(modelAdjusters) Updated adjust() to wrap inputs in np.array() so it handles data coming in as Python nested lists of floats, 2D NumPy arrays, and list of 1D NumPy arrays seamlessly

Author: amkrajewski

pysipfenn/core/modelAdjusters.py

@@ -308,8 +308,8 @@ def adjust(
         assert len(self.targetData) != 0, "The target data must not be empty for the adjustment process."
         assert len(self.descriptorData) == len(self.targetData), "The descriptor and target data must have the same length."
 
-        ddTensor = torch.from_numpy(self.descriptorData).float().to(device=self.device)
-        tdTensor = torch.from_numpy(self.targetData).float().to(device=self.device)
+        ddTensor = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)
+        tdTensor = torch.from_numpy(np.array(self.targetData)).float().to(device=self.device)
         if validation > 0:
             split = int(len(ddTensor) * (1 - validation))
             self.validationLabels = ["Training"]*split + ["Validation"]*(len(ddTensor)-split)