def prob(sigma, sigma0, theta=None, phi=None):
""" Probability mass function of a MM with central ranking sigma0 and
dispersion parameter theta/phi.
Parameters
----------
sigma: ndarray
A pemutation
sigma0: ndarray
central permutation
theta: float
Dispersion parameter (optional, if phi is given)
phi: float
Dispersion parameter (optional, if theta is given)
Returns
-------
float
Probability mass function.
"""
theta, phi = mm.check_theta_phi(theta, phi)
d = distance(sigma, sigma0)
n = len(sigma)
facts_ = np.array([1, 1] + [0] * (n - 1), dtype=np.float)
for i in range(2, n + 1):
facts_[i] = facts_[i - 1] * i
sum = 0
for i in range(n + 1):
sum += (((np.exp(theta) - 1)**i) / facts_[i])
psi = sum * np.exp(-n * theta) * facts_[n]
return np.exp(-d * theta) / psi
def variance_v(n, theta=None, phi=None, k=None):
"""This function computes the variance of the decomposition vector under GMM.
Parameters
----------
n: int
Length of the permutation in the considered model
theta: float, optional (if phi is given)
Real dispersion parameter
phi : float, optional (if theta is given)
Real dispersion parameter
k: int, optional
Length of partial permutations (only top items)
Returns
-------
ndarray
The variance of the decomposition vector.
"""
theta, phi = mm.check_theta_phi(theta, phi)
if k is None:
k = n - 1
if type(phi) != list:
phi = np.full(k, phi)
rnge = np.array(range(k))
var_v = phi[rnge] / (1 - phi[rnge])**2 - (n - rnge)**2 * phi[rnge]**(
n - rnge) / (1 - phi[rnge]**(n - rnge))**2
return var_v
def expected_dist_mm(n, theta=None, phi=None):
"""The function computes the expected value of Hamming distance under Mallows Models (MMs).
Parameters
----------
n: int
Length of the permutation in the considered model
theta: float
Real dispersion parameter, optional (if phi is given)
phi: float
Real dispersion parameter, optional (if theta is given)
Returns
-------
float
The expected distance under MMs.
"""
theta, phi = mm.check_theta_phi(theta, phi)
facts_ = np.array([1, 1] + [0] * (n - 1), dtype=np.float)
for i in range(2, n + 1):
facts_[i] = facts_[i - 1] * i
x_n_1, x_n = 0, 0
for k in range(n + 1):
aux = (np.exp(theta) - 1)**k / facts_[k]
x_n += aux
if k < n: x_n_1 += aux
return (n * x_n - x_n_1 * np.exp(theta)) / x_n
def prob(sigma, sigma0, theta=None, phi=None):
"""Probability mass function of a MM with central ranking sigma0 and
dispersion parameter theta/phi.
Parameters
----------
sigma: ndarray
A pemutation
sigma0: ndarray
central permutation
theta: float
Dispersion parameter (optional, if phi is given)
phi: float
Dispersion parameter (optional, if theta is given)
Returns
-------
float
Probability mass function.
"""
n = len(sigma)
theta, phi = mm.check_theta_phi(theta, phi)
sigma0_inv = pu.inverse(sigma0)
rnge = np.array(range(n - 1))
psi = (1 - np.exp((-n + rnge) * (theta))) / (1 - np.exp(-theta))
psi = np.prod(psi)
dist = distance(pu.compose(sigma, sigma0_inv))
return np.exp(-theta * dist) / psi
def expected_v(n, theta=None, phi=None, k=None): #txapu integrar
"""This function computes the expected decomposition vector.
Parameters
----------
n: int
Length of the permutation in the considered model
theta: float, optional (if phi is given)
Real dispersion parameter
phi : float, optional (if theta is given)
Real dispersion parameter
k: int, optional
Length of partial permutations (only top items)
Returns
-------
ndarray
The expected decomposition vector.
"""
theta, phi = mm.check_theta_phi(theta, phi)
if k is None: k = n - 1
if type(theta) != list: theta = np.full(k, theta)
rnge = np.array(range(k))
expected_v = np.exp(-theta[rnge]) / (1 - np.exp(-theta[rnge])) - (
n - rnge) * np.exp(-(n - rnge) * theta[rnge]) / (
1 - np.exp(-(n - rnge) * theta[rnge]))
return expected_v
def sample(m, n, *, k=None, theta=None, phi=None, s0=None):
"""This function generates m (rankings) according to Mallows Models (if the given parameters
are m, n, k/None, theta/phi: float, s0/None) or Generalized Mallows Models (if the given
parameters are m, n, theta/phi: ndarray, s0/None). Moreover, the parameter k allows the
function to generate top-k rankings only.
Parameters
----------
m: int
Number of rankings to generate
n: int
Length of rankings
theta: float or ndarray, optional (if phi given)
The dispersion parameter theta
phi: float or ndarray, optional (if theta given)
Dispersion parameter phi
k: int
Length of partial permutations (only top items)
s0: ndarray
Consensus ranking
Returns
-------
ndarray
The rankings generated
"""
theta, phi = mm.check_theta_phi(theta, phi)
theta = np.full(n - 1, theta)
if s0 is None:
s0 = np.array(range(n))
rnge = np.array(range(n - 1))
psi = (1 - np.exp(
(-n + rnge) * (theta[rnge]))) / (1 - np.exp(-theta[rnge]))
vprobs = np.zeros((n, n))
for j in range(n - 1):
vprobs[j][0] = 1.0 / psi[j]
for r in range(1, n - j):
vprobs[j][r] = np.exp(-theta[j] * r) / psi[j]
sample = []
vs = []
for samp in range(m):
v = [np.random.choice(n, p=vprobs[i, :]) for i in range(n - 1)]
v += [0]
ranking = v_to_ranking(v, n)
sample.append(ranking)
sample = np.array([s[s0] for s in sample])
if k is not None:
sample_rankings = np.array(
[pu.inverse(ordering) for ordering in sample])
sample_rankings = np.array([ran[s0] for ran in sample_rankings])
sample = np.array([[i if i in range(k) else np.nan for i in ranking]
for ranking in sample_rankings])
return sample
def sample(m, n, *, theta=None, phi=None, s0=None):
"""This function generates m permutations (rankings) according to Mallows Models.
Parameters
----------
m: int
Number of rankings to generate
n: int
Length of rankings
theta: float, optional (if phi given)
Dispersion parameter theta
phi: float, optional (if theta given)
Dispersion parameter phi
s0: ndarray
Consensus ranking
Returns
-------
ndarray
The rankings generated.
"""
sample = np.zeros((m, n))
theta, phi = mm.check_theta_phi(theta, phi)
facts_ = np.array([1, 1] + [0] * (n - 1), dtype=np.float)
deran_num_ = np.array([1, 0] + [0] * (n - 1), dtype=np.float)
for i in range(2, n + 1):
facts_[i] = facts_[i - 1] * i
deran_num_[i] = deran_num_[i - 1] * (i - 1) + deran_num_[i - 2] * (i -
1)
hamm_count_ = np.array([
deran_num_[d] * facts_[n] / (facts_[d] * facts_[n - d])
for d in range(n + 1)
],
dtype=np.float)
probsd = np.array(
[hamm_count_[d] * np.exp(-theta * d) for d in range(n + 1)],
dtype=np.float)
for m_ in range(m):
target_distance = np.random.choice(n + 1, p=probsd / probsd.sum())
sample[m_, :] = sample_at_dist(n, target_distance, s0)
return sample
def psi_mm(n, theta=None, phi=None):
"""This function computes the normalization constant psi.
Parameters
----------
n: int
Length of the permutation in the considered model
theta: float
Real dispersion parameter (optional if phi is given)
phi: float
Real dispersion parameter (optional if theta is given)
Returns
-------
float
The normalization constant psi.
"""
rnge = np.array(range(2, n + 1))
if theta is not None:
return np.prod((1 - np.exp(-theta * rnge)) / (1 - np.exp(-theta)))
if phi is not None:
return np.prod((1 - np.power(phi, rnge)) / (1 - phi))
theta, phi = mm.check_theta_phi(theta, phi)
def expected_dist_mm(n, theta=None, phi=None):
"""The function computes the expected distance of Kendall's-tau distance under Mallows models (MMs).
Parameters
----------
n: int
Length of the permutation in the considered model
theta: float
Real dispersion parameter, optional (if phi is given)
phi: float
Real dispersion parameter, optional (if theta is given)
Returns
-------
float
The expected distance under MMs.
"""
theta, phi = mm.check_theta_phi(theta, phi)
rnge = np.array(range(1, n + 1))
expected_dist = n * np.exp(-theta) / (1 - np.exp(-theta)) - np.sum(
rnge * np.exp(-rnge * theta) / (1 - np.exp(-rnge * theta)))
return expected_dist
def variance_dist_mm(n, theta=None, phi=None):
""" This function returns the variance of Kendall's-tau distance under the MMs.
Parameters
----------
n: int
Length of the permutations
theta: float
Dispersion parameter, optional (if phi is given)
phi : float
Dispersion parameter, optional (if theta is given)
Returns
-------
float
The variance of Kendall's-tau distance under the MMs.
"""
theta, phi = mm.check_theta_phi(theta, phi)
rnge = np.array(range(1, n + 1))
variance = (phi * n) / (1 - phi)**2 - np.sum(
(pow(phi, rnge) * rnge**2) / (1 - pow(phi, rnge))**2)
return variance
def variance_dist_top_k(n, k, theta=None, phi=None):
"""Compute the variance of the distance for top-k rankings, following
a MM under the Kendall's-tau distance.
Parameters
----------
n: int
Length of the permutation in the considered model
k: int
Length of partial permutations (only top items)
theta: float, optional (if phi is given)
Real dispersion parameter
phi : float, optional (if theta is given)
Real dispersion parameter
Returns
-------
float
The variance under the MMs.
"""
theta, phi = mm.check_theta_phi(theta, phi)
rnge = np.array(range(n - k + 1, n + 1))
variance = (phi * k) / (1 - phi)**2 - np.sum(
(pow(phi, rnge) * rnge**2) / (1 - pow(phi, rnge))**2)
return variance
