dgld.utils.common

class dgld.utils.common.ExpRecord(filepath='result.csv')

Bases: object

create a read a existed csv file to record the experiments

Parameters

filepath (str, filepath) – description, by default ‘result.csv’

Examples

>>> exprecord = ExpRecord()
>>> argsdict = vars(args)
>>> argsdict['auc'] = 1.0
>>> argsdict['info'] = "test"
>>> exprecord.add_record(argsdict)

add_record(dict_record)

summary

Parameters

dict_record (Dict) – record to add

load_record()

save_record()

class dgld.utils.common.Multidict2dict

Bases: object

convert multilayer Dict to a single layer Dict

Parameters

• inputs (Dict) –

• Dict (input) – {“a”:{“as”:”value”}}

• like{ (Maybe multilayer) – {“a”:{“as”:”value”}}

• } –

Returns

Dict

Return type

a single layer Dict

Examples

>>> tool = Multidict2dict()
>>> inputs = {
>>>    "1layer":{
>>>        "2layer_one":{
>>>            "3layers1":4,
>>>            "3layers2":2,
>>>        },
>>>        "2layer_two":2
>>>    }
>>> }
>>> result = tool.solve(inputs)
>>> print(result)
>>> {'3layers1': 4, '3layers2': 2, '2layer_two': 2}

solve(inputs)

class dgld.utils.common.ParameterShower

Bases: object

show Parameter using texttable

Examples

>>> inputs = {
>>>         "1layer":{
>>>             "2layer_one":{
>>>                 "3layers1":4,
>>>                 "3layers2":2,
>>>             },
>>>             "2layer_two":2
>>>         }
>>>     }
>>>
>>> tool = ParameterShower()
>>> tool.show_multilayer(inputs)
+------------+-------+
|    Name    | Value |
+============+=======+
| 3layers1   | 4     |
+------------+-------+
| 3layers2   | 2     |
+------------+-------+
| 2layer_two | 2     |
+------------+-------+

show_dict(inputs: Dict) → None

show_multilayer(inputs: Dict) → None

dgld.utils.common.allclose(a, b, rtol=0.0001, atol=0.0001)

This function checks if a and b satisfy the condition: |a - b| <= atol + rtol * |b|

Parameters

• input (Tensor) – first tensor to compare

• other (Tensor) – second tensor to compare

• atol (float, optional) – absolute tolerance. Default: 1e-08

• rtol (float, optional) – relative tolerance. Default: 1e-05

Returns

res – True for close, False for not

Return type

bool

dgld.utils.common.bar_progress(current, total, width=80)

dgld.utils.common.cprint(x, color='green')

dgld.utils.common.is_bidirected(g)

Return whether the graph is a bidirected graph. A graph is bidirected if for any edge \((u, v)\) in \(G\) with weight \(w\), there exists an edge \((v, u)\) in \(G\) with the same weight.

Parameters

g (DGL.graph) –

Returns

res – True for bidirected, False for not

Return type

bool

dgld.utils.common.lcprint(*arr, color='green')

dgld.utils.common.load_ACM(raw_dir='D:\\git repo\\DGLD\\src\\dgld/data/')

load ACM dgl graph

Parameters

raw_dir (str) – Data path. Supports user customization.

Returns

graph

Return type

DGL.graph

Examples

>>> graph=load_ACM()[0]

dgld.utils.common.load_BlogCatalog(raw_dir='D:\\git repo\\DGLD\\src\\dgld/data/')

load BlogCatalog dgl graph

Parameters

raw_dir (str) – Data path. Supports user customization.

Returns

graph

Return type

DGL.graph

Examples

>>> graph=load_BlogCatalog()[0]

dgld.utils.common.load_Flickr(raw_dir='D:\\git repo\\DGLD\\src\\dgld/data/')

load Flickr dgl graph

Parameters

raw_dir (str) – Data path. Supports user customization.

Returns

graph

Return type

DGL.graph

Examples

>>> graph=load_Flickr()[0]

dgld.utils.common.load_mat_data2dgl(data_path, verbose=True)

load data from .mat file

Parameters

• data_path (str) – the file to read in

• verbose (bool, optional) – print info, by default True

Returns

graph – the graph read from data_path

Return type

DGL.graph

dgld.utils.common.load_ogbn_arxiv(raw_dir='D:\\git repo\\DGLD\\src\\dgld/data/')

Read ogbn-arxiv from dgl.

Parameters

raw_dir (str) – Data path. Supports user customization.

Returns

graph – the graph of ogbn-arxiv

Return type

dgl.graph

dgld.utils.common.loadargs_from_json(filename, indent=4)

load args from a format json file

Parameters

• filename (file name) – json filename

• indent (int, optional) – description, by default 4

Returns

Dict – args parameters

Return type

json

dgld.utils.common.move_start_node_fisrt(pace, start_node)

return a new pace in which the start node is in the first place.

Parameters

• pace (list) – the subgraph of start node

• start_node (int) – target node

Returns

pace – subgraph whose first value is start_node

Return type

list

dgld.utils.common.preprocess_features(features)

Functions that process features, here norm in row

Parameters

features (torch.Tensor) – features to be processed

Return type

None

dgld.utils.common.print_format_dict(dict_input)

print dict with json for a decent show

Parameters

dict_input (Dict) – dict to print

dgld.utils.common.print_shape(*a)

dgld.utils.common.ranknorm(input_arr)

return the 1-norm of rankdata of input_arr

Parameters

input_arr (list) – the data to be ranked

Returns

rank – the 1-norm of rankdata

Return type

numpy.ndarray

dgld.utils.common.saveargs2json(jsonobject, filename, indent=4)

save args parameters to json with a decent format

Parameters

• jsonobject (Dict) – dict object to save

• filename (str) – file name

• indent (int, optional) – description, by default 4

dgld.utils.common.seed_everything(seed=42)

dgld.utils.common.tab_printer(args: Dict, thead: Optional[List[str]] = None) → None

Function to print the logs in a nice tabular format.

Parameters

args (Dict) – Parameters used for the model.