update minimization

ml/opt.py

@@ -64,7 +64,8 @@ def opt(b_b_function, c_function, x, initial_point = None):
     min_dic = {}
     for i in range(x.shape[0]):
         print(f'Begining of the {i}th iteration') 
-        x0 = x[i,:]
+        x1 = x.to_numpy()
+        x0 = x1[i,:]
         res = minimize(fun, x0, method='trust-constr', jac='2-point', hess=BFGS(),
                constraints=[linear_constraint, nonlinear_constraint],
                options={'verbose': 0}, bounds=bounds, tol = 1e-5)
@@ -100,7 +101,8 @@ def opt_gp(b_b_function, c_function, x, initial_point = None):
     min_dic = {}
     for i in range(x.shape[0]):
         print(f'Begining of the {i}th iteration') 
-        x0 = x[i,:]
+        x1 = x.to_numpy()
+        x0 = x1[i,:]
         res = minimize(fun, x0, method='trust-constr', jac='2-point', hess=BFGS(),
                constraints=[linear_constraint, nonlinear_constraint],
                options={'verbose': 0}, bounds=bounds, tol = 1e-5)
@@ -108,6 +110,45 @@ def opt_gp(b_b_function, c_function, x, initial_point = None):
         print(f'Result for the {i}th iteration: {res.x} with CO2 emissions of {res.fun}' )
     return min_dic
 
+def opt_gp1(b_b_function, c_function, x, initial_point = None):
+    ''' Standard optimization function '''
+    def fun(x):
+        return co2_emission_fct(x)
+    def con(x):
+        return c_function.predict(x.reshape(1, -1))[0][1]
+    print('Initialization of the constraints')
+    bounds = Bounds([45, 0, 4, 7, 1, 25, 385, 0.3, 1, 0, 0, 0, 0, 0] , [80, 32, 14, 15, 1, 25, 385, 2, 5, 30, 30, 80, 40, 70])
+    m = [[1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+    v = [100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    linear_constraint = LinearConstraint(m,v,v)
+    nonlinear_constraint = NonlinearConstraint(con, 10, np.inf, jac='2-point', hess=BFGS())
+    min_dic = {}
+    for i in range(x.shape[0]):
+        print(f'Begining of the {i}th iteration') 
+        x1 = x.to_numpy()
+        x0 = x1[i,:]
+        res = minimize(fun, x0, method='trust-constr', jac='2-point', hess=BFGS(),
+               constraints=[linear_constraint, nonlinear_constraint],
+               options={'verbose': 0}, bounds=bounds, tol = 1e-2)
+        min_dic[f'Iteration {i}'] = {'recipe': res.x, 'co2_value': res.fun}
+        print(f'Result for the {i}th iteration: {res.x} with CO2 emissions of {res.fun}' )
+    return min_dic
+
+
+
 def opt_ismo(c_function, initial_point):
     ''' Standard optimization function '''
     def fun(x):

prop/elastic_moduli.py

@@ -17,7 +17,7 @@ bulk_moduli_phase = {'CSHQ' : 14.9, # obtain in Modeling the elastic properties
             'Belite': 105.2,
             'C2S' : 105.2,
             'C3A' : 105.2,
-            'C4AF': 105.3,
+            'C4AF': 105.2,
             'Portlandite' : 40.0,
             'C3AH6' :14.9,
             'C4AsH12': 40.0,