Merge branch 'master' into add-flake8-checker

examples/other_examples/linear_regression_pymc3_custom_likelihood.py

@@ -99,7 +99,7 @@ class GaussianLikelihoodPyMC3(tupak.Likelihood):
         with sampler.pymc3_model:
             mdist = sampler.pymc3_priors['m']
             cdist = sampler.pymc3_priors['c']
-            
+
             mu = model(time, mdist, cdist)
 
             # set the likelihood distribution

tupak/core/result.py

@@ -375,21 +375,33 @@ class Result(dict):
         self.posterior['mass_chirp'] = (
             (self.posterior.mass_1 * self.posterior.mass_2) ** 0.6 / (
                 self.posterior.mass_1 + self.posterior.mass_2) ** 0.2)
+        self.search_parameter_keys.append('mass_chirp')
+        self.parameter_labels.append('$\mathcal{M}$')
+
         self.posterior['q'] = self.posterior.mass_2 / self.posterior.mass_1
+        self.search_parameter_keys.append('q')
+        self.parameter_labels.append('$q$')
+
         self.posterior['eta'] = (
             (self.posterior.mass_1 * self.posterior.mass_2) / (
                 self.posterior.mass_1 + self.posterior.mass_2) ** 2)
+        self.search_parameter_keys.append('eta')
+        self.parameter_labels.append('$\eta$')
 
         self.posterior['chi_eff'] = (
-            self.posterior.a_1 * np.cos(self.posterior.tilt_1) +
-            self.posterior.q * self.posterior.a_2 *
-            np.cos(self.posterior.tilt_2)) / (1 + self.posterior.q)
-
-        self.posterior['chi_p'] = np.maximum(
-            self.posterior.a_1 * np.sin(self.posterior.tilt_1),
-            (4 * self.posterior.q + 3) / (3 * self.posterior.q + 4) *
-            self.posterior.q * self.posterior.a_2 *
-            np.sin(self.posterior.tilt_2))
+            (self.posterior.a_1 * np.cos(self.posterior.tilt_1) +
+                self.posterior.q * self.posterior.a_2 *
+                np.cos(self.posterior.tilt_2)) / (1 + self.posterior.q))
+        self.search_parameter_keys.append('chi_eff')
+        self.parameter_labels.append('$\chi_{\mathrm eff}$')
+
+        self.posterior['chi_p'] = (
+            np.maximum(self.posterior.a_1 * np.sin(self.posterior.tilt_1),
+                       (4 * self.posterior.q + 3) / (3 * self.posterior.q + 4) *
+                       self.posterior.q * self.posterior.a_2 *
+                       np.sin(self.posterior.tilt_2)))
+        self.search_parameter_keys.append('chi_p')
+        self.parameter_labels.append('$\chi_{\mathrm p}$')
 
     def check_attribute_match_to_other_object(self, name, other_object):
         """ Check attribute name exists in other_object and is the same

tupak/core/sampler.py

@@ -597,6 +597,9 @@ class Dynesty(Sampler):
         self.result.log_likelihood_evaluations = out.logl
         self.result.log_evidence = out.logz[-1]
         self.result.log_evidence_err = out.logzerr[-1]
+        self.result.nested_samples = pd.DataFrame(
+            out.samples, columns=self.search_parameter_keys)
+        self.result.nested_samples['weights'] = weights
 
         if self.plot:
             self.generate_trace_plots(out)
@@ -1176,6 +1179,9 @@ class Pymc3(Sampler):
         prior_map['Lorentzian'] = prior_map['Cauchy']
         prior_map['FromFile'] = prior_map['Interped']
 
+        # GW specific priors
+        prior_map['UniformComovingVolume'] = prior_map['Interped']
+
         # internally defined mappings for tupak priors
         prior_map['DeltaFunction'] = {'internal': self._deltafunction_prior}
         prior_map['Sine'] = {'internal': self._sine_prior}
@@ -1353,22 +1359,41 @@ class Pymc3(Sampler):
 
         step_methods = {m.__name__.lower(): m.__name__ for m in STEP_METHODS}
         if 'step' in self.__kwargs:
-            step_method = self.__kwargs.pop('step').lower()
+            self.step_method = self.__kwargs.pop('step')
 
-            if step_method not in step_methods:
-                raise ValueError("Using invalid step method '{}'".format(step_method))
+            # 'step' could be a dictionary of methods for different parameters, so check for this
+            if isinstance(self.step_method, (dict, OrderedDict)):
+                for key in self.step_method:
+                    if key not in self.__search_parameter_keys:
+                        raise ValueError("Setting a step method for an unknown parameter '{}'".format(key))
+                    else:
+                        if self.step_method[key].lower() not in step_methods:
+                            raise ValueError("Using invalid step method '{}'".format(self.step_method[key]))
+            else:
+                self.step_method = self.step_method.lower()
+
+                if self.step_method not in step_methods:
+                    raise ValueError("Using invalid step method '{}'".format(self.step_method))
         else:
-            step_method = None
+            self.step_method = None
 
         # initialise the PyMC3 model
         self.pymc3_model = pymc3.Model()
 
-        # set the step method
-        sm = None if step_method is None else pymc3.__dict__[step_methods[step_method]]()
-
         # set the prior
         self.set_prior()
 
+        # set the step method
+        if isinstance(self.step_method, (dict, OrderedDict)):
+            # create list of step methods (any not given will default to NUTS)
+            sm = []
+            with self.pymc3_model:
+                for key in self.step_method:
+                    curmethod = self.step_method[key].lower()
+                    sm.append(pymc3.__dict__[step_methods[curmethod]]([self.pymc3_priors[key]]))
+        else:
+            sm = None if self.step_method is None else pymc3.__dict__[step_methods[self.step_method]]()
+
         # if a custom log_likelihood function requires a `sampler` argument
         # then use that log_likelihood function, with the assumption that it
         # takes in a Pymc3 Sampler, with a pymc3_model attribute, and defines
@@ -1407,7 +1432,7 @@ class Pymc3(Sampler):
 
         self.setup_prior_mapping()
 
-        self.pymc3_priors = dict()
+        self.pymc3_priors = OrderedDict()
 
         pymc3 = self.external_sampler
 
@@ -1472,17 +1497,85 @@ class Pymc3(Sampler):
         Convert any tupak likelihoods to PyMC3 distributions.
         """
 
+        try:
+            import theano
+            import theano.tensor as tt
+            from theano.compile.ops import as_op
+        except ImportError:
+            raise ImportError("Could not import theano")
+
+        from tupak.core.likelihood import GaussianLikelihood, PoissonLikelihood, ExponentialLikelihood, StudentTLikelihood
+        from tupak.gw.likelihood import BasicGravitationalWaveTransient, GravitationalWaveTransient
+
+        # create theano Op for the log likelihood if not using a predefined model
+        class LogLike(tt.Op):
+
+            itypes = [tt.dvector]
+            otypes = [tt.dscalar]
+
+            def __init__(self, parameters, loglike, priors):
+                self.parameters = parameters
+                self.likelihood = loglike
+                self.priors = priors
+
+                # set the fixed parameters
+                for key in self.priors.keys():
+                    if isinstance(self.priors[key], float):
+                        self.likelihood.parameters[key] = self.priors[key]
+
+                self.logpgrad = LogLikeGrad(self.parameters, self.likelihood, self.priors)
+
+            def perform(self, node, inputs, outputs):
+                theta, = inputs
+                for i, key in enumerate(self.parameters):
+                    self.likelihood.parameters[key] = theta[i]
+
+                outputs[0][0] = np.array(self.likelihood.log_likelihood())
+
+            def grad(self, inputs, g):
+                theta, = inputs
+                return [g[0]*self.logpgrad(theta)]
+
+        # create theano Op for calculating the gradient of the log likelihood
+        class LogLikeGrad(tt.Op):
+
+            itypes = [tt.dvector]
+            otypes = [tt.dvector]
+
+            def __init__(self, parameters, loglike, priors):
+                self.parameters = parameters
+                self.Nparams = len(parameters)
+                self.likelihood = loglike
+                self.priors = priors
+
+                # set the fixed parameters
+                for key in self.priors.keys():
+                    if isinstance(self.priors[key], float):
+                        self.likelihood.parameters[key] = self.priors[key]
+
+            def perform(self, node, inputs, outputs):
+                theta, = inputs
+
+                # define version of likelihood function to pass to derivative function
+                def lnlike(values):
+                    for i, key in enumerate(self.parameters):
+                        self.likelihood.parameters[key] = values[i]
+                    return self.likelihood.log_likelihood()
+
+                # calculate gradients
+                grads = utils.derivatives(theta, lnlike, abseps=1e-5, mineps=1e-12, reltol=1e-2)
+
+                outputs[0][0] = grads
+
         pymc3 = self.external_sampler
 
         with self.pymc3_model:
             #  check if it is a predefined likelhood function
-            if self.likelihood.__class__.__name__ == 'GaussianLikelihood':
+            if isinstance(self.likelihood, GaussianLikelihood):
                 # check required attributes exist
                 if (not hasattr(self.likelihood, 'sigma') or
                     not hasattr(self.likelihood, 'x') or
-                    not hasattr(self.likelihood, 'y') or
-                    not hasattr(self.likelihood, 'function') or
-                    not hasattr(self.likelihood, 'function_keys')):
+                    not hasattr(self.likelihood, 'y')):
                     raise ValueError("Gaussian Likelihood does not have all the correct attributes!")
 
                 if 'sigma' in self.pymc3_priors:
@@ -1496,17 +1589,15 @@ class Pymc3(Sampler):
                     if key not in self.likelihood.function_keys:
                         raise ValueError("Prior key '{}' is not a function key!".format(key))
 
-                model = self.likelihood.function(self.likelihood.x, **self.pymc3_priors)
+                model = self.likelihood.func(self.likelihood.x, **self.pymc3_priors)
 
                 # set the distribution
                 pymc3.Normal('likelihood', mu=model, sd=self.likelihood.sigma,
                              observed=self.likelihood.y)
-            elif self.likelihood.__class__.__name__ == 'PoissonLikelihood':
+            elif isinstance(self.likelihood, PoissonLikelihood):
                 # check required attributes exist
                 if (not hasattr(self.likelihood, 'x') or
-                    not hasattr(self.likelihood, 'y') or
-                    not hasattr(self.likelihood, 'function') or
-                    not hasattr(self.likelihood, 'function_keys')):
+                    not hasattr(self.likelihood, 'y')):
                     raise ValueError("Poisson Likelihood does not have all the correct attributes!")
 
                 for key in self.pymc3_priors:
@@ -1514,16 +1605,14 @@ class Pymc3(Sampler):
                         raise ValueError("Prior key '{}' is not a function key!".format(key))
 
                 # get rate function
-                model = self.likelihood.function(self.likelihood.x, **self.pymc3_priors)
+                model = self.likelihood.func(self.likelihood.x, **self.pymc3_priors)
 
                 # set the distribution
                 pymc3.Poisson('likelihood', mu=model, observed=self.likelihood.y)
-            elif self.likelihood.__class__.__name__ == 'ExponentialLikelihood':
+            elif isinstance(self.likelihood, ExponentialLikelihood):
                 # check required attributes exist
                 if (not hasattr(self.likelihood, 'x') or
-                    not hasattr(self.likelihood, 'y') or
-                    not hasattr(self.likelihood, 'function') or
-                    not hasattr(self.likelihood, 'function_keys')):
+                    not hasattr(self.likelihood, 'y')):
                     raise ValueError("Exponential Likelihood does not have all the correct attributes!")
 
                 for key in self.pymc3_priors:
@@ -1531,18 +1620,16 @@ class Pymc3(Sampler):
                         raise ValueError("Prior key '{}' is not a function key!".format(key))
 
                 # get mean function
-                model = self.likelihood.function(self.likelihood.x, **self.pymc3_priors)
+                model = self.likelihood.func(self.likelihood.x, **self.pymc3_priors)
 
                 # set the distribution
                 pymc3.Exponential('likelihood', lam=1. / model, observed=self.likelihood.y)
-            elif self.likelihood.__class__.__name__ == 'StudentTLikelihood':
+            elif isinstance(self.likelihood, StudentTLikelihood):
                 # check required attributes exist
                 if (not hasattr(self.likelihood, 'x') or
                     not hasattr(self.likelihood, 'y') or
                     not hasattr(self.likelihood, 'nu') or
-                    not hasattr(self.likelihood, 'sigma') or
-                    not hasattr(self.likelihood, 'function') or
-                    not hasattr(self.likelihood, 'function_keys')):
+                    not hasattr(self.likelihood, 'sigma')):
                     raise ValueError("StudentT Likelihood does not have all the correct attributes!")
 
                 if 'nu' in self.pymc3_priors:
@@ -1556,10 +1643,25 @@ class Pymc3(Sampler):
                     if key not in self.likelihood.function_keys:
                         raise ValueError("Prior key '{}' is not a function key!".format(key))
 
-                model = self.likelihood.function(self.likelihood.x, **self.pymc3_priors)
+                model = self.likelihood.func(self.likelihood.x, **self.pymc3_priors)
 
                 # set the distribution
                 pymc3.StudentT('likelihood', nu=self.likelihood.nu, mu=model, sd=self.likelihood.sigma, observed=self.likelihood.y)
+            elif isinstance(self.likelihood, (GravitationalWaveTransient, BasicGravitationalWaveTransient)):
+                # set theano Op - pass __search_parameter_keys, which only contains non-fixed variables
+                logl = LogLike(self.__search_parameter_keys, self.likelihood, self.pymc3_priors)
+
+                parameters = OrderedDict()
+                for key in self.__search_parameter_keys:
+                    try:
+                        parameters[key] = self.pymc3_priors[key]
+                    except KeyError:
+                        raise KeyError("Unknown key '{}' when setting GravitationalWaveTransient likelihood".format(key))
+
+                # convert to theano tensor variable
+                values = tt.as_tensor_variable(list(parameters.values()))
+
+                pymc3.DensityDist('likelihood', lambda v: logl(v), observed={'v': values})
             else:
                 raise ValueError("Unknown likelihood has been provided")
 

tupak/core/utils.py

@@ -450,6 +450,141 @@ def set_up_command_line_arguments():
     return args
 
 
+def derivatives(vals, func, releps=1e-3, abseps=None, mineps=1e-9, reltol=1e-3,
+                epsscale=0.5, nonfixedidx=None):
+    """
+    Calculate the partial derivatives of a function at a set of values. The
+    derivatives are calculated using the central difference, using an iterative
+    method to check that the values converge as step size decreases.
+
+    Parameters
+    ----------
+    vals: array_like
+        A set of values, that are passed to a function, at which to calculate
+        the gradient of that function
+    func:
+        A function that takes in an array of values.
+    releps: float, array_like, 1e-3
+        The initial relative step size for calculating the derivative.
+    abseps: float, array_like, None
+        The initial absolute step size for calculating the derivative.
+        This overrides `releps` if set.
+        `releps` is set then that is used.
+    mineps: float, 1e-9
+        The minimum relative step size at which to stop iterations if no
+        convergence is achieved.
+    epsscale: float, 0.5
+        The factor by which releps if scaled in each iteration.
+    nonfixedidx: array_like, None
+        An array of indices in `vals` that are _not_ fixed values and therefore
+        can have derivatives taken. If `None` then derivatives of all values
+        are calculated.
+        
+    Returns
+    -------
+    grads: array_like
+        An array of gradients for each non-fixed value.
+    """
+
+    if nonfixedidx is None:
+        nonfixedidx = range(len(vals))
+    
+    if len(nonfixedidx) > len(vals):
+        raise ValueError("To many non-fixed values")
+        
+    if max(nonfixedidx) >= len(vals) or min(nonfixedidx) < 0:
+        raise ValueError("Non-fixed indexes contain non-existant indices")
+
+    grads = np.zeros(len(nonfixedidx))
+
+    # maximum number of times the gradient can change sign
+    flipflopmax = 10.
+
+    # set steps
+    if abseps is None:
+        if isinstance(releps, float):
+            eps = np.abs(vals)*releps
+            eps[eps == 0.] = releps # if any values are zero set eps to releps
+            teps = releps*np.ones(len(vals))
+        elif isinstance(releps, (list, np.ndarray)):
+            if len(releps) != len(vals):
+                raise ValueError("Problem with input relative step sizes")
+            eps = np.multiply(np.abs(vals), releps)
+            eps[eps == 0.] = np.array(releps)[eps == 0.]
+            teps = releps
+        else:
+            raise RuntimeError("Relative step sizes are not a recognised type!")
+    else:
+        if isinstance(abseps, float):
+            eps = abseps*np.ones(len(vals))
+        elif isinstance(abseps, (list, np.ndarray)):
+            if len(abseps) != len(vals):
+                raise ValueError("Problem with input absolute step sizes")
+            eps = np.array(abseps)
+        else:
+            raise RuntimeError("Absolute step sizes are not a recognised type!")
+        teps = eps
+
+    # for each value in vals calculate the gradient
+    count = 0
+    for i in nonfixedidx:
+        # initial parameter diffs
+        leps = eps[i]
+        cureps = teps[i]
+
+        flipflop = 0
+
+        # get central finite difference
+        fvals = np.copy(vals)
+        bvals = np.copy(vals)
+
+        # central difference
+        fvals[i] += 0.5*leps  # change forwards distance to half eps
+        bvals[i] -= 0.5*leps  # change backwards distance to half eps
+        cdiff = (func(fvals)-func(bvals))/leps
+
+        while 1:
+            fvals[i] -= 0.5*leps  # remove old step
+            bvals[i] += 0.5*leps
+
+            # change the difference by a factor of two
+            cureps *= epsscale
+            if cureps < mineps or flipflop > flipflopmax:
+                # if no convergence set flat derivative (TODO: check if there is a better thing to do instead)
+                logger.warning("Derivative calculation did not converge: setting flat derivative.")
+                grads[count] = 0.
+                break
+            leps *= epsscale
+
+            # central difference
+            fvals[i] += 0.5*leps  # change forwards distance to half eps
+            bvals[i] -= 0.5*leps  # change backwards distance to half eps
+            cdiffnew = (func(fvals)-func(bvals))/leps
+
+            if cdiffnew == cdiff:
+                grads[count] = cdiff
+                break
+
+            # check whether previous diff and current diff are the same within reltol
+            rat = (cdiff/cdiffnew)
+            if np.isfinite(rat) and rat > 0.:
+                # gradient has not changed sign
+                if np.abs(1.-rat) < reltol:
+                    grads[count] = cdiffnew
+                    break
+                else:
+                    cdiff = cdiffnew
+                    continue
+            else:
+                cdiff = cdiffnew
+                flipflop += 1
+                continue
+
+        count += 1
+
+    return grads
+
+
 command_line_args = set_up_command_line_arguments()
 setup_logger(print_version=True)