Awesome
Social-Aware Time Series Imputation
This project implements the social-aware time series imputation method proposed in [1], which is an imputation algorithm for time series data in the social network.
Testing
This project is implemented in Python 3.6
Dependency:
- Python 3.6. Version 3.6.4 has been tested.
- PyTorch. Version 0.4.0 has been tested. Note that the GPU support is encouraged as it greatly boosts training efficiency.
- Other Python modules. Some other Python module dependencies are listed in
requirements.txt
, which can be easily installed with pippip install -r requirements.txt
Testing the Project:
python impute.py -f data/data.npy -n data/network.pkl -o data/imputed_data.npy
Usage
Given an array of users' time series data and the social relationship among these users, this program can be used to replace the missing value in these time series data with reasonable values.
Input Format
The input files are expected to be two parts:
(1) data file: a numpy array (.npy) file which contains users' data shaped N * L * D, where N denotes the number of users, L denotes the sequence length and D denotes the number of channels at each time stamp. The missing data should be marked as -1 (or manually marked).
(2) social network file: a pickle file which contains the social network information formated as the adjacent list:
[[node0 's neighbors], [node1's neighbors],..., nodeN's neighbors]
e.g. [[1], [0,2,3], [1,3,4], [2]]
each node index is corresponding to the index of the row in the data array in (1).
See the sample data in the data
directory.
Output Format
The program outputs to a file named imputed_data.npy
which contains the data after imputation, i.e., the missing elements are replaced by reasonable values.
Main Script
The help of main script can be obtained by excuting command:
python impute.py -h
usage: impute.py [-h] [-f DATA_FILE] [-n SOCIAL_NETWORK] [-o OUTPUT_FILE]
[-m MISSING_MARKER] [-b BATCH_SIZE] [-e NUM_EPOCH]
[-s HIDDEN_SIZE] [-k DIM_MEMORY] [-l LEARNING_RATE]
[-d DROPOUT] [-r DECODER_LEARNING_RATIO] [-w WEIGHT_DECAY]
[--log]
optional arguments:
-h, --help show this help message and exit
-f DATA_FILE, --data_file DATA_FILE
path of input file
-n SOCIAL_NETWORK, --social_network SOCIAL_NETWORK
path of network file
-o OUTPUT_FILE, --output_file OUTPUT_FILE
path of output file
-m MISSING_MARKER, --missing_marker MISSING_MARKER
marker of missing elements, default value is -1
-b BATCH_SIZE, --batch_size BATCH_SIZE
the number of samples in each batch, default value is
256
-e NUM_EPOCH, --num_epoch NUM_EPOCH
number of epoch, default value is 200
-s HIDDEN_SIZE, --hidden_size HIDDEN_SIZE
size of hidden feature in LSTM, default value is 32
-k DIM_MEMORY, --dim_memory DIM_MEMORY
dimension of memory matrix, default value is 32
-l LEARNING_RATE, --learning_rate LEARNING_RATE
-d DROPOUT, --dropout DROPOUT
the dropout rate of output layers, default value is
0.8
-r DECODER_LEARNING_RATIO, --decoder_learning_ratio DECODER_LEARNING_RATIO
ratio between the learning rate of decoder and
encoder, default value is 10
-w WEIGHT_DECAY, --weight_decay WEIGHT_DECAY
--log print log information, you can see the train loss in
each epoch
Reference
[1] Zongtao, L; Yang, Y; Wei, H; Zhongyi, T; Ning, L and Fei, W, 2019, How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation, In WWW, 2019
@inproceedings{liu2019imputation,
title={How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation},
author={Zongtao Liu and Yang Yang and Wei Huang and Zhongyi Tang and Ning Li and Fei Wu},
booktitle={Proceedings of WWW},
year={2019}
}