def test_fitMk_invalidpi_raisesAssertionError(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
try:
discrete.fitMk(tree, chars, pi = "invalid pi")
self.fail("Assertion error not raised")
except AssertionError, e:
self.assertEquals("Pi must be one of: 'Equal', 'Fitzjohn', 'Equilibrium'", e.message)
def test_fitMk_invalidQMatrix_raisesAssertionError(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
Q = np.ones([3, 3])
try:
discrete.fitMk(tree, chars, Q)
self.fail("Assertion error not raised")
except AssertionError, e:
self.assertEquals("rows of q must sum to zero", e.message)
def test_fitMk_invalidQMatrix_raisesAssertionError(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
Q = np.ones([3,3])
try:
discrete.fitMk(tree, chars, Q)
self.fail("Assertion error not raised")
except AssertionError, e:
self.assertEquals("rows of q must sum to zero", e.message)
def test_fitMk_invalidpi_raisesAssertionError(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
try:
discrete.fitMk(tree, chars, pi="invalid pi")
self.fail("Assertion error not raised")
except AssertionError, e:
self.assertEquals(
"Pi must be one of: 'Equal', 'Fitzjohn', 'Equilibrium'",
e.message)
def test_fitMk_invalidQstring_raisesValueError(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
Q = "This is not a valid Q"
try:
discrete.fitMk(tree, chars, Q)
self.fail("Value error not raised")
except ValueError, e:
self.assertEquals("Q str must be one of: 'Equal', 'Sym', 'ARD'",
e.message)
def test_fitMk_invalidQstring_raisesValueError(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
Q = "This is not a valid Q"
try:
discrete.fitMk(tree, chars, Q)
self.fail("Value error not raised")
except ValueError, e:
self.assertEquals("Q str must be one of: 'Equal', 'Sym', 'ARD'",
e.message)
def test_ARDEqualPiQ3traits_matchesPhytools(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
expectedParam = np.array([[-0.31973305, 0.136550, 0.183184],
[0.997779, -0.997779, 0.0000],
[3.315930, 0.0000, -3.315930]])
expectedLogLikelihood = -32.697278
calculated = discrete.fitMk(tree, chars, Q="ARD", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam,
calculatedParam,
atol=1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(
np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_EqualRatesEquilibriumPiQ3_matchesPhytools(self):
"""
The same as a flat pi
"""
tree = self.randTree100
chars = self.simChars100states3
expectedParam = np.array([[-0.556216, 0.278108, 0.278108],
[0.278108, -0.556216, 0.278108],
[0.278108, 0.278108, -0.556216]])
expectedLogLikelihood = -41.508675
calculated = discrete.fitMk(tree, chars, Q="Equal", pi="Equilibrium")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam,
calculatedParam,
atol=1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(
np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_SymRatesEquilibriumPiQ3_matchesPhytools(self):
"""
The same as a flat pi
"""
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
expectedParam = np.array([[-0.220576, 0.129755, 0.090821],
[0.129755, -0.129755, 0.000000],
[0.090821, 0.000000, -0.090821]])
expectedLogLikelihood = -34.398614
calculated = discrete.fitMk(tree, chars, Q="Sym", pi="Equilibrium")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam,
calculatedParam,
atol=1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(
np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_SymRatesEqualPiQ2traits_matchesPhytools(self):
"""
Note that this is the same as an equal-rates 2-trait Q matrix
"""
tree = self.randTree100
chars = self.simChars100states2 # Generated with a 2x2 Q matrix where alpha=beta=0.5
expectedParam = np.array([[-0.4549581, 0.4549581],
[0.4549581, -0.4549581]])
expectedLogLikelihood = -27.26863
calculated = discrete.fitMk(tree, chars, Q="Sym", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try:
np.testing.assert_allclose(expectedParam,
calculatedParam,
atol=1e-5)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(
np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_fitMk_fixedQ_returnsQandLogik(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
Q = np.array([[-.2,.1,.1],[.1,-.2,.1],[.1,.1,-.2]], dtype = np.double)
calculated = discrete.fitMk(tree, chars, Q)
calcq = calculated["Q"]
calclik = calculated["Log-likelihood"]
self.assertTrue(np.array_equal(Q,calcq) & np.isclose(calclik, -34.7215))
def test_fitMk_fixedQ_returnsQandLogik(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
Q = np.array([[-.2, .1, .1], [.1, -.2, .1], [.1, .1, -.2]],
dtype=np.double)
calculated = discrete.fitMk(tree, chars, Q)
calcq = calculated["Q"]
calclik = calculated["Log-likelihood"]
self.assertTrue(
np.array_equal(Q, calcq) & np.isclose(calclik, -34.7215))
def test_EqualRatesEqualPiQ2traits_matchesPhytools(self):
tree = self.randTree100
chars = self.simChars100states2 # Generated with a 2x2 Q matrix where alpha=beta=0.5
expectedParam = np.array([[-0.4549581,0.4549581],[0.4549581,-0.4549581]])
expectedLogLikelihood = -27.26863
calculated = discrete.fitMk(tree, chars, Q="Equal", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try:
np.testing.assert_allclose(expectedParam, calculatedParam, atol = 1e-4)
except:
self.fail("expectedParam != calculatedParam")
self.assertTrue(np.isclose(expectedLogLikelihood,
calculatedLogLikelihood))
def test_ARDEqualPiQ2traits_matchesPhytools(self):
tree = self.randTree100
chars = self.simChars100states2
expectedParam = np.array([[-0.261398, 0.261398],
[0.978787, -0.978787]])
expectedLogLikelihood = -25.813332
calculated = discrete.fitMk(tree, chars, Q="ARD", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam, calculatedParam, atol = 1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_SymRatesEqualPiQ3traits_matchesPhytools(self):
tree = self.randTree100
chars = self.simChars100states3
expectedParam = np.array([[-.631001,0.462874,0.168128],
[0.462874,-0.462874,0.000000],
[0.168128,0.000000,-0.168128]])
expectedLogLikelihood = -39.141458
calculated = discrete.fitMk(tree, chars, Q="Sym", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try:
np.testing.assert_allclose(expectedParam, calculatedParam, atol = 1e-5)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_EqualRatesEqualPiQ3traits_matchesPhytools(self):
tree = self.randTree100
chars = self.simChars100states3
expectedParam = np.array([[-0.556216,0.278108,0.278108],
[0.278108,-0.556216,0.278108],
[0.278108,0.278108,-0.556216]])
expectedLogLikelihood = -41.508675
calculated = discrete.fitMk(tree, chars, Q="Equal", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try:
np.testing.assert_allclose(expectedParam, calculatedParam, atol = 1e-5)
except:
self.fail("expectedParam != calculatedParam")
self.assertTrue(np.isclose(expectedLogLikelihood,
calculatedLogLikelihood))
def test_EqualRatesEquilibriumPiQ2_matchesPhytools(self):
"""
The same as a flat pi
"""
tree = self.randTree100
chars = self.simChars100states2
expectedParam = np.array([[-0.4549581,0.4549581],[0.4549581,-0.4549581]])
expectedLogLikelihood = -27.26863
calculated = discrete.fitMk(tree, chars, Q="Equal", pi="Equilibrium")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam, calculatedParam, atol = 1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_ARDEqualPiQ3traits_matchesPhytools(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
expectedParam = np.array([[-0.31973305, 0.136550, 0.183184],
[0.997779, -0.997779, 0.0000],
[3.315930, 0.0000, -3.315930]])
expectedLogLikelihood = -32.697278
calculated = discrete.fitMk(tree, chars, Q="ARD", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam, calculatedParam, atol = 1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_SymRatesEquilibriumPiQ3_matchesPhytools(self):
"""
The same as a flat pi
"""
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
expectedParam = np.array([[-0.220576,0.129755,0.090821],
[0.129755,-0.129755,0.000000],
[0.090821,0.000000,-0.090821]])
expectedLogLikelihood = -34.398614
calculated = discrete.fitMk(tree, chars, Q="Sym", pi="Equilibrium")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam, calculatedParam, atol = 1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_EqualRatesEqualPiQ3traits_matchesPhytools(self):
tree = self.randTree100
chars = self.simChars100states3
expectedParam = np.array([[-0.556216, 0.278108, 0.278108],
[0.278108, -0.556216, 0.278108],
[0.278108, 0.278108, -0.556216]])
expectedLogLikelihood = -41.508675
calculated = discrete.fitMk(tree, chars, Q="Equal", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try:
np.testing.assert_allclose(expectedParam,
calculatedParam,
atol=1e-5)
except:
self.fail("expectedParam != calculatedParam")
self.assertTrue(
np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_ARDEqualPiQ2traits_matchesPhytools(self):
tree = self.randTree100
chars = self.simChars100states2
expectedParam = np.array([[-0.261398, 0.261398], [0.978787,
-0.978787]])
expectedLogLikelihood = -25.813332
calculated = discrete.fitMk(tree, chars, Q="ARD", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try: # Need high tolerance for this test
np.testing.assert_allclose(expectedParam,
calculatedParam,
atol=1e-3)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(
np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
def test_SymRatesEqualPiQ3traits_matchesPhytools(self):
tree = self.randTree100
chars = self.simChars100states3
expectedParam = np.array([[-.631001, 0.462874, 0.168128],
[0.462874, -0.462874, 0.000000],
[0.168128, 0.000000, -0.168128]])
expectedLogLikelihood = -39.141458
calculated = discrete.fitMk(tree, chars, Q="Sym", pi="Equal")
calculatedParam = calculated["Q"]
calculatedLogLikelihood = calculated["Log-likelihood"]
try:
np.testing.assert_allclose(expectedParam,
calculatedParam,
atol=1e-5)
except AssertionError:
self.fail("expectedParam != calculatedParam")
self.assertTrue(
np.isclose(expectedLogLikelihood, calculatedLogLikelihood))
tree = ivy.tree.read("support/randtree100tipsscale2.newick")
chars = [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1]
Q = np.array([[-2.09613850e-01, 1.204029e-01, 8.921095e-02],
[5.654382e-01, -5.65438217e-01, 1.713339e-08],
[2.415020e-06, 5.958744e-07, -3.01089440e-06]])
mk(tree, chars, Q, pi="Equal", returnPi=True)
q, lik = discrete.fitMk(tree, chars, Q="ARD", pi="Equal")
Q = np.array([[-2.09613850e-01, 1.204029e-01, 8.921095e-02],
[5.654382e-01, -5.65438217e-01, 1.713339e-08],
[2.415020e-06, 5.958744e-07, -3.01089440e-06]])
calculatedLikelihood = discrete.mk(tree, chars, Q,
pi = "Fitzjohn")
calculatedLogLikelihood = math.log(calculatedLikelihood)
calculatedLogLikelihood
tree = ivy.tree.read("../support/randtree100tips.newick")
chars = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
def test_fitMk_ARDQequilibriumpi_matchesPhytools(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
calculated = discrete.fitMk(tree, chars, Q="ARD", pi="Equilibrium")
def test_fitMk_ARDQequilibriumpi_matchesPhytools(self):
tree = self.randTree100Scale2
chars = self.simChars100states3Scale2
calculated = discrete.fitMk(tree, chars, Q="ARD", pi="Equilibrium")
