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. -- https://mail.python.org/mailman/listinfo/python-list
