def __init__(self, model_name, model_base_path, verbose=False):
"""
Initialize the service.
Args:
model_name: The name of the model.
model_base_path: The file path of the model.
Return:
None
"""
super(PmmlInferenceService, self).__init__()
self.model_name = model_name
self.model_base_path = model_base_path
self.model_version_list = [1]
self.model_graph_signature = ""
self.platform = "PMML"
# Load model
from openscoring import Openscoring
openscoring_server_endpoint = "localhost:8080"
kwargs = {"auth" : ("admin", "adminadmin")}
self.openscoring = Openscoring("http://{}/openscoring".format(openscoring_server_endpoint))
self.openscoring.deployFile(self.model_name, self.model_base_path, **kwargs)
self.model_graph_signature = "No signature for PMML models"
self.verbose = verbose
def testReadme(self):
openscoring = Openscoring()
pmml = os.path.join(os.path.dirname(__file__), "resources",
"DecisionTreeIris.pmml")
with open(pmml, "rb") as instream:
pmmlBytes = instream.read()
self.assertTrue(isinstance(pmmlBytes, bytes))
self.assertEqual(4306, len(pmmlBytes))
modelResponse = openscoring.deploy("Iris", pmmlBytes)
self.assertEqual("Iris", modelResponse.id)
modelResponse = openscoring.deployFile("Iris", pmml)
self.assertEqual("Iris", modelResponse.id)
arguments = {
"Sepal_Length": 5.1,
"Sepal_Width": 3.5,
"Petal_Length": 1.4,
"Petal_Width": 0.2
}
result = openscoring.evaluate("Iris", arguments)
self.assertEqual(
{
"Species": "setosa",
"Probability_setosa": 1.0,
"Probability_versicolor": 0.0,
"Probability_virginica": 0.0,
"Node_Id": "2"
}, result)
evaluationRequest = EvaluationRequest("record-001", arguments)
evaluationResponse = openscoring.evaluate("Iris", evaluationRequest)
self.assertEqual(evaluationRequest.id, evaluationResponse.id)
self.assertEqual("setosa", evaluationResponse.result["Species"])
inCsv = os.path.join(os.path.dirname(__file__), "resources",
"input.csv")
outCsv = os.path.join(tempfile.gettempdir(), "output.csv")
df = pandas.read_csv(inCsv, sep=",")
result = openscoring.evaluateCsv("Iris", df)
self.assertEqual(df["Id"].tolist(), result["Id"].tolist())
self.assertEqual(["setosa", "versicolor", "virginica"],
result["Species"].tolist())
self.assertFalse(os.path.isfile(outCsv))
openscoring.evaluateCsvFile("Iris", inCsv, outCsv)
self.assertTrue(
os.path.isfile(outCsv) and os.path.getsize(outCsv) > 10)
os.remove(outCsv)
openscoring.undeploy("Iris")
with self.assertRaises(Exception) as context:
openscoring.evaluate("Iris", arguments)
self.assertEqual("Model \"Iris\" not found", str(context.exception))
def get_predictions(pw, pl, sw, sl):
"""
Get predictions by source
:param pw:
:param pl:
:param sw:
:param sl:
:return:
"""
arguments = {
"sepal_length": float(sl),
"sepal_width": float(sw),
"petal_length": float(pl),
"petal_width": float(pw)
}
print(arguments)
from openscoring import Openscoring
os = Openscoring("http://localhost:8080/openscoring")
result = os.evaluate("Iris", arguments)
print(result)
return html.Div([
html.H3("The class detected was : {}".format(result['species'])),
])
def __init__(self):
self.scoring = Openscoring("http://localhost:8080/openscoring")
self.arguments = {"sourceTaggedAsFakeCount": 2.0, "reporterScore": 3.0}
def testReadme(self):
openscoring = Openscoring(base_url = "http://localhost:8080/openscoring", token = os.getenv("OPENSCORING_TOKEN", None))
pmml = os.path.join(os.path.dirname(__file__), "resources", "DecisionTreeIris.pmml")
with open(pmml, "rb") as instream:
pmmlBytes = instream.read()
self.assertTrue(isinstance(pmmlBytes, bytes))
self.assertEqual(2919, len(pmmlBytes))
modelResponse = openscoring.deploy("Iris", pmmlBytes)
self.assertEqual("Iris", modelResponse.id)
modelResponse = openscoring.deployFile("Iris", pmml)
self.assertEqual("Iris", modelResponse.id)
arguments = {
"Sepal.Length" : 5.1,
"Sepal.Width" : 3.5,
"Petal.Length" : 1.4,
"Petal.Width" : 0.2
}
results = openscoring.evaluate("Iris", arguments)
self.assertEqual({"Species" : "setosa", "probability(setosa)" : 1.0, "probability(versicolor)" : 0.0, "probability(virginica)" : 0.0}, results)
evaluationRequest = EvaluationRequest("record-001", arguments)
evaluationResponse = openscoring.evaluate("Iris", evaluationRequest)
self.assertEqual(evaluationRequest.id, evaluationResponse.id)
self.assertEqual("setosa", evaluationResponse.results["Species"])
batchArguments = [
{
"Petal.Length" : 1.4,
"Petal.Width" : 0.2
},
{
"Petal.Length" : 4.7,
"Petal.Width" : 1.4
},
{
"Petal.Length" : 3.6,
"Petal.Width" : 2.5
}
]
batchResults = openscoring.evaluateBatch("Iris", batchArguments)
self.assertEquals(3, len(batchResults))
self.assertEquals({"Species" : "setosa", "probability(setosa)" : 1.0, "probability(versicolor)" : 0.0, "probability(virginica)" : 0.0}, batchResults[0])
self.assertEquals({"Species" : "versicolor", "probability(setosa)" : 0.0, "probability(versicolor)" : (49.0 / 54.0), "probability(virginica)" : (5.0 / 54.0)}, batchResults[1])
self.assertEquals({"Species" : "virginica", "probability(setosa)" : 0.0, "probability(versicolor)" : (1.0 / 46.0), "probability(virginica)" : (45.0 / 46.0)}, batchResults[2])
evaluationRequests = [EvaluationRequest(None, arguments) for arguments in batchArguments]
batchEvaluationRequest = BatchEvaluationRequest("batch-A", evaluationRequests)
batchEvaluationResponse = openscoring.evaluateBatch("Iris", batchEvaluationRequest)
self.assertEqual(batchEvaluationRequest.id, batchEvaluationResponse.id)
evaluationResponses = batchEvaluationResponse.responses
self.assertEqual(3, len(evaluationResponses))
self.assertEqual("setosa", evaluationResponses[0].results["Species"])
self.assertEqual("versicolor", evaluationResponses[1].results["Species"])
self.assertEqual("virginica", evaluationResponses[2].results["Species"])
inCsv = os.path.join(os.path.dirname(__file__), "resources", "input.csv")
outCsv = os.path.join(tempfile.gettempdir(), "output.csv")
dfArguments = pandas.read_csv(inCsv, sep = ",")
dfResults = openscoring.evaluateCsv("Iris", dfArguments)
self.assertEqual((3, 1 + 4), dfResults.shape)
self.assertEqual(dfArguments["Id"].tolist(), dfResults["Id"].tolist())
self.assertEqual(["setosa", "versicolor", "virginica"], dfResults["Species"].tolist())
self.assertFalse(os.path.isfile(outCsv))
openscoring.evaluateCsvFile("Iris", inCsv, outCsv)
self.assertTrue(os.path.isfile(outCsv) and os.path.getsize(outCsv) > 10)
os.remove(outCsv)
openscoring.undeploy("Iris")
with self.assertRaises(Exception) as context:
openscoring.evaluate("Iris", arguments)
self.assertEqual("Not Found", str(context.exception))
#!/usr/bin/env python
from openscoring import Openscoring
os = Openscoring("http://localhost:8080/openscoring")
kwargs = {"auth": ("admin", "adminadmin")}
model_file_path = "../../models/pmml_iris/DecisionTreeIris.pmml"
model_name = "PmmlModel"
os.deployFile(model_name, model_file_path, **kwargs)
arguments = {
"Sepal_Length": 5.1,
"Sepal_Width": 3.5,
"Petal_Length": 1.4,
"Petal_Width": 0.2
}
result = os.evaluate(model_name, arguments)
print(result)
