# "log\_likelihood" not found in InferenceData

**URL:** <https://discourse.pymc.io/t/log-likelihood-not-found-in-inferencedata/8169>\
**Category:** Questions\
**Created:** [October 13, 2021, 8:18pm UTC](https://discourse.pymc.io/t/log-likelihood-not-found-in-inferencedata/8169 "2021-10-13T20:18:02Z")\
**Posts on this page:** 1\
**Showing post:** 4

<div class="post-metadata">

**Author:** ![karthur](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/karthur/32/4470_2.png) [@karthur](https://discourse.pymc.io/u/karthur)\
**Post date:** [October 16, 2021, 4:34pm UTC](https://discourse.pymc.io/t/log-likelihood-not-found-in-inferencedata/8169/4 "2021-10-16T16:34:43Z")

</div>

I am using ArviZ version 0.11.4, PyMC3 version 3.11.4.

Here is a minimal example in which the `log_likelihood` is not saved in the InferenceData (i.e., not in `trace`).

```auto
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import theano.tensor as tt
from matplotlib import pyplot

NUM_CORES = 4
NUM_DRAWS = 25000

class BlackBoxLikelihood(tt.Op):
    '''
    Specify what type of object will be passed and returned to the Op when it is
    called. In our case we will be passing it a vector of values (the parameters
    that define our model) and returning a single "scalar" value (the
    log-likelihood)
    '''
    itypes = [tt.dvector] # Expects a vector of parameter values when called
    otypes = [tt.dscalar] # Outputs a single scalar value (the log likelihood)

    def __init__ (self, model, observed, x, loglik = None):
        '''
        Initialise the Op with various things that our log-likelihood function
        requires. Below are the things that are needed in this particular
        example.

        Parameters
        ----------
        model : Callable
            An arbitrary "black box" function that takes two arguments: the
            model parameters ("params") and the forcing data ("x")
        observed : numpy.ndarray
            The "observed" data that our log-likelihood function takes in
        x:
            The forcing data (input drivers) that our model requires
        loglik : Callable or None
            The log-likelihood (or whatever) function we've defined
        '''
        self.model = model
        self.observed = observed
        self.x = x
        # Optionally overwrite the Gaussian log-likelihood function
        if loglik is not None:
            self.loglik = loglik

    def loglik(self, params, x, observed):
        predicted = self.model(params, x)
        sigma = params[-1]
        return -0.5 * np.log(2 * np.pi * sigma**2) - (0.5 / sigma**2) *\
            np.nansum((predicted - observed)**2)

    def perform(self, node, inputs, outputs):
        # The method that is used when calling the Op
        (params,) = inputs
        logl = self.loglik(params, self.x, self.observed)
        outputs[0][0] = np.array(logl) # Output the log-likelihood

def main():
    def model(params, x, n = 1000):
        'Our black-box model; really, a simulator of Gaussian random vars'
        return np.random.normal(*params, n)

    # Observed data
    data = np.random.normal(loc = 0, scale = 1, size = 1000)
    loglik = BlackBoxLikelihood(model, data, None)

    basic_model = pm.Model()
    with basic_model:
        # (Stochstic) Priors for unknown model parameters
        a = pm.Normal('a', mu = 0, sd = 5)
        b = pm.HalfNormal('b', sd = 1)

        # Convert model parameters to a tensor vector
        params = tt.as_tensor_variable([a, b])
        pm.Potential('likelihood', loglik(params))

        trace = pm.sample(
            draws = NUM_DRAWS, step = pm.DEMetropolisZ(vars = [a, b]),
            cores = NUM_CORES, chains = NUM_CORES,
            return_inferencedata = True, start = {'a': 1, 'b': 10},
            idata_kwargs = {'log_likelihood': True})

```

Output `trace` looks like the following, even if `idata_kwargs` is omitted:

```auto
>>> trace
Inference data with groups:
	> posterior
	> sample_stats

```

---

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