wehub-resource-sync
387 lines
14 KiB
Python
387 lines
14 KiB
Python
import ast
|
|
import os
|
|
from typing import Callable, List, Optional
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import pydantic as dt
|
|
import yaml
|
|
|
|
from .. import backend as F
|
|
from ..base import dgl_warning, DGLError
|
|
from ..convert import heterograph as dgl_heterograph
|
|
|
|
|
|
class MetaNode(dt.BaseModel):
|
|
"""Class of node_data in YAML. Internal use only."""
|
|
|
|
file_name: str
|
|
ntype: Optional[str] = "_V"
|
|
graph_id_field: Optional[str] = "graph_id"
|
|
node_id_field: Optional[str] = "node_id"
|
|
|
|
|
|
class MetaEdge(dt.BaseModel):
|
|
"""Class of edge_data in YAML. Internal use only."""
|
|
|
|
file_name: str
|
|
etype: Optional[List[str]] = ["_V", "_E", "_V"]
|
|
graph_id_field: Optional[str] = "graph_id"
|
|
src_id_field: Optional[str] = "src_id"
|
|
dst_id_field: Optional[str] = "dst_id"
|
|
|
|
|
|
class MetaGraph(dt.BaseModel):
|
|
"""Class of graph_data in YAML. Internal use only."""
|
|
|
|
file_name: str
|
|
graph_id_field: Optional[str] = "graph_id"
|
|
|
|
|
|
class MetaYaml(dt.BaseModel):
|
|
"""Class of YAML. Internal use only."""
|
|
|
|
version: Optional[str] = "1.0.0"
|
|
dataset_name: str
|
|
separator: Optional[str] = ","
|
|
node_data: List[MetaNode]
|
|
edge_data: List[MetaEdge]
|
|
graph_data: Optional[MetaGraph] = None
|
|
|
|
|
|
def load_yaml_with_sanity_check(yaml_file):
|
|
"""Load yaml and do sanity check. Internal use only."""
|
|
with open(yaml_file) as f:
|
|
yaml_data = yaml.load(f, Loader=yaml.loader.SafeLoader)
|
|
try:
|
|
meta_yaml = MetaYaml(**yaml_data)
|
|
except dt.ValidationError as e:
|
|
print("Details of pydantic.ValidationError:\n{}".format(e.json()))
|
|
raise DGLError(
|
|
"Validation Error for YAML fields. Details are shown above."
|
|
)
|
|
if meta_yaml.version != "1.0.0":
|
|
raise DGLError(
|
|
"Invalid CSVDataset version {}. Supported versions: '1.0.0'".format(
|
|
meta_yaml.version
|
|
)
|
|
)
|
|
ntypes = [meta.ntype for meta in meta_yaml.node_data]
|
|
if len(ntypes) > len(set(ntypes)):
|
|
raise DGLError(
|
|
"Each node CSV file must have a unique node type name, but found duplicate node type: {}.".format(
|
|
ntypes
|
|
)
|
|
)
|
|
etypes = [tuple(meta.etype) for meta in meta_yaml.edge_data]
|
|
if len(etypes) > len(set(etypes)):
|
|
raise DGLError(
|
|
"Each edge CSV file must have a unique edge type name, but found duplicate edge type: {}.".format(
|
|
etypes
|
|
)
|
|
)
|
|
return meta_yaml
|
|
|
|
|
|
def _validate_data_length(data_dict):
|
|
len_dict = {k: len(v) for k, v in data_dict.items()}
|
|
lst = list(len_dict.values())
|
|
res = lst.count(lst[0]) == len(lst)
|
|
if not res:
|
|
raise DGLError(
|
|
"All data are required to have same length while some of them does not. Length of data={}".format(
|
|
str(len_dict)
|
|
)
|
|
)
|
|
|
|
|
|
def _tensor(data, dtype=None):
|
|
"""Float32 is the default dtype for float tensor in DGL
|
|
so let's cast float64 into float32 to avoid dtype mismatch.
|
|
"""
|
|
ret = F.tensor(data, dtype)
|
|
if F.dtype(ret) == F.float64:
|
|
ret = F.tensor(ret, dtype=F.float32)
|
|
return ret
|
|
|
|
|
|
class BaseData:
|
|
"""Class of base data which is inherited by Node/Edge/GraphData. Internal use only."""
|
|
|
|
@staticmethod
|
|
def read_csv(file_name, base_dir, separator):
|
|
csv_path = file_name
|
|
if base_dir is not None:
|
|
csv_path = os.path.join(base_dir, csv_path)
|
|
return pd.read_csv(csv_path, sep=separator)
|
|
|
|
@staticmethod
|
|
def pop_from_dataframe(df: pd.DataFrame, item: str):
|
|
ret = None
|
|
try:
|
|
ret = df.pop(item).to_numpy().squeeze()
|
|
except KeyError:
|
|
pass
|
|
return ret
|
|
|
|
|
|
class NodeData(BaseData):
|
|
"""Class of node data which is used for DGLGraph construction. Internal use only."""
|
|
|
|
def __init__(self, node_id, data, type=None, graph_id=None):
|
|
self.id = np.array(node_id)
|
|
self.data = data
|
|
self.type = type if type is not None else "_V"
|
|
self.graph_id = (
|
|
np.array(graph_id)
|
|
if graph_id is not None
|
|
else np.full(len(node_id), 0)
|
|
)
|
|
_validate_data_length(
|
|
{**{"id": self.id, "graph_id": self.graph_id}, **self.data}
|
|
)
|
|
|
|
@staticmethod
|
|
def load_from_csv(
|
|
meta: MetaNode, data_parser: Callable, base_dir=None, separator=","
|
|
):
|
|
df = BaseData.read_csv(meta.file_name, base_dir, separator)
|
|
node_ids = BaseData.pop_from_dataframe(df, meta.node_id_field)
|
|
graph_ids = BaseData.pop_from_dataframe(df, meta.graph_id_field)
|
|
if node_ids is None:
|
|
raise DGLError(
|
|
"Missing node id field [{}] in file [{}].".format(
|
|
meta.node_id_field, meta.file_name
|
|
)
|
|
)
|
|
ntype = meta.ntype
|
|
ndata = data_parser(df)
|
|
return NodeData(node_ids, ndata, type=ntype, graph_id=graph_ids)
|
|
|
|
@staticmethod
|
|
def to_dict(node_data: List["NodeData"]) -> dict:
|
|
# node_ids could be numeric or non-numeric values, but duplication is not allowed.
|
|
node_dict = {}
|
|
for n_data in node_data:
|
|
graph_ids = np.unique(n_data.graph_id)
|
|
for graph_id in graph_ids:
|
|
idx = n_data.graph_id == graph_id
|
|
ids = n_data.id[idx]
|
|
u_ids, u_indices, u_counts = np.unique(
|
|
ids, return_index=True, return_counts=True
|
|
)
|
|
if len(ids) > len(u_ids):
|
|
raise DGLError(
|
|
"Node IDs are required to be unique but the following ids are duplicate: {}".format(
|
|
u_ids[u_counts > 1]
|
|
)
|
|
)
|
|
if graph_id not in node_dict:
|
|
node_dict[graph_id] = {}
|
|
node_dict[graph_id][n_data.type] = {
|
|
"mapping": {
|
|
index: i for i, index in enumerate(ids[u_indices])
|
|
},
|
|
"data": {
|
|
k: _tensor(v[idx][u_indices])
|
|
for k, v in n_data.data.items()
|
|
},
|
|
"dtype": ids.dtype,
|
|
}
|
|
return node_dict
|
|
|
|
|
|
class EdgeData(BaseData):
|
|
"""Class of edge data which is used for DGLGraph construction. Internal use only."""
|
|
|
|
def __init__(self, src_id, dst_id, data, type=None, graph_id=None):
|
|
self.src = np.array(src_id)
|
|
self.dst = np.array(dst_id)
|
|
self.data = data
|
|
self.type = type if type is not None else ("_V", "_E", "_V")
|
|
self.graph_id = (
|
|
np.array(graph_id)
|
|
if graph_id is not None
|
|
else np.full(len(src_id), 0)
|
|
)
|
|
_validate_data_length(
|
|
{
|
|
**{"src": self.src, "dst": self.dst, "graph_id": self.graph_id},
|
|
**self.data,
|
|
}
|
|
)
|
|
|
|
@staticmethod
|
|
def load_from_csv(
|
|
meta: MetaEdge, data_parser: Callable, base_dir=None, separator=","
|
|
):
|
|
df = BaseData.read_csv(meta.file_name, base_dir, separator)
|
|
src_ids = BaseData.pop_from_dataframe(df, meta.src_id_field)
|
|
if src_ids is None:
|
|
raise DGLError(
|
|
"Missing src id field [{}] in file [{}].".format(
|
|
meta.src_id_field, meta.file_name
|
|
)
|
|
)
|
|
dst_ids = BaseData.pop_from_dataframe(df, meta.dst_id_field)
|
|
if dst_ids is None:
|
|
raise DGLError(
|
|
"Missing dst id field [{}] in file [{}].".format(
|
|
meta.dst_id_field, meta.file_name
|
|
)
|
|
)
|
|
graph_ids = BaseData.pop_from_dataframe(df, meta.graph_id_field)
|
|
etype = tuple(meta.etype)
|
|
edata = data_parser(df)
|
|
return EdgeData(src_ids, dst_ids, edata, type=etype, graph_id=graph_ids)
|
|
|
|
@staticmethod
|
|
def to_dict(edge_data: List["EdgeData"], node_dict: dict) -> dict:
|
|
edge_dict = {}
|
|
for e_data in edge_data:
|
|
(src_type, e_type, dst_type) = e_data.type
|
|
graph_ids = np.unique(e_data.graph_id)
|
|
for graph_id in graph_ids:
|
|
if graph_id in edge_dict and e_data.type in edge_dict[graph_id]:
|
|
raise DGLError(
|
|
f"Duplicate edge type[{e_data.type}] for same graph[{graph_id}], please place the same edge_type for same graph into single EdgeData."
|
|
)
|
|
idx = e_data.graph_id == graph_id
|
|
src_mapping = node_dict[graph_id][src_type]["mapping"]
|
|
dst_mapping = node_dict[graph_id][dst_type]["mapping"]
|
|
orig_src_ids = e_data.src[idx].astype(
|
|
node_dict[graph_id][src_type]["dtype"]
|
|
)
|
|
orig_dst_ids = e_data.dst[idx].astype(
|
|
node_dict[graph_id][dst_type]["dtype"]
|
|
)
|
|
src_ids = [src_mapping[index] for index in orig_src_ids]
|
|
dst_ids = [dst_mapping[index] for index in orig_dst_ids]
|
|
if graph_id not in edge_dict:
|
|
edge_dict[graph_id] = {}
|
|
edge_dict[graph_id][e_data.type] = {
|
|
"edges": (_tensor(src_ids), _tensor(dst_ids)),
|
|
"data": {
|
|
k: _tensor(v[idx]) for k, v in e_data.data.items()
|
|
},
|
|
}
|
|
return edge_dict
|
|
|
|
|
|
class GraphData(BaseData):
|
|
"""Class of graph data which is used for DGLGraph construction. Internal use only."""
|
|
|
|
def __init__(self, graph_id, data):
|
|
self.graph_id = np.array(graph_id)
|
|
self.data = data
|
|
_validate_data_length({**{"graph_id": self.graph_id}, **self.data})
|
|
|
|
@staticmethod
|
|
def load_from_csv(
|
|
meta: MetaGraph, data_parser: Callable, base_dir=None, separator=","
|
|
):
|
|
df = BaseData.read_csv(meta.file_name, base_dir, separator)
|
|
graph_ids = BaseData.pop_from_dataframe(df, meta.graph_id_field)
|
|
if graph_ids is None:
|
|
raise DGLError(
|
|
"Missing graph id field [{}] in file [{}].".format(
|
|
meta.graph_id_field, meta.file_name
|
|
)
|
|
)
|
|
gdata = data_parser(df)
|
|
return GraphData(graph_ids, gdata)
|
|
|
|
@staticmethod
|
|
def to_dict(graph_data: "GraphData", graphs_dict: dict) -> dict:
|
|
missing_ids = np.setdiff1d(
|
|
np.array(list(graphs_dict.keys())), graph_data.graph_id
|
|
)
|
|
if len(missing_ids) > 0:
|
|
raise DGLError(
|
|
"Found following graph ids in node/edge CSVs but not in graph CSV: {}.".format(
|
|
missing_ids
|
|
)
|
|
)
|
|
graph_ids = graph_data.graph_id
|
|
graphs = []
|
|
for graph_id in graph_ids:
|
|
if graph_id not in graphs_dict:
|
|
graphs_dict[graph_id] = dgl_heterograph(
|
|
{("_V", "_E", "_V"): ([], [])}
|
|
)
|
|
for graph_id in graph_ids:
|
|
graphs.append(graphs_dict[graph_id])
|
|
data = {
|
|
k: F.reshape(_tensor(v), (len(graphs), -1))
|
|
for k, v in graph_data.data.items()
|
|
}
|
|
return graphs, data
|
|
|
|
|
|
class DGLGraphConstructor:
|
|
"""Class for constructing DGLGraph from Node/Edge/Graph data. Internal use only."""
|
|
|
|
@staticmethod
|
|
def construct_graphs(node_data, edge_data, graph_data=None):
|
|
if not isinstance(node_data, list):
|
|
node_data = [node_data]
|
|
if not isinstance(edge_data, list):
|
|
edge_data = [edge_data]
|
|
node_dict = NodeData.to_dict(node_data)
|
|
edge_dict = EdgeData.to_dict(edge_data, node_dict)
|
|
graph_dict = DGLGraphConstructor._construct_graphs(node_dict, edge_dict)
|
|
if graph_data is None:
|
|
graph_data = GraphData(np.full(1, 0), {})
|
|
graphs, data = GraphData.to_dict(graph_data, graph_dict)
|
|
return graphs, data
|
|
|
|
@staticmethod
|
|
def _construct_graphs(node_dict, edge_dict):
|
|
graph_dict = {}
|
|
for graph_id in node_dict:
|
|
if graph_id not in edge_dict:
|
|
edge_dict[graph_id][("_V", "_E", "_V")] = {"edges": ([], [])}
|
|
graph = dgl_heterograph(
|
|
{
|
|
etype: edata["edges"]
|
|
for etype, edata in edge_dict[graph_id].items()
|
|
},
|
|
num_nodes_dict={
|
|
ntype: len(ndata["mapping"])
|
|
for ntype, ndata in node_dict[graph_id].items()
|
|
},
|
|
)
|
|
|
|
def assign_data(type, src_data, dst_data):
|
|
for key, value in src_data.items():
|
|
dst_data[type].data[key] = value
|
|
|
|
for type, data in node_dict[graph_id].items():
|
|
assign_data(type, data["data"], graph.nodes)
|
|
for (type), data in edge_dict[graph_id].items():
|
|
assign_data(type, data["data"], graph.edges)
|
|
graph_dict[graph_id] = graph
|
|
return graph_dict
|
|
|
|
|
|
class DefaultDataParser:
|
|
"""Default data parser for CSVDataset. It
|
|
1. ignores any columns which does not have a header.
|
|
2. tries to convert to list of numeric values(generated by
|
|
np.array().tolist()) if cell data is a str separated by ','.
|
|
3. read data and infer data type directly, otherwise.
|
|
"""
|
|
|
|
def __call__(self, df: pd.DataFrame):
|
|
data = {}
|
|
for header in df:
|
|
if "Unnamed" in header:
|
|
dgl_warning("Unnamed column is found. Ignored...")
|
|
continue
|
|
dt = df[header].to_numpy().squeeze()
|
|
if len(dt) > 0 and isinstance(dt[0], str):
|
|
# probably consists of list of numeric values
|
|
dt = np.array([ast.literal_eval(row) for row in dt])
|
|
data[header] = dt
|
|
return data
|