diff --git a/.github/workflows/build-and-test.yml b/.github/workflows/build-and-test.yml index 60e0a004..92eb0d0f 100644 --- a/.github/workflows/build-and-test.yml +++ b/.github/workflows/build-and-test.yml @@ -49,15 +49,33 @@ jobs: sleep 1 # Wait for Memgraph a bit. - name: Install Neo4j run: | - docker run -p 7474:7474 -p 7688:7687 -d -v $HOME/neo4j/data:/data -v $HOME/neo4j/logs:/logs -v $HOME/neo4j/import:/var/lib/neo4j/import -v $HOME/neo4j/plugins:/plugins --env NEO4J_AUTH=neo4j/test neo4j:4.4.7 + # Using Neo4j 4.4.18 which has cgroupv2 compatibility fixes for Ubuntu 24.04 + docker run --name neo4j-test -p 7474:7474 -p 7688:7687 -d -v $HOME/neo4j/data:/data -v $HOME/neo4j/logs:/logs -v $HOME/neo4j/import:/var/lib/neo4j/import -v $HOME/neo4j/plugins:/plugins --env NEO4J_AUTH=neo4j/test neo4j:4.4.18 + - name: Wait for Neo4j to be ready + run: | + echo "Waiting for Neo4j to be ready..." + for i in {1..60}; do + # Use cypher-shell inside the container to verify Neo4j is accepting connections + if docker exec neo4j-test cypher-shell -u neo4j -p test "RETURN 1" 2>/dev/null; then + echo "Neo4j is ready and accepting queries!" + break + fi + echo "Waiting for Neo4j... ($i/60)" + sleep 2 + done + # Show Neo4j logs for debugging + echo "=== Neo4j logs ===" + docker logs neo4j-test 2>&1 | tail -30 || true - name: Install More Packages run: | ps aux | grep memgraph poetry install --all-extras poe install-pyg-cpu poe install-dgl + poe install-tfgnn - name: Run Tests run: | + export TF_USE_LEGACY_KERAS="1" poetry run pytest -vvv -m "not slow and not ubuntu and not docker" - name: Use the Upload Artifact GitHub Action uses: actions/upload-artifact@v4 @@ -132,6 +150,8 @@ jobs: run: | poetry install --all-extras poe install-pyg-cpu + poe install-tfgnn + export TF_USE_LEGACY_KERAS="1" poetry run pytest -vvv -m "not slow and not ubuntu and not docker" - name: Save Memgraph Logs uses: actions/upload-artifact@v4 diff --git a/README.md b/README.md index e89c5f53..f4537be8 100644 --- a/README.md +++ b/README.md @@ -84,6 +84,7 @@ poetry install # No extras poetry install -E arrow # Support for the CSV, Parquet, ORC and IPC/Feather/Arrow formats poetry install -E dgl # DGL support (also includes torch) poetry install -E docker # Docker support +poetry install -E tfgnn # TFGNN support ``` To run the tests, make sure you have an [active Memgraph instance](https://memgraph.com/docs/getting-started), and execute one of the following commands: @@ -100,6 +101,7 @@ If you’ve installed only certain extras, it’s also possible to run their ass poetry run pytest . -k "arrow" poetry run pytest . -k "dgl" poetry run pytest . -k "docker" +poetry run pytest . -k "tfgnn" ``` ## Development (how to build) diff --git a/docs/how-to-guides/translators/export-python-graphs.md b/docs/how-to-guides/translators/export-python-graphs.md index 33a044b3..ff4f7925 100644 --- a/docs/how-to-guides/translators/export-python-graphs.md +++ b/docs/how-to-guides/translators/export-python-graphs.md @@ -1,6 +1,6 @@ # How to export data from Memgraph into Python graphs -GQLAlchemy holds translators that can export Memgraph graphs into Python graphs ([NetworkX](https://networkx.org/), [PyG](https://pytorch-geometric.readthedocs.io/en/latest/) or [DGL](https://www.dgl.ai/) graphs). These translators create a Python graph instance from the graph stored in Memgraph. +GQLAlchemy holds translators that can export Memgraph graphs into Python graphs ([NetworkX](https://networkx.org/), [PyG](https://pytorch-geometric.readthedocs.io/en/latest/), [DGL](https://www.dgl.ai/) or [TF-GNN](https://github.com/tensorflow/gnn) graphs). These translators create a Python graph instance from the graph stored in Memgraph. [![docs-source](https://img.shields.io/badge/source-examples-FB6E00?logo=github&style=for-the-badge)](https://github.com/memgraph/gqlalchemy/tree/main/tests/transformations/translators) [![docs-source](https://img.shields.io/badge/source-translators-FB6E00?logo=github&style=for-the-badge)](https://github.com/memgraph/gqlalchemy/tree/main/gqlalchemy/transformations/translators) @@ -9,17 +9,37 @@ hood](https://img.shields.io/static/v1?label=Related&message=Under%20the%20hood& In this guide you will learn how to: -- [**Export data from Memgraph into NetworkX graph**](#export-data-from-memgraph-into-networkx-graph) -- [**Export data from Memgraph into PyG graph**](#import-pyg-graph-into-memgraph) -- [**Export data from Memgraph into DGL graph**](#import-dgl-graph-into-memgraph) +- [How to export data from Memgraph into Python graphs](#how-to-export-data-from-memgraph-into-python-graphs) + - [General prerequisites](#general-prerequisites) + - [Export data from Memgraph into NetworkX graph](#export-data-from-memgraph-into-networkx-graph) + - [Prerequisites](#prerequisites) + - [Create and run a Python script](#create-and-run-a-python-script) + - [Export data from Memgraph into PyG graph](#export-data-from-memgraph-into-pyg-graph) + - [Prerequisites](#prerequisites-1) + - [Create and run a Python script](#create-and-run-a-python-script-1) + - [Export data from Memgraph into DGL graph](#export-data-from-memgraph-into-dgl-graph) + - [Prerequisites](#prerequisites-2) + - [Create and run a Python script](#create-and-run-a-python-script-2) + - [Export data from Memgraph into TF-GNN graph](#export-data-from-memgraph-into-tf-gnn-graph) + - [Prerequisites](#prerequisites-3) + - [Create and run a Python script](#create-and-run-a-python-script-3) + - [Learn more](#learn-more) ## General prerequisites -You need a running **Memgraph Platform instance**, which includes both the MAGE library and Memgraph Lab, a visual interface. To run the image, open a command-line interpreter and run the following Docker command: +You need **Memgraph Platform** running, which includes both the MAGE library and Memgraph Lab, a visual interface. To run it on Linux/macOS, run the following in your terminal: ``` -docker run -it -p 7687:7687 -p 7444:7444 -p 3000:3000 memgraph/memgraph-platform:latest +curl https://install.memgraph.com | sh ``` +To run it on Windows, execute the following command in the console: + +``` +iwr https://windows.memgraph.com | iex +``` + +The above command runs a script that downloads a Docker Compose file to your system, builds and starts `memgraph-mage` and `memgraph-lab` Docker services in two separate containers. +
To export data from Memgraph, you first have to create a graph in Memgraph. To do that, expand this section and run the given Python script. @@ -188,6 +208,51 @@ You will get the following output: This means that the DGL graph has the correct number of node and edge types, total number of nodes and edges, as well as node and edge features. You can explore it more to see if it has all the required features. +## Export data from Memgraph into TF-GNN graph + +### Prerequisites + +Except for the [**general prerequisites**](#general-prerequisites), you also need to install [**TensorFlow GNN**](https://github.com/tensorflow/gnn). You can install it with: + +```bash +pip install tensorflow-gnn +``` + +Note: TF-GNN requires TensorFlow 2.x. For TensorFlow 2.16 and above, you may need to set `TF_USE_LEGACY_KERAS=1` environment variable. + +### Create and run a Python script + +Create a new Python script `memgraph-to-tfgnn.py`, in the code editor of your choice, with the following code: + +```python +import os +os.environ["TF_USE_LEGACY_KERAS"] = "1" # Required for TensorFlow 2.16+ + +from gqlalchemy.transformations.translators.tfgnn_translator import TFGNNTranslator + +translator = TFGNNTranslator() +graph_tensor = translator.get_instance() + +print("Node sets:", list(graph_tensor.node_sets.keys())) +print("Edge sets:", list(graph_tensor.edge_sets.keys())) + +for node_set_name, node_set in graph_tensor.node_sets.items(): + print(f"Node set '{node_set_name}': {node_set.sizes[0]} nodes") + print(f" Features: {list(node_set.features.keys())}") + +for edge_set_name, edge_set in graph_tensor.edge_sets.items(): + print(f"Edge set '{edge_set_name}': {edge_set.sizes[0]} edges") + print(f" Features: {list(edge_set.features.keys())}") +``` + +To run it, open a command-line interpreter and run the following command: + +```python +python3 memgraph-to-tfgnn.py +``` + +You will get output showing the node sets, edge sets, and their features. The TF-GNN GraphTensor can now be used directly with TensorFlow GNN models for graph neural network training. + ## Learn more Head over to the [**Under the hood**](../../under-the-hood/python-graph-translators.md) section to read about implementation details. If you want to learn more about using NetworkX with Memgraph with interesting resources and courses, head over to the [**Memgraph for NetworkX developers**](https://memgraph.com/memgraph-for-networkx?utm_source=docs&utm_medium=referral&utm_campaign=networkx_ppp&utm_term=docsgqla%2Bhowto&utm_content=textlink) website. If you have any questions or want to connect with the Memgraph community, [**join our Discord server**](https://www.discord.gg/memgraph). diff --git a/docs/how-to-guides/translators/import-python-graphs.md b/docs/how-to-guides/translators/import-python-graphs.md index 30b2a4c7..981fe8f9 100644 --- a/docs/how-to-guides/translators/import-python-graphs.md +++ b/docs/how-to-guides/translators/import-python-graphs.md @@ -1,6 +1,6 @@ # How to import Python graphs into Memgraph -GQLAlchemy holds translators that can import Python graphs ([NetworkX](https://networkx.org/), [PyG](https://pytorch-geometric.readthedocs.io/en/latest/) or [DGL](https://www.dgl.ai/) graphs) into Memgraph. These translators take the Python graph object and translate it to the appropriate Cypher queries. The Cypher queries are then executed to create a graph inside Memgraph. +GQLAlchemy holds translators that can import Python graphs ([NetworkX](https://networkx.org/), [PyG](https://pytorch-geometric.readthedocs.io/en/latest/), [DGL](https://www.dgl.ai/) or [TF-GNN](https://github.com/tensorflow/gnn) graphs) into Memgraph. These translators take the Python graph object and translate it to the appropriate Cypher queries. The Cypher queries are then executed to create a graph inside Memgraph. [![docs-source](https://img.shields.io/badge/source-examples-FB6E00?logo=github&style=for-the-badge)](https://github.com/memgraph/gqlalchemy/tree/main/tests/transformations/translators) [![docs-source](https://img.shields.io/badge/source-translators-FB6E00?logo=github&style=for-the-badge)](https://github.com/memgraph/gqlalchemy/tree/main/gqlalchemy/transformations/translators) @@ -12,14 +12,23 @@ In this guide you will learn how to: - [**Import NetworkX graph into Memgraph**](#import-networkx-graph-into-memgraph) - [**Import PyG graph into Memgraph**](#import-pyg-graph-into-memgraph) - [**Import DGL graph into Memgraph**](#import-dgl-graph-into-memgraph) +- [**Import TF-GNN graph into Memgraph**](#import-tf-gnn-graph-into-memgraph) ## General prerequisites -You need a running **Memgraph Platform instance**, which includes both the MAGE library and Memgraph Lab, a visual interface. To run the image, open a command-line interpreter and run the following Docker command: +You need **Memgraph Platform** running, which includes both the MAGE library and Memgraph Lab, a visual interface. To run it on Linux/macOS, run the following in your terminal: ``` -docker run -it -p 7687:7687 -p 7444:7444 -p 3000:3000 memgraph/memgraph-platform:latest +curl https://install.memgraph.com | sh ``` +To run it on Windows, execute the following command in the console: + +``` +iwr https://windows.memgraph.com | iex +``` + +The above command runs a script that downloads a Docker Compose file to your system, builds and starts `memgraph-mage` and `memgraph-lab` Docker services in two separate containers. + ## Import NetworkX graph into Memgraph ### Prerequisites @@ -207,6 +216,93 @@ Click **Run Query** button to see the results. You can notice that we have nodes labeled with `user` and `movie` and relationships of type `PLUS` and `MINUS`. Besides that, nodes and relationships have randomized array properties ad well as `dgl_id` property. +## Import TF-GNN graph into Memgraph + +### Prerequisites + +Except for the [**general prerequisites**](#general-prerequisites), you also need to install [**TensorFlow GNN**](https://github.com/tensorflow/gnn). You can install it with: + +```bash +pip install tensorflow-gnn +``` + +Note: TF-GNN requires TensorFlow 2.x. For TensorFlow 2.16 and above, you may need to set `TF_USE_LEGACY_KERAS=1` environment variable. + +### Create and run a Python script + +Create a new Python script `tfgnn-graph.py` in the code editor of your choice, with the following code: + +```python +import os +os.environ["TF_USE_LEGACY_KERAS"] = "1" # Required for TensorFlow 2.16+ + +import tensorflow as tf +import tensorflow_gnn as tfgnn +from gqlalchemy import Memgraph +from gqlalchemy.transformations.translators.tfgnn_translator import TFGNNTranslator + +memgraph = Memgraph() +memgraph.drop_database() + +# Create a TF-GNN GraphTensor +graph_tensor = tfgnn.GraphTensor.from_pieces( + node_sets={ + "user": tfgnn.NodeSet.from_fields( + sizes=[3], + features={ + "name": tf.constant(["Alice", "Bob", "Charlie"]), + "age": tf.constant([25, 30, 35], dtype=tf.int64), + } + ), + "movie": tfgnn.NodeSet.from_fields( + sizes=[2], + features={ + "title": tf.constant(["Inception", "Matrix"]), + "rating": tf.constant([8.8, 8.7], dtype=tf.float32), + } + ), + }, + edge_sets={ + "LIKES": tfgnn.EdgeSet.from_fields( + sizes=[3], + adjacency=tfgnn.Adjacency.from_indices( + source=("user", tf.constant([0, 0, 1])), + target=("movie", tf.constant([0, 1, 0])), + ), + features={ + "score": tf.constant([5, 4, 5], dtype=tf.int64), + } + ), + }, +) + +translator = TFGNNTranslator() + +for query in translator.to_cypher_queries(graph_tensor): + memgraph.execute(query) +``` + +First, connect to a running Memgraph instance. Next, drop the database to be sure that it's empty. After that, create a TF-GNN GraphTensor with `user` and `movie` node sets, and a `LIKES` edge set connecting users to movies. The GraphTensor includes node features (`name`, `age` for users; `title`, `rating` for movies) and edge features (`score`). In the end, call `to_cypher_queries` on `TFGNNTranslator` instance to transform the TF-GNN graph to Cypher queries which will be executed in Memgraph. + +To run it, open a command-line interpreter and run the following command: + +```python +python3 tfgnn-graph.py +``` + +### Explore the graph + +[Connect to Memgraph](htps://memgraph.com/docs/data-visualization/install-and-connect) via Memgraph Lab which is running at `localhost:3000`. Open the **Query Execution** section and write the following query: + +```cypher +MATCH (n)-[r]->(m) +RETURN n, r, m; +``` + +Click **Run Query** button to see the results. + +You can notice that we have nodes labeled with `user` and `movie` and relationships of type `LIKES`. Besides that, nodes and relationships have their respective properties as well as `tfgnn_id` property which maps to the node/edge index in the original TF-GNN GraphTensor. + ## Learn more Head over to the [**Under the hood**](../../under-the-hood/python-graph-translators.md) section to read about implementation details. If you want to learn more about using NetworkX with Memgraph with interesting resources and courses, head over to the [**Memgraph for NetworkX developers**](https://memgraph.com/memgraph-for-networkx?utm_source=docs&utm_medium=referral&utm_campaign=networkx_ppp&utm_term=docsgqla%2Bhowto&utm_content=textlink) website. If you have any questions or want to connect with the Memgraph community, [**join our Discord server**](https://www.discord.gg/memgraph). diff --git a/docs/reference/gqlalchemy/transformations/translators/tfgnn_translator.md b/docs/reference/gqlalchemy/transformations/translators/tfgnn_translator.md new file mode 100644 index 00000000..5ad7b27a --- /dev/null +++ b/docs/reference/gqlalchemy/transformations/translators/tfgnn_translator.md @@ -0,0 +1,62 @@ +--- +sidebar_label: tfgnn_translator +title: gqlalchemy.transformations.translators.tfgnn_translator +--- + +## TFGNNTranslator Objects + +```python +class TFGNNTranslator(Translator) +``` + +Translator for converting between Memgraph and TensorFlow GNN (TF-GNN) graph representations. + +TF-GNN represents graphs as GraphTensor objects, which are composite tensors that can be used directly in TensorFlow operations. This translator handles the conversion between Memgraph's graph model and TF-GNN's GraphTensor. + +TF-GNN is particularly useful for building Graph Neural Networks (GNNs) using TensorFlow and Keras. It supports both homogeneous and heterogeneous graphs with node and edge features. + +#### to\_cypher\_queries + +```python +def to_cypher_queries(graph_tensor: tfgnn.GraphTensor) -> List[str] +``` + +Produce Cypher queries for data saved as part of the TF-GNN GraphTensor. The method handles both homogeneous and heterogeneous graphs. If the graph is homogeneous, default labels will be used (`_N` as a node label and `_E` as edge label). The method converts 1D as well as multidimensional features. If there are some isolated nodes inside the GraphTensor, they won't get transferred. Nodes and edges created in Memgraph DB will, for consistency reasons, have property `tfgnn_id` set to the id they have as part of the TF-GNN graph. Note that this method doesn't insert anything inside the database, it just creates Cypher queries. To insert queries the following code can be used: + +```python +from gqlalchemy import Memgraph +from gqlalchemy.transformations.translators.tfgnn_translator import TFGNNTranslator + +memgraph = Memgraph() +graph_tensor = ... # Your TF-GNN GraphTensor +for query in TFGNNTranslator().to_cypher_queries(graph_tensor): + memgraph.execute(query) +``` + +**Arguments**: + +- `graph_tensor` - A reference to the TF-GNN GraphTensor. + +**Returns**: + + List of Cypher queries. + +#### get\_instance + +```python +def get_instance() -> tfgnn.GraphTensor +``` + +Create an instance of TF-GNN GraphTensor from all nodes and edges that are inside Memgraph. + +The translator converts Memgraph node labels to TF-GNN node sets and edge types to edge sets. Node and edge properties are converted to TF-GNN features with the following rules: + +- String, Integer, Float, Boolean properties are converted to corresponding TensorFlow dtypes +- List properties are converted to tensors (dense if same length, ragged if different lengths) +- Missing properties result in ragged tensors with empty values +- Map properties are not supported and will raise an error +- Memgraph-specific types (temporal types, Enum, Point) are converted to string representation + +**Returns**: + + A TF-GNN GraphTensor instance representing the graph stored in Memgraph. diff --git a/gqlalchemy/transformations/constants.py b/gqlalchemy/transformations/constants.py index e60db034..1e296a7e 100644 --- a/gqlalchemy/transformations/constants.py +++ b/gqlalchemy/transformations/constants.py @@ -9,3 +9,4 @@ NUM_NODES = "num_nodes" DEFAULT_NODE_LABEL = "NODE" DEFAULT_EDGE_TYPE = "RELATIONSHIP" +TFGNN_ID = "tfgnn_id" diff --git a/gqlalchemy/transformations/export/graph_transporter.py b/gqlalchemy/transformations/export/graph_transporter.py index 2bcf2ea6..375cb55e 100644 --- a/gqlalchemy/transformations/export/graph_transporter.py +++ b/gqlalchemy/transformations/export/graph_transporter.py @@ -29,6 +29,11 @@ except ModuleNotFoundError: PyGTranslator = None +try: + from gqlalchemy.transformations.translators.tfgnn_translator import TFGNNTranslator +except ModuleNotFoundError: + TFGNNTranslator = None + class GraphTransporter(Transporter): """Here is a possible example for using this module: @@ -52,7 +57,7 @@ def __init__( >>> transporter = GraphTransporter("dgl") graph = transporter.export() Args: - graph_type: dgl, pyg or nx + graph_type: dgl, pyg, nx or tfgnn """ super().__init__() self.graph_type = graph_type.upper() @@ -64,8 +69,11 @@ def __init__( self.translator = PyGTranslator(host, port, username, password, encrypted, client_name, lazy) elif self.graph_type == GraphType.NX.name: self.translator = NxTranslator(host, port, username, password, encrypted, client_name, lazy) + elif self.graph_type == GraphType.TFGNN.name: + raise_if_not_imported(dependency=TFGNNTranslator, dependency_name="tensorflow-gnn") + self.translator = TFGNNTranslator(host, port, username, password, encrypted, client_name, lazy) else: - raise ValueError("Unknown export option. Currently supported are DGL, PyG and NetworkX.") + raise ValueError("Unknown export option. Currently supported are DGL, PyG, NetworkX and TFGNN.") def export(self): """Creates graph instance for the wanted export option.""" diff --git a/gqlalchemy/transformations/graph_type.py b/gqlalchemy/transformations/graph_type.py index 9f7be8d3..f7202283 100644 --- a/gqlalchemy/transformations/graph_type.py +++ b/gqlalchemy/transformations/graph_type.py @@ -1,3 +1,3 @@ from enum import Enum -GraphType = Enum("GraphType", ["DGL", "PYG", "NX"]) +GraphType = Enum("GraphType", ["DGL", "PYG", "NX", "TFGNN"]) diff --git a/gqlalchemy/transformations/translators/tfgnn_translator.py b/gqlalchemy/transformations/translators/tfgnn_translator.py new file mode 100644 index 00000000..591c1a5f --- /dev/null +++ b/gqlalchemy/transformations/translators/tfgnn_translator.py @@ -0,0 +1,402 @@ +# Copyright (c) 2016-2025 Memgraph Ltd. [https://memgraph.com] +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections import defaultdict +from typing import Any, Dict, List, Set, Tuple + +import numpy as np +import tensorflow as tf +import tensorflow_gnn as tfgnn + +from gqlalchemy.memgraph_constants import ( + MG_CLIENT_NAME, + MG_ENCRYPTED, + MG_HOST, + MG_LAZY, + MG_PASSWORD, + MG_PORT, + MG_USERNAME, +) +from gqlalchemy.transformations.constants import DEFAULT_NODE_LABEL, TFGNN_ID +from gqlalchemy.transformations.translators.translator import Translator + + +class TFGNNTranslator(Translator): + """Translator for converting between Memgraph and TF-GNN graph representations. + + TF-GNN represents graphs as GraphTensor objects, which are composite tensors + that can be used directly in TensorFlow operations. This translator handles + the conversion between Memgraph's graph model and TF-GNN's GraphTensor. + """ + + def __init__( + self, + host: str = MG_HOST, + port: int = MG_PORT, + username: str = MG_USERNAME, + password: str = MG_PASSWORD, + encrypted: bool = MG_ENCRYPTED, + client_name: str = MG_CLIENT_NAME, + lazy: bool = MG_LAZY, + ) -> None: + super().__init__(host, port, username, password, encrypted, client_name, lazy) + + def get_instance(self) -> tfgnn.GraphTensor: + """Creates a TF-GNN GraphTensor instance from the data residing inside Memgraph. + + Returns: + tfgnn.GraphTensor: The created GraphTensor instance. + """ + memgraph = self.connection + # Get all nodes + nodes = memgraph.execute_and_fetch("MATCH (n) RETURN id(n), labels(n), properties(n)") + + # node data container + node_data = defaultdict(dict) + for node in nodes: + memgraph_id = node["id(n)"] + labels = node["labels(n)"] or [DEFAULT_NODE_LABEL] + properties = node["properties(n)"] + + for label in labels: + node_data[label][memgraph_id] = properties + + # Create node sets + node_sets = {} + for label, memgraph_id_properties in node_data.items(): + sizes = tf.constant([len(memgraph_id_properties)]) + features = self._convert_properties_to_tensors(memgraph_id_properties.values()) + node_sets[label] = tfgnn.NodeSet.from_fields(sizes=sizes, features=features) + + # Create mapping from Memgraph ID to TF-GNN ID + # TFGNN has node id for each node type starting from 0 to N + # the edges have schema describing the node types for source and target + memgraph_id_to_tfgnn_id = defaultdict(dict) + for label, memgraph_id_properties in node_data.items(): + for tfgnn_id, memgraph_id in enumerate(memgraph_id_properties.keys()): + memgraph_id_to_tfgnn_id[label][memgraph_id] = tfgnn_id + + # Get all edges + edges = memgraph.execute_and_fetch( + "MATCH (source)-[r]->(target) RETURN id(r), type(r), properties(r), id(source), id(target), labels(source), labels(target)" + ) + + # data containers for edges + edges_properties = defaultdict(list) + edges_source_memgraph_ids = defaultdict(list) + edges_target_memgraph_ids = defaultdict(list) + # in tfgnn the edges have schema describing the node types for source and target + edge_type_source_name = defaultdict(set) + edge_type_target_name = defaultdict(set) + + for edge in edges: + edge_type = edge["type(r)"] + memgraph_id_source = edge["id(source)"] + memgraph_id_target = edge["id(target)"] + properties = edge["properties(r)"] + source_labels = edge["labels(source)"] or [DEFAULT_NODE_LABEL] + target_labels = edge["labels(target)"] or [DEFAULT_NODE_LABEL] + + edges_source_memgraph_ids[edge_type].append(memgraph_id_source) + edges_target_memgraph_ids[edge_type].append(memgraph_id_target) + edges_properties[edge_type].append(properties) + + for source_label in source_labels: + for target_label in target_labels: + edge_type_source_name[edge_type].add(source_label) + edge_type_target_name[edge_type].add(target_label) + + edge_types = list(edge_type_source_name.keys()) + edge_sets = {} + for edge_type in edge_types: + if len(edge_type_source_name[edge_type]) > 1 or len(edge_type_target_name[edge_type]) > 1: + multiple_node_types_share_edge_type = True + else: + multiple_node_types_share_edge_type = False + for source_label in edge_type_source_name[edge_type]: + for target_label in edge_type_target_name[edge_type]: + tfgnn_source_ids = [ + memgraph_id_to_tfgnn_id[source_label][i] for i in edges_source_memgraph_ids[edge_type] + ] + tfgnn_target_ids = [ + memgraph_id_to_tfgnn_id[target_label][i] for i in edges_target_memgraph_ids[edge_type] + ] + if multiple_node_types_share_edge_type: + edge_name = f"{source_label}_{edge_type}_{target_label}" + else: + edge_name = edge_type + + edge_sets[edge_name] = tfgnn.EdgeSet.from_fields( + sizes=tf.constant([len(tfgnn_source_ids)]), + adjacency=tfgnn.Adjacency.from_indices( + source=(source_label, tf.constant(tfgnn_source_ids, dtype=tf.int64)), + target=(target_label, tf.constant(tfgnn_target_ids, dtype=tf.int64)), + ), + features=self._convert_properties_to_tensors(edges_properties[edge_type]), + ) + + return tfgnn.GraphTensor.from_pieces(node_sets=node_sets, edge_sets=edge_sets) + + def _infer_dtype_from_value(self, value: Any) -> tf.DType: + """Infer TensorFlow dtype from a Python value. + + Handles standard types (str, int, float, bool) as well as Memgraph-specific + types like temporal types (Duration, Date, LocalTime, LocalDateTime, ZonedDateTime), + Enum, and Point, which are converted to string representation. + + Args: + value: The Python value to infer dtype from. + + Returns: + The corresponding TensorFlow dtype. + + Raises: + ValueError: If the value is a dict (Map type not supported by TFGNN). + """ + if isinstance(value, dict): + raise ValueError(f"Map property type is not supported by TFGNN.") + if isinstance(value, str): + return tf.string + elif isinstance(value, bool): + # bool must come before int since bool is a subclass of int in Python + return tf.bool + elif isinstance(value, int): + return tf.int64 + elif isinstance(value, float): + return tf.float32 + else: + # For other types (temporal types, Enum, Point, etc.), + # convert to string representation + return tf.string + + def _convert_value_for_tensor(self, value: Any) -> Any: + """Convert a value to a format suitable for TensorFlow tensors. + + Standard types (str, int, float, bool) are passed through unchanged. + Memgraph-specific types (temporal types, Enum, Point, etc.) are converted + to their string representation. + + For lists, each element is recursively converted. + + Args: + value: The value to convert. + + Returns: + The converted value. + """ + if isinstance(value, (str, int, float, bool)) or value is None: + return value + elif isinstance(value, list): + return [self._convert_value_for_tensor(v) for v in value] + elif isinstance(value, dict): + # Maps are not supported, but let _infer_dtype_from_value raise the error + return value + else: + # Convert temporal types, Enum, Point, etc. to string + return str(value) + + def _properties_metadata( + self, properties_list: List[Dict[str, Any]] + ) -> Tuple[Set[str], Set[str], Dict[str, tf.DType], Set[str]]: + all_property_names = set() + list_properties = set() + dtypes = {} + + for properties in properties_list: + all_property_names.update(properties.keys()) + for key, value in properties.items(): + if isinstance(value, list): + list_properties.add(key) + if len(value): + value = value[0] + else: + continue + + dtypes[key] = self._infer_dtype_from_value(value) + + properties_with_missing_values = set() + for properties in properties_list: + for key in all_property_names: + if key not in properties: + properties_with_missing_values.add(key) + return all_property_names, list_properties, dtypes, properties_with_missing_values + + def _convert_properties_to_tensors(self, properties_list: List[Dict[str, Any]]) -> Dict[str, tf.Tensor]: + """ + Converts list of properties to TF tensors. + + properties_list example: + [ + {"name": "John", "age": 30}, + {"name": "Jane"} + ] + + Returns a dictionary of TF tensors in one of the following dtypes: tf.string, tf.int64, tf.float32, tf.bool. + + + example: + { + "name": tf.constant(["John", "Jane"], dtype=tf.string), + "age": tf.ragged.constant([[30], []], dtype=tf.int64) + } + + Conversion rules: + + Properties are allowed to be missing for some nodes. + eg: one node can have property 'age' and another node can only have property 'name' + + If properties have different types, an exception will be raised. + eg: if one node has property 'age' as Integer and another node has property 'age' as String, an exception will be raised. + + If a property have type String, Integer, Float, Boolean: + Then if all nodes have the same property value, it is converted to a dense 1D tensor eg: [1, 2, 3] + Otherwise, it is converted to a ragged 2D tensor eg: [[1], [], [3]] + The user can fill the missing value and convert to dense tensor by `ragged_tensor.to_tensor(default_value=0)` + + If a property have type List: + Then if all nodes have values of the same length, it is converted to a dense 2D tensor eg: [[1, 2, 3], [3, 4, 5]] + Otherwise, it is converted to a ragged 2D tensor eg: [[1, 2], [], [3, 4, 5]] + + If a property have type Map: + Then exception will be raised as tfgnn.GraphTensor does not support map property type + + If a property has a Memgraph-specific type (temporal types like Duration, Date, LocalTime, + LocalDateTime, ZonedDateTime, or Enum, Point): + Then it is converted to tf.string using str() representation. + """ + # Convert special Memgraph types to strings before processing + converted_properties_list = [] + for properties in properties_list: + converted_props = {} + for key, value in properties.items(): + converted_props[key] = self._convert_value_for_tensor(value) + converted_properties_list.append(converted_props) + + all_property_names, list_properties, dtypes, properties_with_missing_values = self._properties_metadata( + converted_properties_list + ) + + features = defaultdict(list) + + for properties in converted_properties_list: + for key in all_property_names: + if key not in properties: + # missing value + value = [] + else: + if key in properties_with_missing_values: + if key in list_properties: + value = properties[key] + else: + value = [properties[key]] + else: + value = properties[key] + + features[key].append(value) + + for key, values in features.items(): + if key not in properties_with_missing_values: + if key not in dtypes: + # do not include property if all nodes have empty lists + continue + if key in list_properties: + have_same_shape = True + for value in values: + have_same_shape = have_same_shape and len(value) == len(values[0]) + if have_same_shape: + features[key] = tf.constant(values, dtype=dtypes[key]) + else: + features[key] = tf.ragged.constant(values, dtype=dtypes[key]) + else: + features[key] = tf.constant(values, dtype=dtypes[key]) + else: + features[key] = tf.ragged.constant(values, dtype=dtypes[key]) + return features + + def _convert_tensors_to_properties(self, features: Dict[str, tf.Tensor], idx: int) -> Dict[str, Any]: + """Converts TF tensors to properties dictionary.""" + properties = {} + for key, tensor in features.items(): + value = tensor[idx].numpy() + if not isinstance(value, np.ndarray) and isinstance(value, bytes): + value = value.decode("utf-8") + if isinstance(value, np.ndarray) and (np.issubdtype(value.dtype, np.bytes_) or value.dtype == np.object_): + value = value.astype(str) + if (isinstance(value, np.ndarray) or isinstance(value, list)) and len(value) == 0: + # skip empty values + continue + if isinstance(value, (np.floating, float)) and (np.isnan(value) or np.isinf(value)): + continue + properties[key] = value + + return properties + + def to_cypher_queries(self, graph_tensor: tfgnn.GraphTensor) -> List[str]: + """Produce Cypher queries for data saved as part of the TFGNN graph. If the graph is homogeneous, a default TFGNN's labels will be used (_N as a node label and _E as edge label). The method converts 1D as well as multidimensional features. If there are some isolated nodes inside TFGNN graph, they won't get transferred. Nodes and edges + created in Memgraph DB will, for the consistency reasons, have property `tfgnn_id` set to the id they have as part of the TFGNN graph. Note that this method doesn't insert anything inside the database, + it just creates Cypher queries. To insert queries the following code can be used: + + memgraph = Memgraph() + graph_tensor = TFGNNTranslator(...) + for query in TFGNNTranslator().to_cypher_queries(graph_tensor): + memgraph.execute(query) + """ + + queries = [] + # merge potentially batched graphs to one graph with disjoined graphs + graph_tensor = graph_tensor.merge_batch_to_components() + + for edge_type, edge_set in graph_tensor.edge_sets.items(): + # in GraphTensor, the source and target nodes are explicitly defined in edge set schema + source_node_label = edge_set.adjacency.source_name + dest_node_label = edge_set.adjacency.target_name + + for edge_index in range(edge_set.sizes[0]): + start_node_id = edge_set.adjacency.source[edge_index].numpy() + source_node_properties = self._convert_tensors_to_properties( + graph_tensor.node_sets[source_node_label].features, start_node_id + ) + source_node_properties[TFGNN_ID] = start_node_id + + end_node_id = edge_set.adjacency.target[edge_index].numpy() + dest_node_properties = self._convert_tensors_to_properties( + graph_tensor.node_sets[dest_node_label].features, end_node_id + ) + dest_node_properties[TFGNN_ID] = end_node_id + + edge_properties = self._convert_tensors_to_properties(edge_set.features, edge_index) + edge_properties[TFGNN_ID] = edge_index + + queries.append( + self.create_insert_query( + source_node_label=source_node_label, + source_node_properties=source_node_properties, + edge_type=edge_type, + edge_properties=edge_properties, + dest_node_label=dest_node_label, + dest_node_properties=dest_node_properties, + ) + ) + return queries + + +def to_cypher_index_queries(self, graph_tensor: tfgnn.GraphTensor) -> List[str]: + """ + Creates Cypher index queries for the graph. + """ + queries = [] + for node_name in graph_tensor.node_sets: + queries.append(f"CREATE INDEX ON :{node_name}({TFGNN_ID})") + return queries diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 0b8d8e7b..00000000 --- a/poetry.lock +++ /dev/null @@ -1,2565 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - 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[https://memgraph.com] +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytest +import tensorflow as tf +import tensorflow_gnn as tfgnn +import numpy as np + +from gqlalchemy import Memgraph +from gqlalchemy.transformations.translators.tfgnn_translator import TFGNNTranslator +from gqlalchemy.transformations.constants import DEFAULT_NODE_LABEL + + +def test_export_homogeneous_graph(memgraph: Memgraph): + """ + Test exporting homogeneous graph (nodes without labels) + """ + memgraph.execute( + """ + CREATE (m {name: 'Alice'}), (n {name: 'Bob'}), (m)-[:FRIEND]->(n) + """ + ) + graph_tensor = TFGNNTranslator().get_instance() + + assert graph_tensor.node_sets[DEFAULT_NODE_LABEL].sizes[0] == 2 + assert graph_tensor.node_sets[DEFAULT_NODE_LABEL].features["name"].numpy().tolist() == [b"Alice", b"Bob"] + assert graph_tensor.edge_sets["FRIEND"].sizes[0] == 1 + assert len(graph_tensor.edge_sets["FRIEND"].features) == 0 + + +def test_export_heterogeneous_graph(memgraph: Memgraph): + """ + Test exporting heterogeneous graph (nodes with one label) + """ + memgraph.execute( + """ + CREATE (m:Person {name: 'Alice'}), (n:Person {name: 'Bob'}), (m)-[:FRIEND]->(n) + """ + ) + graph_tensor = TFGNNTranslator().get_instance() + assert graph_tensor.node_sets["Person"].sizes[0] == 2 + assert graph_tensor.edge_sets["FRIEND"].sizes[0] == 1 + + +def test_export_heterogeneous_multi_label_graph(memgraph: Memgraph): + """ + Test exporting heterogeneous graph and nodes with more than one label + GraphTensor will create a node for every label and edge type for each combination of source / target labels + """ + memgraph.execute( + """ + CREATE (m:A:B {name: 'Alice'}), (n:C {name: 'Bob'}), (m)-[:FRIEND]->(n) + """ + ) + + graph_tensor = TFGNNTranslator().get_instance() + assert graph_tensor.node_sets["A"].sizes[0] == 1 + assert graph_tensor.node_sets["B"].sizes[0] == 1 + assert graph_tensor.node_sets["C"].sizes[0] == 1 + + assert graph_tensor.edge_sets["A_FRIEND_C"].sizes[0] == 1 + assert graph_tensor.edge_sets["B_FRIEND_C"].sizes[0] == 1 + + +def test_export_missing_properties(memgraph: Memgraph): + """ + Test exporting graph with missing properties + """ + memgraph.execute( + """ + CREATE (:Person {name: 'Alice'}), (:Person {name: 'Bob', age: 30}), (:Person {name: 'Charlie', height: 6.1}) + """ + ) + graph_tensor = TFGNNTranslator().get_instance() + assert graph_tensor.node_sets["Person"].sizes[0] == 3 + assert graph_tensor.node_sets["Person"].features["name"].numpy().tolist() == [b"Alice", b"Bob", b"Charlie"] + assert graph_tensor.node_sets["Person"].features["age"].to_tensor(default_value=0).numpy().flatten().tolist() == [ + 0, + 30, + 0, + ] + assert np.allclose( + graph_tensor.node_sets["Person"].features["height"].to_tensor(default_value=0).numpy().flatten(), + [0.0, 0.0, 6.1], + ) + + +def test_export_list_properties_same_dimensionality(memgraph: Memgraph): + """ + Test exporting graph with list properties of same dimensionality + """ + memgraph.execute( + """ + CREATE (:Person {name: 'Alice', hobbies: ['reading', 'hiking']}), + (:Person {name: 'Bob', hobbies: ['reading', 'gaming']}), + (:Person {name: 'Charlie', hobbies: ['reading', 'cooking']}) + """ + ) + graph_tensor = TFGNNTranslator().get_instance() + assert graph_tensor.node_sets["Person"].sizes[0] == 3 + assert graph_tensor.node_sets["Person"].features["name"].numpy().tolist() == [b"Alice", b"Bob", b"Charlie"] + assert graph_tensor.node_sets["Person"].features["hobbies"].numpy().tolist() == [ + [b"reading", b"hiking"], + [b"reading", b"gaming"], + [b"reading", b"cooking"], + ] + + +def test_export_list_properties_various_dimensionality(memgraph: Memgraph): + """ + Test exporting graph with list properties of various dimensionality + """ + memgraph.execute( + """ + CREATE (:Person {name: 'Alice', hobbies: ['hiking']}), + (:Person {name: 'Bob', hobbies: ['reading', 'gaming']}), + (:Person) + """ + ) + graph_tensor = TFGNNTranslator().get_instance() + assert graph_tensor.node_sets["Person"].sizes[0] == 3 + assert graph_tensor.node_sets["Person"].features["name"].to_tensor(default_value=b"N/A").numpy().tolist() == [ + [b"Alice"], + [b"Bob"], + [b"N/A"], + ] + assert graph_tensor.node_sets["Person"].features["hobbies"].to_tensor( + default_value=b"reading" + ).numpy().tolist() == [[b"hiking", b"reading"], [b"reading", b"gaming"], [b"reading", b"reading"]] + + +def test_export_map_properties(memgraph: Memgraph): + memgraph.execute( + """ + CREATE (:Person {hobbies: {reading: 1, hiking: 2}}) + """ + ) + with pytest.raises(ValueError, match="Map property type is not supported by TFGNN."): + TFGNNTranslator().get_instance() + + +def test_create_simple_graph(memgraph: Memgraph): + graph_tensor = tfgnn.GraphTensor.from_pieces( + node_sets={ + "Person": tfgnn.NodeSet.from_fields(sizes=[2], features={"name": [b"Alice", b"Bob"]}), + "Dog": tfgnn.NodeSet.from_fields(sizes=[1], features={"name": [b"Rex"]}), + }, + edge_sets={ + "FRIEND": tfgnn.EdgeSet.from_fields( + sizes=[1], + adjacency=tfgnn.Adjacency.from_indices( + source=("Person", tf.constant([0])), target=("Person", tf.constant([1])) + ), + ), + "PETS": tfgnn.EdgeSet.from_fields( + sizes=[1], + features={"bread": [b"Golden Retriever"]}, + adjacency=tfgnn.Adjacency.from_indices( + source=("Person", tf.constant([1])), target=("Dog", tf.constant([0])) + ), + ), + }, + ) + for query in TFGNNTranslator().to_cypher_queries(graph_tensor): + memgraph.execute(query) + + properties = [] + for node in memgraph.execute_and_fetch("MATCH (n: Person) RETURN properties(n)"): + properties.append(node["properties(n)"]) + assert properties == [{"name": "Alice", "tfgnn_id": 0}, {"name": "Bob", "tfgnn_id": 1}] + + # ingest again to check that all nodes are merged and graph is not changed + for query in TFGNNTranslator().to_cypher_queries(graph_tensor): + memgraph.execute(query) + + new_properties = [] + for node in memgraph.execute_and_fetch("MATCH (n: Person) RETURN properties(n)"): + new_properties.append(node["properties(n)"]) + assert new_properties == properties + + properties = [] + for node in memgraph.execute_and_fetch("MATCH (n: Dog) RETURN properties(n)"): + properties.append(node["properties(n)"]) + assert properties == [{"name": "Rex", "tfgnn_id": 0}] + + properties_s = [] + properties_t = [] + for edge in memgraph.execute_and_fetch("MATCH (s)-[r:FRIEND]->(t) RETURN properties(s), properties(t)"): + properties_s.append(edge["properties(s)"]) + properties_t.append(edge["properties(t)"]) + assert properties_s == [{"name": "Alice", "tfgnn_id": 0}] + assert properties_t == [{"name": "Bob", "tfgnn_id": 1}] + + properties_s = [] + properties_r = [] + properties_t = [] + for edge in memgraph.execute_and_fetch( + "MATCH (s)-[r:PETS]->(t) RETURN properties(s), properties(r), properties(t)" + ): + properties_s.append(edge["properties(s)"]) + properties_r.append(edge["properties(r)"]) + properties_t.append(edge["properties(t)"]) + assert properties_s == [{"name": "Bob", "tfgnn_id": 1}] + assert properties_r == [{"bread": "Golden Retriever", "tfgnn_id": 0}] + assert properties_t == [{"name": "Rex", "tfgnn_id": 0}] + + +def test_create_missing_properties(memgraph: Memgraph): + graph_tensor = tfgnn.GraphTensor.from_pieces( + node_sets={ + "Person": tfgnn.NodeSet.from_fields( + sizes=[2], + features={ + "hobbies": tf.ragged.constant([[], [b"reading", b"gaming"]]), + "age": tf.ragged.constant([[25], []]), + }, + ) + }, + edge_sets={ + "FRIEND": tfgnn.EdgeSet.from_fields( + sizes=[1], + adjacency=tfgnn.Adjacency.from_indices( + source=("Person", tf.constant([0])), target=("Person", tf.constant([1])) + ), + ) + }, + ) + for query in TFGNNTranslator().to_cypher_queries(graph_tensor): + memgraph.execute(query) + + properties = [] + for node in memgraph.execute_and_fetch("MATCH (n) RETURN properties(n)"): + properties.append(node["properties(n)"]) + + assert properties == [{"age": [25], "tfgnn_id": 0}, {"hobbies": ["reading", "gaming"], "tfgnn_id": 1}] + + +def test_create_disjoined_nodes(memgraph: Memgraph): + graph_tensor = tfgnn.GraphTensor.from_pieces( + node_sets={"Person": tfgnn.NodeSet.from_fields(sizes=[2], features={"age": tf.ragged.constant([25, 45])})} + ) + for query in TFGNNTranslator().to_cypher_queries(graph_tensor): + memgraph.execute(query) + + result = memgraph.execute_and_fetch("MATCH (n) RETURN n") + assert len(list(result)) == 0