test for get_results and categorical fix (#241)

* test for get_results and categorical fix for #236

ema_workbench/em_framework/callbacks.py

@@ -220,6 +220,9 @@ def __init__(
             columns.append(name)
             dtypes.append("object")
 
+        self.columns = columns
+        self.dtypes = dtypes
+
         index = np.arange(nr_experiments)
         column_dict = {
             name: pd.Series(dtype=dtype, index=index) for name, dtype in zip(columns, dtypes)
@@ -306,6 +309,17 @@ def get_results(self):
                 _logger.warning("some experiments have failed, returning masked result arrays")
                 results[k] = v
 
+        # we want to ensure the dtypes for the columns in the experiments dataframe match
+        # the type of uncertainty. The exception is needed in case their are missing values (i.e. nans).
+        # nans can only ever be a float.
+        for name, dtype in zip(self.columns, self.dtypes):
+            try:
+                if dtype == "object":
+                    dtype = "category"
+                self.cases[name] = self.cases[name].astype(dtype)
+            except Exception:
+                pass
+
         return self.cases, results
 
     def _setup_outcomes_array(self, shape, dtype):

test/test_em_framework/test_callback.py

@@ -4,6 +4,8 @@
 .. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
 """
 import random
+
+import pandas as pd
 import pytest
 
 import numpy as np
@@ -153,6 +155,7 @@ def test_store_cases():
     case = {unc.name: random.random() for unc in uncs}
     case["c"] = int(round(case["c"] * 2))
     case["d"] = int(round(case["d"]))
+    case["e"] = True
 
     model = NamedObject("test")
     policy = Policy("policy")
@@ -166,15 +169,15 @@ def test_store_cases():
     callback(experiment, model_outcomes)
 
     experiments, _ = callback.get_results()
-    design = case
-    design["policy"] = policy.name
-    design["model"] = model.name
-    design["scenario"] = scenario.name
+    # design = case
+    case["policy"] = policy.name
+    case["model"] = model.name
+    case["scenario"] = scenario.name
 
     names = experiments.columns.values.tolist()
     for name in names:
         entry_a = experiments[name][0]
-        entry_b = design[name]
+        entry_b = case[name]
 
         assert entry_a == entry_b, "failed for " + name
 
@@ -213,25 +216,57 @@ def test_get_results(mocker):
     nr_experiments = 3
     uncs = [
         RealParameter("a", 0, 1),
+        CategoricalParameter("b", ["0", "1", "2", "3"]),
+        IntegerParameter("c", 0, 5),
+        BooleanParameter("d"),
     ]
     outcomes = [ScalarOutcome("other_test")]
     outcomes[0].shape = (1,)
 
     callback = DefaultCallback(
         uncs, [], outcomes, nr_experiments=nr_experiments, reporting_interval=1
     )
-
     # test warning
     mock = mocker.patch("ema_workbench.em_framework.callbacks._logger.warning")
     callback.get_results()
     assert mock.call_count == 1
 
     # test without warning
+    callback = DefaultCallback(
+        uncs, [], outcomes, nr_experiments=nr_experiments, reporting_interval=1
+    )
+
+    cases = []
+    for i in range(nr_experiments):
+        model = NamedObject("test")
+        policy = Policy("policy")
+        case = {"a": i * 0.15, "b": f"{i}", "c": i, "d": True if i % 2 == 0 else False}
+        scenario = Scenario(**case)
+        experiment = Experiment(0, model.name, policy, scenario, i)
+        model_outcomes = {outcomes[0].name: i * 1.25}
+        callback(experiment, model_outcomes)
+        cases.append(case)
+
     mock = mocker.patch("ema_workbench.em_framework.callbacks._logger.warning")
     callback.results = {k: v.data for k, v in callback.results.items()}
-    callback.get_results()
+    experiments, results = callback.get_results()
     assert mock.call_count == 0
 
+    # check if experiments dataframe contains the experiments correctly
+    data = pd.DataFrame.from_dict(cases)
+    assert np.all(data == experiments.loc[:, data.columns])
+
+    # check data types of columns in experiments dataframe
+    dtype_mapping = {
+        RealParameter: float,
+        CategoricalParameter: "category",
+        IntegerParameter: int,
+        BooleanParameter: bool,
+    }
+
+    for u in uncs:
+        assert experiments.loc[:, u.name].dtype == dtype_mapping[u.__class__]
+
 
 def test_filebasedcallback(mocker):
     # only most basic assertions are checked