Source code for statsmodels.stats.regularized_covariance
from statsmodels.regression.linear_model import OLS
import numpy as np
def _calc_nodewise_row(exog, idx, alpha):
"""calculates the nodewise_row values for the idxth variable, used to
estimate approx_inv_cov.
Parameters
----------
exog : array_like
The weighted design matrix for the current partition.
idx : scalar
Index of the current variable.
alpha : scalar or array_like
The penalty weight. If a scalar, the same penalty weight
applies to all variables in the model. If a vector, it
must have the same length as `params`, and contains a
penalty weight for each coefficient.
Returns
-------
An array-like object of length p-1
Notes
-----
nodewise_row_i = arg min 1/(2n) ||exog_i - exog_-i gamma||_2^2
+ alpha ||gamma||_1
"""
p = exog.shape[1]
ind = list(range(p))
ind.pop(idx)
# handle array alphas
if not np.isscalar(alpha):
alpha = alpha[ind]
tmod = OLS(exog[:, idx], exog[:, ind])
nodewise_row = tmod.fit_regularized(alpha=alpha).params
return nodewise_row
def _calc_nodewise_weight(exog, nodewise_row, idx, alpha):
"""calculates the nodewise_weightvalue for the idxth variable, used to
estimate approx_inv_cov.
Parameters
----------
exog : array_like
The weighted design matrix for the current partition.
nodewise_row : array_like
The nodewise_row values for the current variable.
idx : scalar
Index of the current variable
alpha : scalar or array_like
The penalty weight. If a scalar, the same penalty weight
applies to all variables in the model. If a vector, it
must have the same length as `params`, and contains a
penalty weight for each coefficient.
Returns
-------
A scalar
Notes
-----
nodewise_weight_i = sqrt(1/n ||exog,i - exog_-i nodewise_row||_2^2
+ alpha ||nodewise_row||_1)
"""
n, p = exog.shape
ind = list(range(p))
ind.pop(idx)
# handle array alphas
if not np.isscalar(alpha):
alpha = alpha[ind]
d = np.linalg.norm(exog[:, idx] - exog[:, ind].dot(nodewise_row))**2
d = np.sqrt(d / n + alpha * np.linalg.norm(nodewise_row, 1))
return d
def _calc_approx_inv_cov(nodewise_row_l, nodewise_weight_l):
"""calculates the approximate inverse covariance matrix
Parameters
----------
nodewise_row_l : list
A list of array-like object where each object corresponds to
the nodewise_row values for the corresponding variable, should
be length p.
nodewise_weight_l : list
A list of scalars where each scalar corresponds to the nodewise_weight
value for the corresponding variable, should be length p.
Returns
------
An array-like object, p x p matrix
Notes
-----
nwr = nodewise_row
nww = nodewise_weight
approx_inv_cov_j = - 1 / nww_j [nwr_j,1,...,1,...nwr_j,p]
"""
p = len(nodewise_weight_l)
approx_inv_cov = -np.eye(p)
for idx in range(p):
ind = list(range(p))
ind.pop(idx)
approx_inv_cov[idx, ind] = nodewise_row_l[idx]
approx_inv_cov *= -1 / nodewise_weight_l[:, None]**2
return approx_inv_cov
[docs]
class RegularizedInvCovariance:
"""
Class for estimating regularized inverse covariance with
nodewise regression
Parameters
----------
exog : array_like
A weighted design matrix for covariance
Attributes
----------
exog : array_like
A weighted design matrix for covariance
alpha : scalar
Regularizing constant
"""
def __init__(self, exog):
self.exog = exog
[docs]
def fit(self, alpha=0):
"""estimates the regularized inverse covariance using nodewise
regression
Parameters
----------
alpha : scalar
Regularizing constant
"""
n, p = self.exog.shape
nodewise_row_l = []
nodewise_weight_l = []
for idx in range(p):
nodewise_row = _calc_nodewise_row(self.exog, idx, alpha)
nodewise_row_l.append(nodewise_row)
nodewise_weight = _calc_nodewise_weight(self.exog, nodewise_row,
idx, alpha)
nodewise_weight_l.append(nodewise_weight)
nodewise_row_l = np.array(nodewise_row_l)
nodewise_weight_l = np.array(nodewise_weight_l)
approx_inv_cov = _calc_approx_inv_cov(nodewise_row_l,
nodewise_weight_l)
self._approx_inv_cov = approx_inv_cov
Last update:
Nov 14, 2024