Hello everyone! I am trying to work with the following likelyhood: [image] I think it has a technical name but I don’t remember it, would love if anyone could tell me :slight_smile: I have set it up in the following way: def logp_exp_lag(t, tau_0, tau): return T.log(T.switch(t < tau_0, 1E-20,T.exp( - (t - tau_0) / tau)/ tau )) with pm.Model() as my_model: tau = pm.Uniform('tau', 0.1, 10, shape=1) tau_0 = pm.Uniform('tau_0', 0.1, 5, shape=1) like = DensityDist('like', logp_exp_lag, observed= { 't': df['tp (ns)'].to_numpy(), 'tau' : tau, 'tau_0' : tau_0 } , shape=1 ) with my_model: my_model_trace = pm.sample(1000, random_seed=RANDOM_SEED, cores=1) And I get the following error: MissingInputError Traceback (most recent call last) in 1 with my_model: ----> 2 my_model_trace = pm.sample(1000, random_seed=RANDOM_SEED, cores=1) 3 #ppc = pm.sample_posterior_predictive(my_model_trace, var_names=["like"], random_seed=RANDOM_SEED, cores=1 ) ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/pymc3/sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, **kwargs) 496 warnings.warn("The number of samples is too small to check convergence reliably.") 497 else: --> 498 trace.report._run_convergence_checks(trace, model) 499 500 trace.report._log_summary() ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/pymc3/backends/report.py in _run_convergence_checks(self, trace, model) 82 varnames.append(rv_name) 83 ---> 84 self._ess = ess = ess(trace, var_names=varnames) 85 self._rhat = rhat = rhat(trace, var_names=varnames) 86 ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/pymc3/stats/__init__.py in wrapped(*args, **kwargs) 22 ) 23 kwargs[new] = kwargs.pop(old) ---> 24 return func(*args, **kwargs) 25 26 return wrapped ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/arviz/stats/diagnostics.py in ess(data, var_names, method, relative, prob) 186 raise TypeError(msg) 187 --> 188 dataset = convert_to_dataset(data, group="posterior") 189 var_names = _var_names(var_names, dataset) 190 ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/arviz/data/converters.py in convert_to_dataset(obj, group, coords, dims) 175 xarray.Dataset 176 """ --> 177 inference_data = convert_to_inference_data(obj, group=group, coords=coords, dims=dims) 178 dataset = getattr(inference_data, group, None) 179 if dataset is None: ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/arviz/data/converters.py in convert_to_inference_data(obj, group, coords, dims, **kwargs) 89 return from_pystan(**kwargs) 90 elif obj.__class__.__name__ == "MultiTrace": # ugly, but doesn't make PyMC3 a requirement ---> 91 return from_pymc3(trace=kwargs.pop(group), **kwargs) 92 elif obj.__class__.__name__ == "EnsembleSampler": # ugly, but doesn't make emcee a requirement 93 return from_emcee(sampler=kwargs.pop(group), **kwargs) ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/arviz/data/io_pymc3.py in from_pymc3(trace, prior, posterior_predictive, log_likelihood, coords, dims, model, save_warmup) 538 dims=dims, 539 model=model, --> 540 save_warmup=save_warmup, 541 ).to_inference_data() 542 ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/arviz/data/io_pymc3.py in __init__(self, trace, prior, posterior_predictive, log_likelihood, predictions, coords, dims, model, save_warmup) 159 self.dims = {**model_dims, **self.dims} 160 --> 161 self.observations, self.multi_observations = self.find_observations() 162 163 def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]: ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/arviz/data/io_pymc3.py in find_observations(self) 172 elif hasattr(obs, "data"): 173 for key, val in obs.data.items(): --> 174 multi_observations[key] = val.eval() if hasattr(val, "eval") else val 175 return observations, multi_observations 176 ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/gof/graph.py in eval(self, inputs_to_values) 520 inputs = tuple(sorted(inputs_to_values.keys(), key=id)) 521 if inputs not in self._fn_cache: --> 522 self._fn_cache[inputs] = theano.function(inputs, self) 523 args = [inputs_to_values[param] for param in inputs] 524 ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/compile/function.py in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input) 315 on_unused_input=on_unused_input, 316 profile=profile, --> 317 output_keys=output_keys) 318 return fn ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/compile/pfunc.py in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys) 484 accept_inplace=accept_inplace, name=name, 485 profile=profile, on_unused_input=on_unused_input, --> 486 output_keys=output_keys) 487 488 ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/compile/function_module.py in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys) 1837 on_unused_input=on_unused_input, 1838 output_keys=output_keys, -> 1839 name=name) 1840 with theano.change_flags(compute_test_value="off"): 1841 fn = m.create(defaults) ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/compile/function_module.py in __init__(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys, name) 1485 # OUTPUT VARIABLES) 1486 fgraph, additional_outputs = std_fgraph(inputs, outputs, -> 1487 accept_inplace) 1488 fgraph.profile = profile 1489 else: ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/compile/function_module.py in std_fgraph(input_specs, output_specs, accept_inplace) 179 180 fgraph = gof.fg.FunctionGraph(orig_inputs, orig_outputs, --> 181 update_mapping=update_mapping) 182 183 for node in fgraph.apply_nodes: ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/gof/fg.py in __init__(self, inputs, outputs, features, clone, update_mapping) 173 174 for output in outputs: --> 175 self.__import_r__(output, reason="init") 176 for i, output in enumerate(outputs): 177 output.clients.append(('output', i)) ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/gof/fg.py in __import_r__(self, variable, reason) 344 # Imports the owners of the variables 345 if variable.owner and variable.owner not in self.apply_nodes: --> 346 self.__import__(variable.owner, reason=reason) 347 elif (variable.owner is None and 348 not isinstance(variable, graph.Constant) and ~/data_partition/bin2/anaconda3/envs/electroporation_modeling/lib/python3.7/site-packages/theano/gof/fg.py in __import__(self, apply_node, check, reason) 389 "for more information on this error." 390 % (node.inputs.index(r), str(node))) --> 391 raise MissingInputError(error_msg, variable=r) 392 393 for node in new_nodes: MissingInputError: Input 0 of the graph (indices start from 0), used to compute sigmoid(tau_interval__), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error. This happens after the sampling. Does anyone have suggestions? By the way, is there a way to add a 0 instead of a 1E-20 it is very hacky. Thanks a lot! Best, Sergio

Found the solution here: [image] Issue with custom likelihood Questions Hi, Thanks for the suggestion. However, for the scale of the data that prior is reasonably informative (data values range from 1e2 to 1e6). I found that the divergences actually came about because, while trying out different things to get the likelihood working, I’d switched the prior on the amplitude to a Normal (as opposed to the half Normal). So ruling out non-physical values probably stopped the leap-frog steps wandering in to a region the lead to divergences. I am now encountering some o…

DensityDist error after sampling

Questions

sperezconesa November 5, 2020, 7:07pm 2

Found the solution here:

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DensityDist error after sampling

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