diff --git a/src/py/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb b/src/py/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb new file mode 100644 index 0000000..1b17831 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Import GLM and pysal\n", + "import os\n", + "import numpy as np\n", + "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/glm')\n", + "from glm import GLM\n", + "import pysal\n", + "import pandas as pd\n", + "import statsmodels.formula.api as smf\n", + "import statsmodels.api as sm\n", + "from family import Gaussian, Binomial, Poisson, QuasiPoisson\n", + "\n", + "from statsmodels.api import families" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Prepare some test data - columbus example\n", + "db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')\n", + "y = np.array(db.by_col(\"HOVAL\"))\n", + "y = np.reshape(y, (49,1))\n", + "X = []\n", + "#X.append(np.ones(len(y)))\n", + "X.append(db.by_col(\"INC\"))\n", + "X.append(db.by_col(\"CRIME\"))\n", + "X = np.array(X).T" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 46.42818268]\n", + " [ 0.62898397]\n", + " [ -0.48488854]]\n" + ] + } + ], + "source": [ + "#First fit pysal OLS model\n", + "from pysal.spreg import ols\n", + "OLS = ols.OLS(y, X)\n", + "print OLS.betas" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "[ 46.42818268 0.62898397 -0.48488854]\n", + "[ 46.42818268 0.62898397 -0.48488854]\n" + ] + } + ], + "source": [ + "#Then fit Gaussian GLM\n", + "\n", + "#create Gaussian GLM model object\n", + "model = GLM(y, X, Gaussian())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()\n", + "\n", + "#Check coefficients - R betas [46.4282, 0.6290, -0.4849]\n", + "print results.params\n", + "\n", + "# Gaussian GLM results from statsmodels\n", + "sm_model = smf.GLM(y, sm.add_constant(X), family=families.Gaussian())\n", + "sm_results = sm_model.fit()\n", + "print sm_results.params" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 2\n", + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "\n", + "\n", + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "print results.df_model, sm_results.df_model\n", + "print np.allclose(results.aic, sm_results.aic)\n", + "print np.allclose(results.bic, sm_results.bic)\n", + "print np.allclose(results.deviance, sm_results.deviance)\n", + "print np.allclose(results.df_model, sm_results.df_model)\n", + "print np.allclose(results.df_resid, sm_results.df_resid)\n", + "print np.allclose(results.llf, sm_results.llf)\n", + "print np.allclose(results.mu, sm_results.mu)\n", + "print np.allclose(results.n, sm_results.nobs)\n", + "print np.allclose(results.null, sm_results.null)\n", + "print np.allclose(results.null_deviance, sm_results.null_deviance)\n", + "print np.allclose(results.params, sm_results.params)\n", + "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n", + "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n", + "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n", + "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n", + "print np.allclose(results.resid_response, sm_results.resid_response)\n", + "print np.allclose(results.resid_working, sm_results.resid_working)\n", + "print np.allclose(results.scale, sm_results.scale)\n", + "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n", + "print np.allclose(results.cov_params(), sm_results.cov_params())\n", + "print np.allclose(results.bse, sm_results.bse)\n", + "print np.allclose(results.conf_int(), sm_results.conf_int())\n", + "print np.allclose(results.pvalues, sm_results.pvalues)\n", + "print np.allclose(results.tvalues, sm_results.tvalues)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "[ 3.92159085 0.01183491 -0.01371397]\n", + "[ 3.92159085 0.01183491 -0.01371397]\n" + ] + } + ], + "source": [ + "#Now fit a Poisson GLM \n", + "\n", + "poisson_y = np.round(y).astype(int)\n", + "\n", + "#create Poisson GLM model object\n", + "model = GLM(poisson_y, X, Poisson())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()\n", + "\n", + "#Check coefficients - R betas [3.91926, 0.01198, -0.01371]\n", + "print results.params.T\n", + "\n", + "# Poisson GLM results from statsmodels\n", + "sm_results = smf.GLM(poisson_y, sm.add_constant(X), family=families.Poisson()).fit()\n", + "print sm_results.params" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "\n", + "\n", + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "[ 0.13049161 0.00511599 0.00193769] [ 0.13049161 0.00511599 0.00193769]\n" + ] + } + ], + "source": [ + "print np.allclose(results.aic, sm_results.aic)\n", + "print np.allclose(results.bic, sm_results.bic)\n", + "print np.allclose(results.deviance, sm_results.deviance)\n", + "print np.allclose(results.df_model, sm_results.df_model)\n", + "print np.allclose(results.df_resid, sm_results.df_resid)\n", + "print np.allclose(results.llf, sm_results.llf)\n", + "print np.allclose(results.mu, sm_results.mu)\n", + "print np.allclose(results.n, sm_results.nobs)\n", + "print np.allclose(results.null, sm_results.null)\n", + "print np.allclose(results.null_deviance, sm_results.null_deviance)\n", + "print np.allclose(results.params, sm_results.params)\n", + "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n", + "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n", + "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n", + "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n", + "print np.allclose(results.resid_response, sm_results.resid_response)\n", + "print np.allclose(results.resid_working, sm_results.resid_working)\n", + "print np.allclose(results.scale, sm_results.scale)\n", + "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n", + "print np.allclose(results.cov_params(), sm_results.cov_params())\n", + "print np.allclose(results.bse, sm_results.bse)\n", + "print np.allclose(results.conf_int(), sm_results.conf_int())\n", + "print np.allclose(results.pvalues, sm_results.pvalues)\n", + "print np.allclose(results.tvalues, sm_results.tvalues)\n", + "print results.bse, sm_results.bse" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-5.33638276 0.0287754 ]\n", + "[-5.33638276 0.0287754 ]\n" + ] + } + ], + "source": [ + "#Now fit a binomial GLM\n", + "londonhp = pd.read_csv('/Users/toshan/projects/londonhp.csv')\n", + "#londonhp = pd.read_csv('/Users/qszhao/Dropbox/pysal/pysal/contrib/gwr/londonhp.csv')\n", + "y = londonhp['BATH2'].values\n", + "y = np.reshape(y, (316,1))\n", + "X = londonhp['FLOORSZ'].values\n", + "X = np.reshape(X, (316,1))\n", + "\n", + "#create logistic GLM model object\n", + "model = GLM(y, X, Binomial())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()\n", + "\n", + "#Check coefficients - R betas [-5.33638, 0.02878]\n", + "print results.params.T\n", + "\n", + "# Logistic GLM results from statsmodels\n", + "sm_results = smf.GLM(y, sm.add_constant(X), family=families.Binomial()).fit()\n", + "print sm_results.params" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "print results.df_model, sm_results.df_model\n", + "print np.allclose(results.aic, sm_results.aic)\n", + "print np.allclose(results.bic, sm_results.bic)\n", + "print np.allclose(results.deviance, sm_results.deviance)\n", + "print np.allclose(results.df_model, sm_results.df_model)\n", + "print np.allclose(results.df_resid, sm_results.df_resid)\n", + "print np.allclose(results.llf, sm_results.llf)\n", + "print np.allclose(results.mu, sm_results.mu)\n", + "print np.allclose(results.n, sm_results.nobs)\n", + "print np.allclose(results.null, sm_results.null)\n", + "print np.allclose(results.null_deviance, sm_results.null_deviance)\n", + "print np.allclose(results.params, sm_results.params)\n", + "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n", + "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n", + "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n", + "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n", + "print np.allclose(results.resid_response, sm_results.resid_response)\n", + "print np.allclose(results.resid_working, sm_results.resid_working)\n", + "print np.allclose(results.scale, sm_results.scale)\n", + "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n", + "print np.allclose(results.cov_params(), sm_results.cov_params())\n", + "print np.allclose(results.bse, sm_results.bse)\n", + "print np.allclose(results.conf_int(), sm_results.conf_int())\n", + "print np.allclose(results.pvalues, sm_results.pvalues)\n", + "print np.allclose(results.tvalues, sm_results.tvalues)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "#create QUasiPoisson GLM model object\n", + "model = GLM(poisson_y, X, QuasiPoisson())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/py/crankshaft/crankshaft/regression/glm/__init__.py b/src/py/crankshaft/crankshaft/regression/glm/__init__.py new file mode 100644 index 0000000..4a468d5 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/__init__.py @@ -0,0 +1,4 @@ +import glm +import family +import utils +import iwls diff --git a/src/py/crankshaft/crankshaft/regression/glm/base.py b/src/py/crankshaft/crankshaft/regression/glm/base.py new file mode 100644 index 0000000..484c1c8 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/base.py @@ -0,0 +1,959 @@ + +from __future__ import print_function +import numpy as np +from scipy import stats +from utils import cache_readonly + +class Results(object): + """ + Class to contain model results + Parameters + ---------- + model : class instance + the previously specified model instance + params : array + parameter estimates from the fit model + """ + def __init__(self, model, params, **kwd): + self.__dict__.update(kwd) + self.initialize(model, params, **kwd) + self._data_attr = [] + + def initialize(self, model, params, **kwd): + self.params = params + self.model = model + if hasattr(model, 'k_constant'): + self.k_constant = model.k_constant + + def predict(self, exog=None, transform=True, *args, **kwargs): + """ + Call self.model.predict with self.params as the first argument. + Parameters + ---------- + exog : array-like, optional + The values for which you want to predict. + transform : bool, optional + If the model was fit via a formula, do you want to pass + exog through the formula. Default is True. E.g., if you fit + a model y ~ log(x1) + log(x2), and transform is True, then + you can pass a data structure that contains x1 and x2 in + their original form. Otherwise, you'd need to log the data + first. + args, kwargs : + Some models can take additional arguments or keywords, see the + predict method of the model for the details. + Returns + ------- + prediction : ndarray or pandas.Series + See self.model.predict + """ + if transform and hasattr(self.model, 'formula') and exog is not None: + from patsy import dmatrix + exog = dmatrix(self.model.data.design_info.builder, + exog) + + if exog is not None: + exog = np.asarray(exog) + if exog.ndim == 1 and (self.model.exog.ndim == 1 or + self.model.exog.shape[1] == 1): + exog = exog[:, None] + exog = np.atleast_2d(exog) # needed in count model shape[1] + + return self.model.predict(self.params, exog, *args, **kwargs) + + +#TODO: public method? +class LikelihoodModelResults(Results): + """ + Class to contain results from likelihood models + Parameters + ----------- + model : LikelihoodModel instance or subclass instance + LikelihoodModelResults holds a reference to the model that is fit. + params : 1d array_like + parameter estimates from estimated model + normalized_cov_params : 2d array + Normalized (before scaling) covariance of params. (dot(X.T,X))**-1 + scale : float + For (some subset of models) scale will typically be the + mean square error from the estimated model (sigma^2) + Returns + ------- + **Attributes** + mle_retvals : dict + Contains the values returned from the chosen optimization method if + full_output is True during the fit. Available only if the model + is fit by maximum likelihood. See notes below for the output from + the different methods. + mle_settings : dict + Contains the arguments passed to the chosen optimization method. + Available if the model is fit by maximum likelihood. See + LikelihoodModel.fit for more information. + model : model instance + LikelihoodResults contains a reference to the model that is fit. + params : ndarray + The parameters estimated for the model. + scale : float + The scaling factor of the model given during instantiation. + tvalues : array + The t-values of the standard errors. + Notes + ----- + The covariance of params is given by scale times normalized_cov_params. + Return values by solver if full_output is True during fit: + 'newton' + fopt : float + The value of the (negative) loglikelihood at its + minimum. + iterations : int + Number of iterations performed. + score : ndarray + The score vector at the optimum. + Hessian : ndarray + The Hessian at the optimum. + warnflag : int + 1 if maxiter is exceeded. 0 if successful convergence. + converged : bool + True: converged. False: did not converge. + allvecs : list + List of solutions at each iteration. + 'nm' + fopt : float + The value of the (negative) loglikelihood at its + minimum. + iterations : int + Number of iterations performed. + warnflag : int + 1: Maximum number of function evaluations made. + 2: Maximum number of iterations reached. + converged : bool + True: converged. False: did not converge. + allvecs : list + List of solutions at each iteration. + 'bfgs' + fopt : float + Value of the (negative) loglikelihood at its minimum. + gopt : float + Value of gradient at minimum, which should be near 0. + Hinv : ndarray + value of the inverse Hessian matrix at minimum. Note + that this is just an approximation and will often be + different from the value of the analytic Hessian. + fcalls : int + Number of calls to loglike. + gcalls : int + Number of calls to gradient/score. + warnflag : int + 1: Maximum number of iterations exceeded. 2: Gradient + and/or function calls are not changing. + converged : bool + True: converged. False: did not converge. + allvecs : list + Results at each iteration. + 'lbfgs' + fopt : float + Value of the (negative) loglikelihood at its minimum. + gopt : float + Value of gradient at minimum, which should be near 0. + fcalls : int + Number of calls to loglike. + warnflag : int + Warning flag: + - 0 if converged + - 1 if too many function evaluations or too many iterations + - 2 if stopped for another reason + converged : bool + True: converged. False: did not converge. + 'powell' + fopt : float + Value of the (negative) loglikelihood at its minimum. + direc : ndarray + Current direction set. + iterations : int + Number of iterations performed. + fcalls : int + Number of calls to loglike. + warnflag : int + 1: Maximum number of function evaluations. 2: Maximum number + of iterations. + converged : bool + True : converged. False: did not converge. + allvecs : list + Results at each iteration. + 'cg' + fopt : float + Value of the (negative) loglikelihood at its minimum. + fcalls : int + Number of calls to loglike. + gcalls : int + Number of calls to gradient/score. + warnflag : int + 1: Maximum number of iterations exceeded. 2: Gradient and/ + or function calls not changing. + converged : bool + True: converged. False: did not converge. + allvecs : list + Results at each iteration. + 'ncg' + fopt : float + Value of the (negative) loglikelihood at its minimum. + fcalls : int + Number of calls to loglike. + gcalls : int + Number of calls to gradient/score. + hcalls : int + Number of calls to hessian. + warnflag : int + 1: Maximum number of iterations exceeded. + converged : bool + True: converged. False: did not converge. + allvecs : list + Results at each iteration. + """ + + # by default we use normal distribution + # can be overwritten by instances or subclasses + use_t = False + + def __init__(self, model, params, normalized_cov_params=None, scale=1., + **kwargs): + super(LikelihoodModelResults, self).__init__(model, params) + self.normalized_cov_params = normalized_cov_params + self.scale = scale + + # robust covariance + # We put cov_type in kwargs so subclasses can decide in fit whether to + # use this generic implementation + if 'use_t' in kwargs: + use_t = kwargs['use_t'] + if use_t is not None: + self.use_t = use_t + if 'cov_type' in kwargs: + cov_type = kwargs.get('cov_type', 'nonrobust') + cov_kwds = kwargs.get('cov_kwds', {}) + + if cov_type == 'nonrobust': + self.cov_type = 'nonrobust' + self.cov_kwds = {'description' : 'Standard Errors assume that the ' + + 'covariance matrix of the errors is correctly ' + + 'specified.'} + else: + from statsmodels.base.covtype import get_robustcov_results + if cov_kwds is None: + cov_kwds = {} + use_t = self.use_t + # TODO: we shouldn't need use_t in get_robustcov_results + get_robustcov_results(self, cov_type=cov_type, use_self=True, + use_t=use_t, **cov_kwds) + + + def normalized_cov_params(self): + raise NotImplementedError + + + def _get_robustcov_results(self, cov_type='nonrobust', use_self=True, + use_t=None, **cov_kwds): + from statsmodels.base.covtype import get_robustcov_results + if cov_kwds is None: + cov_kwds = {} + + if cov_type == 'nonrobust': + self.cov_type = 'nonrobust' + self.cov_kwds = {'description' : 'Standard Errors assume that the ' + + 'covariance matrix of the errors is correctly ' + + 'specified.'} + else: + # TODO: we shouldn't need use_t in get_robustcov_results + get_robustcov_results(self, cov_type=cov_type, use_self=True, + use_t=use_t, **cov_kwds) + + @cache_readonly + def llf(self): + return self.model.loglike(self.params) + + @cache_readonly + def bse(self): + return np.sqrt(np.diag(self.cov_params())) + + @cache_readonly + def tvalues(self): + """ + Return the t-statistic for a given parameter estimate. + """ + return self.params / self.bse + + @cache_readonly + def pvalues(self): + if self.use_t: + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + return stats.t.sf(np.abs(self.tvalues), df_resid)*2 + else: + return stats.norm.sf(np.abs(self.tvalues))*2 + + + def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None, + other=None): + """ + Returns the variance/covariance matrix. + The variance/covariance matrix can be of a linear contrast + of the estimates of params or all params multiplied by scale which + will usually be an estimate of sigma^2. Scale is assumed to be + a scalar. + Parameters + ---------- + r_matrix : array-like + Can be 1d, or 2d. Can be used alone or with other. + column : array-like, optional + Must be used on its own. Can be 0d or 1d see below. + scale : float, optional + Can be specified or not. Default is None, which means that + the scale argument is taken from the model. + other : array-like, optional + Can be used when r_matrix is specified. + Returns + ------- + cov : ndarray + covariance matrix of the parameter estimates or of linear + combination of parameter estimates. See Notes. + Notes + ----- + (The below are assumed to be in matrix notation.) + If no argument is specified returns the covariance matrix of a model + ``(scale)*(X.T X)^(-1)`` + If contrast is specified it pre and post-multiplies as follows + ``(scale) * r_matrix (X.T X)^(-1) r_matrix.T`` + If contrast and other are specified returns + ``(scale) * r_matrix (X.T X)^(-1) other.T`` + If column is specified returns + ``(scale) * (X.T X)^(-1)[column,column]`` if column is 0d + OR + ``(scale) * (X.T X)^(-1)[column][:,column]`` if column is 1d + """ + if (hasattr(self, 'mle_settings') and + self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']): + dot_fun = nan_dot + else: + dot_fun = np.dot + + if (cov_p is None and self.normalized_cov_params is None and + not hasattr(self, 'cov_params_default')): + raise ValueError('need covariance of parameters for computing ' + '(unnormalized) covariances') + if column is not None and (r_matrix is not None or other is not None): + raise ValueError('Column should be specified without other ' + 'arguments.') + if other is not None and r_matrix is None: + raise ValueError('other can only be specified with r_matrix') + + if cov_p is None: + if hasattr(self, 'cov_params_default'): + cov_p = self.cov_params_default + else: + if scale is None: + scale = self.scale + cov_p = self.normalized_cov_params * scale + + if column is not None: + column = np.asarray(column) + if column.shape == (): + return cov_p[column, column] + else: + #return cov_p[column][:, column] + return cov_p[column[:, None], column] + elif r_matrix is not None: + r_matrix = np.asarray(r_matrix) + if r_matrix.shape == (): + raise ValueError("r_matrix should be 1d or 2d") + if other is None: + other = r_matrix + else: + other = np.asarray(other) + tmp = dot_fun(r_matrix, dot_fun(cov_p, np.transpose(other))) + return tmp + else: # if r_matrix is None and column is None: + return cov_p + + #TODO: make sure this works as needed for GLMs + def t_test(self, r_matrix, cov_p=None, scale=None, + use_t=None): + """ + Compute a t-test for a each linear hypothesis of the form Rb = q + Parameters + ---------- + r_matrix : array-like, str, tuple + - array : If an array is given, a p x k 2d array or length k 1d + array specifying the linear restrictions. It is assumed + that the linear combination is equal to zero. + - str : The full hypotheses to test can be given as a string. + See the examples. + - tuple : A tuple of arrays in the form (R, q). If q is given, + can be either a scalar or a length p row vector. + cov_p : array-like, optional + An alternative estimate for the parameter covariance matrix. + If None is given, self.normalized_cov_params is used. + scale : float, optional + An optional `scale` to use. Default is the scale specified + by the model fit. + use_t : bool, optional + If use_t is None, then the default of the model is used. + If use_t is True, then the p-values are based on the t + distribution. + If use_t is False, then the p-values are based on the normal + distribution. + Returns + ------- + res : ContrastResults instance + The results for the test are attributes of this results instance. + The available results have the same elements as the parameter table + in `summary()`. + Examples + -------- + >>> import numpy as np + >>> import statsmodels.api as sm + >>> data = sm.datasets.longley.load() + >>> data.exog = sm.add_constant(data.exog) + >>> results = sm.OLS(data.endog, data.exog).fit() + >>> r = np.zeros_like(results.params) + >>> r[5:] = [1,-1] + >>> print(r) + [ 0. 0. 0. 0. 0. 1. -1.] + r tests that the coefficients on the 5th and 6th independent + variable are the same. + >>> T_test = results.t_test(r) + >>> print(T_test) + + >>> T_test.effect + -1829.2025687192481 + >>> T_test.sd + 455.39079425193762 + >>> T_test.tvalue + -4.0167754636411717 + >>> T_test.pvalue + 0.0015163772380899498 + Alternatively, you can specify the hypothesis tests using a string + >>> from statsmodels.formula.api import ols + >>> dta = sm.datasets.longley.load_pandas().data + >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' + >>> results = ols(formula, dta).fit() + >>> hypotheses = 'GNPDEFL = GNP, UNEMP = 2, YEAR/1829 = 1' + >>> t_test = results.t_test(hypotheses) + >>> print(t_test) + See Also + --------- + tvalues : individual t statistics + f_test : for F tests + patsy.DesignInfo.linear_constraint + """ + from patsy import DesignInfo + names = self.model.data.param_names + LC = DesignInfo(names).linear_constraint(r_matrix) + r_matrix, q_matrix = LC.coefs, LC.constants + num_ttests = r_matrix.shape[0] + num_params = r_matrix.shape[1] + + if (cov_p is None and self.normalized_cov_params is None and + not hasattr(self, 'cov_params_default')): + raise ValueError('Need covariance of parameters for computing ' + 'T statistics') + if num_params != self.params.shape[0]: + raise ValueError('r_matrix and params are not aligned') + if q_matrix is None: + q_matrix = np.zeros(num_ttests) + else: + q_matrix = np.asarray(q_matrix) + q_matrix = q_matrix.squeeze() + if q_matrix.size > 1: + if q_matrix.shape[0] != num_ttests: + raise ValueError("r_matrix and q_matrix must have the same " + "number of rows") + + if use_t is None: + #switch to use_t false if undefined + use_t = (hasattr(self, 'use_t') and self.use_t) + + _t = _sd = None + + _effect = np.dot(r_matrix, self.params) + # nan_dot multiplies with the convention nan * 0 = 0 + + # Perform the test + if num_ttests > 1: + _sd = np.sqrt(np.diag(self.cov_params( + r_matrix=r_matrix, cov_p=cov_p))) + else: + _sd = np.sqrt(self.cov_params(r_matrix=r_matrix, cov_p=cov_p)) + _t = (_effect - q_matrix) * recipr(_sd) + + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + + if use_t: + return ContrastResults(effect=_effect, t=_t, sd=_sd, + df_denom=df_resid) + else: + return ContrastResults(effect=_effect, statistic=_t, sd=_sd, + df_denom=df_resid, + distribution='norm') + + def f_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None): + """ + Compute the F-test for a joint linear hypothesis. + This is a special case of `wald_test` that always uses the F + distribution. + Parameters + ---------- + r_matrix : array-like, str, or tuple + - array : An r x k array where r is the number of restrictions to + test and k is the number of regressors. It is assumed + that the linear combination is equal to zero. + - str : The full hypotheses to test can be given as a string. + See the examples. + - tuple : A tuple of arrays in the form (R, q), ``q`` can be + either a scalar or a length k row vector. + cov_p : array-like, optional + An alternative estimate for the parameter covariance matrix. + If None is given, self.normalized_cov_params is used. + scale : float, optional + Default is 1.0 for no scaling. + invcov : array-like, optional + A q x q array to specify an inverse covariance matrix based on a + restrictions matrix. + Returns + ------- + res : ContrastResults instance + The results for the test are attributes of this results instance. + Examples + -------- + >>> import numpy as np + >>> import statsmodels.api as sm + >>> data = sm.datasets.longley.load() + >>> data.exog = sm.add_constant(data.exog) + >>> results = sm.OLS(data.endog, data.exog).fit() + >>> A = np.identity(len(results.params)) + >>> A = A[1:,:] + This tests that each coefficient is jointly statistically + significantly different from zero. + >>> print(results.f_test(A)) + + Compare this to + >>> results.fvalue + 330.2853392346658 + >>> results.f_pvalue + 4.98403096572e-10 + >>> B = np.array(([0,0,1,-1,0,0,0],[0,0,0,0,0,1,-1])) + This tests that the coefficient on the 2nd and 3rd regressors are + equal and jointly that the coefficient on the 5th and 6th regressors + are equal. + >>> print(results.f_test(B)) + + Alternatively, you can specify the hypothesis tests using a string + >>> from statsmodels.datasets import longley + >>> from statsmodels.formula.api import ols + >>> dta = longley.load_pandas().data + >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' + >>> results = ols(formula, dta).fit() + >>> hypotheses = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)' + >>> f_test = results.f_test(hypotheses) + >>> print(f_test) + See Also + -------- + statsmodels.stats.contrast.ContrastResults + wald_test + t_test + patsy.DesignInfo.linear_constraint + Notes + ----- + The matrix `r_matrix` is assumed to be non-singular. More precisely, + r_matrix (pX pX.T) r_matrix.T + is assumed invertible. Here, pX is the generalized inverse of the + design matrix of the model. There can be problems in non-OLS models + where the rank of the covariance of the noise is not full. + """ + res = self.wald_test(r_matrix, cov_p=cov_p, scale=scale, + invcov=invcov, use_f=True) + return res + + #TODO: untested for GLMs? + def wald_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None, + use_f=None): + """ + Compute a Wald-test for a joint linear hypothesis. + Parameters + ---------- + r_matrix : array-like, str, or tuple + - array : An r x k array where r is the number of restrictions to + test and k is the number of regressors. It is assumed that the + linear combination is equal to zero. + - str : The full hypotheses to test can be given as a string. + See the examples. + - tuple : A tuple of arrays in the form (R, q), ``q`` can be + either a scalar or a length p row vector. + cov_p : array-like, optional + An alternative estimate for the parameter covariance matrix. + If None is given, self.normalized_cov_params is used. + scale : float, optional + Default is 1.0 for no scaling. + invcov : array-like, optional + A q x q array to specify an inverse covariance matrix based on a + restrictions matrix. + use_f : bool + If True, then the F-distribution is used. If False, then the + asymptotic distribution, chisquare is used. If use_f is None, then + the F distribution is used if the model specifies that use_t is True. + The test statistic is proportionally adjusted for the distribution + by the number of constraints in the hypothesis. + Returns + ------- + res : ContrastResults instance + The results for the test are attributes of this results instance. + See also + -------- + statsmodels.stats.contrast.ContrastResults + f_test + t_test + patsy.DesignInfo.linear_constraint + Notes + ----- + The matrix `r_matrix` is assumed to be non-singular. More precisely, + r_matrix (pX pX.T) r_matrix.T + is assumed invertible. Here, pX is the generalized inverse of the + design matrix of the model. There can be problems in non-OLS models + where the rank of the covariance of the noise is not full. + """ + if use_f is None: + #switch to use_t false if undefined + use_f = (hasattr(self, 'use_t') and self.use_t) + + from patsy import DesignInfo + names = self.model.data.param_names + LC = DesignInfo(names).linear_constraint(r_matrix) + r_matrix, q_matrix = LC.coefs, LC.constants + + if (self.normalized_cov_params is None and cov_p is None and + invcov is None and not hasattr(self, 'cov_params_default')): + raise ValueError('need covariance of parameters for computing ' + 'F statistics') + + cparams = np.dot(r_matrix, self.params[:, None]) + J = float(r_matrix.shape[0]) # number of restrictions + if q_matrix is None: + q_matrix = np.zeros(J) + else: + q_matrix = np.asarray(q_matrix) + if q_matrix.ndim == 1: + q_matrix = q_matrix[:, None] + if q_matrix.shape[0] != J: + raise ValueError("r_matrix and q_matrix must have the same " + "number of rows") + Rbq = cparams - q_matrix + if invcov is None: + cov_p = self.cov_params(r_matrix=r_matrix, cov_p=cov_p) + if np.isnan(cov_p).max(): + raise ValueError("r_matrix performs f_test for using " + "dimensions that are asymptotically " + "non-normal") + invcov = np.linalg.inv(cov_p) + + if (hasattr(self, 'mle_settings') and + self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']): + F = nan_dot(nan_dot(Rbq.T, invcov), Rbq) + else: + F = np.dot(np.dot(Rbq.T, invcov), Rbq) + + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + if use_f: + F /= J + return ContrastResults(F=F, df_denom=df_resid, + df_num=invcov.shape[0]) + else: + return ContrastResults(chi2=F, df_denom=J, statistic=F, + distribution='chi2', distargs=(J,)) + + + def wald_test_terms(self, skip_single=False, extra_constraints=None, + combine_terms=None): + """ + Compute a sequence of Wald tests for terms over multiple columns + This computes joined Wald tests for the hypothesis that all + coefficients corresponding to a `term` are zero. + `Terms` are defined by the underlying formula or by string matching. + Parameters + ---------- + skip_single : boolean + If true, then terms that consist only of a single column and, + therefore, refers only to a single parameter is skipped. + If false, then all terms are included. + extra_constraints : ndarray + not tested yet + combine_terms : None or list of strings + Each string in this list is matched to the name of the terms or + the name of the exogenous variables. All columns whose name + includes that string are combined in one joint test. + Returns + ------- + test_result : result instance + The result instance contains `table` which is a pandas DataFrame + with the test results: test statistic, degrees of freedom and + pvalues. + Examples + -------- + >>> res_ols = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", + data).fit() + >>> res_ols.wald_test_terms() + + F P>F df constraint df denom + Intercept 279.754525 2.37985521351e-22 1 51 + C(Duration, Sum) 5.367071 0.0245738436636 1 51 + C(Weight, Sum) 12.432445 3.99943118767e-05 2 51 + C(Duration, Sum):C(Weight, Sum) 0.176002 0.83912310946 2 51 + >>> res_poi = Poisson.from_formula("Days ~ C(Weight) * C(Duration)", + data).fit(cov_type='HC0') + >>> wt = res_poi.wald_test_terms(skip_single=False, + combine_terms=['Duration', 'Weight']) + >>> print(wt) + chi2 P>chi2 df constraint + Intercept 15.695625 7.43960374424e-05 1 + C(Weight) 16.132616 0.000313940174705 2 + C(Duration) 1.009147 0.315107378931 1 + C(Weight):C(Duration) 0.216694 0.897315972824 2 + Duration 11.187849 0.010752286833 3 + Weight 30.263368 4.32586407145e-06 4 + """ + # lazy import + from collections import defaultdict + + result = self + if extra_constraints is None: + extra_constraints = [] + if combine_terms is None: + combine_terms = [] + design_info = getattr(result.model.data.orig_exog, 'design_info', None) + + if design_info is None and extra_constraints is None: + raise ValueError('no constraints, nothing to do') + + + identity = np.eye(len(result.params)) + constraints = [] + combined = defaultdict(list) + if design_info is not None: + for term in design_info.terms: + cols = design_info.slice(term) + name = term.name() + constraint_matrix = identity[cols] + + # check if in combined + for cname in combine_terms: + if cname in name: + combined[cname].append(constraint_matrix) + + k_constraint = constraint_matrix.shape[0] + if skip_single: + if k_constraint == 1: + continue + + constraints.append((name, constraint_matrix)) + + combined_constraints = [] + for cname in combine_terms: + combined_constraints.append((cname, np.vstack(combined[cname]))) + else: + # check by exog/params names if there is no formula info + for col, name in enumerate(result.model.exog_names): + constraint_matrix = identity[col] + + # check if in combined + for cname in combine_terms: + if cname in name: + combined[cname].append(constraint_matrix) + + if skip_single: + continue + + constraints.append((name, constraint_matrix)) + + combined_constraints = [] + for cname in combine_terms: + combined_constraints.append((cname, np.vstack(combined[cname]))) + + use_t = result.use_t + distribution = ['chi2', 'F'][use_t] + + res_wald = [] + index = [] + for name, constraint in constraints + combined_constraints + extra_constraints: + wt = result.wald_test(constraint) + row = [wt.statistic.item(), wt.pvalue, constraint.shape[0]] + if use_t: + row.append(wt.df_denom) + res_wald.append(row) + index.append(name) + + # distribution nerutral names + col_names = ['statistic', 'pvalue', 'df_constraint'] + if use_t: + col_names.append('df_denom') + # TODO: maybe move DataFrame creation to results class + from pandas import DataFrame + table = DataFrame(res_wald, index=index, columns=col_names) + res = WaldTestResults(None, distribution, None, table=table) + # TODO: remove temp again, added for testing + res.temp = constraints + combined_constraints + extra_constraints + return res + + + def conf_int(self, alpha=.05, cols=None, method='default'): + """ + Returns the confidence interval of the fitted parameters. + Parameters + ---------- + alpha : float, optional + The significance level for the confidence interval. + ie., The default `alpha` = .05 returns a 95% confidence interval. + cols : array-like, optional + `cols` specifies which confidence intervals to return + method : string + Not Implemented Yet + Method to estimate the confidence_interval. + "Default" : uses self.bse which is based on inverse Hessian for MLE + "hjjh" : + "jac" : + "boot-bse" + "boot_quant" + "profile" + Returns + -------- + conf_int : array + Each row contains [lower, upper] limits of the confidence interval + for the corresponding parameter. The first column contains all + lower, the second column contains all upper limits. + Examples + -------- + >>> import statsmodels.api as sm + >>> data = sm.datasets.longley.load() + >>> data.exog = sm.add_constant(data.exog) + >>> results = sm.OLS(data.endog, data.exog).fit() + >>> results.conf_int() + array([[-5496529.48322745, -1467987.78596704], + [ -177.02903529, 207.15277984], + [ -0.1115811 , 0.03994274], + [ -3.12506664, -0.91539297], + [ -1.5179487 , -0.54850503], + [ -0.56251721, 0.460309 ], + [ 798.7875153 , 2859.51541392]]) + >>> results.conf_int(cols=(2,3)) + array([[-0.1115811 , 0.03994274], + [-3.12506664, -0.91539297]]) + Notes + ----- + The confidence interval is based on the standard normal distribution. + Models wish to use a different distribution should overwrite this + method. + """ + bse = self.bse + + if self.use_t: + dist = stats.t + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + q = dist.ppf(1 - alpha / 2, df_resid) + else: + dist = stats.norm + q = dist.ppf(1 - alpha / 2) + + if cols is None: + lower = self.params - q * bse + upper = self.params + q * bse + else: + cols = np.asarray(cols) + lower = self.params[cols] - q * bse[cols] + upper = self.params[cols] + q * bse[cols] + return np.asarray(lzip(lower, upper)) + + def save(self, fname, remove_data=False): + ''' + save a pickle of this instance + Parameters + ---------- + fname : string or filehandle + fname can be a string to a file path or filename, or a filehandle. + remove_data : bool + If False (default), then the instance is pickled without changes. + If True, then all arrays with length nobs are set to None before + pickling. See the remove_data method. + In some cases not all arrays will be set to None. + Notes + ----- + If remove_data is true and the model result does not implement a + remove_data method then this will raise an exception. + ''' + + from statsmodels.iolib.smpickle import save_pickle + + if remove_data: + self.remove_data() + + save_pickle(self, fname) + + @classmethod + def load(cls, fname): + ''' + load a pickle, (class method) + Parameters + ---------- + fname : string or filehandle + fname can be a string to a file path or filename, or a filehandle. + Returns + ------- + unpickled instance + ''' + + from statsmodels.iolib.smpickle import load_pickle + return load_pickle(fname) + + def remove_data(self): + '''remove data arrays, all nobs arrays from result and model + This reduces the size of the instance, so it can be pickled with less + memory. Currently tested for use with predict from an unpickled + results and model instance. + .. warning:: Since data and some intermediate results have been removed + calculating new statistics that require them will raise exceptions. + The exception will occur the first time an attribute is accessed + that has been set to None. + Not fully tested for time series models, tsa, and might delete too much + for prediction or not all that would be possible. + The list of arrays to delete is maintained as an attribute of the + result and model instance, except for cached values. These lists could + be changed before calling remove_data. + ''' + def wipe(obj, att): + #get to last element in attribute path + p = att.split('.') + att_ = p.pop(-1) + try: + obj_ = reduce(getattr, [obj] + p) + + #print(repr(obj), repr(att)) + #print(hasattr(obj_, att_)) + if hasattr(obj_, att_): + #print('removing3', att_) + setattr(obj_, att_, None) + except AttributeError: + pass + + model_attr = ['model.' + i for i in self.model._data_attr] + for att in self._data_attr + model_attr: + #print('removing', att) + wipe(self, att) + + data_in_cache = getattr(self, 'data_in_cache', []) + data_in_cache += ['fittedvalues', 'resid', 'wresid'] + for key in data_in_cache: + try: + self._cache[key] = None + except (AttributeError, KeyError): + pass + +def lzip(*args, **kwargs): + return list(zip(*args, **kwargs)) diff --git a/src/py/crankshaft/crankshaft/regression/glm/family.py b/src/py/crankshaft/crankshaft/regression/glm/family.py new file mode 100644 index 0000000..bad22c1 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/family.py @@ -0,0 +1,1845 @@ +''' +The one parameter exponential family distributions used by GLM. +''' +# TODO: quasi, quasibinomial, quasipoisson +# see http://www.biostat.jhsph.edu/~qli/biostatistics_r_doc/library/stats/html/family.html +# for comparison to R, and McCullagh and Nelder + +import numpy as np +from scipy import special +import links as L +import varfuncs as V +FLOAT_EPS = np.finfo(float).eps + + +class Family(object): + """ + The parent class for one-parameter exponential families. + + Parameters + ---------- + link : a link function instance + Link is the linear transformation function. + See the individual families for available links. + variance : a variance function + Measures the variance as a function of the mean probabilities. + See the individual families for the default variance function. + + See Also + -------- + :ref:`links` + + """ + # TODO: change these class attributes, use valid somewhere... + valid = [-np.inf, np.inf] + + links = [] + + def _setlink(self, link): + """ + Helper method to set the link for a family. + + Raises a ValueError exception if the link is not available. Note that + the error message might not be that informative because it tells you + that the link should be in the base class for the link function. + + See glm.GLM for a list of appropriate links for each family but note + that not all of these are currently available. + """ + # TODO: change the links class attribute in the families to hold + # meaningful information instead of a list of links instances such as + # [, + # , + # ] + # for Poisson... + self._link = link + if not isinstance(link, L.Link): + raise TypeError("The input should be a valid Link object.") + if hasattr(self, "links"): + validlink = link in self.links + validlink = max([isinstance(link, _) for _ in self.links]) + if not validlink: + errmsg = "Invalid link for family, should be in %s. (got %s)" + raise ValueError(errmsg % (repr(self.links), link)) + + def _getlink(self): + """ + Helper method to get the link for a family. + """ + return self._link + + # link property for each family is a pointer to link instance + link = property(_getlink, _setlink, doc="Link function for family") + + def __init__(self, link, variance): + self.link = link() + self.variance = variance + + def starting_mu(self, y): + r""" + Starting value for mu in the IRLS algorithm. + + Parameters + ---------- + y : array + The untransformed response variable. + + Returns + ------- + mu_0 : array + The first guess on the transformed response variable. + + Notes + ----- + .. math:: + + \mu_0 = (Y + \overline{Y})/2 + + Notes + ----- + Only the Binomial family takes a different initial value. + """ + return (y + y.mean())/2. + + def weights(self, mu): + r""" + Weights for IRLS steps + + Parameters + ---------- + mu : array-like + The transformed mean response variable in the exponential family + + Returns + ------- + w : array + The weights for the IRLS steps + + Notes + ----- + .. math:: + + w = 1 / (g'(\mu)^2 * Var(\mu)) + """ + return 1. / (self.link.deriv(mu)**2 * self.variance(mu)) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + The deviance function evaluated at (endog,mu,freq_weights,mu). + + Deviance is usually defined as twice the loglikelihood ratio. + + Parameters + ---------- + endog : array-like + The endogenous response variable + mu : array-like + The inverse of the link function at the linear predicted values. + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + Deviance : array + The value of deviance function defined below. + + Notes + ----- + Deviance is defined + + .. math:: + + D = \sum_i (2 * freq\_weights_i * llf(Y_i, Y_i) - 2 * + llf(Y_i, \mu_i)) / scale + + where y is the endogenous variable. The deviance functions are + analytically defined for each family. + """ + raise NotImplementedError + + def resid_dev(self, endog, mu, freq_weights=1., scale=1.): + """ + The deviance residuals + + Parameters + ---------- + endog : array + The endogenous response variable + mu : array + The inverse of the link function at the linear predicted values. + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + Deviance residuals. + + Notes + ----- + The deviance residuals are defined for each family. + """ + raise NotImplementedError + + def fitted(self, lin_pred): + """ + Fitted values based on linear predictors lin_pred. + + Parameters + ----------- + lin_pred : array + Values of the linear predictor of the model. + dot(X,beta) in a classical linear model. + + Returns + -------- + mu : array + The mean response variables given by the inverse of the link + function. + """ + fits = self.link.inverse(lin_pred) + return fits + + def predict(self, mu): + """ + Linear predictors based on given mu values. + + Parameters + ---------- + mu : array + The mean response variables + + Returns + ------- + lin_pred : array + Linear predictors based on the mean response variables. The value + of the link function at the given mu. + """ + return self.link(mu) + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + """ + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + `endog` : array + Usually the endogenous response variable. + `mu` : array + Usually but not always the fitted mean response variable. + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float + The scale parameter. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood evaluated at + (endog,mu,freq_weights,scale) as defined below. + Notes + ----- + This is defined for each family. endog and mu are not restricted to + `endog` and `mu` respectively. For instance, the deviance function + calls both loglike(endog,endog) and loglike(endog,mu) to get the + likelihood ratio. + """ + raise NotImplementedError + + def resid_anscombe(self, endog, mu): + """ + The Anscome residuals. + + See also + -------- + statsmodels.families.family.Family docstring and the `resid_anscombe` + for the individual families for more information. + """ + raise NotImplementedError + + +class Poisson(Family): + """ + Poisson exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the Poisson family is the log link. Available + links are log, identity, and sqrt. See statsmodels.family.links for + more information. + + Attributes + ---------- + Poisson.link : a link instance + The link function of the Poisson instance. + Poisson.variance : varfuncs instance + `variance` is an instance of + statsmodels.genmod.families.family.varfuncs.mu + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.sqrt] + variance = V.mu + valid = [0, np.inf] + safe_links = [L.Log, ] + + def __init__(self, link=L.log): + self.variance = Poisson.variance + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def resid_dev(self, endog, mu, scale=1.): + r"""Poisson deviance residual + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{2 * + (Y_i * \log(Y_i / \mu_i) - (Y_i - \mu_i))} / scale + """ + endog_mu = self._clean(endog / mu) + return (np.sign(endog - mu) * + np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu))) / scale) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r''' + Poisson deviance function + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + The deviance function at (endog,mu,freq_weights,scale) as defined + below. + + Notes + ----- + If a constant term is included it is defined as + + .. math:: + + D = 2 * \sum_i (freq\_weights_i * Y_i * \log(Y_i / \mu_i))/ scale + ''' + endog_mu = self._clean(endog / mu) + return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + The scale parameter, defaults to 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + .. math:: + + llf = scale * \sum_i freq\_weights_i * (Y_i * \log(\mu_i) - \mu_i - + \ln \Gamma(Y_i + 1)) + """ + loglike = np.sum(freq_weights * (endog * np.log(mu) - mu - + special.gammaln(endog + 1))) + return scale * loglike + + def resid_anscombe(self, endog, mu): + r""" + Anscombe residuals for the Poisson exponential family distribution + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscome residuals for the Poisson family defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = (3/2) * (Y_i^{2/3} - \mu_i^{2/3}) / \mu_i^{1/6} + """ + return (3 / 2.) * (endog**(2/3.) - mu**(2 / 3.)) / mu**(1 / 6.) + +class QuasiPoisson(Family): + """ + QuasiPoisson exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the Poisson family is the log link. Available + links are log, identity, and sqrt. See statsmodels.family.links for + more information. + + Attributes + ---------- + Poisson.link : a link instance + The link function of the Poisson instance. + Poisson.variance : varfuncs instance + `variance` is an instance of + statsmodels.genmod.families.family.varfuncs.mu + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.sqrt] + variance = V.mu + valid = [0, np.inf] + safe_links = [L.Log, ] + + def __init__(self, link=L.log): + self.variance = Poisson.variance + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def resid_dev(self, endog, mu, scale=1.): + r"""Poisson deviance residual + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{2 * + (Y_i * \log(Y_i / \mu_i) - (Y_i - \mu_i))} / scale + """ + endog_mu = self._clean(endog / mu) + return (np.sign(endog - mu) * + np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu))) / scale) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r''' + Poisson deviance function + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + The deviance function at (endog,mu,freq_weights,scale) as defined + below. + + Notes + ----- + If a constant term is included it is defined as + + .. math:: + + D = 2 * \sum_i (freq\_weights_i * Y_i * \log(Y_i / \mu_i))/ scale + ''' + endog_mu = self._clean(endog / mu) + return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Returns NaN for QuasiPoisson + + Returns + ------- + None: not applicable for QuasiPoisson + """ + return np.nan + + def resid_anscombe(self, endog, mu): + r""" + Anscombe residuals for the Poisson exponential family distribution + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscome residuals for the Poisson family defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = (3/2) * (Y_i^{2/3} - \mu_i^{2/3}) / \mu_i^{1/6} + """ + return (3 / 2.) * (endog**(2/3.) - mu**(2 / 3.)) / mu**(1 / 6.) + +class Gaussian(Family): + """ + Gaussian exponential family distribution. + + Parameters + ---------- + link : a link instance, optional + The default link for the Gaussian family is the identity link. + Available links are log, identity, and inverse. + See statsmodels.family.links for more information. + + Attributes + ---------- + Gaussian.link : a link instance + The link function of the Gaussian instance + Gaussian.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.constant + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.inverse_power] + variance = V.constant + safe_links = links + + def __init__(self, link=L.identity): + self.variance = Gaussian.variance + self.link = link() + + def resid_dev(self, endog, mu, scale=1.): + r""" + Gaussian deviance residuals + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + -------- + .. math:: + + resid\_dev_i = (Y_i - \mu_i) / \sqrt{Var(\mu_i)} / scale + """ + + return (endog - mu) / np.sqrt(self.variance(mu)) / scale + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Gaussian deviance function + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + The deviance function at (endog,mu,freq_weights,scale) + as defined below. + + Notes + -------- + .. math:: + + D = \sum_i freq\_weights_i * (Y_i - \mu_i)^2 / scale + """ + return np.sum((freq_weights * (endog - mu)**2)) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + Scales the loglikelihood function. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + If the link is the identity link function then the + loglikelihood function is the same as the classical OLS model. + + .. math:: + + llf = -nobs / 2 * (\log(SSR) + (1 + \log(2 \pi / nobs))) + + where + + .. math:: + SSR = \sum_i (Y_i - g^{-1}(\mu_i))^2 + + If the links is not the identity link then the loglikelihood + function is defined as + + .. math:: + + llf = \sum_i freq\_weights_i * ((Y_i * \mu_i - \mu_i^2 / 2) / scale- + Y^2 / (2 * scale) - (1/2) * \log(2 * \pi * scale)) + """ + if isinstance(self.link, L.Power) and self.link.power == 1: + # This is just the loglikelihood for classical OLS + nobs2 = endog.shape[0] / 2. + SSR = np.sum((endog-self.fitted(mu))**2, axis=0) + llf = -np.log(SSR) * nobs2 + llf -= (1+np.log(np.pi/nobs2))*nobs2 + return llf + else: + return np.sum(freq_weights * ((endog * mu - mu**2/2)/scale - + endog**2/(2 * scale) - .5*np.log(2 * np.pi * scale))) + + def resid_anscombe(self, endog, mu): + r""" + The Anscombe residuals for the Gaussian exponential family distribution + + Parameters + ---------- + endog : array + Endogenous response variable + mu : array + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals for the Gaussian family defined below + + Notes + -------- + .. math:: + + resid\_anscombe_i = Y_i - \mu_i + """ + return endog - mu + + +class Gamma(Family): + """ + Gamma exponential family distribution. + + Parameters + ---------- + link : a link instance, optional + The default link for the Gamma family is the inverse link. + Available links are log, identity, and inverse. + See statsmodels.family.links for more information. + + Attributes + ---------- + Gamma.link : a link instance + The link function of the Gamma instance + Gamma.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.mu_squared + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.inverse_power] + variance = V.mu_squared + safe_links = [L.Log, ] + + def __init__(self, link=L.inverse_power): + self.variance = Gamma.variance + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Gamma deviance function + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + .. math:: + + D = 2 * \sum_i freq\_weights_i * ((Y_i - \mu_i)/\mu_i - \log(Y_i / + \mu_i)) + """ + endog_mu = self._clean(endog/mu) + return 2*np.sum(freq_weights*((endog-mu)/mu-np.log(endog_mu))) + + def resid_dev(self, endog, mu, scale=1.): + r""" + Gamma deviance residuals + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) \sqrt{-2 * + (-(Y_i - \mu_i) / \mu_i + \log(Y_i / \mu_i))} + """ + endog_mu = self._clean(endog / mu) + return np.sign(endog - mu) * np.sqrt(-2 * (-(endog - mu)/mu + + np.log(endog_mu))) + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + -------- + .. math:: + + llf = -1 / scale * \sum_i *(Y_i / \mu_i+ \log(\mu_i)+ + (scale -1) * \log(Y) + \log(scale) + scale * + \ln \Gamma(1 / scale)) + """ + return - 1./scale * np.sum((endog/mu + np.log(mu) + (scale - 1) * + np.log(endog) + np.log(scale) + scale * + special.gammaln(1./scale)) * freq_weights) + + # in Stata scale is set to equal 1 for reporting llf + # in R it's the dispersion, though there is a loss of precision vs. + # our results due to an assumed difference in implementation + + def resid_anscombe(self, endog, mu): + r""" + The Anscombe residuals for Gamma exponential family distribution + + Parameters + ---------- + endog : array + Endogenous response variable + mu : array + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals for the Gamma family defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = 3 * (Y_i^{1/3} - \mu_i^{1/3}) / \mu_i^{1/3} + """ + return 3 * (endog**(1/3.) - mu**(1/3.)) / mu**(1/3.) + + +class Binomial(Family): + """ + Binomial exponential family distribution. + + Parameters + ---------- + link : a link instance, optional + The default link for the Binomial family is the logit link. + Available links are logit, probit, cauchy, log, and cloglog. + See statsmodels.family.links for more information. + + Attributes + ---------- + Binomial.link : a link instance + The link function of the Binomial instance + Binomial.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.binary + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + endog for Binomial can be specified in one of three ways. + + """ + + links = [L.logit, L.probit, L.cauchy, L.log, L.cloglog, L.identity] + variance = V.binary # this is not used below in an effort to include n + + # Other safe links, e.g. cloglog and probit are subclasses + safe_links = [L.Logit, L.CDFLink] + + def __init__(self, link=L.logit): # , n=1.): + # TODO: it *should* work for a constant n>1 actually, if freq_weights + # is equal to n + self.n = 1 + # overwritten by initialize if needed but always used to initialize + # variance since endog is assumed/forced to be (0,1) + self.variance = V.Binomial(n=self.n) + self.link = link() + + def starting_mu(self, y): + """ + The starting values for the IRLS algorithm for the Binomial family. + A good choice for the binomial family is :math:`\mu_0 = (Y_i + 0.5)/2` + """ + return (y + .5)/2 + + def initialize(self, endog, freq_weights): + ''' + Initialize the response variable. + + Parameters + ---------- + endog : array + Endogenous response variable + + Returns + -------- + If `endog` is binary, returns `endog` + + If `endog` is a 2d array, then the input is assumed to be in the format + (successes, failures) and + successes/(success + failures) is returned. And n is set to + successes + failures. + ''' + # if not np.all(np.asarray(freq_weights) == 1): + # self.variance = V.Binomial(n=freq_weights) + if (endog.ndim > 1 and endog.shape[1] > 1): + y = endog[:, 0] + # overwrite self.freq_weights for deviance below + self.n = endog.sum(1) + return y*1./self.n, self.n + else: + return endog, np.ones(endog.shape[0]) + + def deviance(self, endog, mu, freq_weights=1, scale=1., axis=None): + r''' + Deviance function for either Bernoulli or Binomial data. + + Parameters + ---------- + endog : array-like + Endogenous response variable (already transformed to a probability + if appropriate). + mu : array + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + -------- + deviance : float + The deviance function as defined below + + Notes + ----- + If the endogenous variable is binary: + + .. math:: + + D = -2 * \sum_i freq\_weights * (I_{1,i} * \log(\mu_i) + I_{0,i} * + \log(1 - \mu_i)) + + where :math:`I_{1,i}` is an indicator function that evalueates to 1 if + :math:`Y_i = 1`. and :math:`I_{0,i}` is an indicator function that + evaluates to 1 if :math:`Y_i = 0`. + + If the model is ninomial: + + .. math:: + + D = 2 * \sum_i freq\_weights * (\log(Y_i / \mu_i) + (n_i - Y_i) * + \log((n_i - Y_i) / n_i - \mu_i)) + + where :math:`Y_i` and :math:`n` are as defined in Binomial.initialize. + ''' + if np.shape(self.n) == () and self.n == 1: + one = np.equal(endog, 1) + return -2 * np.sum((one * np.log(mu + 1e-200) + (1-one) * + np.log(1 - mu + 1e-200)) * freq_weights, axis=axis) + + else: + return 2 * np.sum(self.n * freq_weights * + (endog * np.log(endog/mu + 1e-200) + + (1 - endog) * np.log((1 - endog) / + (1 - mu) + 1e-200)), axis=axis) + + def resid_dev(self, endog, mu, scale=1.): + r""" + Binomial deviance residuals + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + If the endogenous variable is binary: + + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{-2 * + \log(I_{1,i} * \mu_i + I_{0,i} * (1 - \mu_i))} + + where :math:`I_{1,i}` is an indicator function that evalueates to 1 if + :math:`Y_i = 1`. and :math:`I_{0,i}` is an indicator function that + evaluates to 1 if :math:`Y_i = 0`. + + If the endogenous variable is binomial: + + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) \sqrt{2 * n_i * + (Y_i * \log(Y_i / \mu_i) + (1 - Y_i) * + \log(1 - Y_i)/(1 - \mu_i))} + + where :math:`Y_i` and :math:`n` are as defined in Binomial.initialize. + """ + + mu = self.link._clean(mu) + if np.shape(self.n) == () and self.n == 1: + one = np.equal(endog, 1) + return np.sign(endog-mu)*np.sqrt(-2 * + np.log(one * mu + (1 - one) * + (1 - mu)))/scale + else: + return (np.sign(endog - mu) * + np.sqrt(2 * self.n * + (endog * np.log(endog/mu + 1e-200) + + (1 - endog) * np.log((1 - endog)/(1 - mu) + 1e-200)))/scale) + + def loglike(self, endog, mu, freq_weights=1, scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + Not used for the Binomial GLM. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + -------- + If the endogenous variable is binary: + + .. math:: + + llf = scale * \sum_i (y_i * \log(\mu_i/(1-\mu_i)) + \log(1-\mu_i)) * + freq\_weights_i + + If the endogenous variable is binomial: + + .. math:: + + llf = scale * \sum_i freq\_weights_i * (\ln \Gamma(n+1) - + \ln \Gamma(y_i + 1) - \ln \Gamma(n_i - y_i +1) + y_i * + \log(\mu_i / (1 - \mu_i)) + n * \log(1 - \mu_i)) + + where :math:`y_i = Y_i * n_i` with :math:`Y_i` and :math:`n_i` as + defined in Binomial initialize. This simply makes :math:`y_i` the + original number of successes. + """ + + if np.shape(self.n) == () and self.n == 1: + return scale * np.sum((endog * np.log(mu/(1 - mu) + 1e-200) + + np.log(1 - mu)) * freq_weights) + else: + y = endog * self.n # convert back to successes + return scale * np.sum((special.gammaln(self.n + 1) - + special.gammaln(y + 1) - + special.gammaln(self.n - y + 1) + y * + np.log(mu/(1 - mu)) + self.n * + np.log(1 - mu)) * freq_weights) + + def resid_anscombe(self, endog, mu): + ''' + The Anscombe residuals + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals as defined below. + + Notes + ----- + sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.)) + + where cox_snell is defined as + cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.) + where betainc is the incomplete beta function + + The name 'cox_snell' is idiosyncratic and is simply used for + convenience following the approach suggested in Cox and Snell (1968). + Further note that + cox_snell(x) = x**(2/3.)/(2/3.)*hyp2f1(2/3.,1/3.,5/3.,x) + where hyp2f1 is the hypergeometric 2f1 function. The Anscombe + residuals are sometimes defined in the literature using the + hyp2f1 formulation. Both betainc and hyp2f1 can be found in scipy. + + References + ---------- + Anscombe, FJ. (1953) "Contribution to the discussion of H. Hotelling's + paper." Journal of the Royal Statistical Society B. 15, 229-30. + + Cox, DR and Snell, EJ. (1968) "A General Definition of Residuals." + Journal of the Royal Statistical Society B. 30, 248-75. + + ''' + cox_snell = lambda x: (special.betainc(2/3., 2/3., x) + * special.beta(2/3., 2/3.)) + return np.sqrt(self.n) * ((cox_snell(endog) - cox_snell(mu)) / + (mu**(1/6.) * (1 - mu)**(1/6.))) + + +class InverseGaussian(Family): + """ + InverseGaussian exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the inverse Gaussian family is the + inverse squared link. + Available links are inverse_squared, inverse, log, and identity. + See statsmodels.family.links for more information. + + Attributes + ---------- + InverseGaussian.link : a link instance + The link function of the inverse Gaussian instance + InverseGaussian.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.mu_cubed + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + The inverse Guassian distribution is sometimes referred to in the + literature as the Wald distribution. + + """ + + links = [L.inverse_squared, L.inverse_power, L.identity, L.log] + variance = V.mu_cubed + safe_links = [L.inverse_squared, L.Log, ] + + def __init__(self, link=L.inverse_squared): + self.variance = InverseGaussian.variance + self.link = link() + + def resid_dev(self, endog, mu, scale=1.): + r""" + Returns the deviance residuals for the inverse Gaussian family. + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * + \sqrt {(Y_i - \mu_i)^2 / (Y_i * \mu_i^2)} / scale + """ + return np.sign(endog-mu) * np.sqrt((endog-mu)**2/(endog*mu**2))/scale + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Inverse Gaussian deviance function + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + .. math:: + + D = \sum_i freq\_weights_i * ((Y_i - \mu_i)^2 / (Y_i *\mu_i^2)) / + scale + """ + return np.sum(freq_weights*(endog-mu)**2/(endog*mu**2))/scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + .. math:: + + llf = -1/2 * \sum_i freq\_weights_i * ((Y_i - \mu_i)^2 / (Y_i * + \mu_i * scale) + \log(scale * Y_i^3) + \log(2 * \pi)) + """ + return -.5 * np.sum(((endog - mu)**2/(endog * mu**2 * scale) + + np.log(scale * endog**3) + np.log(2 * np.pi)) * + freq_weights) + + def resid_anscombe(self, endog, mu): + r""" + The Anscombe residuals for the inverse Gaussian distribution + + Parameters + ---------- + endog : array + Endogenous response variable + mu : array + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals for the inverse Gaussian distribution as + defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = \log(Y_i / \mu_i) / \sqrt{\mu_i} + """ + return np.log(endog / mu) / np.sqrt(mu) + + +class NegativeBinomial(Family): + """ + Negative Binomial exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the negative binomial family is the log link. + Available links are log, cloglog, identity, nbinom and power. + See statsmodels.family.links for more information. + alpha : float, optional + The ancillary parameter for the negative binomial distribution. + For now `alpha` is assumed to be nonstochastic. The default value + is 1. Permissible values are usually assumed to be between .01 and 2. + + + Attributes + ---------- + NegativeBinomial.link : a link instance + The link function of the negative binomial instance + NegativeBinomial.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.nbinom + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + Power link functions are not yet supported. + + """ + links = [L.log, L.cloglog, L.identity, L.nbinom, L.Power] + # TODO: add the ability to use the power links with an if test + # similar to below + variance = V.nbinom + safe_links = [L.Log, ] + + def __init__(self, link=L.log, alpha=1.): + self.alpha = 1. * alpha # make it at least float + self.variance = V.NegativeBinomial(alpha=self.alpha) + if isinstance(link, L.NegativeBinomial): + self.link = link(alpha=self.alpha) + else: + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Returns the value of the deviance function. + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + :math:`D = \sum_i piecewise_i` where :math:`piecewise_i` is defined as: + + If :math:`Y_{i} = 0`: + + :math:`piecewise_i = 2* \log(1 + \alpha * \mu_i) / \alpha` + + If :math:`Y_{i} > 0`: + + :math:`piecewise_i = 2 * Y_i * \log(Y_i / \mu_i) - (2 / \alpha) * + (1 + \alpha * Y_i) * \ln(1 + \alpha * Y_i) / (1 + \alpha * \mu_i)` + """ + iszero = np.equal(endog, 0) + notzero = 1 - iszero + endog_mu = self._clean(endog/mu) + tmp = iszero * 2 * np.log(1 + self.alpha * mu) / self.alpha + tmp += notzero * (2 * endog * np.log(endog_mu) - 2 / self.alpha * + (1 + self.alpha * endog) * + np.log((1 + self.alpha * endog) / + (1 + self.alpha * mu))) + return np.sum(freq_weights * tmp) / scale + + def resid_dev(self, endog, mu, scale=1.): + r""" + Negative Binomial Deviance Residual + + Parameters + ---------- + endog : array-like + `endog` is the response variable + mu : array-like + `mu` is the fitted value of the model + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + -------- + resid_dev : array + The array of deviance residuals + + Notes + ----- + :math:`resid\_dev_i = sign(Y_i-\mu_i) * \sqrt{piecewise_i}` + + where :math:`piecewise_i` is defined as + + If :math:`Y_i = 0`: + + :math:`piecewise_i = 2 * \log(1 + \alpha * \mu_i)/ \alpha` + + If :math:`Y_i > 0`: + + :math:`piecewise_i = 2 * Y_i * \log(Y_i / \mu_i) - (2 / \alpha) * + (1 + \alpha * Y_i) * \log((1 + \alpha * Y_i) / (1 + \alpha * \mu_i))` + """ + iszero = np.equal(endog, 0) + notzero = 1 - iszero + endog_mu = self._clean(endog / mu) + tmp = iszero * 2 * np.log(1 + self.alpha * mu) / self.alpha + tmp += notzero * (2 * endog * np.log(endog_mu) - 2 / self.alpha * + (1 + self.alpha * endog) * + np.log((1 + self.alpha * endog) / + (1 + self.alpha * mu))) + return np.sign(endog - mu) * np.sqrt(tmp) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + The fitted mean response values + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float + The scale parameter. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + Defined as: + + .. math:: + + llf = \sum_i freq\_weights_i * (Y_i * \log{(\alpha * e^{\eta_i} / + (1 + \alpha * e^{\eta_i}))} - \log{(1 + \alpha * e^{\eta_i})}/ + \alpha + Constant) + + where :math:`Constant` is defined as: + + .. math:: + + Constant = \ln \Gamma{(Y_i + 1/ \alpha )} - \ln \Gamma(Y_i + 1) - + \ln \Gamma{(1/ \alpha )} + """ + lin_pred = self._link(mu) + constant = (special.gammaln(endog + 1 / self.alpha) - + special.gammaln(endog+1)-special.gammaln(1/self.alpha)) + exp_lin_pred = np.exp(lin_pred) + return np.sum((endog * np.log(self.alpha * exp_lin_pred / + (1 + self.alpha * exp_lin_pred)) - + np.log(1 + self.alpha * exp_lin_pred) / + self.alpha + constant) * freq_weights) + + def resid_anscombe(self, endog, mu): + """ + The Anscombe residuals for the negative binomial family + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals as defined below. + + Notes + ----- + `resid_anscombe` = (hyp2f1(-alpha*endog)-hyp2f1(-alpha*mu)+\ + 1.5*(endog**(2/3.)-mu**(2/3.)))/(mu+alpha*mu**2)**(1/6.) + + where hyp2f1 is the hypergeometric 2f1 function parameterized as + hyp2f1(x) = hyp2f1(2/3.,1/3.,5/3.,x) + """ + + hyp2f1 = lambda x : special.hyp2f1(2 / 3., 1 / 3., 5 / 3., x) + return ((hyp2f1(-self.alpha * endog) - hyp2f1(-self.alpha * mu) + + 1.5 * ( endog**(2 / 3.) - mu**(2 / 3.))) / + (mu + self.alpha * mu**2)**(1 / 6.)) + + +class Tweedie(Family): + """ + Tweedie family. + + Parameters + ---------- + link : a link instance, optional + The default link for the Tweedie family is the log link when the + link_power is 0. Otherwise, the power link is default. + Available links are log and Power. + var_power : float, optional + The variance power. + link_power : float, optional + The link power. + + Attributes + ---------- + Tweedie.link : a link instance + The link function of the Tweedie instance + Tweedie.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.Power + Tweedie.link_power : float + The power of the link function, or 0 if its a log link. + Tweedie.var_power : float + The power of the variance function. + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + Logliklihood function not implemented because of the complexity of + calculating an infinite series of summations. The variance power can be + estimated using the `estimate_tweedie_power` function that is part of the + `GLM` class. + """ + links = [L.log, L.Power] + variance = V.Power + safe_links = [L.log, L.Power] + + def __init__(self, link=None, var_power=1., link_power=0): + self.var_power = var_power + self.link_power = link_power + self.variance = V.Power(power=var_power * 1.) + if link_power != 0 and not ((link is L.Power) or (link is None)): + msg = 'link_power of {} not supported specified link' + msg = msg.format(link_power) + raise ValueError(msg) + if (link_power == 0) and ((link is None) or (link is L.Log)): + self.link = L.log() + elif link_power != 0: + self.link = L.Power(power=link_power * 1.) + else: + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, 0, np.inf) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Returns the value of the deviance function. + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + When :math:`p = 1`, + + .. math:: + + resid\_dev_i = \mu + + when :math:`endog = 0` and + + .. math:: + + resid\_dev_i = endog * \log(endog / \mu) + (\mu - endog) + + otherwise. + + When :math:`p = 2`, + + .. math:: + + resid\_dev_i = (endog - \mu) / \mu - \log(endog / \mu) + + For all other p, + + .. math:: + + resid\_dev_i = endog ^{2 - p} / ((1 - p) * (2 - p)) - + endog * \mu ^{1 - p} / (1 - p) + \mu ^{2 - p} / + (2 - p) + + Once :math:`resid\_dev_i` is calculated, then calculate deviance as + + .. math:: + + D = \sum{2 * freq\_weights * resid\_dev_i} + """ + p = self.var_power + if p == 1: + dev = np.where(endog == 0, + mu, + endog * np.log(endog / mu) + (mu - endog)) + elif p == 2: + endog1 = np.clip(endog, FLOAT_EPS, np.inf) + dev = ((endog - mu) / mu) - np.log(endog1 / mu) + else: + dev = (endog ** (2 - p) / ((1 - p) * (2 - p)) - + endog * mu ** (1-p) / (1 - p) + mu ** (2 - p) / (2 - p)) + return np.sum(2 * freq_weights * dev) + + def resid_dev(self, endog, mu, scale=1.): + r""" + Tweedie Deviance Residual + + Parameters + ---------- + endog : array-like + `endog` is the response variable + mu : array-like + `mu` is the fitted value of the model + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + -------- + resid_dev : array + The array of deviance residuals + + Notes + ----- + When :math:`p = 1`, + + .. math:: + + resid\_dev_i = \mu + + when :math:`endog = 0` and + + .. math:: + + resid\_dev_i = endog * \log(endog / \mu) + (\mu - endog) + + otherwise. + + When :math:`p = 2`, + + .. math:: + + resid\_dev_i = (endog - \mu) / \mu - \log(endog / \mu) + + For all other p, + + .. math:: + + resid\_dev_i = endog ^{2 - p} / ((1 - p) * (2 - p)) - + endog * \mu ^{1 - p} / (1 - p) + \mu ^{2 - p} / + (2 - p) + """ + p = self.var_power + if p == 1: + dev = np.where(endog == 0, + mu, + endog * np.log(endog / mu) + (mu - endog)) + elif p == 2: + endog1 = np.clip(endog, FLOAT_EPS, np.inf) + dev = ((endog - mu) / mu) - np.log(endog1 / mu) + else: + dev = (endog ** (2 - p) / ((1 - p) * (2 - p)) - + endog * mu ** (1-p) / (1 - p) + mu ** (2 - p) / (2 - p)) + return np.sign(endog - mu) * np.sqrt(2 * dev) + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + The fitted mean response values + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float + The scale parameter. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + This is not implemented because of the complexity of calculating an + infinite series of sums. + """ + return np.nan + + def resid_anscombe(self, endog, mu): + """ + The Anscombe residuals for the Tweedie family + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals as defined below. + + Notes + ----- + When :math:`p = 3`, then + + .. math:: + + resid\_anscombe_i = (\log(endog) - \log(\mu)) / \sqrt{mu} + + Otherwise, + + .. math:: + + c = (3 - p) / 3 + + .. math:: + + resid\_anscombe_i = (1 / c) * (endog ^ c - \mu ^ c) / \mu ^{p / 6} + """ + if self.var_power == 3: + return (np.log(endog) - np.log(mu)) / np.sqrt(mu) + else: + c = (3. - self.var_power) / 3. + return ((1. / c) * (endog ** c - mu ** c) / + mu ** (self.var_power / 6.)) diff --git a/src/py/crankshaft/crankshaft/regression/glm/glm.py b/src/py/crankshaft/crankshaft/regression/glm/glm.py new file mode 100644 index 0000000..f2fc17d --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/glm.py @@ -0,0 +1,326 @@ + +import numpy as np +import numpy.linalg as la +from pysal.spreg.utils import RegressionPropsY, spdot +import pysal.spreg.user_output as USER +from utils import cache_readonly +from base import LikelihoodModelResults +import family +from iwls import iwls + +__all__ = ['GLM'] + +class GLM(RegressionPropsY): + """ + Generalised linear models. Can currently estimate Guassian, Poisson and + Logisitc regression coefficients. GLM object prepares model input and fit + method performs estimation which then returns a GLMResults object. + + Parameters + ---------- + y : array + n*1, dependent variable. + X : array + n*k, independent variable, exlcuding the constant. + family : string + Model type: 'Gaussian', 'Poisson', 'Binomial' + + Attributes + ---------- + y : array + n*1, dependent variable. + X : array + n*k, independent variable, including constant. + family : string + Model type: 'Gaussian', 'Poisson', 'logistic' + n : integer + Number of observations + k : integer + Number of independent variables + df_model : float + k-1, where k is the number of variables (including + intercept) + df_residual : float + observations minus variables (n-k) + mean_y : float + Mean of y + std_y : float + Standard deviation of y + fit_params : dict + Parameters passed into fit method to define estimation + routine. + normalized_cov_params : array + k*k, approximates [X.T*X]-1 + """ + def __init__(self, y, X, family=family.Gaussian(), constant=True): + """ + Initialize class + """ + self.n = USER.check_arrays(y, X) + USER.check_y(y, self.n) + self.y = y + if constant: + self.X = USER.check_constant(X) + else: + self.X = X + self.family = family + self.k = self.X.shape[1] + self.fit_params = {} + + def fit(self, ini_betas=None, tol=1.0e-6, max_iter=200, solve='iwls'): + """ + Method that fits a model with a particular estimation routine. + + Parameters + ---------- + + ini_betas : array + k*1, initial coefficient values, including constant. + Default is None, which calculates initial values during + estimation. + tol: float + Tolerence for estimation convergence. + max_iter : integer + Maximum number of iterations if convergence not + achieved. + solve :string + Technique to solve MLE equations. + 'iwls' = iteratively (re)weighted least squares (default) + """ + self.fit_params['ini_betas'] = ini_betas + self.fit_params['tol'] = tol + self.fit_params['max_iter'] = max_iter + self.fit_params['solve']=solve + if solve.lower() == 'iwls': + params, predy, w, n_iter = iwls(self.y, self.X, self.family, + ini_betas=ini_betas, tol=tol, max_iter=max_iter) + self.fit_params['n_iter'] = n_iter + return GLMResults(self, params.flatten(), predy, w) + + @cache_readonly + def df_model(self): + return self.X.shape[1] - 1 + + @cache_readonly + def df_resid(self): + return self.n - self.df_model - 1 + +class GLMResults(LikelihoodModelResults): + """ + Results of estimated GLM and diagnostics. + + Parameters + ---------- + model : GLM object + Pointer to GLM object with estimation parameters. + params : array + k*1, estimared coefficients + mu : array + n*1, predicted y values. + w : array + n*1, final weight used for iwls + + Attributes + ---------- + model : GLM Object + Points to GLM object for which parameters have been + estimated. + y : array + n*1, dependent variable. + x : array + n*k, independent variable, including constant. + family : string + Model type: 'Gaussian', 'Poisson', 'Logistic' + n : integer + Number of observations + k : integer + Number of independent variables + df_model : float + k-1, where k is the number of variables (including + intercept) + df_residual : float + observations minus variables (n-k) + fit_params : dict + parameters passed into fit method to define estimation + routine. + scale : float + sigma squared used for subsequent computations. + params : array + n*k, estimared beta coefficients + w : array + n*1, final weight values of x + mu : array + n*1, predicted value of y (i.e., fittedvalues) + cov_params : array + Variance covariance matrix (kxk) of betas which has been + appropriately scaled by sigma-squared + bse : array + k*1, standard errors of betas + pvalues : array + k*1, two-tailed pvalues of parameters + tvalues : array + k*1, the tvalues of the standard errors + null : array + n*1, predicted values of y for null model + deviance : float + value of the deviance function evalued at params; + see family.py for distribution-specific deviance + null_deviance : float + value of the deviance function for the model fit with + a constant as the only regressor + llf : float + value of the loglikelihood function evalued at params; + see family.py for distribution-specific loglikelihoods + llnull : float + value of log-likelihood function evaluated at null + aic : float + AIC + bic : float + BIC + D2 : float + percent deviance explained + adj_D2 : float + adjusted percent deviance explained + pseudo_R2 : float + McFadden's pseudo R2 (coefficient of determination) + adj_pseudoR2 : float + adjusted McFadden's pseudo R2 + resid_response : array + response residuals; defined as y-mu + resid_pearson : array + Pearson residuals; defined as (y-mu)/sqrt(VAR(mu)) + where VAR is the distribution specific variance + function; see family.py and varfuncs.py for more information. + resid_working : array + Working residuals; the working residuals are defined as + resid_response/link'(mu); see links.py for the + derivatives of the link functions. + + resid_anscombe : array + Anscombe residuals; see family.py for + distribution-specific Anscombe residuals. + + resid_deviance : array + deviance residuals; see family.py for + distribution-specific deviance residuals. + + pearson_chi2 : float + chi-Squared statistic is defined as the sum + of the squares of the Pearson residuals + + normalized_cov_params : array + k*k, approximates [X.T*X]-1 + """ + def __init__(self, model, params, mu, w): + self.model = model + self.n = model.n + self.y = model.y.T.flatten() + self.X = model.X + self.k = model.k + self.family = model.family + self.fit_params = model.fit_params + self.params = params + self.w = w + self.mu = mu.flatten() + self._cache = {} + + @cache_readonly + def df_model(self): + return self.model.df_model + + @cache_readonly + def df_resid(self): + return self.model.df_resid + + @cache_readonly + def normalized_cov_params(self): + return la.inv(spdot(self.w.T, self.w)) + + @cache_readonly + def resid_response(self): + return (self.y-self.mu) + + @cache_readonly + def resid_pearson(self): + return ((self.y-self.mu) / + np.sqrt(self.family.variance(self.mu))) + + @cache_readonly + def resid_working(self): + return (self.resid_response / self.family.link.deriv(self.mu)) + + @cache_readonly + def resid_anscombe(self): + return (self.family.resid_anscombe(self.y, self.mu)) + + @cache_readonly + def resid_deviance(self): + return (self.family.resid_dev(self.y, self.mu)) + + @cache_readonly + def pearson_chi2(self): + chisq = (self.y - self.mu)**2 / self.family.variance(self.mu) + chisqsum = np.sum(chisq) + return chisqsum + + @cache_readonly + def null(self): + y = np.reshape(self.y, (-1,1)) + model = self.model + X = np.ones((len(y), 1)) + null_mod = GLM(y, X, family=self.family, constant=False) + return null_mod.fit().mu + + @cache_readonly + def scale(self): + if isinstance(self.family, (family.Binomial, family.Poisson)): + return 1. + else: + return (((np.power(self.resid_response, 2) / + self.family.variance(self.mu))).sum() / + (self.df_resid)) + @cache_readonly + def deviance(self): + return self.family.deviance(self.y, self.mu) + + @cache_readonly + def null_deviance(self): + return self.family.deviance(self.y, self.null) + + @cache_readonly + def llnull(self): + return self.family.loglike(self.y, self.null, scale=self.scale) + + @cache_readonly + def llf(self): + return self.family.loglike(self.y, self.mu, scale=self.scale) + + @cache_readonly + def aic(self): + if isinstance(self.family, family.QuasiPoisson): + return np.nan + else: + return -2 * self.llf + 2*(self.df_model+1) + + @cache_readonly + def bic(self): + return (self.deviance - + (self.model.n - self.df_model - 1) * + np.log(self.model.n)) + + @cache_readonly + def D2(self): + return 1 - (self.deviance / self.null_deviance) + + @cache_readonly + def adj_D2(self): + return 1.0 - (float(self.n) - 1.0)/(float(self.n) - float(self.k)) * (1.0-self.D2) + + @cache_readonly + def pseudoR2(self): + return 1 - (self.llf/self.llnull) + + @cache_readonly + def adj_pseudoR2(self): + return 1 - ((self.llf-self.k)/self.llnull) + diff --git a/src/py/crankshaft/crankshaft/regression/glm/iwls.py b/src/py/crankshaft/crankshaft/regression/glm/iwls.py new file mode 100644 index 0000000..3ea6747 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/iwls.py @@ -0,0 +1,84 @@ +import numpy as np +import numpy.linalg as la +from scipy import sparse as sp +from scipy.sparse import linalg as spla +from pysal.spreg.utils import spdot, spmultiply +from family import Binomial, Poisson + +def _compute_betas(y, x): + """ + compute MLE coefficients using iwls routine + + Methods: p189, Iteratively (Re)weighted Least Squares (IWLS), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + """ + xT = x.T + xtx = spdot(xT, x) + xtx_inv = la.inv(xtx) + xtx_inv = sp.csr_matrix(xtx_inv) + xTy = spdot(xT, y, array_out=False) + betas = spdot(xtx_inv, xTy) + return betas + +def _compute_betas_gwr(y, x, wi): + """ + compute MLE coefficients using iwls routine + + Methods: p189, Iteratively (Re)weighted Least Squares (IWLS), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + """ + xT = (x * wi).T + xtx = np.dot(xT, x) + xtx_inv = la.inv(xtx) + xtx_inv_xt = np.dot(xtx_inv, xT) + betas = np.dot(xtx_inv_xt, y) + return betas, xtx_inv_xt + +def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=None): + """ + Iteratively re-weighted least squares estimation routine + """ + n_iter = 0 + diff = 1.0e6 + if ini_betas is None: + betas = np.zeros((x.shape[1], 1), np.float) + else: + betas = ini_betas + if isinstance(family, Binomial): + y = family.link._clean(y) + if isinstance(family, Poisson): + y_off = y/offset + y_off = family.starting_mu(y_off) + v = family.predict(y_off) + mu = family.starting_mu(y) + else: + mu = family.starting_mu(y) + v = family.predict(mu) + + while diff > tol and n_iter < max_iter: + n_iter += 1 + w = family.weights(mu) + z = v + (family.link.deriv(mu)*(y-mu)) + w = np.sqrt(w) + if type(x) != np.ndarray: + w = sp.csr_matrix(w) + z = sp.csr_matrix(z) + wx = spmultiply(x, w, array_out=False) + wz = spmultiply(z, w, array_out=False) + if wi is None: + n_betas = _compute_betas(wz, wx) + else: + n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi) + v = spdot(x, n_betas) + mu = family.fitted(v) + if isinstance(family, Poisson): + mu = mu * offset + diff = min(abs(n_betas-betas)) + betas = n_betas + + if wi is None: + return betas, mu, wx, n_iter + else: + return betas, mu, v, w, z, xtx_inv_xt, n_iter diff --git a/src/py/crankshaft/crankshaft/regression/glm/links.py b/src/py/crankshaft/crankshaft/regression/glm/links.py new file mode 100644 index 0000000..f45724d --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/links.py @@ -0,0 +1,953 @@ +''' +Defines the link functions to be used with GLM and GEE families. +''' + +import numpy as np +import scipy.stats +FLOAT_EPS = np.finfo(float).eps + + +class Link(object): + """ + A generic link function for one-parameter exponential family. + + `Link` does nothing, but lays out the methods expected of any subclass. + """ + + def __call__(self, p): + """ + Return the value of the link function. This is just a placeholder. + + Parameters + ---------- + p : array-like + Probabilities + + Returns + ------- + g(p) : array-like + The value of the link function g(p) = z + """ + return NotImplementedError + + def inverse(self, z): + """ + Inverse of the link function. Just a placeholder. + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor of the transformed variable + in the IRLS algorithm for GLM. + + Returns + ------- + g^(-1)(z) : array + The value of the inverse of the link function g^(-1)(z) = p + + + """ + return NotImplementedError + + def deriv(self, p): + """ + Derivative of the link function g'(p). Just a placeholder. + + Parameters + ---------- + p : array-like + + Returns + ------- + g'(p) : array + The value of the derivative of the link function g'(p) + """ + return NotImplementedError + + def deriv2(self, p): + """Second derivative of the link function g''(p) + + implemented through numerical differentiation + """ + from statsmodels.tools.numdiff import approx_fprime_cs + # TODO: workaround proplem with numdiff for 1d + return np.diag(approx_fprime_cs(p, self.deriv)) + + def inverse_deriv(self, z): + """ + Derivative of the inverse link function g^(-1)(z). + + Notes + ----- + This reference implementation gives the correct result but is + inefficient, so it can be overriden in subclasses. + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor for a GLM or GEE model. + + Returns + ------- + g'^(-1)(z) : array + The value of the derivative of the inverse of the link function + + """ + return 1 / self.deriv(self.inverse(z)) + + +class Logit(Link): + """ + The logit transform + + Notes + ----- + call and derivative use a private method _clean to make trim p by + machine epsilon so that p is in (0,1) + + Alias of Logit: + logit = Logit() + """ + + def _clean(self, p): + """ + Clip logistic values to range (eps, 1-eps) + + Parameters + ----------- + p : array-like + Probabilities + + Returns + -------- + pclip : array + Clipped probabilities + """ + return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS) + + def __call__(self, p): + """ + The logit transform + + Parameters + ---------- + p : array-like + Probabilities + + Returns + ------- + z : array + Logit transform of `p` + + Notes + ----- + g(p) = log(p / (1 - p)) + """ + p = self._clean(p) + return np.log(p / (1. - p)) + + def inverse(self, z): + """ + Inverse of the logit transform + + Parameters + ---------- + z : array-like + The value of the logit transform at `p` + + Returns + ------- + p : array + Probabilities + + Notes + ----- + g^(-1)(z) = exp(z)/(1+exp(z)) + """ + z = np.asarray(z) + t = np.exp(-z) + return 1. / (1. + t) + + def deriv(self, p): + + """ + Derivative of the logit transform + + Parameters + ---------- + p: array-like + Probabilities + + Returns + ------- + g'(p) : array + Value of the derivative of logit transform at `p` + + Notes + ----- + g'(p) = 1 / (p * (1 - p)) + + Alias for `Logit`: + logit = Logit() + """ + p = self._clean(p) + return 1. / (p * (1 - p)) + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the logit transform + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor for a GLM or GEE model. + + Returns + ------- + g'^(-1)(z) : array + The value of the derivative of the inverse of the logit function + + """ + t = np.exp(z) + return t/(1 + t)**2 + + + def deriv2(self, p): + """ + Second derivative of the logit function. + + Parameters + ---------- + p : array-like + probabilities + + Returns + ------- + g''(z) : array + The value of the second derivative of the logit function + """ + v = p * (1 - p) + return (2*p - 1) / v**2 + +class logit(Logit): + pass + + +class Power(Link): + """ + The power transform + + Parameters + ---------- + power : float + The exponent of the power transform + + Notes + ----- + Aliases of Power: + inverse = Power(power=-1) + sqrt = Power(power=.5) + inverse_squared = Power(power=-2.) + identity = Power(power=1.) + """ + + def __init__(self, power=1.): + self.power = power + + def __call__(self, p): + """ + Power transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + z : array-like + Power transform of x + + Notes + ----- + g(p) = x**self.power + """ + + z = np.power(p, self.power) + return z + + def inverse(self, z): + """ + Inverse of the power transform link function + + Parameters + ---------- + `z` : array-like + Value of the transformed mean parameters at `p` + + Returns + ------- + `p` : array + Mean parameters + + Notes + ----- + g^(-1)(z`) = `z`**(1/`power`) + """ + + p = np.power(z, 1. / self.power) + return p + + def deriv(self, p): + """ + Derivative of the power transform + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + -------- + g'(p) : array + Derivative of power transform of `p` + + Notes + ----- + g'(`p`) = `power` * `p`**(`power` - 1) + """ + return self.power * np.power(p, self.power - 1) + + def deriv2(self, p): + """ + Second derivative of the power transform + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + -------- + g''(p) : array + Second derivative of the power transform of `p` + + Notes + ----- + g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2) + """ + return self.power * (self.power - 1) * np.power(p, self.power - 2) + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the power transform + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor for a GLM or GEE model. + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the power transform + function + """ + return np.power(z, (1 - self.power)/self.power) / self.power + + +class inverse_power(Power): + """ + The inverse transform + + Notes + ----- + g(p) = 1/p + + Alias of statsmodels.family.links.Power(power=-1.) + """ + def __init__(self): + super(inverse_power, self).__init__(power=-1.) + + +class sqrt(Power): + """ + The square-root transform + + Notes + ----- + g(`p`) = sqrt(`p`) + + Alias of statsmodels.family.links.Power(power=.5) + """ + def __init__(self): + super(sqrt, self).__init__(power=.5) + + +class inverse_squared(Power): + """ + The inverse squared transform + + Notes + ----- + g(`p`) = 1/(`p`\ \*\*2) + + Alias of statsmodels.family.links.Power(power=2.) + """ + def __init__(self): + super(inverse_squared, self).__init__(power=-2.) + + +class identity(Power): + """ + The identity transform + + Notes + ----- + g(`p`) = `p` + + Alias of statsmodels.family.links.Power(power=1.) + """ + def __init__(self): + super(identity, self).__init__(power=1.) + + +class Log(Link): + """ + The log transform + + Notes + ----- + call and derivative call a private method _clean to trim the data by + machine epsilon so that p is in (0,1). log is an alias of Log. + """ + + def _clean(self, x): + return np.clip(x, FLOAT_EPS, np.inf) + + def __call__(self, p, **extra): + """ + Log transform link function + + Parameters + ---------- + x : array-like + Mean parameters + + Returns + ------- + z : array + log(x) + + Notes + ----- + g(p) = log(p) + """ + x = self._clean(p) + return np.log(x) + + def inverse(self, z): + """ + Inverse of log transform link function + + Parameters + ---------- + z : array + The inverse of the link function at `p` + + Returns + ------- + p : array + The mean probabilities given the value of the inverse `z` + + Notes + ----- + g^{-1}(z) = exp(z) + """ + return np.exp(z) + + def deriv(self, p): + """ + Derivative of log transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g'(p) : array + derivative of log transform of x + + Notes + ----- + g'(x) = 1/x + """ + p = self._clean(p) + return 1. / p + + def deriv2(self, p): + """ + Second derivative of the log transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g''(p) : array + Second derivative of log transform of x + + Notes + ----- + g''(x) = -1/x^2 + """ + p = self._clean(p) + return -1. / p**2 + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the log transform link function + + Parameters + ---------- + z : array + The inverse of the link function at `p` + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the log function, + the exponential function + """ + return np.exp(z) + + +class log(Log): + """ + The log transform + + Notes + ----- + log is a an alias of Log. + """ + pass + + +# TODO: the CDFLink is untested +class CDFLink(Logit): + """ + The use the CDF of a scipy.stats distribution + + CDFLink is a subclass of logit in order to use its _clean method + for the link and its derivative. + + Parameters + ---------- + dbn : scipy.stats distribution + Default is dbn=scipy.stats.norm + + Notes + ----- + The CDF link is untested. + """ + + def __init__(self, dbn=scipy.stats.norm): + self.dbn = dbn + + def __call__(self, p): + """ + CDF link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + z : array + (ppf) inverse of CDF transform of p + + Notes + ----- + g(`p`) = `dbn`.ppf(`p`) + """ + p = self._clean(p) + return self.dbn.ppf(p) + + def inverse(self, z): + """ + The inverse of the CDF link + + Parameters + ---------- + z : array-like + The value of the inverse of the link function at `p` + + Returns + ------- + p : array + Mean probabilities. The value of the inverse of CDF link of `z` + + Notes + ----- + g^(-1)(`z`) = `dbn`.cdf(`z`) + """ + return self.dbn.cdf(z) + + def deriv(self, p): + """ + Derivative of CDF link + + Parameters + ---------- + p : array-like + mean parameters + + Returns + ------- + g'(p) : array + The derivative of CDF transform at `p` + + Notes + ----- + g'(`p`) = 1./ `dbn`.pdf(`dbn`.ppf(`p`)) + """ + p = self._clean(p) + return 1. / self.dbn.pdf(self.dbn.ppf(p)) + + def deriv2(self, p): + """ + Second derivative of the link function g''(p) + + implemented through numerical differentiation + """ + from statsmodels.tools.numdiff import approx_fprime + p = np.atleast_1d(p) + # Note: special function for norm.ppf does not support complex + return np.diag(approx_fprime(p, self.deriv, centered=True)) + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the CDF transformation link function + + Parameters + ---------- + z : array + The inverse of the link function at `p` + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the logit function + """ + return 1/self.deriv(self.inverse(z)) + + +class probit(CDFLink): + """ + The probit (standard normal CDF) transform + + Notes + -------- + g(p) = scipy.stats.norm.ppf(p) + + probit is an alias of CDFLink. + """ + pass + + +class cauchy(CDFLink): + """ + The Cauchy (standard Cauchy CDF) transform + + Notes + ----- + g(p) = scipy.stats.cauchy.ppf(p) + + cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy + """ + + def __init__(self): + super(cauchy, self).__init__(dbn=scipy.stats.cauchy) + + def deriv2(self, p): + """ + Second derivative of the Cauchy link function. + + Parameters + ---------- + p: array-like + Probabilities + + Returns + ------- + g''(p) : array + Value of the second derivative of Cauchy link function at `p` + """ + a = np.pi * (p - 0.5) + d2 = 2 * np.pi**2 * np.sin(a) / np.cos(a)**3 + return d2 + +class CLogLog(Logit): + """ + The complementary log-log transform + + CLogLog inherits from Logit in order to have access to its _clean method + for the link and its derivative. + + Notes + ----- + CLogLog is untested. + """ + def __call__(self, p): + """ + C-Log-Log transform link function + + Parameters + ---------- + p : array + Mean parameters + + Returns + ------- + z : array + The CLogLog transform of `p` + + Notes + ----- + g(p) = log(-log(1-p)) + """ + p = self._clean(p) + return np.log(-np.log(1 - p)) + + def inverse(self, z): + """ + Inverse of C-Log-Log transform link function + + + Parameters + ---------- + z : array-like + The value of the inverse of the CLogLog link function at `p` + + Returns + ------- + p : array + Mean parameters + + Notes + ----- + g^(-1)(`z`) = 1-exp(-exp(`z`)) + """ + return 1 - np.exp(-np.exp(z)) + + def deriv(self, p): + """ + Derivative of C-Log-Log transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g'(p) : array + The derivative of the CLogLog transform link function + + Notes + ----- + g'(p) = - 1 / ((p-1)*log(1-p)) + """ + p = self._clean(p) + return 1. / ((p - 1) * (np.log(1 - p))) + + def deriv2(self, p): + """ + Second derivative of the C-Log-Log ink function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g''(p) : array + The second derivative of the CLogLog link function + """ + p = self._clean(p) + fl = np.log(1 - p) + d2 = -1 / ((1 - p)**2 * fl) + d2 *= 1 + 1 / fl + return d2 + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the C-Log-Log transform link function + + Parameters + ---------- + z : array-like + The value of the inverse of the CLogLog link function at `p` + + Returns + ------- + g^(-1)'(z) : array + The derivative of the inverse of the CLogLog link function + """ + return np.exp(z - np.exp(z)) + + +class cloglog(CLogLog): + """ + The CLogLog transform link function. + + Notes + ----- + g(`p`) = log(-log(1-`p`)) + + cloglog is an alias for CLogLog + cloglog = CLogLog() + """ + pass + + +class NegativeBinomial(object): + ''' + The negative binomial link function + + Parameters + ---------- + alpha : float, optional + Alpha is the ancillary parameter of the Negative Binomial link + function. It is assumed to be nonstochastic. The default value is 1. + Permissible values are usually assumed to be in (.01, 2). + ''' + + def __init__(self, alpha=1.): + self.alpha = alpha + + def _clean(self, x): + return np.clip(x, FLOAT_EPS, np.inf) + + def __call__(self, p): + ''' + Negative Binomial transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + z : array + The negative binomial transform of `p` + + Notes + ----- + g(p) = log(p/(p + 1/alpha)) + ''' + p = self._clean(p) + return np.log(p/(p + 1/self.alpha)) + + def inverse(self, z): + ''' + Inverse of the negative binomial transform + + Parameters + ----------- + z : array-like + The value of the inverse of the negative binomial link at `p`. + + Returns + ------- + p : array + Mean parameters + + Notes + ----- + g^(-1)(z) = exp(z)/(alpha*(1-exp(z))) + ''' + return -1/(self.alpha * (1 - np.exp(-z))) + + def deriv(self, p): + ''' + Derivative of the negative binomial transform + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g'(p) : array + The derivative of the negative binomial transform link function + + Notes + ----- + g'(x) = 1/(x+alpha*x^2) + ''' + return 1/(p + self.alpha * p**2) + + def deriv2(self,p): + ''' + Second derivative of the negative binomial link function. + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g''(p) : array + The second derivative of the negative binomial transform link + function + + Notes + ----- + g''(x) = -(1+2*alpha*x)/(x+alpha*x^2)^2 + ''' + numer = -(1 + 2 * self.alpha * p) + denom = (p + self.alpha * p**2)**2 + return numer / denom + + def inverse_deriv(self, z): + ''' + Derivative of the inverse of the negative binomial transform + + Parameters + ----------- + z : array-like + Usually the linear predictor for a GLM or GEE model + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the negative + binomial link + ''' + t = np.exp(z) + return t / (self.alpha * (1-t)**2) + + +class nbinom(NegativeBinomial): + """ + The negative binomial link function. + + Notes + ----- + g(p) = log(p/(p + 1/alpha)) + + nbinom is an alias of NegativeBinomial. + nbinom = NegativeBinomial(alpha=1.) + """ + pass diff --git a/src/py/crankshaft/crankshaft/regression/glm/tests/test_glm.py b/src/py/crankshaft/crankshaft/regression/glm/tests/test_glm.py new file mode 100644 index 0000000..b86ad6a --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/tests/test_glm.py @@ -0,0 +1,993 @@ +""" +Tests for generalized linear models. Majority of code either directly borrowed +or closely adapted from statsmodels package. Model results verfiied using glm +function in R and GLM function in statsmodels. +""" + +__author__ = 'Taylor Oshan tayoshan@gmail.com' + +from pysal.contrib.glm.glm import GLM +from pysal.contrib.glm.family import Gaussian, Poisson, Binomial, QuasiPoisson +import numpy as np +import pysal +import unittest +import math + + +class TestGaussian(unittest.TestCase): + """ + Tests for Poisson GLM + """ + + def setUp(self): + db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r') + y = np.array(db.by_col("HOVAL")) + self.y = np.reshape(y, (49,1)) + X = [] + X.append(db.by_col("INC")) + X.append(db.by_col("CRIME")) + self.X = np.array(X).T + + def testIWLS(self): + model = GLM(self.y, self.X, family=Gaussian()) + results = model.fit() + self.assertEqual(results.n, 49) + self.assertEqual(results.df_model, 2) + self.assertEqual(results.df_resid, 46) + self.assertEqual(results.aic, 408.73548964604873) + self.assertEqual(results.bic, 10467.991340493107) + self.assertEqual(results.deviance, 10647.015074206196) + self.assertEqual(results.llf, -201.36774482302437) + self.assertEqual(results.null_deviance, 16367.794631703124) + self.assertEqual(results.scale, 231.45684943926514) + np.testing.assert_allclose(results.params, [ 46.42818268, 0.62898397, + -0.48488854]) + np.testing.assert_allclose(results.bse, [ 13.19175703, 0.53591045, + 0.18267291]) + np.testing.assert_allclose(results.cov_params(), + [[ 1.74022453e+02, -6.52060364e+00, -2.15109867e+00], + [ -6.52060364e+00, 2.87200008e-01, 6.80956787e-02], + [ -2.15109867e+00, 6.80956787e-02, 3.33693910e-02]]) + np.testing.assert_allclose(results.tvalues, [ 3.51948437, 1.17367365, + -2.65440864]) + np.testing.assert_allclose(results.pvalues, [ 0.00043239, 0.24052577, + 0.00794475], atol=1.0e-8) + np.testing.assert_allclose(results.conf_int(), + [[ 20.57281401, 72.28355135], + [ -0.42138121, 1.67934915], + [ -0.84292086, -0.12685622]]) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 7.51857004e-01, -2.81720055e-02, -9.29373521e-03], + [ -2.81720055e-02, 1.24083607e-03, 2.94204638e-04], + [ -9.29373521e-03, 2.94204638e-04, 1.44171110e-04]]) + np.testing.assert_allclose(results.mu, + [ 51.08752105, 50.66601521, 41.61367567, 33.53969014, + 28.90638232, 43.87074227, 51.64910882, 34.92671563, + 42.69267622, 38.49449134, 20.92815471, 25.25228436, + 29.78223486, 25.02403635, 29.07959539, 24.63352275, + 34.71372149, 33.40443052, 27.29864225, 65.86219802, + 33.69854751, 37.44976435, 50.01304928, 36.81219959, + 22.02674837, 31.64775955, 27.63563294, 23.7697291 , + 22.43119725, 21.76987089, 48.51169321, 49.05891819, + 32.31656426, 44.20550354, 35.49244888, 51.27811308, + 36.55047181, 27.37048914, 48.78812922, 57.31744163, + 51.22914162, 54.70515578, 37.06622277, 44.5075759 , + 41.24328983, 49.93821824, 44.85644299, 40.93838609, 47.32045464]) + self.assertEqual(results.pearson_chi2, 10647.015074206196) + np.testing.assert_allclose(results.resid_response, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_working, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_pearson, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_anscombe, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_deviance, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.null, + [ 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, 38.43622447]) + self.assertAlmostEqual(results.D2, .349514377851) + self.assertAlmostEqual(results.adj_D2, 0.32123239427957673) + +class TestPoisson(unittest.TestCase): + + def setUp(self): + db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r') + y = np.array(db.by_col("HOVAL")) + y = np.reshape(y, (49,1)) + self.y = np.round(y).astype(int) + X = [] + X.append(db.by_col("INC")) + X.append(db.by_col("CRIME")) + self.X = np.array(X).T + + def testIWLS(self): + model = GLM(self.y, self.X, family=Poisson()) + results = model.fit() + self.assertEqual(results.n, 49) + self.assertEqual(results.df_model, 2) + self.assertEqual(results.df_resid, 46) + self.assertAlmostEqual(results.aic, 500.85184179938756) + self.assertAlmostEqual(results.bic, 51.436404535087661) + self.assertAlmostEqual(results.deviance, 230.46013824817649) + self.assertAlmostEqual(results.llf, -247.42592089969378) + self.assertAlmostEqual(results.null_deviance, 376.97293610347361) + self.assertEqual(results.scale, 1.0) + np.testing.assert_allclose(results.params, [ 3.92159085, 0.01183491, + -0.01371397], atol=1.0e-8) + np.testing.assert_allclose(results.bse, [ 0.13049161, 0.00511599, + 0.00193769], atol=1.0e-8) + np.testing.assert_allclose(results.cov_params(), + [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04], + [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06], + [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]]) + np.testing.assert_allclose(results.tvalues, [ 30.0524361 , 2.31331634, + -7.07748998]) + np.testing.assert_allclose(results.pvalues, [ 2.02901657e-198, + 2.07052532e-002, 1.46788805e-012]) + np.testing.assert_allclose(results.conf_int(), + [[ 3.66583199e+00, 4.17734972e+00], + [ 1.80774841e-03, 2.18620753e-02], + [ -1.75117666e-02, -9.91616901e-03]]) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04], + [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06], + [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]]) + np.testing.assert_allclose(results.mu, + [ 51.26831574, 50.15022766, 40.06142973, 34.13799739, + 28.76119226, 42.6836241 , 55.64593703, 34.08277997, + 40.90389582, 37.19727958, 23.47459217, 26.12384057, + 29.78303507, 25.96888223, 29.14073823, 26.04369592, + 34.18996367, 32.28924005, 27.42284396, 72.69207879, + 33.05316347, 36.52276972, 49.2551479 , 35.33439632, + 24.07252457, 31.67153709, 27.81699478, 25.38021219, + 24.31759259, 23.13586161, 48.40724678, 48.57969818, + 31.92596006, 43.3679231 , 34.32925819, 51.78908089, + 34.49778584, 27.56236198, 48.34273194, 57.50829097, + 50.66038226, 54.68701352, 35.77103116, 43.21886784, + 40.07615759, 49.98658004, 43.13352883, 40.28520774, 46.28910294]) + self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221) + np.testing.assert_allclose(results.resid_response, + [ 28.73168426, -5.15022766, -14.06142973, -1.13799739, + -5.76119226, -13.6836241 , 19.35406297, 2.91722003, + 12.09610418, 58.80272042, -3.47459217, -6.12384057, + 12.21696493, 17.03111777, -11.14073823, -7.04369592, + 7.81003633, 27.71075995, 3.57715604, 8.30792121, + -13.05316347, -6.52276972, -1.2551479 , 17.66560368, + -6.07252457, -11.67153709, 6.18300522, -2.38021219, + 7.68240741, -1.13586161, -16.40724678, -8.57969818, + -7.92596006, -15.3679231 , -7.32925819, -15.78908089, + 8.50221416, -4.56236198, -8.34273194, 4.49170903, + -8.66038226, -10.68701352, -9.77103116, -9.21886784, + -12.07615759, 26.01341996, -1.13352883, -13.28520774, -10.28910294]) + np.testing.assert_allclose(results.resid_working, + [ 1473.02506034, -258.28508941, -563.32097891, -38.84895192, + -165.69875817, -584.06666725, 1076.97496919, 99.42696848, + 494.77778514, 2187.30123163, -81.56463405, -159.97823479, + 363.858295 , 442.27909165, -324.64933645, -183.44387481, + 267.02485844, 894.75938 , 98.09579187, 603.9200634 , + -431.44834594, -238.2296165 , -61.82249568, 624.20344168, + -146.18099686, -369.65551968, 171.99262399, -60.41029031, + 186.81765356, -26.27913713, -794.22964417, -416.79914795, + -253.04388425, -666.47490701, -251.6079969 , -817.70198717, + 293.30756327, -125.74947222, -403.31045369, 258.31051005, + -438.73827602, -584.440853 , -349.51985996, -398.42903071, + -483.96599444, 1300.32189904, -48.89309853, -535.19735391, + -476.27334527]) + np.testing.assert_allclose(results.resid_pearson, + [ 4.01269878, -0.72726045, -2.221602 , -0.19477008, -1.07425881, + -2.09445239, 2.59451042, 0.49969118, 1.89131202, 9.64143836, + -0.71714142, -1.19813392, 2.23861212, 3.34207756, -2.0637814 , + -1.3802231 , 1.33568403, 4.87662684, 0.68309584, 0.97442591, + -2.27043598, -1.07931992, -0.17884182, 2.97186889, -1.23768025, + -2.07392709, 1.1723155 , -0.47246327, 1.55789092, -0.23614708, + -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503, + -2.19400568, 1.44755952, -0.8690235 , -1.19989348, 0.59230634, + -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306, + 3.67934693, -0.17259375, -2.09312684, -1.51230062]) + np.testing.assert_allclose(results.resid_anscombe, + [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751, + -2.22611959, 2.46352013, 0.49282126, 1.80857757, 8.06444452, + -0.73610811, -1.25061371, 2.10820431, 3.05467547, -2.22437611, + -1.45136173, 1.28939698, 4.35942058, 0.66904552, 0.95674923, + -2.45438937, -1.11429881, -0.17961012, 2.76715848, -1.29658591, + -2.22816691, 1.13269136, -0.48017382, 1.48562248, -0.23812278, + -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484, + -2.32398309, 1.39380683, -0.89495368, -1.23735395, 0.58485202, + -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267, + 3.41729922, -0.17335867, -2.22921828, -1.57470549]) + np.testing.assert_allclose(results.resid_deviance, + [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765, + -2.22466106, 2.46246446, 0.4928057 , 1.80799655, 8.02696525, + -0.73602255, -1.25021555, 2.10699958, 3.05084608, -2.22214376, + -1.45072221, 1.28913747, 4.35106213, 0.6689982 , 0.95669662, + -2.45171913, -1.11410444, -0.17960956, 2.76494217, -1.29609865, + -2.22612429, 1.13247453, -0.48015254, 1.48508549, -0.23812 , + -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892, + -2.32263069, 1.39348459, -0.89482132, -1.23715363, 0.58483655, + -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949, + 3.41437156, -0.1733581 , -2.22765605, -1.57426046]) + np.testing.assert_allclose(results.null, + [ 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, 38.42857143]) + self.assertAlmostEqual(results.D2, .388656011675) + self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)#.375648692774) + + def testQuasi(self): + model = GLM(self.y, self.X, family=QuasiPoisson()) + results = model.fit() + self.assertEqual(results.n, 49) + self.assertEqual(results.df_model, 2) + self.assertEqual(results.df_resid, 46) + self.assertTrue(math.isnan(results.aic)) + self.assertAlmostEqual(results.bic, 51.436404535087661) + self.assertAlmostEqual(results.deviance, 230.46013824817649) + self.assertTrue(math.isnan(results.llf)) + self.assertAlmostEqual(results.null_deviance, 376.97293610347361) + self.assertAlmostEqual(results.scale, 5.7526658548022223) + np.testing.assert_allclose(results.params, [ 3.92159085, 0.01183491, + -0.01371397], atol=1.0e-8) + np.testing.assert_allclose(results.bse, [ 0.31298042, 0.01227057, + 0.00464749], atol=1.0e-8) + np.testing.assert_allclose(results.cov_params(), + [[ 9.79567451e-02, -3.55876238e-03, -1.27356524e-03], + [ -3.55876238e-03, 1.50566777e-04, 3.89741067e-05], + [ -1.27356524e-03, 3.89741067e-05, 2.15991606e-05]]) + np.testing.assert_allclose(results.tvalues, [ 12.52982796, 0.96449604, + -2.95083339]) + np.testing.assert_allclose(results.pvalues, [ 5.12737770e-36, + 3.34797291e-01, 3.16917819e-03]) + np.testing.assert_allclose(results.conf_int(), + [[ 3.3081605 , 4.53502121], + [-0.01221495, 0.03588478], + [-0.02282288, -0.00460506]], atol=1.0e-8) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04], + [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06], + [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]]) + np.testing.assert_allclose(results.mu, + [ 51.26831574, 50.15022766, 40.06142973, 34.13799739, + 28.76119226, 42.6836241 , 55.64593703, 34.08277997, + 40.90389582, 37.19727958, 23.47459217, 26.12384057, + 29.78303507, 25.96888223, 29.14073823, 26.04369592, + 34.18996367, 32.28924005, 27.42284396, 72.69207879, + 33.05316347, 36.52276972, 49.2551479 , 35.33439632, + 24.07252457, 31.67153709, 27.81699478, 25.38021219, + 24.31759259, 23.13586161, 48.40724678, 48.57969818, + 31.92596006, 43.3679231 , 34.32925819, 51.78908089, + 34.49778584, 27.56236198, 48.34273194, 57.50829097, + 50.66038226, 54.68701352, 35.77103116, 43.21886784, + 40.07615759, 49.98658004, 43.13352883, 40.28520774, 46.28910294]) + self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221) + np.testing.assert_allclose(results.resid_response, + [ 28.73168426, -5.15022766, -14.06142973, -1.13799739, + -5.76119226, -13.6836241 , 19.35406297, 2.91722003, + 12.09610418, 58.80272042, -3.47459217, -6.12384057, + 12.21696493, 17.03111777, -11.14073823, -7.04369592, + 7.81003633, 27.71075995, 3.57715604, 8.30792121, + -13.05316347, -6.52276972, -1.2551479 , 17.66560368, + -6.07252457, -11.67153709, 6.18300522, -2.38021219, + 7.68240741, -1.13586161, -16.40724678, -8.57969818, + -7.92596006, -15.3679231 , -7.32925819, -15.78908089, + 8.50221416, -4.56236198, -8.34273194, 4.49170903, + -8.66038226, -10.68701352, -9.77103116, -9.21886784, + -12.07615759, 26.01341996, -1.13352883, -13.28520774, -10.28910294]) + np.testing.assert_allclose(results.resid_working, + [ 1473.02506034, -258.28508941, -563.32097891, -38.84895192, + -165.69875817, -584.06666725, 1076.97496919, 99.42696848, + 494.77778514, 2187.30123163, -81.56463405, -159.97823479, + 363.858295 , 442.27909165, -324.64933645, -183.44387481, + 267.02485844, 894.75938 , 98.09579187, 603.9200634 , + -431.44834594, -238.2296165 , -61.82249568, 624.20344168, + -146.18099686, -369.65551968, 171.99262399, -60.41029031, + 186.81765356, -26.27913713, -794.22964417, -416.79914795, + -253.04388425, -666.47490701, -251.6079969 , -817.70198717, + 293.30756327, -125.74947222, -403.31045369, 258.31051005, + -438.73827602, -584.440853 , -349.51985996, -398.42903071, + -483.96599444, 1300.32189904, -48.89309853, -535.19735391, + -476.27334527]) + np.testing.assert_allclose(results.resid_pearson, + [ 4.01269878, -0.72726045, -2.221602 , -0.19477008, -1.07425881, + -2.09445239, 2.59451042, 0.49969118, 1.89131202, 9.64143836, + -0.71714142, -1.19813392, 2.23861212, 3.34207756, -2.0637814 , + -1.3802231 , 1.33568403, 4.87662684, 0.68309584, 0.97442591, + -2.27043598, -1.07931992, -0.17884182, 2.97186889, -1.23768025, + -2.07392709, 1.1723155 , -0.47246327, 1.55789092, -0.23614708, + -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503, + -2.19400568, 1.44755952, -0.8690235 , -1.19989348, 0.59230634, + -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306, + 3.67934693, -0.17259375, -2.09312684, -1.51230062]) + np.testing.assert_allclose(results.resid_anscombe, + [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751, + -2.22611959, 2.46352013, 0.49282126, 1.80857757, 8.06444452, + -0.73610811, -1.25061371, 2.10820431, 3.05467547, -2.22437611, + -1.45136173, 1.28939698, 4.35942058, 0.66904552, 0.95674923, + -2.45438937, -1.11429881, -0.17961012, 2.76715848, -1.29658591, + -2.22816691, 1.13269136, -0.48017382, 1.48562248, -0.23812278, + -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484, + -2.32398309, 1.39380683, -0.89495368, -1.23735395, 0.58485202, + -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267, + 3.41729922, -0.17335867, -2.22921828, -1.57470549]) + np.testing.assert_allclose(results.resid_deviance, + [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765, + -2.22466106, 2.46246446, 0.4928057 , 1.80799655, 8.02696525, + -0.73602255, -1.25021555, 2.10699958, 3.05084608, -2.22214376, + -1.45072221, 1.28913747, 4.35106213, 0.6689982 , 0.95669662, + -2.45171913, -1.11410444, -0.17960956, 2.76494217, -1.29609865, + -2.22612429, 1.13247453, -0.48015254, 1.48508549, -0.23812 , + -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892, + -2.32263069, 1.39348459, -0.89482132, -1.23715363, 0.58483655, + -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949, + 3.41437156, -0.1733581 , -2.22765605, -1.57426046]) + np.testing.assert_allclose(results.null, + [ 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, 38.42857143]) + self.assertAlmostEqual(results.D2, .388656011675) + self.assertAlmostEqual(results.adj_D2, 0.36207583826952761) + +class TestBinomial(unittest.TestCase): + + def setUp(self): + #London house price data + #y: 'BATH2' + y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 1, 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, 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, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 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, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, + 0, 1, 0, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 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]) + self.y = y.reshape((316,1)) + #X: 'FLOORSZ' + X = np.array([ 77, 75, 64, 95, 107, 100, 81, 151, 98, 260, 171, 161, 91, + 80, 50, 85, 52, 69, 60, 84, 155, 97, 69, 126, 90, 43, + 51, 41, 140, 80, 52, 86, 66, 60, 40, 155, 138, 97, 115, + 148, 206, 60, 53, 96, 88, 160, 31, 43, 154, 60, 131, 60, + 46, 61, 125, 150, 76, 92, 96, 100, 105, 72, 48, 41, 72, + 65, 60, 65, 98, 33, 144, 111, 91, 108, 38, 48, 95, 63, + 98, 129, 108, 51, 131, 66, 48, 127, 76, 68, 52, 64, 57, + 121, 67, 76, 112, 96, 90, 53, 93, 64, 97, 58, 44, 157, + 53, 70, 71, 167, 47, 70, 96, 77, 75, 71, 67, 47, 71, + 90, 69, 64, 65, 95, 60, 60, 65, 54, 121, 105, 50, 85, + 69, 69, 62, 65, 93, 93, 70, 62, 155, 68, 117, 80, 80, + 75, 98, 114, 86, 70, 50, 51, 163, 124, 59, 95, 51, 63, + 85, 53, 46, 102, 114, 83, 47, 40, 63, 123, 100, 63, 110, + 79, 98, 99, 120, 52, 48, 37, 81, 30, 88, 50, 35, 116, + 67, 45, 80, 86, 109, 59, 75, 60, 71, 141, 121, 50, 168, + 90, 51, 133, 75, 133, 127, 37, 68, 105, 61, 123, 151, 110, + 77, 220, 94, 77, 70, 100, 98, 126, 55, 105, 60, 176, 104, + 68, 62, 70, 48, 102, 80, 97, 66, 80, 102, 160, 55, 60, + 71, 125, 85, 85, 190, 137, 48, 41, 42, 51, 57, 60, 114, + 88, 84, 108, 66, 85, 42, 98, 90, 127, 100, 55, 76, 82, + 63, 80, 71, 76, 121, 109, 92, 160, 109, 185, 100, 90, 90, + 86, 88, 95, 116, 135, 61, 74, 60, 235, 76, 66, 100, 49, + 50, 37, 100, 88, 90, 52, 95, 81, 79, 96, 75, 91, 86, + 83, 180, 108, 80, 96, 49, 117, 117, 86, 46, 66, 95, 57, + 120, 137, 68, 240]) + self.X = X.reshape((316,1)) + + def testIWLS(self): + model = GLM(self.y, self.X, family=Binomial()) + results = model.fit() + self.assertEqual(results.n, 316) + self.assertEqual(results.df_model, 1) + self.assertEqual(results.df_resid, 314) + self.assertEqual(results.aic, 155.19347530342466) + self.assertEqual(results.bic, -1656.1095797628657) + self.assertEqual(results.deviance, 151.19347530342466) + self.assertEqual(results.llf, -75.596737651712331) + self.assertEqual(results.null_deviance, 189.16038985881212) + self.assertEqual(results.scale, 1.0) + np.testing.assert_allclose(results.params, [-5.33638276, 0.0287754 ]) + np.testing.assert_allclose(results.bse, [ 0.64499904, 0.00518312], + atol=1.0e-8) + np.testing.assert_allclose(results.cov_params(), + [[ 4.16023762e-01, -3.14338457e-03], + [ -3.14338457e-03, 2.68646833e-05]]) + np.testing.assert_allclose(results.tvalues, [-8.27347396, 5.55175826]) + np.testing.assert_allclose(results.pvalues, [ 1.30111233e-16, + 2.82810512e-08]) + np.testing.assert_allclose(results.conf_int(), + [[-6.60055765, -4.07220787], + [ 0.01861668, 0.03893412]], atol=1.0e-8) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 4.16023762e-01, -3.14338457e-03], + [ -3.14338457e-03, 2.68646833e-05]]) + np.testing.assert_allclose(results.mu, + [ 0.04226237, 0.03999333, 0.02946178, 0.0689636 , 0.09471181, + 0.07879431, 0.04717464, 0.27065598, 0.07471691, 0.89522144, + 0.39752487, 0.33102718, 0.06192993, 0.04589793, 0.01988679, + 0.0526265 , 0.02104007, 0.03386636, 0.02634295, 0.05121018, + 0.29396682, 0.07275173, 0.03386636, 0.15307528, 0.06027915, + 0.01631789, 0.02045547, 0.01541937, 0.2128508 , 0.04589793, + 0.02104007, 0.05407977, 0.0311527 , 0.02634295, 0.01498855, + 0.29396682, 0.20336776, 0.07275173, 0.11637537, 0.25395607, + 0.64367488, 0.02634295, 0.02164101, 0.07083428, 0.05710047, + 0.32468619, 0.01160845, 0.01631789, 0.28803008, 0.02634295, + 0.17267234, 0.02634295, 0.01776301, 0.02709115, 0.14938186, + 0.26501331, 0.04111287, 0.06362285, 0.07083428, 0.07879431, + 0.08989109, 0.03680743, 0.0187955 , 0.01541937, 0.03680743, + 0.03029581, 0.02634295, 0.03029581, 0.07471691, 0.01228768, + 0.23277197, 0.10505173, 0.06192993, 0.09720799, 0.01416217, + 0.0187955 , 0.0689636 , 0.02865003, 0.07471691, 0.16460503, + 0.09720799, 0.02045547, 0.17267234, 0.0311527 , 0.0187955 , + 0.15684317, 0.04111287, 0.03293737, 0.02104007, 0.02946178, + 0.02421701, 0.1353385 , 0.03203302, 0.04111287, 0.10778798, + 0.07083428, 0.06027915, 0.02164101, 0.06535882, 0.02946178, + 0.07275173, 0.02490638, 0.01678627, 0.30605146, 0.02164101, + 0.03482061, 0.03580075, 0.37030921, 0.0182721 , 0.03482061, + 0.07083428, 0.04226237, 0.03999333, 0.03580075, 0.03203302, + 0.0182721 , 0.03580075, 0.06027915, 0.03386636, 0.02946178, + 0.03029581, 0.0689636 , 0.02634295, 0.02634295, 0.03029581, + 0.02225873, 0.1353385 , 0.08989109, 0.01988679, 0.0526265 , + 0.03386636, 0.03386636, 0.02786 , 0.03029581, 0.06535882, + 0.06535882, 0.03482061, 0.02786 , 0.29396682, 0.03293737, + 0.12242534, 0.04589793, 0.04589793, 0.03999333, 0.07471691, + 0.11344884, 0.05407977, 0.03482061, 0.01988679, 0.02045547, + 0.34389327, 0.14576223, 0.02561486, 0.0689636 , 0.02045547, + 0.02865003, 0.0526265 , 0.02164101, 0.01776301, 0.08307425, + 0.11344884, 0.04982997, 0.0182721 , 0.01498855, 0.02865003, + 0.14221564, 0.07879431, 0.02865003, 0.10237696, 0.04465416, + 0.07471691, 0.07673078, 0.13200634, 0.02104007, 0.0187955 , + 0.01376599, 0.04717464, 0.01128289, 0.05710047, 0.01988679, + 0.01300612, 0.11936722, 0.03203302, 0.01726786, 0.04589793, + 0.05407977, 0.09976271, 0.02561486, 0.03999333, 0.02634295, + 0.03580075, 0.21771181, 0.1353385 , 0.01988679, 0.37704374, + 0.06027915, 0.02045547, 0.18104935, 0.03999333, 0.18104935, + 0.15684317, 0.01376599, 0.03293737, 0.08989109, 0.02709115, + 0.14221564, 0.27065598, 0.10237696, 0.04226237, 0.72991785, + 0.06713876, 0.04226237, 0.03482061, 0.07879431, 0.07471691, + 0.15307528, 0.02289366, 0.08989109, 0.02634295, 0.43243779, + 0.08756457, 0.03293737, 0.02786 , 0.03482061, 0.0187955 , + 0.08307425, 0.04589793, 0.07275173, 0.0311527 , 0.04589793, + 0.08307425, 0.32468619, 0.02289366, 0.02634295, 0.03580075, + 0.14938186, 0.0526265 , 0.0526265 , 0.53268924, 0.19874565, + 0.0187955 , 0.01541937, 0.01586237, 0.02045547, 0.02421701, + 0.02634295, 0.11344884, 0.05710047, 0.05121018, 0.09720799, + 0.0311527 , 0.0526265 , 0.01586237, 0.07471691, 0.06027915, + 0.15684317, 0.07879431, 0.02289366, 0.04111287, 0.04848506, + 0.02865003, 0.04589793, 0.03580075, 0.04111287, 0.1353385 , + 0.09976271, 0.06362285, 0.32468619, 0.09976271, 0.49676673, + 0.07879431, 0.06027915, 0.06027915, 0.05407977, 0.05710047, + 0.0689636 , 0.11936722, 0.18973955, 0.02709115, 0.03890304, + 0.02634295, 0.80625182, 0.04111287, 0.0311527 , 0.07879431, + 0.0193336 , 0.01988679, 0.01376599, 0.07879431, 0.05710047, + 0.06027915, 0.02104007, 0.0689636 , 0.04717464, 0.04465416, + 0.07083428, 0.03999333, 0.06192993, 0.05407977, 0.04982997, + 0.46087756, 0.09720799, 0.04589793, 0.07083428, 0.0193336 , + 0.12242534, 0.12242534, 0.05407977, 0.01776301, 0.0311527 , + 0.0689636 , 0.02421701, 0.13200634, 0.19874565, 0.03293737, + 0.82774282], atol=1.0e-8) + self.assertAlmostEqual(results.pearson_chi2, 271.21110541713801) + np.testing.assert_allclose(results.resid_response, + [-0.04226237, -0.03999333, -0.02946178, -0.0689636 , -0.09471181, + -0.07879431, -0.04717464, -0.27065598, -0.07471691, 0.10477856, + -0.39752487, 0.66897282, -0.06192993, -0.04589793, -0.01988679, + -0.0526265 , -0.02104007, -0.03386636, -0.02634295, -0.05121018, + -0.29396682, 0.92724827, -0.03386636, -0.15307528, -0.06027915, + -0.01631789, -0.02045547, -0.01541937, -0.2128508 , -0.04589793, + -0.02104007, -0.05407977, -0.0311527 , -0.02634295, -0.01498855, + -0.29396682, 0.79663224, -0.07275173, -0.11637537, 0.74604393, + -0.64367488, -0.02634295, -0.02164101, -0.07083428, -0.05710047, + -0.32468619, -0.01160845, -0.01631789, -0.28803008, -0.02634295, + -0.17267234, -0.02634295, -0.01776301, -0.02709115, 0.85061814, + 0.73498669, -0.04111287, -0.06362285, -0.07083428, -0.07879431, + 0.91010891, -0.03680743, -0.0187955 , -0.01541937, -0.03680743, + -0.03029581, -0.02634295, -0.03029581, -0.07471691, -0.01228768, + 0.76722803, -0.10505173, -0.06192993, -0.09720799, -0.01416217, + -0.0187955 , -0.0689636 , -0.02865003, -0.07471691, -0.16460503, + -0.09720799, -0.02045547, 0.82732766, -0.0311527 , -0.0187955 , + -0.15684317, -0.04111287, -0.03293737, -0.02104007, -0.02946178, + -0.02421701, -0.1353385 , -0.03203302, -0.04111287, -0.10778798, + -0.07083428, -0.06027915, -0.02164101, -0.06535882, -0.02946178, + -0.07275173, -0.02490638, -0.01678627, -0.30605146, -0.02164101, + -0.03482061, -0.03580075, 0.62969079, -0.0182721 , -0.03482061, + -0.07083428, -0.04226237, -0.03999333, -0.03580075, -0.03203302, + -0.0182721 , -0.03580075, -0.06027915, -0.03386636, -0.02946178, + -0.03029581, -0.0689636 , -0.02634295, -0.02634295, -0.03029581, + -0.02225873, -0.1353385 , -0.08989109, -0.01988679, -0.0526265 , + -0.03386636, -0.03386636, -0.02786 , -0.03029581, -0.06535882, + -0.06535882, -0.03482061, -0.02786 , -0.29396682, -0.03293737, + -0.12242534, -0.04589793, -0.04589793, -0.03999333, -0.07471691, + -0.11344884, -0.05407977, -0.03482061, -0.01988679, -0.02045547, + 0.65610673, 0.85423777, -0.02561486, -0.0689636 , -0.02045547, + -0.02865003, -0.0526265 , -0.02164101, -0.01776301, -0.08307425, + -0.11344884, -0.04982997, -0.0182721 , -0.01498855, -0.02865003, + -0.14221564, -0.07879431, -0.02865003, -0.10237696, -0.04465416, + -0.07471691, -0.07673078, -0.13200634, -0.02104007, -0.0187955 , + -0.01376599, -0.04717464, -0.01128289, 0.94289953, -0.01988679, + -0.01300612, -0.11936722, -0.03203302, -0.01726786, -0.04589793, + -0.05407977, -0.09976271, -0.02561486, -0.03999333, -0.02634295, + -0.03580075, -0.21771181, 0.8646615 , -0.01988679, 0.62295626, + -0.06027915, -0.02045547, -0.18104935, 0.96000667, -0.18104935, + -0.15684317, -0.01376599, -0.03293737, -0.08989109, -0.02709115, + -0.14221564, 0.72934402, -0.10237696, -0.04226237, -0.72991785, + -0.06713876, -0.04226237, -0.03482061, -0.07879431, -0.07471691, + -0.15307528, 0.97710634, 0.91010891, -0.02634295, -0.43243779, + -0.08756457, -0.03293737, -0.02786 , -0.03482061, -0.0187955 , + 0.91692575, -0.04589793, -0.07275173, -0.0311527 , -0.04589793, + -0.08307425, 0.67531381, -0.02289366, -0.02634295, -0.03580075, + -0.14938186, -0.0526265 , -0.0526265 , 0.46731076, -0.19874565, + -0.0187955 , -0.01541937, -0.01586237, -0.02045547, -0.02421701, + -0.02634295, -0.11344884, -0.05710047, -0.05121018, -0.09720799, + 0.9688473 , -0.0526265 , -0.01586237, -0.07471691, -0.06027915, + -0.15684317, -0.07879431, -0.02289366, -0.04111287, -0.04848506, + -0.02865003, -0.04589793, -0.03580075, -0.04111287, -0.1353385 , + -0.09976271, -0.06362285, 0.67531381, -0.09976271, -0.49676673, + -0.07879431, -0.06027915, -0.06027915, -0.05407977, -0.05710047, + -0.0689636 , -0.11936722, -0.18973955, -0.02709115, -0.03890304, + -0.02634295, 0.19374818, -0.04111287, -0.0311527 , -0.07879431, + -0.0193336 , -0.01988679, -0.01376599, -0.07879431, 0.94289953, + -0.06027915, -0.02104007, -0.0689636 , -0.04717464, -0.04465416, + 0.92916572, -0.03999333, -0.06192993, -0.05407977, -0.04982997, + -0.46087756, -0.09720799, -0.04589793, -0.07083428, -0.0193336 , + -0.12242534, -0.12242534, -0.05407977, -0.01776301, -0.0311527 , + -0.0689636 , -0.02421701, -0.13200634, -0.19874565, -0.03293737, + -0.82774282], atol=1.0e-8) + np.testing.assert_allclose(results.resid_working, + [ -1.71062283e-03, -1.53549840e-03, -8.42423701e-04, + -4.42798906e-03, -8.12073047e-03, -5.71934606e-03, + -2.12046213e-03, -5.34278480e-02, -5.16550074e-03, + 9.82823035e-03, -9.52067472e-02, 1.48142818e-01, + -3.59779501e-03, -2.00993083e-03, -3.87619325e-04, + -2.62379729e-03, -4.33370579e-04, -1.10808799e-03, + -6.75670103e-04, -2.48818484e-03, -6.10129090e-02, + 6.25511612e-02, -1.10808799e-03, -1.98451739e-02, + -3.41454749e-03, -2.61928659e-04, -4.09867263e-04, + -2.34090923e-04, -3.56621577e-02, -2.00993083e-03, + 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-0.18139734, -0.20331052, -0.17629229, -0.69186337, -0.30654405, + -0.20622595, -0.33345774, -0.251588 , -0.23106769, -0.17379306, + -0.83437766, 1.78479093, -0.38867448, -0.4974393 , 1.65565332, + -1.43660134, -0.23106769, -0.20918228, -0.38332275, -0.34291558, + -0.88609006, -0.15281596, -0.18139734, -0.82428104, -0.23106769, + -0.61571821, -0.23106769, -0.18932865, -0.234371 , 1.94999969, + 1.62970871, -0.2897651 , -0.36259328, -0.38332275, -0.40514674, + 2.19506559, -0.27386827, -0.19480442, -0.17629229, -0.27386827, + -0.24804925, -0.23106769, -0.24804925, -0.39409528, -0.15725009, + 1.7074519 , -0.47114617, -0.35757692, -0.4522457 , -0.16889886, + -0.19480442, -0.37803944, -0.24111595, -0.39409528, -0.59975102, + -0.4522457 , -0.20331052, 1.87422489, -0.251588 , -0.19480442, + -0.5841272 , -0.2897651 , -0.25881274, -0.20622595, -0.24455876, + -0.22142749, -0.53929061, -0.25517563, -0.2897651 , -0.47760126, + -0.38332275, -0.35262564, -0.20918228, -0.36767536, -0.24455876, + -0.38867448, -0.2245965 , -0.18400413, -0.85481866, -0.20918228, + -0.26623785, -0.27002708, 1.40955093, -0.19204738, -0.26623785, + -0.38332275, -0.29387552, -0.2857098 , -0.27002708, -0.25517563, + -0.19204738, -0.27002708, -0.35262564, -0.26249995, -0.24455876, + -0.24804925, -0.37803944, -0.23106769, -0.23106769, -0.24804925, + -0.21218006, -0.53929061, -0.43402996, -0.20043547, -0.32882173, + -0.26249995, -0.26249995, -0.23772023, -0.24804925, -0.36767536, + -0.36767536, -0.26623785, -0.23772023, -0.83437766, -0.25881274, + -0.51106408, -0.30654405, -0.30654405, -0.2857098 , -0.39409528, + -0.49074728, -0.33345774, -0.26623785, -0.20043547, -0.20331052, + 1.46111186, 1.96253843, -0.22780971, -0.37803944, -0.20331052, + -0.24111595, -0.32882173, -0.20918228, -0.18932865, -0.41648237, + -0.49074728, -0.31973217, -0.19204738, -0.17379306, -0.24111595, + -0.55389988, -0.40514674, -0.24111595, -0.46476893, -0.30226435, + -0.39409528, -0.39958581, -0.53211065, -0.20622595, -0.19480442, + -0.16650295, -0.31088148, -0.15064545, 2.39288231, -0.20043547, + -0.16181126, -0.5042114 , -0.25517563, -0.18664773, -0.30654405, + -0.33345774, -0.45846897, -0.22780971, -0.2857098 , -0.23106769, + -0.27002708, -0.7007597 , 1.99998811, -0.20043547, 1.39670618, + -0.35262564, -0.20331052, -0.63203077, 2.53733821, -0.63203077, + -0.5841272 , -0.16650295, -0.25881274, -0.43402996, -0.234371 , + -0.55389988, 1.61672923, -0.46476893, -0.29387552, -1.61804148, + -0.37282386, -0.29387552, -0.26623785, -0.40514674, -0.39409528, + -0.57644334, 2.74841605, 2.19506559, -0.23106769, -1.06433539, + -0.42810736, -0.25881274, -0.23772023, -0.26623785, -0.19480442, + 2.23070414, -0.30654405, -0.38867448, -0.251588 , -0.30654405, + -0.41648237, 1.49993075, -0.21521982, -0.23106769, -0.27002708, + -0.5688444 , -0.32882173, -0.32882173, 1.12233423, -0.66569789, + -0.19480442, -0.17629229, -0.17882689, -0.20331052, -0.22142749, + -0.23106769, -0.49074728, -0.34291558, -0.32424676, -0.4522457 , + 2.63395309, -0.32882173, -0.17882689, -0.39409528, -0.35262564, + -0.5841272 , -0.40514674, -0.21521982, -0.2897651 , -0.3152773 , + -0.24111595, -0.30654405, -0.27002708, -0.2897651 , -0.53929061, + -0.45846897, -0.36259328, 1.49993075, -0.45846897, -1.17192274, + -0.40514674, -0.35262564, -0.35262564, -0.33345774, -0.34291558, + -0.37803944, -0.5042114 , -0.64869028, -0.234371 , -0.28170899, + -0.23106769, 0.65629132, -0.2897651 , -0.251588 , -0.40514674, + -0.19760028, -0.20043547, -0.16650295, -0.40514674, 2.39288231, + -0.35262564, -0.20622595, -0.37803944, -0.31088148, -0.30226435, + 2.30104857, -0.2857098 , -0.35757692, -0.33345774, -0.31973217, + -1.11158678, -0.4522457 , -0.30654405, -0.38332275, -0.19760028, + -0.51106408, -0.51106408, -0.33345774, -0.18932865, -0.251588 , + -0.37803944, -0.22142749, -0.53211065, -0.66569789, -0.25881274, + -1.87550882]) + np.testing.assert_allclose(results.null, + [ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 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0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759]) + self.assertAlmostEqual(results.D2, .200712816165) + self.assertAlmostEqual(results.adj_D2, 0.19816731557930456) + + + +if __name__ == '__main__': + unittest.main() diff --git a/src/py/crankshaft/crankshaft/regression/glm/utils.py b/src/py/crankshaft/crankshaft/regression/glm/utils.py new file mode 100644 index 0000000..0789675 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/utils.py @@ -0,0 +1,350 @@ + +from __future__ import absolute_import, print_function +import numpy as np +import warnings + + +def _bit_length_26(x): + if x == 0: + return 0 + elif x == 1: + return 1 + else: + return len(bin(x)) - 2 + + +try: + from scipy.lib._version import NumpyVersion +except ImportError: + import re + string_types = basestring + + class NumpyVersion(): + """Parse and compare numpy version strings. + Numpy has the following versioning scheme (numbers given are examples; they + can be >9) in principle): + - Released version: '1.8.0', '1.8.1', etc. + - Alpha: '1.8.0a1', '1.8.0a2', etc. + - Beta: '1.8.0b1', '1.8.0b2', etc. + - Release candidates: '1.8.0rc1', '1.8.0rc2', etc. + - Development versions: '1.8.0.dev-f1234afa' (git commit hash appended) + - Development versions after a1: '1.8.0a1.dev-f1234afa', + '1.8.0b2.dev-f1234afa', + '1.8.1rc1.dev-f1234afa', etc. + - Development versions (no git hash available): '1.8.0.dev-Unknown' + Comparing needs to be done against a valid version string or other + `NumpyVersion` instance. + Parameters + ---------- + vstring : str + Numpy version string (``np.__version__``). + Notes + ----- + All dev versions of the same (pre-)release compare equal. + Examples + -------- + >>> from scipy.lib._version import NumpyVersion + >>> if NumpyVersion(np.__version__) < '1.7.0': + ... print('skip') + skip + >>> NumpyVersion('1.7') # raises ValueError, add ".0" + """ + + def __init__(self, vstring): + self.vstring = vstring + ver_main = re.match(r'\d[.]\d+[.]\d+', vstring) + if not ver_main: + raise ValueError("Not a valid numpy version string") + + self.version = ver_main.group() + self.major, self.minor, self.bugfix = [int(x) for x in + self.version.split('.')] + if len(vstring) == ver_main.end(): + self.pre_release = 'final' + else: + alpha = re.match(r'a\d', vstring[ver_main.end():]) + beta = re.match(r'b\d', vstring[ver_main.end():]) + rc = re.match(r'rc\d', vstring[ver_main.end():]) + pre_rel = [m for m in [alpha, beta, rc] if m is not None] + if pre_rel: + self.pre_release = pre_rel[0].group() + else: + self.pre_release = '' + + self.is_devversion = bool(re.search(r'.dev-', vstring)) + + def _compare_version(self, other): + """Compare major.minor.bugfix""" + if self.major == other.major: + if self.minor == other.minor: + if self.bugfix == other.bugfix: + vercmp = 0 + elif self.bugfix > other.bugfix: + vercmp = 1 + else: + vercmp = -1 + elif self.minor > other.minor: + vercmp = 1 + else: + vercmp = -1 + elif self.major > other.major: + vercmp = 1 + else: + vercmp = -1 + + return vercmp + + def _compare_pre_release(self, other): + """Compare alpha/beta/rc/final.""" + if self.pre_release == other.pre_release: + vercmp = 0 + elif self.pre_release == 'final': + vercmp = 1 + elif other.pre_release == 'final': + vercmp = -1 + elif self.pre_release > other.pre_release: + vercmp = 1 + else: + vercmp = -1 + + return vercmp + + def _compare(self, other): + if not isinstance(other, (string_types, NumpyVersion)): + raise ValueError("Invalid object to compare with NumpyVersion.") + + if isinstance(other, string_types): + other = NumpyVersion(other) + + vercmp = self._compare_version(other) + if vercmp == 0: + # Same x.y.z version, check for alpha/beta/rc + vercmp = self._compare_pre_release(other) + if vercmp == 0: + # Same version and same pre-release, check if dev version + if self.is_devversion is other.is_devversion: + vercmp = 0 + elif self.is_devversion: + vercmp = -1 + else: + vercmp = 1 + + return vercmp + + def __lt__(self, other): + return self._compare(other) < 0 + + def __le__(self, other): + return self._compare(other) <= 0 + + def __eq__(self, other): + return self._compare(other) == 0 + + def __ne__(self, other): + return self._compare(other) != 0 + + def __gt__(self, other): + return self._compare(other) > 0 + + def __ge__(self, other): + return self._compare(other) >= 0 + + def __repr(self): + return "NumpyVersion(%s)" % self.vstring + + +def _next_regular(target): + """ + Find the next regular number greater than or equal to target. + Regular numbers are composites of the prime factors 2, 3, and 5. + Also known as 5-smooth numbers or Hamming numbers, these are the optimal + size for inputs to FFTPACK. + Target must be a positive integer. + """ + if target <= 6: + return target + + # Quickly check if it's already a power of 2 + if not (target & (target - 1)): + return target + + match = float('inf') # Anything found will be smaller + p5 = 1 + while p5 < target: + p35 = p5 + while p35 < target: + # Ceiling integer division, avoiding conversion to float + # (quotient = ceil(target / p35)) + quotient = -(-target // p35) + # Quickly find next power of 2 >= quotient + try: + p2 = 2 ** ((quotient - 1).bit_length()) + except AttributeError: + # Fallback for Python <2.7 + p2 = 2 ** _bit_length_26(quotient - 1) + + N = p2 * p35 + if N == target: + return N + elif N < match: + match = N + p35 *= 3 + if p35 == target: + return p35 + if p35 < match: + match = p35 + p5 *= 5 + if p5 == target: + return p5 + if p5 < match: + match = p5 + return match +if NumpyVersion(np.__version__) >= '1.7.1': + np_matrix_rank = np.linalg.matrix_rank +else: + def np_matrix_rank(M, tol=None): + """ + Return matrix rank of array using SVD method + Rank of the array is the number of SVD singular values of the array that are + greater than `tol`. + Parameters + ---------- + M : {(M,), (M, N)} array_like + array of <=2 dimensions + tol : {None, float}, optional + threshold below which SVD values are considered zero. If `tol` is + None, and ``S`` is an array with singular values for `M`, and + ``eps`` is the epsilon value for datatype of ``S``, then `tol` is + set to ``S.max() * max(M.shape) * eps``. + Notes + ----- + The default threshold to detect rank deficiency is a test on the magnitude + of the singular values of `M`. By default, we identify singular values less + than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with + the symbols defined above). This is the algorithm MATLAB uses [1]. It also + appears in *Numerical recipes* in the discussion of SVD solutions for linear + least squares [2]. + This default threshold is designed to detect rank deficiency accounting for + the numerical errors of the SVD computation. Imagine that there is a column + in `M` that is an exact (in floating point) linear combination of other + columns in `M`. Computing the SVD on `M` will not produce a singular value + exactly equal to 0 in general: any difference of the smallest SVD value from + 0 will be caused by numerical imprecision in the calculation of the SVD. + Our threshold for small SVD values takes this numerical imprecision into + account, and the default threshold will detect such numerical rank + deficiency. The threshold may declare a matrix `M` rank deficient even if + the linear combination of some columns of `M` is not exactly equal to + another column of `M` but only numerically very close to another column of + `M`. + We chose our default threshold because it is in wide use. Other thresholds + are possible. For example, elsewhere in the 2007 edition of *Numerical + recipes* there is an alternative threshold of ``S.max() * + np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe + this threshold as being based on "expected roundoff error" (p 71). + The thresholds above deal with floating point roundoff error in the + calculation of the SVD. However, you may have more information about the + sources of error in `M` that would make you consider other tolerance values + to detect *effective* rank deficiency. The most useful measure of the + tolerance depends on the operations you intend to use on your matrix. For + example, if your data come from uncertain measurements with uncertainties + greater than floating point epsilon, choosing a tolerance near that + uncertainty may be preferable. The tolerance may be absolute if the + uncertainties are absolute rather than relative. + References + ---------- + .. [1] MATLAB reference documention, "Rank" + http://www.mathworks.com/help/techdoc/ref/rank.html + .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, + "Numerical Recipes (3rd edition)", Cambridge University Press, 2007, + page 795. + Examples + -------- + >>> from numpy.linalg import matrix_rank + >>> matrix_rank(np.eye(4)) # Full rank matrix + 4 + >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix + >>> matrix_rank(I) + 3 + >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0 + 1 + >>> matrix_rank(np.zeros((4,))) + 0 + """ + M = np.asarray(M) + if M.ndim > 2: + raise TypeError('array should have 2 or fewer dimensions') + if M.ndim < 2: + return int(not all(M == 0)) + S = np.linalg.svd(M, compute_uv=False) + if tol is None: + tol = S.max() * max(M.shape) * np.finfo(S.dtype).eps + return np.sum(S > tol) + + + +class CacheWriteWarning(UserWarning): + pass + +class CachedAttribute(object): + + def __init__(self, func, cachename=None, resetlist=None): + self.fget = func + self.name = func.__name__ + self.cachename = cachename or '_cache' + self.resetlist = resetlist or () + + def __get__(self, obj, type=None): + if obj is None: + return self.fget + # Get the cache or set a default one if needed + _cachename = self.cachename + _cache = getattr(obj, _cachename, None) + if _cache is None: + setattr(obj, _cachename, resettable_cache()) + _cache = getattr(obj, _cachename) + # Get the name of the attribute to set and cache + name = self.name + _cachedval = _cache.get(name, None) + # print("[_cachedval=%s]" % _cachedval) + if _cachedval is None: + # Call the "fget" function + _cachedval = self.fget(obj) + # Set the attribute in obj + # print("Setting %s in cache to %s" % (name, _cachedval)) + try: + _cache[name] = _cachedval + except KeyError: + setattr(_cache, name, _cachedval) + # Update the reset list if needed (and possible) + resetlist = self.resetlist + if resetlist is not (): + try: + _cache._resetdict[name] = self.resetlist + except AttributeError: + pass + # else: + # print("Reading %s from cache (%s)" % (name, _cachedval)) + return _cachedval + + def __set__(self, obj, value): + errmsg = "The attribute '%s' cannot be overwritten" % self.name + warnings.warn(errmsg, CacheWriteWarning) + + +class _cache_readonly(object): + """ + Decorator for CachedAttribute + """ + + def __init__(self, cachename=None, resetlist=None): + self.func = None + self.cachename = cachename + self.resetlist = resetlist or None + + def __call__(self, func): + return CachedAttribute(func, + cachename=self.cachename, + resetlist=self.resetlist) +cache_readonly = _cache_readonly() + + diff --git a/src/py/crankshaft/crankshaft/regression/glm/varfuncs.py b/src/py/crankshaft/crankshaft/regression/glm/varfuncs.py new file mode 100644 index 0000000..af66d8c --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/glm/varfuncs.py @@ -0,0 +1,284 @@ +""" +Variance functions for use with the link functions in statsmodels.family.links +""" + +__docformat__ = 'restructuredtext' + +import numpy as np +FLOAT_EPS = np.finfo(float).eps + +class VarianceFunction(object): + """ + Relates the variance of a random variable to its mean. Defaults to 1. + + Methods + ------- + call + Returns an array of ones that is the same shape as `mu` + + Notes + ----- + After a variance function is initialized, its call method can be used. + + Alias for VarianceFunction: + constant = VarianceFunction() + + See also + -------- + statsmodels.family.family + """ + + def __call__(self, mu): + """ + Default variance function + + Parameters + ----------- + mu : array-like + mean parameters + + Returns + ------- + v : array + ones(mu.shape) + """ + mu = np.asarray(mu) + return np.ones(mu.shape, np.float64) + + + def deriv(self, mu): + """ + Derivative of the variance function v'(mu) + """ + from statsmodels.tools.numdiff import approx_fprime_cs + # TODO: diag workaround proplem with numdiff for 1d + return np.diag(approx_fprime_cs(mu, self)) + + +constant = VarianceFunction() +constant.__doc__ = """ +The call method of constant returns a constant variance, i.e., a vector of ones. + +constant is an alias of VarianceFunction() +""" + +class Power(object): + """ + Power variance function + + Parameters + ---------- + power : float + exponent used in power variance function + + Methods + ------- + call + Returns the power variance + + Formulas + -------- + V(mu) = numpy.fabs(mu)**power + + Notes + ----- + Aliases for Power: + mu = Power() + mu_squared = Power(power=2) + mu_cubed = Power(power=3) + """ + + def __init__(self, power=1.): + self.power = power + + def __call__(self, mu): + """ + Power variance function + + Parameters + ---------- + mu : array-like + mean parameters + + Returns + ------- + variance : array + numpy.fabs(mu)**self.power + """ + return np.power(np.fabs(mu), self.power) + + + def deriv(self, mu): + """ + Derivative of the variance function v'(mu) + """ + from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime + #return approx_fprime_cs(mu, self) # TODO fix breaks in `fabs + # TODO: diag is workaround problem with numdiff for 1d + return np.diag(approx_fprime(mu, self)) + + +mu = Power() +mu.__doc__ = """ +Returns np.fabs(mu) + +Notes +----- +This is an alias of Power() +""" +mu_squared = Power(power=2) +mu_squared.__doc__ = """ +Returns np.fabs(mu)**2 + +Notes +----- +This is an alias of statsmodels.family.links.Power(power=2) +""" +mu_cubed = Power(power=3) +mu_cubed.__doc__ = """ +Returns np.fabs(mu)**3 + +Notes +----- +This is an alias of statsmodels.family.links.Power(power=3) +""" + +class Binomial(object): + """ + Binomial variance function + + Parameters + ---------- + n : int, optional + The number of trials for a binomial variable. The default is 1 for + p in (0,1) + + Methods + ------- + call + Returns the binomial variance + + Formulas + -------- + V(mu) = p * (1 - p) * n + + where p = mu / n + + Notes + ----- + Alias for Binomial: + binary = Binomial() + + A private method _clean trims the data by machine epsilon so that p is + in (0,1) + """ + + def __init__(self, n=1): + self.n = n + + def _clean(self, p): + return np.clip(p, FLOAT_EPS, 1 - FLOAT_EPS) + + def __call__(self, mu): + """ + Binomial variance function + + Parameters + ----------- + mu : array-like + mean parameters + + Returns + ------- + variance : array + variance = mu/n * (1 - mu/n) * self.n + """ + p = self._clean(mu / self.n) + return p * (1 - p) * self.n + + #TODO: inherit from super + def deriv(self, mu): + """ + Derivative of the variance function v'(mu) + """ + from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime + # TODO: diag workaround proplem with numdiff for 1d + return np.diag(approx_fprime_cs(mu, self)) + + +binary = Binomial() +binary.__doc__ = """ +The binomial variance function for n = 1 + +Notes +----- +This is an alias of Binomial(n=1) +""" + +class NegativeBinomial(object): + ''' + Negative binomial variance function + + Parameters + ---------- + alpha : float + The ancillary parameter for the negative binomial variance function. + `alpha` is assumed to be nonstochastic. The default is 1. + + Methods + ------- + call + Returns the negative binomial variance + + Formulas + -------- + V(mu) = mu + alpha*mu**2 + + Notes + ----- + Alias for NegativeBinomial: + nbinom = NegativeBinomial() + + A private method _clean trims the data by machine epsilon so that p is + in (0,inf) + ''' + + def __init__(self, alpha=1.): + self.alpha = alpha + + def _clean(self, p): + return np.clip(p, FLOAT_EPS, np.inf) + + def __call__(self, mu): + """ + Negative binomial variance function + + Parameters + ---------- + mu : array-like + mean parameters + + Returns + ------- + variance : array + variance = mu + alpha*mu**2 + """ + p = self._clean(mu) + return p + self.alpha*p**2 + + def deriv(self, mu): + """ + Derivative of the negative binomial variance function. + """ + + p = self._clean(mu) + return 1 + 2 * self.alpha * p + +nbinom = NegativeBinomial() +nbinom.__doc__ = """ +Negative Binomial variance function. + +Notes +----- +This is an alias of NegativeBinomial(alpha=1.) +""" diff --git a/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/FBGWR-checkpoint.ipynb b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/FBGWR-checkpoint.ipynb new file mode 100644 index 0000000..e06df51 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/FBGWR-checkpoint.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sys\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pysal as ps\n", + "sys.path.append('/Users/toshan/Dropbox/GWR/PyGWRJing/PyGWR/')\n", + "from M_FBGWR_May2016 import FBGWR\n", + "from M_GWGLM import GWGLM\n", + "from M_selection import Band_Sel\n", + "import scipy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "path = ps.examples.get_path('GData_utm.csv')\n", + "shp = pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Prep data into design matrix and coordinates\n", + "\n", + "#Dependent variable\n", + "y = shp.PctBach.reshape((-1,1))\n", + "\n", + "#Design matrix - covariates - intercept added automatically\n", + "pov = shp.PctPov.reshape((-1,1))\n", + "rural = shp.PctRural.reshape((-1,1))\n", + "blk = shp.PctBlack.reshape((-1,1))\n", + "X = np.hstack([pov, rural, blk])\n", + "labels = ['Intercept', 'PctPov', 'PctRural', 'PctBlack']\n", + "\n", + "#Coordinates for calibration points\n", + "u = shp.X\n", + "v = shp.Y\n", + "coords = zip(u,v)\n", + "\n", + "coords_dict = {}\n", + "for i, x in enumerate(coords):\n", + " coords_dict[i] = x" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60.0, , [(89.0, 1088.4132720309442), (73.0, 1082.7676645259439), (62.0, 1079.8414440181946), (62.0, 1079.8414440181946), (62.0, 1079.8414440181946), (60.0, 1079.6668177916372), (60.0, 1079.6668177916372), (60.0, 1079.6668177916372)])\n", + "CPU times: user 1.75 s, sys: 80.7 ms, total: 1.83 s\n", + "Wall time: 1.75 s\n" + ] + } + ], + "source": [ + "%%time\n", + "print Band_Sel(y, None, X, coords_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pysal.contrib.gwr.sel_bw import Sel_BW\n", + "from pysal.contrib.gwr.gwr import GWR" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "60.0\n", + "CPU times: user 1.1 s, sys: 3.5 ms, total: 1.11 s\n", + "Wall time: 1.1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "print Sel_BW(coords, y, X, [], kernel='bisquare', constant=False).search()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%%time\n", + "results = FBGWR(y, X, coords_dict, tolFB=1e-03)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "%%time\n", + "sel = Sel_BW(coords, y, X, [], kernel='bisquare', fb=True, constant=False)\n", + "results = sel.search(tol_fb=1e-03)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [fbgwr]", + "language": "python", + "name": "Python [fbgwr]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/FBGWR_validation-checkpoint.ipynb b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/FBGWR_validation-checkpoint.ipynb new file mode 100644 index 0000000..286dcb3 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/FBGWR_validation-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/GWR_Georgia_Example-checkpoint.ipynb b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/GWR_Georgia_Example-checkpoint.ipynb new file mode 100644 index 0000000..5a2c09d --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/GWR_Georgia_Example-checkpoint.ipynb @@ -0,0 +1,470 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gp\n", + "import pysal as ps\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%pylab inline\n", + "from sel_bw import Sel_BW\n", + "from gwr import GWR\n", + "from pysal.contrib.glm.family import Gaussian\n", + "from pathos import multiprocessing as mp" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0bah3tE754AOT91OrVCkW79/P/AULzE7cbOHvwfLXyYPKVatxIyI6f8W3qPWt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\nrQL6evEZfC5RPfsLU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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Data\n", + "\n", + "data = ps.open(ps.examples.get_path('GData_utm.csv'))\n", + "shp = gp.read_file('/Users/toshan/dev/pysal/pysal/examples/georgia/G_utm.shp')\n", + "shp.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Prep data into design matrix and coordinates\n", + "\n", + "#Dependent variable\n", + "y = shp.PctBach.reshape((-1,1))\n", + "\n", + "#Design matrix - covariates - intercept added automatically\n", + "pov = shp.PctPov.reshape((-1,1))\n", + "rural = shp.PctRural.reshape((-1,1))\n", + "blk = shp.PctBlack.reshape((-1,1))\n", + "X = np.hstack([pov, rural, blk])\n", + "labels = ['Intercept', 'PctPov', 'PctRural', 'PctBlack']\n", + "\n", + "#Coordinates for calibration points\n", + "u = shp.X\n", + "v = shp.Y\n", + "coords = zip(u,v)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100.0\n", + "100.0\n", + "100.0\n", + "100.0\n", + "1 loop, best of 3: 1.22 s per loop\n" + ] + } + ], + "source": [ + "#Find optimal bandwidth using golden section search to minimize AICc\n", + "\n", + "#Instantiate bandwidth selection class - bisquare NN (adaptive)\n", + "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n", + "\n", + "#Find optimal bandwidth by minimizing AICc using golden section search algorithm\n", + "bw = bw.search(search='golden_section', criterion='AICc')\n", + "print bw" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Instantiate GWR model and then estimate parameters and diagnostics using fit method\n", + "model = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n", + "results = model.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(172, 4)" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Results in a set of mappable results \n", + "results.params.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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Pq3ddpWpehQS6XC6+WbmYns3a5Hu/Zply/Pz4ywDsP3Wc/cknzkth3KdtZ/q0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PGBwXu3cjvAn6LWObg6IvWJm1LxYuoUOmzcxBFfXwDaVyjNmm5t011md/beQ+rOWYZLoTycWzg8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\nSFsZSOXkypWLtSsOkMMmA+2aVMUknZFe7eszZsDM1JgoZeI3e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8V/1GqRzSs10crnL92l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\nFy3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O9rbY29kyZdYqVq7/Eykk+a1TRVGkS4eGdOmQsE4CwIsXvsxdtJnO7RqRLVvaB3cs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\nOBhPeno6z0x/2+xyyb2/K4/tRvfN+wu+JrZypSIFAgBWq5Vundu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FhZw/f57WrVvTq1cvGjVq5HJfRqOxRpWBoqIiFi9ezPHjx8tcW7t2LQUFBdSv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\nQlHOUQaECoVCoVA4gKsMCN2J0gwoFAqFQlH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mzZvkcdHToVAoHj6PkpfAHZTBkE00Gg2VKlbg0JFT1Kt7f72FVet2EuBBl/3C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\npjLfNn+dRKsFo05HAf97waV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DUpYu4oyDx2kn0LBhw+5+rl+/PvXr13d9Ri5QtWp19h88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crmcWJp67ZYK5Xs876N14vn2FtZc+TuHQLDQpnfoiWdyv2U6HwKmYwxderSqUxZqi9dxKtXL9my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5jCzLCAgWBzK6qNW8CAykx9p1hERExDzCDQbCDAYijAZ0BiP6SGNMYazISBORsnmumPPBnLUj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\nfw2LlVIKmPvTjxyQVblVP7//fTVnnVviMjgqleigR6WUSh3dDuv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aNWrw2muvsXz5clq0aMHRo0ddEo9BoVC4jkfp9IFSCpwgJCSEX3/91bZyNplM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zxrksIiKCzp07ExgYyN69e53ihFVURMFvv/0GkK+0yPHx8Xb9QTJFAZw6dSpz5841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\nxeKcZcb9H4Dnjfttge1SyhgpZTS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Vdnp2bU7pkoX5ZfV2Vm/YQ1Dxtg/Uq/1EZbeWbTocDsZOmEuftzqmer1CuRLs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7nGjsNjz2G6uifk6dQpUYQ6JaT95v08JARnCbdK0pN6zkV599csnMdMZ+DAgezcsYO9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\nyGljy54e/SnsIL00e1o4+fgBf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Z+8ab6eQm7eWhs6nY7yE+dy/v4jTgweaNYPbYBynnm4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\n/wOgO+gOU7GQ6wAAAABJRU5ErkJggg==\n", 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mOTmZZcuWWd32QS5evMi1a9cswZEAmjZtysKFC1m2bBmNGzdmz549DonHoFAo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/+eSTfPLJJz6zyJTHbbfdRlJSErNmzeLOO++kefPmHjvvlsZsNvPKK6+QnJzMlClT6Ny5Mxdd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\nUak6Goc5X2u9Uyn1PbATKAIe0v/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zbQPKFcvHmu0HWf/nEbI/fb+Nx5PVyxIY4LrbpsPh4LM5v9CvZ+sUr1cqV5Q9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\nUKoecrvduN3uSntjxowZA8Cjjz4a83NGIpEDh4WqeSsoKCCQbwc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K1KtX74m3FAAkJSUxZ84cTp48meHZ6tWriY+Pp2TJkpm+/7p16xg9ejS+vr7Z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\nUNJw1VJg1Vq3xDYd0E4p1QyYCLy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Map Parameter estimates and T-vals for each covariate\n", + "\n", + "for param in range(results.params.shape[1]):\n", + " shp[str(param)] = results.params[:,param]\n", + " vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n", + " ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='YlOrRd')\n", + " ax.set_title(labels[param] + ' Estimates')\n", + " fig = ax.get_figure()\n", + " cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n", + " sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='YlOrRd')\n", + " sm._A = []\n", + " fig.colorbar(sm, cax=cax)\n", + " \n", + " shp[str(param)] = results.tvalues[:,param]\n", + " vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n", + " ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='Greys')\n", + " ax.set_title(labels[param] + ' T-vals')\n", + " fig = ax.get_figure()\n", + " cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n", + " sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='Greys')\n", + " sm._A = []\n", + " fig.colorbar(sm, cax=cax)\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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33uHzjzqxafNmBg0a5LIOCoVCkVEoQyGdFC5cmIjbt4mKjMA/MNN91w7v3o5O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Oaly5yV4Qs3MKZLM1ZMcKdQ2SqRjitYpjJVmrZj7cxxeJ0/xfMHt/ns74dKpWbtjDFYWlkTGvKF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\no1YAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Map local R-square values which is a weighted R-square at each observation location\n", + "\n", + "shp['localR2'] = results.localR2\n", + "vmin, vmax = np.min(shp['localR2']), np.max(shp['localR2']) \n", + "ax = shp.plot('localR2', vmin=vmin, vmax=vmax, figsize=(8,8), cmap='PuBuGn')\n", + "ax.set_title('Local R-Squared')\n", + "fig = ax.get_figure()\n", + "cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n", + "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='PuBuGn')\n", + "sm._A = []\n", + "fig.colorbar(sm, cax=cax)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import multiprocessing as mp" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def function(bw):\n", + " return func(bw)\n", + "func = lambda bw: GWR(coords, y, X, bw).fit().utu \n", + "bws = [60,70,80,90]\n", + "pool = mp.Pool(processes=2)\n", + "results = [pool.apply_async(func, args=(bw,)) for bw in bws]\n", + "output = [p.get() for p in results]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1862.8840959732197,\n", + " 1951.0076602693341,\n", + " 2029.5771191209799,\n", + " 2087.5580755341939]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "print a.get(timeout=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1862.8840959732197" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GWR(coords, y, X, 60).fit().utu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [multigwr]", + "language": "python", + "name": "Python [multigwr]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/Untitled1-checkpoint.ipynb new file mode 100644 index 0000000..59ce53d --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/Untitled1-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/Untitled2-checkpoint.ipynb b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/Untitled2-checkpoint.ipynb new file mode 100644 index 0000000..286dcb3 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/Untitled2-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/calibration_dists-checkpoint.ipynb b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/calibration_dists-checkpoint.ipynb new file mode 100644 index 0000000..cec8500 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/calibration_dists-checkpoint.ipynb @@ -0,0 +1,571 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pysal as ps\n", + "from pysal.weights.Distance import Kernel\n", + "import sys\n", + "sys.path.append('/Users/toshan/dev/pysal/pysal/weights')\n", + "from Distance import Kernel as kn\n", + "from util import full2W as f2w" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n = 5 #number of observations\n", + "m = 3 #number of calibration points" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x = np.random.randint(1,1000, n)\n", + "y = np.random.randint(1,1000, n)\n", + "D1 = zip(x,y)\n", + "W1 = kn(D1)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x = np.random.randint(1,1000, m)\n", + "y = np.random.randint(1,1000, m)\n", + "D2 = zip(x,y)\n", + "W2 = kn(D2)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.39894228, 0.1009935 , 0.05619733, 0.04896355, 0.32650695,\n", + " 0.37879836, 0.31411809, 0.1073076 ],\n", + " [ 0.1009935 , 0.39894228, 0.36195557, 0.36626279, 0.07720723,\n", + " 0.11754135, 0.03669346, 0.33721825],\n", + " [ 0.05619733, 0.36195557, 0.39894228, 0.39108403, 0.0345001 ,\n", + " 0.06151089, 0.02103471, 0.24420603],\n", + " [ 0.04896355, 0.36626279, 0.39108403, 0.39894228, 0.0335092 ,\n", + " 0.05713611, 0.01604658, 0.26976648],\n", + " [ 0.32650695, 0.07720723, 0.0345001 , 0.0335092 , 0.39894228,\n", + " 0.37224696, 0.18985479, 0.11774827],\n", + " [ 0.37879836, 0.11754135, 0.06151089, 0.05713611, 0.37224696,\n", + " 0.39894228, 0.24197075, 0.14519262],\n", + " [ 0.31411809, 0.03669346, 0.02103471, 0.01604658, 0.18985479,\n", + " 0.24197075, 0.39894228, 0.03119528],\n", + " [ 0.1073076 , 0.33721825, 0.24420603, 0.26976648, 0.11774827,\n", + " 0.14519262, 0.03119528, 0.39894228]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D3 = np.vstack([D1,D2])\n", + "W3 = Kernel(D3, function='gaussian')\n", + "W3 = kn(D3, function='gaussian', truncate=False)\n", + "\n", + "W3.full()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n", + "a\n" + ] + } + ], + "source": [ + "\n", + "coord_ids = np.arange(n)\n", + "points_ids = np.arange(n, n+m)\n", + "all_ids = np.arange(n+m)\n", + "dists = np.zeros((m,n))\n", + "\n", + "\n", + "for i in all_ids:\n", + " if i in points_ids:\n", + " for j in coord_ids:\n", + " if j in W3[j].keys():\n", + " if i >= n:\n", + " print 'a'\n", + " dists[i-n][j] = W3.full()[0][i][j]\n", + " elif j >= m:\n", + " print 'b'\n", + " dists[i][j-m] = W3.full()[0][i][j]\n", + " elif (i >= n) & (j >= m):\n", + " print 'c'\n", + " dists[i-n][j-m] = W3.full()[0][i][j]\n", + " else:\n", + " print 'd'\n", + " dists[i][j] = W3.full()[0][i][j]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.37879836, 0.11754135, 0.06151089, 0.05713611, 0.37224696],\n", + " [ 0.31411809, 0.03669346, 0.02103471, 0.01604658, 0.18985479],\n", + " [ 0.1073076 , 0.33721825, 0.24420603, 0.26976648, 0.11774827]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dists" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "3 is not in list", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf2w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/weights.pyc\u001b[0m in \u001b[0;36mfull\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 953\u001b[0m \"\"\"\n\u001b[0;32m--> 954\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 955\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtowsp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/util.pyc\u001b[0m in \u001b[0;36mfull\u001b[0;34m(w)\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mw_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwij\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 713\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 714\u001b[0m \u001b[0mwfull\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwij\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mwfull\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: 3 is not in list" + ] + } + ], + "source": [ + "w = f2w(dists)\n", + "w.full()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "new_neighbs = {}\n", + "new_weights = {}\n", + "for each in W2.neighbors:\n", + " three = set(W3.neighbors[each])\n", + " two = set(W2.neighbors[each])\n", + " new_neighbs[each] = list(three.difference(two))\n", + " new_weights[each] = {}\n", + " for weight in new_neighbs[each]:\n", + " new_weights[each][weight] = W3[each][weight]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nfor ids in all_ids:\\n if ids in points_ids:\\n _all = set(W3.neighbors[ids])\\n points = set(W2.neighbors[ids])\\n new_neighbs[ids] = list(_all.difference(points))\\n new_weights[ids] = {}\\n for weight in new_neighbs[ids]:\\n new_weights[ids][weight] = W3[ids][weight]\\n\\n else:\\n new_weights[ids] = {}\\n'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coord_ids = W1.id_order\n", + "points_ids = W2.id_order\n", + "all_ids = W3.id_order\n", + "dists = np.zeros((2,3))\n", + "\n", + "'''\n", + "for ids in all_ids:\n", + " if ids in points_ids:\n", + " _all = set(W3.neighbors[ids])\n", + " points = set(W2.neighbors[ids])\n", + " new_neighbs[ids] = list(_all.difference(points))\n", + " new_weights[ids] = {}\n", + " for weight in new_neighbs[ids]:\n", + " new_weights[ids][weight] = W3[ids][weight]\n", + "\n", + " else:\n", + " new_weights[ids] = {}\n", + "''' \n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 0\n", + "a\n", + "3 1\n", + "a\n", + "3 2\n", + "a\n", + "4 0\n", + "a\n", + "4 1\n", + "a\n", + "4 2\n", + "a\n", + "5 0\n", + "a\n", + "5 1\n", + "a\n", + "5 2\n", + "a\n", + "6 0\n", + "a\n", + "6 1\n", + "a\n", + "6 2\n", + "a\n", + "7 0\n", + "a\n", + "7 1\n", + "a\n", + "7 2\n", + "a\n" + ] + } + ], + "source": [ + "n = 3 #number of observations\n", + "m = 2 #number of \n", + "for i in all_ids:\n", + " if i in points_ids:\n", + " for j in coord_ids:\n", + " if j in W3[j].keys():\n", + " print i,j\n", + " if i >= n:\n", + " print 'a'\n", + " dists[i-n][j] = W3.full()[0][i][j]\n", + " elif j >= m:\n", + " print 'b'\n", + " dists[i][j-m] = W3.full()[0][i][j]\n", + " elif (i >= n) & (j >= m):\n", + " print 'c'\n", + " dists[i-n][j-m] = W3.full()[0][i][j]\n", + " else:\n", + " print 'd'\n", + " dists[i][j] = W3.full()[0][i][j]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0.45118883, 0.85404129],\n", + " [ 0.27754126, 0.40233088, 0.20596816],\n", + " [ 0. , 0.37941649, 0.6032733 ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0.35446728, 0.73305802, 0.50022967]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dists" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "points_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "W4 = ps.W(new_neighbs, new_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: [3, 4, 5, 6, 7], 1: [3, 4, 5, 6, 7], 2: [3, 4, 5, 6, 7]}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W4.neighbors" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: {3: -1.0482874068379293,\n", + " 4: 0.36696681575222334,\n", + " 5: 0.678090925231672,\n", + " 6: 0.308555499289517,\n", + " 7: -0.620566108018012},\n", + " 1: {3: 0.5865615659374835,\n", + " 4: -0.8123595700046831,\n", + " 5: -0.5633467635255329,\n", + " 6: -1.1845906645769202,\n", + " 7: 0.42019589403802593},\n", + " 2: {3: 0.8005291815217084,\n", + " 4: -1.212624893235362,\n", + " 5: -0.9337024333901489,\n", + " 6: -1.4259607135415542,\n", + " 7: 0.009238162970930053}}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W4.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W4.id_order" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "3 is not in list", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/util.pyc\u001b[0m in \u001b[0;36mfull\u001b[0;34m(w)\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mw_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwij\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 713\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 714\u001b[0m \u001b[0mwfull\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwij\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mwfull\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: 3 is not in list" + ] + } + ], + "source": [ + "W4.full()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 1}" + ] + }, + "execution_count": 244, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W4." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5])" + ] + }, + "execution_count": 35, + "metadata": 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b/src/py/crankshaft/crankshaft/regression/gwr/.ipynb_checkpoints/profiling-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/__init__.py b/src/py/crankshaft/crankshaft/regression/gwr/base/__init__.py new file mode 100644 index 0000000..eeb63b3 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/__init__.py @@ -0,0 +1,4 @@ +import gwr +import sel_bw +import diagnostics +import kernels diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/diagnostics.py b/src/py/crankshaft/crankshaft/regression/gwr/base/diagnostics.py new file mode 100644 index 0000000..44a75d5 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/diagnostics.py @@ -0,0 +1,81 @@ +""" +Diagnostics for estimated gwr modesl +""" +__author__ = "Taylor Oshan tayoshan@gmail.com" + +import numpy as np +from pysal.contrib.glm.family import Gaussian, Poisson, Binomial + +def get_AICc(gwr): + """ + Get AICc value + + Gaussian: p61, (2.33), Fotheringham, Brunsdon and Charlton (2002) + + GWGLM: AICc=AIC+2k(k+1)/(n-k-1), Nakaya et al. (2005): p2704, (36) + + """ + n = gwr.n + k = gwr.tr_S + if isinstance(gwr.family, Gaussian): + aicc = -2.0*gwr.llf + 2.0*n*(k + 1.0)/(n-k-2.0) + elif isinstance(gwr.family, (Poisson, Binomial)): + aicc = get_AIC(gwr) + 2.0 * k * (k+1.0) / (n - k - 1.0) + return aicc + +def get_AIC(gwr): + """ + Get AIC calue + + Gaussian: p96, (4.22), Fotheringham, Brunsdon and Charlton (2002) + + GWGLM: AIC(G)=D(G) + 2K(G), where D and K denote the deviance and the effective + number of parameters in the model with bandwidth G, respectively. + + """ + k = gwr.tr_S + #deviance = -2*log-likelihood + y = gwr.y + mu = gwr.mu + if isinstance(gwr.family, Gaussian): + aic = -2.0 * gwr.llf + 2.0 * (k+1) + elif isinstance(gwr.family, (Poisson, Binomial)): + aic = np.sum(gwr.family.resid_dev(y, mu)**2) + 2.0 * k + return aic + +def get_BIC(gwr): + """ + Get BIC value + + Gaussian: p61 (2.34), Fotheringham, Brunsdon and Charlton (2002) + BIC = -2log(L)+klog(n) + + GWGLM: BIC = dev + tr_S * log(n) + + """ + n = gwr.n # (scalar) number of observations + k = gwr.tr_S + y = gwr.y + mu = gwr.mu + if isinstance(gwr.family, Gaussian): + bic = -2.0 * gwr.llf + (k+1) * np.log(n) + elif isinstance(gwr.family, (Poisson, Binomial)): + bic = np.sum(gwr.family.resid_dev(y, mu)**2) + k * np.log(n) + return bic + +def get_CV(gwr): + """ + Get CV value + + Gaussian only + + Methods: p60, (2.31) or p212 (9.4) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + Modification: sum of residual squared is divided by n according to GWR4 results + + """ + aa = gwr.resid_response.reshape((-1,1))/(1.0-gwr.influ) + cv = np.sum(aa**2)/gwr.n + return cv + diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/gwr.py b/src/py/crankshaft/crankshaft/regression/gwr/base/gwr.py new file mode 100644 index 0000000..0711c29 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/gwr.py @@ -0,0 +1,1019 @@ +#Main GWR classes + +#Offset does not yet do anyhting and needs to be implemented + +__author__ = "Taylor Oshan Tayoshan@gmail.com" + +import numpy as np +import numpy.linalg as la +from scipy.stats import t +from kernels import * +from diagnostics import get_AIC, get_AICc, get_BIC +import pysal.spreg.user_output as USER +from pysal.contrib.glm.family import Gaussian, Binomial, Poisson +from pysal.contrib.glm.glm import GLM, GLMResults +from pysal.contrib.glm.iwls import iwls +from pysal.contrib.glm.utils import cache_readonly + +fk = {'gaussian': fix_gauss, 'bisquare': fix_bisquare, 'exponential': fix_exp} +ak = {'gaussian': adapt_gauss, 'bisquare': adapt_bisquare, 'exponential': adapt_exp} + +class GWR(GLM): + """ + Geographically weighted regression. Can currently estimate Gaussian, + Poisson, and logistic models(built on a GLM framework). GWR object prepares + model input. Fit method performs estimation and returns a GWRResults object. + + Parameters + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates of + observatons; also used as calibration locations is + 'points' is set to None + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + points : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations; default is set to None, which + uses every observation as a calibration point + + bw : scalar + bandwidth value consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations; + only for Poisson models + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + Attributes + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + bw : scalar + bandwidth value consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept + + n : integer + number of observations + + k : integer + number of independent variables + + mean_y : float + mean of y + + std_y : float + standard deviation of y + + fit_params : dict + parameters passed into fit method to define estimation + routine + + W : array + n*n, spatial weights matrix for weighting all + observations from each calibration point + """ + def __init__(self, coords, y, X, bw, family=Gaussian(), offset=None, + sigma2_v1=False, kernel='bisquare', fixed=False, constant=True): + """ + Initialize class + """ + GLM.__init__(self, y, X, family, constant=constant) + self.sigma2_v1 = sigma2_v1 + self.bw = bw + self.kernel = kernel + self.fixed = fixed + if offset is None: + self.offset = np.ones((self.n, 1)) + else: + self.offset = offset * 1.0 + self.fit_params = {} + self.W = self._build_W(fixed, kernel, coords, bw) + + def _build_W(self, fixed, kernel, coords, bw, points=None): + if points is not None: + all_coords = np.vstack([coords, points]) + else: all_coords = coords + + if fixed: + try: + W = fk[kernel](all_coords, bw) + if points is not None: + W = self._shed(W, coords, points, bw, fk[kernel]) + except: + raise TypeError('Unsupported kernel function ', kernel) + else: + + W = ak[kernel](all_coords, bw) + #if points is not None: + #W = self._shed(W, coords, points, bw, fk[kernel]) + #except: + #raise TypeError('Unsupported kernel function ', kernel) + + return W + + def _shed(self, W, coords, points, bw, function): + W_coords = function(coords, bw) + W_points = function(points, bw) + + + def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'): + """ + Method that fits a model with a particular estimation routine. + + Parameters + ---------- + + ini_betas : array + k*1, initial coefficient values, including constant. + Default is None, which calculates initial values during + estimation + tol: float + Tolerence for estimation convergence + max_iter : integer + Maximum number of iterations if convergence not + achieved + solve : string + Technique to solve MLE equations. + 'iwls' = iteratively (re)weighted least squares (default) + """ + self.fit_params['ini_params'] = ini_params + self.fit_params['tol'] = tol + self.fit_params['max_iter'] = max_iter + self.fit_params['solve']= solve + if solve.lower() == 'iwls': + params = np.zeros((self.n, self.k)) + predy = np.zeros((self.n, 1)) + v = np.zeros((self.n, 1)) + w = np.zeros((self.n, 1)) + z = np.zeros((self.n, self.n)) + S = np.zeros((self.n, self.n)) + R = np.zeros((self.n, self.n)) + CCT = np.zeros((self.n, self.k)) + #f = np.zeros((self.n, self.n)) + p = np.zeros((self.n, 1)) + for i in range(self.n): + wi = self.W[i].reshape((-1,1)) + rslt = iwls(self.y, self.X, self.family, self.offset, + ini_params, tol, max_iter, wi=wi) + params[i,:] = rslt[0].T + predy[i] = rslt[1][i] + v[i] = rslt[2][i] + w[i] = rslt[3][i] + z[i] = rslt[4].flatten() + R[i] = np.dot(self.X[i], rslt[5]) + ri = np.dot(self.X[i], rslt[5]) + S[i] = ri*np.reshape(rslt[4].flatten(), (1,-1)) + #dont need unless f is explicitly passed for + #prediction of non-sampled points + #cf = rslt[5] - np.dot(rslt[5], f) + CCT[i] = np.diag(np.dot(rslt[5], rslt[5].T)) + S = S * (1.0/z) + return GWRResults(self, params, predy, S, CCT, w) + + @cache_readonly + def df_model(self): + raise NotImplementedError('Only computed for fitted model in GWRResults') + + @cache_readonly + def df_resid(self): + raise NotImplementedError('Only computed for fitted model in GWRResults') + +class GWRResults(GLMResults): + """ + Basic class including common properties for all GWR regression models + + Parameters + ---------- + model : GWR object + pointer to GWR object with estimation parameters + + params : array + n*k, estimated coefficients + + predy : array + n*1, predicted y values + + w : array + n*1, final weight used for iteratively re-weighted least + sqaures; default is None + + S : array + n*n, hat matrix + + CCT : array + n*k, scaled variance-covariance matrix + + Attributes + ---------- + model : GWR Object + points to GWR object for which parameters have been + estimated + + params : array + n*k, parameter estimates + + predy : array + n*1, predicted value of y + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, including constant + + family : family object + underlying probability model; provides + distribution-specific calculations + + n : integer + number of observations + + k : integer + number of independent variables + + df_model : integer + model degrees of freedom + + df_resid : integer + residual degrees of freedom + + offset : array + n*1, the offset variable at the ith location. + For Poisson model this term is often the size of + the population at risk or the expected size of + the outcome in spatial epidemiology; Default is + None where Ni becomes 1.0 for all locations + + scale : float + sigma squared used for subsequent computations + + w : array + n*1, final weights from iteratively re-weighted least + sqaures routine + + resid_response : array + n*1, residuals of the repsonse + + resid_ss : scalar + residual sum of sqaures + + W : array + n*n; spatial weights for each observation from each + calibration point + + S : array + n*n, hat matrix + + CCT : array + n*k, scaled variance-covariance matrix + + tr_S : float + trace of S (hat) matrix + + tr_STS : float + trace of STS matrix + + tr_SWSTW : float + trace of weighted STS matrix; weights are those output + from iteratively weighted least sqaures (not spatial + weights) + + y_bar : array + n*1, weighted mean value of y + + TSS : array + n*1, geographically weighted total sum of squares + + RSS : array + n*1, geographically weighted residual sum of squares + + localR2 : array + n*1, local R square + + sigma2_v1 : float + sigma squared, use (n-v1) as denominator + + sigma2_v1v2 : float + sigma squared, use (n-2v1+v2) as denominator + + sigma2_ML : float + sigma squared, estimated using ML + + std_res : array + n*1, standardised residuals + + bse : array + n*k, standard errors of parameters (betas) + + influ : array + n*1, leading diagonal of S matrix + + CooksD : array + n*1, Cook's D + + tvalues : array + n*k, local t-statistics + + adj_alpha : array + 3*1, corrected alpha values to account for multiple + hypothesis testing for the 90%, 95%, and 99% confidence + levels; tvalues with an absolute value larger than the + corrected alpha are considered statistically + significant. + + deviance : array + n*1, local model deviance for each calibration point + + resid_deviance : array + n*1, local sum of residual deviance for each + calibration point + + llf : scalar + log-likelihood of the full model; see + pysal.contrib.glm.family for damily-sepcific + log-likelihoods + + pDev : float + local percent of deviation accounted for; analogous to + r-squared for GLM's + + mu : array + n*, flat one dimensional array of predicted mean + response value from estimator + + fit_params : dict + parameters passed into fit method to define estimation + routine + """ + def __init__(self, model, params, predy, S, CCT, w=None): + GLMResults.__init__(self, model, params, predy, w) + self.W = model.W + self.offset = model.offset + if w is not None: + self.w = w + self.predy = predy + self.S = S + self.CCT = self.cov_params(CCT) + self._cache = {} + + @cache_readonly + def resid_ss(self): + u = self.resid_response.flatten() + return np.dot(u, u.T) + + @cache_readonly + def scale(self): + if isinstance(self.family, Gaussian): + if self.model.sigma2_v1: + scale = self.sigma2_v1 + else: + scale = self.sigma2_v1v2 + else: + scale = 1.0 + return scale + + def cov_params(self, cov): + """ + Returns scaled covariance parameters + Parameters + ---------- + cov : array + estimated covariance parameters + + Returns + ------- + Scaled covariance parameters + + """ + return cov*self.scale + + @cache_readonly + def tr_S(self): + """ + trace of S (hat) matrix + """ + return np.trace(self.S*self.w) + + @cache_readonly + def tr_STS(self): + """ + trace of STS matrix + """ + return np.trace(np.dot(self.S.T*self.w,self.S*self.w)) + + @cache_readonly + def y_bar(self): + """ + weighted mean of y + """ + off = self.offset.reshape((-1,1)) + arr_ybar = np.zeros(shape=(self.n,1)) + for i in range(self.n): + w_i= np.reshape(np.array(self.W[i]), (-1, 1)) + sum_yw = np.sum(self.y.reshape((-1,1)) * w_i) + arr_ybar[i] = 1.0 * sum_yw / np.sum(w_i*off) + return arr_ybar + + @cache_readonly + def TSS(self): + """ + geographically weighted total sum of squares + + Methods: p215, (9.9) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + + """ + TSS = np.zeros(shape=(self.n,1)) + for i in range(self.n): + TSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1)) * + (self.y.reshape((-1,1)) - self.y_bar[i])**2) + return TSS + + @cache_readonly + def RSS(self): + """ + geographically weighted residual sum of squares + + Methods: p215, (9.10) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + resid_response = self.resid_response.reshape((-1,1)) + RSS = np.zeros(shape=(self.n,1)) + for i in range(self.n): + RSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1)) + * resid_response**2) + return RSS + + @cache_readonly + def localR2(self): + """ + local R square + + Methods: p215, (9.8) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + if isinstance(self.family, Gaussian): + return (self.TSS - self.RSS)/self.TSS + else: + raise NotImplementedError('Only applicable to Gaussian') + + @cache_readonly + def sigma2_v1(self): + """ + residual variance + + Methods: p214, (9.6), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + + only use v1 + """ + return (self.resid_ss/(self.n-self.tr_S)) + + @cache_readonly + def sigma2_v1v2(self): + """ + residual variance + + Methods: p55 (2.16)-(2.18) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + + use v1 and v2 #used in GWR4 + """ + if isinstance(self.family, (Poisson, Binomial)): + return self.resid_ss/(self.n - 2.0*self.tr_S + + self.tr_STS) #could be changed to SWSTW - nothing to test against + else: + return self.resid_ss/(self.n - 2.0*self.tr_S + + self.tr_STS) #could be changed to SWSTW - nothing to test against + @cache_readonly + def sigma2_ML(self): + """ + residual variance + + Methods: maximum likelihood + """ + return self.resid_ss/self.n + + @cache_readonly + def std_res(self): + """ + standardized residuals + + Methods: p215, (9.7) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + return self.resid_response.reshape((-1,1))/(np.sqrt(self.scale * (1.0 - self.influ))) + + @cache_readonly + def bse(self): + """ + standard errors of Betas + + Methods: p215, (2.15) and (2.21) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + return np.sqrt(self.CCT) + + @cache_readonly + def influ(self): + """ + Influence: leading diagonal of S Matrix + """ + return np.reshape(np.diag(self.S),(-1,1)) + + @cache_readonly + def cooksD(self): + """ + Influence: leading diagonal of S Matrix + + Methods: p216, (9.11), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + Note: in (9.11), p should be tr(S), that is, the effective number of parameters + """ + return self.std_res**2 * self.influ / (self.tr_S * (1.0-self.influ)) + + @cache_readonly + def deviance(self): + off = self.offset.reshape((-1,1)).T + y = self.y + ybar = self.y_bar + if isinstance(self.family, Gaussian): + raise NotImplementedError('deviance not currently used for Gaussian') + elif isinstance(self.family, Poisson): + dev = np.sum(2.0*self.W*(y*np.log(y/(ybar*off))-(y-ybar*off)),axis=1) + elif isinstance(self.family, Binomial): + dev = self.family.deviance(self.y, self.y_bar, self.W, axis=1) + return dev.reshape((-1,1)) + + @cache_readonly + def resid_deviance(self): + if isinstance(self.family, Gaussian): + raise NotImplementedError('deviance not currently used for Gaussian') + else: + off = self.offset.reshape((-1,1)).T + y = self.y + ybar = self.y_bar + global_dev_res = ((self.family.resid_dev(self.y, self.mu))**2) + dev_res = np.repeat(global_dev_res.flatten(),self.n) + dev_res = dev_res.reshape((self.n, self.n)) + dev_res = np.sum(dev_res * self.W.T, axis=0) + return dev_res.reshape((-1,1)) + + @cache_readonly + def pDev(self): + """ + Local percentage of deviance accounted for. Described in the GWR4 + manual. Equivalent to 1 - (deviance/null deviance) + """ + if isinstance(self.family, Gaussian): + raise NotImplementedError('Not implemented for Gaussian') + else: + return 1.0 - (self.resid_deviance/self.deviance) + + @cache_readonly + def adj_alpha(self): + """ + Corrected alpha (critical) values to account for multiple testing during hypothesis + testing. Includes corrected value for 90% (.1), 95% (.05), and 99% + (.01) confidence levels. Correction comes from: + + da Silva, A. R., & Fotheringham, A. S. (2015). The Multiple Testing Issue in + Geographically Weighted Regression. Geographical Analysis. + + """ + alpha = np.array([.1, .05, .001]) + pe = (2.0 * self.tr_S) - self.tr_STS + p = self.k + return (alpha*p)/pe + + def filter_tvals(self, alpha): + """ + Utility function to set tvalues with an absolute value smaller than the + absolute value of the alpha (critical) value to 0 + + Parameters + ---------- + alpha : scalar + critical value to determine which tvalues are + associated with statistically significant parameter + estimates + + Returns + ------- + filtered : array + n*k; new set of n tvalues for each of k variables + where absolute tvalues less than the absolute value of + alpha have been set to 0. + """ + alpha = np.abs(alpha)/2.0 + n = self.n + critical = stats.t.ppf(1-critical, n-1) + subset = (self.tvalues < alpha) & (self.tvalues > -1.0*alpha) + tvalues = self.tvalues.copy() + tvalues[subset] = 0 + return tvalues + + @cache_readonly + def df_model(self): + return self.n - self.tr_S + + @cache_readonly + def df_resid(self): + return self.n - 2.0*self.tr_S + self.tr_STS + + @cache_readonly + def normalized_cov_params(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def resid_pearson(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def resid_working(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def resid_anscombe(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def pearson_chi2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def null(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def llnull(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def null_deviance(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def aic(self): + return get_AIC(self) + + @cache_readonly + def aicc(self): + return get_AICc(self) + + @cache_readonly + def bic(self): + return get_BIC(self) + + @cache_readonly + def D2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def adj_D2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def pseudoR2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def adj_pseudoR2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def pvalues(self): + raise NotImplementedError('Not implemented for GWR') + +class FBGWR(GWR): + """ + Parameters + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates of + observatons; also used as calibration locations is + 'points' is set to None + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + points : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations; default is set to None, which + uses every observation as a calibration point + + bws : array-like + collection of bandwidth values consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW with fb=True. Order of values should the same as + the order of columns associated with X + XB : array + n*k, product of temporary X and params obtained as through-put + from the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + err : array + n*1, temporary residuals associated with the predicted values from + the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + Attributes + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates of + observatons; also used as calibration locations is + 'points' is set to None + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + points : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations; default is set to None, which + uses every observation as a calibration point + + bws : array-like + collection of bandwidth values consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW with fb=True. Order of values should the same as + the order of columns associated with X + XB : array + n*k, product of temporary X and params obtained as through-put + from the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + err : array + n*1, temporary residuals associated with the predicted values from + the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + + Examples + ------- + TODO + + """ + def __init__(self, coords, y, X, bws, XB, err, family=Gaussian(), offset=None, + sigma2_v1=False, kernel='bisquare', fixed=False, constant=True): + """ + Initialize class + """ + self.coords = coords + self.y = y + self.X = X + self.XB = XB + self.err = err + self.bws = bws + self.family = family + self.offset = offset + self.sigma2_v1 = sigma2_v1 + self.kernel = kernel + self.fixed = fixed + self.constant = constant + if constant: + self.X = USER.check_constant(self.X) + + def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'): + """ + Method that fits a model with a particular estimation routine. + + Parameters + ---------- + + ini_betas : array + k*1, initial coefficient values, including constant. + Default is None, which calculates initial values during + estimation + tol: float + Tolerence for estimation convergence + max_iter : integer + Maximum number of iterations if convergence not + achieved + solve : string + Technique to solve MLE equations. + 'iwls' = iteratively (re)weighted least squares (default) + + """ + params = np.zeros_like(self.X) + err = self.err + for i, bw in enumerate(self.bws): + W = self._build_W(self.fixed, self.kernel, self.coords, bw) + X = self.X[:,i].reshape((-1,1)) + y = self.XB[:,i].reshape((-1,1)) + err + model = GWR(self.coords, y, X, bw, self.family, self.offset, + self.sigma2_v1, self.kernel, self.fixed, constant=False) + results = model.fit(ini_params, tol, max_iter, solve) + params[:,i] = results.params.flatten() + err = results.resid_response.reshape((-1,1)) + return FBGWRResults(self, params) + +class FBGWRResults(object): + """ + Parameters + ---------- + model : GWR object + pointer to FBGWR object with estimation parameters + + params : array + n*k, estimated coefficients + + Attributes + ---------- + model : GWR Object + points to FBGWR object for which parameters have been + estimated + + params : array + n*k, parameter estimates + + predy : array + n*1, predicted value of y + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, including constant + + : array + resid_response n*1, residuals of response + + resid_ss : scalar + residual sum of sqaures + + Examples + ------- + TODO + + """ + def __init__(self, model, params): + """ + Initialize class + """ + self.model = model + self.params = params + self.X = model.X + self.y = model.y + self._cache = {} + + @cache_readonly + def predy(self): + return np.sum(np.multiply(self.params, self.X), axis=1).reshape((-1,1)) + + @cache_readonly + def resid_response(self): + return (self.y - self.predy).reshape((-1,1)) + + @cache_readonly + def resid_ss(self): + u = self.resid_response.flatten() + return np.dot(u, u.T) diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/kernels.py b/src/py/crankshaft/crankshaft/regression/gwr/base/kernels.py new file mode 100644 index 0000000..4f3fb6e --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/kernels.py @@ -0,0 +1,120 @@ +# GWR kernel function specifications + +__author__ = "Taylor Oshan tayoshan@gmail.com" + +#from pysal.weights.Distance import Kernel +import scipy +from scipy.spatial.kdtree import KDTree +import numpy as np + +#adaptive specifications should be parameterized with nn-1 to match original gwr +#implementation. That is, pysal counts self neighbors with knn automatically. + +def fix_gauss(points, bw): + w = _Kernel(points, function='gwr_gaussian', bandwidth=bw, + truncate=False) + return w.kernel + +def adapt_gauss(points, nn): + w = _Kernel(points, fixed=False, k=nn-1, function='gwr_gaussian', + truncate=False) + return w.kernel + +def fix_bisquare(points, bw): + w = _Kernel(points, function='bisquare', bandwidth=bw) + return w.kernel + +def adapt_bisquare(points, nn): + w = _Kernel(points, fixed=False, k=nn-1, function='bisquare') + return w.kernel + +def fix_exp(points, bw): + w = _Kernel(points, function='exponential', bandwidth=bw, + truncate=False) + return w.kernel + +def adapt_exp(points, nn): + w = _Kernel(points, fixed=False, k=nn-1, function='exponential', + truncate=False) + return w.kernel + +from scipy.spatial.distance import cdist + +class _Kernel(object): + """ + + """ + def __init__(self, data, bandwidth=None, fixed=True, k=None, + function='triangular', eps=1.0000001, ids=None, truncate=True): #Added truncate flag + if issubclass(type(data), scipy.spatial.KDTree): + self.kdt = data + self.data = self.kdt.data + data = self.data + else: + self.data = data + self.kdt = KDTree(self.data) + if k is not None: + self.k = int(k) + 1 + else: + self.k = k + self.dmat = cdist(self.data, self.data) + self.function = function.lower() + self.fixed = fixed + self.eps = eps + self.trunc = truncate + if bandwidth: + try: + bandwidth = np.array(bandwidth) + bandwidth.shape = (len(bandwidth), 1) + except: + bandwidth = np.ones((len(data), 1), 'float') * bandwidth + self.bandwidth = bandwidth + else: + self._set_bw() + self.kernel = self._kernel_funcs(self.dmat/self.bandwidth) + + if self.trunc: + mask = np.repeat(self.bandwidth, len(self.bandwidth), axis=1) + kernel_mask = self._kernel_funcs(1.0/mask) + self.kernel[(self.dmat >= mask)] = 0 + + + + def _set_bw(self): + if self.k is not None: + dmat = np.sort(self.dmat)[:,:self.k] + else: + dmat = self.dmat + if self.fixed: + # use max knn distance as bandwidth + bandwidth = dmat.max() * self.eps + n = len(self.data) + self.bandwidth = np.ones((n, 1), 'float') * bandwidth + else: + # use local max knn distance + self.bandwidth = dmat.max(axis=1) * self.eps + self.bandwidth.shape = (self.bandwidth.size, 1) + + + def _kernel_funcs(self, zs): + # functions follow Anselin and Rey (2010) table 5.4 + if self.function == 'triangular': + return 1 - zs + elif self.function == 'uniform': + return np.ones(zi.shape) * 0.5 + elif self.function == 'quadratic': + return (3. / 4) * (1 - zs ** 2) + elif self.function == 'quartic': + return (15. / 16) * (1 - zs ** 2) ** 2 + elif self.function == 'gaussian': + c = np.pi * 2 + c = c ** (-0.5) + return c * np.exp(-(zs ** 2) / 2.) + elif self.function == 'gwr_gaussian': + return np.exp(-0.5*(zs)**2) + elif self.function == 'bisquare': + return (1-(zs)**2)**2 + elif self.function =='exponential': + return np.exp(-zs) + else: + print('Unsupported kernel function', self.function) diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/search.py b/src/py/crankshaft/crankshaft/regression/gwr/base/search.py new file mode 100644 index 0000000..97de4be --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/search.py @@ -0,0 +1,208 @@ +#Bandwidth optimization methods + +__author__ = "Taylor Oshan" + +import numpy as np + +def golden_section(a, c, delta, function, tol, max_iter, int_score=False): + """ + Golden section search routine + Method: p212, 9.6.4 + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + + Parameters + ---------- + a : float + initial max search section value + b : float + initial min search section value + delta : float + constant used to determine width of search sections + function : function + obejective function to be evaluated at different section + values + int_score : boolean + False for float score, True for integer score + tol : float + tolerance used to determine convergence + max_iter : integer + maximum iterations if no convergence to tolerance + + Returns + ------- + opt_val : float + optimal value + opt_score : kernel + optimal score + output : list of tuples + searching history + """ + b = a + delta * np.abs(c-a) + d = c - delta * np.abs(c-a) + score = 0.0 + diff = 1.0e9 + iters = 0 + output = [] + while np.abs(diff) > tol and iters < max_iter: + iters += 1 + if int_score: + b = np.round(b) + d = np.round(d) + + score_a = function(a) + score_b = function(b) + score_c = function(c) + score_d = function(d) + + if score_b <= score_d: + opt_val = b + opt_score = score_b + c = d + d = b + b = a + delta * np.abs(c-a) + #if int_score: + #b = np.round(b) + else: + opt_val = d + opt_score = score_d + a = b + b = d + d = c - delta * np.abs(c-a) + #if int_score: + #d = np.round(b) + + #if int_score: + # opt_val = np.round(opt_val) + output.append((opt_val, opt_score)) + diff = score_b - score_d + score = opt_score + return np.round(opt_val, 2), opt_score, output + +def equal_interval(l_bound, u_bound, interval, function, int_score=False): + """ + Interval search, using interval as stepsize + + Parameters + ---------- + l_bound : float + initial min search section value + u_bound : float + initial max search section value + interval : float + constant used to determine width of search sections + function : function + obejective function to be evaluated at different section + values + int_score : boolean + False for float score, True for integer score + + Returns + ------- + opt_val : float + optimal value + opt_score : kernel + optimal score + output : list of tuples + searching history + """ + a = l_bound + c = u_bound + b = a + interval + if int_score: + a = np.round(a,0) + c = np.round(c,0) + b = np.round(b,0) + + output = [] + + score_a = function(a) + score_c = function(c) + + output.append((a,score_a)) + output.append((c,score_c)) + + if score_a < score_c: + opt_val = a + opt_score = score_a + else: + opt_val = c + opt_score = score_c + + while b < c: + score_b = function(b) + + output.append((b,score_b)) + + if score_b < opt_score: + opt_val = b + opt_score = score_b + b = b + interval + + return opt_val, opt_score, output + + +def flexible_bw(init, y, X, n, k, family, tol, max_iter, rss_score, + gwr_func, bw_func, sel_func): + if init: + bw = sel_func(bw_func(y, X)) + print bw + optim_model = gwr_func(y, X, bw) + err = optim_model.resid_response.reshape((-1,1)) + est = optim_model.params + else: + model = GLM(y, X, family=self.family, constant=False).fit() + err = model.resid_response.reshape((-1,1)) + est = np.repeat(model.params.T, n, axis=0) + + + XB = np.multiply(est, X) + if rss_score: + rss = np.sum((err)**2) + iters = 0 + scores = [] + delta = 1e6 + BWs = [] + VALs = [] + + while delta > tol and iters < max_iter: + iters += 1 + new_XB = np.zeros_like(X) + bws = [] + vals = [] + ests = np.zeros_like(X) + f_XB = XB.copy() + f_err = err.copy() + for i in range(k): + temp_y = XB[:,i].reshape((-1,1)) + temp_y = temp_y + err + temp_X = X[:,i].reshape((-1,1)) + bw_class = bw_func(temp_y, temp_X) + bw = sel_func(bw_class) + optim_model = gwr_func(temp_y, temp_X, bw) + err = optim_model.resid_response.reshape((-1,1)) + est = optim_model.params.reshape((-1,)) + + new_XB[:,i] = np.multiply(est, temp_X.reshape((-1,))) + bws.append(bw) + ests[:,i] = est + vals.append(bw_class.bw[1]) + + predy = np.sum(np.multiply(ests, X), axis=1).reshape((-1,1)) + num = np.sum((new_XB - XB)**2)/n + den = np.sum(np.sum(new_XB, axis=1)**2) + score = (num/den)**0.5 + XB = new_XB + + if rss_score: + new_rss = np.sum((y - predy)**2) + score = np.abs((new_rss - rss)/new_rss) + rss = new_rss + print score + scores.append(score) + delta = score + BWs.append(bws) + VALs.append(vals) + + opt_bws = BWs[-1] + return opt_bws, np.array(BWs), np.array(VALs), np.array(scores), f_XB, f_err diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/sel_bw.py b/src/py/crankshaft/crankshaft/regression/gwr/base/sel_bw.py new file mode 100644 index 0000000..2c26d03 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/sel_bw.py @@ -0,0 +1,286 @@ +# GWR Bandwidth selection class + +#Thinking about removing the search method and just having optimization begin in +#class __init__ + +#x_glob and offset parameters dont yet do anything; former is for semiparametric +#GWR and later is for offset variable for Poisson model + +__author__ = "Taylor Oshan Tayoshan@gmail.com" + +from kernels import * +from search import golden_section, equal_interval, flexible_bw +from gwr import GWR +from pysal.contrib.glm.family import Gaussian, Poisson, Binomial +import pysal.spreg.user_output as USER +from diagnostics import get_AICc, get_AIC, get_BIC, get_CV +from scipy.spatial.distance import pdist, squareform +from pysal.common import KDTree +import numpy as np + +kernels = {1: fix_gauss, 2: adapt_gauss, 3: fix_bisquare, 4: + adapt_bisquare, 5: fix_exp, 6:adapt_exp} +getDiag = {'AICc': get_AICc,'AIC':get_AIC, 'BIC': get_BIC, 'CV': get_CV} + +class Sel_BW(object): + """ + Select bandwidth for kernel + + Methods: p211 - p213, bandwidth selection + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + + Parameters + ---------- + y : array + n*1, dependent variable. + x_glob : array + n*k1, fixed independent variable. + x_loc : array + n*k2, local independent variable, including constant. + coords : list of tuples + (x,y) of points used in bandwidth selection + family : string + GWR model type: 'Gaussian', 'logistic, 'Poisson'' + offset : array + n*1, offset variable for Poisson model + kernel : string + kernel function: 'gaussian', 'bisquare', 'exponetial' + fixed : boolean + True for fixed bandwidth and False for adaptive (NN) + fb : True for flexible (mutliple covaraite-specific) bandwidths + False for a traditional (same for all covariates) + bandwdith; defualt is False. + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + + Attributes + ---------- + y : array + n*1, dependent variable. + x_glob : array + n*k1, fixed independent variable. + x_loc : array + n*k2, local independent variable, including constant. + coords : list of tuples + (x,y) of points used in bandwidth selection + family : string + GWR model type: 'Gaussian', 'logistic, 'Poisson'' + kernel : string + type of kernel used and wether fixed or adaptive + criterion : string + bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV' + search : string + bw search method: 'golden', 'interval' + bw_min : float + min value used in bandwidth search + bw_max : float + max value used in bandwidth search + interval : float + interval increment used in interval search + tol : float + tolerance used to determine convergence + max_iter : integer + max interations if no convergence to tol + fb : True for flexible (mutliple covaraite-specific) bandwidths + False for a traditional (same for all covariates) + bandwdith; defualt is False. + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + """ + def __init__(self, coords, y, x_loc, x_glob=None, family=Gaussian(), + offset=None, kernel='bisquare', fixed=False, fb=False, constant=True): + self.coords = coords + self.y = y + self.x_loc = x_loc + if x_glob is not None: + self.x_glob = x_glob + else: + self.x_glob = [] + self.family=family + self.fixed = fixed + self.kernel = kernel + if offset is None: + self.offset = np.ones((len(y), 1)) + else: + self.offset = offset * 1.0 + self.fb = fb + self.constant = constant + + def search(self, search='golden_section', criterion='AICc', bw_min=0.0, + bw_max=0.0, interval=0.0, tol=1.0e-6, max_iter=200, init_fb=True, + tol_fb=1.0e-5, rss_score=False, max_iter_fb=200): + """ + Parameters + ---------- + criterion : string + bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV' + search : string + bw search method: 'golden', 'interval' + bw_min : float + min value used in bandwidth search + bw_max : float + max value used in bandwidth search + interval : float + interval increment used in interval search + tol : float + tolerance used to determine convergence + max_iter : integer + max iterations if no convergence to tol + init_fb : True to initialize flexible bandwidth search with + esitmates from a traditional GWR and False to + initialize flexible bandwidth search with global + regression estimates + tol_fb : convergence tolerence for the flexible bandwidth + backfitting algorithm; a larger tolerance may stop the + algorith faster though it may result in a less optimal + model + max_iter_fb : max iterations if no convergence to tol for flexible + bandwidth backfittign algorithm + rss_score : True to use the residual sum of sqaures to evaluate + each iteration of the flexible bandwidth backfitting + routine and False to use a smooth function; default is + False + + Returns + ------- + bw : scalar or array + optimal bandwidth value or values; returns scalar for + fb=False and array for fb=True; ordering of bandwidths + matches the ordering of the covariates (columns) of the + designs matrix, X + """ + self.search = search + self.criterion = criterion + self.bw_min = bw_min + self.bw_max = bw_max + self.interval = interval + self.tol = tol + self.max_iter = max_iter + self.init_fb = init_fb + self.tol_fb = tol_fb + self.rss_score = rss_score + self.max_iter_fb = max_iter_fb + + + if self.fixed: + if self.kernel == 'gaussian': + ktype = 1 + elif self.kernel == 'bisquare': + ktype = 3 + elif self.kernel == 'exponential': + ktype = 5 + else: + raise TypeError('Unsupported kernel function ', self.kernel) + else: + if self.kernel == 'gaussian': + ktype = 2 + elif self.kernel == 'bisquare': + ktype = 4 + elif self.kernel == 'exponential': + ktype = 6 + else: + raise TypeError('Unsupported kernel function ', self.kernel) + + function = lambda bw: getDiag[criterion]( + GWR(self.coords, self.y, self.x_loc, bw, family=self.family, + kernel=self.kernel, fixed=self.fixed, offset=self.offset).fit()) + + if ktype % 2 == 0: + int_score = True + else: + int_score = False + self.int_score = int_score + + if self.fb: + self._fbw() + print self.bw[1] + self.XB = self.bw[4] + self.err = self.bw[5] + else: + self._bw() + + return self.bw[0] + + def _bw(self): + gwr_func = lambda bw: getDiag[self.criterion]( + GWR(self.coords, self.y, self.x_loc, bw, family=self.family, + kernel=self.kernel, fixed=self.fixed, constant=self.constant).fit()) + if self.search == 'golden_section': + a,c = self._init_section(self.x_glob, self.x_loc, self.coords, + self.constant) + delta = 0.38197 #1 - (np.sqrt(5.0)-1.0)/2.0 + self.bw = golden_section(a, c, delta, gwr_func, self.tol, + self.max_iter, self.int_score) + elif self.search == 'interval': + self.bw = equal_interval(self.bw_min, self.bw_max, self.interval, + gwr_func, self.int_score) + else: + raise TypeError('Unsupported computational search method ', search) + + def _fbw(self): + y = self.y + if self.constant: + X = USER.check_constant(self.x_loc) + else: + X = self.x_loc + n, k = X.shape + family = self.family + offset = self.offset + kernel = self.kernel + fixed = self.fixed + coords = self.coords + search = self.search + criterion = self.criterion + bw_min = self.bw_min + bw_max = self.bw_max + interval = self.interval + tol = self.tol + max_iter = self.max_iter + gwr_func = lambda y, X, bw: GWR(coords, y, X, bw, family=family, + kernel=kernel, fixed=fixed, offset=offset, constant=False).fit() + bw_func = lambda y, X: Sel_BW(coords, y, X, x_glob=[], family=family, + kernel=kernel, fixed=fixed, offset=offset, constant=False) + sel_func = lambda bw_func: bw_func.search(search=search, + criterion=criterion, bw_min=bw_min, bw_max=bw_max, + interval=interval, tol=tol, max_iter=max_iter) + self.bw = flexible_bw(self.init_fb, y, X, n, k, family, self.tol_fb, + self.max_iter_fb, self.rss_score, gwr_func, bw_func, sel_func) + + + + def _init_section(self, x_glob, x_loc, coords, constant): + if len(x_glob) > 0: + n_glob = x_glob.shape[1] + else: + n_glob = 0 + if len(x_loc) > 0: + n_loc = x_loc.shape[1] + else: + n_loc = 0 + if constant: + n_vars = n_glob + n_loc + 1 + else: + n_vars = n_glob + n_loc + n = np.array(coords).shape[0] + + if self.int_score: + a = 40 + 2 * n_vars + c = n + else: + nn = 40 + 2 * n_vars + sq_dists = squareform(pdist(coords)) + sort_dists = np.sort(sq_dists, axis=1) + min_dists = sort_dists[:,nn-1] + max_dists = sort_dists[:,-1] + a = np.min(min_dists)/2.0 + c = np.max(max_dists)/2.0 + + if a < self.bw_min: + a = self.bw_min + if c > self.bw_max and self.bw_max > 0: + c = self.bw_max + return a, c diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py b/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py new file mode 100644 index 0000000..c5de085 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py @@ -0,0 +1,785 @@ +""" +GWR is tested against results from GWR4 +""" + +import unittest +import pickle as pk +from pysal.contrib.gwr.gwr import GWR, FBGWR +from pysal.contrib.gwr.diagnostics import get_AICc, get_AIC, get_BIC, get_CV +from pysal.contrib.glm.family import Gaussian, Poisson, Binomial +import numpy as np +import pysal + +class TestGWRGaussian(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('GData_utm.csv')) + self.coords = zip(data.by_col('X'), data.by_col('Y')) + self.y = np.array(data.by_col('PctBach')).reshape((-1,1)) + rural = np.array(data.by_col('PctRural')).reshape((-1,1)) + pov = np.array(data.by_col('PctPov')).reshape((-1,1)) + black = np.array(data.by_col('PctBlack')).reshape((-1,1)) + self.X = np.hstack([rural, pov, black]) + self.BS_F = pysal.open(pysal.examples.get_path('georgia_BS_F_listwise.csv')) + self.BS_NN = pysal.open(pysal.examples.get_path('georgia_BS_NN_listwise.csv')) + self.GS_F = pysal.open(pysal.examples.get_path('georgia_GS_F_listwise.csv')) + self.GS_NN = pysal.open(pysal.examples.get_path('georgia_GS_NN_listwise.csv')) + self.FB = pk.load(open(pysal.examples.get_path('FB.p'), 'r')) + self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r')) + self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r')) + + def test_BS_F(self): + est_Int = self.BS_F.by_col(' est_Intercept') + se_Int = self.BS_F.by_col(' se_Intercept') + t_Int = self.BS_F.by_col(' t_Intercept') + est_rural = self.BS_F.by_col(' est_PctRural') + se_rural = self.BS_F.by_col(' se_PctRural') + t_rural = self.BS_F.by_col(' t_PctRural') + est_pov = self.BS_F.by_col(' est_PctPov') + se_pov = self.BS_F.by_col(' se_PctPov') + t_pov = self.BS_F.by_col(' t_PctPov') + est_black = self.BS_F.by_col(' est_PctBlack') + se_black = self.BS_F.by_col(' se_PctBlack') + t_black = self.BS_F.by_col(' t_PctBlack') + yhat = self.BS_F.by_col(' yhat') + res = np.array(self.BS_F.by_col(' residual')) + std_res = np.array(self.BS_F.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.BS_F.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.BS_F.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.BS_F.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=209267.689, fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + self.assertAlmostEquals(np.floor(AICc), 894.0) + self.assertAlmostEquals(np.floor(AIC), 890.0) + self.assertAlmostEquals(np.floor(BIC), 944.0) + self.assertAlmostEquals(np.round(CV,2), 18.25) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_BS_NN(self): + est_Int = self.BS_NN.by_col(' est_Intercept') + se_Int = self.BS_NN.by_col(' se_Intercept') + t_Int = self.BS_NN.by_col(' t_Intercept') + est_rural = self.BS_NN.by_col(' est_PctRural') + se_rural = self.BS_NN.by_col(' se_PctRural') + t_rural = self.BS_NN.by_col(' t_PctRural') + est_pov = self.BS_NN.by_col(' est_PctPov') + se_pov = self.BS_NN.by_col(' se_PctPov') + t_pov = self.BS_NN.by_col(' t_PctPov') + est_black = self.BS_NN.by_col(' est_PctBlack') + se_black = self.BS_NN.by_col(' se_PctBlack') + t_black = self.BS_NN.by_col(' t_PctBlack') + yhat = self.BS_NN.by_col(' yhat') + res = np.array(self.BS_NN.by_col(' residual')) + std_res = np.array(self.BS_NN.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.BS_NN.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.BS_NN.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.BS_NN.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=90.000, fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + self.assertAlmostEquals(np.floor(AICc), 896.0) + self.assertAlmostEquals(np.floor(AIC), 892.0) + self.assertAlmostEquals(np.floor(BIC), 941.0) + self.assertAlmostEquals(np.around(CV, 2), 19.19) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_GS_F(self): + est_Int = self.GS_F.by_col(' est_Intercept') + se_Int = self.GS_F.by_col(' se_Intercept') + t_Int = self.GS_F.by_col(' t_Intercept') + est_rural = self.GS_F.by_col(' est_PctRural') + se_rural = self.GS_F.by_col(' se_PctRural') + t_rural = self.GS_F.by_col(' t_PctRural') + est_pov = self.GS_F.by_col(' est_PctPov') + se_pov = self.GS_F.by_col(' se_PctPov') + t_pov = self.GS_F.by_col(' t_PctPov') + est_black = self.GS_F.by_col(' est_PctBlack') + se_black = self.GS_F.by_col(' se_PctBlack') + t_black = self.GS_F.by_col(' t_PctBlack') + yhat = self.GS_F.by_col(' yhat') + res = np.array(self.GS_F.by_col(' residual')) + std_res = np.array(self.GS_F.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.GS_F.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.GS_F.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.GS_F.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=87308.298, + kernel='gaussian', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + self.assertAlmostEquals(np.floor(AICc), 895.0) + self.assertAlmostEquals(np.floor(AIC), 890.0) + self.assertAlmostEquals(np.floor(BIC), 943.0) + self.assertAlmostEquals(np.around(CV, 2), 18.21) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_GS_NN(self): + est_Int = self.GS_NN.by_col(' est_Intercept') + se_Int = self.GS_NN.by_col(' se_Intercept') + t_Int = self.GS_NN.by_col(' t_Intercept') + est_rural = self.GS_NN.by_col(' est_PctRural') + se_rural = self.GS_NN.by_col(' se_PctRural') + t_rural = self.GS_NN.by_col(' t_PctRural') + est_pov = self.GS_NN.by_col(' est_PctPov') + se_pov = self.GS_NN.by_col(' se_PctPov') + t_pov = self.GS_NN.by_col(' t_PctPov') + est_black = self.GS_NN.by_col(' est_PctBlack') + se_black = self.GS_NN.by_col(' se_PctBlack') + t_black = self.GS_NN.by_col(' t_PctBlack') + yhat = self.GS_NN.by_col(' yhat') + res = np.array(self.GS_NN.by_col(' residual')) + std_res = np.array(self.GS_NN.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.GS_NN.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.GS_NN.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.GS_NN.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=49.000, + kernel='gaussian', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + self.assertAlmostEquals(np.floor(AICc), 896) + self.assertAlmostEquals(np.floor(AIC), 894.0) + self.assertAlmostEquals(np.floor(BIC), 922.0) + self.assertAlmostEquals(np.around(CV, 2), 17.91) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_FBGWR(self): + model = FBGWR(self.coords, self.y, self.X, [157.0, 65.0, 52.0], + XB=self.XB, err=self.err, constant=False) + rslt = model.fit() + + np.testing.assert_allclose(rslt.predy, self.FB['predy'], atol=1e-07) + np.testing.assert_allclose(rslt.params, self.FB['params'], atol=1e-07) + np.testing.assert_allclose(rslt.resid_response, self.FB['u'], atol=1e-05) + np.testing.assert_almost_equal(rslt.resid_ss, 6339.3497144025841) + +class TestGWRPoisson(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('Tokyomortality.csv'), mode='Ur') + self.coords = zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID')) + self.y = np.array(data.by_col('db2564')).reshape((-1,1)) + self.off = np.array(data.by_col('eb2564')).reshape((-1,1)) + OCC = np.array(data.by_col('OCC_TEC')).reshape((-1,1)) + OWN = np.array(data.by_col('OWNH')).reshape((-1,1)) + POP = np.array(data.by_col('POP65')).reshape((-1,1)) + UNEMP = np.array(data.by_col('UNEMP')).reshape((-1,1)) + self.X = np.hstack([OCC,OWN,POP,UNEMP]) + self.BS_F = pysal.open(pysal.examples.get_path('tokyo_BS_F_listwise.csv')) + self.BS_NN = pysal.open(pysal.examples.get_path('tokyo_BS_NN_listwise.csv')) + self.GS_F = pysal.open(pysal.examples.get_path('tokyo_GS_F_listwise.csv')) + self.GS_NN = pysal.open(pysal.examples.get_path('tokyo_GS_NN_listwise.csv')) + self.BS_NN_OFF = pysal.open(pysal.examples.get_path('tokyo_BS_NN_OFF_listwise.csv')) + + def test_BS_F(self): + est_Int = self.BS_F.by_col(' est_Intercept') + se_Int = self.BS_F.by_col(' se_Intercept') + t_Int = self.BS_F.by_col(' t_Intercept') + est_OCC = self.BS_F.by_col(' est_OCC_TEC') + se_OCC = self.BS_F.by_col(' se_OCC_TEC') + t_OCC = self.BS_F.by_col(' t_OCC_TEC') + est_OWN = self.BS_F.by_col(' est_OWNH') + se_OWN = self.BS_F.by_col(' se_OWNH') + t_OWN = self.BS_F.by_col(' t_OWNH') + est_POP = self.BS_F.by_col(' est_POP65') + se_POP = self.BS_F.by_col(' se_POP65') + t_POP = self.BS_F.by_col(' t_POP65') + est_UNEMP = self.BS_F.by_col(' est_UNEMP') + se_UNEMP = self.BS_F.by_col(' se_UNEMP') + t_UNEMP = self.BS_F.by_col(' t_UNEMP') + yhat = self.BS_F.by_col(' yhat') + pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=26029.625, family=Poisson(), + kernel='bisquare', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 13294.0) + self.assertAlmostEquals(np.floor(AIC), 13247.0) + self.assertAlmostEquals(np.floor(BIC), 13485.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-05) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-03) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-03) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-03) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-03) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-04) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + + def test_BS_NN(self): + est_Int = self.BS_NN.by_col(' est_Intercept') + se_Int = self.BS_NN.by_col(' se_Intercept') + t_Int = self.BS_NN.by_col(' t_Intercept') + est_OCC = self.BS_NN.by_col(' est_OCC_TEC') + se_OCC = self.BS_NN.by_col(' se_OCC_TEC') + t_OCC = self.BS_NN.by_col(' t_OCC_TEC') + est_OWN = self.BS_NN.by_col(' est_OWNH') + se_OWN = self.BS_NN.by_col(' se_OWNH') + t_OWN = self.BS_NN.by_col(' t_OWNH') + est_POP = self.BS_NN.by_col(' est_POP65') + se_POP = self.BS_NN.by_col(' se_POP65') + t_POP = self.BS_NN.by_col(' t_POP65') + est_UNEMP = self.BS_NN.by_col(' est_UNEMP') + se_UNEMP = self.BS_NN.by_col(' se_UNEMP') + t_UNEMP = self.BS_NN.by_col(' t_UNEMP') + yhat = self.BS_NN.by_col(' yhat') + pdev = np.array(self.BS_NN.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), + kernel='bisquare', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 13285) + self.assertAlmostEquals(np.floor(AIC), 13259.0) + self.assertAlmostEquals(np.floor(BIC), 13442.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_BS_NN_Offset(self): + est_Int = self.BS_NN_OFF.by_col(' est_Intercept') + se_Int = self.BS_NN_OFF.by_col(' se_Intercept') + t_Int = self.BS_NN_OFF.by_col(' t_Intercept') + est_OCC = self.BS_NN_OFF.by_col(' est_OCC_TEC') + se_OCC = self.BS_NN_OFF.by_col(' se_OCC_TEC') + t_OCC = self.BS_NN_OFF.by_col(' t_OCC_TEC') + est_OWN = self.BS_NN_OFF.by_col(' est_OWNH') + se_OWN = self.BS_NN_OFF.by_col(' se_OWNH') + t_OWN = self.BS_NN_OFF.by_col(' t_OWNH') + est_POP = self.BS_NN_OFF.by_col(' est_POP65') + se_POP = self.BS_NN_OFF.by_col(' se_POP65') + t_POP = self.BS_NN_OFF.by_col(' t_POP65') + est_UNEMP = self.BS_NN_OFF.by_col(' est_UNEMP') + se_UNEMP = self.BS_NN_OFF.by_col(' se_UNEMP') + t_UNEMP = self.BS_NN_OFF.by_col(' t_UNEMP') + yhat = self.BS_NN_OFF.by_col(' yhat') + pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(), + kernel='bisquare', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 367.0) + self.assertAlmostEquals(np.floor(AIC), 361.0) + self.assertAlmostEquals(np.floor(BIC), 451.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02, + atol=1e-02) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03, + atol=1e-02) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04, + atol=1e-02) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03, + atol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04, + atol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02, + atol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-03, atol=1e-02) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-04, atol=1e-02) + + def test_GS_F(self): + est_Int = self.GS_F.by_col(' est_Intercept') + se_Int = self.GS_F.by_col(' se_Intercept') + t_Int = self.GS_F.by_col(' t_Intercept') + est_OCC = self.GS_F.by_col(' est_OCC_TEC') + se_OCC = self.GS_F.by_col(' se_OCC_TEC') + t_OCC = self.GS_F.by_col(' t_OCC_TEC') + est_OWN = self.GS_F.by_col(' est_OWNH') + se_OWN = self.GS_F.by_col(' se_OWNH') + t_OWN = self.GS_F.by_col(' t_OWNH') + est_POP = self.GS_F.by_col(' est_POP65') + se_POP = self.GS_F.by_col(' se_POP65') + t_POP = self.GS_F.by_col(' t_POP65') + est_UNEMP = self.GS_F.by_col(' est_UNEMP') + se_UNEMP = self.GS_F.by_col(' se_UNEMP') + t_UNEMP = self.GS_F.by_col(' t_UNEMP') + yhat = self.GS_F.by_col(' yhat') + pdev = np.array(self.GS_F.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=8764.474, family=Poisson(), + kernel='gaussian', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 11283.0) + self.assertAlmostEquals(np.floor(AIC), 11211.0) + self.assertAlmostEquals(np.floor(BIC), 11497.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-03) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-03) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_GS_NN(self): + est_Int = self.GS_NN.by_col(' est_Intercept') + se_Int = self.GS_NN.by_col(' se_Intercept') + t_Int = self.GS_NN.by_col(' t_Intercept') + est_OCC = self.GS_NN.by_col(' est_OCC_TEC') + se_OCC = self.GS_NN.by_col(' se_OCC_TEC') + t_OCC = self.GS_NN.by_col(' t_OCC_TEC') + est_OWN = self.GS_NN.by_col(' est_OWNH') + se_OWN = self.GS_NN.by_col(' se_OWNH') + t_OWN = self.GS_NN.by_col(' t_OWNH') + est_POP = self.GS_NN.by_col(' est_POP65') + se_POP = self.GS_NN.by_col(' se_POP65') + t_POP = self.GS_NN.by_col(' t_POP65') + est_UNEMP = self.GS_NN.by_col(' est_UNEMP') + se_UNEMP = self.GS_NN.by_col(' se_UNEMP') + t_UNEMP = self.GS_NN.by_col(' t_UNEMP') + yhat = self.GS_NN.by_col(' yhat') + pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), + kernel='gaussian', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 21070.0) + self.assertAlmostEquals(np.floor(AIC), 21069.0) + self.assertAlmostEquals(np.floor(BIC), 21111.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + +class TestGWRBinomial(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('landslides.csv')) + self.coords = zip(data.by_col('X'), data.by_col('Y')) + self.y = np.array(data.by_col('Landslid')).reshape((-1,1)) + ELEV = np.array(data.by_col('Elev')).reshape((-1,1)) + SLOPE = np.array(data.by_col('Slope')).reshape((-1,1)) + SIN = np.array(data.by_col('SinAspct')).reshape((-1,1)) + COS = np.array(data.by_col('CosAspct')).reshape((-1,1)) + SOUTH = np.array(data.by_col('AbsSouth')).reshape((-1,1)) + DIST = np.array(data.by_col('DistStrm')).reshape((-1,1)) + self.X = np.hstack([ELEV, SLOPE, SIN, COS, SOUTH, DIST]) + self.BS_F = pysal.open(pysal.examples.get_path('clearwater_BS_F_listwise.csv')) + self.BS_NN = pysal.open(pysal.examples.get_path('clearwater_BS_NN_listwise.csv')) + self.GS_F = pysal.open(pysal.examples.get_path('clearwater_GS_F_listwise.csv')) + self.GS_NN = pysal.open(pysal.examples.get_path('clearwater_GS_NN_listwise.csv')) + + def test_BS_F(self): + est_Int = self.BS_F.by_col(' est_Intercept') + se_Int = self.BS_F.by_col(' se_Intercept') + t_Int = self.BS_F.by_col(' t_Intercept') + est_elev = self.BS_F.by_col(' est_Elev') + se_elev = self.BS_F.by_col(' se_Elev') + t_elev = self.BS_F.by_col(' t_Elev') + est_slope = self.BS_F.by_col(' est_Slope') + se_slope = self.BS_F.by_col(' se_Slope') + t_slope = self.BS_F.by_col(' t_Slope') + est_sin = self.BS_F.by_col(' est_SinAspct') + se_sin = self.BS_F.by_col(' se_SinAspct') + t_sin = self.BS_F.by_col(' t_SinAspct') + est_cos = self.BS_F.by_col(' est_CosAspct') + se_cos = self.BS_F.by_col(' se_CosAspct') + t_cos = self.BS_F.by_col(' t_CosAspct') + est_south = self.BS_F.by_col(' est_AbsSouth') + se_south = self.BS_F.by_col(' se_AbsSouth') + t_south = self.BS_F.by_col(' t_AbsSouth') + est_strm = self.BS_F.by_col(' est_DistStrm') + se_strm = self.BS_F.by_col(' se_DistStrm') + t_strm = self.BS_F.by_col(' t_DistStrm') + yhat = self.BS_F.by_col(' yhat') + pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=19642.170, family=Binomial(), + kernel='bisquare', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 275.0) + self.assertAlmostEquals(np.floor(AIC), 271.0) + self.assertAlmostEquals(np.floor(BIC), 349.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_BS_NN(self): + est_Int = self.BS_NN.by_col(' est_Intercept') + se_Int = self.BS_NN.by_col(' se_Intercept') + t_Int = self.BS_NN.by_col(' t_Intercept') + est_elev = self.BS_NN.by_col(' est_Elev') + se_elev = self.BS_NN.by_col(' se_Elev') + t_elev = self.BS_NN.by_col(' t_Elev') + est_slope = self.BS_NN.by_col(' est_Slope') + se_slope = self.BS_NN.by_col(' se_Slope') + t_slope = self.BS_NN.by_col(' t_Slope') + est_sin = self.BS_NN.by_col(' est_SinAspct') + se_sin = self.BS_NN.by_col(' se_SinAspct') + t_sin = self.BS_NN.by_col(' t_SinAspct') + est_cos = self.BS_NN.by_col(' est_CosAspct') + se_cos = self.BS_NN.by_col(' se_CosAspct') + t_cos = self.BS_NN.by_col(' t_CosAspct') + est_south = self.BS_NN.by_col(' est_AbsSouth') + se_south = self.BS_NN.by_col(' se_AbsSouth') + t_south = self.BS_NN.by_col(' t_AbsSouth') + est_strm = self.BS_NN.by_col(' est_DistStrm') + se_strm = self.BS_NN.by_col(' se_DistStrm') + t_strm = self.BS_NN.by_col(' t_DistStrm') + yhat = self.BS_NN.by_col(' yhat') + pdev = self.BS_NN.by_col(' localpdev') + + model = GWR(self.coords, self.y, self.X, bw=158, family=Binomial(), + kernel='bisquare', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 277.0) + self.assertAlmostEquals(np.floor(AIC), 271.0) + self.assertAlmostEquals(np.floor(BIC), 358.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e03) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e03) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_GS_F(self): + est_Int = self.GS_F.by_col(' est_Intercept') + se_Int = self.GS_F.by_col(' se_Intercept') + t_Int = self.GS_F.by_col(' t_Intercept') + est_elev = self.GS_F.by_col(' est_Elev') + se_elev = self.GS_F.by_col(' se_Elev') + t_elev = self.GS_F.by_col(' t_Elev') + est_slope = self.GS_F.by_col(' est_Slope') + se_slope = self.GS_F.by_col(' se_Slope') + t_slope = self.GS_F.by_col(' t_Slope') + est_sin = self.GS_F.by_col(' est_SinAspct') + se_sin = self.GS_F.by_col(' se_SinAspct') + t_sin = self.GS_F.by_col(' t_SinAspct') + est_cos = self.GS_F.by_col(' est_CosAspct') + se_cos = self.GS_F.by_col(' se_CosAspct') + t_cos = self.GS_F.by_col(' t_CosAspct') + est_south = self.GS_F.by_col(' est_AbsSouth') + se_south = self.GS_F.by_col(' se_AbsSouth') + t_south = self.GS_F.by_col(' t_AbsSouth') + est_strm = self.GS_F.by_col(' est_DistStrm') + se_strm = self.GS_F.by_col(' se_DistStrm') + t_strm = self.GS_F.by_col(' t_DistStrm') + yhat = self.GS_F.by_col(' yhat') + pdev = self.GS_F.by_col(' localpdev') + + model = GWR(self.coords, self.y, self.X, bw=8929.061, family=Binomial(), + kernel='gaussian', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 276.0) + self.assertAlmostEquals(np.floor(AIC), 272.0) + self.assertAlmostEquals(np.floor(BIC), 341.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_GS_NN(self): + est_Int = self.GS_NN.by_col(' est_Intercept') + se_Int = self.GS_NN.by_col(' se_Intercept') + t_Int = self.GS_NN.by_col(' t_Intercept') + est_elev = self.GS_NN.by_col(' est_Elev') + se_elev = self.GS_NN.by_col(' se_Elev') + t_elev = self.GS_NN.by_col(' t_Elev') + est_slope = self.GS_NN.by_col(' est_Slope') + se_slope = self.GS_NN.by_col(' se_Slope') + t_slope = self.GS_NN.by_col(' t_Slope') + est_sin = self.GS_NN.by_col(' est_SinAspct') + se_sin = self.GS_NN.by_col(' se_SinAspct') + t_sin = self.GS_NN.by_col(' t_SinAspct') + est_cos = self.GS_NN.by_col(' est_CosAspct') + se_cos = self.GS_NN.by_col(' se_CosAspct') + t_cos = self.GS_NN.by_col(' t_CosAspct') + est_south = self.GS_NN.by_col(' est_AbsSouth') + se_south = self.GS_NN.by_col(' se_AbsSouth') + t_south = self.GS_NN.by_col(' t_AbsSouth') + est_strm = self.GS_NN.by_col(' est_DistStrm') + se_strm = self.GS_NN.by_col(' se_DistStrm') + t_strm = self.GS_NN.by_col(' t_DistStrm') + yhat = self.GS_NN.by_col(' yhat') + pdev = self.GS_NN.by_col(' localpdev') + + model = GWR(self.coords, self.y, self.X, bw=64, family=Binomial(), + kernel='gaussian', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + self.assertAlmostEquals(np.floor(AICc), 276.0) + self.assertAlmostEquals(np.floor(AIC), 273.0) + self.assertAlmostEquals(np.floor(BIC), 331.0) + np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-00) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + +if __name__ == '__main__': + unittest.main() diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py b/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py new file mode 100644 index 0000000..ea044b9 --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py @@ -0,0 +1,84 @@ +import unittest +import numpy as np +import pysal +from pysal.contrib.gwr.kernels import * + +PEGP = pysal.examples.get_path + +class TestKernels(unittest.TestCase): + def setUp(self): + np.random.seed(1234) + x = np.arange(1,6) + y = np.arange(5,0, -1) + np.random.shuffle(x) + np.random.shuffle(y) + self.coords = np.array(zip(x, y)) + self.fix_gauss_kern = np.array([ + [ 1. , 0.38889556, 0.48567179, 0.48567179, 0.89483932], + [ 0.38889556, 1. , 0.89483932, 0.64118039, 0.48567179], + [ 0.48567179, 0.89483932, 1. , 0.89483932, 0.48567179], + [ 0.48567179, 0.64118039, 0.89483932, 1. , 0.38889556], + [ 0.89483932, 0.48567179, 0.48567179, 0.38889556, 1. ]]) + self.adapt_gauss_kern = np.array([ + [ 1. , 0.52004183, 0.60653072, 0.60653072, 0.92596109], + [ 0.34559083, 1. , 0.88249692, 0.60653072, 0.44374738], + [ 0.03877423, 0.60653072, 1. , 0.60653072, 0.03877423], + [ 0.44374738, 0.60653072, 0.88249692, 1. , 0.34559083], + [ 0.92596109, 0.60653072, 0.60653072, 0.52004183, 1. ]]) + self.fix_bisquare_kern = np.array([ + [ 1. , 0. , 0. , 0. , 0.60493827], + [ 0. , 1. , 0.60493827, 0.01234568, 0. ], + [ 0. , 0.60493827, 1. , 0.60493827, 0. ], + [ 0. , 0.01234568, 0.60493827, 1. , 0. ], + [ 0.60493827, 0. , 0. , 0. , 1. ]]) + self.adapt_bisquare_kern = np.array([ + [ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 3.99999881e-14, 7.15976383e-01], + [ 0.00000000e+00, 1.00000000e+00, 5.62500075e-01, + 3.99999881e-14, 0.00000000e+00], + [ 0.00000000e+00, 3.99999881e-14, 1.00000000e+00, + 3.99999881e-14, 0.00000000e+00], + [ 0.00000000e+00, 3.99999881e-14, 5.62500075e-01, + 1.00000000e+00, 0.00000000e+00], + [ 7.15976383e-01, 0.00000000e+00, 3.99999881e-14, + 0.00000000e+00, 1.00000000e+00]]) + self.fix_exp_kern = np.array([ + [ 1. , 0.2529993 , 0.30063739, 0.30063739, 0.62412506], + [ 0.2529993 , 1. , 0.62412506, 0.38953209, 0.30063739], + [ 0.30063739, 0.62412506, 1. , 0.62412506, 0.30063739], + [ 0.30063739, 0.38953209, 0.62412506, 1. , 0.2529993 ], + [ 0.62412506, 0.30063739, 0.30063739, 0.2529993 , 1. ]]) + self.adapt_exp_kern = np.array([ + [ 1. , 0.31868771, 0.36787948, 0.36787948, 0.67554721], + [ 0.23276223, 1. , 0.60653069, 0.36787948, 0.27949951], + [ 0.07811997, 0.36787948, 1. , 0.36787948, 0.07811997], + [ 0.27949951, 0.36787948, 0.60653069, 1. , 0.23276223], + [ 0.67554721, 0.36787948, 0.36787948, 0.31868771, 1. ]]) + + def test_fix_gauss(self): + kern = fix_gauss(self.coords, 3) + np.testing.assert_allclose(kern, self.fix_gauss_kern) + + def test_adapt_gauss(self): + kern = adapt_gauss(self.coords, 3) + np.testing.assert_allclose(kern, self.adapt_gauss_kern) + + def test_fix_biqsquare(self): + kern = fix_bisquare(self.coords, 3) + np.testing.assert_allclose(kern, self.fix_bisquare_kern, + atol=1e-01) + + def test_adapt_bisqaure(self): + kern = adapt_bisquare(self.coords, 3) + np.testing.assert_allclose(kern, self.adapt_bisquare_kern, atol=1e-012) + + def test_fix_exp(self): + kern = fix_exp(self.coords, 3) + np.testing.assert_allclose(kern, self.fix_exp_kern) + + def test_adapt_exp(self): + kern = adapt_exp(self.coords, 3) + np.testing.assert_allclose(kern, self.adapt_exp_kern) + +if __name__ == '__main__': + unittest.main() diff --git a/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py b/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py new file mode 100644 index 0000000..47c6d9d --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py @@ -0,0 +1,139 @@ + +""" +GWR is tested against results from GWR4 +""" + +import unittest +import pickle as pk +from pysal.contrib.glm.family import Gaussian, Poisson, Binomial +from pysal.contrib.gwr.sel_bw import Sel_BW +import numpy as np +import pysal + +class TestSelBW(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('GData_utm.csv')) + self.coords = zip(data.by_col('X'), data.by_col('Y')) + self.y = np.array(data.by_col('PctBach')).reshape((-1,1)) + rural = np.array(data.by_col('PctRural')).reshape((-1,1)) + pov = np.array(data.by_col('PctPov')).reshape((-1,1)) + black = np.array(data.by_col('PctBlack')).reshape((-1,1)) + self.X = np.hstack([rural, pov, black]) + self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r')) + self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r')) + + def test_golden_fixed_AICc(self): + bw1 = 211027.34 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=True).search(criterion='AICc') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_AICc(self): + bw1 = 93.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=False).search(criterion='AICc') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_fixed_AIC(self): + bw1 = 76169.15 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='AIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_AIC(self): + bw1 = 50.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='AIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_fixed_BIC(self): + bw1 = 279451.43 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='BIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_BIC(self): + bw1 = 62.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='BIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_fixed_CV(self): + bw1 = 130406.67 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='CV') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_CV(self): + bw1 = 68.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='CV') + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_AICc(self): + bw1 = 211025.0#211027.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=True).search(criterion='AICc', search='interval', bw_min=211001., + bw_max=211035.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_AICc(self): + bw1 = 93.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=False).search(criterion='AICc', search='interval', + bw_min=90.0, bw_max=95.0, interval=1) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_AIC(self): + bw1 = 76175.0#76169.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='AIC', search='interval', + bw_min=76161.0, bw_max=76175.0, interval=1) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_AIC(self): + bw1 = 40.0#50.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='AIC', search='interval', bw_min=40.0, + bw_max=60.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_BIC(self): + bw1 = 279461.0#279451.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='BIC', search='interval', bw_min=279441.0, + bw_max=279461.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_BIC(self): + bw1 = 62.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='BIC', search='interval', + bw_min=52.0, bw_max=72.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_CV(self): + bw1 = 130400.0#130406.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='CV', search='interval', bw_min=130400.0, + bw_max=130410.0, interval=1) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_CV(self): + bw1 = 62.0#68.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='CV', search='interval', bw_min=60.0, + bw_max=76.0 , interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_FBGWR_AIC(self): + bw1 = [157.0, 65.0, 52.0] + sel = Sel_BW(self.coords, self.y, self.X, fb=True, kernel='bisquare', + constant=False) + bw2 = sel.search(tol_fb=1e-03) + np.testing.assert_allclose(bw1, bw2) + np.testing.assert_allclose(sel.XB, self.XB, atol=1e-05) + np.testing.assert_allclose(sel.err, self.err, atol=1e-05) + +if __name__ == '__main__': + unittest.main() diff --git a/src/py/crankshaft/crankshaft/regression/gwr/gwr.py b/src/py/crankshaft/crankshaft/regression/gwr/gwr.py new file mode 100644 index 0000000..155b95b --- /dev/null +++ b/src/py/crankshaft/crankshaft/regression/gwr/gwr.py @@ -0,0 +1,42 @@ +import numpy as np +from base.gwr import GWR +from base.sel_bw import Sel_BW + +def gwr(subquery, dep_var, ind_vars, fixed=False, kernel='bisquare'): + """ + subquery: 'select * from interesting_table' + dep_var: 'pctbachelor' + ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack'] + fixed: False (kNN) or True ('distance') + kernel: 'bisquare' (default), or 'exponential', 'gaussian' + """ + + query_result = subquery + rowid = np.array(query_result[0]['rowid']) + + x = np.array(query_result[0]['x']) + y = np.array(query_result[0]['y']) + coords = zip(x,y) + + Y = query_result[0]['dep'].reshape((-1,1)) + n = Y.shape[0] + k = len(ind_vars) + X = np.zeros((n, k)) + + for attr in range(0,k): + attr_name = 'attr' + str(attr+1) + X[:, attr] = np.array(query_result[0][attr_name]).flatten() + + bw = Sel_BW(coords, Y, X, fixed=fixed, kernel=kernel).search() + model = GWR(coords, Y, X, bw, fixed=fixed, kernel=kernel).fit() + + coefficients = model.params.reshape((-1,)) + t_vals = model.tvalues.reshape((-1,)) + stand_errs = model.bse.reshape((-1)) + predicted = np.repeat(model.predy.reshape((-1,)), k+1) + residuals = np.repeat(model.resid_response.reshape((-1,)), k+1) + r_squared = np.tile(model.localR2.reshape((-1,)), k+1) + rowid = np.tile(rowid, k+1).reshape((-1,)) + var_name = np.tile(ind_vars, k+1).reshape((-1,)) + + return zip(coefficients, stand_errs, t_vals, predicted, residuals, r_squared, rowid, var_name)