Robert wrote: > Hi, > > I follow code example at link: > > https://users.obs.carnegiescience.edu/cburns/ipynbs/PyMC.html > > > There is the following code line: > > sampler = pymc.MCMC([alpha,betax,betay,eps,model,tau,z_obs,x_true,y_true]) > > > I want to know the detail of pymc.MCMC, then I get help content of it > with: > > ///////////// > help(pymc.MCMC) > Help on class MCMC in module pymc.MCMC: > > class MCMC(pymc.Model.Sampler) > | This class fits probability models using Markov Chain Monte Carlo. > | Each stochastic variable is assigned a StepMethod object, which makes > | it take a single MCMC step conditional on the rest of the model. These > | step methods are called in turn. > | > | >>> A = MCMC(input, db, verbose=0) > | > \\\\\\\\\\\\\\\\\\ > > > help('pymc.Model.Sampler') > no Python documentation found for 'pymc.Model.Sampler' > > > help('pymc.Model') > Help on class Model in pymc: > > pymc.Model = class Model(pymc.Container.ObjectContainer) > | The base class for all objects that fit probability models. Model is > | initialized with: > | > | >>> A = Model(input, verbose=0) > | > | :Parameters: > | - input : module, list, tuple, dictionary, set, object or nothing. > | Model definition, in terms of Stochastics, Deterministics, > | Potentials and Containers. If nothing, all nodes are collected > | from the base namespace. > | > | Attributes: > | - deterministics > | - stochastics (with observed=False) > | - data (stochastic variables with observed=True) > | - variables > | - potentials > | - containers > | - nodes > | - all_objects > | - status: Not useful for the Model base class, but may be used by > | subclasses. > | > | The following attributes only exist after the appropriate method is > | called: > | - moral_neighbors: The edges of the moralized graph. A dictionary, > | keyed by stochastic variable, > | whose values are sets of stochastic variables. Edges exist between > | the key variable and all variables in the value. Created by method > | _moralize. > | - extended_children: The extended children of self's stochastic > | variables. See the docstring of > | extend_children. This is a dictionary keyed by stochastic > | variable. > | - generations: A list of sets of stochastic variables. The members > | of each element only have parents in > | previous elements. Created by method find_generations. > | > | Methods: > | - sample_model_likelihood(iter): Generate and return iter samples > | of p(data and potentials|model). > | Can be used to generate Bayes' factors. > | > | :SeeAlso: Sampler, MAP, NormalApproximation, weight, Container, graph. > | > | Method resolution order: > | Model > | pymc.Container.ObjectContainer > | pymc.six.NewBase > | pymc.Node.ContainerBase > | __builtin__.object > | > | Methods defined here: > | > | __init__(self, input=None, name=None, verbose=-1) > | Initialize a Model instance. > | > | :Parameters: > | - input : module, list, tuple, dictionary, set, object or > | nothing. > | Model definition, in terms of Stochastics, Deterministics, > | Potentials and Containers. If nothing, all nodes are > | collected from the base namespace. > | > | draw_from_prior(self) > | Sets all variables to random values drawn from joint 'prior', > | meaning contributions of data and potentials to the joint > | distribution are not considered. > | > | get_node(self, node_name) > | Retrieve node with passed name > | > | seed(self) > | Seed new initial values for the stochastics. > | > | ---------------------------------------------------------------------- > | Data descriptors defined here: > | > | generations > | > | ---------------------------------------------------------------------- > | Data and other attributes defined here: > | > | __slotnames__ = [] > | > | register = False > | > | ---------------------------------------------------------------------- > | Methods inherited from pymc.Container.ObjectContainer: > | > | replace(self, item, new_container, key) > | > | ---------------------------------------------------------------------- > | Data descriptors inherited from pymc.Container.ObjectContainer: > | > | value > | A copy of self, with all variables replaced by their values. > | > | ---------------------------------------------------------------------- > | Methods inherited from pymc.Node.ContainerBase: > | > | assimilate(self, new_container) > | > | ---------------------------------------------------------------------- > | Data descriptors inherited from pymc.Node.ContainerBase: > | > | __dict__ > | dictionary for instance variables (if defined) > | > | __weakref__ > | list of weak references to the object (if defined) > | > | logp > | The summed log-probability of all stochastic variables (data > | or otherwise) and factor potentials in self. > | > | ---------------------------------------------------------------------- > | Data and other attributes inherited from pymc.Node.ContainerBase: > | > | change_methods = [] > | > | containing_classes = [] > --------- > > > Now, I have puzzles on the class constructor input parameter: > [alpha,betax,betay,eps,model,tau,z_obs,x_true,y_true] > > 1. 'class MCMC(pymc.Model.Sampler)' says its inheritance is from > 'pymc.Model.Sampler' > > 2. When I try to get help on 'pymc.Model.Sampler', it says: > 'no Python documentation found for 'pymc.Model.Sampler' > > 3. When I continue to catch help on 'pymc.Model.Sampler', I don't see > content mentions 'Sampler'. This complete help message is shown above. > > So, what is 'pymc.Model.Sampler'?
Unfortunately there is a module pymc.Model and a class pymc.Model.Model, and in pymc.__init__.py there is code that overwrites the module with the class. Therefore when you write pymc.Model you get pymc.Model.Model as you can see when you type >>> import pymc >>> pymc.Model <class 'pymc.Model.Model'> To get around this bad naming use >>> from pymc.Model import Sampler >>> help(Sampler) -- https://mail.python.org/mailman/listinfo/python-list
