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Help on class understanding in pymc code

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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

#100400 — Help on class understanding in pymc code

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'?


BTW, I use Enthought Canopy, Python 2.7.

Thanks,

 

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#100401

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)

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#100402

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.

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#100408

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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