Пример #1
0
    def stats(self, alpha=0.05, start=0, batches=100):
        """
        Generate posterior statistics for node.
        """
        from utils import hpd, quantiles
        from numpy import sqrt

        try:
            trace = np.squeeze(np.array(self.trace(), float)[start:])

            n = len(trace)
            if not n:
                print 'Cannot generate statistics for zero-length trace in', self.__name__
                return


            return {
                'n': n,
                'standard deviation': trace.std(0),
                'mean': trace.mean(0),
                '%s%s HPD interval' % (int(100*(1-alpha)),'%'): hpd(trace, alpha),
                'mc error': batchsd(trace, batches),
                'quantiles': quantiles(trace)
            }
        except:
            print 'Could not generate output statistics for', self.__name__
            return
 def write_vals(self,saveTo=None):
     """ Saves estimated parameters to csv file. 
     
     Input:
     - m (Model)
     - saveTo (str) - path and name of csv file where results should be 
     saved. Defaults to mod.saveTo+'-posteriorValues.csv'.
     """
     m=self
     if saveTo==None:
         fname=m.saveTo+'-posteriorValues.csv'
     else:
         fname=saveTo
     f=open(fname,'w')
     f.write('\t'.join(['Parameter','mean','median','95% HPD','std'])+'\n')
     for v in m.parameters:
         trac=getattr(m,v+'s')
         hpdi=ut.hpd(trac,0.95)
         form='%.2f'
         if v.startswith('p') or v.startswith('k') or v.startswith('e'):
             form='%.2e'
         
         f.write('\t'.join([v,form%trac.mean(),form%sap(trac,50),('['+form+', '+form+']')%hpdi,form%trac.std()])+'\n')
     
     f.close()
     if saveTo==None:
         print "Saved posterior median and confidence intervals for each parameter, see "+ m.name+'-posterior_values.csv'
     else:
         print "Saved posterior median and confidence intervals for each parameter, see "+ saveTo
Пример #3
0
def histogram(data, name, nbins=None, datarange=(None, None), format='png', suffix='', path='./', rows=1, columns=1, num=1, last=True, fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}, verbose=1):

    # Internal histogram specification for handling nested arrays
    try:

        # Stand-alone plot or subplot?
        standalone = rows==1 and columns==1 and num==1
        if standalone:
            if verbose>0:
                print 'Generating histogram of', name
            figure()

        subplot(rows, columns, num)

        #Specify number of bins (10 as default)
        uniquevals = len(unique(data))
        nbins = nbins or uniquevals*(uniquevals<=25) or int(4 + 1.5*log(len(data)))

        # Generate histogram
        hist(data.tolist(), nbins, histtype='stepfilled')

        xlim(datarange)

        # Plot options
        title('\n\n   %s hist'%name, x=0., y=1., ha='left', va='top', fontsize='medium')

        ylabel("Frequency", fontsize='x-small')

        # Plot vertical lines for median and 95% HPD interval
        quant = calc_quantiles(data)
        axvline(x=quant[50], linewidth=2, color='black')
        for q in hpd(data, 0.05):
            axvline(x=q, linewidth=2, color='grey', linestyle='dotted')

        # Smaller tick labels
        tlabels = gca().get_xticklabels()
        setp(tlabels, 'fontsize', fontmap[rows])
        tlabels = gca().get_yticklabels()
        setp(tlabels, 'fontsize', fontmap[rows])

        if standalone:
            if not os.path.exists(path):
                os.mkdir(path)
            if not path.endswith('/'):
                path += '/'
            # Save to file
            savefig("%s%s%s.%s" % (path, name, suffix, format))
            #close()

    except OverflowError:
        print '... cannot generate histogram'
def plot_coefs(coefs, label, offset, errorbars=True, marker='o'):
    "Plots the coefficients of a logistic regression analysis"
    x = np.array((0, 1, 2, 3))
    if errorbars:
        y = [coef_series.mean() for coef_series in coefs]
        yerr = [[], []]
        for yy, coef_series in zip(y, coefs):
            uplim, lolim = utils.hpd(coef_series)
            yerr[0].append(yy - lolim)
            yerr[1].append(uplim - yy)
        plt.errorbar(x + offset, y, yerr, fmt='none', ecolor='black')
    else:
        y = coefs
    plt.plot(x + offset, y, marker, label=label)
def plot_stay_probs(probs, condition, legend):
    "Plots stay probabilities with error bars"
    y = [prob.median() for prob in probs]
    plt.title(condition)
    plt.ylim(0, 1)
    plt.bar((0, 2), (y[0], y[2]), color=COLOR_COMMON, label='Common')
    plt.bar((1, 3), (y[1], y[3]), color=COLOR_RARE, label='Rare')
    plt.xticks((0.5, 2.5), ('Rewarded', 'Unrewarded'))
    yerr = [[], []]
    for yy, prob_series in zip(y, probs):
        uplim, lolim = hpd(prob_series)
        yerr[0].append(yy - lolim)
        yerr[1].append(uplim - yy)
    plt.errorbar((0, 1, 2, 3), y, yerr, fmt='none', ecolor='black')
    plt.xlabel('Previous outcome')
    plt.ylabel('Stay probability')
    if legend:
        plt.legend(loc='best', title='Previous transition')
Пример #6
0
    def stats(self, alpha=0.05, start=0, batches=100, chain=None):
        """
        Generate posterior statistics for node.
        
        :Parameters:
        alpha : float
          The alpha level for generating posterior intervals. Defaults to
          0.05.

        start : int
          The starting index from which to summarize (each) chain. Defaults
          to zero.
          
        batches : int
          Batch size for calculating standard deviation for non-independent
          samples. Defaults to 100.
          
        chain : int
          The index for which chain to summarize. Defaults to None (all
          chains).
        """
        from utils import hpd, quantiles
        from numpy import sqrt

        try:
            trace = np.squeeze(np.array(self.trace(burn=start, chain=chain), float))

            n = len(trace)
            if not n:
                print 'Cannot generate statistics for zero-length trace in', self.__name__
                return


            return {
                'n': n,
                'standard deviation': trace.std(0),
                'mean': trace.mean(0),
                '%s%s HPD interval' % (int(100*(1-alpha)),'%'): hpd(trace, alpha),
                'mc error': batchsd(trace, batches),
                'quantiles': quantiles(trace)
            }
        except:
            print 'Could not generate output statistics for', self.__name__
            return
Пример #7
0
 def interval(self, alpha):
     return tuple(hpd(self, 1 - alpha))