Update app.py

app.py

@@ -145,6 +145,9 @@ def predict_inverse(hardness_original_target, ys_original_target, metals_to_use,
         else:
             domain.append({'name': str(c), 'type': 'discrete', 'domain': (0,1)})
 
+    print("************")
+    print("Domain")
+    print(domain)
     # Constraints 
     constraints = []
     constrained_columns = ['Single/Multiphase', 'Preprocessing method', 'BCC/FCC/other'] #'PROPERTY: Metal']#, 'Microstructure']
@@ -156,7 +159,9 @@ def predict_inverse(hardness_original_target, ys_original_target, metals_to_use,
                 sum_string = sum_string+"+x[:," + str(i) + "]"
         constraints.append({'name': constraint + "+1", 'constraint': sum_string + '-1'})
         constraints.append({'name': constraint + "-1", 'constraint': '-1*(' + sum_string + ')+1'})
-
+    print("********************")
+    print("Constraints")
+    print(constraints)
 
     def fit_outputs(x):
         return fit_outputs_constraints(x, hardness_target, ys_target, request)
@@ -174,6 +179,8 @@ def predict_inverse(hardness_original_target, ys_original_target, metals_to_use,
         [el['name'] for el in domain],
         [[x] for x in x_best]))
     optimized_x = pd.DataFrame.from_dict(best_params)
+    print("Optimized parameters")
+    print(optimized_x.columns)
     for c in optimized_x.columns:
         if c in continuous_variables:
             if c in ['PROPERTY: Calculated Density (g/cm$^3$)', 'PROPERTY: Calculated Young modulus (GPa)']:
@@ -181,10 +188,7 @@ def predict_inverse(hardness_original_target, ys_original_target, metals_to_use,
                 print(optimized_x[c])
                 optimized_x[c]=round(optimized_x[c]*(scaling_factors[c][1]-scaling_factors[c][0])+scaling_factors[c][0], 2)
     result = optimized_x
-    print(result)
     result = result[result>0.0].dropna(axis=1)
-    print("-------------")
-    print(result)
 
     # Normalize metals outputs
     sum_metals = np.sum(result[c] for c in list(result.columns) if c.startswith("PROPERTY: Metal"))