m1

Model_Result_Overview.py

@@ -8,7 +8,7 @@
 #         else:
 #             subprocess.check_call([sys.executable, "-m", "conda", "install", package_name, "-y"])
 #     except subprocess.CalledProcessError as e:
-#         print(f"Failed to install {package_name}: {e}")
+#         # print(f"Failed to install {package_name}: {e}")
 
 # # Example usage
 # install_conda_package("plotly-orca", channel="plotly")
@@ -69,7 +69,7 @@ def save_table(df,prs):
     # Add the DataFrame rows to the table
     for row_idx, row in df.iterrows():
         for col_idx, value in enumerate(row):
-            # # print(value)
+            # # # print(value)
             if isinstance(value, int):
                 table.cell(row_idx + 1, col_idx).text = str(value)
 
@@ -141,7 +141,7 @@ def save_ppt_file(fig1,fig2,fig3,fig4,fig6,fig7,figw,start_date,end_date,shares_
 
     # prs.save('MMM_Model_Result Overview.pptx')
 
-    # print("PowerPoint slides created successfully.")
+    # # print("PowerPoint slides created successfully.")
 
     # Save to a BytesIO object
     ppt_stream = BytesIO()
@@ -154,7 +154,7 @@ def get_random_effects(media_data, panel_col, mdf):
     random_eff_df = pd.DataFrame(columns=[panel_col, "random_effect"])
 
     for i, market in enumerate(media_data[panel_col].unique()):
-        # # print(i, end='\r')
+        # # # print(i, end='\r')
         intercept = mdf.random_effects[market].values[0]
         random_eff_df.loc[i, 'random_effect'] = intercept
         random_eff_df.loc[i, panel_col] = market

Streamlit_functions.py

@@ -473,8 +473,8 @@ def waterfall2(start_date1,end_date1,start_date2,end_date2):
         font=dict(size=16),
         # align='left'
     )
-    # # # print(cur_data)
-    # # # print(prev_data)
+    # # # # print(cur_data)
+    # # # # print(prev_data)
     # fig.show()
     return fig
 def waterfall(start_date,end_date,btn_chart):
@@ -624,8 +624,8 @@ def waterfall(start_date,end_date,btn_chart):
         font=dict(size=16),
         # align='left'
     )
-    # # # print(cur_data)
-    # # # print(prev_data)
+    # # # # print(cur_data)
+    # # # # print(prev_data)
     # fig.show()
     return fig
 
@@ -1084,7 +1084,7 @@ def media_decomp():
 
     media_cols = media_decomp_df.columns
     for i in range(2,len(media_cols)):
-    #     # # print(media_cols[i])
+    #     # # # print(media_cols[i])
         cumulative_df[media_cols[i]] = cumulative_df[media_cols[i]] + cumulative_df[media_cols[i-1]]
     # cumulative_df
 
@@ -1506,16 +1506,16 @@ def scenario_spend_forecasting(delta_df,start_date,end_date):
     key_df["Channel_name"] = ["Email","DisplayRetargeting","\xa0Video","BroadcastTV","SocialRetargeting","Connected&OTTTV","SearchBrand","Audio","SocialProspecting","CableTV","DisplayProspecting","SearchNon-brand","DigitalPartners"]
     key_df["Channels"] = ["EMAIL","DISPLAY RETARGETING","VIDEO","BROADCAST TV","SOCIAL RETARGETING","CONNECTED & OTT TV","SEARCH BRAND","AUDIO","SOCIAL PROSPECTING","CABLE TV","DISPLAY PROSPECTING","SEARCH NON-BRAND","DIGITAL PARTNERS"]
     delta_df = delta_df.merge(key_df,on = "Channel_name",how = "inner")
-    # print(delta_df)
+    # # print(delta_df)
 
     data3 = data2.copy()
     for channel in delta_df["Channels"]:
-        # print(channel)
+        # # print(channel)
         delta_percent = delta_df[delta_df["Channels"]==channel]["Delta_percent"].iloc[0]
-        # print(delta_percent)
+        # # print(delta_percent)
         data3[channel] = data3[channel]*(1+delta_percent/100)
-    # print(data2)
-    # print(data3)
+    # # print(data2)
+    # # print(data3)
 
 
     ###### output dataframes
@@ -1523,20 +1523,20 @@ def scenario_spend_forecasting(delta_df,start_date,end_date):
 
     #### percent change dataframe
     delta_df2 = pd.DataFrame(data = delta_df["Delta_percent"].values,index = delta_df["Channels"])
-    # print(delta_df2)
+    # # print(delta_df2)
     output_df1 = (pd.DataFrame(data2.sum()).transpose()).append(pd.DataFrame(data3.sum()).transpose()).append(delta_df2.transpose())
     output_df1.index = ["Last Year Spends", "Forecasted Spends","Spends Change"]
 
       
 
-    # print(output_df1)
+    # # print(output_df1)
 # 
 
 
 
-    # print (data3)
+    # # print (data3)
     # data3 = data2.append(key_df)
-    # print (data2)
+    # # print (data2)
     # cur_data = cur_data[spend_cols]
     # cur_data.columns = channels
     # data1 = pd.DataFrame(cur_data[channels].sum().transpose()).reset_index()
@@ -1636,7 +1636,7 @@ def scenario_spend_forecasting2(delta_df,start_date,end_date):
     #         return "Invalid month number"
     
     # data2["Month year"] = data2["Month"].apply(get_month_name) + ' ' +(data2["Date"].dt.year+1).astype(str)   
-    # # # print(data2.columns)
+    # # # # print(data2.columns)
     # data2 = data2[['Month year' ,'BROADCAST TV', 'CABLE TV',
     #    'CONNECTED & OTT TV', 'VIDEO', 'DISPLAY PROSPECTING',
     #    'DISPLAY RETARGETING', 'SOCIAL PROSPECTING', 'SOCIAL RETARGETING',

classes.py

@@ -100,7 +100,7 @@ class Channel:
         self.modified_total_sales = self.modified_sales.sum()
         self.delta_spends = self.modified_total_spends - self.actual_total_spends
         self.delta_sales = self.modified_total_sales - self.actual_total_sales
-        # # # print(self.actual_total_spends)
+        # # # # print(self.actual_total_spends)
     def update_penalty(self, penalty):
         self.penalty = penalty
 
@@ -108,25 +108,25 @@ class Channel:
         return spends_array * total_spends / spends_array.sum()
 
     def modify_spends(self, total_spends):
-        # # # print(total_spends)
+        # # # # print(total_spends)
         self.modified_spends[0] = total_spends 
         # (
         #     self.modified_spends * total_spends / self.modified_spends.sum()
         # )
-        # # # print("in spends")
-        # # # print(self.modified_spends,self.modified_spends.sum())
+        # # # # print("in spends")
+        # # # # print(self.modified_spends,self.modified_spends.sum())
 
     def calculate_sales(self):
-        # # # print("in calc_sales")
-        # # # print(self.modified_spends)
+        # # # # print("in calc_sales")
+        # # # # print(self.modified_spends)
         return self.response_curve(self.modified_spends)
 
     def hill_equation(x, Kd, n):
         return x**n / (Kd**n + x**n)
     def response_curve(self, x):
-        # # # print(x)
+        # # # # print(x)
         # if self.penalty:
-        #     # # print("in penalty")
+        #     # # # print("in penalty")
         #     x = np.where(
         #         x < self.upper_limit,
         #         x,
@@ -134,14 +134,14 @@ class Channel:
         #     )
         if self.response_curve_type == "hill-eq":
             # dividing_parameter = check_dividing_parameter()
-            # # # print("lalala")
-            # # # # print(self.name)\
-            # # # print(len(x))
-            # # # print("in response curve function")
-            # # # print(x)
+            # # # # print("lalala")
+            # # # # # print(self.name)\
+            # # # # print(len(x))
+            # # # # print("in response curve function")
+            # # # # print(x)
             if len(x) == 1:
                 dividing_rate = self.response_curve_params["num_pos_obsv"]
-                # # # print(dividing_rate)
+                # # # # print(dividing_rate)
                 # x = np.sum(x)
             else:
                 dividing_rate = 1
@@ -155,14 +155,14 @@ class Channel:
             x_max= self.response_curve_params["x_max"]
             y_min= self.response_curve_params["y_min"]
             y_max= self.response_curve_params['y_max']  
-            # # # # print(x_min)
-            # # # # print(Kd,n,x_min,x_max,y_min,y_max)
-            # # # # print(np.sum(x)/104)
+            # # # # # print(x_min)
+            # # # # # print(Kd,n,x_min,x_max,y_min,y_max)
+            # # # # # print(np.sum(x)/104)
             x_inp = ( x/dividing_rate- x_min) / (x_max - x_min)
-            # # # # print("x",x)
-            # # # # print("x_inp",x_inp)
+            # # # # # print("x",x)
+            # # # # # print("x_inp",x_inp)
             x_out = x_inp**n / (Kd**n + x_inp**n) #self.hill_equation(x_inp,Kd, n)
-            # # # # print("x_out",x_out)
+            # # # # # print("x_out",x_out)
 
 
             x_val_inv = (x_out*x_max + (1 - x_out) * x_min)
@@ -170,12 +170,12 @@ class Channel:
             # sales = ((x_max - x_min)*x_out + x_min)*dividing_rate
 
             sales[np.isnan(sales)] = 0
-            # # # # print(sales) 
-            # # # # print(np.sum(sales))
-            # # # # print("sales",sales)
-            # # # print("aa")
-            # # # print(sales)
-            # # # print("aa1")
+            # # # # # print(sales) 
+            # # # # # print(np.sum(sales))
+            # # # # # print("sales",sales)
+            # # # # print("aa")
+            # # # # print(sales)
+            # # # # print("aa1")
         if self.response_curve_type == "s-curve":
             if self.power >= 0:
                 x = x / 10**self.power
@@ -292,24 +292,24 @@ class Scenario:
 
     def calculate_actual_total_sales(self):
         total_actual_sales = 0#self.constant.sum()  + self.correction.sum()
-        # # # print("a")
+        # # # # print("a")
         for channel in self.channels.values():
             total_actual_sales += channel.actual_total_sales
-            # # # # print(channel.actual_total_sales)
-        # # # # print(total_actual_sales)
+            # # # # # print(channel.actual_total_sales)
+        # # # # # print(total_actual_sales)
         return total_actual_sales
 
     def calculate_modified_total_sales(self):
 
         total_modified_sales = 0 #self.constant.sum() + self.correction.sum()
-        # # # print(total_modified_sales)
+        # # # # print(total_modified_sales)
         for channel in self.channels.values():
-            # # # print(channel,channel.modified_total_sales)
+            # # # # print(channel,channel.modified_total_sales)
             total_modified_sales += channel.modified_total_sales
         return total_modified_sales
 
     def update(self, channel_name, modified_spends):
-        # # # print("in updtw")
+        # # # # print("in updtw")
         self.channels[channel_name].update(modified_spends)
         self.modified_total_sales = self.calculate_modified_total_sales()
         self.modified_total_spends = self.calculate_modified_total_spends()
@@ -368,7 +368,7 @@ class Scenario:
 
     def optimize_spends(self, sales_percent, channels_list, algo="trust-constr"):
         num_channels = len(channels_list)
-        # # # # print("%"*100)
+        # # # # # print("%"*100)
         desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
 
         def constraint(x):
@@ -379,7 +379,7 @@ class Scenario:
         # def calc_overall_bounds(channels_list):
         #     total_spends=0
         #     for ch in zip(channels_list):
-        #         print(ch)
+        #         # print(ch)
         #         total_spends= total_spends+self.channels[ch].actual_total_spends
         #     return total_spends
 
@@ -393,7 +393,7 @@ class Scenario:
             lb = (1- int(self.channels[ch].channel_bounds_min) / 100) * self.channels[ch].actual_total_spends
             ub = (1+  int(self.channels[ch].channel_bounds_max) / 100)  * self.channels[ch].actual_total_spends
             bounds.append((lb,ub))
-            # # # # print(self.channels[ch].actual_total_spends)
+            # # # # # print(self.channels[ch].actual_total_spends)
         initial_point = []
         for bound in bounds:
             initial_point.append(bound[0])
@@ -429,11 +429,11 @@ class Scenario:
         for channel_name in channels_list:
             # spends_constraint += self.channels[channel_name].modified_total_spends
             spends_constant.append(self.channels[channel_name].conversion_rate)
-            # # # print(spends_constant)
+            # # # # print(spends_constant)
             spends_constraint += (
                 self.channels[channel_name].actual_total_spends+ self.channels[channel_name].delta_spends #st.session_state["total_spends_change_abs_slider_options"]
             )
-            # # # print("delta spends",self.channels[channel_name].delta_spends)
+            # # # # print("delta spends",self.channels[channel_name].delta_spends)
         # spends_constraint = spends_constraint * (1 + spends_percent / 100)
         constraint= LinearConstraint(np.ones((num_channels,)), lb = spends_constraint, ub = spends_constraint)
         # constraint = LinearConstraint(
@@ -455,8 +455,8 @@ class Scenario:
             lb = (1- int(_channel_class.channel_bounds_min) / 100) * _channel_class.actual_total_spends
             ub = (1+  int(_channel_class.channel_bounds_max) / 100)  * _channel_class.actual_total_spends
             bounds.append((lb,ub))
-            # # # print("aaaaaa")
-            # # print((_channel_class.channel_bounds_max,_channel_class.channel_bounds_min))
+            # # # # print("aaaaaa")
+            # # # print((_channel_class.channel_bounds_max,_channel_class.channel_bounds_min))
             # _channel_class.channel_bounds_min
             # _channel_class.channel_bounds_max
         def cost_func1(channel,x):
@@ -471,29 +471,29 @@ class Scenario:
             y_max= param_dicts['y_max'][channel] 
             division_parameter = param_dicts['num_pos_obsv'][channel] 
             x_inp = ( x/division_parameter- x_min) / (x_max - x_min)
-        #     # # print(x_inp)
+        #     # # # print(x_inp)
             x_out = x_inp**n / (Kd**n + x_inp**n)
             x_val_inv = (x_out*x_max + (1 - x_out) * x_min)
             sales = (x_val_inv*y_min/y_max)*division_parameter
             if np.isnan(sales):
-        #         # # print(sales,channel)
+        #         # # # print(sales,channel)
                 sales = 0
-            # # # print(sales,channel)
+            # # # # print(sales,channel)
             return sales
         def objective_function(x):
             sales = 0
             it = 0
             for channel_name, modified_spends in zip(channels_list, x):
                 # sales = sales + cost_func1(channel_name,modified_spends)
-                # print(channel_name, modified_spends,cost_func1(channel_name, modified_spends))
+                # # print(channel_name, modified_spends,cost_func1(channel_name, modified_spends))
                 it+=1
                 self.update(channel_name, modified_spends)
-            # # # print(self.modified_total_sales)
-            # # # print(channel_name, modified_spends)
+            # # # # print(self.modified_total_sales)
+            # # # # print(channel_name, modified_spends)
             return -1 * self.modified_total_sales
 
-        # # # print(bounds)
-        # # # # print("$"*100)
+        # # # # print(bounds)
+        # # # # # print("$"*100)
         res = minimize(
             lambda x: objective_function(x)/1e3,
             method="trust-constr",
@@ -504,11 +504,11 @@ class Scenario:
         )
 
         for channel_name, modified_spends in zip(channels_list, res.x):
-            # # # print("aaaaaaaaaaaaaa")
+            # # # # print("aaaaaaaaaaaaaa")
             self.update(channel_name, modified_spends)
-            # # # print(channel_name, modified_spends,cost_func1(channel_name, modified_spends))
+            # # # # print(channel_name, modified_spends,cost_func1(channel_name, modified_spends))
             
-            # print(it)
+            # # print(it)
 
         return zip(channels_list, res.x)
     
@@ -533,7 +533,7 @@ class Scenario:
 
     #     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)
+    #     # # # # print(x_vars)
     # #     x_vars,lower_bounds
         
     #     # Define the objective function to minimize
@@ -541,8 +541,8 @@ class Scenario:
     #     spends = 0 
     #     i = 0
     #     for i,c in enumerate(channels_list):
-    #         # # # # print(c)
-    #         # # # # print(x_vars[i])
+    #         # # # # # print(c)
+    #         # # # # # print(x_vars[i])
     #         cost = cost + (self.cost_func(c, x_vars[i]))
     #         spends = spends +x_vars[i]
 
@@ -557,7 +557,7 @@ class Scenario:
     #     m.solve(disp=True)
         
     #     for i, var in enumerate(x_vars):
-    #         # # # print(f"x{i+1} = {var.value[0]}")
+    #         # # # # 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])

pages/1_Model_Quality.py

@@ -30,20 +30,20 @@ def save_ppt_file():
     title_1 = slide_1.shapes.title
     title_1.text = "Model Quality"
     i = 0 
-    # print (i)
+    # # print (i)
     # Generate Plotly chart
     fig = sf.mmm_model_quality()
 
     # Add the Plotly chart to the slide
     add_plotly_chart_to_slide(slide_1, fig, Inches(1), Inches(2), width=Inches(9), height=Inches(4.5))
     i = i+1
-    # print (i)
+    # # print (i)
     # Slide 2: Media Data Elasticity
     slide_2 = prs.slides.add_slide(prs.slide_layouts[5])
     title_2 = slide_2.shapes.title
     title_2.text = "Media Data Elasticity"
     i = i+1
-    # print (i)
+    # # print (i)
     # Generate Elasticity chart
     media_df = sf.media_data()
     fig = sf.elasticity(media_df)
@@ -52,17 +52,17 @@ def save_ppt_file():
         # xaxis=dict(tickangle=-45)  # Rotate x-axis labels if needed
     )
     i = i+1
-    # print (i)
+    # # print (i)
     # Add the Plotly chart to the slide
     add_plotly_chart_to_slide(slide_2, fig, Inches(1), Inches(2), width=Inches(8), height=Inches(4.5))
     i = i+1
-    # print (i)
+    # # print (i)
     # Slide 3: Half-Life Analysis
     slide_3 = prs.slides.add_slide(prs.slide_layouts[5])
     title_3 = slide_3.shapes.title
     title_3.text = "Half-Life Analysis"
     i = i+1
-    # print (i)
+    # # print (i)
     # Generate Half-Life chart
     fig = sf.half_life(media_df)
     fig.update_layout(
@@ -70,11 +70,11 @@ def save_ppt_file():
         # xaxis=dict(tickangle=-45)  # Rotate x-axis labels if needed
     )
     i = i+1
-    # print (i)
+    # # print (i)
     # Add the Plotly chart to the slide
     add_plotly_chart_to_slide(slide_3, fig, Inches(1), Inches(2), width=Inches(8), height=Inches(4.5))
     i = i+1
-    # print (i)
+    # # print (i)
     # Slide 4: Response Curves
 
     # Generate Response Curves chart
@@ -98,7 +98,7 @@ def save_ppt_file():
         title_4 = slide_4.shapes.title
         title_4.text = "Response Curves"
         i = i+1
-        # print (i)
+        # # print (i)
         selected_option = channel_name 
         selected_option2 = 'View Line Plot'
         fig = rc1.response_curves(selected_option, selected_option2)
@@ -106,7 +106,7 @@ def save_ppt_file():
         add_plotly_chart_to_slide(slide_4, fig, Inches(1), Inches(2), width=Inches(6), height=Inches(4.5))
     # Save the PowerPoint presentation
     # prs.save('MMM_Model_Quality_Presentation.pptx')
-    # # print("PowerPoint slides created successfully.")
+    # # # print("PowerPoint slides created successfully.")
 
     # Save to a BytesIO object
     ppt_stream = BytesIO()
@@ -123,9 +123,9 @@ st.header("Model Quality")
 
 st.plotly_chart(sf.mmm_model_quality(),use_container_width=True)
 fig = sf.mmm_model_quality()
-# print("aaa")
+# # print("aaa")
 # fig.write_image("chart.png")
-# print("bbb")
+# # print("bbb")
 
 media_df = sf.media_data()
     # Create two columns for start date and end date input

pages/2_Scenario_Planner.py

@@ -65,7 +65,7 @@ def save_ppt_file(summary_df_sorted,fig1,fig2,fig3):
         slide.shapes.add_picture(img_stream, left, top, width, height)
 
     for i in range(0,len(channels_list)):
-        # print(channels_list[i])
+        # # print(channels_list[i])
         slide_1 = prs.slides.add_slide(prs.slide_layouts[6])
         fig = rc.response_curves(channels_list[i], summary_df_sorted["Optimized_spend"][channels_list[i]], summary_df_sorted["New_sales"][channels_list[i]])
         add_plotly_chart_to_slide(slide_1, fig, Inches(0.1), Inches(0.1), width=Inches(9), height=Inches(7))
@@ -163,15 +163,15 @@ def optimize(key, status_placeholder):
         if key.lower() == "media spends":
             with status_placeholder:
                 with st.spinner("Optimizing"):
-                    # # # print(channel_list)
-                    # # # print(st.session_state["total_spends_change"])
+                    # # # # print(channel_list)
+                    # # # # print(st.session_state["total_spends_change"])
                     result = st.session_state["scenario"].optimize(
                         st.session_state["total_spends_change"], channel_list
                         # result = st.session_state["scenario"].spends_optimisation(
                         # st.session_state["total_spends_change"], channel_list
                     )
-                    # print("")
-                    # print(list(zip(*result)))
+                    # # print("")
+                    # # print(list(zip(*result)))
 
     
 
@@ -214,7 +214,7 @@ def save_scenario(scenario_name):
         st.session_state["scenario"]
     )
     st.session_state["scenario_input"] = ""
-    # # # print(type(st.session_state['saved_scenarios']))
+    # # # # print(type(st.session_state['saved_scenarios']))
     with open("../saved_scenarios.pkl", "wb") as f:
         pickle.dump(st.session_state["saved_scenarios"], f)
 
@@ -291,9 +291,9 @@ def update_sales():
 # def update_all_spends_abs_slider():
 #     actual_spends = _scenario.actual_total_spends
 #     if validate_input(st.session_state["total_spends_change_abs_slider"]):
-#         # # print("#" * 100)
-#         # # print(st.session_state["total_spends_change_abs_slider"])C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulatorAldi\pages\8_Scenario_Planner.py
-#         # # print("#" * 100)
+#         # # # print("#" * 100)
+#         # # # print(st.session_state["total_spends_change_abs_slider"])C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulatorAldi\pages\8_Scenario_Planner.py
+#         # # # print("#" * 100)
 
 #         modified_spends = extract_number_for_string(
 #             st.session_state["total_spends_change_abs_slider"]
@@ -315,8 +315,8 @@ def update_all_spends_abs():
     #     in st.session_state["total_spends_change_abs_slider_options"]
     # ):
     #     st.session_state["allow_spends_update"] = True
-    #     print st.session_state["total_spends_change_abs_slider_options"]
-    #     print("not allowed")
+    #     # print st.session_state["total_spends_change_abs_slider_options"]
+    #     # print("not allowed")
     # else:
     #     st.session_state["allow_spends_update"] = False
     #     # st.warning("Invalid Input")
@@ -507,7 +507,7 @@ def update_penalty():
 
 
 def reset_scenario(panel_selected, file_selected, updated_rcs):
-    # ## # print(st.session_state['default_scenario_dict'])
+    # ## # # print(st.session_state['default_scenario_dict'])
     # st.session_state['scenario']  = class_from_dict(st.session_state['default_scenario_dict'])
     # for channel in st.session_state['scenario'].channels.values():
     #     st.session_state[channel.name] = float(channel.actual_total_spends * channel.conversion_rate)
@@ -646,13 +646,13 @@ def calculate_rgba(
 
 
 def debug_temp(x_test, power, K, b, a, x0):
-    # # # print("*" * 100)
+    # # # # print("*" * 100)
     # Calculate the count of bins
     count_lower_bin = sum(1 for x in x_test if x <= 2524)
     count_center_bin = sum(1 for x in x_test if x > 2524 and x <= 3377)
     count_ = sum(1 for x in x_test if x > 3377)
 
-    # # # print(
+    # # # # print(
     #     f"""
     #         lower : {count_lower_bin}
     #         center : {count_center_bin}
@@ -737,14 +737,14 @@ def upload_file_prospects_calc(df):
     params = pd.read_excel(r"response_curves_parameters.xlsx",index_col = "channel")
     param_dicts = {col: params[col].to_dict() for col in params.columns}
     df.index = df.channel
-#     # # print(param_dicts)
+#     # # # print(param_dicts)
     for col in df.channel:
         x = df["Spends"][col]
         dividing_rate = 104
         # st.write(x)
         x_inp = ( x/dividing_rate- param_dicts["x_min"][col]) / (param_dicts["x_max"][col] - param_dicts["x_min"][col])
         x_out = x_inp**param_dicts["n"][col] / (param_dicts["Kd"][col]**param_dicts["n"][col] + x_inp**param_dicts["n"][col]) #self.hill_equation(x_inp,Kd, n)
-            # # # print("x_out",x_out)
+            # # # # print("x_out",x_out)
 
 
         x_val_inv = (x_out*param_dicts["x_max"][col] + (1 - x_out) * param_dicts["x_min"][col])
@@ -755,7 +755,7 @@ def upload_file_prospects_calc(df):
         # x_out = x**param_dicts["n"][col]/(param_dicts["Kd"][col]**param_dicts["n"][col]+ x**param_dicts["n"][col])
         # x_out_inv = (x_out*(param_dicts["y_max"][col]-param_dicts["y_min"][col])+param_dicts["y_min"][col])*104
         df["Prospects"][col] = sales
-    # # # print(df)
+    # # # # print(df)
     return df
     
 
@@ -1225,7 +1225,7 @@ if auth_status == True:
         # ========================== UI ========================== #
         # ======================================================== #
 
-        # # # print(list(st.session_state.keys()))
+        # # # # print(list(st.session_state.keys()))
         main_header = st.columns((2, 2))
         sub_header = st.columns((1, 1, 1, 1))
         _scenario = st.session_state["scenario"]
@@ -1770,7 +1770,7 @@ if auth_status == True:
                     # st.write(channel_spends)
                     # st.write(min_value)
                     # st.write(max_value)
-                    ### # print(st.session_state[channel_name])
+                    ### # # print(st.session_state[channel_name])
                     # st.write(_channel_class.channel_bounds_min,channel_bounds_min)
                     # st.write(_channel_class.channel_bounds_max,channel_bounds_max)
                     _columns_min = st.columns(2)
@@ -2043,7 +2043,7 @@ if auth_status == True:
                 #     current_channel_spends,
                 # )
 
-                # # # print(st.session_state["acutual_predicted"])
+                # # # # print(st.session_state["acutual_predicted"])
                 summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
                 # (pd.DataFrame(st.session_state["acutual_predicted"])).to_excel("test.xlsx")
                 # st.dataframe(summary_df)
@@ -2065,12 +2065,12 @@ if auth_status == True:
                 a = (summary_df_sorted[summary_df_sorted['Channel_name']== col]).reset_index()['new_efficiency'][0]
                 b = (summary_df_sorted[summary_df_sorted['Channel_name']== col]).reset_index()['old_efficiency'][0]
                 # st.write(a)
-                # print(a)
-                # print(summary_df_sorted['Actual_spend'].sum())
-                # print(summary_df_sorted['Actual_spend'])
-                # print(col,summary_df_sorted)
-                # print(summary_df_sorted['Old_sales'])
-                # print(col, "old efficiency ", a)
+                # # print(a)
+                # # print(summary_df_sorted['Actual_spend'].sum())
+                # # print(summary_df_sorted['Actual_spend'])
+                # # print(col,summary_df_sorted)
+                # # print(summary_df_sorted['Old_sales'])
+                # # print(col, "old efficiency ", a)
                 with bin_placeholder:
                     if a> 1:
                         fill_color_box = "#6bbf6b"

pages/3_Saved_Scenarios.py

@@ -40,7 +40,7 @@ def comparison_scenarios_df():
     summary_df_spend = None
     summary_df_prospect = None
     # summary_df_efficiency = None
-    #=# # print(scenarios_to_download)
+    #=# # # print(scenarios_to_download)
     for scenario_name in scenarios_to_compare:
         scenario_dict =  st.session_state['saved_scenarios'][scenario_name]
         _spends = []
@@ -125,7 +125,7 @@ def generate_color_gradient(start_color, end_color, num_colors):
         colortype='hex'  # Use 'rgb' to get colors in RGB format
     )
     
-    # print(colors)
+    # # print(colors)
     # Convert RGB tuples to hex
     # hex_colors = [rgb_to_hex(color) for color in colors]
     return colors
@@ -141,8 +141,8 @@ def generate_color_gradient(start_color, end_color, num_colors):
 #     return custom_colors
 
 def plot_comparison_chart(df,metric,custom_colors):
-    # print(metric)
-    # print(custom_colors)
+    # # print(metric)
+    # # print(custom_colors)
     custom_colors = [
         "#4169E1",  # Royal Blue
     "#ADD8E6",  # Light Blue
@@ -161,8 +161,8 @@ def plot_comparison_chart(df,metric,custom_colors):
     # Create traces for each column
     traces = []
     for i,column in enumerate(df.columns):
-        # print(i)
-        # print(custom_colors[i])
+        # # print(i)
+        # # print(custom_colors[i])
         traces.append(go.Bar(
             x=df.index,
             y=df[column],
@@ -418,7 +418,7 @@ def download_scenarios():
     wb.remove(wb.active)
     st.session_state['xlsx_buffer'] = io.BytesIO()
     summary_df = None
-    ## # print(scenarios_to_download)
+    ## # # print(scenarios_to_download)
     for scenario_name in scenarios_to_download:
         scenario_dict =  st.session_state['saved_scenarios'][scenario_name]
         _spends = []
@@ -508,7 +508,7 @@ auth_status = st.session_state.get('authentication_status')
 if auth_status == True:
     is_state_initiaized = st.session_state.get('initialized',False)
     if not is_state_initiaized:
-        ## # print("Scenario page state reloaded")
+        ## # # print("Scenario page state reloaded")
         initialize_data(target_file = "Overview_data_test_panel@#prospects.xlsx")
 
 

response_curves_model_quality.py

@@ -112,7 +112,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
     for i in range(len(y_fit_inv_ext)):
         y_fit_inv_v2_ext.append(y_fit_inv_ext[i][0])
     
-#     # # print(x_ext_data)
+#     # # # print(x_ext_data)
     ext_df = pd.DataFrame()
     ext_df[f'{channel}_Spends'] = x_ext_data
     ext_df[fit_col] = y_fit_inv_v2_ext
@@ -125,7 +125,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
 
     ext_df['MAT'] = ["ext","ext","ext"]
     
-    # # # print(ext_df)
+    # # # # print(ext_df)
     plot_df= plot_df.append(ext_df)
     return plot_df
 
@@ -148,7 +148,7 @@ def input_data(df,spend_col,prospect_col):
     return X,y,x_data,y_data,x_minmax,y_minmax
 
 def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
-    # # # print(x_max)
+    # # # # print(x_max)
     x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
 #     x_ext_data = [1500000,2000000,2500000]
 #     x_ext_data = [x_max+100,x_max+200,x_max+5000]
@@ -157,7 +157,7 @@ def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
     for i in range(len(x_scaled)):
         x_data.append(x_scaled[i][0])
         
-    # # # print(x_data)
+    # # # # print(x_data)
     y_fit = hill_equation(x_data, Kd_fit, n_fit)
     y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
     
@@ -170,8 +170,8 @@ def fit_data(spend_col,prospect_col,channel):
 
     X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
     y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
-    # # # print('k: ',Kd_fit)
-    # # # print('n: ', n_fit)
+    # # # # print('k: ',Kd_fit)
+    # # # # print('n: ', n_fit)
     
     ##### extend_s_curve
     x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
@@ -183,7 +183,7 @@ plotly_data = fit_data(spend_cols[0],prospect_cols[0],channel_cols[0])
 plotly_data.tail()
 
 for i in range(1,13):
-    # # # print(i)
+    # # # # print(i)
     pdf =  fit_data(spend_cols[i],prospect_cols[i],channel_cols[i])
     plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
     
@@ -211,7 +211,7 @@ def response_curves(channel,x_modified,y_modified):
     
     plotly_data2 = plotly_data.copy()
     plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
-    # print(plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col])
+    # # print(plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col])
     # .dropna(subset=[x_col]).reset_index(inplace = True)
     fig.add_trace(go.Scatter(
         x=plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col],
@@ -358,7 +358,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
     for i in range(len(y_fit_inv_ext)):
         y_fit_inv_v2_ext.append(y_fit_inv_ext[i][0])
     
-#     # # print(x_ext_data)
+#     # # # print(x_ext_data)
     ext_df = pd.DataFrame()
     ext_df[f'{channel}_Spends'] = x_ext_data
     ext_df[fit_col] = y_fit_inv_v2_ext
@@ -371,7 +371,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
 
     ext_df['MAT'] = ["ext","ext","ext"]
     
-    # # # print(ext_df)
+    # # # # print(ext_df)
     plot_df= plot_df.append(ext_df)
     return plot_df
 
@@ -394,7 +394,7 @@ def input_data(df,spend_col,prospect_col):
     return X,y,x_data,y_data,x_minmax,y_minmax
 
 def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
-    # # # print(x_max)
+    # # # # print(x_max)
     x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
 #     x_ext_data = [1500000,2000000,2500000]
 #     x_ext_data = [x_max+100,x_max+200,x_max+5000]
@@ -403,7 +403,7 @@ def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
     for i in range(len(x_scaled)):
         x_data.append(x_scaled[i][0])
         
-    # # # print(x_data)
+    # # # # print(x_data)
     y_fit = hill_equation(x_data, Kd_fit, n_fit)
     y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
     
@@ -416,8 +416,8 @@ def fit_data(spend_col,prospect_col,channel):
 
     X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
     y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
-    # # # print('k: ',Kd_fit)
-    # # # print('n: ', n_fit)
+    # # # # print('k: ',Kd_fit)
+    # # # # print('n: ', n_fit)
     
     ##### extend_s_curve
     x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
@@ -429,7 +429,7 @@ plotly_data = fit_data(spend_cols[0],prospect_cols[0],channel_cols[0])
 plotly_data.tail()
 
 for i in range(1,13):
-    # # # print(i)
+    # # # # print(i)
     pdf =  fit_data(spend_cols[i],prospect_cols[i],channel_cols[i])
     plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
     
@@ -457,7 +457,7 @@ def response_curves(channel,x_modified,y_modified):
     ))
 
     dividing_parameter = len(plotly_data1[plotly_data1[x_col].isnull()==False])
-    print(dividing_parameter)
+    # print(dividing_parameter)
     plotly_data2 = plotly_data.copy()
     plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
     plotly_data2 = plotly_data2[plotly_data2["MAT"]!="ext"]
@@ -473,7 +473,7 @@ def response_curves(channel,x_modified,y_modified):
         name="Current Spends"
     ))
 
-    # print(dividing_parameter)
+    # # print(dividing_parameter)
     fig.add_trace(go.Scatter(
         x=[x_modified/dividing_parameter],
         y=[y_modified/dividing_parameter],

response_curves_model_quality_base.py

@@ -111,7 +111,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
     for i in range(len(y_fit_inv_ext)):
         y_fit_inv_v2_ext.append(y_fit_inv_ext[i][0])
     
-#     # # print(x_ext_data)
+#     # # # print(x_ext_data)
     ext_df = pd.DataFrame()
     ext_df[f'{channel}_Spends'] = x_ext_data
     ext_df[f'{channel}_Prospects'] = y_fit_inv_v2_ext
@@ -125,7 +125,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
 
     ext_df['MAT'] = ["ext","ext","ext"]
     
-    # # # print(ext_df.columns)
+    # # # # print(ext_df.columns)
     plot_df= plot_df.append(ext_df)
     return plot_df
 
@@ -148,7 +148,7 @@ def input_data(df,spend_col,prospect_col):
     return X,y,x_data,y_data,x_minmax,y_minmax
 
 def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
-    # # # print(x_max)
+    # # # # print(x_max)
     x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
 #     x_ext_data = [1500000,2000000,2500000]
 #     x_ext_data = [x_max+100,x_max+200,x_max+5000]
@@ -157,7 +157,7 @@ def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
     for i in range(len(x_scaled)):
         x_data.append(x_scaled[i][0])
         
-    # # # print(x_data)
+    # # # # print(x_data)
     y_fit = hill_equation(x_data, Kd_fit, n_fit)
     y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
     
@@ -170,8 +170,8 @@ def fit_data(spend_col,prospect_col,channel):
 
     X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
     y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
-    # # # print('k: ',Kd_fit)
-    # # # print('n: ', n_fit)
+    # # # # print('k: ',Kd_fit)
+    # # # # print('n: ', n_fit)
     
     ##### extend_s_curve
     x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
@@ -183,7 +183,7 @@ plotly_data = fit_data(spend_cols[0],prospect_cols[0],channel_cols[0])
 plotly_data.tail()
 
 for i in range(1,13):
-    # # print(i)
+    # # # print(i)
     pdf =  fit_data(spend_cols[i],prospect_cols[i],channel_cols[i])
     plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
     
@@ -247,7 +247,7 @@ def response_curves(channel,chart_typ):
     
     plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
     plotly_data2 = plotly_data2[plotly_data2["MAT"]!="ext"]
-    # # print(plotly_data2[x_col].mean(),plotly_data2[y_col].mean())
+    # # # print(plotly_data2[x_col].mean(),plotly_data2[y_col].mean())
     # import steamlit as st
     # st.dataframe()    
     fig.add_trace(go.Scatter(

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:397ea0787076aeec49755f959ff2d8ce788a2263d166f7a1711e60ce72c45489
+oid sha256:576e0d173ca100c08b5b592f57ac43bc52a32ffaa46cd1b542e5cbe1690f045c
 size 1822

utilities.py

@@ -211,7 +211,7 @@ def initialize_data(
     # uopx_conv_rates = {'streaming_impressions' : 0.007,'digital_impressions' : 0.007,'search_clicks' : 0.00719,'tv_impressions' : 0.000173,
     #                    "digital_clicks":0.005,"streaming_clicks":0.004,'streaming_spends':1,"tv_spends":1,"search_spends":1,
     #                    "digital_spends":1}
-    # # # print('State initialized')
+    # # # # print('State initialized')
 
     excel = pd.read_excel(target_file, sheet_name=None)
 
@@ -305,16 +305,16 @@ def initialize_data(
         if updated_rcs is not None and updated_rcs_key in list(updated_rcs.keys()):
             response_curves[inp_col] = updated_rcs[updated_rcs_key]
 
-        # # # print(response_curves)
+        # # # # print(response_curves)
         ## conversion rates
         spend_col = [
             _col
             for _col in spend_df.columns
             if _col.startswith(inp_col.rsplit("_", 1)[0])
         ][0]
-        # # # print(spend_col)
-        # # # print('## # printing spendssss')
-        # # # print(spend_col)
+        # # # # print(spend_col)
+        # # # # print('## # # printing spendssss')
+        # # # # print(spend_col)
         conv = (
             spend_df.set_index("Week")[spend_col]
             / input_df.set_index("Date")[inp_col].clip(lower=1)
@@ -324,10 +324,10 @@ def initialize_data(
         conv_rates[inp_col] = list(conv.drop("Week", axis=1).mean().to_dict().values())[
             0
         ]
-        # # # print(conv_rates)
-        ### # print('Before',conv_rates[inp_col])
+        # # # # print(conv_rates)
+        ### # # print('Before',conv_rates[inp_col])
         # conv_rates[inp_col] = uopx_conv_rates[inp_col]
-        ### # print('After',(conv_rates[inp_col]))
+        ### # # print('After',(conv_rates[inp_col]))
 
         channel = Channel(
             name=inp_col,
@@ -355,13 +355,13 @@ def initialize_data(
             sales = channel.actual_sales
         else:
             sales += channel.actual_sales
-    # # # print(actual_output_dic)
+    # # # # print(actual_output_dic)
     other_contributions = (
         output_df.drop([*output_cols], axis=1).sum(axis=1, numeric_only=True).values
     )
     correction = output_df.drop("Date", axis=1).sum(axis=1).values - (sales + other_contributions)
-    # # # print(other_contributions)
-    # # # print(correction)
+    # # # # print(other_contributions)
+    # # # # print(correction)
     scenario = Scenario(
         name="default",
         channels=channels,

utilities_with_panel.py

@@ -98,7 +98,7 @@ DATA_PATH = './data'
 
 IMAGES_PATH = './data/images_224_224'
 
-# New - S# # print 2
+# New - S# # # print 2
 if 'bin_dict' not in st.session_state:
     
     with open("data_import.pkl", "rb") as f:
@@ -395,7 +395,7 @@ def initialize_data(target_col,selected_markets):
     # uopx_conv_rates = {'streaming_impressions' : 0.007,'digital_impressions' : 0.007,'search_clicks' : 0.00719,'tv_impressions' : 0.000173,
     #                    "digital_clicks":0.005,"streaming_clicks":0.004,'streaming_spends':1,"tv_spends":1,"search_spends":1,
     #                    "digital_spends":1}
-    ## # print('State initialized')
+    ## # # print('State initialized')
     # excel = pd.read_excel("data_test_overview_panel.xlsx",sheet_name=None)
     #excel = pd.read_excel("Overview_data_test_panel@#revenue.xlsx" + target_col + ".xlsx",sheet_name=None)
     
@@ -469,7 +469,7 @@ def initialize_data(target_col,selected_markets):
     for inp_col in channel_list:
         #st.write(inp_col)
 
-        # # New - S# # print 2
+        # # New - S# # # print 2
         # if is_panel:
         #     input_df1 = input_df.groupby([date_col]).agg({inp_col:'sum'}).reset_index() # aggregate spends on date
         #     spends = input_df1[inp_col].values
@@ -484,7 +484,7 @@ def initialize_data(target_col,selected_markets):
 
 
         # contribution
-        # New - S# # print 2
+        # New - S# # # print 2
         out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
         if is_panel :
             output_df1 = output_df.groupby([date_col]).agg({out_col:'sum'}).reset_index()
@@ -505,12 +505,12 @@ def initialize_data(target_col,selected_markets):
             
         x = x.astype('float64')
         y = y.astype('float64')
-        ## # print('## # printing yyyyyyyyy')
-        ## # print(inp_col)
-        ## # print(x.max())
-        ## # print(y.max())
+        ## # # print('## # # printing yyyyyyyyy')
+        ## # # print(inp_col)
+        ## # # print(x.max())
+        ## # # print(y.max())
         # st.write(y.max(),x.max())
-        # # print(y.max(),x.max())
+        # # # print(y.max(),x.max())
         if y.max()<=0.01:
             if x.max()<=0.01 :
                 st.write("here-here")
@@ -539,15 +539,15 @@ def initialize_data(target_col,selected_markets):
         ## conversion rates
         spend_col = [_col for _col in spend_df.columns if _col.startswith(inp_col.rsplit('_',1)[0])][0]
 
-        ## # print('## # printing spendssss')
-        ## # print(spend_col)
+        ## # # print('## # # printing spendssss')
+        ## # # print(spend_col)
         conv = (spend_df.set_index('Week')[spend_col] / input_df.set_index('Date')[inp_col].clip(lower=1)).reset_index()
         conv.rename(columns={'index':'Week'},inplace=True)
         conv['year'] = conv.Week.dt.year
         conv_rates[inp_col] = list(conv.drop('Week',axis=1).mean().to_dict().values())[0]
-        ### # print('Before',conv_rates[inp_col])
+        ### # # print('Before',conv_rates[inp_col])
         # conv_rates[inp_col] = uopx_conv_rates[inp_col]
-        ### # print('After',(conv_rates[inp_col]))
+        ### # # print('After',(conv_rates[inp_col]))
         
         
         channel = Channel(name=inp_col,dates=dates,
@@ -617,7 +617,7 @@ def initialize_data(target_col,selected_markets):
 #     channel_list = []
 #     for col in raw_df.columns:
 #         if 'click' in col.lower() or 'spend' in col.lower() or 'imp' in col.lower():
-#             ### # print(col)
+#             ### # # print(col)
 #             channel_list.append(col)
 #         else:
 #             pass
@@ -708,7 +708,7 @@ def create_channel_summary(scenario):
         if name_mod.lower().endswith(' imp'):
             name_mod = name_mod.replace('Imp', ' Impressions')
 
-        # # print(name_mod, channel.actual_total_spends, channel.conversion_rate,
+        # # # print(name_mod, channel.actual_total_spends, channel.conversion_rate,
         channel.actual_total_spends * channel.conversion_rate
 
         summary_columns.append(name_mod)