ch

README.md

@@ -5,7 +5,7 @@ colorFrom: indigo
 colorTo: pink
 sdk: streamlit
 sdk_version: 1.32.1
-app_file: Model_Result_Overview.py
+app_file: Scenario_Planner.py
 pinned: false
 ---
 

pages/8_Scenario_Planner.py → Scenario_Planner.py

@@ -1007,7 +1007,7 @@ if auth_status == True:
     #     on_change=reset_inputs,
     # )
 
-    metrics_selected='revenue'
+    metrics_selected='prospects'
     # Target
     target = name_formating(metrics_selected)
 
@@ -1483,34 +1483,34 @@ if auth_status == True:
                     unsafe_allow_html=True,
                 )
 
-    with st.expander("See Response Curves", expanded=True):
-        fig = plot_response_curves()
-        st.plotly_chart(fig, use_container_width=True)
-
-    _columns = st.columns(2)
-    # with _columns[0]:
-    st.subheader("Save Scenario")
-    scenario_name = st.text_input(
-        "Scenario name",
-        key="scenario_input",
-        placeholder="Scenario name",
-        label_visibility="collapsed",
-    )
-    st.button(
-        "Save",
-        on_click=lambda: save_scenario(scenario_name),
-        disabled=len(st.session_state["scenario_input"]) == 0,use_container_width=True
-    )
+    # with st.expander("See Response Curves", expanded=True):
+    #     fig = plot_response_curves()
+    #     st.plotly_chart(fig, use_container_width=True)
+
+    # _columns = st.columns(2)
+    # # with _columns[0]:
+    # st.subheader("Save Scenario")
+    # scenario_name = st.text_input(
+    #     "Scenario name",
+    #     key="scenario_input",
+    #     placeholder="Scenario name",
+    #     label_visibility="collapsed",
+    # )
+    # st.button(
+    #     "Save",
+    #     on_click=lambda: save_scenario(scenario_name),
+    #     disabled=len(st.session_state["scenario_input"]) == 0,use_container_width=True
+    # )
 
-    summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
-    summary_df.drop_duplicates(subset="Channel_name", keep="last", inplace=True)
+    # summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
+    # summary_df.drop_duplicates(subset="Channel_name", keep="last", inplace=True)
 
-    summary_df_sorted = summary_df.sort_values(by="Delta", ascending=False)
-    summary_df_sorted["Delta_percent"] = np.round(
-        ((summary_df_sorted["Optimized_spend"] / summary_df_sorted["Actual_spend"]) - 1)
-        * 100,
-        2,
-    )
+    # summary_df_sorted = summary_df.sort_values(by="Delta", ascending=False)
+    # summary_df_sorted["Delta_percent"] = np.round(
+    #     ((summary_df_sorted["Optimized_spend"] / summary_df_sorted["Actual_spend"]) - 1)
+    #     * 100,
+    #     2,
+    # )
 
     with open("summary_df.pkl", "wb") as f:
         pickle.dump(summary_df_sorted, f)

Test/output_df.csv

@@ -1,29 +1,105 @@
-Date,const,TV,Radio,Online-Search,Online-Social,Online-Display
-2023-10-06,5600,59756.0844,142203.9149,28601.88742,50917.24656,33.0
-2023-10-07,5600,4729.790691,13687.27868,4533.499686,4661.526339,28.99845778
-2023-10-08,5600,3813.711153,13169.55523,2333.559147,4516.38624,28.99845778
-2023-10-09,5600,2914.944625,12174.66075,2333.559147,4458.082645,31.00582633
-2023-10-10,5600,2397.607895,11382.13944,866.696237,4633.786327,36.57315649
-2023-10-11,5600,2030.213987,11728.5212,4753.433627,4344.815321,42.85967447
-2023-10-12,5600,1830.984812,12685.90092,3927.458,3674.378572,38.21471097
-2023-10-13,5600,1579.931074,12929.94788,2769.207621,3563.619842,37.97022099
-2023-10-14,5600,1751.457923,12334.35123,4654.32756,4076.330336,44.67969146
-2023-10-15,5600,1553.758322,12366.09162,4390.452107,3972.140169,16.3174922
-2023-10-16,5600,2157.741503,11953.27821,3528.841092,3810.866204,73.52925638
-2023-10-17,5600,2733.488482,11421.58186,2940.240073,3989.162779,83.23761245
-2023-10-18,5600,2648.588677,11990.4392,5147.61113,3878.666893,72.9103301
-2023-10-19,5600,2608.080302,13763.92965,3745.619183,3766.169273,84.58450258
-2023-10-20,5600,2100.502694,13922.19601,4523.837232,3767.96304,67.10249285
-2023-10-21,5600,1644.795022,14150.51864,9221.915556,4055.068091,95.52759293
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-2023-10-24,5600,1332.448786,14092.05212,8761.717336,4830.23677,176.6417753
-2023-10-25,5600,1216.112934,11929.61991,5451.34576,4850.424513,131.0169787
-2023-10-26,5600,1299.676521,11481.0426,5823.157794,4887.425273,101.2440651
-2023-10-27,5600,1252.148046,11147.22245,7472.096231,4618.373003,119.262777
-2023-10-28,5600,1008.991192,10861.33851,9926.506067,4096.021098,141.6552351
-2023-10-29,5600,613.6573777,10198.12195,9518.260835,3955.613007,103.2800534
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-2023-11-03,5600,150.5666759,9532.980532,5166.812906,3988.123646,98.15641792
+Date,const,BroadcastTV,CableTV,Connected&OTTTV,DisplayProspecting,DisplayRetargeting, Video,SocialProspecting,SocialRetargeting,SearchBrand,SearchNon-brand,DigitalPartners,Audio,Email
+2022-02-27,1,605.7688941,880.7115151,135.0516981,208.2711327,19.41746001,297.0007496,345.010799,51.00935797,214.3019417,892.607461,833.4551364,316.7036224,0.0
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Test/scenario_test_df.csv

@@ -1,29 +1,105 @@
 other_contributions,correction,sales
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classes.py

@@ -275,6 +275,7 @@ class Scenario:
     #     return zip(channels_list, res.x)
 
     def optimize_spends(self, sales_percent, channels_list, algo="trust-constr"):
+        print("%"*100)
         desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
 
         def constraint(x):
@@ -285,7 +286,7 @@ class Scenario:
         bounds = []
         for ch in channels_list:
             bounds.append(
-                (1 + np.array([-50.0, 100.0]) / 100.0)
+                (1+np.array([-50.0, 100.0]) / 100.0)
                 * self.channels[ch].actual_total_spends
             )
 
@@ -303,7 +304,7 @@ class Scenario:
             x0=initial_point,
             constraints=constraints,
             method=algo,
-            options={"maxiter": int(2e7), "xtol": 100},
+            options={"maxiter": int(2e7), "xtol": 10},
         )
 
         for channel_name, modified_spends in zip(channels_list, res.x):
@@ -339,20 +340,22 @@ class Scenario:
                 (1 + spends_percent / 100)
             )
             old_spends.append(channel_actual_total_spends)
-            bounds.append((1 + channel_bounds / 100) * channel_actual_total_spends)
+            bounds.append((1+ channel_bounds / 100) * channel_actual_total_spends)
 
         def objective_function(x):
             for channel_name, modified_spends in zip(channels_list, x):
                 self.update(channel_name, modified_spends)
             return -1 * self.modified_total_sales
 
+        print(bounds)
+        print("$"*100)
         res = minimize(
-            lambda x: objective_function(x) / 1e8,
+            lambda x: objective_function(x)  / 1e3,
             method="trust-constr",
             x0=old_spends,
             constraints=constraint,
             bounds=bounds,
-            options={"maxiter": int(1e7), "xtol": 100},
+            options={"maxiter": int(1e7), "xtol": 50},
         )
         # res = dual_annealing(
         # objective_function,

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e87645c85f167c009e00eae80fbe54392be6fb73096f5959d7b108c00aa72708
-size 1233
+oid sha256:d1b0b3954664cb2e6a7ed10e74ff599cc25479153bb9ee953a602ce03710de55
+size 2269

utilities.py

@@ -39,7 +39,7 @@ color_palette = [
 ]
 
 
-CURRENCY_INDICATOR = '€'
+CURRENCY_INDICATOR = '$'
 
 import streamlit_authenticator as stauth
 
@@ -206,7 +206,7 @@ def panel_level(input_df, date_column="Date"):
 
 
 def initialize_data(
-    panel=None, target_file="Overview_data_test_panel@#revenue.xlsx", updated_rcs=None, metrics=None
+    panel=None, target_file="Overview_data_test_panel@#prospects.xlsx", updated_rcs=None, metrics=None
 ):
     # 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,