Help on class understanding in pymc code

[Robert]

On Sunday, December 13, 2015 at 8:10:25 PM UTC-5, Peter Otten wrote:
> 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)

Thanks. Your answer does solve the problem, but I cannot follow your words.
When you run below code, what is 'pymc.Model'?


>>> import pymc
>>> pymc.Model 
<class 'pymc.Model.Model'>


When I run:
aaa=pymc.Model

type(aaa)
Out[160]: pymc.Node.ContainerMeta

type(pymc.Model)
Out[161]: pymc.Node.ContainerMeta

I see that it is not '<class 'pymc.Model.Model'>'.


Thanks again.