Python pymc3.find_MAP函数代码示例

    def _fit_time_series_model(self, signal, target, samples):
        
        model_randomwalk = pm.Model()
        with model_randomwalk:

            sigma_alpha = pm.Exponential('sigma_alpha', 1. / .02, testval=.1)
            sigma_beta = pm.Exponential('sigma_beta', 1. / .02, testval=.1)

            alpha = GaussianRandomWalk('alpha', sigma_alpha ** -2, shape=len(tar))
            beta = GaussianRandomWalk('beta', sigma_beta ** -2, shape=len(tar))

            # Define regression
            regression = alpha + beta * rev.values

            # Assume prices are Normally distributed, the mean comes from the regression.
            sd = pm.Uniform('sd', 0, 20)
            likelihood = pm.Normal('y', 
                                   mu=regression, 
                                   sd=sd, 
                                   observed=tar.values)
        
            # First optimize random walk
            start = pm.find_MAP(vars=[alpha, beta], fmin=optimize.fmin_l_bfgs_b)
            step = pm.NUTS(scaling=start)
            trace = pm.sample(10, step, start)

            # Sample
            start2 = trace.point(-1)
            step = pm.NUTS(scaling=start2)
            trace_rw = pm.sample(samples, step, start=start)
            
            

def init_nuts(init='advi', n_init=500000, model=None, **kwargs):
    """Initialize and sample from posterior of a continuous model.

    This is a convenience function. NUTS convergence and sampling speed is extremely
    dependent on the choice of mass/scaling matrix. In our experience, using ADVI
    to estimate a diagonal covariance matrix and using this as the scaling matrix
    produces robust results over a wide class of continuous models.

    Parameters
    ----------
    init : str {'advi', 'advi_map', 'map', 'nuts'}
        Initialization method to use.
        * advi : Run ADVI to estimate posterior mean and diagonal covariance matrix.
        * advi_map: Initialize ADVI with MAP and use MAP as starting point.
        * map : Use the MAP as starting point.
        * nuts : Run NUTS and estimate posterior mean and covariance matrix.
    n_init : int
        Number of iterations of initializer
        If 'advi', number of iterations, if 'metropolis', number of draws.
    model : Model (optional if in `with` context)
    **kwargs : keyword arguments
        Extra keyword arguments are forwarded to pymc3.NUTS.

    Returns
    -------
    start, nuts_sampler

    start : pymc3.model.Point
        Starting point for sampler
    nuts_sampler : pymc3.step_methods.NUTS
        Instantiated and initialized NUTS sampler object
    """

    model = pm.modelcontext(model)

    pm._log.info('Initializing NUTS using {}...'.format(init))

    if init == 'advi':
        v_params = pm.variational.advi(n=n_init)
        start = pm.variational.sample_vp(v_params, 1, progressbar=False)[0]
        cov = np.power(model.dict_to_array(v_params.stds), 2)
    elif init == 'advi_map':
        start = pm.find_MAP()
        v_params = pm.variational.advi(n=n_init, start=start)
        cov = np.power(model.dict_to_array(v_params.stds), 2)
    elif init == 'map':
        start = pm.find_MAP()
        cov = pm.find_hessian(point=start)

    elif init == 'nuts':
        init_trace = pm.sample(step=pm.NUTS(), draws=n_init)
        cov = pm.trace_cov(init_trace[n_init//2:])

        start = {varname: np.mean(init_trace[varname]) for varname in init_trace.varnames}
    else:
        raise NotImplemented('Initializer {} is not supported.'.format(init))

    step = pm.NUTS(scaling=cov, is_cov=True, **kwargs)

    return start, step

    def fit(self, x, y, mcmc_samples=1000):
        t = x.shape[0] - 1  # number of additive components
        varnames = ["xc", "w", "decay", "sigma", "b", "lam"]

        with pm.Model() as model:
            # Priors for additive predictor
            w = pm.Normal("w", mu=0, sd=1, shape=t)
            decay = pm.HalfNormal("decay", sd=200, shape=t)
            # Prior for likelihood
            sigma = pm.Uniform("sigma", 0, 0.3)
            b = pm.Normal("b", mu=0, sd=20)
            lam = pm.Uniform("lam", 0, 0.3)

            # Building linear predictor
            lin_pred = 0
            for ii in range(1, t + 1):
                lin_pred += self.bias(w[ii - 1], decay[ii - 1])(x[ii, :])

            phi2 = pm.Deterministic("phi2", 0.5 * lam + (1 - lam) * phi(b + lin_pred + x[0, :] / sigma))
            y = pm.Bernoulli("y", p=phi2, observed=y)

        with model:
            # Inference
            start = pm.find_MAP()  # Find starting value by optimization
            print("MAP found:")
            # step = pm.NUTS(scaling = start)
            # step = pm.Slice()
            step = pm.NUTS(scaling=start)
            trace = pm.sample(mcmc_samples, step, start=start, progressbar=True)  # draw posterior samples

        return trace, model

def model_returns_t(data, samples=500):
    """Run Bayesian model assuming returns are normally distributed.

    Parameters
    ----------
    returns : pandas.Series
        Series of simple returns of an algorithm or stock.
    samples : int, optional
        Number of posterior samples to draw.

    Returns
    -------
    pymc3.sampling.BaseTrace object
        A PyMC3 trace object that contains samples for each parameter
        of the posterior.

    """

    with pm.Model():
        mu = pm.Normal('mean returns', mu=0, sd=.01, testval=data.mean())
        sigma = pm.HalfCauchy('volatility', beta=1, testval=data.std())
        nu = pm.Exponential('nu_minus_two', 1. / 10., testval=3.)

        returns = pm.T('returns', nu=nu + 2, mu=mu, sd=sigma, observed=data)
        pm.Deterministic('annual volatility',
                         returns.distribution.variance**.5 * np.sqrt(252))

        pm.Deterministic('sharpe', returns.distribution.mean /
                         returns.distribution.variance**.5 *
                         np.sqrt(252))

        start = pm.find_MAP(fmin=sp.optimize.fmin_powell)
        step = pm.NUTS(scaling=start)
        trace = pm.sample(samples, step, start=start)
    return trace

	def fit(self,xdata,ydata,yerr,arange=[-100.,100],brange=[-100.,100]):
		trace = None
		with pm.Model() as model:
		    # alpha = pm.Normal('alpha', mu=1.0e7, sd=1.0e6)
		    # beta  = pm.Normal('beta', mu=1.0e7, sd=1.0e6)
		    # sigma = pm.Uniform('sigma', lower=0, upper=20)
		    alpha = pm.Uniform('alpha', lower=arange[0], upper=arange[1])
		    beta  = pm.Uniform('beta',  lower=brange[0], upper=brange[1])
		    sigma = yerr
		    
		    y_est = alpha + beta * xdata
		    
		    likelihood = pm.Normal('y', mu=y_est, sd=sigma, observed=ydata)
		    
		    # obtain starting values via MAP
		    start = pm.find_MAP()
		    step  = pm.NUTS(state=start)
		    trace = pm.sample(2000, step, start=start, progressbar=False)
		    
		    # pm.traceplot(trace)

		# plt.show()
		# pprint(trace['alpha'].mean())
		# pprint(trace['alpha'].std())
		# print pm.summary(trace)
		# print pm.summary(trace, ['alpha'])
		# print pm.stats()
		# print(trace.__dict__)

		# Return the traces
		return [trace['alpha'], trace['beta']]

def sample_pymc3(d, samples=2000, njobs=2):
    with pm.Model() as model:
        dfc = pm.Normal(mu=0.0, sd=d['sigma_fc'], name='dfc')
        Q = pm.Gamma(mu=d['mu_Q'], sd=d['sigma_Q'], name='Q')
        Pdet = pm.Gamma(mu=d['mu_Pdet'], sd=d['sigma_Pdet'], name='Pdet')
        kc = pm.Gamma(mu=d['mu_kc'], sd=d['sigma_kc'], name='kc')

        M = d['M']
        T = d['T']
        scale=d['scale']
        mu_fc = d['mu_fc']
        f = d['f']


        like = pm.Gamma(alpha=M, beta=(M/(((2 * 1.381e-5 * T) / (np.pi * Q * kc)) / scale * (dfc + mu_fc)**3 /
                    ((f * f - (dfc + mu_fc)**2) * (f * f - (dfc + mu_fc)**2) + f * f * (dfc + mu_fc)**2 / Q**2)
                    + Pdet)),
                            observed=d['y'],
                            name='like')

        start = pm.find_MAP()
        step = pm.NUTS(state=start)
        
        trace = pm.sample(samples, step=step, start=start, progressbar=True, njobs=njobs)
    return trace

def lin_fit(t, y, yerr=None, samples=10000, sampler="NUTS", alphalims=[-100,100]):
    """
    Bayesian linear fitting function.
    See Jake Vanderplas' blog post on how to be a
    bayesian in python for more details

    uses pymc3 MCMC sampling

    inputs:
        t    ::    Vector of values at which the function is evaluated ("x" values)
        y    ::    Vector of dependent values (observed y(t))
        yerr (optional = None) :: Errors on y values.  If not provided, errors are taken to be the same for each dta point,
            with a 1/sigma (jefferys) prior.
        samples (optional = 1000)  :: Number of samples to draw from MCMC
        sampler (optional = "NUTS")  :: Type of MCMC sampler to use.  "NUTS" or "Metropolis"
        alphalims (optional = [-100,100])  ::  Length 2 vector of endpoints for uniform prior on intercept of the line
    """
    with pm.Model() as model:
            #Use uninformative priors on slope/intercept of line
            alpha = pm.Uniform('alpha',alphalims[0],alphalims[1])
            #this defines an uninformative prior on slope.  See Jake's blog post
            beta = pm.DensityDist('beta',lambda value: -1.5 * T.log(1 + value**2.),testval=0)
            #if yerr not given, assume all values have same errorbar
            if yerr is None:
                sigma = pm.DensityDist('sigma', lambda value: -T.log(T.abs_(value)),testval=1)
            else:
                sigma = yerr
            like = pm.Normal('likelihood',mu=alpha+beta*t, sd=sigma, observed=y)
            #start the sampler at the maximum a-posteriori value
            start = pm.find_MAP()
            step = select_sampler(sampler,start)
            trace = pm.sample(draws=samples,start=start,step=step)
    return trace

    def test_linear_component(self):
        vars_to_create = {
            'sigma',
            'sigma_interval__',
            'y_obs',
            'lm_x0',
            'lm_Intercept'
        }
        with Model() as model:
            lm = LinearComponent(
                self.data_linear['x'],
                self.data_linear['y'],
                name='lm'
            )   # yields lm_x0, lm_Intercept
            sigma = Uniform('sigma', 0, 20)     # yields sigma_interval__
            Normal('y_obs', mu=lm.y_est, sigma=sigma, observed=self.y_linear)  # yields y_obs
            start = find_MAP(vars=[sigma])
            step = Slice(model.vars)
            trace = sample(500, tune=0, step=step, start=start,
                           progressbar=False, random_seed=self.random_seed)

            assert round(abs(np.mean(trace['lm_Intercept'])-self.intercept), 1) == 0
            assert round(abs(np.mean(trace['lm_x0'])-self.slope), 1) == 0
            assert round(abs(np.mean(trace['sigma'])-self.sd), 1) == 0
        assert vars_to_create == set(model.named_vars.keys())

    def run(self, samples=1000, find_map=True, verbose=True, step='nuts',
            burn=0.5, **kwargs):
        ''' Run the model.
        Args:
            samples (int): Number of MCMC samples to generate
            find_map (bool): passed to find_map argument of pm.sample()
            verbose (bool): if True, prints additional information
            step (str or PyMC3 Sampler): either an instantiated PyMC3 sampler,
                or the name of the sampler to use (either 'nuts' or
                'metropolis').
            start: Optional starting point to pass onto sampler.
            burn (int or float): Number or proportion of samples to treat as
                burn-in; passed onto the BayesianModelResults instance returned
                by this method.
            kwargs (dict): optional keyword arguments passed on to the sampler.

        Returns: an instance of class BayesianModelResults.

        '''
        with self.model:
            njobs = kwargs.pop('njobs', 1)
            start = kwargs.pop('start', pm.find_MAP() if find_map else None)
            chain = kwargs.pop('chain', 0)
            if isinstance(step, string_types):
                step = {
                    'nuts': pm.NUTS,
                    'metropolis': pm.Metropolis
                }[step.lower()](**kwargs)

            self.start = start
            trace = pm.sample(
                samples, start=start, step=step, progressbar=verbose, njobs=njobs, chain=chain)
            self.last_trace = trace  # for convenience
            return BayesianModelResults(trace)

def run(n=5000):
    with model_1:
        xstart = pm.find_MAP()
        xstep = pm.Slice()
        trace = pm.sample(5000, xstep, xstart, random_seed=123, progressbar=True)

        pm.summary(trace)

def model_returns_t_alpha_beta(data, bmark, samples=2000):
    """Run Bayesian alpha-beta-model with T distributed returns.

    This model estimates intercept (alpha) and slope (beta) of two
    return sets. Usually, these will be algorithm returns and
    benchmark returns (e.g. S&P500). The data is assumed to be T
    distributed and thus is robust to outliers and takes tail events
    into account.

    Parameters
    ----------
    returns : pandas.Series
        Series of simple returns of an algorithm or stock.
    bmark : pandas.Series
        Series of simple returns of a benchmark like the S&P500.
        If bmark has more recent returns than returns_train, these dates
        will be treated as missing values and predictions will be
        generated for them taking market correlations into account.
    samples : int (optional)
        Number of posterior samples to draw.

    Returns
    -------
    pymc3.sampling.BaseTrace object
        A PyMC3 trace object that contains samples for each parameter
        of the posterior.
    """

    if len(data) != len(bmark):
        # pad missing data
        data = pd.Series(data, index=bmark.index)

    data_no_missing = data.dropna()

    with pm.Model():
        sigma = pm.HalfCauchy(
            'sigma',
            beta=1,
            testval=data_no_missing.values.std())
        nu = pm.Exponential('nu_minus_two', 1. / 10., testval=.3)

        # alpha and beta
        beta_init, alpha_init = sp.stats.linregress(
            bmark.loc[data_no_missing.index],
            data_no_missing)[:2]

        alpha_reg = pm.Normal('alpha', mu=0, sd=.1, testval=alpha_init)
        beta_reg = pm.Normal('beta', mu=0, sd=1, testval=beta_init)

        pm.T('returns',
             nu=nu + 2,
             mu=alpha_reg + beta_reg * bmark,
             sd=sigma,
             observed=data)
        start = pm.find_MAP(fmin=sp.optimize.fmin_powell)
        step = pm.NUTS(scaling=start)
        trace = pm.sample(samples, step, start=start)

    return trace

def run(n=1000):
    if n == "short":
        n = 50
    with model:
        start = pm.find_MAP()
        step = pm.NUTS(scaling=start)
        trace = pm.sample(n, step=step, start=start)
    return trace

    def _inference(self, reinit=True):
        with self.cached_model:
            if reinit or (self.cached_start is None) or (self.cached_sampler is None):
                self.cached_start = pm.find_MAP(fmin=sp.optimize.fmin_powell)
                self.cached_sampler = pm.NUTS(scaling=self.cached_start)

            trace = pm.sample(self.samples, self.cached_sampler, start=self.cached_start)

        return trace

def learn_model(model, draws=50000):
    with model:
        start = pm.find_MAP()
        #step = pm.Slice()  # It is very slow when the model has many parameters
        #step = pm.HamiltonianMC(scaling=start)  # It leads to constant samples
        #step = pm.NUTS(scaling=start)           # It leads to constant samples
        step = pm.Metropolis()
        trace = pm.sample(draws, step, start=start)
    return trace

 def fit(self, X, y, sampling_iterations):
     X = self._force_shape(X)
     self.input_data_dimension = len(X[0])
     model, w, b = self._build_model(X, y)
     with model:
         self.map_estimate = pymc3.find_MAP(model=model, vars=[w, b])
         step = pymc3.NUTS(scaling=self.map_estimate)
         trace = pymc3.sample(sampling_iterations, step, start=self.map_estimate)
     self.samples = trace