Merge branch 'testing' into mcminn-test

Author: xuyuting

demo/Cost-penalized custom objective.ipynb

@@ -232,7 +232,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for iter in range(3):\n",
+    "for i in range(3):\n",
     "    campaign.fit()\n",
     "    X_suggest, eval_suggest = campaign.suggest(m_batch=3)\n",
     "    y_iter = pd.DataFrame(simulator.simulate(X_suggest))\n",

demo/Simple single objective.ipynb

@@ -190,7 +190,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for iter in range(3):\n",
+    "for i in range(3):\n",
     "    campaign.fit()\n",
     "    X_suggest, eval_suggest = campaign.suggest(m_batch=3)\n",
     "    y_iter = pd.DataFrame(simulator.simulate(X_suggest))\n",

obsidian/parameters/transforms.py

@@ -1,6 +1,6 @@
 """Transformation functions to normalize output responses"""
 
-from torch import logit, sigmoid
+from torch import logit, sigmoid, zeros_like
 from abc import ABC, abstractmethod
 import warnings
 from torch import Tensor
@@ -84,7 +84,12 @@ def forward(self,
             self.params = {'mu': X_v.mean(), 'sd': X_v.std()}
         else:
             self._validate_fit()
-        return (X-self.params['mu'])/self.params['sd']
+        if self.params["sd"] == 0:
+            # In the edge case where `X` is degenerate, avoid 0 divided by 0
+            warnings.warn('Transform constant target values by mean subtraction', UserWarning)
+            return zeros_like(X)
+        else:
+            return (X-self.params['mu'])/self.params['sd']
 
     def inverse(self, X):
         """Inverse transform the transformed data X_t"""

obsidian/plotting/plotly.py

@@ -147,7 +147,7 @@ def MDS_plot(campaign: Campaign) -> Figure:
     except ImportError:
         raise ImportError(
             "The `sklearn` package (>1.0) is required for the MDS plot. \
-            Please install it using `pip install scikit-learn`"
+                          Please install it using `pip install scikit-learn`"
         )
 
     mds = MDS(n_components=2)

obsidian/tests/test_parameters.py

@@ -255,7 +255,16 @@ def test_target_validation():
     with pytest.warns(UserWarning):
         transform_func = Logit_Scaler(range_response=100)
         transform_func.forward(test_neg_response, fit=False)
-        
-        
+
+    # Transform constant target values
+    test_constant_response = torch.zeros(10) + 9.0
+    with pytest.warns(UserWarning):
+        Target('Response1', f_transform='Standard').transform_f(test_constant_response, fit=True)
+
+    # Corner case for Logit_Scaler
+    transform_func = Logit_Scaler(standardize=False)
+    transform_func.forward(test_response, fit=True)
+
+
 if __name__ == '__main__':
     pytest.main([__file__, '-m', 'fast'])