diff --git a/KDEy/kdey_devel.py b/KDEy/kdey_devel.py
index fa3713b..0506fde 100644
--- a/KDEy/kdey_devel.py
+++ b/KDEy/kdey_devel.py
@@ -68,11 +68,11 @@ class KDEyMLauto(KDEyML):
                         current_prevalence, current_bandwidth = self.optim_minimize_both_fine(current_bandwidth, current_prevalence, tr_posteriors, tr_y, te_posteriors, classes)
 
                     # check converngece
-                    prev_convergence = all(np.isclose(previous_prevalence, current_prevalence, atol=0.0001))
+                    prev_convergence = all(np.isclose(previous_prevalence, current_prevalence, atol=0.01))
                     if isinstance(current_bandwidth, float):
-                        band_convergence = np.isclose(previous_bandwidth, current_bandwidth, atol=0.0001)
+                        band_convergence = np.isclose(previous_bandwidth, current_bandwidth, atol=0.001)
                     else:
-                        band_convergence = all(np.isclose(previous_bandwidth, current_bandwidth, atol=0.0001))
+                        band_convergence = all(np.isclose(previous_bandwidth, current_bandwidth, atol=0.001))
 
                     convergence = band_convergence and prev_convergence
 
@@ -94,17 +94,21 @@ class KDEyMLauto(KDEyML):
 
     def optim_minimize_bandwidth(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
         def neg_loglikelihood_bandwidth(bandwidth):
-            mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth[0])
+            # bandwidth = bandwidth[0]
+            mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
             test_densities = [self.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
             test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(current_prev, test_densities))
             test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
-            return -np.sum(test_loglikelihood)
+            nll = -np.sum(test_loglikelihood)
+            # print(f'\t{bandwidth=:.10f}\t{nll=:.10f}')
+            return nll
 
-        bounds = [(0.00001, 1)]
-        r = optimize.minimize(neg_loglikelihood_bandwidth, x0=[current_bandwidth], method='SLSQP', bounds=bounds)
-        print(f'iterations-bandwidth={r.nit}')
-        assert r.success, 'Process did not converge!'
-        return r.x[0]
+        # bounds = [(0.00001, 0.2)]
+        # r = optimize.minimize(neg_loglikelihood_bandwidth, x0=[current_bandwidth], method='SLSQP', bounds=bounds)
+        r = optimize.minimize_scalar(neg_loglikelihood_bandwidth, bounds=(0.00001, 0.2))
+        # print(f'iterations-bandwidth={r.nit}')
+        assert r.success, f'Process did not converge! {r.message}'
+        return r.x
 
     def optim_minimize_both(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
         n_classes = len(current_prev)
@@ -117,7 +121,7 @@ class KDEyMLauto(KDEyML):
             test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
             return -np.sum(test_loglikelihood)
 
-        bounds = [(0, 1) for _ in range(n_classes)] + [(0.00001, 1)]
+        bounds = [(0, 1) for _ in range(n_classes)] + [(0.00001, 0.2)]
         constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x[:n_classes])})
         prevalence_bandwidth = np.append(current_prev, current_bandwidth)
         r = optimize.minimize(neg_loglikelihood_bandwidth, x0=prevalence_bandwidth, method='SLSQP', bounds=bounds, constraints=constraints)
diff --git a/KDEy/quantification_evaluation.py b/KDEy/quantification_evaluation.py
index 9cc8a8e..bfe8f58 100644
--- a/KDEy/quantification_evaluation.py
+++ b/KDEy/quantification_evaluation.py
@@ -35,7 +35,7 @@ METHODS = [
     ('PACC', PACC(newLR()), wrap_hyper(logreg_grid)),
     ('EMQ', EMQ(newLR()), wrap_hyper(logreg_grid)),
     ('KDEy-ML',  KDEyML(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
-    ('KDEy-MLred',  KDEyMLred(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
+    # ('KDEy-MLred',  KDEyMLred(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
     ('KDEy-ML-scott', KDEyML(newLR(), bandwidth='scott'), wrap_hyper(logreg_grid)),
     ('KDEy-ML-silver', KDEyML(newLR(), bandwidth='silverman'), wrap_hyper(logreg_grid)),
     ('KDEy-ML-autoLike',  KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood'), wrap_hyper(logreg_grid)),
@@ -55,7 +55,7 @@ TRANSDUCTIVE_METHODS = [
     #('TKDEy-ML',  KDEyMLauto(newLR()), None),
     # ('TKDEy-MLboth',  KDEyMLauto(newLR(), optim='both'), None),
     # ('TKDEy-MLbothfine',  KDEyMLauto(newLR(), optim='both_fine'), None),
-    # ('TKDEy-ML2',  KDEyMLauto(newLR()), None),
+    ('TKDEy-ML2',  KDEyMLauto(newLR(), optim='two_steps'), None),
     # ('TKDEy-MLike',  KDEyMLauto(newLR(), optim='max_likelihood'), None),
     # ('TKDEy-MLike2',  KDEyMLauto(newLR(), optim='max_likelihood2'), None),
     #('TKDEy-ML3',  KDEyMLauto(newLR()), None),