def test_predictDiscrete(self):
s1 = self.createSignature(1, -1)
s2 = self.createSignature(2, -2)
s3 = self.createSignature(3, -3)
s4 = self.createSignature(4, -4)
trainFts = FeatureSet_Discrete()
trainFts.AddSignature(s1, 0)
trainFts.AddSignature(s2, 0)
trainFts.AddSignature(s3, 1)
trainFts.AddSignature(s4, 1)
tmp = trainFts.ContiguousDataMatrix()
# Values are chosen so that different weights give different predictions
s5 = self.createSignature(0, -5)
s6 = self.createSignature(5, 0)
testFts = FeatureSet_Discrete()
testFts.AddSignature(s5, 0)
testFts.AddSignature(s6, 0)
tmp = testFts.ContiguousDataMatrix()
weights = FisherFeatureWeights()
weights.names = ['ft [0]', 'ft [1]']
weights.values = [2.0, 1.0]
pred = DiscreteBatchClassificationResult.New(trainFts, testFts,
weights)
self.assertEqual(len(pred.individual_results), 2)
r1, r2 = pred.individual_results
np.testing.assert_almost_equal(r1.marginal_probabilities,
[0.975, 0.025],
decimal=3)
np.testing.assert_almost_equal(r2.marginal_probabilities,
[0.025, 0.975],
decimal=3)
weights = FisherFeatureWeights()
weights.names = ['ft [0]', 'ft [1]']
weights.values = [1.0, 2.0]
pred = DiscreteBatchClassificationResult.New(trainFts, testFts,
weights)
self.assertEqual(len(pred.individual_results), 2)
r1, r2 = pred.individual_results
np.testing.assert_almost_equal(r1.marginal_probabilities,
[0.025, 0.975],
decimal=3)
np.testing.assert_almost_equal(r2.marginal_probabilities,
[0.975, 0.025],
decimal=3)
def test_thresholdWeights(self):
w = FisherFeatureWeights()
w.names = ["a", "b", "c"]
w.values = [1.0, 2.0, 4.0]
w1 = w.Threshold(2)
self.assertEqual(w1.names, ["c", "b"])
self.assertAlmostEqual(w1.values, [4.0, 2.0])
def test_thresholdWeights(self):
w = FisherFeatureWeights()
w.names = ['a', 'b', 'c']
w.values = [1.0, 2.0, 4.0]
w1 = w.Threshold(2)
self.assertEqual(w1.names, ['c', 'b'])
self.assertAlmostEqual(w1.values, [4.0, 2.0])
def test_thresholdWeights(self):
w = FisherFeatureWeights()
w.names = ['a', 'b', 'c']
w.values = [1.0, 2.0, 4.0]
w1 = w.Threshold(2)
self.assertEqual(w1.names, ['c', 'b'])
self.assertAlmostEqual(w1.values, [4.0, 2.0])
def test_predictDiscrete(self):
s1 = self.createSignature(1, -1)
s2 = self.createSignature(2, -2)
s3 = self.createSignature(3, -3)
s4 = self.createSignature(4, -4)
trainFts = FeatureSet_Discrete()
trainFts.AddSignature(s1, 0)
trainFts.AddSignature(s2, 0)
trainFts.AddSignature(s3, 1)
trainFts.AddSignature(s4, 1)
tmp = trainFts.ContiguousDataMatrix()
# Values are chosen so that different weights give different predictions
s5 = self.createSignature(0, -5)
s6 = self.createSignature(5, 0)
testFts = FeatureSet_Discrete()
testFts.AddSignature(s5, 0)
testFts.AddSignature(s6, 0)
tmp = testFts.ContiguousDataMatrix()
weights = FisherFeatureWeights()
weights.names = ['ft [0]', 'ft [1]']
weights.values = [2.0, 1.0]
pred = DiscreteBatchClassificationResult.New(trainFts, testFts, weights)
self.assertEqual(len(pred.individual_results), 2)
r1, r2 = pred.individual_results
np.testing.assert_almost_equal(
r1.marginal_probabilities, [0.975, 0.025], decimal=3)
np.testing.assert_almost_equal(
r2.marginal_probabilities, [0.025, 0.975], decimal=3)
weights = FisherFeatureWeights()
weights.names = ['ft [0]', 'ft [1]']
weights.values = [1.0, 2.0]
pred = DiscreteBatchClassificationResult.New(trainFts, testFts, weights)
self.assertEqual(len(pred.individual_results), 2)
r1, r2 = pred.individual_results
np.testing.assert_almost_equal(
r1.marginal_probabilities, [0.025, 0.975], decimal=3)
np.testing.assert_almost_equal(
r2.marginal_probabilities, [0.975, 0.025], decimal=3)
