%global _empty_manifest_terminate_build 0 Name: python-kgx Version: 2.1.0 Release: 1 Summary: A Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the Biolink Model. License: BSD URL: https://pypi.org/project/kgx/ Source0: https://mirrors.aliyun.com/pypi/web/packages/0d/6b/983f1ecccbf7efc900b9eb93081bfcb42a774673f0206d1cc5eb59e1a457/kgx-2.1.0.tar.gz BuildArch: noarch Requires: python3-Click Requires: python3-SPARQLWrapper Requires: python3-Sphinx Requires: python3-bmt Requires: python3-cachetools Requires: python3-closurizer Requires: python3-deprecation Requires: python3-docker Requires: python3-docutils Requires: python3-ijson Requires: python3-inflection Requires: python3-jsonlines Requires: python3-jsonstreams Requires: python3-linkml Requires: python3-linkml-runtime Requires: python3-mypy Requires: python3-neo4j Requires: python3-networkx Requires: python3-ordered-set Requires: python3-pandas Requires: python3-prefixcommons Requires: python3-prologterms Requires: python3-pytest Requires: python3-dateutil Requires: python3-pyyaml Requires: python3-rdflib Requires: python3-recommonmark Requires: python3-shexjsg Requires: python3-stringcase Requires: python3-terminaltables Requires: python3-tox Requires: python3-validators %description # Knowledge Graph Exchange [![Python](https://img.shields.io/badge/python-3.9+-blue.svg)]() ![Run tests](https://github.com/biolink/kgx/workflows/Run%20tests/badge.svg)[![Documentation Status](https://readthedocs.org/projects/kgx/badge/?version=latest)](https://kgx.readthedocs.io/en/latest/?badge=latest) [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=alert_status)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=sqale_rating)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![Coverage](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=coverage)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![PyPI](https://img.shields.io/pypi/v/kgx)](https://img.shields.io/pypi/v/kgx) [![Docker](https://img.shields.io/static/v1?label=Docker&message=biolink/kgx:latest&color=orange&logo=docker)](https://hub.docker.com/r/biolink/kgx) KGX (Knowledge Graph Exchange) is a Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the [Biolink Model](https://biolink.github.io/biolink-model/). The core datamodel is a [Property Graph](https://neo4j.com/developer/graph-database/) (PG), represented internally in Python using a [networkx MultiDiGraph model](https://networkx.github.io/documentation/stable/reference/classes/generated/networkx.MultiDiGraph.edges.html). KGX allows conversion to and from: * RDF serializations (read/write) and SPARQL endpoints (read) * Neo4j endpoints (read) or Neo4j dumps (write) * CSV/TSV and JSON (see [associated data formats](./data-preparation.md) and [example script to load CSV/TSV to Neo4j](./examples/scripts/load_csv_to_neo4j.py)) * Reasoner Standard API format * OBOGraph JSON format KGX will also provide validation, to ensure the KGs are conformant to the Biolink Model: making sure nodes are categorized using Biolink classes, edges are labeled using valid Biolink relationship types, and valid properties are used. Internal representation is a property graph, specifically a networkx MultiDiGraph. The structure of this graph is expected to conform to the Biolink Model standard, as specified in the [KGX format specification](specification/kgx-format.md). In addition to the main code-base, KGX also provides a series of [command line operations](https://kgx.readthedocs.io/en/latest/examples.html#using-kgx-cli). ### Example usage Validate: ```bash poetry run kgx validate -i tsv tests/resources/merge/test2_nodes.tsv tests/resources/merge/test2_edges.tsv ``` Merge: ```bash poetry run kgx merge —merge-config tests/resources/test-merge.yaml ``` Graph Summary: ```bash poetry run kgx graph-summary -i tests/resources/graph_nodes.tsv -o summary.txt ``` Transform: ```bash poetry run kgx transform —transform-config tests/resources/test-transform-tsv-rdf.yaml ``` ### Error Detection and Reporting Non-redundant JSON-formatted structured error logging is now provided in KGX Transformer, Validator, GraphSummary and MetaKnowledgeGraph operations. See the various unit tests for the general design pattern (using the Validator as an example here): ```python from kgx.validator import Validator from kgx.transformer import Transformer Validator.set_biolink_model("2.11.0") # Validator assumes the currently set Biolink Release validator = Validator() transformer = Transformer(stream=True) transformer.transform( input_args = { "filename": [ "graph_nodes.tsv", "graph_edges.tsv", ], "format": "tsv", }, output_args={ "format": "null" }, inspector=validator, ) # Both the Validator and the Transformer can independently capture errors # The Validator, from the overall semantics of the graph... # Here, we just report severe Errors from the Validator (no Warnings) validator.write_report(open("validation_errors.json", "w"), "Error") # The Transformer, from the syntax of the input files... # Here, we catch *all* Errors and Warnings (by not providing a filter) transformer.write_report(open("input_errors.json", "w")) ``` The JSON error outputs will look something like this: ```json { "ERROR": { "MISSING_EDGE_PROPERTY": { "Required edge property 'id' is missing": [ "A:123->X:1", "B:456->Y:2" ], "Required edge property 'object' is missing": [ "A:123->X:1" ], "Required edge property 'predicate' is missing": [ "A:123->X:1" ], "Required edge property 'subject' is missing": [ "A:123->X:1", "B:456->Y:2" ] } }, "WARNING": { "DUPLICATE_NODE": { "Node 'id' duplicated in input data": [ "MONDO:0010011", "REACT:R-HSA-5635838" ] } } } ``` This system reduces the significant redundancies of earlier line-oriented KGX logging text output files, in that graph entities with the same class of error are simply aggregated in lists of names/identifiers at the leaf level of the JSON structure. The top level JSON tags originate from the `MessageLevel` class and the second level tags from the `ErrorType` class in the [error_detection](kgx/error_detection.py) module, while the third level messages are hard coded as `log_error` method messages in the code. It is likely that additional error conditions within KGX can be efficiently captured and reported in the future using this general framework. ## Installation #### Installing from PyPI KGX is available on PyPI and can be installed using [pip](https://pip.pypa.io/en/stable/installing/) as follows, ```bash pip install kgx ``` To install a particular version of KGX, be sure to specify the version number, ```bash pip install kgx==0.5.0 ``` #### Installing from GitHub Clone the GitHub repository and then install, ```bash git clone https://github.com/biolink/kgx cd kgx poetry install ``` ### Setting up a testing environment for Neo4j This release of KGX supports graph source and sink transactions with the 4.3 release of Neo4j. KGX has a suite of tests that rely on Docker containers to run Neo4j specific tests. To set up the required containers, first install [Docker](https://docs.docker.com/get-docker/) on your local machine. Once Docker is up and running, run the following commands: ```bash docker run -d --rm --name kgx-neo4j-integration-test -p 7474:7474 -p 7687:7687 --env NEO4J_AUTH=neo4j/test neo4j:4.3 ``` ```bash docker run -d --rm --name kgx-neo4j-unit-test -p 8484:7474 -p 8888:7687 --env NEO4J_AUTH=neo4j/test neo4j:4.3 ``` **Note:** Setting up the Neo4j container is optional. If there is no container set up then the tests that rely on them are skipped. %package -n python3-kgx Summary: A Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the Biolink Model. Provides: python-kgx BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-kgx # Knowledge Graph Exchange [![Python](https://img.shields.io/badge/python-3.9+-blue.svg)]() ![Run tests](https://github.com/biolink/kgx/workflows/Run%20tests/badge.svg)[![Documentation Status](https://readthedocs.org/projects/kgx/badge/?version=latest)](https://kgx.readthedocs.io/en/latest/?badge=latest) [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=alert_status)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=sqale_rating)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![Coverage](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=coverage)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![PyPI](https://img.shields.io/pypi/v/kgx)](https://img.shields.io/pypi/v/kgx) [![Docker](https://img.shields.io/static/v1?label=Docker&message=biolink/kgx:latest&color=orange&logo=docker)](https://hub.docker.com/r/biolink/kgx) KGX (Knowledge Graph Exchange) is a Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the [Biolink Model](https://biolink.github.io/biolink-model/). The core datamodel is a [Property Graph](https://neo4j.com/developer/graph-database/) (PG), represented internally in Python using a [networkx MultiDiGraph model](https://networkx.github.io/documentation/stable/reference/classes/generated/networkx.MultiDiGraph.edges.html). KGX allows conversion to and from: * RDF serializations (read/write) and SPARQL endpoints (read) * Neo4j endpoints (read) or Neo4j dumps (write) * CSV/TSV and JSON (see [associated data formats](./data-preparation.md) and [example script to load CSV/TSV to Neo4j](./examples/scripts/load_csv_to_neo4j.py)) * Reasoner Standard API format * OBOGraph JSON format KGX will also provide validation, to ensure the KGs are conformant to the Biolink Model: making sure nodes are categorized using Biolink classes, edges are labeled using valid Biolink relationship types, and valid properties are used. Internal representation is a property graph, specifically a networkx MultiDiGraph. The structure of this graph is expected to conform to the Biolink Model standard, as specified in the [KGX format specification](specification/kgx-format.md). In addition to the main code-base, KGX also provides a series of [command line operations](https://kgx.readthedocs.io/en/latest/examples.html#using-kgx-cli). ### Example usage Validate: ```bash poetry run kgx validate -i tsv tests/resources/merge/test2_nodes.tsv tests/resources/merge/test2_edges.tsv ``` Merge: ```bash poetry run kgx merge —merge-config tests/resources/test-merge.yaml ``` Graph Summary: ```bash poetry run kgx graph-summary -i tests/resources/graph_nodes.tsv -o summary.txt ``` Transform: ```bash poetry run kgx transform —transform-config tests/resources/test-transform-tsv-rdf.yaml ``` ### Error Detection and Reporting Non-redundant JSON-formatted structured error logging is now provided in KGX Transformer, Validator, GraphSummary and MetaKnowledgeGraph operations. See the various unit tests for the general design pattern (using the Validator as an example here): ```python from kgx.validator import Validator from kgx.transformer import Transformer Validator.set_biolink_model("2.11.0") # Validator assumes the currently set Biolink Release validator = Validator() transformer = Transformer(stream=True) transformer.transform( input_args = { "filename": [ "graph_nodes.tsv", "graph_edges.tsv", ], "format": "tsv", }, output_args={ "format": "null" }, inspector=validator, ) # Both the Validator and the Transformer can independently capture errors # The Validator, from the overall semantics of the graph... # Here, we just report severe Errors from the Validator (no Warnings) validator.write_report(open("validation_errors.json", "w"), "Error") # The Transformer, from the syntax of the input files... # Here, we catch *all* Errors and Warnings (by not providing a filter) transformer.write_report(open("input_errors.json", "w")) ``` The JSON error outputs will look something like this: ```json { "ERROR": { "MISSING_EDGE_PROPERTY": { "Required edge property 'id' is missing": [ "A:123->X:1", "B:456->Y:2" ], "Required edge property 'object' is missing": [ "A:123->X:1" ], "Required edge property 'predicate' is missing": [ "A:123->X:1" ], "Required edge property 'subject' is missing": [ "A:123->X:1", "B:456->Y:2" ] } }, "WARNING": { "DUPLICATE_NODE": { "Node 'id' duplicated in input data": [ "MONDO:0010011", "REACT:R-HSA-5635838" ] } } } ``` This system reduces the significant redundancies of earlier line-oriented KGX logging text output files, in that graph entities with the same class of error are simply aggregated in lists of names/identifiers at the leaf level of the JSON structure. The top level JSON tags originate from the `MessageLevel` class and the second level tags from the `ErrorType` class in the [error_detection](kgx/error_detection.py) module, while the third level messages are hard coded as `log_error` method messages in the code. It is likely that additional error conditions within KGX can be efficiently captured and reported in the future using this general framework. ## Installation #### Installing from PyPI KGX is available on PyPI and can be installed using [pip](https://pip.pypa.io/en/stable/installing/) as follows, ```bash pip install kgx ``` To install a particular version of KGX, be sure to specify the version number, ```bash pip install kgx==0.5.0 ``` #### Installing from GitHub Clone the GitHub repository and then install, ```bash git clone https://github.com/biolink/kgx cd kgx poetry install ``` ### Setting up a testing environment for Neo4j This release of KGX supports graph source and sink transactions with the 4.3 release of Neo4j. KGX has a suite of tests that rely on Docker containers to run Neo4j specific tests. To set up the required containers, first install [Docker](https://docs.docker.com/get-docker/) on your local machine. Once Docker is up and running, run the following commands: ```bash docker run -d --rm --name kgx-neo4j-integration-test -p 7474:7474 -p 7687:7687 --env NEO4J_AUTH=neo4j/test neo4j:4.3 ``` ```bash docker run -d --rm --name kgx-neo4j-unit-test -p 8484:7474 -p 8888:7687 --env NEO4J_AUTH=neo4j/test neo4j:4.3 ``` **Note:** Setting up the Neo4j container is optional. If there is no container set up then the tests that rely on them are skipped. %package help Summary: Development documents and examples for kgx Provides: python3-kgx-doc %description help # Knowledge Graph Exchange [![Python](https://img.shields.io/badge/python-3.9+-blue.svg)]() ![Run tests](https://github.com/biolink/kgx/workflows/Run%20tests/badge.svg)[![Documentation Status](https://readthedocs.org/projects/kgx/badge/?version=latest)](https://kgx.readthedocs.io/en/latest/?badge=latest) [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=alert_status)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=sqale_rating)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![Coverage](https://sonarcloud.io/api/project_badges/measure?project=biolink_kgx&metric=coverage)](https://sonarcloud.io/dashboard?id=biolink_kgx) [![PyPI](https://img.shields.io/pypi/v/kgx)](https://img.shields.io/pypi/v/kgx) [![Docker](https://img.shields.io/static/v1?label=Docker&message=biolink/kgx:latest&color=orange&logo=docker)](https://hub.docker.com/r/biolink/kgx) KGX (Knowledge Graph Exchange) is a Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the [Biolink Model](https://biolink.github.io/biolink-model/). The core datamodel is a [Property Graph](https://neo4j.com/developer/graph-database/) (PG), represented internally in Python using a [networkx MultiDiGraph model](https://networkx.github.io/documentation/stable/reference/classes/generated/networkx.MultiDiGraph.edges.html). KGX allows conversion to and from: * RDF serializations (read/write) and SPARQL endpoints (read) * Neo4j endpoints (read) or Neo4j dumps (write) * CSV/TSV and JSON (see [associated data formats](./data-preparation.md) and [example script to load CSV/TSV to Neo4j](./examples/scripts/load_csv_to_neo4j.py)) * Reasoner Standard API format * OBOGraph JSON format KGX will also provide validation, to ensure the KGs are conformant to the Biolink Model: making sure nodes are categorized using Biolink classes, edges are labeled using valid Biolink relationship types, and valid properties are used. Internal representation is a property graph, specifically a networkx MultiDiGraph. The structure of this graph is expected to conform to the Biolink Model standard, as specified in the [KGX format specification](specification/kgx-format.md). In addition to the main code-base, KGX also provides a series of [command line operations](https://kgx.readthedocs.io/en/latest/examples.html#using-kgx-cli). ### Example usage Validate: ```bash poetry run kgx validate -i tsv tests/resources/merge/test2_nodes.tsv tests/resources/merge/test2_edges.tsv ``` Merge: ```bash poetry run kgx merge —merge-config tests/resources/test-merge.yaml ``` Graph Summary: ```bash poetry run kgx graph-summary -i tests/resources/graph_nodes.tsv -o summary.txt ``` Transform: ```bash poetry run kgx transform —transform-config tests/resources/test-transform-tsv-rdf.yaml ``` ### Error Detection and Reporting Non-redundant JSON-formatted structured error logging is now provided in KGX Transformer, Validator, GraphSummary and MetaKnowledgeGraph operations. See the various unit tests for the general design pattern (using the Validator as an example here): ```python from kgx.validator import Validator from kgx.transformer import Transformer Validator.set_biolink_model("2.11.0") # Validator assumes the currently set Biolink Release validator = Validator() transformer = Transformer(stream=True) transformer.transform( input_args = { "filename": [ "graph_nodes.tsv", "graph_edges.tsv", ], "format": "tsv", }, output_args={ "format": "null" }, inspector=validator, ) # Both the Validator and the Transformer can independently capture errors # The Validator, from the overall semantics of the graph... # Here, we just report severe Errors from the Validator (no Warnings) validator.write_report(open("validation_errors.json", "w"), "Error") # The Transformer, from the syntax of the input files... # Here, we catch *all* Errors and Warnings (by not providing a filter) transformer.write_report(open("input_errors.json", "w")) ``` The JSON error outputs will look something like this: ```json { "ERROR": { "MISSING_EDGE_PROPERTY": { "Required edge property 'id' is missing": [ "A:123->X:1", "B:456->Y:2" ], "Required edge property 'object' is missing": [ "A:123->X:1" ], "Required edge property 'predicate' is missing": [ "A:123->X:1" ], "Required edge property 'subject' is missing": [ "A:123->X:1", "B:456->Y:2" ] } }, "WARNING": { "DUPLICATE_NODE": { "Node 'id' duplicated in input data": [ "MONDO:0010011", "REACT:R-HSA-5635838" ] } } } ``` This system reduces the significant redundancies of earlier line-oriented KGX logging text output files, in that graph entities with the same class of error are simply aggregated in lists of names/identifiers at the leaf level of the JSON structure. The top level JSON tags originate from the `MessageLevel` class and the second level tags from the `ErrorType` class in the [error_detection](kgx/error_detection.py) module, while the third level messages are hard coded as `log_error` method messages in the code. It is likely that additional error conditions within KGX can be efficiently captured and reported in the future using this general framework. ## Installation #### Installing from PyPI KGX is available on PyPI and can be installed using [pip](https://pip.pypa.io/en/stable/installing/) as follows, ```bash pip install kgx ``` To install a particular version of KGX, be sure to specify the version number, ```bash pip install kgx==0.5.0 ``` #### Installing from GitHub Clone the GitHub repository and then install, ```bash git clone https://github.com/biolink/kgx cd kgx poetry install ``` ### Setting up a testing environment for Neo4j This release of KGX supports graph source and sink transactions with the 4.3 release of Neo4j. KGX has a suite of tests that rely on Docker containers to run Neo4j specific tests. To set up the required containers, first install [Docker](https://docs.docker.com/get-docker/) on your local machine. Once Docker is up and running, run the following commands: ```bash docker run -d --rm --name kgx-neo4j-integration-test -p 7474:7474 -p 7687:7687 --env NEO4J_AUTH=neo4j/test neo4j:4.3 ``` ```bash docker run -d --rm --name kgx-neo4j-unit-test -p 8484:7474 -p 8888:7687 --env NEO4J_AUTH=neo4j/test neo4j:4.3 ``` **Note:** Setting up the Neo4j container is optional. If there is no container set up then the tests that rely on them are skipped. %prep %autosetup -n kgx-2.1.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-kgx -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 2.1.0-1 - Package Spec generated