| Name | adbpyg-adapter JSON |
| Version |
1.1.3
JSON |
| download |
| home_page | |
| Summary | Convert ArangoDB graphs to PyG & vice-versa. |
| upload_time | 2024-02-09 14:33:32 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
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| keywords |
arangodb
pyg
pytorch
pytorch geometric
adapter
|
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# ArangoDB-PyG Adapter
[](https://github.com/arangoml/pyg-adapter/actions/workflows/build.yml)
[](https://github.com/arangoml/pyg-adapter/actions/workflows/analyze.yml)
[](https://coveralls.io/github/arangoml/pyg-adapter)
[](https://github.com/arangoml/pyg-adapter/commits/master)
[](https://pypi.org/project/adbpyg-adapter/)
[](https://pypi.org/project/adbpyg-adapter/)
[](https://github.com/arangoml/pyg-adapter/blob/master/LICENSE)
[](https://github.com/psf/black)
[](https://pepy.tech/project/adbpyg-adapter)
<a href="https://www.arangodb.com/" rel="arangodb.com"></a>
<a href="https://www.pyg.org/" rel="pyg.org"><img src="https://raw.githubusercontent.com/pyg-team/pyg_sphinx_theme/master/pyg_sphinx_theme/static/img/pyg_logo_text.svg?sanitize=true" width=40% /></a>
The ArangoDB-PyG Adapter exports Graphs from ArangoDB, the multi-model database for graph & beyond, into PyTorch Geometric (PyG), a PyTorch-based Graph Neural Network library, and vice-versa.
## About PyG
**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`DataPipe` support](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py), distributed graph learning via [Quiver](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/quiver), a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/notes/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
## Installation
#### Latest Release
```
pip install torch
pip install adbpyg-adapter
```
#### Current State
```
pip install torch
pip install git+https://github.com/arangoml/pyg-adapter.git
```
## Quickstart
[](https://colab.research.google.com/github/arangoml/pyg-adapter/blob/master/examples/ArangoDB_PyG_Adapter.ipynb)
Also available as an ArangoDB Lunch & Learn session on YouTube: [Graph & Beyond Course: ArangoDB-PyG Adapter](https://www.youtube.com/watch?v=QtGR95NN8bA)
```py
import torch
import pandas
from torch_geometric.datasets import FakeHeteroDataset
from arango import ArangoClient
from adbpyg_adapter import ADBPyG_Adapter, ADBPyG_Controller
from adbpyg_adapter.encoders import IdentityEncoder, CategoricalEncoder
# Connect to ArangoDB
db = ArangoClient().db()
# Instantiate the adapter
adbpyg_adapter = ADBPyG_Adapter(db)
# Create a PyG Heterogeneous Graph
data = FakeHeteroDataset(
num_node_types=2,
num_edge_types=3,
avg_num_nodes=20,
avg_num_channels=3, # avg number of features per node
edge_dim=2, # number of features per edge
num_classes=3, # number of unique label values
)[0]
```
### PyG to ArangoDB
Note: If the PyG graph contains `_key`, `_v_key`, or `_e_key` properties for any node / edge types, the adapter will assume to persist those values as [ArangoDB document keys](https://www.arangodb.com/docs/stable/data-modeling-naming-conventions-document-keys.html). See the `Full Cycle (ArangoDB -> PyG -> ArangoDB)` section below for an example.
```py
#############################
# 1.1: without a Metagraph #
#############################
adb_g = adbpyg_adapter.pyg_to_arangodb("FakeData", data)
#########################
# 1.2: with a Metagraph #
#########################
# Specifying a Metagraph provides customized adapter behaviour
metagraph = {
"nodeTypes": {
"v0": {
"x": "features", # 1) You can specify a string value if you want to rename your PyG data when stored in ArangoDB
"y": y_tensor_to_2_column_dataframe, # 2) you can specify a function for user-defined handling, as long as the function returns a Pandas DataFrame
},
# 3) You can specify set of strings if you want to preserve the same PyG attribute names for the node/edge type
"v1": {"x"} # this is equivalent to {"x": "x"}
},
"edgeTypes": {
("v0", "e0", "v0"): {
# 4) You can specify a list of strings for tensor dissasembly (if you know the number of node/edge features in advance)
"edge_attr": [ "a", "b"]
},
},
}
def y_tensor_to_2_column_dataframe(pyg_tensor: torch.Tensor, adb_df: pandas.DataFrame) -> pandas.DataFrame:
"""A user-defined function to create two
ArangoDB attributes out of the 'user' label tensor
:param pyg_tensor: The PyG Tensor containing the data
:type pyg_tensor: torch.Tensor
:param adb_df: The ArangoDB DataFrame to populate, whose
size is preset to the length of **pyg_tensor**.
:type adb_df: pandas.DataFrame
:return: The populated ArangoDB DataFrame
:rtype: pandas.DataFrame
"""
label_map = {0: "Kiwi", 1: "Blueberry", 2: "Avocado"}
adb_df["label_num"] = pyg_tensor.tolist()
adb_df["label_str"] = adb_df["label_num"].map(label_map)
return adb_df
adb_g = adbpyg_adapter.pyg_to_arangodb("FakeData", data, metagraph, explicit_metagraph=False)
#######################################################
# 1.3: with a Metagraph and `explicit_metagraph=True` #
#######################################################
# With `explicit_metagraph=True`, the node & edge types omitted from the metagraph will NOT be converted to ArangoDB.
adb_g = adbpyg_adapter.pyg_to_arangodb("FakeData", data, metagraph, explicit_metagraph=True)
########################################
# 1.4: with a custom ADBPyG Controller #
########################################
class Custom_ADBPyG_Controller(ADBPyG_Controller):
def _prepare_pyg_node(self, pyg_node: dict, node_type: str) -> dict:
"""Optionally modify a PyG node object before it gets inserted into its designated ArangoDB collection.
:param pyg_node: The PyG node object to (optionally) modify.
:param node_type: The PyG Node Type of the node.
:return: The PyG Node object
"""
pyg_node["foo"] = "bar"
return pyg_node
def _prepare_pyg_edge(self, pyg_edge: dict, edge_type: tuple) -> dict:
"""Optionally modify a PyG edge object before it gets inserted into its designated ArangoDB collection.
:param pyg_edge: The PyG edge object to (optionally) modify.
:param edge_type: The Edge Type of the PyG edge. Formatted
as (from_collection, edge_collection, to_collection)
:return: The PyG Edge object
"""
pyg_edge["bar"] = "foo"
return pyg_edge
adb_g = ADBPyG_Adapter(db, Custom_ADBPyG_Controller()).pyg_to_arangodb("FakeData", data)
```
### ArangoDB to PyG
```py
# Start from scratch!
db.delete_graph("FakeData", drop_collections=True, ignore_missing=True)
adbpyg_adapter.pyg_to_arangodb("FakeData", data)
#######################
# 2.1: via Graph name #
#######################
# Due to risk of ambiguity, this method does not transfer attributes
pyg_g = adbpyg_adapter.arangodb_graph_to_pyg("FakeData")
#############################
# 2.2: via Collection names #
#############################
# Due to risk of ambiguity, this method does not transfer attributes
pyg_g = adbpyg_adapter.arangodb_collections_to_pyg("FakeData", v_cols={"v0", "v1"}, e_cols={"e0"})
######################
# 2.3: via Metagraph #
######################
# Transfers attributes "as is", meaning they are already formatted to PyG data standards.
metagraph_v1 = {
"vertexCollections": {
# Move the "x" & "y" ArangoDB attributes to PyG as "x" & "y" Tensors
"v0": {"x", "y"}, # equivalent to {"x": "x", "y": "y"}
"v1": {"v1_x": "x"}, # store the 'x' feature matrix as 'v1_x' in PyG
},
"edgeCollections": {
"e0": {"edge_attr"},
},
}
pyg_g = adbpyg_adapter.arangodb_to_pyg("FakeData", metagraph_v1)
#################################################
# 2.4: via Metagraph with user-defined encoders #
#################################################
# Transforms attributes via user-defined encoders
# For more info on user-defined encoders in PyG, see https://pytorch-geometric.readthedocs.io/en/latest/notes/load_csv.html
metagraph_v2 = {
"vertexCollections": {
"Movies": {
"x": { # Build a feature matrix from the "Action" & "Drama" document attributes
"Action": IdentityEncoder(dtype=torch.long),
"Drama": IdentityEncoder(dtype=torch.long),
},
"y": "Comedy",
},
"Users": {
"x": {
"Gender": CategoricalEncoder(mapping={"M": 0, "F": 1}),
"Age": IdentityEncoder(dtype=torch.long),
}
},
},
"edgeCollections": {
"Ratings": { "edge_weight": "Rating" } # Use the 'Rating' attribute for the PyG 'edge_weight' property
},
}
pyg_g = adbpyg_adapter.arangodb_to_pyg("imdb", metagraph_v2)
##################################################
# 2.5: via Metagraph with user-defined functions #
##################################################
# Transforms attributes via user-defined functions
metagraph_v3 = {
"vertexCollections": {
"v0": {
"x": udf_v0_x, # supports named functions
"y": lambda df: torch.tensor(df["y"].to_list()), # also supports lambda functions
},
"v1": {"x": udf_v1_x},
},
"edgeCollections": {
"e0": {"edge_attr": (lambda df: torch.tensor(df["edge_attr"].to_list()))},
},
}
def udf_v0_x(v0_df: pandas.DataFrame) -> torch.Tensor:
# v0_df["x"] = ...
return torch.tensor(v0_df["x"].to_list())
def udf_v1_x(v1_df: pandas.DataFrame) -> torch.Tensor:
# v1_df["x"] = ...
return torch.tensor(v1_df["x"].to_list())
pyg_g = adbpyg_adapter.arangodb_to_pyg("FakeData", metagraph_v3)
```
### Full Cycle (ArangoDB -> PyG -> ArangoDB)
```py
# With `preserve_adb_keys=True`, the adapter will preserve the ArangoDB vertex & edge _key values into the (newly created) PyG graph.
# Users can then re-import their PyG graph into ArangoDB using the same _key values
pyg_g = adbpyg_adapter.arangodb_graph_to_pyg("imdb", preserve_adb_keys=True)
# pyg_g["Movies"]["_key"] --> ["1", "2", ..., "1682"]
# pyg_g["Users"]["_key"] --> ["1", "2", ..., "943"]
# pyg_g[("Users", "Ratings", "Movies")]["_key"] --> ["2732620466", ..., "2730643624"]
# Let's add a new PyG User Node by updating the _key property
pyg_g["Users"]["_key"].append("new-user-here-944")
# Note: Prior to the re-import, we must manually set the number of nodes in the PyG graph, since the `arangodb_graph_to_pyg` API creates featureless node data
pyg_g["Movies"].num_nodes = len(pyg_g["Movies"]["_key"]) # 1682
pyg_g["Users"].num_nodes = len(pyg_g["Users"]["_key"]) # 944 (prev. 943)
# Re-import PyG graph into ArangoDB
adbpyg_adapter.pyg_to_arangodb("imdb", pyg_g, on_duplicate="update")
```
## Development & Testing
Prerequisite: `arangorestore`
1. `git clone https://github.com/arangoml/pyg-adapter.git`
2. `cd pyg-adapter`
3. (create virtual environment of choice)
4. `pip install torch`
5. `pip install -e .[dev]`
6. (create an ArangoDB instance with method of choice)
7. `pytest --url <> --dbName <> --username <> --password <>`
**Note**: A `pytest` parameter can be omitted if the endpoint is using its default value:
```python
def pytest_addoption(parser):
parser.addoption("--url", action="store", default="http://localhost:8529")
parser.addoption("--dbName", action="store", default="_system")
parser.addoption("--username", action="store", default="root")
parser.addoption("--password", action="store", default="")
```
Raw data
{
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"home_page": "",
"name": "adbpyg-adapter",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "arangodb,pyg,pytorch,pytorch geometric,adapter",
"author": "",
"author_email": "Anthony Mahanna <anthony.mahanna@arangodb.com>",
"download_url": "https://files.pythonhosted.org/packages/6e/a3/a5651e13516a0745fe859cd0c11a3f045602cd3190674f65c71117bda1f5/adbpyg_adapter-1.1.3.tar.gz",
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"description": "# ArangoDB-PyG Adapter\n\n[](https://github.com/arangoml/pyg-adapter/actions/workflows/build.yml)\n[](https://github.com/arangoml/pyg-adapter/actions/workflows/analyze.yml)\n[](https://coveralls.io/github/arangoml/pyg-adapter)\n[](https://github.com/arangoml/pyg-adapter/commits/master)\n\n[](https://pypi.org/project/adbpyg-adapter/)\n[](https://pypi.org/project/adbpyg-adapter/)\n\n[](https://github.com/arangoml/pyg-adapter/blob/master/LICENSE)\n[](https://github.com/psf/black)\n[](https://pepy.tech/project/adbpyg-adapter)\n\n\n<a href=\"https://www.arangodb.com/\" rel=\"arangodb.com\"></a>\n<a href=\"https://www.pyg.org/\" rel=\"pyg.org\"><img src=\"https://raw.githubusercontent.com/pyg-team/pyg_sphinx_theme/master/pyg_sphinx_theme/static/img/pyg_logo_text.svg?sanitize=true\" width=40% /></a>\n\nThe ArangoDB-PyG Adapter exports Graphs from ArangoDB, the multi-model database for graph & beyond, into PyTorch Geometric (PyG), a PyTorch-based Graph Neural Network library, and vice-versa.\n\n## About PyG\n\n**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.\n\nIt consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.\nIn addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`DataPipe` support](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py), distributed graph learning via [Quiver](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/quiver), a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/notes/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.\n\n## Installation\n\n#### Latest Release\n```\npip install torch\npip install adbpyg-adapter\n```\n#### Current State\n```\npip install torch\npip install git+https://github.com/arangoml/pyg-adapter.git\n```\n\n## Quickstart\n\n[](https://colab.research.google.com/github/arangoml/pyg-adapter/blob/master/examples/ArangoDB_PyG_Adapter.ipynb)\n\nAlso available as an ArangoDB Lunch & Learn session on YouTube: [Graph & Beyond Course: ArangoDB-PyG Adapter](https://www.youtube.com/watch?v=QtGR95NN8bA)\n\n```py\nimport torch\nimport pandas\nfrom torch_geometric.datasets import FakeHeteroDataset\n\nfrom arango import ArangoClient\nfrom adbpyg_adapter import ADBPyG_Adapter, ADBPyG_Controller\nfrom adbpyg_adapter.encoders import IdentityEncoder, CategoricalEncoder\n\n# Connect to ArangoDB\ndb = ArangoClient().db()\n\n# Instantiate the adapter\nadbpyg_adapter = ADBPyG_Adapter(db)\n\n# Create a PyG Heterogeneous Graph\ndata = FakeHeteroDataset(\n num_node_types=2,\n num_edge_types=3,\n avg_num_nodes=20,\n avg_num_channels=3, # avg number of features per node\n edge_dim=2, # number of features per edge\n num_classes=3, # number of unique label values\n)[0]\n```\n\n### PyG to ArangoDB\n\nNote: If the PyG graph contains `_key`, `_v_key`, or `_e_key` properties for any node / edge types, the adapter will assume to persist those values as [ArangoDB document keys](https://www.arangodb.com/docs/stable/data-modeling-naming-conventions-document-keys.html). See the `Full Cycle (ArangoDB -> PyG -> ArangoDB)` section below for an example.\n\n```py\n#############################\n# 1.1: without a Metagraph #\n#############################\n\nadb_g = adbpyg_adapter.pyg_to_arangodb(\"FakeData\", data)\n\n#########################\n# 1.2: with a Metagraph #\n#########################\n\n# Specifying a Metagraph provides customized adapter behaviour\nmetagraph = {\n \"nodeTypes\": {\n \"v0\": {\n \"x\": \"features\", # 1) You can specify a string value if you want to rename your PyG data when stored in ArangoDB\n \"y\": y_tensor_to_2_column_dataframe, # 2) you can specify a function for user-defined handling, as long as the function returns a Pandas DataFrame\n },\n # 3) You can specify set of strings if you want to preserve the same PyG attribute names for the node/edge type\n \"v1\": {\"x\"} # this is equivalent to {\"x\": \"x\"}\n },\n \"edgeTypes\": {\n (\"v0\", \"e0\", \"v0\"): {\n # 4) You can specify a list of strings for tensor dissasembly (if you know the number of node/edge features in advance)\n \"edge_attr\": [ \"a\", \"b\"] \n },\n },\n}\n\ndef y_tensor_to_2_column_dataframe(pyg_tensor: torch.Tensor, adb_df: pandas.DataFrame) -> pandas.DataFrame:\n \"\"\"A user-defined function to create two\n ArangoDB attributes out of the 'user' label tensor\n\n :param pyg_tensor: The PyG Tensor containing the data\n :type pyg_tensor: torch.Tensor\n :param adb_df: The ArangoDB DataFrame to populate, whose\n size is preset to the length of **pyg_tensor**.\n :type adb_df: pandas.DataFrame\n :return: The populated ArangoDB DataFrame\n :rtype: pandas.DataFrame\n \"\"\"\n label_map = {0: \"Kiwi\", 1: \"Blueberry\", 2: \"Avocado\"}\n\n adb_df[\"label_num\"] = pyg_tensor.tolist()\n adb_df[\"label_str\"] = adb_df[\"label_num\"].map(label_map)\n\n return adb_df\n\n\nadb_g = adbpyg_adapter.pyg_to_arangodb(\"FakeData\", data, metagraph, explicit_metagraph=False)\n\n#######################################################\n# 1.3: with a Metagraph and `explicit_metagraph=True` #\n#######################################################\n\n# With `explicit_metagraph=True`, the node & edge types omitted from the metagraph will NOT be converted to ArangoDB.\nadb_g = adbpyg_adapter.pyg_to_arangodb(\"FakeData\", data, metagraph, explicit_metagraph=True)\n\n########################################\n# 1.4: with a custom ADBPyG Controller #\n########################################\n\nclass Custom_ADBPyG_Controller(ADBPyG_Controller):\n def _prepare_pyg_node(self, pyg_node: dict, node_type: str) -> dict:\n \"\"\"Optionally modify a PyG node object before it gets inserted into its designated ArangoDB collection.\n\n :param pyg_node: The PyG node object to (optionally) modify.\n :param node_type: The PyG Node Type of the node.\n :return: The PyG Node object\n \"\"\"\n pyg_node[\"foo\"] = \"bar\"\n return pyg_node\n\n def _prepare_pyg_edge(self, pyg_edge: dict, edge_type: tuple) -> dict:\n \"\"\"Optionally modify a PyG edge object before it gets inserted into its designated ArangoDB collection.\n\n :param pyg_edge: The PyG edge object to (optionally) modify.\n :param edge_type: The Edge Type of the PyG edge. Formatted\n as (from_collection, edge_collection, to_collection)\n :return: The PyG Edge object\n \"\"\"\n pyg_edge[\"bar\"] = \"foo\"\n return pyg_edge\n\n\nadb_g = ADBPyG_Adapter(db, Custom_ADBPyG_Controller()).pyg_to_arangodb(\"FakeData\", data)\n```\n\n### ArangoDB to PyG\n```py\n# Start from scratch!\ndb.delete_graph(\"FakeData\", drop_collections=True, ignore_missing=True)\nadbpyg_adapter.pyg_to_arangodb(\"FakeData\", data)\n\n#######################\n# 2.1: via Graph name #\n#######################\n\n# Due to risk of ambiguity, this method does not transfer attributes\npyg_g = adbpyg_adapter.arangodb_graph_to_pyg(\"FakeData\")\n\n#############################\n# 2.2: via Collection names #\n#############################\n\n# Due to risk of ambiguity, this method does not transfer attributes\npyg_g = adbpyg_adapter.arangodb_collections_to_pyg(\"FakeData\", v_cols={\"v0\", \"v1\"}, e_cols={\"e0\"})\n\n######################\n# 2.3: via Metagraph #\n######################\n\n# Transfers attributes \"as is\", meaning they are already formatted to PyG data standards.\nmetagraph_v1 = {\n \"vertexCollections\": {\n # Move the \"x\" & \"y\" ArangoDB attributes to PyG as \"x\" & \"y\" Tensors\n \"v0\": {\"x\", \"y\"}, # equivalent to {\"x\": \"x\", \"y\": \"y\"}\n \"v1\": {\"v1_x\": \"x\"}, # store the 'x' feature matrix as 'v1_x' in PyG\n },\n \"edgeCollections\": {\n \"e0\": {\"edge_attr\"},\n },\n}\n\npyg_g = adbpyg_adapter.arangodb_to_pyg(\"FakeData\", metagraph_v1)\n\n#################################################\n# 2.4: via Metagraph with user-defined encoders #\n#################################################\n\n# Transforms attributes via user-defined encoders\n# For more info on user-defined encoders in PyG, see https://pytorch-geometric.readthedocs.io/en/latest/notes/load_csv.html\nmetagraph_v2 = {\n \"vertexCollections\": {\n \"Movies\": {\n \"x\": { # Build a feature matrix from the \"Action\" & \"Drama\" document attributes\n \"Action\": IdentityEncoder(dtype=torch.long),\n \"Drama\": IdentityEncoder(dtype=torch.long),\n },\n \"y\": \"Comedy\",\n },\n \"Users\": {\n \"x\": {\n \"Gender\": CategoricalEncoder(mapping={\"M\": 0, \"F\": 1}),\n \"Age\": IdentityEncoder(dtype=torch.long),\n }\n },\n },\n \"edgeCollections\": {\n \"Ratings\": { \"edge_weight\": \"Rating\" } # Use the 'Rating' attribute for the PyG 'edge_weight' property\n },\n}\n\npyg_g = adbpyg_adapter.arangodb_to_pyg(\"imdb\", metagraph_v2)\n\n##################################################\n# 2.5: via Metagraph with user-defined functions #\n##################################################\n\n# Transforms attributes via user-defined functions\nmetagraph_v3 = {\n \"vertexCollections\": {\n \"v0\": {\n \"x\": udf_v0_x, # supports named functions\n \"y\": lambda df: torch.tensor(df[\"y\"].to_list()), # also supports lambda functions\n },\n \"v1\": {\"x\": udf_v1_x},\n },\n \"edgeCollections\": {\n \"e0\": {\"edge_attr\": (lambda df: torch.tensor(df[\"edge_attr\"].to_list()))},\n },\n}\n\ndef udf_v0_x(v0_df: pandas.DataFrame) -> torch.Tensor:\n # v0_df[\"x\"] = ...\n return torch.tensor(v0_df[\"x\"].to_list())\n\n\ndef udf_v1_x(v1_df: pandas.DataFrame) -> torch.Tensor:\n # v1_df[\"x\"] = ...\n return torch.tensor(v1_df[\"x\"].to_list())\n\npyg_g = adbpyg_adapter.arangodb_to_pyg(\"FakeData\", metagraph_v3)\n```\n\n### Full Cycle (ArangoDB -> PyG -> ArangoDB)\n```py\n# With `preserve_adb_keys=True`, the adapter will preserve the ArangoDB vertex & edge _key values into the (newly created) PyG graph.\n# Users can then re-import their PyG graph into ArangoDB using the same _key values \npyg_g = adbpyg_adapter.arangodb_graph_to_pyg(\"imdb\", preserve_adb_keys=True)\n\n# pyg_g[\"Movies\"][\"_key\"] --> [\"1\", \"2\", ..., \"1682\"]\n# pyg_g[\"Users\"][\"_key\"] --> [\"1\", \"2\", ..., \"943\"]\n# pyg_g[(\"Users\", \"Ratings\", \"Movies\")][\"_key\"] --> [\"2732620466\", ..., \"2730643624\"]\n\n# Let's add a new PyG User Node by updating the _key property\npyg_g[\"Users\"][\"_key\"].append(\"new-user-here-944\")\n\n# Note: Prior to the re-import, we must manually set the number of nodes in the PyG graph, since the `arangodb_graph_to_pyg` API creates featureless node data\npyg_g[\"Movies\"].num_nodes = len(pyg_g[\"Movies\"][\"_key\"]) # 1682\npyg_g[\"Users\"].num_nodes = len(pyg_g[\"Users\"][\"_key\"]) # 944 (prev. 943)\n\n# Re-import PyG graph into ArangoDB\nadbpyg_adapter.pyg_to_arangodb(\"imdb\", pyg_g, on_duplicate=\"update\")\n```\n\n## Development & Testing\n\nPrerequisite: `arangorestore`\n\n1. `git clone https://github.com/arangoml/pyg-adapter.git`\n2. `cd pyg-adapter`\n3. (create virtual environment of choice)\n4. `pip install torch`\n5. `pip install -e .[dev]`\n6. (create an ArangoDB instance with method of choice)\n7. `pytest --url <> --dbName <> --username <> --password <>`\n\n**Note**: A `pytest` parameter can be omitted if the endpoint is using its default value:\n```python\ndef pytest_addoption(parser):\n parser.addoption(\"--url\", action=\"store\", default=\"http://localhost:8529\")\n parser.addoption(\"--dbName\", action=\"store\", default=\"_system\")\n parser.addoption(\"--username\", action=\"store\", default=\"root\")\n parser.addoption(\"--password\", action=\"store\", default=\"\")\n```\n",
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