty |
| Training Parameter | epoch=600 |
| Optimizer | Adam |
| Loss Function | BPR loss |
| Recall@20 | 0.1534 |
| NDCG@20 | 0.0912 |
| Total time | 30min |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/gnn/bgcf |
# [Description of random situation](#contents)
BGCF model contains lots of dropout operations, if you want to disable dropout, set the neighbor_dropout to [0.0, 0.0, 0.0] in src/config.py.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](http://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
if [ $# != 1 ]
then
echo "Usage: sh run_process_data_ascend.sh [SRC_PATH] "
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
SRC_PATH=$(get_real_path $1)
echo $SRC_PATH
if [ ! -d data_mr ]; then
mkdir data_mr
else
echo data_mr exist
fi
MINDRECORD_PATH=`pwd`/data_mr
rm -rf ${MINDRECORD_PATH:?}/*
INTER_FILE_DIR=$MINDRECORD_PATH/InterFile
mkdir -p $INTER_FILE_DIR
cd ../../../../utils/graph_to_mindrecord || exit
echo "Start to converting data."
python amazon_beauty/converting_data.py --src_path $SRC_PATH --out_path $INTER_FILE_DIR
echo "Start to generate train_mr."
python writer.py --mindrecord_script amazon_beauty \
--mindrecord_file "$MINDRECORD_PATH/train_mr" \
--mindrecord_partitions 1 \
--mindrecord_header_size_by_bit 18 \
--mindrecord_page_size_by_bit 20 \
--graph_api_args "$INTER_FILE_DIR/user.csv:$INTER_FILE_DIR/item.csv:$INTER_FILE_DIR/rating_train.csv"
echo "Start to generate test_mr."
python writer.py --mindrecord_script amazon_beauty \
--mindrecord_file "$MINDRECORD_PATH/test_mr" \
--mindrecord_partitions 1 \
--mindrecord_header_size_by_bit 18 \
--mindrecord_page_size_by_bit 20 \
--graph_api_args "$INTER_FILE_DIR/user.csv:$INTER_FILE_DIR/item.csv:$INTER_FILE_DIR/rating_test.csv"
for id in {0..4}
do
echo "Start to generate sampled${id}_mr."
python writer.py --mindrecord_script amazon_beauty \
--mindrecord_file "${MINDRECORD_PATH}/sampled${id}_mr" \
--mindrecord_partitions 1 \
--mindrecord_header_size_by_bit 18 \
--mindrecord_page_size_by_bit 20 \
--graph_api_args "$INTER_FILE_DIR/user.csv:$INTER_FILE_DIR/item.csv:$INTER_FILE_DIR/rating_sampled${id}.csv"
done

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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
ulimit -u unlimited
export DEVICE_NUM=1
export RANK_SIZE=$DEVICE_NUM
export DEVICE_ID=0
export RANK_ID=0
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp ../*.py ./train
cp *.sh ./train
cp -r ../src ./train
cd ./train || exit
mkdir ./ckpts
env > env.log
echo "start training for device $DEVICE_ID"
python train.py --datapath=../data_mr &> log &
cd ..

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Architecture"""
import mindspore.nn as nn
from mindspore import Parameter
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
from mindspore.common.initializer import initializer
class MeanConv(nn.Cell):
"""
BGCF mean aggregate layer.
Args:
feature_in_dim (int): The input feature dimension.
feature_out_dim (int): The output feature dimension.
activation (str): Activation function applied to the output of the layer, eg. 'relu'. Default: 'tanh'.
dropout (float): Dropout ratio for the dropout layer. Default: 0.2.
Inputs:
- self_feature (Tensor) - Tensor of shape :math:`(batch_size, feature_dim)`.
- neigh_feature (Tensor) - Tensor of shape :math:`(batch_size, neighbour_num, feature_dim)`.
Outputs:
Tensor, output tensor.
"""
def __init__(self,
name,
feature_in_dim,
feature_out_dim,
activation,
dropout=0.2):
super(MeanConv, self).__init__()
self.out_weight = Parameter(
initializer("XavierUniform", [feature_in_dim * 2, feature_out_dim], dtype=mstype.float32),
name=name + 'out_weight')
if activation == "tanh":
self.act = P.Tanh()
elif activation == "relu":
self.act = P.ReLU()
else:
raise ValueError("activation should be tanh or relu")
self.cast = P.Cast()
self.matmul = P.MatMul()
self.concat = P.Concat(axis=1)
self.reduce_mean = P.ReduceMean(keep_dims=False)
self.dropout = nn.Dropout(keep_prob=1 - dropout)
def construct(self, self_feature, neigh_feature):
neigh_matrix = self.reduce_mean(neigh_feature, 1)
neigh_matrix = self.dropout(neigh_matrix)
output = self.concat((self_feature, neigh_matrix))
output = self.act(self.matmul(output, self.out_weight))
return output
class AttenConv(nn.Cell):
"""
BGCF attention aggregate layer.
Args:
feature_in_dim (int): The input feature dimension.
feature_out_dim (int): The output feature dimension.
dropout (float): Dropout ratio for the dropout layer. Default: 0.2.
Inputs:
- self_feature (Tensor) - Tensor of shape :math:`(batch_size, feature_dim)`.
- neigh_feature (Tensor) - Tensor of shape :math:`(batch_size, neighbour_num, feature_dim)`.
Outputs:
Tensor, output tensor.
"""
def __init__(self,
name,
feature_in_dim,
feature_out_dim,
dropout=0.2):
super(AttenConv, self).__init__()
self.out_weight = Parameter(
initializer("XavierUniform", [feature_in_dim * 2, feature_out_dim], dtype=mstype.float32),
name=name + 'out_weight')
self.cast = P.Cast()
self.squeeze = P.Squeeze(1)
self.concat = P.Concat(axis=1)
self.expanddims = P.ExpandDims()
self.softmax = P.Softmax(axis=-1)
self.matmul = P.MatMul()
self.matmul_3 = P.BatchMatMul()
self.matmul_t = P.BatchMatMul(transpose_b=True)
self.dropout = nn.Dropout(keep_prob=1 - dropout)
def construct(self, self_feature, neigh_feature):
"""Attention aggregation"""
query = self.expanddims(self_feature, 1)
neigh_matrix = self.dropout(neigh_feature)
score = self.matmul_t(query, neigh_matrix)
score = self.softmax(score)
atten_agg = self.matmul_3(score, neigh_matrix)
atten_agg = self.squeeze(atten_agg)
output = self.matmul(self.concat((atten_agg, self_feature)), self.out_weight)
return output
class BGCF(nn.Cell):
"""
BGCF architecture.
Args:
dataset_argv (list[int]): A list of the dataset argv.
architect_argv (list[int]): A list of the model layer argv.
activation (str): Activation function applied to the output of the layer, eg. 'relu'. Default: 'tanh'.
neigh_drop_rate (list[float]): A list of the dropout ratio.
num_user (int): The num of user.
num_item (int): The num of item.
input_dim (int): The feature dim.
"""
def __init__(self,
dataset_argv,
architect_argv,
activation,
neigh_drop_rate,
num_user,
num_item,
input_dim):
super(BGCF, self).__init__()
self.user_embeddings = Parameter(initializer("XavierUniform", [num_user, input_dim], dtype=mstype.float32),
name='user_embed')
self.item_embeddings = Parameter(initializer("XavierUniform", [num_item, input_dim], dtype=mstype.float32),
name='item_embed')
self.cast = P.Cast()
self.tanh = P.Tanh()
self.shape = P.Shape()
self.split = P.Split(0, 2)
self.gather = P.GatherV2()
self.reshape = P.Reshape()
self.concat_0 = P.Concat(0)
self.concat_1 = P.Concat(1)
(self.input_dim, self.num_user, self.num_item) = dataset_argv
self.layer_dim = architect_argv
self.gnew_agg_mean = MeanConv('gnew_agg_mean', self.input_dim, self.layer_dim,
activation=activation, dropout=neigh_drop_rate[1])
self.gnew_agg_mean.to_float(mstype.float16)
self.gnew_agg_user = AttenConv('gnew_agg_att_user', self.input_dim,
self.layer_dim, dropout=neigh_drop_rate[2])
self.gnew_agg_user.to_float(mstype.float16)
self.gnew_agg_item = AttenConv('gnew_agg_att_item', self.input_dim,
self.layer_dim, dropout=neigh_drop_rate[2])
self.gnew_agg_item.to_float(mstype.float16)
self.user_feature_dim = self.input_dim
self.item_feature_dim = self.input_dim
self.final_weight = Parameter(
initializer("XavierUniform", [self.input_dim * 3, self.input_dim * 3], dtype=mstype.float32),
name='final_weight')
self.raw_agg_funcs_user = MeanConv('raw_agg_user', self.input_dim, self.layer_dim,
activation=activation, dropout=neigh_drop_rate[0])
self.raw_agg_funcs_user.to_float(mstype.float16)
self.raw_agg_funcs_item = MeanConv('raw_agg_item', self.input_dim, self.layer_dim,
activation=activation, dropout=neigh_drop_rate[0])
self.raw_agg_funcs_item.to_float(mstype.float16)
def construct(self,
u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs,
neg_item_num):
"""Aggregate user and item embeddings"""
all_user_embed = self.gather(self.user_embeddings, self.concat_0((u_id, pos_users)), 0)
u_self_matrix_at_layers = self.gather(self.user_embeddings, u_group_nodes, 0)
u_neigh_matrix_at_layers = self.gather(self.item_embeddings, u_neighs, 0)
u_output_mean = self.raw_agg_funcs_user(u_self_matrix_at_layers, u_neigh_matrix_at_layers)
u_gnew_neighs_matrix = self.gather(self.item_embeddings, u_gnew_neighs, 0)
u_output_from_gnew_mean = self.gnew_agg_mean(u_self_matrix_at_layers, u_gnew_neighs_matrix)
u_output_from_gnew_att = self.gnew_agg_user(u_self_matrix_at_layers,
self.concat_1((u_neigh_matrix_at_layers, u_gnew_neighs_matrix)))
u_output = self.concat_1((u_output_mean, u_output_from_gnew_mean, u_output_from_gnew_att))
all_user_rep = self.tanh(u_output)
all_pos_item_embed = self.gather(self.item_embeddings, self.concat_0((pos_item_id, pos_items)), 0)
i_self_matrix_at_layers = self.gather(self.item_embeddings, i_group_nodes, 0)
i_neigh_matrix_at_layers = self.gather(self.user_embeddings, i_neighs, 0)
i_output_mean = self.raw_agg_funcs_item(i_self_matrix_at_layers, i_neigh_matrix_at_layers)
i_gnew_neighs_matrix = self.gather(self.user_embeddings, i_gnew_neighs, 0)
i_output_from_gnew_mean = self.gnew_agg_mean(i_self_matrix_at_layers, i_gnew_neighs_matrix)
i_output_from_gnew_att = self.gnew_agg_item(i_self_matrix_at_layers,
self.concat_1((i_neigh_matrix_at_layers, i_gnew_neighs_matrix)))
i_output = self.concat_1((i_output_mean, i_output_from_gnew_mean, i_output_from_gnew_att))
all_pos_item_rep = self.tanh(i_output)
neg_item_embed = self.gather(self.item_embeddings, neg_item_id, 0)
neg_self_matrix_at_layers = self.gather(self.item_embeddings, neg_group_nodes, 0)
neg_neigh_matrix_at_layers = self.gather(self.user_embeddings, neg_neighs, 0)
neg_output_mean = self.raw_agg_funcs_item(neg_self_matrix_at_layers, neg_neigh_matrix_at_layers)
neg_gnew_neighs_matrix = self.gather(self.user_embeddings, neg_gnew_neighs, 0)
neg_output_from_gnew_mean = self.gnew_agg_mean(neg_self_matrix_at_layers, neg_gnew_neighs_matrix)
neg_output_from_gnew_att = self.gnew_agg_item(neg_self_matrix_at_layers,
self.concat_1(
(neg_neigh_matrix_at_layers, neg_gnew_neighs_matrix)))
neg_output = self.concat_1((neg_output_mean, neg_output_from_gnew_mean, neg_output_from_gnew_att))
neg_output = self.tanh(neg_output)
neg_output_shape = self.shape(neg_output)
neg_item_rep = self.reshape(neg_output,
(self.shape(neg_item_embed)[0], neg_item_num, neg_output_shape[-1]))
return all_user_embed, all_user_rep, all_pos_item_embed, all_pos_item_rep, neg_item_embed, neg_item_rep

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
callback
"""
import numpy as np
from mindspore import nn
from mindspore import Tensor
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
from mindspore.common.parameter import ParameterTuple
from src.utils import convert_item_id
def TestBGCF(forward_net, num_user, num_item, input_dim, test_graph_dataset):
"""BGCF test wrapper"""
user_reps = np.zeros([num_user, input_dim * 3])
item_reps = np.zeros([num_item, input_dim * 3])
for _ in range(50):
test_graph_dataset.random_select_sampled_graph()
u_test_neighs, u_test_gnew_neighs = test_graph_dataset.get_user_sapmled_neighbor()
i_test_neighs, i_test_gnew_neighs = test_graph_dataset.get_item_sampled_neighbor()
u_test_neighs = Tensor(convert_item_id(u_test_neighs, num_user), mstype.int32)
u_test_gnew_neighs = Tensor(convert_item_id(u_test_gnew_neighs, num_user), mstype.int32)
i_test_neighs = Tensor(i_test_neighs, mstype.int32)
i_test_gnew_neighs = Tensor(i_test_gnew_neighs, mstype.int32)
users = Tensor(np.arange(num_user).reshape(-1,), mstype.int32)
items = Tensor(np.arange(num_item).reshape(-1,), mstype.int32)
neg_items = Tensor(np.arange(num_item).reshape(-1, 1), mstype.int32)
user_rep, item_rep = forward_net(users,
items,
neg_items,
u_test_neighs,
u_test_gnew_neighs,
i_test_neighs,
i_test_gnew_neighs)
user_reps += user_rep.asnumpy()
item_reps += item_rep.asnumpy()
user_reps /= 50
item_reps /= 50
return user_reps, item_reps
class ForwardBGCF(nn.Cell):
"""Calculate the forward output"""
def __init__(self,
network):
super(ForwardBGCF, self).__init__()
self.network = network
def construct(self, users, items, neg_items, u_neighs, u_gnew_neighs, i_neighs, i_gnew_neighs):
"""Calculate the user and item representation"""
_, user_rep, _, item_rep, _, _, = self.network(users,
items,
neg_items,
users,
items,
users,
u_neighs,
u_gnew_neighs,
items,
i_neighs,
i_gnew_neighs,
items,
i_neighs,
i_gnew_neighs,
1)
return user_rep, item_rep
class BGCFLoss(nn.Cell):
"""BGCF loss with user and item embedding"""
def __init__(self, neg_item_num, l2_embed, dist_reg):
super(BGCFLoss, self).__init__()
self.neg_item_num = neg_item_num
self.l2_embed = l2_embed
self.dist_reg = dist_reg
self.log = P.Log()
self.pow = P.Pow()
self.cast = P.Cast()
self.tile = P.Tile()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.concat = P.Concat(1)
self.concat2 = P.Concat(2)
self.split = P.Split(0, 2)
self.reduce_sum = P.ReduceSum()
self.expand_dims = P.ExpandDims()
self.multiply = P.Mul()
self.matmul = P.BatchMatMul()
self.squeeze = P.Squeeze(1)
self.transpose = P.Transpose()
self.l2_loss = P.L2Loss()
self.sigmoid = P.Sigmoid()
def construct(self, all_user_embed, all_user_rep, all_pos_item_embed,
all_pos_item_rep, neg_item_embed, neg_item_rep):
"""Calculate loss"""
all_user_embed = self.cast(all_user_embed, mstype.float16)
all_user_rep = self.concat((all_user_rep, all_user_embed))
user_rep, pos_user_rep = self.split(all_user_rep)
user_embed, pos_user_embed = self.split(all_user_embed)
user_user_distance = self.reduce_sum(self.pow(user_rep - pos_user_rep, 2)) \
+ self.reduce_sum(self.pow(user_embed - pos_user_embed, 2))
user_user_distance = self.cast(user_user_distance, mstype.float32)
user_rep = self.expand_dims(user_rep, 1)
all_pos_item_embed = self.cast(all_pos_item_embed, mstype.float16)
all_pos_item_rep = self.concat((all_pos_item_rep, all_pos_item_embed))
pos_item_rep, pos_item_neigh_rep = self.split(all_pos_item_rep)
pos_item_embed, pos_item_neigh_embed = self.split(all_pos_item_embed)
pos_item_item_distance = self.reduce_sum(self.pow(pos_item_rep - pos_item_neigh_rep, 2)) \
+ self.reduce_sum(self.pow(pos_item_embed - pos_item_neigh_embed, 2))
pos_item_item_distance = self.cast(pos_item_item_distance, mstype.float32)
neg_item_embed = self.cast(neg_item_embed, mstype.float16)
neg_item_rep = self.concat2((neg_item_rep, neg_item_embed))
item_rep = self.concat((self.expand_dims(pos_item_rep, 1), neg_item_rep))
pos_rating = self.reduce_sum(self.multiply(self.squeeze(user_rep), pos_item_rep), 1)
pos_rating = self.expand_dims(pos_rating, 1)
pos_rating = self.tile(pos_rating, (1, self.neg_item_num))
pos_rating = self.reshape(pos_rating, (self.shape(pos_rating)[0] * self.neg_item_num, 1))
pos_rating = self.cast(pos_rating, mstype.float32)
batch_neg_item_embedding = self.transpose(neg_item_rep, (0, 2, 1))
neg_rating = self.matmul(user_rep, batch_neg_item_embedding)
neg_rating = self.squeeze(neg_rating)
neg_rating = self.reshape(neg_rating, (self.shape(neg_rating)[0] * self.neg_item_num, 1))
neg_rating = self.cast(neg_rating, mstype.float32)
bpr_loss = pos_rating - neg_rating
bpr_loss = self.sigmoid(bpr_loss)
bpr_loss = - self.log(bpr_loss)
bpr_loss = self.reduce_sum(bpr_loss)
reg_loss = self.l2_embed * (self.l2_loss(user_rep) + self.l2_loss(item_rep))
loss = bpr_loss + reg_loss + self.dist_reg * (user_user_distance + pos_item_item_distance)
return loss
class LossWrapper(nn.Cell):
"""
Wraps the BGCF model with loss.
Args:
network (Cell): BGCF network.
neg_item_num (Number): The num of negative instances for a positive instance.
l2_embed (Number): The coefficient of l2 loss.
dist_reg (Number): The coefficient of distance loss.
"""
def __init__(self, network, neg_item_num, l2_embed, dist_reg=0.002):
super(LossWrapper, self).__init__(auto_prefix=False)
self.network = network
self.loss_func = BGCFLoss(neg_item_num, l2_embed, dist_reg)
def construct(self,
u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs):
"""Return loss"""
all_user_embed, all_user_rep, all_pos_item_embed, \
all_pos_item_rep, neg_item_embed, neg_item_rep = self.network(u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs,
10)
loss = self.loss_func(all_user_embed, all_user_rep, all_pos_item_embed,
all_pos_item_rep, neg_item_embed, neg_item_rep)
return loss
class TrainOneStepCell(nn.Cell):
r"""
Network training package class.
Wraps the network with an optimizer. The resulting Cell be trained with sample inputs.
Backward graph will be created in the construct function to do parameter updating. Different
parallel models are available to run the training.
Args:
network (Cell): The training network.
optimizer (Cell): Optimizer for updating the weights.
sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
Outputs:
Tensor, a scalar Tensor with shape :math:`()`.
Examples:
>>> net = Net()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
>>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> loss_net = nn.WithLossCell(net, loss_fn)
>>> train_net = nn.TrainOneStepCell(loss_net, optim)
"""
def __init__(self, network, optimizer, sens=1.0):
super(TrainOneStepCell, self).__init__(auto_prefix=False)
self.network = network
self.network.add_flags(defer_inline=True)
self.weights = ParameterTuple(network.trainable_params())
self.optimizer = optimizer
self.grad = C.GradOperation(get_by_list=True, sens_param=True)
self.sens = sens
def construct(self,
u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs):
"""Grad process"""
weights = self.weights
loss = self.network(u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs)
sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
grads = self.grad(self.network, weights)(u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs,
sens)
return F.depend(loss, self.optimizer(grads))
class TrainBGCF(nn.Cell):
"""
Wraps the BGCF model with optimizer.
Args:
network (Cell): BGCF network.
neg_item_num (Number): The num of negative instances for a positive instance.
l2_embed (Number): The coefficient of l2 loss.
learning_rate (Number): The learning rate.
epsilon (Number):The term added to the denominator to improve numerical stability.
dist_reg (Number): The coefficient of distance loss.
"""
def __init__(self,
network,
neg_item_num,
l2_embed,
learning_rate,
epsilon,
dist_reg=0.002):
super(TrainBGCF, self).__init__(auto_prefix=False)
self.network = network
loss_net = LossWrapper(network,
neg_item_num,
l2_embed,
dist_reg)
optimizer = nn.Adam(loss_net.trainable_params(),
learning_rate=learning_rate,
eps=epsilon)
self.loss_train_net = TrainOneStepCell(loss_net, optimizer)
def construct(self,
u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs):
"""Return loss"""
loss = self.loss_train_net(u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs)
return loss

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
network config setting, will be used in train.py
"""
import argparse
def parser_args():
"""Config for BGCF"""
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--dataset", type=str, default="Beauty")
parser.add_argument("-dpath", "--datapath", type=str, default="./scripts/data_mr")
parser.add_argument("-de", "--device", type=str, default='0')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--Ks', type=list, default=[5, 10, 20, 100])
parser.add_argument('--test_ratio', type=float, default=0.2)
parser.add_argument('--val_ratio', type=float, default=None)
parser.add_argument('-w', '--workers', type=int, default=10)
parser.add_argument("-eps", "--epsilon", type=float, default=1e-8)
parser.add_argument("-lr", "--learning_rate", type=float, default=1e-3)
parser.add_argument("-l2", "--l2", type=float, default=0.03)
parser.add_argument("-wd", "--weight_decay", type=float, default=0.01)
parser.add_argument("-act", "--activation", type=str, default='tanh', choices=['relu', 'tanh'])
parser.add_argument("-ndrop", "--neighbor_dropout", type=list, default=[0.0, 0.2, 0.3])
parser.add_argument("-log", "--log_name", type=str, default='test')
parser.add_argument("-e", "--num_epoch", type=int, default=600)
parser.add_argument('-input', '--input_dim', type=int, default=64, choices=[64, 128])
parser.add_argument("-b", "--batch_pairs", type=int, default=5000)
parser.add_argument('--eval_interval', type=int, default=20)
parser.add_argument("-neg", "--num_neg", type=int, default=10)
parser.add_argument('-max', '--max_degree', type=str, default='[128,128]')
parser.add_argument("-g1", "--raw_neighs", type=int, default=40)
parser.add_argument("-g2", "--gnew_neighs", type=int, default=20)
parser.add_argument("-emb", "--embedded_dimension", type=int, default=64)
parser.add_argument('-dist', '--distance', type=str, default='iou')
parser.add_argument('--dist_reg', type=float, default=0.003)
parser.add_argument('-ng', '--num_graphs', type=int, default=5)
parser.add_argument('-geps', '--graph_epsilon', type=float, default=0.01)
return parser.parse_args()

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
preprocess raw data; generate batched data and sample neighbors on graph for training and test;
Amazon Beauty datasets are supported by our example, the original versions of these datasets are as follows:
@article{Amazon Beauty,
title = {Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering},
author = {R. He, J. McAuley},
journal = {WWW},
year = {2016},
url = {http://jmcauley.ucsd.edu/data/amazon}
}
"""
import numpy as np
import mindspore.dataset as ds
class RandomBatchedSampler(ds.Sampler):
"""RandomBatchedSampler generate random sequence without replacement in a batched manner"""
sampled_graph_index = 0
def __init__(self, index_range, num_edges_per_sample):
super().__init__()
self.index_range = index_range
self.num_edges_per_sample = num_edges_per_sample
def __iter__(self):
self.sampled_graph_index += 1
indices = [i for i in range(self.index_range)]
np.random.shuffle(indices)
for i in range(0, self.index_range, self.num_edges_per_sample):
if i + self.num_edges_per_sample <= self.index_range:
result = indices[i: i + self.num_edges_per_sample]
result.append(self.sampled_graph_index)
yield result
class TrainGraphDataset():
"""Sample node neighbors on graphs for training"""
def __init__(self, train_graph, sampled_graphs, batch_num, num_samples, num_bgcn_neigh, num_neg):
self.g = train_graph
self.batch_num = batch_num
self.sampled_graphs = sampled_graphs
self.sampled_graph_num = len(sampled_graphs)
self.num_samples = num_samples
self.num_bgcn_neigh = num_bgcn_neigh
self.num_neg = num_neg
def __len__(self):
return self.g.graph_info()['edge_num'][0] // self.batch_num
def __getitem__(self, index):
"""
Sample negative items with their neighbors, user neighbors, pos item neighbors
based on the user-item pairs
"""
sampled_graph_index = index[-1] % self.sampled_graph_num
index = index[0:-1]
train_graph = self.g
sampled_graph = self.sampled_graphs[sampled_graph_index]
rating = train_graph.get_nodes_from_edges(index.astype(np.int32))
users = rating[:, 0]
u_group_nodes = train_graph.get_sampled_neighbors(
node_list=users, neighbor_nums=[1], neighbor_types=[0])
pos_users = u_group_nodes[:, 1]
u_group_nodes = np.concatenate((users, pos_users), axis=0)
u_group_nodes = u_group_nodes.reshape(-1,).tolist()
u_neighs = train_graph.get_sampled_neighbors(
node_list=u_group_nodes, neighbor_nums=[self.num_samples], neighbor_types=[1])
u_neighs = u_neighs[:, 1:]
u_gnew_neighs = sampled_graph.get_sampled_neighbors(
node_list=u_group_nodes, neighbor_nums=[self.num_bgcn_neigh], neighbor_types=[1])
u_gnew_neighs = u_gnew_neighs[:, 1:]
items = rating[:, 1]
i_group_nodes = train_graph.get_sampled_neighbors(
node_list=items, neighbor_nums=[1], neighbor_types=[1])
pos_items = i_group_nodes[:, 1]
i_group_nodes = np.concatenate((items, pos_items), axis=0)
i_group_nodes = i_group_nodes.reshape(-1,).tolist()
i_neighs = train_graph.get_sampled_neighbors(
node_list=i_group_nodes, neighbor_nums=[self.num_samples], neighbor_types=[0])
i_neighs = i_neighs[:, 1:]
i_gnew_neighs = sampled_graph.get_sampled_neighbors(
node_list=i_group_nodes, neighbor_nums=[self.num_bgcn_neigh], neighbor_types=[0])
i_gnew_neighs = i_gnew_neighs[:, 1:]
neg_item_id = train_graph.get_neg_sampled_neighbors(
node_list=users, neg_neighbor_num=self.num_neg, neg_neighbor_type=1)
neg_item_id = neg_item_id[:, 1:]
neg_group_nodes = neg_item_id.reshape(-1,)
neg_neighs = train_graph.get_sampled_neighbors(
node_list=neg_group_nodes, neighbor_nums=[self.num_samples], neighbor_types=[0])
neg_neighs = neg_neighs[:, 1:]
neg_gnew_neighs = sampled_graph.get_sampled_neighbors(
node_list=neg_group_nodes, neighbor_nums=[self.num_bgcn_neigh], neighbor_types=[0])
neg_gnew_neighs = neg_gnew_neighs[:, 1:]
return users, items, neg_item_id, pos_users, pos_items, u_group_nodes, u_neighs, u_gnew_neighs, \
i_group_nodes, i_neighs, i_gnew_neighs, neg_group_nodes, neg_neighs, neg_gnew_neighs
class TestGraphDataset():
"""Sample node neighbors on graphs for test"""
def __init__(self, g, sampled_graphs, num_samples, num_bgcn_neigh, num_neg):
self.g = g
self.sampled_graphs = sampled_graphs
self.sampled_graph_index = 0
self.num_samples = num_samples
self.num_bgcn_neigh = num_bgcn_neigh
self.num_neg = num_neg
self.num_user = self.g.graph_info()["node_num"][0]
self.num_item = self.g.graph_info()["node_num"][1]
def random_select_sampled_graph(self):
self.sampled_graph_index = np.random.randint(len(self.sampled_graphs))
def get_user_sapmled_neighbor(self):
"""Sample all users neighbors for test"""
users = np.arange(self.num_user, dtype=np.int32)
u_neighs = self.g.get_sampled_neighbors(
node_list=users, neighbor_nums=[self.num_samples], neighbor_types=[1])
u_neighs = u_neighs[:, 1:]
sampled_graph = self.sampled_graphs[self.sampled_graph_index]
u_gnew_neighs = sampled_graph.get_sampled_neighbors(
node_list=users, neighbor_nums=[self.num_bgcn_neigh], neighbor_types=[1])
u_gnew_neighs = u_gnew_neighs[:, 1:]
return u_neighs, u_gnew_neighs
def get_item_sampled_neighbor(self):
"""Sample all items neighbors for test"""
items = np.arange(self.num_user, self.num_user + self.num_item, dtype=np.int32)
i_neighs = self.g.get_sampled_neighbors(
node_list=items, neighbor_nums=[self.num_samples], neighbor_types=[0])
i_neighs = i_neighs[:, 1:]
sampled_graph = self.sampled_graphs[self.sampled_graph_index]
i_gnew_neighs = sampled_graph.get_sampled_neighbors(
node_list=items, neighbor_nums=[self.num_bgcn_neigh], neighbor_types=[0])
i_gnew_neighs = i_gnew_neighs[:, 1:]
return i_neighs, i_gnew_neighs
def load_graph(data_path):
"""Load train graph, test graph and sampled graph"""
train_graph = ds.GraphData(
data_path + "/train_mr", num_parallel_workers=8)
test_graph = ds.GraphData(
data_path + "/test_mr", num_parallel_workers=8)
sampled_graph_list = []
for i in range(0, 5):
sampled_graph = ds.GraphData(
data_path + "/sampled" + str(i) + "_mr", num_parallel_workers=8)
sampled_graph_list.append(sampled_graph)
return train_graph, test_graph, sampled_graph_list
def create_dataset(train_graph, sampled_graph_list, batch_size=32, repeat_size=1, num_samples=40, num_bgcn_neigh=20,
num_neg=10):
"""Data generator for training"""
edge_num = train_graph.graph_info()['edge_num'][0]
out_column_names = ["users", "items", "neg_item_id", "pos_users", "pos_items", "u_group_nodes", "u_neighs",
"u_gnew_neighs", "i_group_nodes", "i_neighs", "i_gnew_neighs", "neg_group_nodes",
"neg_neighs", "neg_gnew_neighs"]
train_graph_dataset = TrainGraphDataset(
train_graph, sampled_graph_list, batch_size, num_samples, num_bgcn_neigh, num_neg)
dataset = ds.GeneratorDataset(source=train_graph_dataset, column_names=out_column_names,
sampler=RandomBatchedSampler(edge_num, batch_size), num_parallel_workers=8)
dataset = dataset.repeat(repeat_size)
return dataset

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Recommendation metrics
"""
import math
import heapq
from multiprocessing import Pool
import numpy as np
from src.utils import convert_item_id
def ndcg_k(actual, predicted, topk):
"""Calculates the normalized discounted cumulative gain at k"""
idcg = idcg_k(actual, topk)
res = 0
dcg_k = sum([int(predicted[j] in set(actual)) / math.log(j + 2, 2) for j in range(topk)])
res += dcg_k / idcg
return res
def idcg_k(actual, k):
"""Calculates the ideal discounted cumulative gain at k"""
res = sum([1.0 / math.log(i + 2, 2) for i in range(min(k, len(actual)))])
return 1.0 if not res else res
def recall_at_k_2(r, k, all_pos_num):
"""Calculates the recall at k"""
r = np.asfarray(r)[:k]
return np.sum(r) / all_pos_num
def novelty_at_k(topk_items, item_degree_dict, num_user, k):
"""Calculate the novelty at k"""
avg_nov = []
for item in topk_items[:k]:
avg_nov.append(-np.log2(item_degree_dict[item] / num_user))
return np.mean(avg_nov)
def ranklist_by_heapq(user_pos_test, test_items, rating, Ks):
"""Return the n largest score from the item_score by heap algorithm"""
item_score = {}
for i in test_items:
item_score[i] = rating[i]
K_max = max(Ks)
K_max_item_score = heapq.nlargest(K_max, item_score, key=item_score.get)
r = []
for i in K_max_item_score:
if i in user_pos_test:
r.append(1)
else:
r.append(0)
return r, K_max_item_score
def get_performance(user_pos_test, r, K_max_item, item_degree_dict, num_user, Ks):
"""Wraps the model metrics"""
recall, ndcg, novelty = [], [], []
for K in Ks:
recall.append(recall_at_k_2(r, K, len(user_pos_test)))
ndcg.append(ndcg_k(user_pos_test, K_max_item, K))
novelty.append(novelty_at_k(K_max_item, item_degree_dict, num_user, K))
return {'recall': np.array(recall), 'ndcg': np.array(ndcg), 'nov': np.array(novelty)}
class BGCFEvaluate:
"""
Evaluate the model recommendation performance
"""
def __init__(self, parser, train_graph, test_graph, Ks):
self.num_user = train_graph.graph_info()["node_num"][0]
self.num_item = train_graph.graph_info()["node_num"][1]
self.Ks = Ks
self.test_set = []
self.train_set = []
for i in range(0, self.num_user):
train_item = train_graph.get_all_neighbors(node_list=[i], neighbor_type=1)
train_item = train_item[1:]
self.train_set.append(train_item)
for i in range(0, self.num_user):
test_item = test_graph.get_all_neighbors(node_list=[i], neighbor_type=1)
test_item = test_item[1:]
self.test_set.append(test_item)
self.train_set = convert_item_id(self.train_set, self.num_user).tolist()
self.test_set = convert_item_id(self.test_set, self.num_user).tolist()
self.item_deg_dict = {}
self.item_full_set = []
for i in range(self.num_user, self.num_user + self.num_item):
train_users = train_graph.get_all_neighbors(node_list=[i], neighbor_type=0)
train_users = train_users.tolist()
if isinstance(train_users, int):
train_users = []
else:
train_users = train_users[1:]
self.item_deg_dict[i - self.num_user] = len(train_users)
test_users = test_graph.get_all_neighbors(node_list=[i], neighbor_type=0)
test_users = test_users.tolist()
if isinstance(test_users, int):
test_users = []
else:
test_users = test_users[1:]
self.item_full_set.append(train_users + test_users)
def test_one_user(self, x):
"""Calculate one user metrics"""
rating = x[0]
u = x[1]
training_items = self.train_set[u]
user_pos_test = self.test_set[u]
all_items = set(range(self.num_item))
test_items = list(all_items - set(training_items))
r, k_max_items = ranklist_by_heapq(user_pos_test, test_items, rating, self.Ks)
return get_performance(user_pos_test, r, k_max_items, self.item_deg_dict, self.num_user, self.Ks), \
[k_max_items[:self.Ks[x]] for x in range(len(self.Ks))]
def eval_with_rep(self, user_rep, item_rep, parser):
"""Evaluation with user and item rep"""
result = {'recall': np.zeros(len(self.Ks)), 'ndcg': np.zeros(len(self.Ks)),
'nov': np.zeros(len(self.Ks))}
pool = Pool(parser.workers)
user_indexes = np.arange(self.num_user)
rating_preds = user_rep @ item_rep.transpose()
user_rating_uid = zip(rating_preds, user_indexes)
all_result = pool.map(self.test_one_user, user_rating_uid)
top20 = []
for re in all_result:
result['recall'] += re[0]['recall'] / self.num_user
result['ndcg'] += re[0]['ndcg'] / self.num_user
result['nov'] += re[0]['nov'] / self.num_user
top20.append(re[1][2])
pool.close()
sedp = [[] for i in range(len(self.Ks) - 1)]
num_all_links = np.sum([len(x) for x in self.item_full_set])
for k in range(len(self.Ks) - 1):
for u in range(self.num_user):
diff = []
pred_items_at_k = all_result[u][1][k]
for item in pred_items_at_k:
if item in self.test_set[u]:
avg_prob_all_user = len(self.item_full_set[item]) / num_all_links
diff.append(max((self.Ks[k] - pred_items_at_k.index(item) - 1)
/ (self.Ks[k] - 1) - avg_prob_all_user, 0))
one_user_sedp = sum(diff) / self.Ks[k]
sedp[k].append(one_user_sedp)
sedp = np.array(sedp).mean(1)
return result['recall'].tolist(), result['ndcg'].tolist(), \
[sedp[1], sedp[2]], result['nov'].tolist()

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 for training BGCF"""
import os
import sys
import glob
import shutil
import pickle as pkl
import numpy as np
def load_pickle(path, name):
"""Load pickle"""
with open(path + name, 'rb') as f:
return pkl.load(f, encoding='latin1')
class BGCFLogger:
"""log the output metrics"""
def __init__(self, logname, now, foldername, copy):
self.terminal = sys.stdout
self.file = None
path = os.path.join(foldername, logname, now)
os.makedirs(path)
if copy:
filenames = glob.glob('*.py')
for filename in filenames:
shutil.copy(filename, path)
def open(self, file, mode=None):
if mode is None:
mode = 'w'
self.file = open(file, mode)
def write(self, message, is_terminal=True, is_file=True):
"""Write log"""
if '\r' in message:
is_file = False
if is_terminal:
self.terminal.write(message)
self.terminal.flush()
if is_file:
self.file.write(message)
self.file.flush()
def convert_item_id(item_list, num_user):
"""Convert the graph node id into item id"""
return np.array(item_list) - num_user

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
BGCF training script.
"""
import os
import datetime
from mindspore import Tensor
import mindspore.context as context
from mindspore.common import dtype as mstype
from mindspore.train.serialization import save_checkpoint, load_checkpoint
from src.bgcf import BGCF
from src.metrics import BGCFEvaluate
from src.config import parser_args
from src.utils import BGCFLogger, convert_item_id
from src.callback import ForwardBGCF, TrainBGCF, TestBGCF
from src.dataset import load_graph, create_dataset, TestGraphDataset
def train_and_eval():
"""Train and eval"""
num_user = train_graph.graph_info()["node_num"][0]
num_item = train_graph.graph_info()["node_num"][1]
num_pairs = train_graph.graph_info()['edge_num'][0]
bgcfnet = BGCF([parser.input_dim, num_user, num_item],
parser.embedded_dimension,
parser.activation,
parser.neighbor_dropout,
num_user,
num_item,
parser.input_dim)
train_net = TrainBGCF(bgcfnet, parser.num_neg, parser.l2, parser.learning_rate,
parser.epsilon, parser.dist_reg)
train_net.set_train(True)
eval_class = BGCFEvaluate(parser, train_graph, test_graph, parser.Ks)
itr = train_ds.create_dict_iterator(parser.num_epoch)
num_iter = int(num_pairs / parser.batch_pairs)
for _epoch in range(1, parser.num_epoch + 1):
iter_num = 1
for data in itr:
u_id = Tensor(data["users"], mstype.int32)
pos_item_id = Tensor(convert_item_id(data["items"], num_user), mstype.int32)
neg_item_id = Tensor(convert_item_id(data["neg_item_id"], num_user), mstype.int32)
pos_users = Tensor(data["pos_users"], mstype.int32)
pos_items = Tensor(convert_item_id(data["pos_items"], num_user), mstype.int32)
u_group_nodes = Tensor(data["u_group_nodes"], mstype.int32)
u_neighs = Tensor(convert_item_id(data["u_neighs"], num_user), mstype.int32)
u_gnew_neighs = Tensor(convert_item_id(data["u_gnew_neighs"], num_user), mstype.int32)
i_group_nodes = Tensor(convert_item_id(data["i_group_nodes"], num_user), mstype.int32)
i_neighs = Tensor(data["i_neighs"], mstype.int32)
i_gnew_neighs = Tensor(data["i_gnew_neighs"], mstype.int32)
neg_group_nodes = Tensor(convert_item_id(data["neg_group_nodes"], num_user), mstype.int32)
neg_neighs = Tensor(data["neg_neighs"], mstype.int32)
neg_gnew_neighs = Tensor(data["neg_gnew_neighs"], mstype.int32)
train_loss = train_net(u_id,
pos_item_id,
neg_item_id,
pos_users,
pos_items,
u_group_nodes,
u_neighs,
u_gnew_neighs,
i_group_nodes,
i_neighs,
i_gnew_neighs,
neg_group_nodes,
neg_neighs,
neg_gnew_neighs)
if iter_num == num_iter:
print('Epoch', '%03d' % _epoch, 'iter', '%02d' % iter_num,
'loss',
'{}'.format(train_loss))
iter_num += 1
if _epoch % parser.eval_interval == 0:
if os.path.exists("ckpts/bgcf.ckpt"):
os.remove("ckpts/bgcf.ckpt")
save_checkpoint(bgcfnet, "ckpts/bgcf.ckpt")
bgcfnet_test = BGCF([parser.input_dim, num_user, num_item],
parser.embedded_dimension,
parser.activation,
[0.0, 0.0, 0.0],
num_user,
num_item,
parser.input_dim)
load_checkpoint("ckpts/bgcf.ckpt", net=bgcfnet_test)
forward_net = ForwardBGCF(bgcfnet_test)
user_reps, item_reps = TestBGCF(forward_net, num_user, num_item, parser.input_dim, test_graph_dataset)
test_recall_bgcf, test_ndcg_bgcf, \
test_sedp, test_nov = eval_class.eval_with_rep(user_reps, item_reps, parser)
if parser.log_name:
log.write(
'epoch:%03d, recall_@10:%.5f, recall_@20:%.5f, ndcg_@10:%.5f, ndcg_@20:%.5f, '
'sedp_@10:%.5f, sedp_@20:%.5f, nov_@10:%.5f, nov_@20:%.5f\n' % (_epoch,
test_recall_bgcf[1],
test_recall_bgcf[2],
test_ndcg_bgcf[1],
test_ndcg_bgcf[2],
test_sedp[0],
test_sedp[1],
test_nov[1],
test_nov[2]))
else:
print('epoch:%03d, recall_@10:%.5f, recall_@20:%.5f, ndcg_@10:%.5f, ndcg_@20:%.5f, '
'sedp_@10:%.5f, sedp_@20:%.5f, nov_@10:%.5f, nov_@20:%.5f\n' % (_epoch,
test_recall_bgcf[1],
test_recall_bgcf[2],
test_ndcg_bgcf[1],
test_ndcg_bgcf[2],
test_sedp[0],
test_sedp[1],
test_nov[1],
test_nov[2]))
if __name__ == "__main__":
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
save_graphs=False)
parser = parser_args()
train_graph, test_graph, sampled_graph_list = load_graph(parser.datapath)
train_ds = create_dataset(train_graph, sampled_graph_list, batch_size=parser.batch_pairs)
test_graph_dataset = TestGraphDataset(train_graph, sampled_graph_list, num_samples=parser.raw_neighs,
num_bgcn_neigh=parser.gnew_neighs,
num_neg=parser.num_neg)
if parser.log_name:
now = datetime.datetime.now().strftime("%b_%d_%H_%M_%S")
name = "bgcf" + '-' + parser.log_name + '-' + parser.dataset
log_save_path = './log-files/' + name + '/' + now
log = BGCFLogger(logname=name, now=now, foldername='log-files', copy=False)
log.open(log_save_path + '/log.train.txt', mode='a')
for arg in vars(parser):
log.write(arg + '=' + str(getattr(parser, arg)) + '\n')
else:
for arg in vars(parser):
print(arg + '=' + str(getattr(parser, arg)))
train_and_eval()

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ==============================================================================
"""
Preprocess data.
"""
import time
import random
import argparse
import pickle as pkl
from copy import deepcopy
from functools import partial
from multiprocessing import Pool
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from sklearn.neighbors import kneighbors_graph
from sklearn.model_selection import train_test_split
def load_pickle(path, name):
"""Load pickle"""
with open(path + name, 'rb') as f:
return pkl.load(f, encoding='latin1')
def generate_inverse_mapping(data_list):
"""Generate inverse id map"""
ds_matrix_mapping = dict()
for inner_id, true_id in enumerate(data_list):
ds_matrix_mapping[true_id] = inner_id
return ds_matrix_mapping
def convert_to_inner_index(user_records, user_mapping, item_mapping):
"""Convert real id to inner id"""
inner_user_records = []
user_inverse_mapping = generate_inverse_mapping(user_mapping)
item_inverse_mapping = generate_inverse_mapping(item_mapping)
for user_id, _ in enumerate(user_mapping):
real_user_id = user_mapping[user_id]
item_list = list(user_records[real_user_id])
for index, real_item_id in enumerate(item_list):
item_list[index] = item_inverse_mapping[real_item_id]
inner_user_records.append(item_list)
return inner_user_records, user_inverse_mapping, item_inverse_mapping
def split_data_randomly(user_records, test_ratio, seed=0):
"""Split data"""
print('seed %d ' % seed)
train_set = []
test_set = []
for _, item_list in enumerate(user_records):
tmp_train_sample, tmp_test_sample = train_test_split(
item_list, test_size=test_ratio, random_state=seed)
train_sample = []
for place in item_list:
if place not in tmp_test_sample:
train_sample.append(place)
test_sample = []
for place in tmp_test_sample:
if place not in tmp_train_sample:
test_sample.append(place)
train_set.append(train_sample)
test_set.append(test_sample)
return train_set, test_set
def create_adj_matrix(train_matrix):
"""Create adj matrix"""
user2item, item2user = {}, {}
user_item_ratings = train_matrix.toarray()
for i, _ in enumerate(user_item_ratings):
neigh_items = np.where(user_item_ratings[i] != 0)[0].tolist()
user2item[i] = set(neigh_items)
item_user_ratings = user_item_ratings.transpose()
for j, _ in enumerate(item_user_ratings):
neigh_users = np.where(item_user_ratings[j] != 0)[0].tolist()
item2user[j] = set(neigh_users)
return user2item, item2user
def generate_rating_matrix(train_set, num_users, num_items, user_shift=0, item_shift=0):
"""Generate rating matrix"""
row = []
col = []
data = []
for user_id, article_list in enumerate(train_set):
for article in article_list:
row.append(user_id + user_shift)
col.append(article + item_shift)
data.append(1)
row = np.array(row)
col = np.array(col)
data = np.array(data)
rating_matrix = csr_matrix(
(data, (row, col)), shape=(num_users, num_items))
return rating_matrix
def flatten(distance, adj, thre=10):
"""Flatten the distance matrix for the smoother sampling"""
print('start flattening the dataset with threshold = {}'.format(thre))
top_ids = np.argsort(distance, 1)[:, -thre:]
flat_distance = np.zeros_like(distance)
values = 1 / thre
for i, _ in enumerate(flat_distance):
adj_len = len(adj[i])
if adj_len == 0 or adj_len > thre:
flat_distance[i][top_ids[i]] = values
else:
flat_distance[i][top_ids[i][thre - adj_len:]] = 1 / adj_len
return flat_distance
def sample_graph_copying(node_neighbors_dict, distances, epsilon=0.01, seed=0, set_seed=False):
"""node copying node by node"""
if set_seed:
np.random.seed(seed)
random.seed(seed)
N = len(distances)
sampled_graph = dict()
nodes = np.arange(0, N).astype(np.int)
for i in range(N):
if random.uniform(0, 1) < 1 - epsilon:
sampled_node = np.random.choice(nodes, 1, p=distances[i])
else:
sampled_node = [i]
sampled_graph[i] = node_neighbors_dict[sampled_node[0]]
return sampled_graph
def remove_infrequent_users(data, min_counts=10):
"""Remove infrequent users"""
df = deepcopy(data)
counts = df['user_id'].value_counts()
df = df[df["user_id"].isin(counts[counts >= min_counts].index)]
print("users with < {} interactoins are removed".format(min_counts))
return df
def remove_infrequent_items(data, min_counts=5):
"""Remove infrequent items"""
df = deepcopy(data)
counts = df['item_id'].value_counts()
df = df[df["item_id"].isin(counts[counts >= min_counts].index)]
print("items with < {} interactoins are removed".format(min_counts))
return df
def save_obj(obj, data_path, name):
"""Save object"""
with open(data_path + "/" + name + '.pkl', 'wb') as f:
pkl.dump(obj, f)
def preprocess_data(data_path, data_name):
"""Preprocess data"""
rating_file = 'ratings_{}.csv'.format(data_name)
col_names = ['user_id', 'item_id', 'rating', 'timestamp']
data_records = pd.read_csv(data_path + "/" + rating_file, sep=',', names=col_names, engine='python')
data_records.loc[data_records.rating != 0, 'rating'] = 1
data_records = data_records[data_records.rating > 0]
filtered_data = remove_infrequent_users(data_records, 10)
filtered_data = remove_infrequent_items(filtered_data, 10)
data = filtered_data.groupby('user_id')['item_id'].apply(list)
unique_data = filtered_data.groupby('user_id')['item_id'].nunique()
data = data[unique_data[unique_data >= 5].index]
user_item_dict = data.to_dict()
user_mapping = []
item_set = set()
for user_id, item_list in data.iteritems():
user_mapping.append(user_id)
for item_id in item_list:
item_set.add(item_id)
item_mapping = list(item_set)
return user_item_dict, user_mapping, item_mapping
def iou_set(set1, set2):
"""Calculate iou_set """
union = set1.union(set2)
return len(set1.intersection(set2)) / len(union) if union else 0
def build_func(train_set, data):
"""Build function"""
res = []
res.append([iou_set(set(train_set), x) for x in data.values()])
return res
def build_distance_mp_map(train_set, u_adj_list, v_adj_list, num_workers=5, tag='user', norm=True):
"""Build distance matrix"""
start = time.time()
pool = Pool(processes=num_workers)
if tag == 'user':
results = pool.map_async(partial(build_func, data=u_adj_list), train_set)
if tag == 'item':
results = pool.map_async(partial(build_func, data=v_adj_list), train_set)
results.wait()
pool.close()
pool.join()
distances = np.array(results.get()).squeeze(1)
np.fill_diagonal(distances, 0)
print('=== info: elapsed time with mp for building ' + tag + ' distance matrix: ', time.time() - start)
for i, _ in enumerate(distances):
if sum(distances[i]) == 0:
distances[i] = 1.
if norm:
distances = distances / np.sum(distances, axis=1).reshape(-1, 1)
distances.astype(np.float16)
return distances
def trans(src_path, data_name, out_path):
"""Convert into MindSpore data"""
print('=== loading datasets')
user_records, user_mapping, item_mapping = preprocess_data(src_path, data_name)
inner_data_records, user_inverse_mapping, \
item_inverse_mapping = convert_to_inner_index(
user_records, user_mapping, item_mapping)
test_ratio = 0.2
train_set, test_set = split_data_randomly(
inner_data_records, test_ratio=test_ratio, seed=0)
train_matrix = generate_rating_matrix(
train_set, len(user_mapping), len(item_mapping))
u_adj_list, v_adj_list = create_adj_matrix(train_matrix)
num_user, num_item = train_matrix.shape
print('=== building user-user grpah and item-item graph')
num_self_neigh = 10
user_user_graph = kneighbors_graph(train_matrix, num_self_neigh,
mode='connectivity', include_self=False)
user_self_neighs = user_user_graph.tocoo().col
user_self_neighs = np.array(np.array_split(user_self_neighs, num_user)).tolist()
item_item_graph = kneighbors_graph(train_matrix.transpose(), num_self_neigh,
mode='connectivity', include_self=False)
item_self_neighs = item_item_graph.tocoo().col
item_self_neighs = np.array(np.array_split(item_self_neighs, num_item)).tolist()
assert len(train_set) == len(user_self_neighs)
user_distances = build_distance_mp_map(train_set, u_adj_list, v_adj_list, num_workers=10, tag='user', norm=True)
user_distances = flatten(user_distances, u_adj_list,
thre=10)
item_start_id = num_user
user_file = out_path + "/user.csv"
item_file = out_path + "/item.csv"
train_file = out_path + "/rating_train.csv"
test_file = out_path + "/rating_test.csv"
with open(user_file, 'a+') as user_f:
for k in user_inverse_mapping:
print(k + ',' + str(user_inverse_mapping[k]), file=user_f)
with open(item_file, 'a+') as item_f:
for k in item_inverse_mapping:
print(k + ',' + str(item_inverse_mapping[k] + item_start_id), file=item_f)
with open(train_file, 'a+') as train_f:
print("src_id,dst_id,type", file=train_f)
for user in u_adj_list:
for item in sorted(list(u_adj_list[user])):
print(str(user) + ',' + str(item + item_start_id) + ',0', file=train_f)
for item in v_adj_list:
for user in v_adj_list[item]:
print(str(item + item_start_id) + ',' + str(user) + ',1', file=train_f)
src_user = 0
for users in user_self_neighs:
for dst_user in users:
print(str(src_user) + ',' + str(dst_user) + ',2', file=train_f)
src_user += 1
src_item = 0
for items in item_self_neighs:
for dst_item in items:
print(str(src_item + item_start_id) + ',' + str(dst_item + item_start_id) + ',3', file=train_f)
src_item += 1
with open(test_file, 'a+') as test_f:
print("src_id,dst_id,type", file=test_f)
user = 0
for items in test_set:
for item in items:
print(str(user) + ',' + str(item + item_start_id) + ',0', file=test_f)
user += 1
user = 0
for items in test_set:
for item in items:
print(str(item + item_start_id) + ',' + str(user) + ',1', file=test_f)
user += 1
print('start generating sampled graphs...')
num_graphs = 5
for i in range(num_graphs):
print('=== info: sampling graph {} / {}'.format(i + 1, num_graphs))
sampled_user_graph = sample_graph_copying(node_neighbors_dict=u_adj_list,
distances=user_distances)
print('avg. sampled user-item graph degree: ',
np.mean([len(x) for x in [*sampled_user_graph.values()]]))
sampled_item_graph = {x: set() for x in range(num_item)}
for k, items in sampled_user_graph.items():
for x in items:
sampled_item_graph[x].add(k)
print('avg. sampled item-user graph degree: ',
np.mean([len(x) for x in [*sampled_item_graph.values()]]))
sampled_file = out_path + "/rating_sampled" + str(i) + ".csv"
with open(sampled_file, 'a+') as sampled_f:
print("src_id,dst_id,type", file=sampled_f)
for user in sampled_user_graph:
for item in sampled_user_graph[user]:
print(str(user) + ',' + str(item + item_start_id) + ',0', file=sampled_f)
for item in sampled_item_graph:
for user in sampled_item_graph[item]:
print(str(item + item_start_id) + ',' + str(user) + ',1', file=sampled_f)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Converting Data')
parser.add_argument('--src_path', type=str, default="/tmp/",
help='source data directory')
parser.add_argument('--out_path', type=str, default="/tmp/",
help='output directory')
args = parser.parse_args()
trans(args.src_path + "/", "Beauty", args.out_path + "/")

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ==============================================================================
"""
User-defined API for MindRecord GNN writer.
"""
import os
import csv
args = os.environ['graph_api_args'].split(':')
USER_FILE = args[0]
ITEM_FILE = args[1]
RATING_FILE = args[2]
node_profile = (0, [], [])
edge_profile = (0, [], [])
def yield_nodes(task_id=0):
"""
Generate node data
Yields:
data (dict): data row which is dict.
"""
print("Node task is {}".format(task_id))
with open(USER_FILE) as user_file:
user_reader = csv.reader(user_file, delimiter=',')
line_count = 0
for row in user_reader:
node = {'id': int(row[1]), 'type': 0}
yield node
line_count += 1
print('Processed {} lines for users.'.format(line_count))
with open(ITEM_FILE) as item_file:
item_reader = csv.reader(item_file, delimiter=',')
line_count = 0
for row in item_reader:
node = {'id': int(row[1]), 'type': 1,}
yield node
line_count += 1
print('Processed {} lines for items.'.format(line_count))
def yield_edges(task_id=0):
"""
Generate edge data
Yields:
data (dict): data row which is dict.
"""
print("Edge task is {}".format(task_id))
with open(RATING_FILE) as rating_file:
rating_reader = csv.reader(rating_file, delimiter=',')
line_count = 0
for row in rating_reader:
if line_count == 0:
line_count += 1
continue
edge = {'id': line_count - 1, 'src_id': int(row[0]), 'dst_id': int(row[1]), 'type': int(row[2])}
yield edge
line_count += 1
print('Processed {} lines for edges.'.format(line_count))