Merge pull request #109 from jbboin/dtype_fix

Prevent int parameters from being cast to float in Bayesian optimization

sherpa/algorithms/bayesian_optimization.py

@@ -261,7 +261,7 @@ def _reverse_to_sherpa_format(X_next, parameters):
             transform = ParameterTransform.from_parameter(p)
             col_dict[p.name] = transform.gpyopt_design_format_to_list_in_sherpa_format(X_next[:, i])
 
-        return list(pandas.DataFrame(col_dict).T.to_dict().values())
+        return list(pandas.DataFrame(col_dict).astype(numpy.object).T.to_dict().values())
 
 
 class ParameterTransform(object):

tests/test_gpyopt.py

@@ -403,4 +403,23 @@ def f(x, sd=1):
     # rval = study.get_best_result()
     # print(rval)
     print(study.results.query("Status=='COMPLETED'"))
-    # assert numpy.sqrt((rval['Objective'] - 3.)**2) < 0.2
+    # assert numpy.sqrt((rval['Objective'] - 3.)**2) < 0.2
+
+
+def test_mixed_dtype():
+    algorithm = GPyOpt(max_num_trials=4)
+    parameters = [
+        sherpa.Choice('param_int', [0, 1]),
+        sherpa.Choice('param_float', [0.1, 1.1]),
+    ]
+    study = sherpa.Study(
+        parameters=parameters,
+        algorithm=algorithm,
+        lower_is_better=True,
+        disable_dashboard=True,
+    )
+    for trial in study:
+        study.add_observation(trial, iteration=0, objective=0)
+        study.finalize(trial)
+    assert type(trial.parameters['param_int']) == int
+    assert type(trial.parameters['param_float']) == float