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J'ai une table de comptes de résultats binaires et je voudrais ajuster une distribution binomiale bêta pour estimer les paramètres $ \ alpha $ et $ \ beta $, mais j'ai des erreurs quand j'essaie de échantillonner la distribution du modèle ainsi que je fais pour les autres cas:Utilisation de BetaBinomial dans PyMC3
import pymc3 as pm
import pandas as pd
df = pd.read_csv('~/data.csv', low_memory=False)
df = df[df.Clicks >= 0]
C0=df.C.values
I0=df.N.values
N0 = C0 + I0
with pm.Model() as model:
C=pm.constant(C0)
I=pm.constant(I0)
C1=pm.constant(C0 + 1)
I1=pm.constant(I0 + 1)
N=pm.constant(N0)
alpha = pm.Exponential('alpha', 1/(C0.sum()+1))
beta = pm.Exponential('beta', 1/(I0.sum()+1))
obs = pm.BetaBinomial('obs', alpha, beta, N, observed=C0)
with model:
advi_fit = pm.variational.advi(n=int(1e4))
trace1 = pm.variational.sample_vp(advi_fit, draws=int(1e4))
pm.traceplot(trace1[::10])
with model:
step = pm.NUTS()
#step = pm.Metropolis() # <== same problem
trace2 = pm.sample(int(1e3), step)
pm.traceplot(trace2[::10])
Dans les deux cas, l'échantillonnage échoue avec:
MissingInputError: ("An input of the graph, used to compute Elemwise{neg,no_inplace}(P_logodds_), was not provided and not given a value.Use the Theano flag exception_verbosity='high',for more information on this error.", P_logodds
Dans le cas advi la trace complète de la pile est:
MissingInputError Traceback (most recent call last)
<ipython-input-46-8947c7c798e5> in <module>()
----> 1 import codecs, os;__pyfile = codecs.open('''/tmp/py7996Jip''', encoding='''utf-8''');__code = __pyfile.read().encode('''utf-8''');__pyfile.close();os.remove('''/tmp/py7996Jip''');exec(compile(__code, '''/home/dmahler/Scripts/adops-bayes2.py''', 'exec'));
/home/dmahler/Scripts/adops-bayes2.py in <module>()
59 advi_fit = pm.variational.advi(n=int(J*6.4e4), learning_rate=1e-3/J, epsilon=1e-8, accurate_elbo=False)
60 #advi_fit = pm.variational.advi_minibatch(minibatch_RVs=[alpha, beta, p], minibatch_tensors=[C,I,N])
---> 61 trace = pm.variational.sample_vp(advi_fit, draws=int(2e4))
62
63 pm.traceplot(trace[::10])
/home/dmahler/Anaconda/lib/python2.7/site-packages/pymc3/variational/advi.pyc in sample_vp(vparams, draws, model, local_RVs, random_seed, hide_transformed)
317
318 varnames = [str(var) for var in model.unobserved_RVs]
--> 319 trace = NDArray(model=model, vars=vars_sampled)
320 trace.setup(draws=draws, chain=0)
321
/home/dmahler/Anaconda/lib/python2.7/site-packages/pymc3/backends/ndarray.pyc in __init__(self, name, model, vars)
21 """
22 def __init__(self, name=None, model=None, vars=None):
---> 23 super(NDArray, self).__init__(name, model, vars)
24 self.draw_idx = 0
25 self.draws = None
/home/dmahler/Anaconda/lib/python2.7/site-packages/pymc3/backends/base.pyc in __init__(self, name, model, vars)
34 self.vars = vars
35 self.varnames = [var.name for var in vars]
---> 36 self.fn = model.fastfn(vars)
37
38
/home/dmahler/Anaconda/lib/python2.7/site-packages/pymc3/model.pyc in fastfn(self, outs, mode, *args, **kwargs)
374 Compiled Theano function as point function.
375 """
--> 376 f = self.makefn(outs, mode, *args, **kwargs)
377 return FastPointFunc(f)
378
/home/dmahler/Anaconda/lib/python2.7/site-packages/pymc3/memoize.pyc in memoizer(*args, **kwargs)
12
13 if key not in cache:
---> 14 cache[key] = obj(*args, **kwargs)
15
16 return cache[key]
/home/dmahler/Anaconda/lib/python2.7/site-packages/pymc3/model.pyc in makefn(self, outs, mode, *args, **kwargs)
344 on_unused_input='ignore',
345 accept_inplace=True,
--> 346 mode=mode, *args, **kwargs)
347
348 def fn(self, outs, mode=None, *args, **kwargs):
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/compile/function.pyc in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
318 on_unused_input=on_unused_input,
319 profile=profile,
--> 320 output_keys=output_keys)
321 # We need to add the flag check_aliased inputs if we have any mutable or
322 # borrowed used defined inputs
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/compile/pfunc.pyc in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)
477 accept_inplace=accept_inplace, name=name,
478 profile=profile, on_unused_input=on_unused_input,
--> 479 output_keys=output_keys)
480
481
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/compile/function_module.pyc in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)
1774 profile=profile,
1775 on_unused_input=on_unused_input,
-> 1776 output_keys=output_keys).create(
1777 defaults)
1778
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/compile/function_module.pyc in __init__(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys)
1426 # OUTPUT VARIABLES)
1427 fgraph, additional_outputs = std_fgraph(inputs, outputs,
-> 1428 accept_inplace)
1429 fgraph.profile = profile
1430 else:
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/compile/function_module.pyc in std_fgraph(input_specs, output_specs, accept_inplace)
175
176 fgraph = gof.fg.FunctionGraph(orig_inputs, orig_outputs,
--> 177 update_mapping=update_mapping)
178
179 for node in fgraph.apply_nodes:
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/gof/fg.pyc in __init__(self, inputs, outputs, features, clone, update_mapping)
169
170 for output in outputs:
--> 171 self.__import_r__(output, reason="init")
172 for i, output in enumerate(outputs):
173 output.clients.append(('output', i))
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/gof/fg.pyc in __import_r__(self, variable, reason)
358 # Imports the owners of the variables
359 if variable.owner and variable.owner not in self.apply_nodes:
--> 360 self.__import__(variable.owner, reason=reason)
361 if (variable.owner is None and
362 not isinstance(variable, graph.Constant) and
/home/dmahler/Anaconda/lib/python2.7/site-packages/theano/gof/fg.pyc in __import__(self, apply_node, check, reason)
472 "for more information on this error."
473 % str(node)),
--> 474 r)
475
476 for node in new_nodes:
MissingInputError: ("An input of the graph, used to compute Elemwise{neg,no_inplace}(P_logodds_), was not provided and not given a value.Use the Theano flag exception_verbosity='high',for more information on this error.", P_logodds_)
> /home/dmahler/Anaconda/lib/python2.7/site-packages/theano/gof/fg.py(474)__import__()
472 "for more information on this error."
473 % str(node)),
--> 474 r)
475
476 for node in new_nodes:
Avant que je connaissais pymc3.BetaBinomial je convenait d'essayer d'obtenir le même résultat en utilisant des instances Beta et Binomial séparés:
with pm.Model() as model:
C=pm.constant(C0)
I=pm.constant(I0)
C1=pm.constant(C0 + 1)
I1=pm.constant(I0 + 1)
N=pm.constant(N0)
alpha = pm.Exponential('alpha', 1/(C0.sum()+1))
beta = pm.Exponential('beta', 1/(I0.sum()+1))
p = pm.Beta('P', alpha, beta, shape=K)
b = pm.Binomial('B', N, p, observed=C0)
Ceci termine avec succès, mais différentes méthodes produisent des résultats assez différents. Je pensais que cela pourrait être en partie dû au niveau supplémentaire d'indirection entre les prieurs et les observations rend l'espace de recherche plus grand. Quand je suis tombé sur BetaBinomial je me suis dit que cela rendrait la recherche plus facile tout en étant la bonne chose^TM. Sinon, je crois que les modèles doivent être logiquement équivalents. Malheureusement, je ne peux pas comprendre comment faire batebinomial de travail et je suis incapable de trouver des exemples en utilisant BetaBinomial sur Internet.
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