Update test_thermodynamic_integration (#1385)

* Lowered samples, removed comparison of two different amount of chains

* Removed even argument, due to scipy wanting to go for "simpson" and depracating "even"

* Removed warning for `test_thermodynamic_integration`

---------

Co-authored-by: Jonas Arruda <69197639+arrjon@users.noreply.github.com>
Co-authored-by: Dilan Pathirana <59329744+dilpath@users.noreply.github.com>

pypesto/sample/metropolis.py

@@ -129,6 +129,14 @@ def _perform_step(
         # compute log prior
         lprior_new = -self.neglogprior(x_new)
 
+        # if lpost_new is -inf, x_new will not be accepted
+        if lpost_new == -np.inf:
+            # update proposal
+            self._update_proposal(
+                x, lpost, -np.inf, len(self.trace_neglogpost) + 1
+            )
+            return x, lpost, lprior
+
         if not self.temper_lpost:
             # extract current log likelihood value
             llh = lpost - lprior

pypesto/sample/parallel_tempering.py

@@ -260,7 +260,6 @@ def compute_log_evidence(
                 # integrate from low to high temperature
                 y=mean_loglike_per_beta,
                 x=temps,
-                even="last",
             )
         else:
             raise ValueError(

test/sample/test_sample.py

@@ -796,42 +796,43 @@ def test_thermodynamic_integration():
     problem = gaussian_problem()
 
     # approximation should be better for more chains
-    for n_chains, tol in zip([10, 20], [1, 1e-1]):
-        sampler = sample.ParallelTemperingSampler(
-            internal_sampler=sample.AdaptiveMetropolisSampler(),
-            options={"show_progress": False, "beta_init": "beta_decay"},
-            n_chains=n_chains,
-        )
+    n_chains = 10
+    tol = 1
+    sampler = sample.ParallelTemperingSampler(
+        internal_sampler=sample.AdaptiveMetropolisSampler(),
+        options={"show_progress": False, "beta_init": "beta_decay"},
+        n_chains=n_chains,
+    )
 
-        result = optimize.minimize(
-            problem,
-            progress_bar=False,
-        )
+    result = optimize.minimize(
+        problem,
+        progress_bar=False,
+    )
 
-        result = sample.sample(
-            problem,
-            n_samples=10000,
-            result=result,
-            sampler=sampler,
-        )
+    result = sample.sample(
+        problem,
+        n_samples=2000,
+        result=result,
+        sampler=sampler,
+    )
 
-        # compute the log evidence using trapezoid and simpson rule
-        log_evidence = sampler.compute_log_evidence(result, method="trapezoid")
-        log_evidence_not_all = sampler.compute_log_evidence(
-            result, method="trapezoid", use_all_chains=False
-        )
-        log_evidence_simps = sampler.compute_log_evidence(
-            result, method="simpson"
-        )
+    # compute the log evidence using trapezoid and simpson rule
+    log_evidence = sampler.compute_log_evidence(result, method="trapezoid")
+    log_evidence_not_all = sampler.compute_log_evidence(
+        result, method="trapezoid", use_all_chains=False
+    )
+    log_evidence_simps = sampler.compute_log_evidence(result, method="simpson")
 
-        # compute evidence
-        evidence = quad(
-            lambda x: 1 / (problem.ub - problem.lb) * np.exp(gaussian_llh(x)),
-            a=problem.lb,
-            b=problem.ub,
-        )
+    # compute evidence
+    evidence = quad(
+        lambda x: 1
+        / (problem.ub[0] - problem.lb[0])
+        * np.exp(gaussian_llh(x)),
+        a=problem.lb[0],
+        b=problem.ub[0],
+    )
 
-        # compare to known value
-        assert np.isclose(log_evidence, np.log(evidence[0]), atol=tol)
-        assert np.isclose(log_evidence_not_all, np.log(evidence[0]), atol=tol)
-        assert np.isclose(log_evidence_simps, np.log(evidence[0]), atol=tol)
+    # compare to known value
+    assert np.isclose(log_evidence, np.log(evidence[0]), atol=tol)
+    assert np.isclose(log_evidence_not_all, np.log(evidence[0]), atol=tol)
+    assert np.isclose(log_evidence_simps, np.log(evidence[0]), atol=tol)