Code Release

SmartLOC

This repository is the official PyTorch implementation of the SmartLoc reported in the paper:
SmartLOC: Indoor Localization with Smartphone Anchors for On-Demand Delivery.

Source Code

SmartLoc

Installation

Requirements: Python >= 3.5, Anaconda3

Update conda:
```
conda update -n base -c defaults conda
```

Install basic dependencies to virtual environment and activate it:

conda env create -f environment.yml
conda activate degnn-env

Install PyTorch >= 1.4.0 and torch-geometric >= 1.5.0

conda install pytorch=1.4.0 torchvision cudatoolkit=10.1 -c pytorch
pip install torch-scatter==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-sparse==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-cluster==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-spline-conv==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-geometric

The latest tested combination is: Python 3.8.2 + Pytorch 1.4.0 + torch-geometric 1.5.0.

Quick Start

```
python main.py
```

Usage Summary

Interface for SmartLOC framework [-h] [--data_path DATA_PATH] [--feature_path FEATURE_PATH] [--inshop_path INSHOP_PATH] [--THRE THRE] [--TIME_INTERVAL TIME_INTERVAL] [--test_date TEST_DATE] [--test_ratio TEST_RATIO] [--data_usage DATA_USAGE] [--cat_num CAT_NUM]
                                    [--loc_cat_num LOC_CAT_NUM] [--model {DE-GNN,GIN,GCN,GraphSAGE,GAT,PGNN}] [--layers LAYERS] [--hidden_features HIDDEN_FEATURES] [--feature FEATURE] [--metric {acc,auc}] [--directed DIRECTED] [--prop_depth PROP_DEPTH]
                                    [--use_degree USE_DEGREE] [--use_attributes USE_ATTRIBUTES] [--loc_layers LOC_LAYERS] [--lstm_num_layers LSTM_NUM_LAYERS] [--range RANGE] [--loc_hidden_features LOC_HIDDEN_FEATURES] [--rw_depth RW_DEPTH] [--max_sp MAX_SP]
                                    [--anchor_num ANCHOR_NUM] [--cat_embed_dim CAT_EMBED_DIM] [--seed SEED] [--gpu GPU] [--parallel] [--epoch EPOCH] [--loc_epoch LOC_EPOCH] [--batch_size BATCH_SIZE] [--bs BS] [--lr LR] [--loc_lr LOC_LR] [--optimizer OPTIMIZER]
                                    [--l2 L2] [--dropout DROPOUT] [--log_dir LOG_DIR] [--model_dir MODEL_DIR] [--result_dir RESULT_DIR] [--cache_dir CACHE_DIR] [--version_name VERSION_NAME] [--summary_file SUMMARY_FILE] [--debug] [--use_cache USE_CACHE]

Optinal Arguments

  -h, --help            show this help message and exit
  --data_path DATA_PATH
                    dataset path
  --feature_path FEATURE_PATH
                    feature path
  --inshop_path INSHOP_PATH
                    inshop model result path
  --THRE THRE           time diff filter
  --TIME_INTERVAL TIME_INTERVAL
                    time interval between two events
  --test_date TEST_DATE
                    test date
  --test_ratio TEST_RATIO
                    ratio of the test against whole
  --data_usage DATA_USAGE
                    use partial dataset
  --cat_num CAT_NUM     cat_num
  --loc_cat_num LOC_CAT_NUM
                    loc_cat_num
  --model {DE-GNN,GIN,GCN,GraphSAGE,GAT,PGNN}
                    model to use for merchant GNN
  --layers LAYERS       largest number of layers
  --hidden_features HIDDEN_FEATURES
                    hidden dimension
  --feature FEATURE     distance encoding category: shortest path or random walk (landing probabilities)
  --metric {acc,auc}    metric for evaluating performance
  --directed DIRECTED   (Currently unavailable) whether to treat the graph as directed
  --prop_depth PROP_DEPTH
                    propagation depth (number of hops) for one layer
  --use_degree USE_DEGREE
                    whether to use node degree as the initial feature
  --use_attributes USE_ATTRIBUTES
                    whether to use node attributes as the initial feature
  --loc_layers LOC_LAYERS
                    largest number of localization layers
  --lstm_num_layers LSTM_NUM_LAYERS
                    largest number of lstm layers
  --range RANGE         lstm range
  --loc_hidden_features LOC_HIDDEN_FEATURES
                    loc_hidden_features
  --rw_depth RW_DEPTH   random walk steps
  --max_sp MAX_SP       maximum distance to be encoded for shortest path feature
  --anchor_num ANCHOR_NUM
  --cat_embed_dim CAT_EMBED_DIM
  --seed SEED           seed to initialize all the random modules
  --gpu GPU             gpu id
  --parallel            (Currently unavailable) whether to use multi cpu cores to prepare data
  --epoch EPOCH         number of epochs for GNN to train
  --loc_epoch LOC_EPOCH
                    number of epochs for LOC to train
  --batch_size BATCH_SIZE
                    batch size
  --bs BS               minibatch size
  --lr LR               learning rate
  --loc_lr LOC_LR       learning rate of localization
  --optimizer OPTIMIZER
                    optimizer to use
  --l2 L2               l2 regularization weight
  --dropout DROPOUT     dropout rate
  --log_dir LOG_DIR     log directory
  --model_dir MODEL_DIR
                    model directory
  --result_dir RESULT_DIR
                    result directory
  --cache_dir CACHE_DIR
                    Cache directory
  --version_name VERSION_NAME
                    version name
  --summary_file SUMMARY_FILE
                    brief summary of training result
  --debug               whether to use debug mode
  --use_cache USE_CACHE
                    whether to use cache

P2Loc

This repository is the official PyTorch implementation of the P2Loc reported in the paper:
P2-Loc: A Person-2-Person Indoor Localization System in On-Demand Delivery.

Source Code

P2Loc

Installation

Requirements: Python >= 3.5, Anaconda3

Update conda:
```
conda update -n base -c defaults conda
```

Install basic dependencies to virtual environment and activate it:

conda env create -f environment.yml
conda activate degnn-env

Install PyTorch >= 1.4.0 and torch-geometric >= 1.5.0

conda install pytorch=1.4.0 torchvision cudatoolkit=10.1 -c pytorch
pip install torch-scatter==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-sparse==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-cluster==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-spline-conv==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
pip install torch-geometric

The latest tested combination is: Python 3.8.2 + Pytorch 1.4.0 + torch-geometric 1.5.0.

Quick Start

```
python main.py
```

Yi Ding

SmartLOC

Source Code

Installation

Quick Start

Usage Summary

Optinal Arguments

P2Loc

Source Code

Installation

Quick Start