Improve ptemcee

This is a total rewrite of the ptemcee implementation. Originally, we had based ptemcee on emcee, but emcee has changed quite significantly and I anticipate it will be easier to maintain the two uncoupled from hereon. This adds sufficient functionality to ptemcee for it to be usable on gravitational wave events. Here is a summary of the changes

1. Add automatic convergence to the user interface: users now specify nsamples as a the primary input. The sampler will run until it has nsamples independent samples from the posterior. The user now has access to a reduced set of the ptemcee full functionality (e.g. iterations), but this isn't required given the convergence checking.
2. Add diagnostic plots for the autocorrelation time, e.g.:
3. Improve the traceplots to show the convergence and the current posterior samples
4. Add dumping of posterior samples to a .dat file
5. Add multi-processing support, tested on a 16 core machine to give a significant reduction in compute time (exact timings yet to be calculated).
6. Add regular 10-minute checkpointing

Note, I have intentionally put much of the primary logic into functions rather than class methods. This is because eventually we should port changes to parallel bilby and potentially other samplers and this is most easily done from a functional interface (at least for parallel bilby where inheritance causes issues with the parallel aspects).

bilby/core/sampler/ptemcee.py

@@ -7,6 +7,7 @@ import signal
 import sys
 import time
 import dill
+from collections import namedtuple
 
 import numpy as np
 import pandas as pd
@@ -16,6 +17,23 @@ from ..utils import logger
 from .base_sampler import SamplerError, MCMCSampler
 
 
+ConvergenceInputs = namedtuple(
+    "ConvergenceInputs",
+    [
+        "autocorr_c",
+        "autocorr_tol",
+        "autocorr_tau",
+        "safety",
+        "burn_in_nact",
+        "thin_by_nact",
+        "frac_threshold",
+        "nsamples",
+        "ignore_keys_for_tau",
+        "min_tau",
+    ],
+)
+
+
 class Ptemcee(MCMCSampler):
     """bilby wrapper ptemcee (https://github.com/willvousden/ptemcee)
 
@@ -93,14 +111,10 @@ class Ptemcee(MCMCSampler):
         Tmax=None,
         betas=None,
         a=2.0,
-        loglargs=[],
-        logpargs=[],
-        loglkwargs={},
-        logpkwargs={},
         adaptation_lag=10000,
         adaptation_time=100,
         random=None,
-        adapt=False,
+        adapt=True,
         swap_ratios=False,
     )
 
@@ -143,70 +157,114 @@ class Ptemcee(MCMCSampler):
             **kwargs
         )
 
+        self.nwalkers = self.sampler_init_kwargs["nwalkers"]
+        self.ntemps = self.sampler_init_kwargs["ntemps"]
+        self.max_steps = 500
+
+        # Setup up signal handling
         signal.signal(signal.SIGTERM, self.write_current_state_and_exit)
         signal.signal(signal.SIGINT, self.write_current_state_and_exit)
         signal.signal(signal.SIGALRM, self.write_current_state_and_exit)
 
+        # Checkpointing inputs
+        self.exit_code = exit_code
         self.resume = resume
-        self.autocorr_c = autocorr_c
-        self.safety = safety
-        self.burn_in_nact = burn_in_nact
-        self.thin_by_nact = thin_by_nact
-        self.frac_threshold = frac_threshold
-        self.nsamples = nsamples
-        self.autocorr_tol = autocorr_tol
-        self.autocorr_tau = autocorr_tau
-        self.min_tau = min_tau
         self.check_point_deltaT = check_point_deltaT
-
-        self.threads = threads
-        self.store_walkers = store_walkers
-        self.ignore_keys_for_tau = ignore_keys_for_tau
-        self.pos0 = pos0
-
         self.check_point_plot = check_point_plot
         self.resume_file = "{}/{}_checkpoint_resume.pickle".format(
             self.outdir, self.label
         )
-        self.exit_code = exit_code
+
+        # Store convergence checking inputs in a named tuple
+        convergence_inputs_dict = dict(
+            autocorr_c=autocorr_c,
+            autocorr_tol=autocorr_tol,
+            autocorr_tau=autocorr_tau,
+            safety=safety,
+            burn_in_nact=burn_in_nact,
+            thin_by_nact=thin_by_nact,
+            frac_threshold=frac_threshold,
+            nsamples=nsamples,
+            ignore_keys_for_tau=ignore_keys_for_tau,
+            min_tau=min_tau,
+        )
+        self.convergence_inputs = ConvergenceInputs(**convergence_inputs_dict)
+
+        # MultiProcessing inputs
+        self.threads = threads
+
+        # Misc inputs
+        self.store_walkers = store_walkers
+        self.pos0 = pos0
 
     @property
     def sampler_function_kwargs(self):
+        """ Kwargs passed to samper.sampler() """
         keys = ["adapt", "swap_ratios"]
         return {key: self.kwargs[key] for key in keys}
 
     @property
     def sampler_init_kwargs(self):
+        """ Kwargs passed to initialize ptemcee.Sampler() """
         return {
             key: value
             for key, value in self.kwargs.items()
             if key not in self.sampler_function_kwargs
         }
 
+    def _translate_kwargs(self, kwargs):
+        """ Translate kwargs """
+        if "nwalkers" not in kwargs:
+            for equiv in self.nwalkers_equiv_kwargs:
+                if equiv in kwargs:
+                    kwargs["nwalkers"] = kwargs.pop(equiv)
+
     def get_pos0_from_prior(self):
-        """ for ptemcee, the pos0 has the shape ntemps, nwalkers, ndim """
+        """ Draw the initial positions from the prior
+
+        Returns
+        -------
+        pos0: list
+            The initial postitions of the walkers, with shape (ntemps, nwalkers, ndim)
+
+        """
         logger.info("Generating pos0 samples")
         return [
             [
                 self.get_random_draw_from_prior()
-                for _ in range(self.sampler_init_kwargs["nwalkers"])
+                for _ in range(self.nwalkers)
             ]
             for _ in range(self.kwargs["ntemps"])
         ]
 
     def get_pos0_from_minimize(self, minimize_list=None):
-        logger.info("Attempting to set pos0 from minimize")
+        """ Draw the initial positions using an initial minimization step
+
+        See pos0 in the class initialization for details.
+
+        Returns
+        -------
+        pos0: list
+            The initial postitions of the walkers, with shape (ntemps, nwalkers, ndim)
+
+        """
+
         from scipy.optimize import minimize
 
+        # Set up the minimize list: keys not in this list will have initial
+        # positions drawn from the prior
         if minimize_list is None:
             minimize_list = self.search_parameter_keys
             pos0 = np.zeros((self.kwargs["ntemps"], self.kwargs["nwalkers"], self.ndim))
         else:
             pos0 = np.array(self.get_pos0_from_prior())
 
+        logger.info("Attempting to set pos0 for {} from minimize".format(minimize_list))
+
         likelihood_copy = copy.copy(self.likelihood)
 
         def neg_log_like(params):
+            """ Internal function to minimize """
             likelihood_copy.parameters.update(
                 {key: val for key, val in zip(minimize_list, params)}
             )
@@ -215,10 +273,13 @@ class Ptemcee(MCMCSampler):
             except RuntimeError:
                 return +np.inf
 
+        # Bounds used in the minimization
         bounds = [
             (self.priors[key].minimum, self.priors[key].maximum)
             for key in minimize_list
         ]
+
+        # Run the minimization step several times to get a range of values
         trials = 0
         success = []
         while True:
@@ -235,6 +296,7 @@ class Ptemcee(MCMCSampler):
             if len(success) >= 10:
                 break
 
+        # Initialize positions from the range of values
         success = np.array(success)
         for i, key in enumerate(minimize_list):
             pos0_min = np.min(success[:, i])
@@ -251,6 +313,7 @@ class Ptemcee(MCMCSampler):
         return pos0
 
     def setup_sampler(self):
+        """ Either initialize the sampelr or read in the resume file """
         import ptemcee
 
         if os.path.isfile(self.resume_file) and self.resume is True:
@@ -258,18 +321,22 @@ class Ptemcee(MCMCSampler):
             with open(self.resume_file, "rb") as file:
                 data = dill.load(file)
 
+            # Extract the check-point data
             self.sampler = data["sampler"]
+            self.iteration = data["iteration"]
+            self.chain_array = data["chain_array"]
+            self.log_likelihood_array = data["log_likelihood_array"]
+            self.pos0 = data["pos0"]
             self.tau_list = data["tau_list"]
             self.tau_list_n = data["tau_list_n"]
             self.time_per_check = data["time_per_check"]
 
+            # Initialize the pool
             self.sampler.pool = self.pool
             self.sampler.threads = self.threads
 
-            pos0 = None
-
             logger.info(
-                "Resuming from previous run with time={}".format(self.sampler.time)
+                "Resuming from previous run with time={}".format(self.iteration)
             )
 
         else:
@@ -295,17 +362,25 @@ class Ptemcee(MCMCSampler):
                     self.search_parameter_keys, use_ratio=self.use_ratio
                 )
 
-            # Set up empty lists
+            # Initialize storing results
+            self.iteration = 0
+            self.chain_array = self.get_zero_chain_array()
+            self.log_likelihood_array = self.get_zero_log_likelihood_array()
             self.tau_list = []
             self.tau_list_n = []
             self.time_per_check = []
+            self.pos0 = self.get_pos0()
+
+        return self.sampler
 
-            # Initialize the walker postitions
-            pos0 = self.get_pos0()
+    def get_zero_chain_array(self):
+        return np.zeros((self.nwalkers, self.max_steps, self.ndim))
 
-        return self.sampler, pos0
+    def get_zero_log_likelihood_array(self):
+        return np.zeros((self.ntemps, self.nwalkers, self.max_steps))
 
     def get_pos0(self):
+        """ Master logic for setting pos0 """
         if isinstance(self.pos0, str) and self.pos0.lower() == "prior":
             return self.get_pos0_from_prior()
         elif isinstance(self.pos0, str) and self.pos0.lower() == "minimize":
@@ -316,6 +391,7 @@ class Ptemcee(MCMCSampler):
             raise SamplerError("pos0={} not implemented".format(self.pos0))
 
     def setup_pool(self):
+        """ If threads > 1, setup a MultiPool, else run in serial mode """
         if self.threads > 1:
             import schwimmbad
 
@@ -328,105 +404,48 @@ class Ptemcee(MCMCSampler):
 
     def run_sampler(self):
         self.setup_pool()
-        out = self.run_sampler_internal()
-        if self.pool:
-            self.pool.close()
-        return out
-
-    def run_sampler_internal(self):
-        import emcee
-
-        sampler, pos0 = self.setup_sampler()
+        sampler = self.setup_sampler()
 
         t0 = datetime.datetime.now()
         logger.info("Starting to sample")
         while True:
-            for (pos0, _, _) in sampler.sample(pos0, **self.sampler_function_kwargs):
+            for (pos0, log_posterior, log_likelihood) in sampler.sample(
+                    self.pos0, storechain=False, **self.sampler_function_kwargs):
                 pass
 
+            if self.iteration == self.chain_array.shape[1]:
+                self.chain_array = np.concatenate((
+                    self.chain_array, self.get_zero_chain_array()), axis=1)
+                self.log_likelihood_array = np.concatenate((
+                    self.log_likelihood_array, self.get_zero_log_likelihood_array()),
+                    axis=2)
+            self.pos0 = pos0
+            self.chain_array[:, self.iteration, :] = pos0[0, :, :]
+            self.log_likelihood_array[:, :, self.iteration] = log_likelihood
+
             # Calculate time per iteration
             self.time_per_check.append((datetime.datetime.now() - t0).total_seconds())
             t0 = datetime.datetime.now()
 
-            # Compute ACT tau for 0-temperature chains
-            samples = sampler.chain[0, :, : sampler.time, :]
-            taus = []
-            for ii in range(sampler.nwalkers):
-                tau_ii = []
-                for jj, key in enumerate(self.search_parameter_keys):
-                    if self.ignore_keys_for_tau and self.ignore_keys_for_tau in key:
-                        continue
-                    try:
-                        tau_ii.append(
-                            emcee.autocorr.integrated_time(
-                                samples[ii, :, jj], c=self.autocorr_c, tol=0
-                            )[0]
-                        )
-                    except emcee.autocorr.AutocorrError:
-                        taus.append(np.inf)
-                taus.append(tau_ii)
-
-            tau = np.max(np.mean(taus, axis=0))
-
-            # Apply multiplicitive safety factor
-            tau = self.safety * tau
-
-            # Store for convergence checking and plotting
-            self.tau_list.append(np.mean(taus, axis=0))
-            self.tau_list_n.append(sampler.time)
-
-            # Convert to an integer
-            tau_int = int(np.ceil(tau)) if not np.isnan(tau) else tau
-            self.tau_int = tau_int
-
-            if np.isnan(tau_int) or np.isinf(tau_int):
-                print_progress(
-                    self.sampler,
-                    self.time_per_check,
-                    self.nsamples,
-                    np.nan,
-                    np.nan,
-                    np.nan,
-                    [np.nan],
-                    False,
-                )
-                continue
-
-            # Calculate the effective number of samples available
-            self.nburn = int(self.burn_in_nact * tau_int)
-            self.thin = int(np.max([1, self.thin_by_nact * tau_int]))
-            samples_per_check = sampler.nwalkers / self.thin
-            self.nsamples_effective = int(
-                sampler.nwalkers * (sampler.time - self.nburn) / self.thin
-            )
-
-            # Calculate convergence boolean
-            converged = self.nsamples < self.nsamples_effective
-
-            # Calculate fractional change in tau from previous iterations
-            check_taus = np.array(self.tau_list[-tau_int * self.autocorr_tau :])
-            if not np.any(np.isnan(check_taus)):
-                frac = (tau - check_taus) / tau
-                tau_usable = np.all(frac < self.frac_threshold)
-            else:
-                tau_usable = False
-
-            if sampler.time < tau_int * self.autocorr_tol or tau_int < self.min_tau:
-                tau_usable = False
-
-            # Print an update on the progress
-            print_progress(
-                self.sampler,
-                self.time_per_check,
-                self.nsamples,
+            (
+                stop,
+                self.nburn,
+                self.thin,
+                self.tau_int,
                 self.nsamples_effective,
-                samples_per_check,
-                tau_int,
-                check_taus,
-                tau_usable,
+            ) = check_iteration(
+                self.chain_array[:, :self.iteration + 1, :],
+                sampler,
+                self.convergence_inputs,
+                self.search_parameter_keys,
+                self.time_per_check,
+                self.tau_list,
+                self.tau_list_n,
             )
 
-            if converged and tau_usable:
+            self.iteration += 1
+
+            if stop:
                 logger.info("Finished sampling")
                 break
 
@@ -443,12 +462,11 @@ class Ptemcee(MCMCSampler):
         self.write_current_state(plot=self.check_point_plot)
 
         # Get 0-likelihood samples and store in the result
-        samples = sampler.chain[0, :, :, :]  # nwalkers, nsteps, ndim
-        self.result.samples = samples[
-            :, self.nburn : sampler.time : self.thin, :
+        self.result.samples = self.chain_array[
+            :, self.nburn : self.iteration : self.thin, :
         ].reshape((-1, self.ndim))
-        loglikelihood = sampler.loglikelihood[
-            0, :, self.nburn : sampler.time : self.thin
+        loglikelihood = self.log_likelihood_array[
+            0, :, self.nburn : self.iteration : self.thin
         ]  # nwalkers, nsteps
         self.result.log_likelihood_evaluations = loglikelihood.reshape((-1))
 
@@ -457,7 +475,8 @@ class Ptemcee(MCMCSampler):
         self.result.nburn = self.nburn
 
         log_evidence, log_evidence_err = compute_evidence(
-            sampler, self.outdir, self.label, self.nburn, self.thin
+            sampler, self.log_likelihood_array, self.outdir, self.label, self.nburn,
+            self.thin, self.iteration,
         )
         self.result.log_evidence = log_evidence
         self.result.log_evidence_err = log_evidence_err
@@ -466,6 +485,9 @@ class Ptemcee(MCMCSampler):
             seconds=np.sum(self.time_per_check)
         )
 
+        if self.pool:
+            self.pool.close()
+
         return self.result
 
     def write_current_state_and_exit(self, signum=None, frame=None):
@@ -479,6 +501,7 @@ class Ptemcee(MCMCSampler):
 
     def write_current_state(self, plot=True):
         checkpoint(
+            self.iteration,
             self.outdir,
             self.label,
             self.nsamples_effective,
@@ -487,6 +510,9 @@ class Ptemcee(MCMCSampler):
             self.thin,
             self.search_parameter_keys,
             self.resume_file,
+            self.log_likelihood_array,
+            self.chain_array,
+            self.pos0,
             self.tau_list,
             self.tau_list_n,
             self.time_per_check,
@@ -495,7 +521,7 @@ class Ptemcee(MCMCSampler):
         if plot and not np.isnan(self.nburn):
             # Generate the walkers plot diagnostic
             plot_walkers(
-                self.sampler.chain[0, :, : self.sampler.time, :],
+                self.chain_array[:, : self.iteration, :],
                 self.nburn,
                 self.thin,
                 self.search_parameter_keys,
@@ -511,18 +537,127 @@ class Ptemcee(MCMCSampler):
                 self.outdir,
                 self.label,
                 self.tau_int,
-                self.autocorr_tau,
+                self.convergence_inputs.autocorr_tau,
             )
 
 
+def check_iteration(
+    samples,
+    sampler,
+    convergence_inputs,
+    search_parameter_keys,
+    time_per_check,
+    tau_list,
+    tau_list_n,
+):
+    """ Per-iteration logic to calculate the convergence check
+
+    Parameters
+    ----------
+    convergence_inputs: bilby.core.sampler.ptemcee.ConvergenceInputs
+        A named tuple of the convergence checking inputs
+    search_parameter_keys: list
+        A list of the search parameter keys
+    time_per_check, tau_list, tau_list_n: list
+        Lists used for tracking the run
+
+    Returns
+    -------
+    stop: bool
+        A boolean flag, True if the stoping criteria has been met
+    burn: int
+        The number of burn-in steps to discard
+    thin: int
+        The thin-by factor to apply
+    tau_int: int
+        The integer estimated ACT
+    nsamples_effective: int
+        The effective number of samples after burning and thinning
+    """
+    import emcee
+
+    ci = convergence_inputs
+    nwalkers, iteration, ndim = samples.shape
+
+    # Compute ACT tau for 0-temperature chains
+    tau_array = np.zeros((nwalkers, ndim))
+    for ii in range(nwalkers):
+        for jj, key in enumerate(search_parameter_keys):
+            if ci.ignore_keys_for_tau and ci.ignore_keys_for_tau in key:
+                continue
+            try:
+                tau_array[ii, jj] = emcee.autocorr.integrated_time(
+                    samples[ii, :, jj], c=ci.autocorr_c, tol=0)[0]
+            except emcee.autocorr.AutocorrError:
+                tau_array[ii, jj] = np.inf
+
+    # Maximum over paramters, mean over walkers
+    tau = np.max(np.mean(tau_array, axis=0))
+
+    # Apply multiplicitive safety factor
+    tau = ci.safety * tau
+
+    # Store for convergence checking and plotting
+    tau_list.append(list(np.mean(tau_array, axis=0)))
+    tau_list_n.append(iteration)
+
+    # Convert to an integer
+    tau_int = int(np.ceil(tau)) if not np.isnan(tau) else tau
+
+    if np.isnan(tau_int) or np.isinf(tau_int):
+        print_progress(
+            iteration, sampler, time_per_check, ci.nsamples, np.nan, np.nan, np.nan, np.nan, False,
+        )
+        return False, np.nan, np.nan, np.nan, np.nan
+
+    # Calculate the effective number of samples available
+    nburn = int(ci.burn_in_nact * tau_int)
+    thin = int(np.max([1, ci.thin_by_nact * tau_int]))
+    samples_per_check = nwalkers / thin
+    nsamples_effective = int(nwalkers * (iteration - nburn) / thin)
+
+    # Calculate convergence boolean
+    converged = ci.nsamples < nsamples_effective
+
+    # Calculate fractional change in tau from previous iteration
+    check_taus = np.array(tau_list[-tau_int * ci.autocorr_tau :])
+    taus_per_parameter = check_taus[-1, :]
+    if not np.any(np.isnan(check_taus)):
+        frac = (taus_per_parameter - check_taus) / taus_per_parameter
+        max_frac = np.max(frac)
+        tau_usable = np.all(frac < ci.frac_threshold)
+    else:
+        max_frac = np.nan
+        tau_usable = False
+
+    if iteration < tau_int * ci.autocorr_tol or tau_int < ci.min_tau:
+        tau_usable = False
+
+    # Print an update on the progress
+    print_progress(
+        iteration,
+        sampler,
+        time_per_check,
+        ci.nsamples,
+        nsamples_effective,
+        samples_per_check,
+        tau_int,
+        max_frac,
+        tau_usable,
+    )
+    stop = converged and tau_usable
+    return stop, nburn, thin, tau_int, nsamples_effective
+
+
 def print_progress(
+    iteration,
     sampler,
     time_per_check,
     nsamples,
     nsamples_effective,
     samples_per_check,
     tau_int,
-    tau_list,
+    max_frac,
     tau_usable,
 ):
     # Setup acceptance string
@@ -544,7 +679,10 @@ def print_progress(
 
     sampling_time = datetime.timedelta(seconds=np.sum(time_per_check))
 
-    tau_str = "{}:{:0.1f}->{:0.1f}".format(tau_int, np.min(tau_list), np.max(tau_list))
+    if max_frac >= 0:
+        tau_str = "{}(+{:0.1f})".format(tau_int, max_frac)
+    else:
+        tau_str = "{}({:0.1f})".format(tau_int, max_frac)
     if tau_usable:
         tau_str = "={}".format(tau_str)
     else:
@@ -552,13 +690,13 @@ def print_progress(
 
     evals_per_check = sampler.nwalkers * sampler.ntemps
 
-    ncalls = "{:1.1e}".format(sampler.time * sampler.nwalkers * sampler.ntemps)
+    ncalls = "{:1.1e}".format(iteration * sampler.nwalkers * sampler.ntemps)
     eval_timing = "{:1.1f}ms/ev".format(1e3 * ave_time_per_check / evals_per_check)
     samp_timing = "{:1.1f}ms/sm".format(1e3 * ave_time_per_check / samples_per_check)
 
     print(
         "{}| {} | nc:{}| a0:{}| swp:{}| n:{}<{}| tau{}| {}| {}".format(
-            sampler.time,
+            iteration,
             str(sampling_time).split(".")[0],
             ncalls,
             acceptance_str,
@@ -574,6 +712,7 @@ def print_progress(
 
 
 def checkpoint(
+    iteration,
     outdir,
     label,
     nsamples_effective,
@@ -582,6 +721,9 @@ def checkpoint(
     thin,
     search_parameter_keys,
     resume_file,
+    log_likelihood_array,
+    chain_array,
+    pos0,
     tau_list,
     tau_list_n,
     time_per_check,
@@ -592,7 +734,7 @@ def checkpoint(
     # Store the samples if possible
     if nsamples_effective > 0:
         filename = "{}/{}_samples.txt".format(outdir, label)
-        samples = sampler.chain[0, :, nburn : sampler.time : thin, :].reshape(
+        samples = np.array(chain_array)[:, nburn : iteration : thin, :].reshape(
             (-1, ndim)
         )
         df = pd.DataFrame(samples, columns=search_parameter_keys)
@@ -601,16 +743,16 @@ def checkpoint(
     # Pickle the resume artefacts
     sampler_copy = copy.copy(sampler)
     del sampler_copy.pool
-    sampler_copy._chain = sampler._chain[:, :, : sampler.time, :]
-    sampler_copy._logposterior = sampler._logposterior[:, :, : sampler.time]
-    sampler_copy._loglikelihood = sampler._loglikelihood[:, :, : sampler.time]
-    sampler_copy._beta_history = sampler._beta_history[:, : sampler.time]
 
     data = dict(
+        iteration=iteration,
         sampler=sampler_copy,
         tau_list=tau_list,
         tau_list_n=tau_list_n,
         time_per_check=time_per_check,
+        log_likelihood_array=log_likelihood_array,
+       chain_array=chain_array,
+        pos0=pos0,
     )
 
     with open(resume_file, "wb") as file:
@@ -652,7 +794,9 @@ def plot_walkers(walkers, nburn, thin, parameter_labels, outdir, label):
     plt.close(fig)
 
 
-def plot_tau(tau_list_n, tau_list, search_parameter_keys, outdir, label, tau, autocorr_tau):
+def plot_tau(
+    tau_list_n, tau_list, search_parameter_keys, outdir, label, tau, autocorr_tau
+):
     fig, ax = plt.subplots()
     for i, key in enumerate(search_parameter_keys):
         ax.plot(tau_list_n, np.array(tau_list)[:, i], label=key)
@@ -664,11 +808,12 @@ def plot_tau(tau_list_n, tau_list, search_parameter_keys, outdir, label, tau, au
     plt.close(fig)
 
 
-def compute_evidence(sampler, outdir, label, nburn, thin, make_plots=True):
+def compute_evidence(sampler, log_likelihood_array, outdir, label, nburn, thin,
+                     iteration, make_plots=True):
     """ Computes the evidence using thermodynamic integration """
     betas = sampler.betas
     # We compute the evidence without the burnin samples, but we do not thin
-    lnlike = sampler.loglikelihood[:, :, nburn : sampler.time]
+    lnlike = log_likelihood_array[:, :, nburn : iteration]
     mean_lnlikes = np.mean(np.mean(lnlike, axis=1), axis=1)
 
     mean_lnlikes = mean_lnlikes[::-1]
@@ -701,8 +846,7 @@ def compute_evidence(sampler, outdir, label, nburn, thin, make_plots=True):
 
         ax2.semilogx(min_betas, evidence, "-o")
         ax2.set_ylabel(
-            r"$\int_{\beta_{min}}^{\beta=1}"
-            + r"\langle \log(\mathcal{L})\rangle d\beta$",
+            r"$\int_{\beta_{min}}^{\beta=1}" + r"\langle \log(\mathcal{L})\rangle d\beta$",
             size=16,
         )
         ax2.set_xlabel(r"$\beta_{min}$")