dmlc--dgl
Rhett Ying
643 行
22 KiB
Python
643 行
22 KiB
Python
"""GraphBolt OnDiskDataset."""
|
|
|
|
import os
|
|
from copy import deepcopy
|
|
from typing import Dict, List, Union
|
|
|
|
import pandas as pd
|
|
import torch
|
|
import yaml
|
|
|
|
import dgl
|
|
|
|
from ...base import dgl_warning
|
|
from ...data.utils import download, extract_archive
|
|
from ..base import etype_str_to_tuple
|
|
from ..dataset import Dataset, Task
|
|
from ..internal import copy_or_convert_data, get_attributes, read_data
|
|
from ..itemset import ItemSet, ItemSetDict
|
|
from ..sampling_graph import SamplingGraph
|
|
from .fused_csc_sampling_graph import from_dglgraph, FusedCSCSamplingGraph
|
|
from .ondisk_metadata import (
|
|
OnDiskGraphTopology,
|
|
OnDiskMetaData,
|
|
OnDiskTaskData,
|
|
OnDiskTVTSet,
|
|
)
|
|
from .torch_based_feature_store import TorchBasedFeatureStore
|
|
|
|
__all__ = ["OnDiskDataset", "preprocess_ondisk_dataset", "BuiltinDataset"]
|
|
|
|
|
|
def preprocess_ondisk_dataset(
|
|
dataset_dir: str, include_original_edge_id: bool = False
|
|
) -> str:
|
|
"""Preprocess the on-disk dataset. Parse the input config file,
|
|
load the data, and save the data in the format that GraphBolt supports.
|
|
|
|
Parameters
|
|
----------
|
|
dataset_dir : str
|
|
The path to the dataset directory.
|
|
include_original_edge_id : bool, optional
|
|
Whether to include the original edge id in the FusedCSCSamplingGraph.
|
|
|
|
Returns
|
|
-------
|
|
output_config_path : str
|
|
The path to the output config file.
|
|
"""
|
|
# Check if the dataset path is valid.
|
|
if not os.path.exists(dataset_dir):
|
|
raise RuntimeError(f"Invalid dataset path: {dataset_dir}")
|
|
|
|
# Check if the dataset_dir is a directory.
|
|
if not os.path.isdir(dataset_dir):
|
|
raise RuntimeError(
|
|
f"The dataset must be a directory. But got {dataset_dir}"
|
|
)
|
|
|
|
# 0. Check if the dataset is already preprocessed.
|
|
preprocess_metadata_path = os.path.join("preprocessed", "metadata.yaml")
|
|
if os.path.exists(os.path.join(dataset_dir, preprocess_metadata_path)):
|
|
print("The dataset is already preprocessed.")
|
|
return os.path.join(dataset_dir, preprocess_metadata_path)
|
|
|
|
print("Start to preprocess the on-disk dataset.")
|
|
processed_dir_prefix = "preprocessed"
|
|
|
|
# Check if the metadata.yaml exists.
|
|
metadata_file_path = os.path.join(dataset_dir, "metadata.yaml")
|
|
if not os.path.exists(metadata_file_path):
|
|
raise RuntimeError("metadata.yaml does not exist.")
|
|
|
|
# Read the input config.
|
|
with open(metadata_file_path, "r") as f:
|
|
input_config = yaml.safe_load(f)
|
|
|
|
# 1. Make `processed_dir_abs` directory if it does not exist.
|
|
os.makedirs(os.path.join(dataset_dir, processed_dir_prefix), exist_ok=True)
|
|
output_config = deepcopy(input_config)
|
|
|
|
# 2. Load the edge data and create a DGLGraph.
|
|
if "graph" not in input_config:
|
|
raise RuntimeError("Invalid config: does not contain graph field.")
|
|
is_homogeneous = "type" not in input_config["graph"]["nodes"][0]
|
|
if is_homogeneous:
|
|
# Homogeneous graph.
|
|
num_nodes = input_config["graph"]["nodes"][0]["num"]
|
|
edge_data = pd.read_csv(
|
|
os.path.join(
|
|
dataset_dir, input_config["graph"]["edges"][0]["path"]
|
|
),
|
|
names=["src", "dst"],
|
|
)
|
|
src, dst = edge_data["src"].to_numpy(), edge_data["dst"].to_numpy()
|
|
|
|
g = dgl.graph((src, dst), num_nodes=num_nodes)
|
|
else:
|
|
# Heterogeneous graph.
|
|
# Construct the num nodes dict.
|
|
num_nodes_dict = {}
|
|
for node_info in input_config["graph"]["nodes"]:
|
|
num_nodes_dict[node_info["type"]] = node_info["num"]
|
|
# Construct the data dict.
|
|
data_dict = {}
|
|
for edge_info in input_config["graph"]["edges"]:
|
|
edge_data = pd.read_csv(
|
|
os.path.join(dataset_dir, edge_info["path"]),
|
|
names=["src", "dst"],
|
|
)
|
|
src = torch.tensor(edge_data["src"])
|
|
dst = torch.tensor(edge_data["dst"])
|
|
data_dict[etype_str_to_tuple(edge_info["type"])] = (src, dst)
|
|
# Construct the heterograph.
|
|
g = dgl.heterograph(data_dict, num_nodes_dict)
|
|
|
|
# 3. Load the sampling related node/edge features and add them to
|
|
# the sampling-graph.
|
|
if input_config["graph"].get("feature_data", None):
|
|
for graph_feature in input_config["graph"]["feature_data"]:
|
|
in_memory = (
|
|
True
|
|
if "in_memory" not in graph_feature
|
|
else graph_feature["in_memory"]
|
|
)
|
|
if graph_feature["domain"] == "node":
|
|
node_data = read_data(
|
|
os.path.join(dataset_dir, graph_feature["path"]),
|
|
graph_feature["format"],
|
|
in_memory=in_memory,
|
|
)
|
|
g.ndata[graph_feature["name"]] = node_data
|
|
if graph_feature["domain"] == "edge":
|
|
edge_data = read_data(
|
|
os.path.join(dataset_dir, graph_feature["path"]),
|
|
graph_feature["format"],
|
|
in_memory=in_memory,
|
|
)
|
|
g.edata[graph_feature["name"]] = edge_data
|
|
|
|
# 4. Convert the DGLGraph to a FusedCSCSamplingGraph.
|
|
fused_csc_sampling_graph = from_dglgraph(
|
|
g, is_homogeneous, include_original_edge_id
|
|
)
|
|
|
|
# 5. Save the FusedCSCSamplingGraph and modify the output_config.
|
|
output_config["graph_topology"] = {}
|
|
output_config["graph_topology"]["type"] = "FusedCSCSamplingGraph"
|
|
output_config["graph_topology"]["path"] = os.path.join(
|
|
processed_dir_prefix, "fused_csc_sampling_graph.pt"
|
|
)
|
|
|
|
torch.save(
|
|
fused_csc_sampling_graph,
|
|
os.path.join(
|
|
dataset_dir,
|
|
output_config["graph_topology"]["path"],
|
|
),
|
|
)
|
|
del output_config["graph"]
|
|
|
|
# 6. Load the node/edge features and do necessary conversion.
|
|
if input_config.get("feature_data", None):
|
|
for feature, out_feature in zip(
|
|
input_config["feature_data"], output_config["feature_data"]
|
|
):
|
|
# Always save the feature in numpy format.
|
|
out_feature["format"] = "numpy"
|
|
out_feature["path"] = os.path.join(
|
|
processed_dir_prefix, feature["path"].replace("pt", "npy")
|
|
)
|
|
in_memory = (
|
|
True if "in_memory" not in feature else feature["in_memory"]
|
|
)
|
|
copy_or_convert_data(
|
|
os.path.join(dataset_dir, feature["path"]),
|
|
os.path.join(dataset_dir, out_feature["path"]),
|
|
feature["format"],
|
|
output_format=out_feature["format"],
|
|
in_memory=in_memory,
|
|
is_feature=True,
|
|
)
|
|
|
|
# 7. Save tasks and train/val/test split according to the output_config.
|
|
if input_config.get("tasks", None):
|
|
for input_task, output_task in zip(
|
|
input_config["tasks"], output_config["tasks"]
|
|
):
|
|
for set_name in ["train_set", "validation_set", "test_set"]:
|
|
if set_name not in input_task:
|
|
continue
|
|
for input_set_per_type, output_set_per_type in zip(
|
|
input_task[set_name], output_task[set_name]
|
|
):
|
|
for input_data, output_data in zip(
|
|
input_set_per_type["data"], output_set_per_type["data"]
|
|
):
|
|
# Always save the feature in numpy format.
|
|
output_data["format"] = "numpy"
|
|
output_data["path"] = os.path.join(
|
|
processed_dir_prefix,
|
|
input_data["path"].replace("pt", "npy"),
|
|
)
|
|
copy_or_convert_data(
|
|
os.path.join(dataset_dir, input_data["path"]),
|
|
os.path.join(dataset_dir, output_data["path"]),
|
|
input_data["format"],
|
|
output_data["format"],
|
|
)
|
|
|
|
# 8. Save the output_config.
|
|
output_config_path = os.path.join(dataset_dir, preprocess_metadata_path)
|
|
with open(output_config_path, "w") as f:
|
|
yaml.dump(output_config, f)
|
|
print("Finish preprocessing the on-disk dataset.")
|
|
|
|
# 9. Return the absolute path of the preprocessing yaml file.
|
|
return output_config_path
|
|
|
|
|
|
class OnDiskTask:
|
|
"""An on-disk task.
|
|
|
|
An on-disk task is for ``OnDiskDataset``. It contains the metadata and the
|
|
train/val/test sets.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
metadata: Dict,
|
|
train_set: Union[ItemSet, ItemSetDict],
|
|
validation_set: Union[ItemSet, ItemSetDict],
|
|
test_set: Union[ItemSet, ItemSetDict],
|
|
):
|
|
"""Initialize a task.
|
|
|
|
Parameters
|
|
----------
|
|
metadata : Dict
|
|
Metadata.
|
|
train_set : Union[ItemSet, ItemSetDict]
|
|
Training set.
|
|
validation_set : Union[ItemSet, ItemSetDict]
|
|
Validation set.
|
|
test_set : Union[ItemSet, ItemSetDict]
|
|
Test set.
|
|
"""
|
|
self._metadata = metadata
|
|
self._train_set = train_set
|
|
self._validation_set = validation_set
|
|
self._test_set = test_set
|
|
|
|
@property
|
|
def metadata(self) -> Dict:
|
|
"""Return the task metadata."""
|
|
return self._metadata
|
|
|
|
@property
|
|
def train_set(self) -> Union[ItemSet, ItemSetDict]:
|
|
"""Return the training set."""
|
|
return self._train_set
|
|
|
|
@property
|
|
def validation_set(self) -> Union[ItemSet, ItemSetDict]:
|
|
"""Return the validation set."""
|
|
return self._validation_set
|
|
|
|
@property
|
|
def test_set(self) -> Union[ItemSet, ItemSetDict]:
|
|
"""Return the test set."""
|
|
return self._test_set
|
|
|
|
def __repr__(self) -> str:
|
|
return _ondisk_task_str(self)
|
|
|
|
|
|
class OnDiskDataset(Dataset):
|
|
"""An on-disk dataset which reads graph topology, feature data and
|
|
Train/Validation/Test set from disk.
|
|
|
|
Due to limited resources, the data which are too large to fit into RAM will
|
|
remain on disk while others reside in RAM once ``OnDiskDataset`` is
|
|
initialized. This behavior could be controled by user via ``in_memory``
|
|
field in YAML file. All paths in YAML file are relative paths to the
|
|
dataset directory.
|
|
|
|
A full example of YAML file is as follows:
|
|
|
|
.. code-block:: yaml
|
|
|
|
dataset_name: graphbolt_test
|
|
graph:
|
|
nodes:
|
|
- type: paper # could be omitted for homogeneous graph.
|
|
num: 1000
|
|
- type: author
|
|
num: 1000
|
|
edges:
|
|
- type: author:writes:paper # could be omitted for homogeneous graph.
|
|
format: csv # Can be csv only.
|
|
path: edge_data/author-writes-paper.csv
|
|
- type: paper:cites:paper
|
|
format: csv
|
|
path: edge_data/paper-cites-paper.csv
|
|
feature_data:
|
|
- domain: node
|
|
type: paper # could be omitted for homogeneous graph.
|
|
name: feat
|
|
format: numpy
|
|
in_memory: false # If not specified, default to true.
|
|
path: node_data/paper-feat.npy
|
|
- domain: edge
|
|
type: "author:writes:paper"
|
|
name: feat
|
|
format: numpy
|
|
in_memory: false
|
|
path: edge_data/author-writes-paper-feat.npy
|
|
tasks:
|
|
- name: "edge_classification"
|
|
num_classes: 10
|
|
train_set:
|
|
- type: paper # could be omitted for homogeneous graph.
|
|
data: # multiple data sources could be specified.
|
|
- name: node_pairs
|
|
format: numpy # Can be numpy or torch.
|
|
in_memory: true # If not specified, default to true.
|
|
path: set/paper-train-node_pairs.npy
|
|
- name: labels
|
|
format: numpy
|
|
path: set/paper-train-labels.npy
|
|
validation_set:
|
|
- type: paper
|
|
data:
|
|
- name: node_pairs
|
|
format: numpy
|
|
path: set/paper-validation-node_pairs.npy
|
|
- name: labels
|
|
format: numpy
|
|
path: set/paper-validation-labels.npy
|
|
test_set:
|
|
- type: paper
|
|
data:
|
|
- name: node_pairs
|
|
format: numpy
|
|
path: set/paper-test-node_pairs.npy
|
|
- name: labels
|
|
format: numpy
|
|
path: set/paper-test-labels.npy
|
|
|
|
Parameters
|
|
----------
|
|
path: str
|
|
The YAML file path.
|
|
include_original_edge_id: bool, optional
|
|
Whether to include the original edge id in the FusedCSCSamplingGraph.
|
|
"""
|
|
|
|
def __init__(
|
|
self, path: str, include_original_edge_id: bool = False
|
|
) -> None:
|
|
# Always call the preprocess function first. If already preprocessed,
|
|
# the function will return the original path directly.
|
|
self._dataset_dir = path
|
|
yaml_path = preprocess_ondisk_dataset(path, include_original_edge_id)
|
|
with open(yaml_path) as f:
|
|
self._yaml_data = yaml.load(f, Loader=yaml.loader.SafeLoader)
|
|
self._loaded = False
|
|
|
|
def _convert_yaml_path_to_absolute_path(self):
|
|
"""Convert the path in YAML file to absolute path."""
|
|
if "graph_topology" in self._yaml_data:
|
|
self._yaml_data["graph_topology"]["path"] = os.path.join(
|
|
self._dataset_dir, self._yaml_data["graph_topology"]["path"]
|
|
)
|
|
if "feature_data" in self._yaml_data:
|
|
for feature in self._yaml_data["feature_data"]:
|
|
feature["path"] = os.path.join(
|
|
self._dataset_dir, feature["path"]
|
|
)
|
|
if "tasks" in self._yaml_data:
|
|
for task in self._yaml_data["tasks"]:
|
|
for set_name in ["train_set", "validation_set", "test_set"]:
|
|
if set_name not in task:
|
|
continue
|
|
for set_per_type in task[set_name]:
|
|
for data in set_per_type["data"]:
|
|
data["path"] = os.path.join(
|
|
self._dataset_dir, data["path"]
|
|
)
|
|
|
|
def load(self):
|
|
"""Load the dataset."""
|
|
self._convert_yaml_path_to_absolute_path()
|
|
self._meta = OnDiskMetaData(**self._yaml_data)
|
|
self._dataset_name = self._meta.dataset_name
|
|
self._graph = self._load_graph(self._meta.graph_topology)
|
|
self._feature = TorchBasedFeatureStore(self._meta.feature_data)
|
|
self._tasks = self._init_tasks(self._meta.tasks)
|
|
self._all_nodes_set = self._init_all_nodes_set(self._graph)
|
|
self._loaded = True
|
|
return self
|
|
|
|
@property
|
|
def yaml_data(self) -> Dict:
|
|
"""Return the YAML data."""
|
|
return self._yaml_data
|
|
|
|
@property
|
|
def tasks(self) -> List[Task]:
|
|
"""Return the tasks."""
|
|
self._check_loaded()
|
|
return self._tasks
|
|
|
|
@property
|
|
def graph(self) -> SamplingGraph:
|
|
"""Return the graph."""
|
|
self._check_loaded()
|
|
return self._graph
|
|
|
|
@property
|
|
def feature(self) -> TorchBasedFeatureStore:
|
|
"""Return the feature."""
|
|
self._check_loaded()
|
|
return self._feature
|
|
|
|
@property
|
|
def dataset_name(self) -> str:
|
|
"""Return the dataset name."""
|
|
self._check_loaded()
|
|
return self._dataset_name
|
|
|
|
@property
|
|
def all_nodes_set(self) -> Union[ItemSet, ItemSetDict]:
|
|
"""Return the itemset containing all nodes."""
|
|
self._check_loaded()
|
|
return self._all_nodes_set
|
|
|
|
def _init_tasks(self, tasks: List[OnDiskTaskData]) -> List[OnDiskTask]:
|
|
"""Initialize the tasks."""
|
|
ret = []
|
|
if tasks is None:
|
|
return ret
|
|
for task in tasks:
|
|
ret.append(
|
|
OnDiskTask(
|
|
task.extra_fields,
|
|
self._init_tvt_set(task.train_set),
|
|
self._init_tvt_set(task.validation_set),
|
|
self._init_tvt_set(task.test_set),
|
|
)
|
|
)
|
|
return ret
|
|
|
|
def _check_loaded(self):
|
|
assert self._loaded, (
|
|
"Please ensure that you have called the OnDiskDataset.load() method"
|
|
+ " to properly load the data."
|
|
)
|
|
|
|
def _load_graph(
|
|
self, graph_topology: OnDiskGraphTopology
|
|
) -> FusedCSCSamplingGraph:
|
|
"""Load the graph topology."""
|
|
if graph_topology is None:
|
|
return None
|
|
if graph_topology.type == "FusedCSCSamplingGraph":
|
|
return torch.load(graph_topology.path)
|
|
raise NotImplementedError(
|
|
f"Graph topology type {graph_topology.type} is not supported."
|
|
)
|
|
|
|
def _init_tvt_set(
|
|
self, tvt_set: List[OnDiskTVTSet]
|
|
) -> Union[ItemSet, ItemSetDict]:
|
|
"""Initialize the TVT set."""
|
|
ret = None
|
|
if (tvt_set is None) or (len(tvt_set) == 0):
|
|
return ret
|
|
if tvt_set[0].type is None:
|
|
assert (
|
|
len(tvt_set) == 1
|
|
), "Only one TVT set is allowed if type is not specified."
|
|
ret = ItemSet(
|
|
tuple(
|
|
read_data(data.path, data.format, data.in_memory)
|
|
for data in tvt_set[0].data
|
|
),
|
|
names=tuple(data.name for data in tvt_set[0].data),
|
|
)
|
|
else:
|
|
data = {}
|
|
for tvt in tvt_set:
|
|
data[tvt.type] = ItemSet(
|
|
tuple(
|
|
read_data(data.path, data.format, data.in_memory)
|
|
for data in tvt.data
|
|
),
|
|
names=tuple(data.name for data in tvt.data),
|
|
)
|
|
ret = ItemSetDict(data)
|
|
return ret
|
|
|
|
def _init_all_nodes_set(self, graph) -> Union[ItemSet, ItemSetDict]:
|
|
if graph is None:
|
|
dgl_warning(
|
|
"`all_node_set` is returned as None, since graph is None."
|
|
)
|
|
return None
|
|
num_nodes = graph.num_nodes
|
|
if isinstance(num_nodes, int):
|
|
return ItemSet(num_nodes, names="seed_nodes")
|
|
else:
|
|
data = {
|
|
node_type: ItemSet(num_node, names="seed_nodes")
|
|
for node_type, num_node in num_nodes.items()
|
|
}
|
|
return ItemSetDict(data)
|
|
|
|
|
|
class BuiltinDataset(OnDiskDataset):
|
|
"""A utility class to download built-in dataset from AWS S3 and load it as
|
|
:class:`OnDiskDataset`.
|
|
|
|
Available built-in datasets include:
|
|
|
|
**cora**
|
|
The cora dataset is a homogeneous citation network dataset, which is
|
|
designed for the node classification task.
|
|
|
|
**ogbn-mag**
|
|
The ogbn-mag dataset is a heterogeneous network composed of a subset of
|
|
the Microsoft Academic Graph (MAG). See more details in
|
|
`ogbn-mag <https://ogb.stanford.edu/docs/nodeprop/#ogbn-mag>`_.
|
|
|
|
.. note::
|
|
Reverse edges are added to the original graph and duplicated
|
|
edges are removed.
|
|
|
|
**ogbl-citation2**
|
|
The ogbl-citation2 dataset is a directed graph, representing the
|
|
citation network between a subset of papers extracted from MAG. See
|
|
more details in `ogbl-citation2
|
|
<https://ogb.stanford.edu/docs/linkprop/#ogbl-citation2>`_.
|
|
|
|
.. note::
|
|
Reverse edges are added to the original graph and duplicated
|
|
edges are removed.
|
|
|
|
**ogbn-arxiv**
|
|
The ogbn-arxiv dataset is a directed graph, representing the citation
|
|
network between all Computer Science (CS) arXiv papers indexed by MAG.
|
|
See more details in `ogbn-arxiv
|
|
<https://ogb.stanford.edu/docs/nodeprop/#ogbn-arxiv>`_.
|
|
|
|
.. note::
|
|
Reverse edges are added to the original graph and duplicated
|
|
edges are removed.
|
|
|
|
**ogbn-products**
|
|
The ogbn-products dataset is an undirected and unweighted graph,
|
|
representing an Amazon product co-purchasing network. See more details
|
|
in `ogbn-products
|
|
<https://ogb.stanford.edu/docs/nodeprop/#ogbn-products>`_.
|
|
|
|
.. note::
|
|
Reverse edges are added to the original graph.
|
|
Node features are stored as float32.
|
|
|
|
**ogb-lsc-mag240m**
|
|
The ogb-lsc-mag240m dataset is a heterogeneous academic graph extracted
|
|
from the Microsoft Academic Graph (MAG). See more details in
|
|
`ogb-lsc-mag240m <https://ogb.stanford.edu/docs/lsc/mag240m/>`_.
|
|
|
|
.. note::
|
|
Reverse edges are added to the original graph.
|
|
|
|
Parameters
|
|
----------
|
|
name : str
|
|
The name of the builtin dataset.
|
|
root : str, optional
|
|
The root directory of the dataset. Default ot ``datasets``.
|
|
"""
|
|
|
|
# For dataset that is smaller than 30GB, we use the base url.
|
|
# Otherwise, we use the accelerated url.
|
|
_base_url = "https://data.dgl.ai/dataset/graphbolt/"
|
|
_accelerated_url = (
|
|
"https://dgl-data.s3-accelerate.amazonaws.com/dataset/graphbolt/"
|
|
)
|
|
_datasets = [
|
|
"cora",
|
|
"ogbn-mag",
|
|
"ogbl-citation2",
|
|
"ogbn-products",
|
|
"ogbn-arxiv",
|
|
]
|
|
_large_datasets = ["ogb-lsc-mag240m"]
|
|
_all_datasets = _datasets + _large_datasets
|
|
|
|
def __init__(self, name: str, root: str = "datasets") -> OnDiskDataset:
|
|
dataset_dir = os.path.join(root, name)
|
|
if not os.path.exists(dataset_dir):
|
|
if name not in self._all_datasets:
|
|
raise RuntimeError(
|
|
f"Dataset {name} is not available. Available datasets are "
|
|
f"{self._all_datasets}."
|
|
)
|
|
url = (
|
|
self._accelerated_url
|
|
if name in self._large_datasets
|
|
else self._base_url
|
|
)
|
|
url += name + ".zip"
|
|
os.makedirs(root, exist_ok=True)
|
|
zip_file_path = os.path.join(root, name + ".zip")
|
|
download(url, path=zip_file_path)
|
|
extract_archive(zip_file_path, root, overwrite=True)
|
|
os.remove(zip_file_path)
|
|
super().__init__(dataset_dir)
|
|
|
|
|
|
def _ondisk_task_str(task: OnDiskTask) -> str:
|
|
final_str = "OnDiskTask("
|
|
indent_len = len(final_str)
|
|
|
|
def _add_indent(_str, indent):
|
|
lines = _str.split("\n")
|
|
lines = [lines[0]] + [" " * indent + line for line in lines[1:]]
|
|
return "\n".join(lines)
|
|
|
|
attributes = get_attributes(task)
|
|
attributes.reverse()
|
|
for name in attributes:
|
|
if name[0] == "_":
|
|
continue
|
|
val = getattr(task, name)
|
|
final_str += (
|
|
f"{name}={_add_indent(str(val), indent_len + len(name) + 1)},\n"
|
|
+ " " * indent_len
|
|
)
|
|
return final_str[:-indent_len] + ")"
|