def testNoWinningLabels(self):
"""Inferring 0/4 classes should return 0 winning labels."""
model = ClassificationModel()
inferenceResult = numpy.array([0, 0, 0, 0])
topLabels = model.getWinningLabels(inferenceResult)
self.assertFalse(topLabels)
def testNoWinningLabels(self):
"""Inferring 0/4 classes should return 0 winning labels."""
model = ClassificationModel()
inferenceResult = numpy.array([0, 0, 0, 0])
topLabels = model.getWinningLabels(inferenceResult)
self.assertFalse(topLabels)
def testWinningLabels(self):
"""
Tests whether classification base class returns multiple labels correctly.
"""
model = ClassificationModel()
inferenceResult = numpy.array([3, 1, 4, 0, 1, 0])
topLabels = model.getWinningLabels(inferenceResult, numLabels=1)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2])),
"Output should be label 2.")
topLabels = model.getWinningLabels(inferenceResult, numLabels=2)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2, 0])),
"Output should be labels 2 and 0.")
# Test only nonzero labels are returned.
inferenceResult = numpy.array([3, 0, 4, 0, 0, 0])
topLabels = model.getWinningLabels(inferenceResult, numLabels=5)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2, 0])),
"Output should be labels 2 and 0.")
def testWinningLabels(self):
"""
Tests whether classification base class returns multiple labels correctly.
"""
model = ClassificationModel()
inferenceResult = numpy.array([3, 1, 4, 0, 1, 0])
topLabels = model.getWinningLabels(inferenceResult, numLabels=1)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2])),
"Output should be label 2.")
topLabels = model.getWinningLabels(inferenceResult, numLabels=2)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2, 0])),
"Output should be labels 2 and 0.")
# Test only nonzero labels are returned.
inferenceResult = numpy.array([3, 0, 4, 0, 0, 0])
topLabels = model.getWinningLabels(inferenceResult, numLabels=5)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2, 0])),
"Output should be labels 2 and 0.")
def testWinningLabels(self):
"""
Tests whether classification base class returns multiple labels correctly.
"""
model = ClassificationModel()
inferenceResult = numpy.array([3, 1, 4, 0, 1, 0])
topLabels = model.getWinningLabels(inferenceResult, numLabels=1)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2])), "Output should be label 2.")
topLabels = model.getWinningLabels(inferenceResult, numLabels=2)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2, 0])), "Output should be labels 2 and 0.")
# Test tie is broken by the later index; label 4 beats out label 1.
topLabels = model.getWinningLabels(inferenceResult, numLabels=3)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2, 0, 4])), "Output should be labels 2, 0, and 1.")
# Test only nonzero labels are returned.
topLabels = model.getWinningLabels(inferenceResult, numLabels=5)
self.assertTrue(numpy.allclose(topLabels, numpy.array([2, 0, 4, 1])), "Output should be labels 2, 0, 1, and 4.")