| Name | adbdgl-adapter JSON |
| Version |
3.0.1
JSON |
| download |
| home_page | |
| Summary | Convert ArangoDB graphs to DGL & vice-versa. |
| upload_time | 2024-01-19 15:04:43 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
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| keywords |
arangodb
dgl
deep graph library
adapter
|
| VCS |
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| requirements |
No requirements were recorded.
|
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# ArangoDB-DGL Adapter
[](https://github.com/arangoml/dgl-adapter/actions/workflows/build.yml)
[](https://github.com/arangoml/dgl-adapter/actions/workflows/analyze.yml)
[](https://coveralls.io/github/arangoml/dgl-adapter)
[](https://github.com/arangoml/dgl-adapter/commits/master)
[](https://pypi.org/project/adbdgl-adapter/)
[](https://pypi.org/project/adbdgl-adapter/)
[](https://github.com/arangoml/dgl-adapter/blob/master/LICENSE)
[](https://github.com/psf/black)
[](https://pepy.tech/project/adbdgl-adapter)
<a href="https://www.arangodb.com/" rel="arangodb.com"></a>
<a href="https://www.dgl.ai/" rel="dgl.ai"><img src="https://raw.githubusercontent.com/arangoml/dgl-adapter/master/examples/assets/dgl_logo.png" width=40% /></a>
The ArangoDB-DGL Adapter exports Graphs from ArangoDB, the multi-model database for graph & beyond, into Deep Graph Library (DGL), a python package for graph neural networks, and vice-versa.
Note: The ArangoDB-DGL Adapter currently only supports the use of PyTorch as the [DGL backend](https://docs.dgl.ai/en/0.8.x/install/#backends). Support for MXNet and Tensorflow will be added in the future.
## About DGL
The Deep Graph Library (DGL) is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.
* [Website](https://www.dgl.ai/)
* [Documentation](https://docs.dgl.ai/)
* [Highlighted Features](https://github.com/dmlc/dgl#highlighted-features)
## Installation
#### Latest Release
```
pip install adbdgl-adapter
```
#### Current State
```
pip install git+https://github.com/arangoml/dgl-adapter.git
```
## Quickstart
[](https://colab.research.google.com/github/arangoml/dgl-adapter/blob/master/examples/ArangoDB_DGL_Adapter.ipynb)
Also available as an ArangoDB Lunch & Learn session: [Graph & Beyond Course #2.8](https://www.arangodb.com/resources/lunch-sessions/graph-beyond-lunch-break-2-8-dgl-adapter/)
```py
import dgl
import torch
import pandas
from arango import ArangoClient
from adbdgl_adapter import ADBDGL_Adapter, ADBDGL_Controller
from adbdgl_adapter.encoders import IdentityEncoder, CategoricalEncoder
# Connect to ArangoDB
db = ArangoClient().db()
# Instantiate the adapter
adbdgl_adapter = ADBDGL_Adapter(db)
# Create a DGL Heterogeneous Graph
fake_hetero = dgl.heterograph({
("user", "follows", "user"): (torch.tensor([0, 1]), torch.tensor([1, 2])),
("user", "follows", "topic"): (torch.tensor([1, 1]), torch.tensor([1, 2])),
("user", "plays", "game"): (torch.tensor([0, 3]), torch.tensor([3, 4])),
})
fake_hetero.nodes["user"].data["features"] = torch.tensor([21, 44, 16, 25])
fake_hetero.nodes["user"].data["label"] = torch.tensor([1, 2, 0, 1])
fake_hetero.nodes["game"].data["features"] = torch.tensor([[0, 0], [0, 1], [1, 0], [1, 1], [1, 1]])
fake_hetero.edges[("user", "plays", "game")].data["features"] = torch.tensor([[6, 1], [1000, 0]])
```
### DGL to ArangoDB
```py
############################
# 1.1: without a Metagraph #
############################
adb_g = adbdgl_adapter.dgl_to_arangodb("FakeHetero", fake_hetero)
#########################
# 1.2: with a Metagraph #
#########################
# Specifying a Metagraph provides customized adapter behaviour
metagraph = {
"nodeTypes": {
"user": {
"features": "user_age", # 1) you can specify a string value for attribute renaming
"label": label_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 DGL attribute names for the node/edge type
"game": {"features"} # this is equivalent to {"features": "features"}
},
"edgeTypes": {
("user", "plays", "game"): {
# 4) you can specify a list of strings for tensor dissasembly (if you know the number of node/edge features in advance)
"features": ["hours_played", "is_satisfied_with_game"]
},
},
}
def label_tensor_to_2_column_dataframe(dgl_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 dgl_tensor: The DGL Tensor containing the data
:type dgl_tensor: torch.Tensor
:param adb_df: The ArangoDB DataFrame to populate, whose
size is preset to the length of **dgl_tensor**.
:type adb_df: pandas.DataFrame
:return: The populated ArangoDB DataFrame
:rtype: pandas.DataFrame
"""
label_map = {0: "Class A", 1: "Class B", 2: "Class C"}
adb_df["label_num"] = dgl_tensor.tolist()
adb_df["label_str"] = adb_df["label_num"].map(label_map)
return adb_df
adb_g = adbdgl_adapter.dgl_to_arangodb("FakeHetero", fake_hetero, 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 = adbdgl_adapter.dgl_to_arangodb("FakeHetero", fake_hetero, metagraph, explicit_metagraph=True)
########################################
# 1.4: with a custom ADBDGL Controller #
########################################
class Custom_ADBDGL_Controller(ADBDGL_Controller):
def _prepare_dgl_node(self, dgl_node: dict, node_type: str) -> dict:
"""Optionally modify a DGL node object before it gets inserted into its designated ArangoDB collection.
:param dgl_node: The DGL node object to (optionally) modify.
:param node_type: The DGL Node Type of the node.
:return: The DGL Node object
"""
dgl_node["foo"] = "bar"
return dgl_node
def _prepare_dgl_edge(self, dgl_edge: dict, edge_type: tuple) -> dict:
"""Optionally modify a DGL edge object before it gets inserted into its designated ArangoDB collection.
:param dgl_edge: The DGL edge object to (optionally) modify.
:param edge_type: The Edge Type of the DGL edge. Formatted
as (from_collection, edge_collection, to_collection)
:return: The DGL Edge object
"""
dgl_edge["bar"] = "foo"
return dgl_edge
adb_g = ADBDGL_Adapter(db, Custom_ADBDGL_Controller()).dgl_to_arangodb("FakeHetero", fake_hetero)
```
### ArangoDB to DGL
```py
# Start from scratch!
db.delete_graph("FakeHetero", drop_collections=True, ignore_missing=True)
adbdgl_adapter.dgl_to_arangodb("FakeHetero", fake_hetero)
#######################
# 2.1: via Graph name #
#######################
# Due to risk of ambiguity, this method does not transfer attributes
dgl_g = adbdgl_adapter.arangodb_graph_to_dgl("FakeHetero")
#############################
# 2.2: via Collection names #
#############################
# Due to risk of ambiguity, this method does not transfer attributes
dgl_g = adbdgl_adapter.arangodb_collections_to_dgl("FakeHetero", v_cols={"user", "game"}, e_cols={"plays"})
######################
# 2.3: via Metagraph #
######################
# Transfers attributes "as is", meaning they are already formatted to DGL data standards.
# Learn more about the DGL Data Standards here: https://docs.dgl.ai/guide/graph.html#guide-graph
metagraph_v1 = {
"vertexCollections": {
# Move the "features" & "label" ArangoDB attributes to DGL as "features" & "label" Tensors
"user": {"features", "label"}, # equivalent to {"features": "features", "label": "label"}
"game": {"dgl_game_features": "features"},
"topic": {},
},
"edgeCollections": {
"plays": {"dgl_plays_features": "features"},
"follows": {}
},
}
dgl_g = adbdgl_adapter.arangodb_to_dgl("FakeHetero", metagraph_v1)
#################################################
# 2.4: via Metagraph with user-defined encoders #
#################################################
# Transforms attributes via user-defined encoders
metagraph_v2 = {
"vertexCollections": {
"Movies": {
"features": { # Build a feature matrix from the "Action" & "Drama" document attributes
"Action": IdentityEncoder(dtype=torch.long),
"Drama": IdentityEncoder(dtype=torch.long),
},
"label": "Comedy",
},
"Users": {
"features": {
"Gender": CategoricalEncoder(), # CategoricalEncoder(mapping={"M": 0, "F": 1}),
"Age": IdentityEncoder(dtype=torch.long),
}
},
},
"edgeCollections": {"Ratings": {"weight": "Rating"}},
}
dgl_g = adbdgl_adapter.arangodb_to_dgl("imdb", metagraph_v2)
##################################################
# 2.5: via Metagraph with user-defined functions #
##################################################
# Transforms attributes via user-defined functions
metagraph_v3 = {
"vertexCollections": {
"user": {
"features": udf_user_features, # supports named functions
"label": lambda df: torch.tensor(df["label"].to_list()), # also supports lambda functions
},
"game": {"features": udf_game_features},
},
"edgeCollections": {
"plays": {"features": (lambda df: torch.tensor(df["features"].to_list()))},
},
}
def udf_user_features(user_df: pandas.DataFrame) -> torch.Tensor:
# user_df["features"] = ...
return torch.tensor(user_df["features"].to_list())
def udf_game_features(game_df: pandas.DataFrame) -> torch.Tensor:
# game_df["features"] = ...
return torch.tensor(game_df["features"].to_list())
dgl_g = adbdgl_adapter.arangodb_to_dgl("FakeHetero", metagraph_v3)
```
## Development & Testing
Prerequisite: `arangorestore`
1. `git clone https://github.com/arangoml/dgl-adapter.git`
2. `cd dgl-adapter`
3. (create virtual environment of choice)
4. `pip install -e .[dev]`
5. (create an ArangoDB instance with method of choice)
6. `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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"name": "adbdgl-adapter",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "arangodb,dgl,deep graph library,adapter",
"author": "",
"author_email": "Anthony Mahanna <anthony.mahanna@arangodb.com>",
"download_url": "https://files.pythonhosted.org/packages/fa/78/74fde3a53cc990a3c3bac203267bbf327e00ab98aac6cd8c1f91b7b47567/adbdgl_adapter-3.0.1.tar.gz",
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"description": "# ArangoDB-DGL Adapter\n\n[](https://github.com/arangoml/dgl-adapter/actions/workflows/build.yml)\n[](https://github.com/arangoml/dgl-adapter/actions/workflows/analyze.yml)\n[](https://coveralls.io/github/arangoml/dgl-adapter)\n[](https://github.com/arangoml/dgl-adapter/commits/master)\n\n[](https://pypi.org/project/adbdgl-adapter/)\n[](https://pypi.org/project/adbdgl-adapter/)\n\n[](https://github.com/arangoml/dgl-adapter/blob/master/LICENSE)\n[](https://github.com/psf/black)\n[](https://pepy.tech/project/adbdgl-adapter)\n\n\n<a href=\"https://www.arangodb.com/\" rel=\"arangodb.com\"></a>\n<a href=\"https://www.dgl.ai/\" rel=\"dgl.ai\"><img src=\"https://raw.githubusercontent.com/arangoml/dgl-adapter/master/examples/assets/dgl_logo.png\" width=40% /></a>\n\nThe ArangoDB-DGL Adapter exports Graphs from ArangoDB, the multi-model database for graph & beyond, into Deep Graph Library (DGL), a python package for graph neural networks, and vice-versa.\n\nNote: The ArangoDB-DGL Adapter currently only supports the use of PyTorch as the [DGL backend](https://docs.dgl.ai/en/0.8.x/install/#backends). Support for MXNet and Tensorflow will be added in the future.\n\n## About DGL\n\nThe Deep Graph Library (DGL) is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.\n\n* [Website](https://www.dgl.ai/)\n* [Documentation](https://docs.dgl.ai/)\n* [Highlighted Features](https://github.com/dmlc/dgl#highlighted-features)\n\n## Installation\n\n#### Latest Release\n```\npip install adbdgl-adapter\n```\n#### Current State\n```\npip install git+https://github.com/arangoml/dgl-adapter.git\n```\n\n## Quickstart\n\n[](https://colab.research.google.com/github/arangoml/dgl-adapter/blob/master/examples/ArangoDB_DGL_Adapter.ipynb)\n\nAlso available as an ArangoDB Lunch & Learn session: [Graph & Beyond Course #2.8](https://www.arangodb.com/resources/lunch-sessions/graph-beyond-lunch-break-2-8-dgl-adapter/)\n\n```py\nimport dgl\nimport torch\nimport pandas\n\nfrom arango import ArangoClient\nfrom adbdgl_adapter import ADBDGL_Adapter, ADBDGL_Controller\nfrom adbdgl_adapter.encoders import IdentityEncoder, CategoricalEncoder\n\n# Connect to ArangoDB\ndb = ArangoClient().db()\n\n# Instantiate the adapter\nadbdgl_adapter = ADBDGL_Adapter(db)\n\n# Create a DGL Heterogeneous Graph\nfake_hetero = dgl.heterograph({\n (\"user\", \"follows\", \"user\"): (torch.tensor([0, 1]), torch.tensor([1, 2])),\n (\"user\", \"follows\", \"topic\"): (torch.tensor([1, 1]), torch.tensor([1, 2])),\n (\"user\", \"plays\", \"game\"): (torch.tensor([0, 3]), torch.tensor([3, 4])),\n})\nfake_hetero.nodes[\"user\"].data[\"features\"] = torch.tensor([21, 44, 16, 25])\nfake_hetero.nodes[\"user\"].data[\"label\"] = torch.tensor([1, 2, 0, 1])\nfake_hetero.nodes[\"game\"].data[\"features\"] = torch.tensor([[0, 0], [0, 1], [1, 0], [1, 1], [1, 1]])\nfake_hetero.edges[(\"user\", \"plays\", \"game\")].data[\"features\"] = torch.tensor([[6, 1], [1000, 0]])\n```\n\n### DGL to ArangoDB\n```py\n############################\n# 1.1: without a Metagraph #\n############################\n\nadb_g = adbdgl_adapter.dgl_to_arangodb(\"FakeHetero\", fake_hetero)\n\n#########################\n# 1.2: with a Metagraph #\n#########################\n\n# Specifying a Metagraph provides customized adapter behaviour\nmetagraph = {\n \"nodeTypes\": {\n \"user\": {\n \"features\": \"user_age\", # 1) you can specify a string value for attribute renaming\n \"label\": label_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 DGL attribute names for the node/edge type\n \"game\": {\"features\"} # this is equivalent to {\"features\": \"features\"}\n },\n \"edgeTypes\": {\n (\"user\", \"plays\", \"game\"): {\n # 4) you can specify a list of strings for tensor dissasembly (if you know the number of node/edge features in advance)\n \"features\": [\"hours_played\", \"is_satisfied_with_game\"]\n },\n },\n}\n\ndef label_tensor_to_2_column_dataframe(dgl_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 dgl_tensor: The DGL Tensor containing the data\n :type dgl_tensor: torch.Tensor\n :param adb_df: The ArangoDB DataFrame to populate, whose\n size is preset to the length of **dgl_tensor**.\n :type adb_df: pandas.DataFrame\n :return: The populated ArangoDB DataFrame\n :rtype: pandas.DataFrame\n \"\"\"\n label_map = {0: \"Class A\", 1: \"Class B\", 2: \"Class C\"}\n\n adb_df[\"label_num\"] = dgl_tensor.tolist()\n adb_df[\"label_str\"] = adb_df[\"label_num\"].map(label_map)\n\n return adb_df\n\n\nadb_g = adbdgl_adapter.dgl_to_arangodb(\"FakeHetero\", fake_hetero, 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 = adbdgl_adapter.dgl_to_arangodb(\"FakeHetero\", fake_hetero, metagraph, explicit_metagraph=True)\n\n########################################\n# 1.4: with a custom ADBDGL Controller #\n########################################\n\nclass Custom_ADBDGL_Controller(ADBDGL_Controller):\n def _prepare_dgl_node(self, dgl_node: dict, node_type: str) -> dict:\n \"\"\"Optionally modify a DGL node object before it gets inserted into its designated ArangoDB collection.\n\n :param dgl_node: The DGL node object to (optionally) modify.\n :param node_type: The DGL Node Type of the node.\n :return: The DGL Node object\n \"\"\"\n dgl_node[\"foo\"] = \"bar\"\n return dgl_node\n\n def _prepare_dgl_edge(self, dgl_edge: dict, edge_type: tuple) -> dict:\n \"\"\"Optionally modify a DGL edge object before it gets inserted into its designated ArangoDB collection.\n\n :param dgl_edge: The DGL edge object to (optionally) modify.\n :param edge_type: The Edge Type of the DGL edge. Formatted\n as (from_collection, edge_collection, to_collection)\n :return: The DGL Edge object\n \"\"\"\n dgl_edge[\"bar\"] = \"foo\"\n return dgl_edge\n\n\nadb_g = ADBDGL_Adapter(db, Custom_ADBDGL_Controller()).dgl_to_arangodb(\"FakeHetero\", fake_hetero)\n```\n\n### ArangoDB to DGL\n```py\n# Start from scratch!\ndb.delete_graph(\"FakeHetero\", drop_collections=True, ignore_missing=True)\nadbdgl_adapter.dgl_to_arangodb(\"FakeHetero\", fake_hetero)\n\n#######################\n# 2.1: via Graph name #\n#######################\n\n# Due to risk of ambiguity, this method does not transfer attributes\ndgl_g = adbdgl_adapter.arangodb_graph_to_dgl(\"FakeHetero\")\n\n#############################\n# 2.2: via Collection names #\n#############################\n\n# Due to risk of ambiguity, this method does not transfer attributes\ndgl_g = adbdgl_adapter.arangodb_collections_to_dgl(\"FakeHetero\", v_cols={\"user\", \"game\"}, e_cols={\"plays\"})\n\n######################\n# 2.3: via Metagraph #\n######################\n\n# Transfers attributes \"as is\", meaning they are already formatted to DGL data standards.\n# Learn more about the DGL Data Standards here: https://docs.dgl.ai/guide/graph.html#guide-graph\nmetagraph_v1 = {\n \"vertexCollections\": {\n # Move the \"features\" & \"label\" ArangoDB attributes to DGL as \"features\" & \"label\" Tensors\n \"user\": {\"features\", \"label\"}, # equivalent to {\"features\": \"features\", \"label\": \"label\"}\n \"game\": {\"dgl_game_features\": \"features\"},\n \"topic\": {},\n },\n \"edgeCollections\": {\n \"plays\": {\"dgl_plays_features\": \"features\"}, \n \"follows\": {}\n },\n}\n\ndgl_g = adbdgl_adapter.arangodb_to_dgl(\"FakeHetero\", metagraph_v1)\n\n#################################################\n# 2.4: via Metagraph with user-defined encoders #\n#################################################\n\n# Transforms attributes via user-defined encoders\nmetagraph_v2 = {\n \"vertexCollections\": {\n \"Movies\": {\n \"features\": { # Build a feature matrix from the \"Action\" & \"Drama\" document attributes\n \"Action\": IdentityEncoder(dtype=torch.long),\n \"Drama\": IdentityEncoder(dtype=torch.long),\n },\n \"label\": \"Comedy\",\n },\n \"Users\": {\n \"features\": {\n \"Gender\": CategoricalEncoder(), # CategoricalEncoder(mapping={\"M\": 0, \"F\": 1}),\n \"Age\": IdentityEncoder(dtype=torch.long),\n }\n },\n },\n \"edgeCollections\": {\"Ratings\": {\"weight\": \"Rating\"}},\n}\n\ndgl_g = adbdgl_adapter.arangodb_to_dgl(\"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 \"user\": {\n \"features\": udf_user_features, # supports named functions\n \"label\": lambda df: torch.tensor(df[\"label\"].to_list()), # also supports lambda functions\n },\n \"game\": {\"features\": udf_game_features},\n },\n \"edgeCollections\": {\n \"plays\": {\"features\": (lambda df: torch.tensor(df[\"features\"].to_list()))},\n },\n}\n\ndef udf_user_features(user_df: pandas.DataFrame) -> torch.Tensor:\n # user_df[\"features\"] = ...\n return torch.tensor(user_df[\"features\"].to_list())\n\n\ndef udf_game_features(game_df: pandas.DataFrame) -> torch.Tensor:\n # game_df[\"features\"] = ...\n return torch.tensor(game_df[\"features\"].to_list())\n\n\ndgl_g = adbdgl_adapter.arangodb_to_dgl(\"FakeHetero\", metagraph_v3)\n```\n\n## Development & Testing\n\nPrerequisite: `arangorestore`\n\n1. `git clone https://github.com/arangoml/dgl-adapter.git`\n2. `cd dgl-adapter`\n3. (create virtual environment of choice)\n4. `pip install -e .[dev]`\n5. (create an ArangoDB instance with method of choice)\n6. `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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