m1

classes.py

@@ -3,7 +3,7 @@ from scipy.optimize import minimize, LinearConstraint, NonlinearConstraint
 from collections import OrderedDict
 import pandas as pd
 from numerize.numerize import numerize
-from gekko import GEKKO
+# from gekko import GEKKO
 
 def class_to_dict(class_instance):
     attr_dict = {}
@@ -447,51 +447,51 @@ class Scenario:
         return (param_dicts["y_max"][channel] - param_dicts["y_min"][channel])*(x_out + param_dicts["y_min"][channel])*104
         
 
-    def spends_optimisation(self, spends_percent,channels_list):
-        m = GEKKO(remote=False)
-        # Define variables        
-        # Initialize 13 variables with specific bounds
-        response_curve_params = pd.read_excel(r"C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulator uploaded - Copy\Simulator-UOPX\response_curves_parameters.xlsx",index_col = "channel")
-        param_dicts = {col: response_curve_params[col].to_dict() for col in response_curve_params.columns}
+    # def spends_optimisation(self, spends_percent,channels_list):
+    #     m = GEKKO(remote=False)
+    #     # Define variables        
+    #     # Initialize 13 variables with specific bounds
+    #     response_curve_params = pd.read_excel(r"C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulator uploaded - Copy\Simulator-UOPX\response_curves_parameters.xlsx",index_col = "channel")
+    #     param_dicts = {col: response_curve_params[col].to_dict() for col in response_curve_params.columns}
         
-        initial_values = list(param_dicts["x_min"].values())
-        current_spends = list(param_dicts["current_spends"].values()) 
-        lower_bounds = list(param_dicts["x_min"].values()) 
+    #     initial_values = list(param_dicts["x_min"].values())
+    #     current_spends = list(param_dicts["current_spends"].values()) 
+    #     lower_bounds = list(param_dicts["x_min"].values()) 
         
-        num_channels = len(channels_list)
+    #     num_channels = len(channels_list)
 
-        x_vars=[]
-        x_vars = [m.Var(value=param_dicts["current_spends"][_], lb=param_dicts["x_min"][_]*104, ub=5*param_dicts["current_spends"][_]) for _ in channels_list]
-        print(x_vars)
-    #     x_vars,lower_bounds
+    #     x_vars=[]
+    #     x_vars = [m.Var(value=param_dicts["current_spends"][_], lb=param_dicts["x_min"][_]*104, ub=5*param_dicts["current_spends"][_]) for _ in channels_list]
+    #     print(x_vars)
+    # #     x_vars,lower_bounds
         
-        # Define the objective function to minimize
-        cost = 0
-        spends = 0 
-        i = 0
-        for i,c in enumerate(channels_list):
-            # print(c)
-            # print(x_vars[i])
-            cost = cost + (self.cost_func(c, x_vars[i]))
-            spends = spends +x_vars[i]
+    #     # Define the objective function to minimize
+    #     cost = 0
+    #     spends = 0 
+    #     i = 0
+    #     for i,c in enumerate(channels_list):
+    #         # print(c)
+    #         # print(x_vars[i])
+    #         cost = cost + (self.cost_func(c, x_vars[i]))
+    #         spends = spends +x_vars[i]
 
            
-        m.Maximize(cost)
+    #     m.Maximize(cost)
         
-        # Define constraints
-        m.Equation(spends == sum(current_spends)*(1 + spends_percent / 100))
-        m.Equation(spends <= sum(current_spends)*0.5)
-        m.Equation(spends >= sum(current_spends)*1.5)
+    #     # Define constraints
+    #     m.Equation(spends == sum(current_spends)*(1 + spends_percent / 100))
+    #     m.Equation(spends <= sum(current_spends)*0.5)
+    #     m.Equation(spends >= sum(current_spends)*1.5)
 
-        m.solve(disp=True)
+    #     m.solve(disp=True)
         
-        for i, var in enumerate(x_vars):
-            print(f"x{i+1} = {var.value[0]}")
+    #     for i, var in enumerate(x_vars):
+    #         print(f"x{i+1} = {var.value[0]}")
         
-        for channel_name, modified_spends in zip(channels_list, x_vars):
-            self.update(channel_name, modified_spends.value[0])
+    #     for channel_name, modified_spends in zip(channels_list, x_vars):
+    #         self.update(channel_name, modified_spends.value[0])
 
-        return zip(channels_list, x_vars)
+    #     return zip(channels_list, x_vars)
 
     def save(self):
         details = {}