4.6 Loading data from CSV files

Comma Separated Value (CSV) is a widely used data storage format. DGL provides CSVDataset for loading and parsing graph data stored in CSV format.

To create a CSVDataset object:

import dgl
ds = dgl.data.CSVDataset('/path/to/dataset')

The returned ds object is a standard DGLDataset. For example, one can get graph samples using __getitem__ as well as node/edge features using ndata/edata.

# A demonstration of how to use the loaded dataset. The feature names
# may vary depending on the CSV contents.
g = ds[0] # get the graph
label = g.ndata['label']
feat = g.ndata['feat']

Data folder structure

/path/to/dataset/
|-- meta.yaml     # metadata of the dataset
|-- edges_0.csv   # edge data including src_id, dst_id, feature, label and so on
|-- ...           # you can have as many CSVs for edge data as you want
|-- nodes_0.csv   # node data including node_id, feature, label and so on
|-- ...           # you can have as many CSVs for node data as you want
|-- graphs.csv    # graph-level features

Node/edge/graph-level data are stored in CSV files. meta.yaml is a metadata file specifying where to read nodes/edges/graphs data and how to parse them to construct the dataset object. A minimal data folder contains one meta.yaml and two CSVs, one for node data and one for edge data, in which case the dataset contains only a single graph with no graph-level data.

Dataset of a single feature-less graph

When the dataset contains only one graph with no node or edge features, there need only three files in the data folder: meta.yaml, one CSV for node IDs and one CSV for edges:

./mini_featureless_dataset/
|-- meta.yaml
|-- nodes.csv
|-- edges.csv

meta.yaml contains the following information:

dataset_name: mini_featureless_dataset
edge_data:
- file_name: edges.csv
node_data:
- file_name: nodes.csv

nodes.csv lists the node IDs under the node_id field:

node_id
0
1
2
3
4

edges.csv lists all the edges in two columns (src_id and dst_id) specifying the source and destination node ID of each edge:

src_id,dst_id
4,4
4,1
3,0
4,1
4,0
1,2
1,3
3,3
1,1
4,1

After loaded, the dataset has one graph without any features:

>>> import dgl
>>> dataset = dgl.data.CSVDataset('./mini_featureless_dataset')
>>> g = dataset[0]  # only one graph
>>> print(g)
Graph(num_nodes=5, num_edges=10,
      ndata_schemes={}
      edata_schemes={})

Note

Non-integer node IDs are allowed. When constructing the graph, CSVDataset will map each raw ID to an integer ID starting from zero. If the node IDs are already distinct integers from 0 to num_nodes-1, no mapping is applied.

Note

Edges are always directed. To have both directions, add reversed edges in the edge CSV file or use AddReverse to transform the loaded graph.

A graph without any feature is often of less interest. In the next example, we will show how to load and parse node or edge features.

Dataset of a single graph with features and labels

When the dataset contains a single graph with node or edge features and labels, there still need only three files in the data folder: meta.yaml, one CSV for node IDs and one CSV for edges:

./mini_feature_dataset/
|-- meta.yaml
|-- nodes.csv
|-- edges.csv

meta.yaml:

dataset_name: mini_feature_dataset
edge_data:
- file_name: edges.csv
node_data:
- file_name: nodes.csv

edges.csv with five synthetic edge data (label, train_mask, val_mask, test_mask, feat):

src_id,dst_id,label,train_mask,val_mask,test_mask,feat
4,0,2,False,True,True,"0.5477868606453535, 0.4470617033458436, 0.936706701616337"
4,0,0,False,False,True,"0.9794634290792008, 0.23682038840665198, 0.049629338970987646"
0,3,1,True,True,True,"0.8586722047523594, 0.5746912787380253, 0.6462162561249654"
0,1,2,True,False,False,"0.2730008213674695, 0.5937484188166621, 0.765544096939567"
0,2,1,True,True,True,"0.45441619816038514, 0.1681403185591509, 0.9952376085297715"
0,0,0,False,False,False,"0.4197669213305396, 0.849983324532477, 0.16974127573016262"
2,2,1,False,True,True,"0.5495035052928215, 0.21394654203489705, 0.7174910641836348"
1,0,2,False,True,False,"0.008790817766266334, 0.4216530595907526, 0.529195480661293"
3,0,0,True,True,True,"0.6598715708878852, 0.1932390907048961, 0.9774471538377553"
4,0,1,False,False,False,"0.16846068931179736, 0.41516080644186737, 0.002158116134429955"

nodes.csv with five synthetic node data (label, train_mask, val_mask, test_mask, feat):

node_id,label,train_mask,val_mask,test_mask,feat
0,1,False,True,True,"0.07816474278491703, 0.9137336384979067, 0.4654086994009452"
1,1,True,True,True,"0.05354099924658973, 0.8753101998792645, 0.33929432608774135"
2,1,True,False,True,"0.33234211884156384, 0.9370522452510665, 0.6694943496824788"
3,0,False,True,False,"0.9784264442230887, 0.22131880861864428, 0.3161154827254189"
4,1,True,True,False,"0.23142237259162102, 0.8715767748481147, 0.19117861103555467"

After loaded, the dataset has one graph. Node/edge features are stored in ndata and edata with the same column names. The example demonstrates how to specify a vector-shaped feature using comma-separated list enclosed by double quotes "...".

>>> import dgl
>>> dataset = dgl.data.CSVDataset('./mini_feature_dataset')
>>> g = dataset[0]  # only one graph
>>> print(g)
Graph(num_nodes=5, num_edges=10,
      ndata_schemes={'label': Scheme(shape=(), dtype=torch.int64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'val_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool), 'feat': Scheme(shape=(3,), dtype=torch.float64)}
      edata_schemes={'label': Scheme(shape=(), dtype=torch.int64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'val_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool), 'feat': Scheme(shape=(3,), dtype=torch.float64)})

Note

By default, CSVDatatset assumes all feature data to be numerical values (e.g., int, float, bool or list) and missing values are not allowed. Users could provide custom data parser for these cases. See Custom Data Parser for more details.

Dataset of a single heterogeneous graph

One can specify multiple node and edge CSV files (each for one type) to represent a heterogeneous graph. Here is an example data with two node types and two edge types:

./mini_hetero_dataset/
|-- meta.yaml
|-- nodes_0.csv
|-- nodes_1.csv
|-- edges_0.csv
|-- edges_1.csv

The meta.yaml specifies the node type name (using ntype) and edge type name (using etype) of each CSV file. The edge type name is a string triplet containing the source node type name, relation name and the destination node type name.

dataset_name: mini_hetero_dataset
edge_data:
- file_name: edges_0.csv
  etype: [user, follow, user]
- file_name: edges_1.csv
  etype: [user, like, item]
node_data:
- file_name: nodes_0.csv
  ntype: user
- file_name: nodes_1.csv
  ntype: item

The node and edge CSV files follow the same format as in homogeneous graphs. Here are some synthetic data for demonstration purposes:

edges_0.csv and edges_1.csv:

src_id,dst_id,label,feat
4,4,1,"0.736833152378035,0.10522806046048205,0.9418796835016118"
3,4,2,"0.5749339182767451,0.20181320245665535,0.490938012147181"
1,4,2,"0.7697294432580938,0.49397782380750765,0.10864079337442234"
0,4,0,"0.1364240150959487,0.1393107840629273,0.7901988878812207"
2,3,1,"0.42988138237505735,0.18389137408509248,0.18431292077750894"
0,4,2,"0.8613368738351794,0.67985810014162,0.6580438064356824"
2,4,1,"0.6594951663841697,0.26499036865016423,0.7891429392727503"
4,1,0,"0.36649684241348557,0.9511783938523962,0.8494919263589972"
1,1,2,"0.698592283371875,0.038622249776255946,0.5563827995742111"
0,4,1,"0.5227112950269823,0.3148264185956532,0.47562693094002173"

nodes_0.csv and nodes_1.csv:

node_id,label,feat
0,2,"0.5400687466285844,0.7588441197954202,0.4268254673041745"
1,1,"0.08680051341900807,0.11446843700743892,0.7196969604886617"
2,2,"0.8964389655603473,0.23368113896545695,0.8813472954005022"
3,1,"0.5454703921677284,0.7819383771535038,0.3027939452162367"
4,1,"0.5365210052235699,0.8975240205792763,0.7613943085507672"

After loaded, the dataset has one heterograph with features and labels:

>>> import dgl
>>> dataset = dgl.data.CSVDataset('./mini_hetero_dataset')
>>> g = dataset[0]  # only one graph
>>> print(g)
Graph(num_nodes={'item': 5, 'user': 5},
      num_edges={('user', 'follow', 'user'): 10, ('user', 'like', 'item'): 10},
      metagraph=[('user', 'user', 'follow'), ('user', 'item', 'like')])
>>> g.nodes['user'].data
{'label': tensor([2, 1, 2, 1, 1]), 'feat': tensor([[0.5401, 0.7588, 0.4268],
        [0.0868, 0.1145, 0.7197],
        [0.8964, 0.2337, 0.8813],
        [0.5455, 0.7819, 0.3028],
        [0.5365, 0.8975, 0.7614]], dtype=torch.float64)}
>>> g.edges['like'].data
{'label': tensor([1, 2, 2, 0, 1, 2, 1, 0, 2, 1]), 'feat': tensor([[0.7368, 0.1052, 0.9419],
        [0.5749, 0.2018, 0.4909],
        [0.7697, 0.4940, 0.1086],
        [0.1364, 0.1393, 0.7902],
        [0.4299, 0.1839, 0.1843],
        [0.8613, 0.6799, 0.6580],
        [0.6595, 0.2650, 0.7891],
        [0.3665, 0.9512, 0.8495],
        [0.6986, 0.0386, 0.5564],
        [0.5227, 0.3148, 0.4756]], dtype=torch.float64)}

Dataset of multiple graphs

When there are multiple graphs, one can include an additional CSV file for storing graph-level features. Here is an example:

./mini_multi_dataset/
|-- meta.yaml
|-- nodes.csv
|-- edges.csv
|-- graphs.csv

Accordingly, the meta.yaml should include an extra graph_data key to tell which CSV file to load graph-level features from.

dataset_name: mini_multi_dataset
edge_data:
- file_name: edges.csv
node_data:
- file_name: nodes.csv
graph_data:
  file_name: graphs.csv

To distinguish nodes and edges of different graphs, the node.csv and edge.csv must contain an extra column graph_id:

edges.csv:

graph_id,src_id,dst_id,feat
0,0,4,"0.39534097273254654,0.9422093637539785,0.634899790318452"
0,3,0,"0.04486384200747007,0.6453746567017163,0.8757520744192612"
0,3,2,"0.9397636966928355,0.6526403892728874,0.8643238446466464"
0,1,1,"0.40559906615287566,0.9848072295736628,0.493888090726854"
0,4,1,"0.253458867276219,0.9168191778828504,0.47224962583565544"
0,0,1,"0.3219496197945605,0.3439899477636117,0.7051530741717352"
0,2,1,"0.692873149428549,0.4770019763881086,0.21937428942781778"
0,4,0,"0.620118223673067,0.08691420300562658,0.86573472329756"
0,2,1,"0.00743445923710373,0.5251800239734318,0.054016385555202384"
0,4,1,"0.6776417760682221,0.7291568018841328,0.4523600060547709"
1,1,3,"0.6375445528248924,0.04878384701995819,0.4081642382536248"
1,0,4,"0.776002616178397,0.8851294998284638,0.7321742043493028"
1,1,0,"0.0928555079874982,0.6156748364694707,0.6985674921582508"
1,0,2,"0.31328748118329997,0.8326121496142408,0.04133991340612775"
1,1,0,"0.36786902637778773,0.39161865931662243,0.9971749359397111"
1,1,1,"0.4647410679872376,0.8478810655406659,0.6746269314422184"
1,0,2,"0.8117650553546695,0.7893727601272978,0.41527155506593394"
1,1,3,"0.40707309111756307,0.2796588354307046,0.34846782265758314"
1,1,0,"0.18626464175355095,0.3523777809254057,0.7863421810531344"
1,3,0,"0.28357022069634585,0.13774964202156292,0.5913335505943637"

nodes.csv:

graph_id,node_id,feat
0,0,"0.5725330322207948,0.8451870383322376,0.44412796119211184"
0,1,"0.6624186423087752,0.6118386331195641,0.7352138669985214"
0,2,"0.7583372765843964,0.15218126307872892,0.6810484348765842"
0,3,"0.14627522432017592,0.7457985352827006,0.1037097085190507"
0,4,"0.49037522512771525,0.8778998699783784,0.0911194482288028"
1,0,"0.11158102039672668,0.08543289788089736,0.6901745368284345"
1,1,"0.28367647637469273,0.07502571020414439,0.01217200152200748"
1,2,"0.2472495901894738,0.24285506608575758,0.6494437360242048"
1,3,"0.5614197853127827,0.059172654879085296,0.4692371689047904"
1,4,"0.17583413999295983,0.5191278830882644,0.8453123358491914"

The graphs.csv contains a graph_id column and arbitrary number of feature columns. The example dataset here has two graphs, each with a feat and a label graph-level data.

graph_id,feat,label
0,"0.7426272601929126,0.5197462471155317,0.8149104951283953",0
1,"0.534822233529295,0.2863627767733977,0.1154897249106891",0

After loaded, the dataset has multiple homographs with features and labels:

>>> import dgl
>>> dataset = dgl.data.CSVDataset('./mini_multi_dataset')
>>> print(len(dataset))
2
>>> graph0, data0 = dataset[0]
>>> print(graph0)
Graph(num_nodes=5, num_edges=10,
      ndata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)}
      edata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)})
>>> print(data0)
{'feat': tensor([0.7426, 0.5197, 0.8149], dtype=torch.float64), 'label': tensor(0)}
>>> graph1, data1 = dataset[1]
>>> print(graph1)
Graph(num_nodes=5, num_edges=10,
      ndata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)}
      edata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)})
>>> print(data1)
{'feat': tensor([0.5348, 0.2864, 0.1155], dtype=torch.float64), 'label': tensor(0)}

If there is a single feature column in graphs.csv, data0 will directly be a tensor for the feature.

Custom Data Parser

By default, CSVDataset assumes that all the stored node-/edge-/graph- level data are numerical values. Users can provide custom DataParser to CSVDataset to handle more complex data type. A DataParser needs to implement the __call__ method which takes in the pandas.DataFrame object created from CSV file and should return a dictionary of parsed feature data. The parsed feature data will be saved to the ndata and edata of the corresponding DGLGraph object, and thus must be tensors or numpy arrays. Below shows an example DataParser which converts string type labels to integers:

Given a dataset as follows,

./customized_parser_dataset/
|-- meta.yaml
|-- nodes.csv
|-- edges.csv

meta.yaml:

dataset_name: customized_parser_dataset
edge_data:
- file_name: edges.csv
node_data:
- file_name: nodes.csv

edges.csv:

src_id,dst_id,label
4,0,positive
4,0,negative
0,3,positive
0,1,positive
0,2,negative
0,0,positive
2,2,negative
1,0,positive
3,0,negative
4,0,positive

nodes.csv:

node_id,label
0,positive
1,negative
2,positive
3,negative
4,positive

To parse the string type labels, one can define a DataParser class as follows:

import numpy as np
import pandas as pd

class MyDataParser:
    def __call__(self, df: pd.DataFrame):
        parsed = {}
        for header in df:
            if 'Unnamed' in header:  # Handle Unnamed column
                print("Unnamed column is found. Ignored...")
                continue
            dt = df[header].to_numpy().squeeze()
            if header == 'label':
                dt = np.array([1 if e == 'positive' else 0 for e in dt])
            parsed[header] = dt
        return parsed

Create a CSVDataset using the defined DataParser:

>>> import dgl
>>> dataset = dgl.data.CSVDataset('./customized_parser_dataset',
...                               ndata_parser=MyDataParser(),
...                               edata_parser=MyDataParser())
>>> print(dataset[0].ndata['label'])
tensor([1, 0, 1, 0, 1])
>>> print(dataset[0].edata['label'])
tensor([1, 0, 1, 1, 0, 1, 0, 1, 0, 1])

Note

To specify different DataParsers for different node/edge types, pass a dictionary to ndata_parser and edata_parser, where the key is type name (a single string for node type; a string triplet for edge type) and the value is the DataParser to use.

Full YAML Specification

CSVDataset allows more flexible control over the loading and parsing process. For example, one can change the ID column names via meta.yaml. The example below lists all the supported keys.

version: 1.0.0
dataset_name: some_complex_data
separator: ','                   # CSV separator symbol. Default: ','
edge_data:
- file_name: edges_0.csv
  etype: [user, follow, user]
  src_id_field: src_id           # Column name for source node IDs. Default: src_id
  dst_id_field: dst_id           # Column name for destination node IDs. Default: dst_id
- file_name: edges_1.csv
  etype: [user, like, item]
  src_id_field: src_id
  dst_id_field: dst_id
node_data:
- file_name: nodes_0.csv
  ntype: user
  node_id_field: node_id         # Column name for node IDs. Default: node_id
- file_name: nodes_1.csv
  ntype: item
  node_id_field: node_id         # Column name for node IDs. Default: node_id
graph_data:
  file_name: graphs.csv
  graph_id_field: graph_id       # Column name for graph IDs. Default: graph_id

Top-level

At the top level, only 6 keys are available:

• version: Optional. String. It specifies which version of meta.yaml is used. More feature may be added in the future.

• dataset_name: Required. String. It specifies the dataset name.

• separator: Optional. String. It specifies how to parse data in CSV files. Default: ','.

• edge_data: Required. List of EdgeData. Meta data for parsing edge CSV files.

• node_data: Required. List of NodeData. Meta data for parsing node CSV files.

• graph_data: Optional. GraphData. Meta data for parsing the graph CSV file.

EdgeData

There are 4 keys:

• file_name: Required. String. The CSV file to load data from.

• etype: Optional. List of string. Edge type name in string triplet: [source node type, relation type, destination node type].

• src_id_field: Optional. String. Which column to read for source node IDs. Default: src_id.

• dst_id_field: Optional. String. Which column to read for destination node IDs. Default: dst_id.

NodeData

There are 3 keys:

• file_name: Required. String. The CSV file to load data from.

• ntype: Optional. String. Node type name.

• node_id_field: Optional. String. Which column to read for node IDs. Default: node_id.

GraphData

There are 2 keys:

• file_name: Required. String. The CSV file to load data from.

• graph_id_field: Optional. String. Which column to read for graph IDs. Default: graph_id.