|
|
@@ -1,105 +1,99 @@
|
|
|
-import rdflib
|
|
|
-from rdflib import RDF, RDFS, ConjunctiveGraph, Literal, URIRef
|
|
|
-from rdflib.plugins.sparql.evalutils import _eval
|
|
|
-
|
|
|
-from rdflib_endpoint import SparqlEndpoint
|
|
|
-
|
|
|
-
|
|
|
-def custom_concat(query_results, ctx, part, eval_part):
|
|
|
- """
|
|
|
- Concat 2 string and return the length as additional Length variable
|
|
|
- \f
|
|
|
- :param query_results: An array with the query results objects
|
|
|
- :param ctx: <class 'rdflib.plugins.sparql.sparql.QueryContext'>
|
|
|
- :param part: Part of the query processed (e.g. Extend or BGP) <class 'rdflib.plugins.sparql.parserutils.CompValue'>
|
|
|
- :param eval_part: Part currently evaluated
|
|
|
- :return: the same query_results provided in input param, with additional results
|
|
|
- """
|
|
|
- argument1 = str(_eval(part.expr.expr[0], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
- argument2 = str(_eval(part.expr.expr[1], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
- evaluation = []
|
|
|
- scores = []
|
|
|
- concat_string = argument1 + argument2
|
|
|
- reverse_string = argument2 + argument1
|
|
|
- # Append the concatenated string to the results
|
|
|
- evaluation.append(concat_string)
|
|
|
- evaluation.append(reverse_string)
|
|
|
- # Append the scores for each row of results
|
|
|
- scores.append(len(concat_string))
|
|
|
- scores.append(len(reverse_string))
|
|
|
- # Append our results to the query_results
|
|
|
- for i, result in enumerate(evaluation):
|
|
|
- query_results.append(
|
|
|
- eval_part.merge({part.var: Literal(result), rdflib.term.Variable(part.var + "Length"): Literal(scores[i])})
|
|
|
- )
|
|
|
- return query_results, ctx, part, eval_part
|
|
|
-
|
|
|
-
|
|
|
-def most_similar(query_results, ctx, part, eval_part):
|
|
|
- """
|
|
|
- Get most similar entities for a given entity
|
|
|
-
|
|
|
- PREFIX openpredict: <https://w3id.org/um/openpredict/>
|
|
|
- SELECT ?drugOrDisease ?mostSimilar ?mostSimilarScore WHERE {
|
|
|
- BIND("OMIM:246300" AS ?drugOrDisease)
|
|
|
- BIND(openpredict:most_similar(?drugOrDisease) AS ?mostSimilar)
|
|
|
- """
|
|
|
- # argumentEntity = str(_eval(part.expr.expr[0], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
- # try:
|
|
|
- # argumentLimit = str(_eval(part.expr.expr[1], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
- # except:
|
|
|
- # argumentLimit = None
|
|
|
-
|
|
|
- # Using stub data
|
|
|
- similarity_results = [{"mostSimilar": "DRUGBANK:DB00001", "score": 0.42}]
|
|
|
-
|
|
|
- evaluation = []
|
|
|
- scores = []
|
|
|
- for most_similar in similarity_results:
|
|
|
- evaluation.append(most_similar["mostSimilar"])
|
|
|
- scores.append(most_similar["score"])
|
|
|
-
|
|
|
- # Append our results to the query_results
|
|
|
- for i, result in enumerate(evaluation):
|
|
|
- query_results.append(
|
|
|
- eval_part.merge({part.var: Literal(result), rdflib.term.Variable(part.var + "Score"): Literal(scores[i])})
|
|
|
- )
|
|
|
- return query_results, ctx, part, eval_part
|
|
|
-
|
|
|
-
|
|
|
-example_query = """PREFIX myfunctions: <https://w3id.org/um/sparql-functions/>
|
|
|
-SELECT ?concat ?concatLength WHERE {
|
|
|
- BIND("First" AS ?first)
|
|
|
- BIND(myfunctions:custom_concat(?first, "last") AS ?concat)
|
|
|
-}"""
|
|
|
-
|
|
|
-# Use ConjunctiveGraph to support nquads and graphs in SPARQL queries
|
|
|
-# identifier is the default graph
|
|
|
-g = ConjunctiveGraph(
|
|
|
- # store="Oxigraph",
|
|
|
- identifier=URIRef("https://w3id.org/um/sparql-functions/graph/default"),
|
|
|
-)
|
|
|
-
|
|
|
-# Example to add a nquad to the exposed graph
|
|
|
-g.add((URIRef("http://subject"), RDF.type, URIRef("http://object"), URIRef("http://graph")))
|
|
|
-g.add((URIRef("http://subject"), RDFS.label, Literal("foo"), URIRef("http://graph")))
|
|
|
-
|
|
|
-# Start the SPARQL endpoint based on the RDFLib Graph
|
|
|
-app = SparqlEndpoint(
|
|
|
- graph=g,
|
|
|
- functions={
|
|
|
- "https://w3id.org/um/openpredict/most_similar": most_similar,
|
|
|
- "https://w3id.org/um/sparql-functions/custom_concat": custom_concat,
|
|
|
- },
|
|
|
- title="SPARQL endpoint for RDFLib graph",
|
|
|
- description="A SPARQL endpoint to serve machine learning models, or any other logic implemented in Python. \n[Source code](https://github.com/vemonet/rdflib-endpoint)",
|
|
|
- version="0.1.0",
|
|
|
- public_url="https://service.openpredict.137.120.31.102.nip.io/sparql",
|
|
|
- cors_enabled=True,
|
|
|
- example_query=example_query,
|
|
|
-)
|
|
|
-
|
|
|
-## Uncomment to run it directly with python app/main.py
|
|
|
-# if __name__ == "__main__":
|
|
|
-# import uvicorn
|
|
|
-# uvicorn.run(app, host="0.0.0.0", port=8000)
|
|
|
+import rdflib
|
|
|
+from rdflib import RDF, RDFS, ConjunctiveGraph, Literal, URIRef
|
|
|
+from rdflib.plugins.sparql.evalutils import _eval
|
|
|
+
|
|
|
+from rdflib_endpoint import SparqlEndpoint
|
|
|
+
|
|
|
+
|
|
|
+def custom_concat(query_results, ctx, part, eval_part):
|
|
|
+ """
|
|
|
+ Concat 2 string and return the length as additional Length variable
|
|
|
+ \f
|
|
|
+ :param query_results: An array with the query results objects
|
|
|
+ :param ctx: <class 'rdflib.plugins.sparql.sparql.QueryContext'>
|
|
|
+ :param part: Part of the query processed (e.g. Extend or BGP) <class 'rdflib.plugins.sparql.parserutils.CompValue'>
|
|
|
+ :param eval_part: Part currently evaluated
|
|
|
+ :return: the same query_results provided in input param, with additional results
|
|
|
+ """
|
|
|
+ argument1 = str(_eval(part.expr.expr[0], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
+ argument2 = str(_eval(part.expr.expr[1], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
+ evaluation = []
|
|
|
+ scores = []
|
|
|
+ concat_string = argument1 + argument2
|
|
|
+ reverse_string = argument2 + argument1
|
|
|
+ # Append the concatenated string to the results
|
|
|
+ evaluation.append(concat_string)
|
|
|
+ evaluation.append(reverse_string)
|
|
|
+ # Append the scores for each row of results
|
|
|
+ scores.append(len(concat_string))
|
|
|
+ scores.append(len(reverse_string))
|
|
|
+ # Append our results to the query_results
|
|
|
+ for i, result in enumerate(evaluation):
|
|
|
+ query_results.append(
|
|
|
+ eval_part.merge({part.var: Literal(result), rdflib.term.Variable(part.var + "Length"): Literal(scores[i])})
|
|
|
+ )
|
|
|
+ return query_results, ctx, part, eval_part
|
|
|
+
|
|
|
+
|
|
|
+def most_similar(query_results, ctx, part, eval_part):
|
|
|
+ """
|
|
|
+ Get most similar entities for a given entity
|
|
|
+
|
|
|
+ PREFIX openpredict: <https://w3id.org/um/openpredict/>
|
|
|
+ SELECT ?drugOrDisease ?mostSimilar ?mostSimilarScore WHERE {
|
|
|
+ BIND("OMIM:246300" AS ?drugOrDisease)
|
|
|
+ BIND(openpredict:most_similar(?drugOrDisease) AS ?mostSimilar)
|
|
|
+ """
|
|
|
+ # argumentEntity = str(_eval(part.expr.expr[0], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
+ # try:
|
|
|
+ # argumentLimit = str(_eval(part.expr.expr[1], eval_part.forget(ctx, _except=part.expr._vars)))
|
|
|
+ # except:
|
|
|
+ # argumentLimit = None
|
|
|
+
|
|
|
+ # Using stub data
|
|
|
+ similarity_results = [{"mostSimilar": "DRUGBANK:DB00001", "score": 0.42}]
|
|
|
+
|
|
|
+ evaluation = []
|
|
|
+ scores = []
|
|
|
+ for most_similar in similarity_results:
|
|
|
+ evaluation.append(most_similar["mostSimilar"])
|
|
|
+ scores.append(most_similar["score"])
|
|
|
+
|
|
|
+ # Append our results to the query_results
|
|
|
+ for i, result in enumerate(evaluation):
|
|
|
+ query_results.append(
|
|
|
+ eval_part.merge({part.var: Literal(result), rdflib.term.Variable(part.var + "Score"): Literal(scores[i])})
|
|
|
+ )
|
|
|
+ return query_results, ctx, part, eval_part
|
|
|
+
|
|
|
+
|
|
|
+example_query = """
|
|
|
+PREFIX myfunctions: <https://w3id.org/um/sparql-functions/>
|
|
|
+SELECT ?concat ?concatLength WHERE {
|
|
|
+ BIND("First" AS ?first)
|
|
|
+ BIND(myfunctions:custom_concat(?first, "last") AS ?concat)
|
|
|
+}
|
|
|
+"""
|
|
|
+
|
|
|
+g = ConjunctiveGraph(
|
|
|
+ identifier=URIRef("https://w3id.org/um/sparql-functions/graph/default"),
|
|
|
+)
|
|
|
+
|
|
|
+# Example to add a nquad to the exposed graph
|
|
|
+g.add((URIRef("http://subject"), RDF.type, URIRef("http://object"), URIRef("http://graph")))
|
|
|
+g.add((URIRef("http://subject"), RDFS.label, Literal("foo"), URIRef("http://graph")))
|
|
|
+
|
|
|
+# Start the SPARQL endpoint based on the RDFLib Graph
|
|
|
+app = SparqlEndpoint(
|
|
|
+ graph=g,
|
|
|
+ functions={
|
|
|
+ "https://w3id.org/um/openpredict/most_similar": most_similar,
|
|
|
+ "https://w3id.org/um/sparql-functions/custom_concat": custom_concat,
|
|
|
+ },
|
|
|
+ title="SPARQL endpoint for RDFLib graph",
|
|
|
+ description="A SPARQL endpoint to serve machine learning models, or any other logic implemented in Python. \n[Source code](https://github.com/vemonet/rdflib-endpoint)",
|
|
|
+ version="0.1.0",
|
|
|
+ public_url="https://service.openpredict.137.120.31.102.nip.io/sparql",
|
|
|
+ cors_enabled=True,
|
|
|
+ example_query=example_query,
|
|
|
+)
|
