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renamed test dir
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@dariomalchiodi
dariomalchiodi committed Oct 11, 2024
1 parent 2dda4c4 commit 40a6cf9
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57 changes: 30 additions & 27 deletions tests/test_fuzzifier.py → test/test_fuzzifier.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,12 @@
from unittest import TestCase

from tests.fuzzifier import *
from tests.__init__ import *
import numpy as np
import unittest

from mulearn import FuzzyInductor
import mulearn.fuzzifier as fuzz



class TestCrispFuzzifier(TestCase):
class TestCrispFuzzifier(unittest.TestCase):
def test_compute(self):

X = np.array([[0.91232935],
Expand All @@ -25,27 +25,28 @@ def test_compute(self):
[0.95579843],
[0.23678875]])

mus = np.array([0.00836525, 0.99818461, 0.00124992, 0.5013639 , 0.91687722,
0.83942725, 0.25137643, 0.0040433 , 0.35836777, 0.98317438,
0.72841124, 0.77240852, 0.16950794, 0.00289302, 0.14237189])
mus = np.array(
[0.00836525, 0.99818461, 0.00124992, 0.5013639 , 0.91687722,
0.83942725, 0.25137643, 0.0040433 , 0.35836777, 0.98317438,
0.72841124, 0.77240852, 0.16950794, 0.00289302, 0.14237189])

f = CrispFuzzifier()
f = fuzz.CrispFuzzifier()

m = FuzzyInductor(fuzzifier=f).fit(X, mus)

result = list(m.fuzzifier.get_membership()(X))
result = m.predict(X)

self.assertEqual(result, [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1])

f = CrispFuzzifier(profile='infer')
f = fuzz.CrispFuzzifier(profile='infer')

m = FuzzyInductor(fuzzifier=f).fit(X, mus)

result = list(m.fuzzifier.get_membership()(X))
result = m.predict(X)

self.assertEqual(result, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
self.assertEqual(result, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])

class TestLinearFuzzifier(TestCase):
class TestLinearFuzzifier(unittest.TestCase):
def test_compute(self):

X = np.array([[0.91232935],
Expand All @@ -64,15 +65,16 @@ def test_compute(self):
[0.95579843],
[0.23678875]])

mus = np.array([0.00836525, 0.99818461, 0.00124992, 0.5013639 , 0.91687722,
0.83942725, 0.25137643, 0.0040433 , 0.35836777, 0.98317438,
0.72841124, 0.77240852, 0.16950794, 0.00289302, 0.14237189])
mus = np.array(
[0.00836525, 0.99818461, 0.00124992, 0.5013639 , 0.91687722,
0.83942725, 0.25137643, 0.0040433 , 0.35836777, 0.98317438,
0.72841124, 0.77240852, 0.16950794, 0.00289302, 0.14237189])

f = LinearFuzzifier()
f = fuzz.LinearFuzzifier()

m = FuzzyInductor(fuzzifier=f).fit(X, mus)

result = list(m.fuzzifier.get_membership()(X))
result = m.predict(X)

correct = [0.19171390089298312,
0.9623537182595308,
Expand All @@ -91,13 +93,13 @@ def test_compute(self):
0.5148178265851036]

for chi, chi_opt in zip(result, correct):
self.assertAlmostEqual(chi, chi_opt, places=5)
self.assertAlmostEqual(float(chi), chi_opt, places=5)

f = LinearFuzzifier(profile='infer')
f = fuzz.LinearFuzzifier(profile='infer')

m = FuzzyInductor(fuzzifier=f).fit(X, mus)

result = list(m.fuzzifier.get_membership()(X))
result = m.predict(X)

correct = [0.0,
1.0,
Expand All @@ -111,12 +113,13 @@ def test_compute(self):
0.9709822271100618,
0.7436342714864057,
0.7436342709951314,
0.16644887611636805,
0.0,
0.11758680761161522]
0.16644887611636805,
0.0,
0.11758680761161522]

for chi, chi_opt in zip(result, correct):
self.assertAlmostEqual(chi, chi_opt, places=5)



if __name__ == '__main__':
unittest.main()
149 changes: 149 additions & 0 deletions test/test_kernel.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,149 @@

import numpy as np
import unittest

import mulearn.kernel as kernel


class Test_LinearKernel(unittest.TestCase):
def test_compute(self):
k =kernel.LinearKernel()
self.assertEqual(k.compute(np.array([1, 0, 1]).reshape(1,-1),
np.array([2, 2, 2]).reshape(1,-1)), 4)
self.assertEqual(k.compute(np.array((1, 0, 2)).reshape(1,-1),
np.array((-1, 2, 5)).reshape(1,-1)), 9)

self.assertAlmostEqual(k.compute(
np.array([1.2, -0.4, -2]).reshape(1,-1),
np.array([4, 1.2, .5]).reshape(1,-1))[0], 3.32)
self.assertAlmostEqual(k.compute(
np.array((1.2, -0.4, -2)).reshape(1,-1),
np.array([4, 1.2, .5]).reshape(1,-1))[0], 3.32)

with self.assertRaises(ValueError):
k.compute(np.array([1, 0, 1]).reshape(1,-1),
np.array([2, 2]).reshape(1,-1))


class TestPolynomialKernel(unittest.TestCase):
def test_compute(self):
with self.assertRaises(ValueError):
kernel.PolynomialKernel(3.2)

with self.assertRaises(ValueError):
kernel.PolynomialKernel(-2)

p = kernel.PolynomialKernel(2)
self.assertEqual(p.compute(np.array((1, 0, 2)).reshape(1,-1),
np.array((-1, 2, 5)).reshape(1,-1)), 100)
self.assertAlmostEqual(p.compute(
np.array([1.2, -0.4, -2]).reshape(1,-1),
np.array([4, 1.2, .5]).reshape(1,-1)), 18.6624)

p = kernel.PolynomialKernel(5)
self.assertEqual(p.compute(np.array((1, 0, 2)).reshape(1,-1),
np.array([-1, 2, 5]).reshape(1,-1)), 10 ** 5)
self.assertAlmostEqual(p.compute(
np.array((1.2, -0.4, -2)).reshape(1,-1),
np.array((4, 1.2, .5)).reshape(1,-1)),
1504.59195, delta=10**-6)

with self.assertRaises(ValueError):
p.compute(np.array((1, 0, 2)).reshape(1,-1),
np.array((-1, 2)).reshape(1,-1))



class TestHomogeneousPolynomialKernel(unittest.TestCase):
def test_compute(self):
with self.assertRaises(ValueError):
kernel.HomogeneousPolynomialKernel(3.2)

with self.assertRaises(ValueError):
kernel.HomogeneousPolynomialKernel(-2)

h = kernel.HomogeneousPolynomialKernel(2)
self.assertEqual(h.compute(np.array((1, 0, 2)).reshape(1,-1),
np.array((-1, 2, 5)).reshape(1,-1)), 81.0)
self.assertAlmostEqual(h.compute(
np.array([1.2, -0.4, -2]).reshape(1,-1),
np.array([4, 1.2, .5]).reshape(1,-1))[0], 11.0224)

h = kernel.HomogeneousPolynomialKernel(5)
self.assertEqual(h.compute(
np.array((1, 0, 2)).reshape(1,-1),
np.array([-1, 2, 5]).reshape(1,-1)) , 59049.0)
self.assertAlmostEqual(h.compute(
np.array((1.2, -0.4, -2)).reshape(1,-1),
np.array((4, 1.2, .5)).reshape(1,-1)),
403.357761, delta=10**-6)

with self.assertRaises(ValueError):
h.compute(np.array((1, 0, 2)).reshape(1,-1),
np.array((-1, 2)).reshape(1,-1))


class TestGaussianKernel(unittest.TestCase):
def test_compute(self):
with self.assertRaises(ValueError):
kernel.GaussianKernel(-5)

k = kernel.GaussianKernel(1)
self.assertAlmostEqual(k.compute(np.array((1, 0, 1)).reshape(1,-1),
np.array((0, 0, 1)).reshape(1,-1))[0],
0.60653065)
self.assertAlmostEqual(k.compute(
np.array([-3, 1, 0.5]).reshape(1,-1),
np.array([1, 1.2, -8]).reshape(1,-1))[0], 6.73e-20)
self.assertAlmostEqual(k.compute(
np.array([-1, -4, 3.5]).reshape(1,-1),
np.array((1, 3.2, 6)).reshape(1,-1))[0], 3.29e-14)

with self.assertRaises(ValueError):
k.compute(np.array([-1, 3.5]).reshape(1,-1),
np.array((1, 3.2, 6)).reshape(1,-1))


class TestHyperbolicKernel(unittest.TestCase):
def test_compute(self):
k = kernel.HyperbolicKernel(1, 5)
self.assertAlmostEqual(k.compute(
np.array((1, 0, 1)).reshape(1,-1),
np.array((0, 0, 1)).reshape(1,-1))[0], 0.9999877)
self.assertAlmostEqual(k.compute(
np.array([-3, 1, 0.5]).reshape(1,-1),
np.array([1, 1.2, -8]).reshape(1,-1))[0],
-0.6640367, delta=10**-7)
self.assertAlmostEqual(k.compute(
np.array([-1, -4, 3.5]).reshape(1,-1),
np.array((1, 3.2, 6)).reshape(1,-1))[0],
0.9999999, delta=10**-7)

with self.assertRaises(ValueError):
k.compute(np.array([-1, 3.5]).reshape(1,-1),
np.array((1, 3.2, 6)).reshape(1,-1))


class TestPrecomputedKernel(unittest.TestCase):
def test_compute(self):
with self.assertRaises(ValueError):
kernel.PrecomputedKernel(np.array(((1, 2), (3, 4, 5))))

k = kernel.PrecomputedKernel(np.array(((1, 2), (3, 4))))
self.assertEqual(k.compute(
np.array([1]).reshape(1,-1),
np.array([1]).reshape(1,-1)), 4.0)
self.assertEqual(k.compute(
np.array([1]).reshape(1,-1),
np.array([0]).reshape(1,-1)), 3.0)

with self.assertRaises(IndexError):
k.compute(np.array([1]).reshape(1,-1), np.array([2]).reshape(1,-1))

with self.assertRaises(IndexError):
k.compute(np.array([0]).reshape(1,-1),
np.array([1.6]).reshape(1,-1))


if __name__ == '__main__':
unittest.main()
98 changes: 98 additions & 0 deletions test/test_optimization.py
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import numpy as np
import unittest

from mulearn import FuzzyInductor
from mulearn.optimization import GurobiSolver


class TestGurobiSolver(unittest.TestCase):

def testCompute(self):

s = GurobiSolver()

xs = np.array([[0.2526861], [0.77908776], [0.5120937],
[0.52646533], [0.01438627]])

mus = np.array([0.63883086, 0.56515446, 0.99892903,
0.99488161, 0.17768801])

c = 1

#obtained from the original mulearn module
chis_opt = [0.26334825774012194, 0.5651531004941153,
-0.0010709737624955377, -0.00511839469274798,
0.17768801022078584]

k = np.array([[1. , 0.8706202, 0.9669135, 0.9632160, 0.9720059],
[0.8706202, 1. , 0.9649848, 0.9685946, 0.7464816],
[0.9669135, 0.9649848, 1. , 0.9998967, 0.8835067],
[0.9632160, 0.9685946, 0.9998967, 1. , 0.8771191],
[0.9720059 ,0.7464816, 0.8835067, 0.8771191, 1. ]])

for chi, chi_opt in zip(s.solve(xs, mus, c, k), chis_opt):
self.assertAlmostEqual(chi, chi_opt, places=5)


self.assertEqual(GurobiSolver().__repr__(), 'GurobiSolver()')

self.assertEqual(GurobiSolver(adjustment='auto').__repr__(),
'GurobiSolver(adjustment=auto)')

self.assertEqual(GurobiSolver(time_limit=1000).__repr__(),
'GurobiSolver(time_limit=1000)')

self.assertEqual(GurobiSolver(initial_values=(1,2,3,4,5)).__repr__(),
'GurobiSolver(initial_values=(1, 2, 3, 4, 5))')

self.assertEqual(GurobiSolver(initial_values=(1,2,3,4,5),
adjustment='auto').__repr__(),
'GurobiSolver(adjustment=auto, initial_values=(1, 2, 3, 4, 5))')


model = FuzzyInductor()
xs = np.array([
[0.0529931],[0.0083108],[0.5267421],[0.2486910],[0.0251151],
[0.1652516],[0.6417665],[0.1196049],[0.9794178],[0.1612251],
[0.4119931],[0.3356147],[0.4766746],[0.7740397],[0.6974139],
[0.0767959],[0.4183671],[0.0911699],[0.8647524],[0.7957013],
[0.6492061],[0.0777168],[0.0107658],[0.3432774],[0.5856382],
[0.2890628],[0.0132868],[0.4368564],[0.6674424],[0.6966816],
[0.0819116],[0.2468026],[0.3826838],[0.8054723],[0.4339475],
[0.8033825],[0.8945941],[0.3637644],[0.8285576],[0.1784889],
[0.1342601],[0.8580456],[0.2053696],[0.5880164],[0.1460810],
[0.1117827],[0.5497901],[0.0229859],[0.2912594],[0.1610982]])

mus = np.array([0.0085146, 0.0031310, 0.9830871, 0.2217082, 0.0046125,
0.0690616, 0.6191740, 0.0317020, 0.0041603, 0.0647358,
0.8313222, 0.5249052, 0.9871067, 0.1667561, 0.3947267,
0.0139551, 0.8530416, 0.0185618, 0.0418628, 0.1242336,
0.5880158, 0.0142166, 0.0033161, 0.5566333, 0.8395180,
0.3460139, 0.0035165, 0.9092821, 0.5123567, 0.3974534,
0.0154637, 0.2167269, 0.7201648, 0.1079920, 0.9011676,
0.1113196, 0.0243836, 0.6423038, 0.0761680, 0.0849619,
0.0411487, 0.0469942, 0.1261214, 0.8312888, 0.0504056,
0.0274667, 0.9425840, 0.0043948, 0.3537064, 0.0646031])

chis_opt = np.array(
[ 0.0085146, 0.0031310, -0.0169128, 0.2217082, 0.0046125,
0.0690616, -0.3808259, 0.0317020, 0.0041603, 0.0647358,
-0.1686777, 0.0723333, -0.0128932, 0.1667561, 0.3947267,
0.0139551, -0.1469583, 0.0185618, 0.0418628, 0.1242336,
-0.4119841, 0.0142166, 0.0033161, -0.4433666, -0.1604819,
0.3460139, 0.0035165, -0.0907178, 0.4388794, 0.3974534,
0.0154637, 0.2167269, -0.2798351, 0.1079920, -0.0988323,
0.1113196, 0.0243836, -0.3576962, 0.0761680, 0.0849619,
0.0411487, 0.0469942, 0.1261214, -0.1687111, 0.0504056,
0.0274667, -0.0574159, 0.0043948, 0.3537064, 0.0646031])

model.fit(xs, mus)

for chi, chi_opt in zip(model.chis_, chis_opt):
self.assertAlmostEqual(chi, chi_opt, places=5)


if __name__ == '__main__':
unittest.main()

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