Path: csiph.com!fu-berlin.de!uni-berlin.de!not-for-mail From: Peter Otten <__peter__@web.de> Newsgroups: comp.lang.python Subject: Re: Help on class understanding in pymc code Date: Mon, 14 Dec 2015 02:09:51 +0100 Organization: None Lines: 202 Message-ID: References: <8914575b-92c4-4b0e-adbf-ccb63c5edde5@googlegroups.com> Mime-Version: 1.0 Content-Type: text/plain; charset="ISO-8859-1" Content-Transfer-Encoding: 7Bit X-Trace: news.uni-berlin.de 6RxqWaf9cnErddbivZQv7AxKOyFK2Y/ariSGORAwUQwA== Return-Path: X-Original-To: python-list@python.org Delivered-To: python-list@mail.python.org X-Spam-Status: OK 0.000 X-Spam-Evidence: '*H*': 1.00; '*S*': 0.00; '---------': 0.05; 'elements.': 0.05; 'initialize': 0.05; 'skip:p 60': 0.05; 'attributes': 0.07; 'constructor': 0.07; 'line:': 0.07; 'skip:/ 10': 0.07; 'skip:\\ 10': 0.07; 'subject:code': 0.07; 'variable,': 0.07; "'no": 0.09; 'considered.': 0.09; 'definition,': 0.09; 'inherited': 0.09; 'instance.': 0.09; 'item,': 0.09; 'key)': 0.09; 'keyed': 0.09; 'mentions': 0.09; 'models.': 0.09; 'otherwise)': 0.09; 'received:80.91': 0.09; 'received:80.91.229': 0.09; 'received:gmane.org': 0.09; 'received:list': 0.09; 'subject:Help': 0.10; 'python': 0.10; 'appropriate': 0.14; 'value.': 0.15; 'variables': 0.15; '(data': 0.16; 'attributes:': 0.16; 'conditional': 0.16; 'container,': 0.16; 'containers': 0.16; 'descriptors': 0.16; 'dictionary,': 0.16; 'docstring': 0.16; 'fits': 0.16; 'inheritance': 0.16; 'name=none,': 0.16; 'namespace.': 0.16; 'naming': 0.16; 'nodes': 0.16; 'parameter:': 0.16; 'received:80.91.229.3': 0.16; 'received:dip0.t-ipconnect.de': 0.16; 'received:io': 0.16; 'received:plane.gmane.org': 0.16; 'received:psf.io': 0.16; 'received:t-ipconnect.de': 0.16; 'set,': 0.16; 'skip:[ 50': 0.16; 'subclasses.': 0.16; 'subject:class': 0.16; 'tuple,': 0.16; 'variable.': 0.16; 'weight,': 0.16; 'wrote:': 0.16; 'element': 0.18; 'module,': 0.18; 'input': 0.18; 'variable': 0.18; '>>>': 0.20; 'class,': 0.22; 'variables.': 0.22; 'defined': 0.23; 'fit': 0.23; 'references': 0.23; 'sets': 0.23; 'import': 0.24; 'url:edu': 0.24; 'module': 0.25; 'header:User-Agent:1': 0.26; 'example': 0.26; 'header:X-Complaints-To:1': 0.26; 'rest': 0.26; 'skip:m 30': 0.27; 'object,': 0.27; 'values': 0.28; 'initial': 0.28; 'resolution': 0.28; 'dictionary': 0.29; 'factor': 0.29; 'initialized': 0.29; 'node': 0.29; 'seed': 0.29; 'subject:skip:u 10': 0.29; 'weak': 0.29; 'objects': 0.29; 'random': 0.29; 'code': 0.30; 'class.': 0.30; 'skip:s 30': 0.31; 'skip:_ 10': 0.32; 'says': 0.32; 'useful': 0.33; 'class': 0.33; 'values.': 0.33; "skip:' 20": 0.34; 'previous': 0.34; 'skip:d 20': 0.34; 'list': 0.34; 'so,': 0.35; 'exist': 0.35; 'false': 0.35; 'instance': 0.35; 'nothing.': 0.35; 'replaced': 0.35; 'robert': 0.35; 'skip:p 30': 0.35; 'step': 0.36; 'but': 0.36; 'list,': 0.36; 'there': 0.36; 'created': 0.36; 'assigned': 0.36; 'to:addr:python-list': 0.36; 'subject:: ': 0.37; 'method': 0.37; 'received:org': 0.37; '(with': 0.38; 'detail': 0.38; 'skip:p 20': 0.38; 'hi,': 0.38; 'data': 0.39; 'to:addr:python.org': 0.40; 'received:de': 0.40; 'called': 0.40; 'skip:u 10': 0.61; 'skip:n 10': 0.62; 'complete': 0.63; 'here:': 0.63; 'between': 0.65; 'therefore': 0.67; 'link:': 0.69; 'status:': 0.72; 'order:': 0.84; 'potentials': 0.84; 'stochastic': 0.84; 'monte': 0.91 X-Injected-Via-Gmane: http://gmane.org/ X-Gmane-NNTP-Posting-Host: p57bd8403.dip0.t-ipconnect.de User-Agent: KNode/4.13.3 X-BeenThere: python-list@python.org X-Mailman-Version: 2.1.20+ Precedence: list List-Id: General discussion list for the Python programming language List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Xref: csiph.com comp.lang.python: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 To get around this bad naming use >>> from pymc.Model import Sampler >>> help(Sampler)