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Re: Help on class understanding in pymc code

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Robert wrote:

> 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:

import pymc

This imports the pymc package. Technically this is achieved by executing

pymc/__init__.py

In __init__.py there is a line

from Model import *

This line puts all names in pymc.Model.__all__ into the current namespace 
and is roughly equivalent to

import Model as _tmp
Model = _tmp.Model
Sampler = _tmp.Sampler
del _tmp

so that after the star import pymc.Model is the class pymc.Model.Model.

> aaa=pymc.Model

Now aaa is (a name for) that class, too.

> type(aaa)

The type() of a class is its "metaclass". The relationship 

metaclass --> class 

is the same as

class --> instance

i. e. a Python class is an instance of its metaclass like 42 is an instance 
of int.

> Out[160]: pymc.Node.ContainerMeta

By default all classes derived from object are of type "type". So that's a 
custom metaclass. You probably don't care about that at this time in your 
career as a pythonista.

> type(pymc.Model)
> Out[161]: pymc.Node.ContainerMeta
> 
> I see that it is not '<class 'pymc.Model.Model'>'.

Because pymc.Model is pymc.Model.Model already. The equivalent for
int would be to type

>>> type(int)

and expecting it to return int.

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Help on class understanding in pymc code Robert <rxjwg98@gmail.com> - 2015-12-13 16:40 -0800
  Re: Help on class understanding in pymc code Peter Otten <__peter__@web.de> - 2015-12-14 02:09 +0100
    Re: Help on class understanding in pymc code Robert <rxjwg98@gmail.com> - 2015-12-13 17:45 -0800
      Re: Help on class understanding in pymc code Peter Otten <__peter__@web.de> - 2015-12-14 09:56 +0100

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