├── LICENSE ├── README.md ├── baselines.py ├── checkpoints ├── GCN-LSTM_6_bs512model.pt ├── PAG_6_bs512_completed.pt ├── PAG_6_bs512_simplified.pt └── Proposed_6_bs512model.pt ├── datasets ├── SZ_districts │ ├── SZ_districts.cpg │ ├── SZ_districts.dbf │ ├── SZ_districts.prj │ ├── SZ_districts.sbn │ ├── SZ_districts.sbx │ ├── SZ_districts.shp │ └── SZ_districts.shx ├── SZweather20220619-20220718.xls ├── SZweather_Header.txt ├── Shenzhen.qgz ├── adj.csv ├── distance.csv ├── duration.csv ├── information.csv ├── occupancy.csv ├── price.csv ├── time.csv └── volume.csv ├── figs ├── PAG.png ├── map.png ├── map_v2.png ├── statistics.png └── urbanev.png ├── functions.py ├── learner.py ├── main.py ├── models.py ├── plot.py ├── requirements.txt └── results └── plot_data.csv /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 IntelligentSystemsLab 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Spatio-temporal EVCDP (Shenzhen) Datasets 2 | This project is dedicated to offering high-quality, real-world, open datasets for researching spatio-temporal electric vehicle (EV) charging demand in urban areas. We have publicly released two well-structured datasets: 3 | 1. **ST-EVCDP** (available in this repository): This dataset includes information on 18,061 public charging piles, covering a period of 30 days with data recorded at a minimum interval of 5 minutes. 4 | 2. **UrbanEV** (accessible at [GitHub - IntelligentSystemsLab/UrbanEV](https://github.com/IntelligentSystemsLab/UrbanEV)): This dataset comprises data from 24,798 public charging piles over six months, with data intervals of both 5 minutes and 1 hour. 5 | 6 | ## Updates 7 | * March 28, 2025: Our dataset paper, *UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction*, has been published online at [Scientific Data](https://www.nature.com/articles/s41597-025-04874-4)! 8 | * **Feb. 13, 2025: An updated ST-EVCDP-v2 is released at [UrbanEV](https://github.com/IntelligentSystemsLab/UrbanEV), which includes many detailed and processed charging data with a long time span of six months.** 9 | * Nov. 23, 2024: Update 'occupancy.csv', 'duration.csv', 'volume.csv', 'e_price.csv', and 's_price.csv' in ST-EVCDP-v2. 10 | * **Oct. 24, 2024: We have recently endeavored to forecast EV charging demand utilizing LLMs, i.e., ChatEV. The paper is now available at [TRD](https://www.sciencedirect.com/science/article/abs/pii/S1361920924004279?CMX_ID=&SIS_ID=&dgcid=STMJ_219742_AUTH_SERV_PA&utm_acid=285873158&utm_campaign=STMJ_219742_AUTH_SERV_PA&utm_in=DM517501&utm_medium=email&utm_source=AC), with the code at [Github-ChatEV](https://github.com/Quhaoh233/ChatEV).** 11 | * **Sep. 26, 2024: Our data analysis paper, which explores the relationship between price changes and demand, is now accessible at [Sustainable Cities and Society](https://www.sciencedirect.com/science/article/pii/S2210670724006607?casa_token=iZXxEsQ6voEAAAAA:D5MgoyJf3LNAHF_VKKiwFBG51CeKOE86SY974d0Sj_RLy6_o0D093PecRoWPO_rA8h5Tc85y8A).** 12 | * July 11, 2024: Update 'duration.csv' in ST-EVCDP-v2. 13 | * July 8, 2024: We uploaded meteorological data collected from two weather stations for ST-EVCDP-v2, see link below. 14 | * **July 2, 2024: We are excited to announce the release of the early access version of ST-EVCDP-v2! You can download the data from [Google Drive Link](https://drive.google.com/drive/folders/1sqOUEpMh8VMiJhrT-MOn5OsB1-KirUVq?usp=drive_link).** 15 | * May 30, 2024: The ST-EVCDP-V2 dataset is still being compiled... We will make every effort to release it as quickly as we can. 16 | * May 14, 2024: Our paper has been accepted by [IEEE T-ITS](https://ieeexplore.ieee.org/document/10539613)! 17 | * ST-EVCDP-Version2 is coming soon. The data will span from September 2022 to September 2023, with granularity to charging stations, including coordinates, numbers of chargers, occupancy, and price. Other information will also be gradually released after being desensitized for academic research purposes. 18 | * May 12, 2024: We uploaded the data of weather conditions in the studied areas, namely `SZweather20220619-20220718.csv` and `SZweather_Header.txt`. 19 | * March 15, 2024: We uploaded the data of charging duration and volume in the studied areas. 20 | 21 | ## Data Description 22 | 23 | ### ST-EVCDP 24 | The data used in this study is drawn from a publicly available mobile application, which provides the real-time availability of charging piles (i.e., idle or not). Within **Shenzhen**, China, a total of 18,061 public charging piles are covered during the studied period from 19 June to 18 July 2022 (30 days) with a minimum interval of 5 minutes and `8640 timestamps`. As shown in Figure 1, the city is constructed into a graph-structure data with `247 nodes` (traffic zones) and `1006 edges` (adjacent relationships). 25 | 26 | ![avatar](figs/map.png) Figure 1. Spatial distribution of the 18,061 public EV charging piles in ST-EVCDP. 27 | 28 | Besides, the pricing schemes for the studied charging piles are also collected. Among the 247 traffic zones, 57 of them (enclosed in red lines) deploy time-based pricing schemes, while others use fixed ones. More statistical details are illustrated in the following table. 29 | 30 | ![avatar](figs/statistics.png) 31 | 32 | ### UrbanEV (i.e., ST-EVCDP-v2) 33 | Expanding on the foundation of ST-EVCDP, we have gathered an extensive dataset called UrbanEV, specifically tailored for EV-related research. This dataset covers a timeframe of **six month**, spanning from September 2022 to Feburary 2023, which includes comprehensive information such as coordinates, charging occupancy, duration, volume, and price for a total of 1,682 public charging stations with 24,798 public charging piles. Notably, it provides detailed information on charging stations, with a granularity that allows analysis at the charging station level. And its temporal interval is one hour. The dataset is available at the github repo: [UrbanEV](https://github.com/IntelligentSystemsLab/UrbanEV) and the Dryad repo: [Dryad-UrbanEV](https://datadryad.org/dataset/doi:10.5061/dryad.np5hqc04z). 34 | 35 | ![avatar](figs/urbanev.png) Figure 2. Spatial distribution of the 24,798 public EV charging piles in UrbanEV. 36 | 37 | ## Files 38 | ### ST-EVCDP 39 | * `adj.csv`: The adjacent matrix of studied areas, 1 indicates the two traffic zones are neighboring, vice versa. 40 | * `distance.csv`: Distances between nodes. 41 | * `information.csv`: Several basis information about the data, including pile capacity, longitude, latitude, whether or not located in the central business district (1:yes, 0:no), and whether or not on a time-based pricing scheme (1:yes, 0:no). 42 | * `occupancy.csv`: The real-time EV charging occupancy in studied areas. 43 | * `duration.csv`: The real-time EV charging duration in studied areas, i.e., the sum of charging time for all charging piles, unit in hour. 44 | * `volume.csv`: The real-time EV charging volume in studied areas, i.e., the total power consumption of all charging piles, unit in kWh. 45 | * `price.csv`: The real-time EV charging pricing in studied areas. 46 | * `time.csv`: The timestamps of studied period. 47 | * `Shenzhen.qgz`: The QGIS map file of Shenzhen city. 48 | 49 | ### UrbanEV/ST-EVCDP-v2 50 | * `inf.csv`: Important information of the charging stations, including coordinates and charging capacities. 51 | * `occupancy.csv`: Hourly EV charging occupancy (busy count) in certain stations. 52 | * `duration.csv`: Hourly EV charging duration in specific stations (Unit: hour). 53 | * `volume.csv`: Hourly EV charging volume in specific stations (Unit: kWh). 54 | * `e_price.csv`: Electricity price for specific stations (Unit: Yuan/kWh). 55 | * `s_price.csv`: Service price for specific stations (Unit: Yuan/kWh). 56 | * `weather_airport.csv`: Weather data collected from the meteorological station at Bao'an Airport (Shenzhen). 57 | * `weather_central.csv`: Weather data collected from Futian Meteorological Station located in the city centre area of Shenzhen. 58 | * `weather_header.csv`: Descriptions of the table headers presented in `weather_airport.csv` and `weather_central.csv`. 59 | 60 | Notes: Our occupancy data is gathered from an availability perspective, while the duration and volume data is collected from a utilization standpoint. Specifically, the occupancy data records all unavailable or busy charging piles. In contrast, the duration and volume data only account for the piles actively providing electricity. You can select the data according to your research purpose. 61 | 62 | ## Enviroment Requirement 63 | ```shell 64 | pip install -r requirements.txt 65 | ``` 66 | 67 | ## An simple example to run Spatio-temporal Prediction on the dataset 68 | 69 | We developed a physics-informed and attention-based approach for spatio-temporal EV charging demand prediction, named **PAG**. Expect that, some representative methods are included, e.g., LSTM, and GCN-LSTM, GAT-LSTM. You can train and test the proposed model through the following procedures: 70 | 71 | 1. Choose your model in line 45 of `main.py` or use the default model (PAG) by skipping this procedure. 72 | 2. Run `main.py` via Pycharm, etc. or change your ROOT_PATH and command: 73 | 74 | ```shell 75 | cd [path] && python main.py 76 | ``` 77 | 78 | ## Extend: 79 | * If you want to run your own models on the datasets we offer, you should go to `models.py` and replace the model in `main.py`. 80 | * We have also released a simple repository for a **Large Language Model**-based electric vehicle charging demand predictor, available at [ChatEV](https://github.com/Quhaoh233/ChatEV). 81 | 82 | ## Citations: 83 | If this project is helpful to your research, please cite our papers: 84 | 85 | >Qu, H., Kuang, H., Li, J., & You, L. (2023). A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction. IEEE Transactions on Intellgent Transportation Systems. [Paper in IEEE Explore](https://ieeexplore.ieee.org/document/10539613) [Paper in arXiv](https://arxiv.org/abs/2309.05259) 86 | 87 | >Kuang, H., Zhang, X., Qu, H., and You, L., and Zhu, R. and Li, J. (2024). Unravelling the effect of electricity price on electric vehicle charging behavior: A case study in Shenzhen, China. Sustainable Cities and Society. [DOI](https://doi.org/10.1016/j.scs.2024.105836) 88 | 89 | >Haohao Qu, Han Li, Linlin You, Rui Zhu, Jinyue Yan, Paolo Santi, Carlo Ratti, Chau Yuen. (2024) ChatEV: Predicting electric vehicle charging demand as natural language processing. Transportation Research Part D: Transport and Environment. [Paper in TRD](https://doi.org/10.1016/j.trd.2024.104470) [Code in Github](https://github.com/Quhaoh233/ChatEV) 90 | 91 | >Li, H., Qu, H., Tan, X. et al. (2025) UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction. Scientific Data. [Paper in Spring Nature](https://doi.org/10.1038/s41597-025-04874-4) 92 | 93 | ```shell 94 | @Article{qu2024a, 95 | author={Qu, Haohao and Kuang, Haoxuan and Wang, Qiuxuan and Li, Jun and You, Linlin}, 96 | journal={IEEE Transactions on Intelligent Transportation Systems}, 97 | title={A Physics-Informed and Attention-Based Graph Learning Approach for Regional Electric Vehicle Charging Demand Prediction}, 98 | year={2024}, 99 | pages={1-14}, 100 | doi={10.1109/TITS.2024.3401850}} 101 | 102 | @article{kuang2024unravelling, 103 | title={Unravelling the effect of electricity price on electric vehicle charging behavior: A case study in Shenzhen, China}, 104 | author={Kuang, Haoxuan and Zhang, Xinyu and Qu, Haohao and You, Linlin and Zhu, Rui and Li, Jun}, 105 | journal={Sustainable Cities and Society}, 106 | pages={105836}, 107 | year={2024}, 108 | publisher={Elsevier} 109 | } 110 | 111 | @article{qu2024chatev, 112 | title = {ChatEV: Predicting electric vehicle charging demand as natural language processing}, 113 | journal = {Transportation Research Part D: Transport and Environment}, 114 | volume = {136}, 115 | pages = {104470}, 116 | year = {2024}, 117 | issn = {1361-9209}, 118 | author = {Haohao Qu and Han Li and Linlin You and Rui Zhu and Jinyue Yan and Paolo Santi and Carlo Ratti and Chau Yuen}, 119 | } 120 | 121 | @article{li2025urbanev, 122 | title = {UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction}, 123 | journal = {Scientific Data}, 124 | volume = {12}, 125 | pages = {523}, 126 | year = {2025}, 127 | issn = {2052-4463}, 128 | author = {Li, Han and Qu, Haohao and Tan, Xiaojun and You, Linlin and Zhu, Rui and Fan, Wenqi}, 129 | } 130 | ``` 131 | 132 | Author: Haohao Qu (haohao.qu@connect.polyu.hk) 133 | -------------------------------------------------------------------------------- /baselines.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import models 4 | import torch.nn.functional as F 5 | import functions as fn 6 | import copy 7 | 8 | use_cuda = True 9 | device = torch.device("cuda:0" if use_cuda and torch.cuda.is_available() else "cpu") 10 | fn.set_seed(seed=2023, flag=True) 11 | 12 | 13 | class VAR(nn.Module): 14 | def __init__(self, node=247, seq=12, feature=2): # input_dim = seq_length 15 | super(VAR, self).__init__() 16 | self.linear = nn.Linear(node*seq*feature, node) 17 | 18 | def forward(self, occ, prc): 19 | x = torch.cat((occ, prc), dim=2) 20 | x = torch.flatten(x, 1, 2) 21 | x = self.linear(x) 22 | return x 23 | 24 | 25 | class LSTM(nn.Module): 26 | def __init__(self, seq, n_fea, node=247): 27 | super(LSTM, self).__init__() 28 | self.nodes = node 29 | self.encoder = nn.Conv2d(self.nodes, self.nodes, (n_fea, n_fea)) # input.shape: [batch, channel, width, height] 30 | self.lstm = nn.LSTM(self.nodes, self.nodes, num_layers=2, batch_first=True) 31 | self.decoder = nn.Linear(seq-n_fea+1, 1) 32 | 33 | def forward(self, occ, prc): # occ.shape = [batch, node, seq] 34 | x = torch.stack([occ, prc], dim=3) 35 | x = self.encoder(x) 36 | x = torch.transpose(x.squeeze(), 1, 2) # shape [batch, seq-n_fea+1, node] 37 | x, _ = self.lstm(x) 38 | x = torch.transpose(x, 1, 2) # shape [batch, node, seq-n_fea+1] 39 | x = self.decoder(x) 40 | x = torch.squeeze(x) 41 | return x 42 | 43 | 44 | class GCN(nn.Module): 45 | def __init__(self, seq, n_fea, adj_dense): 46 | super(GCN, self).__init__() 47 | self.nodes = adj_dense.shape[0] 48 | self.encoder = nn.Conv2d(self.nodes, self.nodes, (n_fea, n_fea)) 49 | self.gcn_l1 = nn.Linear(seq-n_fea+1, seq-n_fea+1) 50 | self.gcn_l2 = nn.Linear(seq-n_fea+1, seq-n_fea+1) 51 | self.A = adj_dense 52 | self.act = nn.ReLU() 53 | self.decoder = nn.Linear(seq-n_fea+1, 1) 54 | 55 | # calculate A_delta matrix 56 | deg = torch.sum(adj_dense, dim=0) 57 | deg = torch.diag(deg) 58 | deg_delta = torch.linalg.inv(torch.sqrt(deg)) 59 | a_delta = torch.matmul(torch.matmul(deg_delta, adj_dense), deg_delta) 60 | self.A = a_delta 61 | 62 | def forward(self, occ, prc): # occ.shape = [batch, node, seq] 63 | x = torch.stack([occ, prc], dim=3) 64 | x = self.encoder(x) 65 | # l1 66 | x = self.gcn_l1(x) 67 | x = torch.matmul(self.A, x) 68 | x = self.act(x) 69 | # l2 70 | x = self.gcn_l2(x) 71 | x = torch.matmul(self.A, x) 72 | x = self.act(x) 73 | x = self.decoder(x) 74 | return x 75 | 76 | 77 | class LstmGcn(nn.Module): 78 | def __init__(self, seq, n_fea, adj_dense): 79 | super(LstmGcn, self).__init__() 80 | self.A = adj_dense 81 | self.nodes = adj_dense.shape[0] 82 | self.encoder = nn.Conv2d(self.nodes, self.nodes, (n_fea, n_fea), device=device) 83 | self.gcn_l1 = nn.Linear(seq - n_fea + 1, seq - n_fea + 1, device=device) 84 | self.gcn_l2 = nn.Linear(seq - n_fea + 1, seq - n_fea + 1, device=device) 85 | self.lstm = nn.LSTM(self.nodes, self.nodes, num_layers=2, batch_first=True) 86 | self.act = nn.ReLU() 87 | self.decoder = nn.Linear(seq - n_fea + 1, 1, device=device) 88 | 89 | # calculate A_delta matrix 90 | deg = torch.sum(adj_dense, dim=0) 91 | deg = torch.diag(deg) 92 | deg_delta = torch.linalg.inv(torch.sqrt(deg)) 93 | a_delta = torch.matmul(torch.matmul(deg_delta, adj_dense), deg_delta) 94 | self.A = a_delta 95 | 96 | def forward(self, occ, prc): # occ.shape = [batch, node, seq] 97 | x = torch.stack([occ, prc], dim=3) 98 | x = self.encoder(x) 99 | x = torch.squeeze(x) 100 | # l1 101 | x = self.gcn_l1(x) 102 | x = torch.matmul(self.A, x) 103 | x = self.act(x) 104 | # l2 105 | x = self.gcn_l2(x) 106 | x = torch.matmul(self.A, x) 107 | x = self.act(x) 108 | # lstm 109 | x = x.transpose(1, 2) 110 | x, _ = self.lstm(x) 111 | x = x.transpose(1, 2) 112 | x = self.decoder(x) 113 | x = torch.squeeze(x) 114 | return x 115 | 116 | 117 | class LstmGat(nn.Module): 118 | def __init__(self, seq, n_fea, adj_dense, adj_sparse): 119 | super(LstmGat, self).__init__() 120 | self.nodes = adj_dense.shape[0] 121 | self.gcn = nn.Linear(in_features=seq - n_fea + 1, out_features=seq - n_fea + 1, device=device) 122 | self.encoder = nn.Conv2d(self.nodes, self.nodes, (n_fea, n_fea), device=device) 123 | self.gat_l1 = models.MultiHeadsGATLayer(adj_sparse, seq - n_fea + 1, seq - n_fea + 1, 4, 0, 0.2) 124 | self.gat_l2 = models.MultiHeadsGATLayer(adj_sparse, seq - n_fea + 1, seq - n_fea + 1, 4, 0, 0.2) 125 | self.lstm = nn.LSTM(self.nodes, self.nodes, num_layers=2, batch_first=True) 126 | self.decoder = nn.Linear(seq - n_fea + 1, 1, device=device) 127 | 128 | # Activation 129 | self.dropout = nn.Dropout(p=0.5) 130 | self.LeakyReLU = nn.LeakyReLU() 131 | 132 | def forward(self, occ, prc): # occ.shape = [batch, node, seq] 133 | x = torch.stack([occ, prc], dim=3) 134 | x = self.encoder(x) 135 | x = torch.squeeze(x) 136 | 137 | # first layer 138 | atts_mat = self.gat_l1(x) # attention matrix, dense(nodes, nodes) 139 | occ_conv1 = torch.matmul(atts_mat, x) # (b, n, s) 140 | occ_conv1 = self.dropout(self.LeakyReLU(self.gcn(occ_conv1))) 141 | 142 | # second layer 143 | atts_mat2 = self.gat_l2(occ_conv1) # attention matrix, dense(nodes, nodes) 144 | occ_conv2 = torch.matmul(atts_mat2, occ_conv1) # (b, n, s) 145 | occ_conv2 = self.dropout(self.LeakyReLU(self.gcn(occ_conv2))) 146 | 147 | # lstm 148 | x = occ_conv2.transpose(1, 2) 149 | x, _ = self.lstm(x) 150 | x = x.transpose(x, 1, 2) 151 | 152 | # decode 153 | x = self.decoder(x) 154 | x = torch.squeeze(x) 155 | return x 156 | 157 | 158 | class TPA(nn.Module): 159 | def __init__(self, seq, n_fea): 160 | super(TPA, self).__init__() 161 | self.encoder = nn.Conv2d(self.nodes, self.nodes, (n_fea, n_fea), device=device) 162 | # TPA 163 | self.lstm = nn.LSTM(2, 2, num_layers=2, batch_first=True, device=device) 164 | self.fc1 = nn.Linear(in_features=self.seq - 1, out_features=2, device=device) 165 | self.fc2 = nn.Linear(in_features=2, out_features=2, device=device) 166 | self.fc3 = nn.Linear(in_features=2 + 2, out_features=1, device=device) 167 | self.decoder = nn.Linear(self.seq, 1, device=device) 168 | 169 | def forward(self, occ, prc): # occ.shape = [batch, node, seq] 170 | x = torch.stack([occ, prc], dim=3) 171 | x = self.encoder(x) 172 | x = torch.squeeze(x) 173 | 174 | # TPA 175 | lstm_out, (_, _) = self.lstm(x) # b*n, s, 2 176 | ht = lstm_out[:, -1, :] # ht 177 | hw = lstm_out[:, :-1, :] # from h(t-1) to h1 178 | hw = torch.transpose(hw, 1, 2) 179 | Hc = self.fc1(hw) 180 | Hn = self.fc2(Hc) 181 | ht = torch.unsqueeze(ht, dim=2) 182 | a = torch.bmm(Hn, ht) 183 | a = torch.sigmoid(a) 184 | a = torch.transpose(a, 1, 2) 185 | vt = torch.matmul(a, Hc) 186 | ht = torch.transpose(ht, 1, 2) 187 | hx = torch.cat((vt, ht), dim=2) 188 | y = self.fc3(hx) 189 | print(y.shape) 190 | return y 191 | 192 | 193 | # https://doi.org/10.1016/j.trc.2023.104205 194 | class HSTGCN(nn.Module): 195 | def __init__(self, seq, n_fea, adj_distance, adj_demand, alpha=0.5): 196 | super(HSTGCN, self).__init__() 197 | # hyper-params 198 | self.nodes = adj_distance.shape[0] 199 | self.alpha = alpha 200 | hidden = seq - n_fea + 1 201 | 202 | # network components 203 | self.encoder = nn.Conv2d(self.nodes, self.nodes, (n_fea, n_fea)) 204 | self.linear = nn.Linear(hidden, hidden) 205 | self.distance_gcn_l1 = nn.Linear(hidden, hidden) 206 | self.distance_gcn_l2 = nn.Linear(hidden, hidden) 207 | self.gru1 = nn.GRU(self.nodes, self.nodes, num_layers=2, batch_first=True) 208 | self.demand_gcn_l1 = nn.Linear(hidden, hidden) 209 | self.demand_gcn_l2 = nn.Linear(hidden, hidden) 210 | self.gru2 = nn.GRU(self.nodes, self.nodes, num_layers=2, batch_first=True) 211 | self.decoder = nn.Sequential(nn.Linear(hidden, 16), 212 | nn.ReLU(), 213 | nn.Linear(16, 1) 214 | ) 215 | 216 | self.act = nn.ReLU() 217 | self.dropout = nn.Dropout(p=0.5) 218 | 219 | # calculate A_delta matrix 220 | deg = torch.sum(adj_distance, dim=0) 221 | deg = torch.diag(deg) 222 | deg_delta = torch.linalg.inv(torch.sqrt(deg)) 223 | a_delta = torch.matmul(torch.matmul(deg_delta, adj_distance), deg_delta) 224 | self.A_dis = a_delta 225 | 226 | deg = torch.sum(adj_demand, dim=0) 227 | deg = torch.diag(deg) 228 | deg_delta = torch.linalg.inv(torch.sqrt(deg)) 229 | a_delta = torch.matmul(torch.matmul(deg_delta, adj_demand), deg_delta) 230 | self.A_dem = a_delta 231 | 232 | def forward(self, occ, prc): # occ.shape = [batch, node, seq] 233 | x = torch.stack([occ, prc], dim=3) 234 | x = self.encoder(x) 235 | x = torch.squeeze(x) 236 | x = self.act(self.linear(x)) 237 | 238 | # distance-based graph propagation 239 | # l1 240 | x1 = self.distance_gcn_l1(x) 241 | x1 = torch.matmul(self.A_dis, x1) 242 | x1 = self.dropout(self.act(x1)) 243 | # l2 244 | x1 = self.distance_gcn_l2(x1) 245 | x1 = torch.matmul(self.A_dis, x1) 246 | x1 = self.dropout(self.act(x1)) 247 | # gru 248 | x1 = x1.transpose(1, 2) 249 | x1, _ = self.gru1(x1) 250 | x1 = x1.transpose(1, 2) 251 | 252 | # demand-based graph propagation 253 | # l1 254 | x2 = self.demand_gcn_l1(x) 255 | x2 = torch.matmul(self.A_dem, x2) 256 | x2 = self.dropout(self.act(x2)) 257 | # l2 258 | x2 = self.demand_gcn_l2(x2) 259 | x2 = torch.matmul(self.A_dem, x2) 260 | x2 = self.dropout(self.act(x2)) 261 | # gru 262 | x2 = x2.transpose(1, 2) 263 | x2, _ = self.gru2(x2) 264 | x2 = x2.transpose(1, 2) 265 | 266 | # decode 267 | output = self.alpha * x1 + (1-self.alpha) * x2 268 | output = self.decoder(output) 269 | output = torch.squeeze(output) 270 | return output 271 | 272 | 273 | # https://arxiv.org/abs/2311.06190 274 | class FGN(nn.Module): 275 | def __init__(self, pre_length=1, embed_size=64, 276 | feature_size=0, seq_length=12, hidden_size=32, hard_thresholding_fraction=1, hidden_size_factor=1, sparsity_threshold=0.01): 277 | super().__init__() 278 | self.embed_size = embed_size 279 | self.hidden_size = hidden_size 280 | self.number_frequency = 1 281 | self.pre_length = pre_length 282 | self.feature_size = feature_size 283 | self.seq_length = seq_length 284 | self.frequency_size = self.embed_size // self.number_frequency 285 | self.hidden_size_factor = hidden_size_factor 286 | self.sparsity_threshold = sparsity_threshold 287 | self.hard_thresholding_fraction = hard_thresholding_fraction 288 | self.scale = 0.02 289 | self.embeddings = nn.Parameter(torch.randn(1, self.embed_size)) 290 | 291 | self.encoder = nn.Linear(2, 1) 292 | self.w1 = nn.Parameter( 293 | self.scale * torch.randn(2, self.frequency_size, self.frequency_size * self.hidden_size_factor)) 294 | self.b1 = nn.Parameter(self.scale * torch.randn(2, self.frequency_size * self.hidden_size_factor)) 295 | self.w2 = nn.Parameter( 296 | self.scale * torch.randn(2, self.frequency_size * self.hidden_size_factor, self.frequency_size)) 297 | self.b2 = nn.Parameter(self.scale * torch.randn(2, self.frequency_size)) 298 | self.w3 = nn.Parameter( 299 | self.scale * torch.randn(2, self.frequency_size, 300 | self.frequency_size * self.hidden_size_factor)) 301 | self.b3 = nn.Parameter( 302 | self.scale * torch.randn(2, self.frequency_size * self.hidden_size_factor)) 303 | self.embeddings_10 = nn.Parameter(torch.randn(self.seq_length, 8)) 304 | self.fc = nn.Sequential( 305 | nn.Linear(self.embed_size * 8, 64), 306 | nn.LeakyReLU(), 307 | nn.Linear(64, self.hidden_size), 308 | nn.LeakyReLU(), 309 | nn.Linear(self.hidden_size, self.pre_length) 310 | ) 311 | self.to('cuda:0') 312 | 313 | def tokenEmb(self, x): 314 | x = x.unsqueeze(2) 315 | y = self.embeddings 316 | return x * y 317 | 318 | # FourierGNN 319 | def fourierGC(self, x, B, N, L): 320 | o1_real = torch.zeros([B, (N*L)//2 + 1, self.frequency_size * self.hidden_size_factor], 321 | device=x.device) 322 | o1_imag = torch.zeros([B, (N*L)//2 + 1, self.frequency_size * self.hidden_size_factor], 323 | device=x.device) 324 | o2_real = torch.zeros(x.shape, device=x.device) 325 | o2_imag = torch.zeros(x.shape, device=x.device) 326 | 327 | o3_real = torch.zeros(x.shape, device=x.device) 328 | o3_imag = torch.zeros(x.shape, device=x.device) 329 | 330 | o1_real = F.relu( 331 | torch.einsum('bli,ii->bli', x.real, self.w1[0]) - \ 332 | torch.einsum('bli,ii->bli', x.imag, self.w1[1]) + \ 333 | self.b1[0] 334 | ) 335 | 336 | o1_imag = F.relu( 337 | torch.einsum('bli,ii->bli', x.imag, self.w1[0]) + \ 338 | torch.einsum('bli,ii->bli', x.real, self.w1[1]) + \ 339 | self.b1[1] 340 | ) 341 | 342 | # 1 layer 343 | y = torch.stack([o1_real, o1_imag], dim=-1) 344 | y = F.softshrink(y, lambd=self.sparsity_threshold) 345 | 346 | o2_real = F.relu( 347 | torch.einsum('bli,ii->bli', o1_real, self.w2[0]) - \ 348 | torch.einsum('bli,ii->bli', o1_imag, self.w2[1]) + \ 349 | self.b2[0] 350 | ) 351 | 352 | o2_imag = F.relu( 353 | torch.einsum('bli,ii->bli', o1_imag, self.w2[0]) + \ 354 | torch.einsum('bli,ii->bli', o1_real, self.w2[1]) + \ 355 | self.b2[1] 356 | ) 357 | 358 | # 2 layer 359 | x = torch.stack([o2_real, o2_imag], dim=-1) 360 | x = F.softshrink(x, lambd=self.sparsity_threshold) 361 | x = x + y 362 | 363 | o3_real = F.relu( 364 | torch.einsum('bli,ii->bli', o2_real, self.w3[0]) - \ 365 | torch.einsum('bli,ii->bli', o2_imag, self.w3[1]) + \ 366 | self.b3[0] 367 | ) 368 | 369 | o3_imag = F.relu( 370 | torch.einsum('bli,ii->bli', o2_imag, self.w3[0]) + \ 371 | torch.einsum('bli,ii->bli', o2_real, self.w3[1]) + \ 372 | self.b3[1] 373 | ) 374 | 375 | # 3 layer 376 | z = torch.stack([o3_real, o3_imag], dim=-1) 377 | z = F.softshrink(z, lambd=self.sparsity_threshold) 378 | z = z + x 379 | z = torch.view_as_complex(z) 380 | return z 381 | 382 | def forward(self, occ, prc): 383 | x = torch.stack([occ, prc], dim=3) 384 | x = self.encoder(x) 385 | x = torch.squeeze(x) 386 | 387 | B, N, L = x.shape 388 | # B*N*L ==> B*NL 389 | x = x.reshape(B, -1) 390 | # embedding B*NL ==> B*NL*D 391 | x = self.tokenEmb(x) 392 | 393 | # FFT B*NL*D ==> B*NT/2*D 394 | x = torch.fft.rfft(x, dim=1, norm='ortho') 395 | 396 | x = x.reshape(B, (N*L)//2+1, self.frequency_size) 397 | 398 | bias = x 399 | 400 | # FourierGNN 401 | x = self.fourierGC(x, B, N, L) 402 | 403 | x = x + bias 404 | 405 | x = x.reshape(B, (N*L)//2+1, self.embed_size) 406 | 407 | # ifft 408 | x = torch.fft.irfft(x, n=N*L, dim=1, norm="ortho") 409 | 410 | x = x.reshape(B, N, L, self.embed_size) 411 | x = x.permute(0, 1, 3, 2) # B, N, D, L 412 | 413 | # projection 414 | x = torch.matmul(x, self.embeddings_10) 415 | x = x.reshape(B, N, -1) 416 | x = self.fc(x) 417 | x = torch.squeeze(x) 418 | return x 419 | 420 | # Other baselines refer to its own original code. 421 | -------------------------------------------------------------------------------- /checkpoints/GCN-LSTM_6_bs512model.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/checkpoints/GCN-LSTM_6_bs512model.pt -------------------------------------------------------------------------------- /checkpoints/PAG_6_bs512_completed.pt: -------------------------------------------------------------------------------- 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PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0]] -------------------------------------------------------------------------------- /datasets/SZ_districts/SZ_districts.sbn: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/datasets/SZ_districts/SZ_districts.sbn -------------------------------------------------------------------------------- /datasets/SZ_districts/SZ_districts.sbx: -------------------------------------------------------------------------------- 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metres above the earth's surface over the 10-minute periodimmediately preceding the observation (meters persecond). 9 | ff10: Maximum gust value at a height of 10-12 metres above the earth's surface over the 10-minute period immediately preceding the observation (meters per second). 10 | WW: Special present weather phenomena observed at or near the aerodrome. 11 | W'W': Recent weather phenomena of operational significance. 12 | c: Total cloud cover. 13 | VV: Horizontal visibility (km). 14 | Td: Dewpoint temperature at a height of 2 metres above the earth's surface (degrees Celsius). 15 | -------------------------------------------------------------------------------- /datasets/Shenzhen.qgz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/datasets/Shenzhen.qgz -------------------------------------------------------------------------------- /datasets/adj.csv: -------------------------------------------------------------------------------- 1 | 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263,1062,143,2,141,2.07,114.0533,22.53673,1,0 189 | 266,1066,40,0,40,1.57,113.9103,22.52129,1,0 190 | 267,1067,49,0,49,1.52,113.906,22.51218,1,0 191 | 268,1068,309,4,305,1.41,113.9187,22.51784,1,0 192 | 269,1071,72,0,72,1.1,113.9362,22.52924,1,0 193 | 270,1072,68,0,68,1.17,113.9454,22.53025,1,1 194 | 271,1074,82,0,82,0.99,113.9214,22.5394,1,0 195 | 272,1075,16,0,16,1.01,113.9147,22.54925,1,0 196 | 273,1076,123,0,123,1.33,113.9234,22.54958,1,1 197 | 274,1081,47,0,47,1.06,113.9132,22.48642,0,0 198 | 275,1082,21,0,21,0.59,113.916,22.49459,0,0 199 | 276,1083,44,2,42,1.49,113.9298,22.48623,0,0 200 | 277,1085,83,0,83,0.38,113.9258,22.52152,1,0 201 | 278,1086,12,0,12,0.66,113.9321,22.52104,1,0 202 | 280,1088,220,8,212,0.76,113.9242,22.51267,1,1 203 | 281,1090,34,0,34,0.54,113.9307,22.50352,0,0 204 | 282,1092,24,0,24,0.57,113.923,22.50533,1,0 205 | 283,1093,6,0,6,0.92,113.9257,22.49366,0,0 206 | 285,1095,29,0,29,0.97,113.9096,22.53069,1,0 207 | 286,1096,70,12,58,1.07,113.9199,22.53143,1,0 208 | 288,1098,74,28,46,1.52,113.896,22.55854,1,0 209 | 289,1099,18,0,18,0.77,114.0446,22.51649,1,0 210 | 290,1100,69,0,69,1.05,114.0474,22.52499,1,0 211 | 291,1101,20,0,20,0.47,114.0555,22.51732,1,0 212 | 292,1102,108,0,108,1.19,114.0551,22.525,1,0 213 | 293,1104,27,0,27,0.47,114.0646,22.52713,1,0 214 | 294,1105,28,0,28,0.85,114.0351,22.52086,1,0 215 | 295,1106,22,0,22,1.26,114.0396,22.52866,1,0 216 | 296,1107,12,0,12,0.78,114.0396,22.54603,1,0 217 | 297,1109,62,0,62,0.74,114.0387,22.55278,1,0 218 | 298,1110,33,0,33,1.18,114.0345,22.55901,1,0 219 | 299,1111,59,0,59,1.09,114.0033,22.53855,1,0 220 | 300,1112,18,0,18,1.48,114.0008,22.54379,1,0 221 | 301,1113,55,0,55,1.29,114.0141,22.54929,1,0 222 | 302,1114,45,2,43,0.6,114.0178,22.54145,1,0 223 | 303,1115,62,0,62,2.23,114.0243,22.55313,1,0 224 | 305,1119,113,0,113,1.05,114.1166,22.54006,0,0 225 | 307,1121,37,0,37,0.73,114.1043,22.5559,0,0 226 | 308,1122,62,0,62,1,114.1044,22.54872,0,0 227 | 309,1124,61,37,24,1.07,114.1096,22.56802,0,1 228 | 311,1126,42,0,42,0.56,114.1028,22.562,0,0 229 | 313,1131,92,86,6,1.06,114.1073,22.58458,0,1 230 | 314,1134,58,0,58,0.96,114.1304,22.574,0,0 231 | 316,1137,216,87,129,1.66,114.1225,22.58215,0,1 232 | 317,1138,16,0,16,0.9,114.1154,22.58799,0,0 233 | 318,1143,26,0,26,0.42,114.1764,22.559,0,0 234 | 319,1144,68,0,68,1.85,114.1774,22.56502,0,0 235 | 321,1149,48,0,48,1.31,114.2428,22.56276,0,0 236 | 322,1154,22,0,22,1.23,114.2244,22.55129,0,0 237 | 324,1156,30,0,30,0.57,114.2318,22.55387,0,0 238 | 325,1159,114,0,114,1.95,114.2599,22.59085,0,0 239 | 326,1160,14,2,12,1.38,114.2527,22.5833,0,0 240 | 327,1162,22,0,22,1.4,114.2459,22.5929,0,0 241 | 328,1163,18,0,18,1.07,114.1095,22.60267,0,0 242 | 329,1164,116,0,116,1.88,114.12,22.59568,0,0 243 | 330,1166,76,0,76,2.37,114.1272,22.60459,0,0 244 | 331,1167,382,55,327,6.13,114.1453,22.59922,0,1 245 | 332,1168,43,12,31,1.43,114.0487,22.57147,0,0 246 | 333,1170,10,0,10,2.16,114.0327,22.56949,0,0 247 | 335,1172,47,0,47,1.1,114.057,22.57284,0,0 248 | 336,1173,44,0,44,1.28,114.0664,22.5739,0,0 249 | -------------------------------------------------------------------------------- /figs/PAG.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/figs/PAG.png -------------------------------------------------------------------------------- /figs/map.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/figs/map.png -------------------------------------------------------------------------------- /figs/map_v2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/figs/map_v2.png -------------------------------------------------------------------------------- /figs/statistics.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/figs/statistics.png -------------------------------------------------------------------------------- /figs/urbanev.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IntelligentSystemsLab/ST-EVCDP/78a3b4555adb7bb1319f27421e3acc18c3267d45/figs/urbanev.png -------------------------------------------------------------------------------- /functions.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import copy 4 | import torch 5 | from torch.utils.data import Dataset 6 | from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score,mean_absolute_percentage_error 7 | 8 | 9 | def read_dataset(): 10 | occ = pd.read_csv('datasets/occupancy.csv', index_col=0, header=0) 11 | inf = pd.read_csv('datasets/information.csv', index_col=None, header=0) 12 | prc = pd.read_csv('datasets/price.csv', index_col=0, header=0) 13 | adj = pd.read_csv('datasets/adj.csv', index_col=0, header=0) # check 14 | dis = pd.read_csv('datasets/distance.csv', index_col=0, header=0) 15 | time = pd.read_csv('datasets/time.csv', index_col=None, header=0) 16 | 17 | col = occ.columns 18 | cap = np.array(inf['count'], dtype=float).reshape(1, -1) # parking_capability 19 | occ = np.array(occ, dtype=float) / cap 20 | prc = np.array(prc, dtype=float) 21 | adj = np.array(adj, dtype=float) 22 | dis = np.array(dis, dtype=float) 23 | time = pd.to_datetime(time, dayfirst=True) 24 | return occ, prc, adj, col, dis, cap, time, inf 25 | 26 | 27 | # ---------data transform----------- 28 | def create_rnn_data(dataset, lookback, predict_time): 29 | x = [] 30 | y = [] 31 | for i in range(len(dataset) - lookback - predict_time): 32 | x.append(dataset[i:i + lookback]) 33 | y.append(dataset[i + lookback + predict_time - 1]) 34 | return np.array(x), np.array(y) 35 | 36 | 37 | def get_a_delta(adj): # D^-1/2 * A * D^-1/2 38 | # adj.shape = np.size(node, node) 39 | deg = np.sum(adj, axis=0) 40 | deg = np.diag(deg) 41 | deg_delta = np.linalg.inv(np.sqrt(deg)) 42 | a_delta = np.matmul(np.matmul(deg_delta, adj), deg_delta) 43 | return a_delta 44 | 45 | 46 | def division(data, train_rate, valid_rate, test_rate): 47 | data_length = len(data) 48 | train_division_index = int(data_length * train_rate) 49 | valid_division_index = int(data_length * (train_rate + valid_rate)) 50 | test_division_index = int(data_length * (1 - test_rate)) 51 | train_data = data[:train_division_index, :] 52 | valid_data = data[train_division_index:valid_division_index, :] 53 | test_data = data[test_division_index:, :] 54 | return train_data, valid_data, test_data 55 | 56 | 57 | def set_seed(seed, flag): 58 | if flag == True: 59 | torch.manual_seed(seed) 60 | torch.cuda.manual_seed(seed) 61 | torch.cuda.manual_seed_all(seed) 62 | 63 | 64 | def metrics(test_pre, test_real): 65 | eps = 0.01 66 | MAPE_test_real = test_real 67 | MAPE_test_pre = test_pre 68 | MAPE_test_real[np.where(MAPE_test_real == 0)] = MAPE_test_real[np.where(MAPE_test_real == 0)] + eps 69 | MAPE_test_pre[np.where(MAPE_test_real == 0)] = MAPE_test_pre[np.where(MAPE_test_real == 0)] + eps 70 | MAPE = mean_absolute_percentage_error(MAPE_test_real, MAPE_test_pre) 71 | MAE = mean_absolute_error(test_real, test_pre) 72 | MSE = mean_squared_error(test_real, test_pre) 73 | RMSE = np.sqrt(MSE) 74 | R2 = r2_score(test_real, test_pre) 75 | RAE = np.sum(abs(test_pre - test_real)) / np.sum(abs(np.mean(test_real) - test_real)) 76 | print('MAPE: {}'.format(MAPE)) 77 | print('MAE:{}'.format(MAE)) 78 | print('MSE:{}'.format(MSE)) 79 | print('RMSE:{}'.format(RMSE)) 80 | print('R2:{}'.format(R2)) 81 | print(('RAE:{}'.format(RAE))) 82 | output_list = [MSE, RMSE, MAPE, RAE, MAE, R2] 83 | return output_list 84 | 85 | 86 | class CreateDataset(Dataset): 87 | def __init__(self, occ, prc, lb, pt, device, adj): # adj 88 | occ, label = create_rnn_data(occ, lb, pt) 89 | prc, _ = create_rnn_data(prc, lb, pt) 90 | self.occ = torch.Tensor(occ) 91 | self.prc = torch.Tensor(prc) 92 | self.label = torch.Tensor(label) 93 | self.device = device 94 | 95 | def __len__(self): 96 | return len(self.occ) 97 | 98 | def __getitem__(self, idx): # occ: batch, seq, node 99 | output_occ = torch.transpose(self.occ[idx, :, :], 0, 1).to(self.device) 100 | output_prc = torch.transpose(self.prc[idx, :, :], 0, 1).to(self.device) 101 | output_label = self.label[idx, :].to(self.device) 102 | return output_occ, output_prc, output_label 103 | 104 | 105 | class CreateFastDataset(Dataset): 106 | def __init__(self, occ, prc, lb, pt, law, device, adj, num_layers=2, prob=0.6): # adj 107 | occ, label = create_rnn_data(occ, lb, pt) 108 | prc, _ = create_rnn_data(prc, lb, pt) 109 | self.occ = torch.Tensor(occ) 110 | self.prc = torch.Tensor(prc) 111 | self.label = torch.Tensor(label) 112 | self.device = device 113 | self.adj = adj 114 | self.eye = torch.eye(adj.shape[0]) 115 | self.deg = torch.sum(adj, dim=0) 116 | self.num_layers = num_layers 117 | self.law = -law 118 | 119 | # price 120 | chg = torch.randn(size=[self.occ.shape[2]]) / 2 121 | chg[torch.where(chg < prob)] = 0 122 | self.prc_chg = chg # [node, ] 123 | 124 | # label 125 | chg = torch.unsqueeze(chg, dim=1) # [node, 1] 126 | deg = torch.unsqueeze(self.deg, dim=1) # [node, 1] 127 | label_chg = [-chg] 128 | hop_chg = chg 129 | for n in range(self.num_layers): # graph propagation 130 | hop_chg = torch.matmul(self.adj-self.eye, hop_chg) * (1 / deg) 131 | label_chg.append(hop_chg) 132 | label_chg = torch.stack(label_chg, dim=1) # [node, num_layers] 133 | label_chg = torch.sum(label_chg, dim=1) # [node, ] 134 | self.label_chg = torch.squeeze(label_chg, dim=1) 135 | 136 | def __len__(self): 137 | return len(self.occ) 138 | 139 | def __getitem__(self, idx): # occ: batch, seq, node 140 | # Pseudo Sampling 141 | prc_ch = torch.Tensor(self.prc[idx, :, :] * (1+self.prc_chg)) # [node, seq] 142 | label_ch = torch.tan(torch.Tensor(self.label[idx, :] * (1+self.label_chg/self.law))) # [node, ] 143 | 144 | # to device 145 | output_occ = torch.transpose(self.occ[idx, :, :], 0, 1).to(self.device) 146 | output_prc = torch.transpose(self.prc[idx, :, :], 0, 1).to(self.device) 147 | output_label = self.label[idx, :].to(self.device) 148 | output_prc_ch = torch.transpose(prc_ch, 0, 1).to(self.device) 149 | output_label_ch = label_ch.to(self.device) 150 | return output_occ, output_prc, output_label, output_prc_ch, output_label_ch 151 | 152 | 153 | class PseudoDataset(Dataset): 154 | def __init__(self, occ, prc, lb, pt, device, adj, law, num_layers=2, prop=0.4): # adj 155 | occ, label = create_rnn_data(occ, lb, pt) 156 | prc, _ = create_rnn_data(prc, lb, pt) 157 | self.occ = torch.Tensor(occ) 158 | self.prc = torch.Tensor(prc) 159 | self.label = torch.Tensor(label) 160 | self.device = device 161 | self.adj = adj 162 | self.eye = torch.eye(adj.shape[0]) 163 | self.deg = torch.sum(adj, dim=0) 164 | self.num_layers = num_layers 165 | self.prop = prop # Proportion of nodes with price changes 166 | self.law = -law 167 | 168 | # price changes 169 | node_score = torch.rand(size=[self.occ.shape[2]]) 170 | shred = torch.quantile(node_score, self.prop) 171 | prc_chg = torch.randn_like(node_score) / 2 # Percentage change in price 172 | prc_chg[torch.where(node_score > self.prop)] = 0 173 | self.prc_chg = prc_chg 174 | 175 | # label changes 176 | label_chg = self.law * prc_chg # Percentage change in occupancy 177 | label_chg = torch.unsqueeze(label_chg, dim=1) # [node, 1] 178 | hop_chg = -label_chg 179 | label_chg = [label_chg] 180 | deg = torch.unsqueeze(self.deg, dim=1) # [node, 1] 181 | for n in range(self.num_layers): # graph propagation 182 | hop_chg = torch.matmul(self.adj-self.eye, hop_chg) * (1 / deg) 183 | label_chg.append(hop_chg) 184 | label_chg = torch.stack(label_chg, dim=1) # [node, num_layers] 185 | label_chg = torch.sum(label_chg, dim=1) # [node, ] 186 | self.label_chg = torch.squeeze(label_chg, dim=1) 187 | 188 | def __len__(self): 189 | return len(self.occ) 190 | 191 | def __getitem__(self, idx): # occ: batch, seq, node 192 | # sampling 193 | pseudo_prc = torch.Tensor(self.prc[idx, :, :] * (1+self.prc_chg)) # [node, seq] 194 | pseudo_label = torch.tan(torch.Tensor(self.label[idx, :] * (1+self.label_chg))) # [node, ] 195 | 196 | # to device 197 | output_occ = torch.transpose(self.occ[idx, :, :], 0, 1).to(self.device) 198 | output_prc = torch.transpose(self.prc[idx, :, :], 0, 1).to(self.device) 199 | output_label = self.label[idx, :].to(self.device) 200 | output_pseudo_prc = torch.transpose(pseudo_prc, 0, 1).to(self.device) 201 | output_pseudo_label = pseudo_label.to(self.device) 202 | 203 | return output_occ, output_prc, output_label, output_pseudo_prc, output_pseudo_label 204 | 205 | 206 | def meta_division(data, support_rate, query_rate): 207 | data_length = len(data) 208 | support_division_index = int(data_length * support_rate) 209 | supprot_set = data[:support_division_index, :] 210 | query_set = data[support_division_index:, :] 211 | return supprot_set, query_set 212 | 213 | 214 | def zero_init_global_gradient(model): 215 | grads = dict() 216 | for name, param in model.named_parameters(): 217 | param.requires_grad_(True) 218 | grads[name] = 0 219 | return grads 220 | 221 | 222 | def data_mix(ori_data, pse_data, mix_ratio): 223 | shred = int(ori_data.shape[0] * mix_ratio) 224 | mix_data = ori_data 225 | mix_data[shred:] = pse_data[shred:] # mix on the 1st dimension: batch 226 | return mix_data 227 | -------------------------------------------------------------------------------- /learner.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.utils.data import DataLoader 3 | import pandas as pd 4 | import numpy as np 5 | import functions as fn 6 | import copy 7 | from tqdm import tqdm 8 | 9 | # 10 | def physics_informed_meta_learning(law_list, global_model, model_name, p_epoch, bs, train_occupancy, train_price, seq_l, pre_l, device, adj_dense): 11 | support_occ, query_occ = fn.meta_division(train_occupancy, support_rate=0.5, query_rate=0.5) 12 | support_prc, query_prc = fn.meta_division(train_price, support_rate=0.5, query_rate=0.5) 13 | 14 | # pre-training data generation 15 | n_laws = len(law_list) 16 | support_dataset_dict = dict() 17 | query_dataset_dict = dict() 18 | support_dataloader_dict = dict() 19 | query_dataloader_dict = dict() 20 | for n in range(n_laws): 21 | support_dataset_dict[n] = fn.PseudoDataset(support_occ, support_prc, seq_l, pre_l, device, adj_dense, law_list[n]) 22 | query_dataset_dict[n] = fn.PseudoDataset(query_occ, query_prc, seq_l, pre_l, device, adj_dense, law_list[n]) 23 | support_dataloader_dict[n] = DataLoader(support_dataset_dict[n], batch_size=bs, shuffle=True, drop_last=True) 24 | query_dataloader_dict[n] = DataLoader(query_dataset_dict[n], batch_size=query_occ.shape[0], shuffle=False) 25 | 26 | # meta-learning process 27 | torch.save(global_model, './checkpoints' + '/meta_' + model_name + '_' + str(pre_l) + '_bs' + str(bs) + 'model.pt') 28 | loss_function = torch.nn.MSELoss() 29 | # outer loop 30 | global_model.train() 31 | for epoch in tqdm(range(p_epoch), desc='Pre-training'): 32 | query_loss = 100 33 | global_grads = fn.zero_init_global_gradient(global_model) 34 | 35 | # inner loop 36 | for n in range(n_laws): 37 | temp_model = torch.load('./checkpoints' + '/meta_' + model_name + '_' + str(pre_l) + '_bs' + str(bs) + 'model.pt').to(device) 38 | temp_optimizer = torch.optim.Adam(temp_model.parameters(), weight_decay=0.00001) 39 | temp_model.train() 40 | # support 41 | for j, data in enumerate(support_dataloader_dict[n]): 42 | ''' 43 | occupancy = (batch, seq, node) 44 | price = (batch, seq, node) 45 | label = (batch, node) 46 | ''' 47 | occupancy, price, label, pseudo_price, pseudo_label = data 48 | mix_ratio = (j+1) * occupancy.shape[0] / len(train_occupancy) 49 | mix_prc = fn.data_mix(price, pseudo_price, mix_ratio) 50 | mix_label = fn.data_mix(label, pseudo_label, mix_ratio) 51 | temp_optimizer.zero_grad() 52 | predict = temp_model(occupancy, mix_prc) 53 | loss = loss_function(predict, mix_label) 54 | loss.backward() 55 | temp_optimizer.step() 56 | # query 57 | for j, data in enumerate(query_dataloader_dict[n]): 58 | ''' 59 | occupancy = (batch, seq, node) 60 | price = (batch, seq, node) 61 | label = (batch, node) 62 | ''' 63 | occupancy, price, label, pseudo_price, pseudo_label = data 64 | temp_optimizer.zero_grad() 65 | predict = temp_model(occupancy, price) 66 | loss = loss_function(predict, label) 67 | loss.backward() 68 | for name, param in temp_model.named_parameters(): 69 | if param.grad is not None: 70 | global_grads[name] += param.grad 71 | 72 | # global updating: BGD 73 | for name, param in global_model.named_parameters(): 74 | param = param - 0.02 * global_grads[name] / n_laws 75 | 76 | if query_loss > loss: 77 | loss = query_loss 78 | torch.save(global_model, './checkpoints' + '/meta_' + model_name + '_' + str(pre_l) + '_bs' + str(bs) + 'model.pt') 79 | 80 | return global_model 81 | 82 | 83 | def fast_learning(law_list, model, model_name, p_epoch, bs, train_occupancy, train_price, seq_l, pre_l, device, adj_dense): 84 | n_laws = len(law_list) 85 | fast_datasets = dict() 86 | fast_loaders = dict() 87 | for n in range(n_laws): 88 | fast_datasets[n] = fn.CreateFastDataset(train_occupancy, train_price, seq_l, pre_l, law_list[n], device, adj_dense) 89 | fast_loaders[n] = DataLoader(fast_datasets[n], batch_size=bs, shuffle=True, drop_last=True) 90 | 91 | optimizer = torch.optim.Adam(model.parameters(), weight_decay=0.00001) 92 | loss_function = torch.nn.MSELoss() 93 | for epoch in tqdm(range(p_epoch), desc='Pre-training'): 94 | for n in range(n_laws): 95 | for j, data in enumerate(fast_loaders[n]): 96 | ''' 97 | occupancy = (batch, seq, node) 98 | price = (batch, seq, node) 99 | label = (batch, node) 100 | ''' 101 | occupancy, price, label, prc_ch, label_ch = data 102 | optimizer.zero_grad() 103 | predict = model(occupancy, prc_ch) 104 | loss = loss_function(predict, label_ch) 105 | loss.backward() 106 | optimizer.step() 107 | 108 | for j, data in enumerate(fast_loaders[n]): 109 | ''' 110 | occupancy = (batch, seq, node) 111 | price = (batch, seq, node) 112 | label = (batch, node) 113 | ''' 114 | occupancy, price, label, prc_ch, label_ch = data 115 | optimizer.zero_grad() 116 | predict = model(occupancy, prc_ch) 117 | loss = loss_function(predict, label_ch) 118 | loss.backward() 119 | optimizer.step() 120 | 121 | return model 122 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import baselines 3 | import torch 4 | import numpy as np 5 | import pandas as pd 6 | import functions as fn 7 | from torch.utils.data import DataLoader 8 | from tqdm import tqdm 9 | import models 10 | import learner 11 | 12 | # system configuration 13 | use_cuda = True 14 | device = torch.device("cuda:0" if use_cuda and torch.cuda.is_available() else "cpu") 15 | fn.set_seed(seed=2023, flag=True) 16 | 17 | # hyper params 18 | model_name = 'PAG' 19 | seq_l = 12 20 | pre_l = 6 21 | bs = 512 22 | p_epoch = 200 23 | n_epoch = 1000 24 | law_list = np.array([-1.48, -0.74]) # price elasticities of demand for EV charging. Recommend: up to 5 elements. 25 | is_train = True 26 | mode = 'completed' # 'simplified' or 'completed' 27 | is_pre_train = True 28 | 29 | # input data 30 | occ, prc, adj, col, dis, cap, time, inf = fn.read_dataset() 31 | adj_dense = torch.Tensor(adj) 32 | adj_dense_cuda = adj_dense.to(device) 33 | adj_sparse = adj_dense.to_sparse_coo().to(device) 34 | 35 | # dataset division 36 | train_occupancy, valid_occupancy, test_occupancy = fn.division(occ, train_rate=0.6, valid_rate=0.2, test_rate=0.2) 37 | train_price, valid_price, test_price = fn.division(prc, train_rate=0.6, valid_rate=0.2, test_rate=0.2) 38 | 39 | # data 40 | train_dataset = fn.CreateDataset(train_occupancy, train_price, seq_l, pre_l, device, adj_dense) 41 | train_loader = DataLoader(train_dataset, batch_size=bs, shuffle=True, drop_last=True) 42 | valid_dataset = fn.CreateDataset(valid_occupancy, valid_price, seq_l, pre_l, device, adj_dense) 43 | valid_loader = DataLoader(valid_dataset, batch_size=len(valid_occupancy), shuffle=False) 44 | test_dataset = fn.CreateDataset(test_occupancy, test_price, seq_l, pre_l, device, adj_dense) 45 | test_loader = DataLoader(test_dataset, batch_size=len(test_occupancy), shuffle=False) 46 | 47 | # training setting 48 | model = models.PAG(a_sparse=adj_sparse).to(device) # init model 49 | # model = FGN().to(device) 50 | # model = baselines.LSTM(seq_l, 2).to(device) 51 | # model = baselines.LstmGcn(seq_l, 2, adj_dense_cuda).to(device) 52 | optimizer = torch.optim.Adam(model.parameters(), weight_decay=0.00001) 53 | loss_function = torch.nn.MSELoss() 54 | valid_loss = 100 55 | 56 | if is_train is True: 57 | model.train() 58 | if is_pre_train is True: 59 | if mode == 'simplified': # a simplified way of physics-informed meta-learning 60 | model = learner.fast_learning(law_list, model, model_name, p_epoch, bs, train_occupancy, train_price, seq_l, pre_l, device, adj_dense) 61 | 62 | elif mode == 'completed': # the completed process 63 | model = learner.physics_informed_meta_learning(law_list, model, model_name, p_epoch, bs, train_occupancy, train_price, seq_l, pre_l, device, adj_dense) 64 | else: 65 | print("Mode error, skip the pre-training process.") 66 | 67 | for epoch in tqdm(range(n_epoch), desc='Fine-tuning'): 68 | for j, data in enumerate(train_loader): 69 | ''' 70 | occupancy = (batch, seq, node) 71 | price = (batch, seq, node) 72 | label = (batch, node) 73 | ''' 74 | model.train() 75 | occupancy, price, label = data 76 | 77 | optimizer.zero_grad() 78 | predict = model(occupancy, price) 79 | loss = loss_function(predict, label) 80 | loss.backward() 81 | optimizer.step() 82 | 83 | # validation 84 | model.eval() 85 | for j, data in enumerate(valid_loader): 86 | ''' 87 | occupancy = (batch, seq, node) 88 | price = (batch, seq, node) 89 | label = (batch, node) 90 | ''' 91 | model.train() 92 | occupancy, price, label = data 93 | predict = model(occupancy, price) 94 | loss = loss_function(predict, label) 95 | if loss.item() < valid_loss: 96 | valid_loss = loss.item() 97 | torch.save(model, './checkpoints' + '/' + model_name + '_' + str(pre_l) + '_bs' + str(bs) + '_' + mode + '.pt') 98 | 99 | model = torch.load('./checkpoints' + '/' + model_name + '_' + str(pre_l) + '_bs' + str(bs) + '_' + mode + '.pt') 100 | # test 101 | model.eval() 102 | result_list = [] 103 | predict_list = np.zeros([1, adj_dense.shape[1]]) 104 | label_list = np.zeros([1, adj_dense.shape[1]]) 105 | for j, data in enumerate(test_loader): 106 | occupancy, price, label = data # occupancy.shape = [batch, seq, node] 107 | print('occupancy:', occupancy.shape, 'price:', price.shape, 'label:', label.shape) 108 | with torch.no_grad(): 109 | predict = model(occupancy, price) 110 | predict = predict.cpu().detach().numpy() 111 | label = label.cpu().detach().numpy() 112 | predict_list = np.concatenate((predict_list, predict), axis=0) 113 | label_list = np.concatenate((label_list, label), axis=0) 114 | 115 | output_no_noise = fn.metrics(test_pre=predict_list[1:, :], test_real=label_list[1:, :]) 116 | result_list.append(output_no_noise) 117 | result_df = pd.DataFrame(columns=['MSE', 'RMSE', 'MAPE', 'RAE', 'MAE', 'R2'], data=result_list) 118 | result_df.to_csv('./results' + '/' + model_name + '_' + str(pre_l) + 'bs' + str(bs) + '.csv', encoding='gbk') 119 | -------------------------------------------------------------------------------- /models.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import functions as fn 5 | import copy 6 | 7 | use_cuda = True 8 | device = torch.device("cuda:0" if use_cuda and torch.cuda.is_available() else "cpu") 9 | fn.set_seed(seed=2023, flag=True) 10 | 11 | 12 | class MultiHeadsGATLayer(nn.Module): 13 | def __init__(self, a_sparse, input_dim, out_dim, head_n, dropout, alpha): # input_dim = seq_length 14 | super(MultiHeadsGATLayer, self).__init__() 15 | 16 | self.head_n = head_n 17 | self.heads_dict = dict() 18 | for n in range(head_n): 19 | self.heads_dict[n, 0] = nn.Parameter(torch.zeros(size=(input_dim, out_dim), device=device)) 20 | self.heads_dict[n, 1] = nn.Parameter(torch.zeros(size=(1, 2 * out_dim), device=device)) 21 | nn.init.xavier_normal_(self.heads_dict[n, 0], gain=1.414) 22 | nn.init.xavier_normal_(self.heads_dict[n, 1], gain=1.414) 23 | self.linear = nn.Linear(head_n, 1, device=device) 24 | 25 | # regularization 26 | self.leakyrelu = nn.LeakyReLU(alpha) 27 | self.dropout = nn.Dropout(dropout) 28 | self.softmax = nn.Softmax(dim=0) 29 | 30 | # sparse matrix 31 | self.a_sparse = a_sparse 32 | self.edges = a_sparse.indices() 33 | self.values = a_sparse.values() 34 | self.N = a_sparse.shape[0] 35 | a_dense = a_sparse.to_dense() 36 | a_dense[torch.where(a_dense == 0)] = -1000000000 37 | a_dense[torch.where(a_dense == 1)] = 0 38 | self.mask = a_dense 39 | 40 | def forward(self, x): 41 | b, n, s = x.shape 42 | x = x.reshape(b*n, s) 43 | 44 | atts_stack = [] 45 | # multi-heads attention 46 | for n in range(self.head_n): 47 | h = torch.matmul(x, self.heads_dict[n, 0]) 48 | edge_h = torch.cat((h[self.edges[0, :], :], h[self.edges[1, :], :]), dim=1).t() # [Ni, Nj] 49 | atts = self.heads_dict[n, 1].mm(edge_h).squeeze() 50 | atts = self.leakyrelu(atts) 51 | atts_stack.append(atts) 52 | 53 | mt_atts = torch.stack(atts_stack, dim=1) 54 | mt_atts = self.linear(mt_atts) 55 | new_values = self.values * mt_atts.squeeze() 56 | atts_mat = torch.sparse_coo_tensor(self.edges, new_values) 57 | atts_mat = atts_mat.to_dense() + self.mask 58 | atts_mat = self.softmax(atts_mat) 59 | return atts_mat 60 | 61 | 62 | class MLP(nn.Module): 63 | def __init__(self, in_channel, out_channel): 64 | super(MLP, self).__init__() 65 | self.l1 = nn.Linear(in_features=in_channel, out_features=256) 66 | self.l2 = nn.Linear(in_features=256, out_features=256) 67 | self.l3 = nn.Linear(in_features=256, out_features=out_channel) 68 | # self.dropout = nn.Dropout(p=0.5) 69 | self.relu = nn.ReLU() 70 | 71 | def forward(self, x): 72 | x = self.l1(x) 73 | x = self.relu(x) 74 | x = self.l2(x) 75 | x = self.relu(x) 76 | x = self.l3(x) 77 | return x 78 | 79 | 80 | class PAG(nn.Module): 81 | def __init__(self, a_sparse, seq=12, kcnn=2, k=6, m=2): 82 | super(PAG, self).__init__() 83 | self.feature = seq 84 | self.seq = seq-kcnn+1 85 | self.alpha = 0.5 86 | self.m = m 87 | self.a_sparse = a_sparse 88 | self.nodes = a_sparse.shape[0] 89 | 90 | # GAT 91 | self.conv2d = nn.Conv2d(1, 1, (kcnn, 2)) # input.shape = [batch, channel, width, height] 92 | self.gat_lyr = MultiHeadsGATLayer(a_sparse, self.seq, self.seq, 4, 0, 0.2) 93 | self.gcn = nn.Linear(in_features=self.seq, out_features=self.seq) 94 | 95 | # TPA 96 | self.lstm = nn.LSTM(m, m, num_layers=2, batch_first=True) 97 | self.fc1 = nn.Linear(in_features=self.seq - 1, out_features=k) 98 | self.fc2 = nn.Linear(in_features=k, out_features=m) 99 | self.fc3 = nn.Linear(in_features=k + m, out_features=1) 100 | self.decoder = nn.Linear(self.seq, 1) 101 | 102 | # Activation 103 | self.dropout = nn.Dropout(p=0.5) 104 | self.LeakyReLU = nn.LeakyReLU() 105 | 106 | # 107 | adj1 = copy.deepcopy(self.a_sparse.to_dense()) 108 | adj2 = copy.deepcopy(self.a_sparse.to_dense()) 109 | for i in range(self.nodes): 110 | adj1[i, i] = 0.000000001 111 | adj2[i, i] = 0 112 | degree = 1.0 / (torch.sum(adj1, dim=0)) 113 | degree_matrix = torch.zeros((self.nodes, self.feature), device=device) 114 | for i in range(12): 115 | degree_matrix[:, i] = degree 116 | self.degree_matrix = degree_matrix 117 | self.adj2 = adj2 118 | 119 | def forward(self, occ, prc): # occ.shape = [batch,node, seq] 120 | b, n, s = occ.shape 121 | data = torch.stack([occ, prc], dim=3).reshape(b*n, s, -1).unsqueeze(1) 122 | data = self.conv2d(data) 123 | data = data.squeeze().reshape(b, n, -1) 124 | 125 | # first layer 126 | atts_mat = self.gat_lyr(data) # attention matrix, dense(nodes, nodes) 127 | occ_conv1 = torch.matmul(atts_mat, data) # (b, n, s) 128 | occ_conv1 = self.dropout(self.LeakyReLU(self.gcn(occ_conv1))) 129 | 130 | # second layer 131 | atts_mat2 = self.gat_lyr(occ_conv1) # attention matrix, dense(nodes, nodes) 132 | occ_conv2 = torch.matmul(atts_mat2, occ_conv1) # (b, n, s) 133 | occ_conv2 = self.dropout(self.LeakyReLU(self.gcn(occ_conv2))) 134 | 135 | occ_conv1 = (1 - self.alpha) * occ_conv1 + self.alpha * data 136 | occ_conv2 = (1 - self.alpha) * occ_conv2 + self.alpha * occ_conv1 137 | occ_conv1 = occ_conv1.view(b * n, self.seq) 138 | occ_conv2 = occ_conv2.view(b * n, self.seq) 139 | 140 | x = torch.stack([occ_conv1, occ_conv2], dim=2) # best 141 | lstm_out, (_, _) = self.lstm(x) # b*n, s, 2 142 | 143 | # TPA 144 | ht = lstm_out[:, -1, :] # ht 145 | hw = lstm_out[:, :-1, :] # from h(t-1) to h1 146 | hw = torch.transpose(hw, 1, 2) 147 | Hc = self.fc1(hw) 148 | Hn = self.fc2(Hc) 149 | ht = torch.unsqueeze(ht, dim=2) 150 | a = torch.bmm(Hn, ht) 151 | a = torch.sigmoid(a) 152 | a = torch.transpose(a, 1, 2) 153 | vt = torch.matmul(a, Hc) 154 | ht = torch.transpose(ht, 1, 2) 155 | hx = torch.cat((vt, ht), dim=2) 156 | y = self.fc3(hx) 157 | y = y.view(b, n) 158 | return y 159 | -------------------------------------------------------------------------------- /plot.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | import seaborn as sns 5 | 6 | 7 | # input data 8 | data = pd.read_csv('results/plot_data.csv') 9 | impulse = np.array(data['Impulse'], dtype=float).reshape(-1, 1) 10 | Proposed = np.array(data['Proposed'], dtype=float).reshape(-1, 1) 11 | GCN_LSTM = np.array(data['GCN-LSTM'], dtype=float).reshape(-1, 1) 12 | GAT_LSTM = np.array(data['AST-GAT'], dtype=float).reshape(-1, 1) 13 | GTA = np.array(data['PAG-'], dtype=float).reshape(-1, 1) 14 | 15 | temp = np.ones_like(impulse) 16 | GCN_LSTM_id = temp * 3 17 | GAT_LSTM_id = temp * 2 18 | GTA_id = temp * 1 19 | 20 | ids = np.concatenate((GTA_id, GAT_LSTM_id, GCN_LSTM_id), axis=0) 21 | impulses = np.concatenate((impulse, impulse, impulse), axis=0) 22 | responses = np.concatenate((GTA, GAT_LSTM, GCN_LSTM), axis=0) 23 | 24 | plot_data = np.concatenate((ids, impulses, responses), axis=1) 25 | plot_data = pd.DataFrame(plot_data, columns=['ids', 'impulse', 'response']) 26 | 27 | rc = {'font.sans-serif': ['Arial']} 28 | sns.set(style='ticks', color_codes=True, font_scale=1.6, rc=rc) 29 | sns.scatterplot(x='impulse', y='response', hue='ids', style='ids', palette="Set2", data=plot_data) 30 | plt.ylim(-11, 11) 31 | plt.twinx() 32 | sns.scatterplot(x=np.squeeze(impulse), y=np.squeeze(Proposed)) 33 | plt.ylim(-0.6, 0.6) 34 | plt.tight_layout() 35 | plt.show() 36 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | python==3.9 2 | numpy==1.24.1 3 | pandas 4 | scikit-learn==1.3.2 5 | matplotlib 6 | seaborn 7 | torch==2.0.1 8 | torchaudio==2.0.2 9 | torchvision==0.15.2 10 | tqdm 11 | geopy -------------------------------------------------------------------------------- /results/plot_data.csv: -------------------------------------------------------------------------------- 1 | Impulse,Proposed,GCN-LSTM,AST-GAT,PAG- 2 | -0.405798078,0.289778352,-0.58664286,-1.2319298,2.0666213 3 | -0.38783434,0.260221273,-0.20436765,-2.5654917,1.6719426 4 | -0.387670994,0.299909562,-10.081264,-1.4864572,6.7974987 5 | -0.382315755,0.237625599,-6.5725074,-1.126765,7.048565 6 | -0.377201349,0.227767259,-0.5723982,-0.45433068,5.439013 7 | -0.377201349,0.243065715,-1.8914468,-1.2688505,4.9504566 8 | -0.377201349,0.216667384,-0.3711326,-0.24231058,3.4321165 9 | -0.362277448,0.215424284,-0.38130248,-6.4946117,1.3453286 10 | -0.353639275,0.207309097,-1.5805433,-0.35935378,10.47899 11 | -0.312296301,0.210177958,-1.5590018,-2.4542465,4.7496266 12 | 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28 | -0.107557453,0.069450691,-1.9632411,-1.4779646,2.7720804 29 | -0.104167633,0.073879771,-0.6045579,-0.9373981,1.36712 30 | -0.100590467,0.051606413,-0.16876899,-0.81197214,1.4812973 31 | -0.099538527,0.076215707,-0.13329998,-1.7938418,1.6125609 32 | -0.096838452,0.066589437,-1.1134254,-0.7322225,0.99088943 33 | -0.090872571,0.057069588,-1.3059429,-0.21352051,1.2793047 34 | -0.08968734,0.06290932,-0.15599987,-0.39575854,0.7611482 35 | -0.08945819,0.050789397,-0.07408654,-0.32684773,0.6199312 36 | -0.085566789,0.061505243,-0.12226246,-0.2590725,0.8015822 37 | -0.076690465,0.047481924,-0.010588099,-0.3641652,0.51468134 38 | -0.070827655,0.048615627,-0.068530336,-0.3963993,0.7525296 39 | -0.069731601,0.04253158,-5.140072,-1.10761,0.86184824 40 | -0.069456026,0.044423681,-0.009020298,-3.4123886,0.5823264 41 | -0.068912297,0.048079874,-0.13734782,-0.20779689,0.60169613 42 | -0.064016618,0.040339805,-0.09172733,-0.11104924,0.7645104 43 | 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