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QGCN-0.0.9
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مشاهده بیشترتوضیحات
a qgcn model package
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | QGCN-0.0.9 |
| نام | QGCN |
| نسخه کتابخانه | 0.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | YOLO LAB |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/QGCN/ |
| مجوز | - |
# QGCN
QGCN method for graph classification: https://arxiv.org/abs/2104.06750
## Installation
required packages:
- scipy~=1.8.0
- pandas~=1.4.2
- networkx~=2.8.3
- numpy~=1.22.3
- torch~=1.11.0
- scikit-learn~=1.1.1
- bokeh~=2.4.2
- matplotlib~=3.5.1
- bitstring~=3.1.9
- python-louvain~=0.16
- graph-measures~=0.1.44
You can download the package by the command:
```
pip install QGCN
```
## How to use
### Graph representing
To use this package you will need to provide the following files as input:
* Graphs csv file - files that contain the graphs for input and their labels.
The format of the file is flexible, but it must contain headers for any column, and there must be a column provided for:
- graph id
- source node id
- destination node id
- label id (every graph id can be attached to only one label)
- External data file - external data for every node (Optional)
The format of this file is also flexible, but it must contain headers for any column, and there must be a column provided for:
**note!! every node must get a value**
- graph id
- node id
- column for every external feature (if the value is not numeric then it can be handled with embeddings)
Example for such files: <br>
graph csv file:
```csv
g_id,src,dst,label
6678,_1,_2,i
6678,_1,_3,i
6678,_2,_4,i
6678,_3,_5,i
```
External data file:
```csv
g_id,node,charge,chem,symbol,x,y
6678,_1,0,1,C,4.5981,-0.25
6678,_2,0,1,C,5.4641,0.25
6678,_3,0,1,C,3.7321,0.25
6678,_4,0,1,C,6.3301,-0.25
```
### Parameters passing
After creating these file, you should define the parameters of the model. This can be done with a json file, or with data classes:
* Example json file:
- (Notice that if an external file is not provided, you should put the associated parameters as None.)
```json
{
"dataset_name": "Aids",
"external": {
"file_path": "./data/AIDS_external_data_all.csv",
"graph_col": "g_id",
"node_col": "node",
"embeddings": ["chem", "symbol"],
"continuous": ["charge", "x", "y"]
},
"graphs_data": {
"file_path": "./data/AIDS_all.csv",
"graph_col": "g_id",
"src_col": "src",
"dst_col": "dst",
"label_col": "label",
"directed": "False",
"features": ["DEG", "CENTRALITY", "BFS"],
"adjacency_norm": "NORM_REDUCED",
"percentage": 1,
"standardization": "zscore"
},
"model": {
"label_type": "binary",
"num_classes": 2,
"use_embeddings": "True",
"embeddings_dim": [10, 10],
"activation": "relu_",
"dropout": 0,
"lr": 1e-3,
"optimizer": "ADAM_",
"L2_regularization": 0,
"f": "c_x0",
"GCN_layers": [
{ "in_dim": "None", "out_dim": 100 },
{ "in_dim": 100, "out_dim": 50 },
{ "in_dim": 50, "out_dim": 25 }
]
},
"activator": {
"epochs": 3,
"batch_size": 128,
"loss_func": "binary_cross_entropy_with_logits_",
"train": 0.3467,
"dev": 0.1153,
"test": 0.538
}
}
```
* Example dataclass objects:
```python
from QGCN.params import GraphsDataParams, ExternalParams, ModelParams, ActivatorParams
external_params = ExternalParams(file_path="./data/Mutagenicity_external_data_all.csv",
embeddings=["chem"],
continuous=[])
graphs_data_params = GraphsDataParams(file_path="../src/QGCN/data/Mutagenicity_all.csv",
standardization="min_max")
model_params = ModelParams(label_type="binary",
use_embeddings="True",
embeddings_dim=[10],
activation="srss_",
GCN_layers=[
{"in_dim": "None", "out_dim": 250},
{"in_dim": 250, "out_dim": 100}])
activator_params = ActivatorParams(epochs=100)
```
### Executing the model
Once you have these files, you can use the QGCNModel from QGCN.activator with the path to the parameters file or the dataclass objects:
```python
from torch.utils.data import DataLoader
from QGCN.params import GraphsDataParams, ExternalParams, ModelParams, ActivatorParams
from QGCN.activator import QGCNModel, QGCNDataSet
# sets the parameters of the dataset:
external = ExternalParams(file_path="./data/Mutagenicity_external_data_all.csv",
graph_col="g_id", node_col="node",
embeddings=["chem"], continuous=[])
graphs_data = GraphsDataParams(file_path="../src/QGCN/data/Mutagenicity_all.csv",
standardization="min_max")
# sets the parameters of the model:
model = ModelParams(label_type="binary", num_classes=2, use_embeddings="True", embeddings_dim=[10],
activation="srss_", dropout=0.2, lr=0.005, optimizer="ADAM_", L2_regularization=0.005, f="x1_x0",
GCN_layers=[
{"in_dim": "None", "out_dim": 250},
{"in_dim": 250, "out_dim": 100}])
activator = ActivatorParams(epochs=100)
qgcn_model = QGCNModel("Mutagen", graphs_data, external, model, activator)
qgcn_model.train(should_print=False)
ds = QGCNDataSet("Mutagen", graphs_data, external)
loader = DataLoader(
ds.get_dataset(),
shuffle=False
)
for _, (A, x0, embed, label) in enumerate(loader):
output = qgcn_model.predict(A, x0, embed)
print(output, label)
```
## Links
The datasets can be download here: https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets . Notice you will have to change their format to ours. You can see an example data here (gitHub link) the conventor in datasets -> change_data_format.py
Mail address for more information: 123shovalf@gmail.com
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl QGCN-0.0.9:
pip install QGCN-0.0.9.whl
نصب پکیج tar.gz QGCN-0.0.9:
pip install QGCN-0.0.9.tar.gz