diff --git a/.github/workflows/build-and-test.yml b/.github/workflows/build-and-test.yml index d20e69ee..de240763 100644 --- a/.github/workflows/build-and-test.yml +++ b/.github/workflows/build-and-test.yml @@ -55,8 +55,10 @@ jobs: 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 @@ -131,6 +133,8 @@ 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..07fc4810 100644 --- a/docs/how-to-guides/translators/export-python-graphs.md +++ b/docs/how-to-guides/translators/export-python-graphs.md @@ -14,12 +14,20 @@ In this guide you will learn how to: - [**Export data from Memgraph into DGL graph**](#import-dgl-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. +
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. diff --git a/docs/how-to-guides/translators/import-python-graphs.md b/docs/how-to-guides/translators/import-python-graphs.md index 30b2a4c7..3f352373 100644 --- a/docs/how-to-guides/translators/import-python-graphs.md +++ b/docs/how-to-guides/translators/import-python-graphs.md @@ -14,12 +14,20 @@ In this guide you will learn how to: - [**Import DGL graph into Memgraph**](#import-dgl-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 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..f5577db1 --- /dev/null +++ b/gqlalchemy/transformations/translators/tfgnn_translator.py @@ -0,0 +1,345 @@ +# 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 typing import List, Dict, Any, Set, Tuple +import tensorflow as tf +import tensorflow_gnn as tfgnn +from collections import defaultdict +from gqlalchemy.transformations.constants import DEFAULT_NODE_LABEL, TFGNN_ID +from gqlalchemy.transformations.translators.translator import Translator +import numpy as np + +from gqlalchemy.memgraph_constants import ( + MG_HOST, + MG_PORT, + MG_USERNAME, + MG_PASSWORD, + MG_ENCRYPTED, + MG_CLIENT_NAME, + MG_LAZY, +) + + +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: + 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, int): + return tf.int64 + elif isinstance(value, float): + return tf.float32 + elif isinstance(value, bool): + return tf.bool + else: + raise ValueError(f"Unexpected type: {type(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) + } + + Convertion 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 + """ + all_property_names, list_properties, dtypes, properties_with_missing_values = self._properties_metadata( + properties_list + ) + + features = defaultdict(list) + + for properties in 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 `dgl_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 index 5e3b9eb1..70c21974 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,23 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= { version = "^6.1.3", optional = true } @@ -52,8 +55,9 @@ priority = "explicit" [tool.poetry.extras] arrow = ["pyarrow"] +tfgnn = ["tensorflow-gnn", "tensorflow-macos", "tensorflow", "tf-keras"] dgl = ["torch", "dgl"] -all = ["pyarrow", "torch", "dgl", "docker"] +all = ["pyarrow", "torch", "dgl", "docker", "tensorflow-gnn"] torch_pyg = ["torch"] docker = ["docker"] @@ -70,6 +74,7 @@ pre-commit = ">=2.15.0,<4.0.0" [tool.poe.tasks] install-dgl = "pip install dgl==2.4.0 -f https://data.dgl.ai/wheels/torch-2.4/repo.html --no-build-isolation" install-pyg-cpu = "pip install 'torch>=2.4.0,<=2.5.0' torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-2.4.0+cpu.html --no-build-isolation" +install-tfgnn = "pip install tensorflow-gnn>=1.0.0,<2.0.0" [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/tests/transformations/export/test_export.py b/tests/transformations/export/test_export.py index fc81f7fc..0bb24d27 100644 --- a/tests/transformations/export/test_export.py +++ b/tests/transformations/export/test_export.py @@ -23,6 +23,16 @@ def test_export_pyg(): transporter.export() # even with empty graph we should check that something doesn't fail +@pytest.mark.extras +@pytest.mark.tfgnn +def test_export_tfgnn(): + TFGNNTranslator = pytest.importorskip("gqlalchemy.transformations.translators.tfgnn_translator.TFGNNTranslator") + + transporter = GraphTransporter(graph_type="tfgnn") + assert isinstance(transporter.translator, TFGNNTranslator) + transporter.export() # even with empty graph we should check that something doesn't fail + + def test_export_nx(): from gqlalchemy.transformations.translators.nx_translator import NxTranslator diff --git a/tests/transformations/translators/test_tfgnn_transformations.py b/tests/transformations/translators/test_tfgnn_transformations.py new file mode 100644 index 00000000..1cb7ab21 --- /dev/null +++ b/tests/transformations/translators/test_tfgnn_transformations.py @@ -0,0 +1,258 @@ +# 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. + + +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_homoginious_graph(memgraph: Memgraph): + """ + Test exporting homoginious 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_heteroghinious_graph(memgraph: Memgraph): + """ + Test exporting heteroghinious 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_heteroghinious_multi_label_graph(memgraph: Memgraph): + """ + Test exporting heteroghinious 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