# imports python
import unittest
-import exceptions
-from exceptions import RuntimeError
import os
import shutil
self.tmpDir += "PmmlUnitTest";
self.tmpDir += os.sep ;
if ( not os.path.exists(self.tmpDir) ):
- os.mkdir(self.tmpDir);
+ os.makedirs(self.tmpDir);
pass
pass
model = "sANNName";
exportPyScript = self.tmpDir + "swigTestExportPythonNeuralNet.py";
refPyFilename = self.resourcesDir + "unittest_ref_ann_model.py";
- refLines = file(refPyFilename).readlines();
+ with open(refPyFilename,"r") as f:
+ refLines = f.readlines();
#
p = PMMLlib( pmmlFile );
p.SetCurrentModel( model, kANN );
p.ExportPython( exportPyScript, "myTestFunc",
"File used by unit test\n PMMLBasicsTest1::testExportNeuralNetworkPython" );
- myLines = file(exportPyScript).readlines();
+ with open(exportPyScript,"r") as f:
+ myLines = f.readlines();
self.assertEqual( len(myLines), len(refLines) );
for (i,line) in enumerate(myLines):
self.assertEqual( line, refLines[i] );
model = "Modeler[LinearRegression]Tds[steamplant]Predictor[x6:x8:x6x8:x6x6x8]Target[x1]";
exportPyScript = self.tmpDir + "swigTestExportPythonRegression.py";
refPyFilename = self.resourcesDir + "unittest_ref_lr_model.py";
- refLines = file(refPyFilename).readlines();
+ with open(refPyFilename,"r") as f:
+ refLines = f.readlines();
#
p = PMMLlib( pmmlFile );
p.SetCurrentModel( model, kLR );
p.ExportPython( exportPyScript, "myTestFunc",
"File used by unit test\n PMMLBasicsTest1::testExportLinearRegressionPython" );
- myLines = file(exportPyScript).readlines();
+ with open(exportPyScript,"r") as f:
+ myLines = f.readlines();
self.assertEqual( len(myLines), len(refLines) );
for (i,line) in enumerate(myLines):
self.assertEqual( line, refLines[i] );