"""
Multivariate Conditional and Unconditional Kernel Density Estimation
with Mixed Data Types.
References
----------
[1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice.
Princeton University Press. (2007)
[2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation
and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008)
http://dx.doi.org/10.1561/0800000009
[3] Racine, J., Li, Q. "Nonparametric Estimation of Distributions
with Categorical and Continuous Data." Working Paper. (2000)
[4] Racine, J. Li, Q. "Kernel Estimation of Multivariate Conditional
Distributions Annals of Economics and Finance 5, 211-235 (2004)
[5] Liu, R., Yang, L. "Kernel estimation of multivariate
cumulative distribution function."
Journal of Nonparametric Statistics (2008)
[6] Li, R., Ju, G. "Nonparametric Estimation of Multivariate CDF
with Categorical and Continuous Data." Working Paper
[7] Li, Q., Racine, J. "Cross-validated local linear nonparametric
regression" Statistica Sinica 14(2004), pp. 485-512
[8] Racine, J.: "Consistent Significance Testing for Nonparametric
Regression" Journal of Business & Economics Statistics
[9] Racine, J., Hart, J., Li, Q., "Testing the Significance of
Categorical Predictor Variables in Nonparametric Regression
Models", 2006, Econometric Reviews 25, 523-544
"""
from __future__ import division
# TODO: make default behavior efficient=True above a certain n_obs
from statsmodels.compat.python import range, next
import numpy as np
from . import kernels
from ._kernel_base import GenericKDE, EstimatorSettings, gpke, \
LeaveOneOut, _adjust_shape
__all__ = ['KDEMultivariate', 'KDEMultivariateConditional', 'EstimatorSettings']
[docs]class KDEMultivariate(GenericKDE):
"""
Multivariate kernel density estimator.
This density estimator can handle univariate as well as multivariate data,
including mixed continuous / ordered discrete / unordered discrete data.
It also provides cross-validated bandwidth selection methods (least
squares, maximum likelihood).
Parameters
----------
data: list of ndarrays or 2-D ndarray
The training data for the Kernel Density Estimation, used to determine
the bandwidth(s). If a 2-D array, should be of shape
(num_observations, num_variables). If a list, each list element is a
separate observation.
var_type: str
The type of the variables:
- c : continuous
- u : unordered (discrete)
- o : ordered (discrete)
The string should contain a type specifier for each variable, so for
example ``var_type='ccuo'``.
bw: array_like or str, optional
If an array, it is a fixed user-specified bandwidth. If a string,
should be one of:
- normal_reference: normal reference rule of thumb (default)
- cv_ml: cross validation maximum likelihood
- cv_ls: cross validation least squares
defaults: EstimatorSettings instance, optional
The default values for (efficient) bandwidth estimation.
Attributes
----------
bw: array_like
The bandwidth parameters.
See Also
--------
KDEMultivariateConditional
Examples
--------
>>> import statsmodels.api as sm
>>> nobs = 300
>>> np.random.seed(1234) # Seed random generator
>>> c1 = np.random.normal(size=(nobs,1))
>>> c2 = np.random.normal(2, 1, size=(nobs,1))
Estimate a bivariate distribution and display the bandwidth found:
>>> dens_u = sm.nonparametric.KDEMultivariate(data=[c1,c2],
... var_type='cc', bw='normal_reference')
>>> dens_u.bw
array([ 0.39967419, 0.38423292])
"""
def __init__(self, data, var_type, bw=None, defaults=EstimatorSettings()):
self.var_type = var_type
self.k_vars = len(self.var_type)
self.data = _adjust_shape(data, self.k_vars)
self.data_type = var_type
self.nobs, self.k_vars = np.shape(self.data)
if self.nobs <= self.k_vars:
raise ValueError("The number of observations must be larger " \
"than the number of variables.")
self._set_defaults(defaults)
if not self.efficient:
self.bw = self._compute_bw(bw)
else:
self.bw = self._compute_efficient(bw)
def __repr__(self):
"""Provide something sane to print."""
rpr = "KDE instance\n"
rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n"
rpr += "Number of samples: nobs = " + str(self.nobs) + "\n"
rpr += "Variable types: " + self.var_type + "\n"
rpr += "BW selection method: " + self._bw_method + "\n"
return rpr
[docs] def loo_likelihood(self, bw, func=lambda x: x):
r"""
Returns the leave-one-out likelihood function.
The leave-one-out likelihood function for the unconditional KDE.
Parameters
----------
bw: array_like
The value for the bandwidth parameter(s).
func: callable, optional
Function to transform the likelihood values (before summing); for
the log likelihood, use ``func=np.log``. Default is ``f(x) = x``.
Notes
-----
The leave-one-out kernel estimator of :math:`f_{-i}` is:
.. math:: f_{-i}(X_{i})=\frac{1}{(n-1)h}
\sum_{j=1,j\neq i}K_{h}(X_{i},X_{j})
where :math:`K_{h}` represents the generalized product kernel
estimator:
.. math:: K_{h}(X_{i},X_{j}) =
\prod_{s=1}^{q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right)
"""
LOO = LeaveOneOut(self.data)
L = 0
for i, X_not_i in enumerate(LOO):
f_i = gpke(bw, data=-X_not_i, data_predict=-self.data[i, :],
var_type=self.var_type)
L += func(f_i)
return -L
[docs] def pdf(self, data_predict=None):
r"""
Evaluate the probability density function.
Parameters
----------
data_predict: array_like, optional
Points to evaluate at. If unspecified, the training data is used.
Returns
-------
pdf_est: array_like
Probability density function evaluated at `data_predict`.
Notes
-----
The probability density is given by the generalized product kernel
estimator:
.. math:: K_{h}(X_{i},X_{j}) =
\prod_{s=1}^{q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right)
"""
if data_predict is None:
data_predict = self.data
else:
data_predict = _adjust_shape(data_predict, self.k_vars)
pdf_est = []
for i in range(np.shape(data_predict)[0]):
pdf_est.append(gpke(self.bw, data=self.data,
data_predict=data_predict[i, :],
var_type=self.var_type) / self.nobs)
pdf_est = np.squeeze(pdf_est)
return pdf_est
[docs] def cdf(self, data_predict=None):
r"""
Evaluate the cumulative distribution function.
Parameters
----------
data_predict: array_like, optional
Points to evaluate at. If unspecified, the training data is used.
Returns
-------
cdf_est: array_like
The estimate of the cdf.
Notes
-----
See http://en.wikipedia.org/wiki/Cumulative_distribution_function
For more details on the estimation see Ref. [5] in module docstring.
The multivariate CDF for mixed data (continuous and ordered/unordered
discrete) is estimated by:
.. math::
F(x^{c},x^{d})=n^{-1}\sum_{i=1}^{n}\left[G(\frac{x^{c}-X_{i}}{h})\sum_{u\leq x^{d}}L(X_{i}^{d},x_{i}^{d}, \lambda)\right]
where G() is the product kernel CDF estimator for the continuous
and L() for the discrete variables.
Used bandwidth is ``self.bw``.
"""
if data_predict is None:
data_predict = self.data
else:
data_predict = _adjust_shape(data_predict, self.k_vars)
cdf_est = []
for i in range(np.shape(data_predict)[0]):
cdf_est.append(gpke(self.bw, data=self.data,
data_predict=data_predict[i, :],
var_type=self.var_type,
ckertype="gaussian_cdf",
ukertype="aitchisonaitken_cdf",
okertype='wangryzin_cdf') / self.nobs)
cdf_est = np.squeeze(cdf_est)
return cdf_est
[docs] def imse(self, bw):
r"""
Returns the Integrated Mean Square Error for the unconditional KDE.
Parameters
----------
bw: array_like
The bandwidth parameter(s).
Returns
-------
CV: float
The cross-validation objective function.
Notes
-----
See p. 27 in [1]_ for details on how to handle the multivariate
estimation with mixed data types see p.6 in [2]_.
The formula for the cross-validation objective function is:
.. math:: CV=\frac{1}{n^{2}}\sum_{i=1}^{n}\sum_{j=1}^{N}
\bar{K}_{h}(X_{i},X_{j})-\frac{2}{n(n-1)}\sum_{i=1}^{n}
\sum_{j=1,j\neq i}^{N}K_{h}(X_{i},X_{j})
Where :math:`\bar{K}_{h}` is the multivariate product convolution
kernel (consult [2]_ for mixed data types).
References
----------
.. [1] Racine, J., Li, Q. Nonparametric econometrics: theory and
practice. Princeton University Press. (2007)
.. [2] Racine, J., Li, Q. "Nonparametric Estimation of Distributions
with Categorical and Continuous Data." Working Paper. (2000)
"""
#F = 0
#for i in range(self.nobs):
# k_bar_sum = gpke(bw, data=-self.data,
# data_predict=-self.data[i, :],
# var_type=self.var_type,
# ckertype='gauss_convolution',
# okertype='wangryzin_convolution',
# ukertype='aitchisonaitken_convolution')
# F += k_bar_sum
## there is a + because loo_likelihood returns the negative
#return (F / self.nobs**2 + self.loo_likelihood(bw) * \
# 2 / ((self.nobs) * (self.nobs - 1)))
# The code below is equivalent to the commented-out code above. It's
# about 20% faster due to some code being moved outside the for-loops
# and shared by gpke() and loo_likelihood().
F = 0
kertypes = dict(c=kernels.gaussian_convolution,
o=kernels.wang_ryzin_convolution,
u=kernels.aitchison_aitken_convolution)
nobs = self.nobs
data = -self.data
var_type = self.var_type
ix_cont = np.array([c == 'c' for c in var_type])
_bw_cont_product = bw[ix_cont].prod()
Kval = np.empty(data.shape)
for i in range(nobs):
for ii, vtype in enumerate(var_type):
Kval[:, ii] = kertypes[vtype](bw[ii],
data[:, ii],
data[i, ii])
dens = Kval.prod(axis=1) / _bw_cont_product
k_bar_sum = dens.sum(axis=0)
F += k_bar_sum # sum of prod kernel over nobs
kertypes = dict(c=kernels.gaussian,
o=kernels.wang_ryzin,
u=kernels.aitchison_aitken)
LOO = LeaveOneOut(self.data)
L = 0 # leave-one-out likelihood
Kval = np.empty((data.shape[0]-1, data.shape[1]))
for i, X_not_i in enumerate(LOO):
for ii, vtype in enumerate(var_type):
Kval[:, ii] = kertypes[vtype](bw[ii],
-X_not_i[:, ii],
data[i, ii])
dens = Kval.prod(axis=1) / _bw_cont_product
L += dens.sum(axis=0)
# CV objective function, eq. (2.4) of Ref. [3]
return (F / nobs**2 - 2 * L / (nobs * (nobs - 1)))
def _get_class_vars_type(self):
"""Helper method to be able to pass needed vars to _compute_subset."""
class_type = 'KDEMultivariate'
class_vars = (self.var_type, )
return class_type, class_vars
[docs]class KDEMultivariateConditional(GenericKDE):
"""
Conditional multivariate kernel density estimator.
Calculates ``P(Y_1,Y_2,...Y_n | X_1,X_2...X_m) =
P(X_1, X_2,...X_n, Y_1, Y_2,..., Y_m)/P(X_1, X_2,..., X_m)``.
The conditional density is by definition the ratio of the two densities,
see [1]_.
Parameters
----------
endog: list of ndarrays or 2-D ndarray
The training data for the dependent variables, used to determine
the bandwidth(s). If a 2-D array, should be of shape
(num_observations, num_variables). If a list, each list element is a
separate observation.
exog: list of ndarrays or 2-D ndarray
The training data for the independent variable; same shape as `endog`.
dep_type: str
The type of the dependent variables:
c : Continuous
u : Unordered (Discrete)
o : Ordered (Discrete)
The string should contain a type specifier for each variable, so for
example ``dep_type='ccuo'``.
indep_type: str
The type of the independent variables; specifed like `dep_type`.
bw: array_like or str, optional
If an array, it is a fixed user-specified bandwidth. If a string,
should be one of:
- normal_reference: normal reference rule of thumb (default)
- cv_ml: cross validation maximum likelihood
- cv_ls: cross validation least squares
defaults: Instance of class EstimatorSettings
The default values for the efficient bandwidth estimation
Attributes
---------
bw: array_like
The bandwidth parameters
See Also
--------
KDEMultivariate
References
----------
.. [1] http://en.wikipedia.org/wiki/Conditional_probability_distribution
Examples
--------
>>> import statsmodels.api as sm
>>> nobs = 300
>>> c1 = np.random.normal(size=(nobs,1))
>>> c2 = np.random.normal(2,1,size=(nobs,1))
>>> dens_c = sm.nonparametric.KDEMultivariateConditional(endog=[c1],
... exog=[c2], dep_type='c', indep_type='c', bw='normal_reference')
>>> dens_c.bw # show computed bandwidth
array([ 0.41223484, 0.40976931])
"""
def __init__(self, endog, exog, dep_type, indep_type, bw,
defaults=EstimatorSettings()):
self.dep_type = dep_type
self.indep_type = indep_type
self.data_type = dep_type + indep_type
self.k_dep = len(self.dep_type)
self.k_indep = len(self.indep_type)
self.endog = _adjust_shape(endog, self.k_dep)
self.exog = _adjust_shape(exog, self.k_indep)
self.nobs, self.k_dep = np.shape(self.endog)
self.data = np.column_stack((self.endog, self.exog))
self.k_vars = np.shape(self.data)[1]
self._set_defaults(defaults)
if not self.efficient:
self.bw = self._compute_bw(bw)
else:
self.bw = self._compute_efficient(bw)
def __repr__(self):
"""Provide something sane to print."""
rpr = "KDEMultivariateConditional instance\n"
rpr += "Number of independent variables: k_indep = " + \
str(self.k_indep) + "\n"
rpr += "Number of dependent variables: k_dep = " + \
str(self.k_dep) + "\n"
rpr += "Number of observations: nobs = " + str(self.nobs) + "\n"
rpr += "Independent variable types: " + self.indep_type + "\n"
rpr += "Dependent variable types: " + self.dep_type + "\n"
rpr += "BW selection method: " + self._bw_method + "\n"
return rpr
[docs] def loo_likelihood(self, bw, func=lambda x: x):
"""
Returns the leave-one-out conditional likelihood of the data.
If `func` is not equal to the default, what's calculated is a function
of the leave-one-out conditional likelihood.
Parameters
----------
bw: array_like
The bandwidth parameter(s).
func: callable, optional
Function to transform the likelihood values (before summing); for
the log likelihood, use ``func=np.log``. Default is ``f(x) = x``.
Returns
-------
L: float
The value of the leave-one-out function for the data.
Notes
-----
Similar to ``KDE.loo_likelihood`, but substitute ``f(y|x)=f(x,y)/f(x)``
for ``f(x)``.
"""
yLOO = LeaveOneOut(self.data)
xLOO = LeaveOneOut(self.exog).__iter__()
L = 0
for i, Y_j in enumerate(yLOO):
X_not_i = next(xLOO)
f_yx = gpke(bw, data=-Y_j, data_predict=-self.data[i, :],
var_type=(self.dep_type + self.indep_type))
f_x = gpke(bw[self.k_dep:], data=-X_not_i,
data_predict=-self.exog[i, :],
var_type=self.indep_type)
f_i = f_yx / f_x
L += func(f_i)
return -L
[docs] def pdf(self, endog_predict=None, exog_predict=None):
r"""
Evaluate the probability density function.
Parameters
----------
endog_predict: array_like, optional
Evaluation data for the dependent variables. If unspecified, the
training data is used.
exog_predict: array_like, optional
Evaluation data for the independent variables.
Returns
-------
pdf: array_like
The value of the probability density at `endog_predict` and `exog_predict`.
Notes
-----
The formula for the conditional probability density is:
.. math:: f(y|x)=\frac{f(x,y)}{f(x)}
with
.. math:: f(x)=\prod_{s=1}^{q}h_{s}^{-1}k
\left(\frac{x_{is}-x_{js}}{h_{s}}\right)
where :math:`k` is the appropriate kernel for each variable.
"""
if endog_predict is None:
endog_predict = self.endog
else:
endog_predict = _adjust_shape(endog_predict, self.k_dep)
if exog_predict is None:
exog_predict = self.exog
else:
exog_predict = _adjust_shape(exog_predict, self.k_indep)
pdf_est = []
data_predict = np.column_stack((endog_predict, exog_predict))
for i in range(np.shape(data_predict)[0]):
f_yx = gpke(self.bw, data=self.data,
data_predict=data_predict[i, :],
var_type=(self.dep_type + self.indep_type))
f_x = gpke(self.bw[self.k_dep:], data=self.exog,
data_predict=exog_predict[i, :],
var_type=self.indep_type)
pdf_est.append(f_yx / f_x)
return np.squeeze(pdf_est)
[docs] def cdf(self, endog_predict=None, exog_predict=None):
r"""
Cumulative distribution function for the conditional density.
Parameters
----------
endog_predict: array_like, optional
The evaluation dependent variables at which the cdf is estimated.
If not specified the training dependent variables are used.
exog_predict: array_like, optional
The evaluation independent variables at which the cdf is estimated.
If not specified the training independent variables are used.
Returns
-------
cdf_est: array_like
The estimate of the cdf.
Notes
-----
For more details on the estimation see [2]_, and p.181 in [1]_.
The multivariate conditional CDF for mixed data (continuous and
ordered/unordered discrete) is estimated by:
.. math::
F(y|x)=\frac{n^{-1}\sum_{i=1}^{n}G(\frac{y-Y_{i}}{h_{0}}) W_{h}(X_{i},x)}{\widehat{\mu}(x)}
where G() is the product kernel CDF estimator for the dependent (y)
variable(s) and W() is the product kernel CDF estimator for the
independent variable(s).
References
----------
.. [1] Racine, J., Li, Q. Nonparametric econometrics: theory and
practice. Princeton University Press. (2007)
.. [2] Liu, R., Yang, L. "Kernel estimation of multivariate cumulative
distribution function." Journal of Nonparametric
Statistics (2008)
"""
if endog_predict is None:
endog_predict = self.endog
else:
endog_predict = _adjust_shape(endog_predict, self.k_dep)
if exog_predict is None:
exog_predict = self.exog
else:
exog_predict = _adjust_shape(exog_predict, self.k_indep)
N_data_predict = np.shape(exog_predict)[0]
cdf_est = np.empty(N_data_predict)
for i in range(N_data_predict):
mu_x = gpke(self.bw[self.k_dep:], data=self.exog,
data_predict=exog_predict[i, :],
var_type=self.indep_type) / self.nobs
mu_x = np.squeeze(mu_x)
cdf_endog = gpke(self.bw[0:self.k_dep], data=self.endog,
data_predict=endog_predict[i, :],
var_type=self.dep_type,
ckertype="gaussian_cdf",
ukertype="aitchisonaitken_cdf",
okertype='wangryzin_cdf', tosum=False)
cdf_exog = gpke(self.bw[self.k_dep:], data=self.exog,
data_predict=exog_predict[i, :],
var_type=self.indep_type, tosum=False)
S = (cdf_endog * cdf_exog).sum(axis=0)
cdf_est[i] = S / (self.nobs * mu_x)
return cdf_est
[docs] def imse(self, bw):
r"""
The integrated mean square error for the conditional KDE.
Parameters
----------
bw: array_like
The bandwidth parameter(s).
Returns
-------
CV: float
The cross-validation objective function.
Notes
-----
For more details see pp. 156-166 in [1]_. For details on how to
handle the mixed variable types see [2]_.
The formula for the cross-validation objective function for mixed
variable types is:
.. math:: CV(h,\lambda)=\frac{1}{n}\sum_{l=1}^{n}
\frac{G_{-l}(X_{l})}{\left[\mu_{-l}(X_{l})\right]^{2}}-
\frac{2}{n}\sum_{l=1}^{n}\frac{f_{-l}(X_{l},Y_{l})}{\mu_{-l}(X_{l})}
where
.. math:: G_{-l}(X_{l}) = n^{-2}\sum_{i\neq l}\sum_{j\neq l}
K_{X_{i},X_{l}} K_{X_{j},X_{l}}K_{Y_{i},Y_{j}}^{(2)}
where :math:`K_{X_{i},X_{l}}` is the multivariate product kernel and
:math:`\mu_{-l}(X_{l})` is the leave-one-out estimator of the pdf.
:math:`K_{Y_{i},Y_{j}}^{(2)}` is the convolution kernel.
The value of the function is minimized by the ``_cv_ls`` method of the
`GenericKDE` class to return the bw estimates that minimize the
distance between the estimated and "true" probability density.
References
----------
.. [1] Racine, J., Li, Q. Nonparametric econometrics: theory and
practice. Princeton University Press. (2007)
.. [2] Racine, J., Li, Q. "Nonparametric Estimation of Distributions
with Categorical and Continuous Data." Working Paper. (2000)
"""
zLOO = LeaveOneOut(self.data)
CV = 0
nobs = float(self.nobs)
expander = np.ones((self.nobs - 1, 1))
for ii, Z in enumerate(zLOO):
X = Z[:, self.k_dep:]
Y = Z[:, :self.k_dep]
Ye_L = np.kron(Y, expander)
Ye_R = np.kron(expander, Y)
Xe_L = np.kron(X, expander)
Xe_R = np.kron(expander, X)
K_Xi_Xl = gpke(bw[self.k_dep:], data=Xe_L,
data_predict=self.exog[ii, :],
var_type=self.indep_type, tosum=False)
K_Xj_Xl = gpke(bw[self.k_dep:], data=Xe_R,
data_predict=self.exog[ii, :],
var_type=self.indep_type, tosum=False)
K2_Yi_Yj = gpke(bw[0:self.k_dep], data=Ye_L,
data_predict=Ye_R, var_type=self.dep_type,
ckertype='gauss_convolution',
okertype='wangryzin_convolution',
ukertype='aitchisonaitken_convolution',
tosum=False)
G = (K_Xi_Xl * K_Xj_Xl * K2_Yi_Yj).sum() / nobs**2
f_X_Y = gpke(bw, data=-Z, data_predict=-self.data[ii, :],
var_type=(self.dep_type + self.indep_type)) / nobs
m_x = gpke(bw[self.k_dep:], data=-X,
data_predict=-self.exog[ii, :],
var_type=self.indep_type) / nobs
CV += (G / m_x ** 2) - 2 * (f_X_Y / m_x)
return CV / nobs
def _get_class_vars_type(self):
"""Helper method to be able to pass needed vars to _compute_subset."""
class_type = 'KDEMultivariateConditional'
class_vars = (self.k_dep, self.dep_type, self.indep_type)
return class_type, class_vars
