Source code for statsmodels.nonparametric.kernel_density

"""
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=None): 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.") defaults = EstimatorSettings() if defaults is None else defaults 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=None): 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] defaults = EstimatorSettings() if defaults is None else defaults 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