Python pymc.sample函数代码示例

    def run_DREAM(self,nsamples=100000):
        model = pymc.Model()
        with model:
            params = pymc.Normal('params', mu=self.start_parameters, 
                                 sd=np.array([1.0
                                              ]*len(self.start_parameters)),
                                shape=(len(self.start_parameters)))
            #params = pymc.Flat('params',shape=(len(self.start_parameters)))           
              
            global cost_function
            cost_function = self.cost_function
            error = pymc.Potential('error', DREAM_cost(params))
            
            nseedchains = 10*len(self.model.parameters_rules())
            step = pymc.Dream(variables=[params],
                              nseedchains=nseedchains, 
                              blocked=True,
                              start_random=False,
                              save_history=True,
                              parallel=True,
                              adapt_crossover=False,
                              verbose=False,)
         
            trace = pymc.sample(nsamples, step,
                                start=self.pso_results, 
                                njobs=self.nchains,
                                use_mpi=False,
                                progressbar=False,) 

            
            cont_flag = True
            while cont_flag:
                cont_flag = False
                conv_stats = gelman_rubin(trace)
                for i in conv_stats['params']:
                    if i>1.2:
                        print "Parameters have not converged, will continue run."
                        print "Value so far is %s"%i
                        cont_flag = True
                        break
                trace = pymc.sample(int(nsamples*.1), step,
                                    #start=self.pso_results, 
                                    njobs=self.nchains,
                                    use_mpi=False,
                                    trace = trace,
                                    progressbar=False,)
            conv_stats = gelman_rubin(trace)
            for i in conv_stats['params']:
                print i,i<1.2
            #pymc.traceplot(trace,vars=[params,error])
            #plt.show()            
            return trace

def bayesian_random_effects(data, labels, group, n_samples=2000, n_burnin=500):
    import pymc as pm
    #preparing the data
    donors = data[group].unique()
    donors_lookup = dict(zip(donors, range(len(donors))))
    data['donor_code'] = data[group].replace(donors_lookup).values
    n_donors = len(data[group].unique())
    donor_idx = data['donor_code'].values
    
    #setting up the model
    with pm.Model() as hierarchical_model:
        # Hyperpriors for group nodes
        group_intercept_mean = pm.Normal('group intercept (mean)', mu=0., sd=100**2)
        group_intercept_variance = pm.Uniform('group intercept (variance)', lower=0, upper=100)
        group_slope_mean = pm.Normal('group slope (mean)', mu=0., sd=100**2)
        group_slope_variance = pm.Uniform('group slope (variance)', lower=0, upper=100)
        
        individual_intercepts = pm.Normal('individual intercepts', mu=group_intercept_mean, sd=group_intercept_variance, shape=n_donors)
        individual_slopes = pm.Normal('individual slopes', mu=group_slope_mean, sd=group_slope_variance, shape=n_donors)
        
        # Model error
        residuals = pm.Uniform('residuals', lower=0, upper=100)
        
        expression_est =  individual_slopes[donor_idx] * data[labels[0]].values + individual_intercepts[donor_idx]
        
        # Data likelihood
        expression_like = pm.Normal('expression_like', mu=expression_est, sd=residuals, observed=data[labels[1]])

        start = pm.find_MAP()
        step = pm.NUTS(scaling=start)
        hierarchical_trace = pm.sample(n_samples, step, start=start, progressbar=True)
        
    mean_slope = hierarchical_trace['group slope (mean)'][n_burnin:].mean()
    zero_percentile = percentileofscore(hierarchical_trace['group slope (mean)'][n_burnin:], 0)
    #print "Mean group level slope was %g (zero was %g percentile of the posterior distribution)"%(mean_slope, zero_percentile)
    
    #pm.summary(hierarchical_trace[n_burnin:], vars=['group slope (mean)'])
        
    #pm.traceplot(hierarchical_trace[n_burnin:])
    
    #selection = donors
    #fig, axis = plt.subplots(2, 3, figsize=(12, 6), sharey=True, sharex=True)
    #axis = axis.ravel()
    #xvals = np.linspace(data[labels[0]].min(), data[labels[0]].max())
    #for i, c in enumerate(selection):
    #    c_data = data.ix[data[group] == c]
    #    c_data = c_data.reset_index(drop = True)
    #    z = list(c_data['donor_code'])[0]
    #    for a_val, b_val in zip(hierarchical_trace['individual intercepts'][n_burnin::10][z], hierarchical_trace['individual slopes'][n_burnin::10][z]):
    #        axis[i].plot(xvals, a_val + b_val * xvals, 'g', alpha=.1)
    #    axis[i].plot(xvals, hierarchical_trace['individual intercepts'][n_burnin:][z].mean() + hierarchical_trace['individual slopes'][n_burnin:][z].mean() * xvals, 
    #                 'g', alpha=1, lw=2.)
    #    axis[i].hexbin(c_data[labels[0]], c_data[labels[1]], mincnt=1, cmap=plt.cm.YlOrRd_r)
    #    axis[i].set_title(c)
    #    axis[i].set_xlabel(labels[0])
    #    axis[i].set_ylabel(labels[1])
    #    
    #plt.show()
        
    return mean_slope, zero_percentile

def get_traces_hierarchical(x, y, idxs, max_iter=100000):
    """ sample hierarchical model """
    
    idx_size = len(np.unique(idxs))
    with pm.Model() as hierarchical_model:

        # hyperpriors for group nodes, all uninformative
        alpha_mu = pm.Normal('alpha_mu', mu=0., sd=100**2)
        alpha_sigma = pm.Uniform('alpha_sigma', lower=0, upper=100)
        beta_mu = pm.Normal('beta_mu', mu=0., sd=100**2)
        beta_sigma = pm.Uniform('beta_sigma', lower=0, upper=100)

        # Intercept for each testtype, distributed around group mean mu_a
        # Above mu & sd are fixed value while below we plug in a common 
        # group distribution for all a & b (which are vectors of length idx_size).

        # priors for alpha, beta and model error, uninformative  
        alpha = pm.Normal('alpha', mu=alpha_mu, sd=alpha_sigma, shape=idx_size)
        beta = pm.Normal('beta', mu=beta_mu, sd=beta_sigma, shape=idx_size)
        epsilon = pm.Uniform('epsilon', lower=0, upper=100)

        # hierarchical linear model
        y_est = alpha[idxs] + beta[idxs] * x
        likelihood = pm.Normal('likelihood', mu=y_est, sd=epsilon, observed=y)
        traces = pm.sample(max_iter, step=pm.Metropolis()
                                       ,start=pm.find_MAP(), progressbar=True)
    return traces

def MCMC(model):
    import time
    with model:
        n = 6000
        START = time.time()
        try:
            start = pm.find_MAP()
        except AssertionError:
            return model, {'error':'AssertionError in pm.find_MAP()'}
        init_time = time.time()-START
        print 'Time to initialize: %ds' % (init_time)

        START = time.time()
        trace = pm.sample(n,pm.Metropolis(),start)
        duration = time.time()-START
        print 'Time to sample (MH): %ds' % (duration)

        # START = time.time()
        # trace = pm.sample(n,pm.Slice(),start)
        # print 'Time to sample (Slice): %ds' % (time.time()-START)

        # START = time.time()
        # trace = pm.sample(n,pm.HamiltonianMC(),start)
        # print 'Time to sample (HMC): %ds' % (time.time()-START)

        # error_b, error_x, output = error(trace,model.data.A,model.data.x_true,
        #                          model.data.b_obs,model.data.scaling)

        # fig = pm.traceplot(trace)
        # plot(error_b,error_x)
        # plt.show()
    return model, trace, init_time, duration

def poisson_regression(targets, predictors, iters=2000):
    """ Return the posterior of a Bayesian Poisson regression model.

        This function takes the targets and predictors and builds a Poisson
        regression model. The predictor coefficients are found by sampling
        from the posterior using PyMC (NUTS in particular).

        The posterior is returned as an MxN array, where M is the number of
        samples and N is the number of predictors. The first column is the 
        coefficient for the first predictor and so on.

        Requires PyMC3

    """
    with pm.Model() as poisson_model:
        # priors for coefficients
        coeffs = pm.Uniform('coeffs', -10, 10, shape=(1, predictors.shape[1]))
        
        p = t.exp(pm.sum(coeffs*predictors.values, 1))
        
        obs = pm.Poisson('obs', p, observed=targets)

        start = pm.find_MAP()
        step = pm.NUTS(scaling=start)
        poisson_trace = pm.sample(iters, step, start=start, progressbar=False)

    return poisson_trace['coeffs'].squeeze()

def test_summary_1d_variable_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, shape=2, testval=[.1, .1])
        step = Metropolis(model.vars, np.diag([1.]))
        trace = pm.sample(100, step=step)
    pm.summary(trace)

def test_summary_0d_variable_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, testval=.1)
        step = Metropolis(model.vars, np.diag([1.]), blocked=True)
        trace = pm.sample(100, step=step)
    pm.summary(trace)

    def foo(self, discrete):
        student_ids  = []
        timestep_ids = []
        y = []

        ids = collections.defaultdict(itertools.count().next)
        for t in range(0, len(self)):
            student_ids  +=  [ids[o.id] for o in self[t]]
            timestep_ids +=  [t         for o in self[t]]
            y            +=  [o.value   for o in self[t]]

        n_students  =  len(set(student_ids))
        n_timesteps = len(self)

        print student_ids, "!", n_students

        with pm.Model() as hierarchical_model:
            # Hyperpriors for group nodes
            mu_student = pm.Normal('mu_student', mu=0., sd=100**2)
            sigma_student = pm.Uniform('sigma_student', lower=0, upper=100)

            #mu_timestep = pm.Normal('mu_beta', mu=0., sd=100**2)
            #sigma_timestep = pm.Uniform('sigma_beta', lower=0, upper=100)

            student  = pm.Normal('student', mu=mu_student, sd=sigma_student, shape=n_students) #random effect
            timestep = pm.Normal('timestep', mu=0, sd=100**2, shape=n_timesteps)  #fixed effect

            # Model error
            eps = pm.Uniform('eps', lower=0, upper=100)


            theta = student[student_ids] + timestep[timestep_ids]

            # Data likelihood
            if discrete:
                ll = pm.Bernoulli('theta', p=self.invlogit(theta), observed=y)
            else:
                ll = pm.Normal('theta', mu=theta, sd=eps, observed=y)

        with hierarchical_model:
            print "Find MAP..."
            start = pm.find_MAP()
            #if discrete:
            #     step = pm.BinaryMetropolis(scaling=start)
            # else:
            print "NUTS..."
            step = pm.NUTS(scaling=start)
            print "Samples..."
            hierarchical_trace = pm.sample(2000, step, start=start, progressbar=False)
        print "done..."
        print "Plot..."

        pl.figure(figsize=(10,10))
        f = pm.traceplot(hierarchical_trace[500:])
        f.savefig("a.png")
        return hierarchical_trace

def test_plots_multidimensional():

    # Test single trace
    from .models import multidimensional_model

    start, model, _ = multidimensional_model()
    with model as model:
        h = np.diag(find_hessian(start))
        step = Metropolis(model.vars, h)
        trace = sample(3000, step, start)

        traceplot(trace)

def run_sig():
    signal_responses = binom.rvs(100, 0.69, size=1)
    noise_responses  = binom.rvs(100, 0.30, size=1)
    m = sig_detect(signal_responses, noise_responses, 1, 100)
    with m:
        #step = pm.Metropolis(blocked=False)
        step = pm.HamiltonianMC()
        start = pm.find_MAP()
        #start = {'Pr. mean discrim.':0.0, 'Pr. mean bias':0.0,
        #         'taud':0.001, 'tauc':0.001}
        trace = pm.sample(5000, step, start, tune=500, njobs=2)
    return trace[1000:]

def run_pl():
    x = arange(180)
    x = concatenate((x,x,x,x))
    xx = piecewise_predictor(x, 90, 50, 1, 2)
    y = xx + 20*(np.random.randn(len(x)))
    y = y-y.mean()
    m = piecewise_linear(y,x)
    with m:
        step = pm.Metropolis()
        #step = pm.NUTS()
        trace = pm.sample(2000, step, {}, tune=50, njobs=1, progressbar=True)
    return trace

def test_multichain_plots():

    from pymc.examples import disaster_model as dm

    with dm.model as model:
        # Run sampler
        step1 = Slice([dm.early_mean, dm.late_mean])
        step2 = Metropolis([dm.switchpoint])
        start = {'early_mean': 2., 'late_mean': 3., 'switchpoint': 50}
        ptrace = sample(1000, [step1, step2], start, njobs=2)

    forestplot(ptrace, vars=['early_mean', 'late_mean'])

    autocorrplot(ptrace, vars=['switchpoint'])

def run_fixdur():
    import cPickle
    dur, fa, obs = cPickle.load(open('durtest.pickle'))
    m = piecewise_durations(dur, fa, obs-1)
    with m:
        start = pm.find_MAP() #cPickle.load(open('fixdur_map.pickle'))
        step = pm.Metropolis(vars=[m.named_vars['Mean_offset'],
            m.named_vars['Mean_slope1'], m.named_vars['Mean_slope2'], 
            m.named_vars['Mean_split']], blocked=False)
        step2 = pm.Metropolis(vars=[m.named_vars['Slope1'], 
            m.named_vars['Slope2'], m.named_vars['Offsets'], m.named_vars['Breakpoint']])
        trace = pm.sample(5000, [step, step2], start, tune=1000, njobs=1,
                progressbar=True)
    return trace

def test_plots():

    # Test single trace
    from pymc.examples import arbitrary_stochastic as asmod

    with asmod.model as model:

        start = model.test_point
        h = find_hessian(start)
        step = Metropolis(model.vars, h)
        trace = sample(3000, step, start)

        forestplot(trace)

        autocorrplot(trace)

def run_banova():
    y = 10+hstack((np.random.randn(100),np.random.randn(100)+1,
        np.random.randn(100)+2))
    y = y-y.mean()
    y = y/y.std()
    x = concatenate(([1.0]*100,[0.0]*200))
    X = vstack((x, np.roll(x,100), np.roll(x,200)))
    m = oneway_banova(y.astype(float),X.astype(float))
    start = {'offset': 0.0,
        'alphas': array([0,1,2.])}
    with m:
        step = pm.Metropolis()
        #step = pm.NUTS()
        trace = pm.sample(150000, step, start, tune=1500, njobs=1, progressbar=True)
        pm.traceplot(trace[::2])
    show()