Imprpoved obsidian.campaign code coverage

obsidian/campaign/campaign.py

@@ -98,7 +98,7 @@ def add_data(self, df: pd.DataFrame):
 
     def clear_data(self):
         """Clears campaign data"""
-        self.data = None
+        self.data = pd.DataFrame()
 
     @property
     def optimizer(self) -> Optimizer:
@@ -189,12 +189,11 @@ def y(self) -> pd.Series | pd.DataFrame:
         """
         Experimental response data
 
-        Raises:
-            ValueError: If no target(s) are specified.
         """
-        if not self.target:
-            raise ValueError('No target(s) specified')
-        return self.data[self.y_names]
+        if not self.data.empty:
+            return self.data[self.y_names]
+        else:
+            return None
 
     @property
     def response_max(self) -> float | pd.Series:
@@ -296,10 +295,7 @@ def load_state(cls,
         new_campaign.data = pd.DataFrame(obj_dict['data'])
         new_campaign.data.index = new_campaign.data.index.astype('int')
 
-        try:
-            new_campaign.iter = new_campaign.data['Iteration'].astype('int').max()
-        except KeyError:
-            new_campaign.iter = 0
+        new_campaign.iter = new_campaign.data['Iteration'].astype('int').max()
 
         return new_campaign
 

obsidian/campaign/explainer.py

@@ -2,6 +2,7 @@
 
 from obsidian.parameters import Param_Continuous, ParamSpace
 from obsidian.optimizer import Optimizer
+from obsidian.exceptions import UnfitError
 
 import shap
 from shap import KernelExplainer, Explanation
@@ -36,7 +37,7 @@ def __init__(self,
                  X_space: ParamSpace | None = None) -> None:
 
         if not optimizer.is_fit:
-            raise ValueError('Surrogate model in optimizer is not fit to data. ')
+            raise UnfitError('Surrogate model in optimizer is not fit to data. ')
 
         self.set_optimizer(optimizer)
         self.X_space = optimizer.X_space if X_space is None else X_space
@@ -117,7 +118,7 @@ def pred_func(X):
     def shap_summary(self) -> Figure:
         """SHAP Summary Plot (Beeswarm)"""
         if not self.shap:
-            raise ValueError('shap explainer is not fit.')
+            raise UnfitError('SHAP explainer is not fit.')
 
         fig = plt.figure()
         shap.summary_plot(self.shap['values'], self.shap['X_sample'],
@@ -129,7 +130,7 @@ def shap_summary(self) -> Figure:
     def shap_summary_bar(self) -> Figure:
         """SHAP Summary Plot (Bar Plot / Importance)"""
         if not self.shap:
-            raise ValueError('shap explainer is not fit.')
+            raise UnfitError('SHAP explainer is not fit.')
 
         fig = plt.figure()
         shap.plots.bar(self.shap['explanation'],
@@ -159,7 +160,7 @@ def shap_pdp_ice(self,
                     
         """
         if not self.shap:
-            raise ValueError('shap explainer is not fit.')
+            raise UnfitError('SHAP explainer is not fit.')
 
         fig, ax = partial_dependence(
                 ind=ind,
@@ -193,7 +194,7 @@ def shap_single_point(self,
         
         """
         if not self.shap:
-            raise ValueError('shap explainer is not fit.')
+            raise UnfitError('SHAP explainer is not fit.')
 
         if isinstance(X_new, pd.Series):
             X_new = X_new.copy().to_frame().T

obsidian/tests/test_campaign.py

@@ -7,6 +7,7 @@
 from obsidian.objectives import Identity_Objective, Scalar_WeightedNorm, Feature_Objective, \
     Objective_Sequence, Utopian_Distance, Index_Objective, Bounded_Target
 from obsidian.plotting import plot_interactions, plot_ofat_ranges
+from obsidian.exceptions import IncompatibleObjectiveError, UnfitError
 
 
 from obsidian.tests.utils import DEFAULT_MOO_PATH
@@ -35,12 +36,18 @@ def test_campaign(X_space, sim_fcn, target):
     y0 = simulator.simulate(X0)
     Z0 = pd.concat([X0, y0], axis=1)
     campaign.m_exp
+
+    # Test some conditional usage
     campaign.add_data(Z0)
+    campaign.fit()
     campaign.clear_data()
+    campaign.y
+
     Z0['Iteration'] = 5
     campaign.add_data(Z0)
     campaign.y
     campaign.fit()
+    campaign.response_max
 
     obj_dict = campaign.save_state()
     campaign2 = Campaign.load_state(obj_dict)
@@ -70,6 +77,7 @@ def test_campaign_objectives(obj):
     campaign.set_objective(obj)
     if campaign.objective:
         campaign.objective.__repr__()
+    campaign.o
 
     obj_dict = campaign.save_state()
     campaign2 = Campaign.load_state(obj_dict)
@@ -80,6 +88,7 @@ def test_campaign_objectives(obj):
 
 def test_explain():
     exp = Explainer(campaign.optimizer)
+    exp.__repr__
     exp.shap_explain(n=50)
 
     exp.shap_summary()
@@ -103,10 +112,73 @@ def test_explain():
 
 @pytest.mark.parametrize('X_ref', X_ref_test)
 def test_analysis(X_ref):
+    ofat_ranges, _ = calc_ofat_ranges(campaign.optimizer, threshold=0.5, X_ref=X_ref, calc_interacts=False)
     ofat_ranges, cor = calc_ofat_ranges(campaign.optimizer, threshold=0.5, X_ref=X_ref)
-
     plot_interactions(campaign.optimizer, cor)
     plot_ofat_ranges(campaign.optimizer, ofat_ranges)
+
+    ofat_ranges, cor = calc_ofat_ranges(campaign.optimizer, threshold=9999, X_ref=X_ref)
+    plot_interactions(campaign.optimizer, cor)
+    plot_ofat_ranges(campaign.optimizer, ofat_ranges)
+
+
+@pytest.mark.fast
+def test_campaign_validation():
+
+    random_data = pd.DataFrame(data={'A': [1, 2, 3], 'B': [4, 5, 6]})
+    with pytest.raises(KeyError):
+        campaign.add_data(random_data)
+
+    with pytest.raises(KeyError):
+        campaign.add_data(campaign.X)
+
+    with pytest.raises(IncompatibleObjectiveError):
+        campaign.set_objective(Identity_Objective(mo=False))
+
+    with pytest.raises(ValueError):
+        campaign2 = Campaign(X_space, target)
+        campaign2.fit()
+
+
+@pytest.mark.fast
+def test_explainer_validation():
+
+    campaign2 = Campaign(X_space, target)
+    with pytest.raises(UnfitError):
+        exp = Explainer(campaign2.optimizer)
+
+    exp = Explainer(campaign.optimizer)
+    with pytest.raises(UnfitError):
+        exp.shap_summary()
+
+    with pytest.raises(UnfitError):
+        exp.shap_summary_bar()
+
+    with pytest.raises(UnfitError):
+        exp.shap_single_point(X_new=campaign.X_space.mean())
+
+    random_data = pd.DataFrame(data={'A': [1], 'B': [4]})
+    long_data = pd.DataFrame(data={'Parameter 1': [1, 2], 'Parameter 2': [1, 2]})
+
+    with pytest.raises(ValueError):
+        exp.shap_explain(n=50, X_ref=random_data)
+
+    with pytest.raises(ValueError):
+        exp.shap_explain(n=50, X_ref=long_data)
+
+    exp.shap_explain(n=50)
+
+    with pytest.raises(ValueError):
+        exp.shap_single_point(X_new=random_data)
+
+    with pytest.raises(ValueError):
+        exp.shap_single_point(X_new=campaign.X_space.mean(), X_ref=random_data)
+
+    with pytest.raises(ValueError):
+        exp.sensitivity(X_ref=random_data)
+
+    with pytest.raises(ValueError):
+        exp.sensitivity(X_ref=long_data)
 
 
 if __name__ == '__main__':