Update Model_Result_Overview.py

Model_Result_Overview.py

@@ -38,175 +38,175 @@ load_local_css('styles.css')
 set_header()
 
 
-def get_random_effects(media_data, panel_col, mdf):
-    random_eff_df = pd.DataFrame(columns=[panel_col, "random_effect"])
+# 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')
-        intercept = mdf.random_effects[market].values[0]
-        random_eff_df.loc[i, 'random_effect'] = intercept
-        random_eff_df.loc[i, panel_col] = market
+#     for i, market in enumerate(media_data[panel_col].unique()):
+#         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
 
-    return random_eff_df
+#     return random_eff_df
 
 
-def process_train_and_test(train, test, features, panel_col, target_col):
-    X1 = train[features]
+# def process_train_and_test(train, test, features, panel_col, target_col):
+#     X1 = train[features]
 
-    ss = MinMaxScaler()
-    X1 = pd.DataFrame(ss.fit_transform(X1), columns=X1.columns)
+#     ss = MinMaxScaler()
+#     X1 = pd.DataFrame(ss.fit_transform(X1), columns=X1.columns)
 
-    X1[panel_col] = train[panel_col]
-    X1[target_col] = train[target_col]
+#     X1[panel_col] = train[panel_col]
+#     X1[target_col] = train[target_col]
 
-    if test is not None:
-        X2 = test[features]
-        X2 = pd.DataFrame(ss.transform(X2), columns=X2.columns)
-        X2[panel_col] = test[panel_col]
-        X2[target_col] = test[target_col]
-        return X1, X2
-    return X1
+#     if test is not None:
+#         X2 = test[features]
+#         X2 = pd.DataFrame(ss.transform(X2), columns=X2.columns)
+#         X2[panel_col] = test[panel_col]
+#         X2[target_col] = test[target_col]
+#         return X1, X2
+#     return X1
 
-def mdf_predict(X_df, mdf, random_eff_df) :
-    X=X_df.copy()
-    X=pd.merge(X, random_eff_df[[panel_col,'random_effect']], on=panel_col, how='left')
-    X['pred_fixed_effect'] = mdf.predict(X)
+# def mdf_predict(X_df, mdf, random_eff_df) :
+#     X=X_df.copy()
+#     X=pd.merge(X, random_eff_df[[panel_col,'random_effect']], on=panel_col, how='left')
+#     X['pred_fixed_effect'] = mdf.predict(X)
 
-    X['pred'] = X['pred_fixed_effect'] + X['random_effect']
-    X.to_csv('Test/merged_df_contri.csv',index=False)
-    X.drop(columns=['pred_fixed_effect', 'random_effect'], inplace=True)
+#     X['pred'] = X['pred_fixed_effect'] + X['random_effect']
+#     X.to_csv('Test/merged_df_contri.csv',index=False)
+#     X.drop(columns=['pred_fixed_effect', 'random_effect'], inplace=True)
 
-    return X
+#     return X
 
 
-target_col='Revenue'
-target='Revenue'
+# target_col='Revenue'
+# target='Revenue'
 
-# is_panel=False
-# is_panel = st.session_state['is_panel']
-#panel_col = [col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in  st.session_state['bin_dict']['Panel Level 1']  ] [0]# set the panel column
-panel_col='Panel'
-date_col = 'date'
+# # is_panel=False
+# # is_panel = st.session_state['is_panel']
+# #panel_col = [col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in  st.session_state['bin_dict']['Panel Level 1']  ] [0]# set the panel column
+# panel_col='Panel'
+# date_col = 'date'
 
-#st.write(media_data)
+# #st.write(media_data)
 
-is_panel = True 
+# is_panel = True 
 
-# panel_col='markets'
-date_col = 'date'
-for k, v in st.session_state.items():
+# # panel_col='markets'
+# date_col = 'date'
+# for k, v in st.session_state.items():
 
-    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
-        st.session_state[k] = v
+#     if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+#         st.session_state[k] = v
 
-authenticator = st.session_state.get('authenticator')
+# authenticator = st.session_state.get('authenticator')
 
-if authenticator is None:
-    authenticator = load_authenticator()
+# if authenticator is None:
+#     authenticator = load_authenticator()
     
-name, authentication_status, username = authenticator.login('Login', 'main')
-auth_status = st.session_state['authentication_status']
+# name, authentication_status, username = authenticator.login('Login', 'main')
+# auth_status = st.session_state['authentication_status']
 
-if auth_status:
-    authenticator.logout('Logout', 'main')
+# if auth_status:
+#     authenticator.logout('Logout', 'main')
     
-    is_state_initiaized = st.session_state.get('initialized',False)
-    if not is_state_initiaized:
-        a=1
+#     is_state_initiaized = st.session_state.get('initialized',False)
+#     if not is_state_initiaized:
+#         a=1
 
-    def panel_fetch(file_selected):
-        raw_data_mmm_df = pd.read_excel(file_selected, sheet_name="RAW DATA MMM")
+#     def panel_fetch(file_selected):
+#         raw_data_mmm_df = pd.read_excel(file_selected, sheet_name="RAW DATA MMM")
 
-        if "Panel" in raw_data_mmm_df.columns:
-            panel = list(set(raw_data_mmm_df["Panel"]))
-        else:
-            raw_data_mmm_df = None
-            panel = None
+#         if "Panel" in raw_data_mmm_df.columns:
+#             panel = list(set(raw_data_mmm_df["Panel"]))
+#         else:
+#             raw_data_mmm_df = None
+#             panel = None
 
-        return panel
+#         return panel
 
-    def rerun():
-        st.rerun()
+#     def rerun():
+#         st.rerun()
 
-    metrics_selected='revenue'
+#     metrics_selected='revenue'
 
-    file_selected = (
-            f"Overview_data_test_panel@#{metrics_selected}.xlsx"
-        )
-    panel_list = panel_fetch(file_selected)
+#     file_selected = (
+#             f"Overview_data_test_panel@#{metrics_selected}.xlsx"
+#         )
+#     panel_list = panel_fetch(file_selected)
 
-    if "selected_markets" not in st.session_state:
-        st.session_state['selected_markets']='DMA1'
+#     if "selected_markets" not in st.session_state:
+#         st.session_state['selected_markets']='DMA1'
 
 
-    st.header('Overview of previous spends')
+#     st.header('Overview of previous spends')
 
-    selected_market= st.selectbox(
-            "Select Markets",
-            ["Total Market"] + panel_list
-        )
+#     selected_market= st.selectbox(
+#             "Select Markets",
+#             ["Total Market"] + panel_list
+#         )
 
 
 
-    initialize_data(target_col,selected_market)
-    scenario = st.session_state['scenario']
-    raw_df = st.session_state['raw_df']
-    # st.write(scenario.actual_total_spends)
-    # st.write(scenario.actual_total_sales)
-    columns = st.columns((1,1,3))
+#     initialize_data(target_col,selected_market)
+#     scenario = st.session_state['scenario']
+#     raw_df = st.session_state['raw_df']
+#     # st.write(scenario.actual_total_spends)
+#     # st.write(scenario.actual_total_sales)
+#     columns = st.columns((1,1,3))
 
-    with columns[0]:
-        st.metric(label='Spends', value=format_numbers(float(scenario.actual_total_spends)))
-    ###print(f"##################### {scenario.actual_total_sales} ##################")
-    with columns[1]:
-        st.metric(label=target, value=format_numbers(float(scenario.actual_total_sales),include_indicator=False))
+#     with columns[0]:
+#         st.metric(label='Spends', value=format_numbers(float(scenario.actual_total_spends)))
+#     ###print(f"##################### {scenario.actual_total_sales} ##################")
+#     with columns[1]:
+#         st.metric(label=target, value=format_numbers(float(scenario.actual_total_sales),include_indicator=False))
 
 
-    actual_summary_df = create_channel_summary(scenario)
-    actual_summary_df['Channel'] = actual_summary_df['Channel'].apply(channel_name_formating) 
+#     actual_summary_df = create_channel_summary(scenario)
+#     actual_summary_df['Channel'] = actual_summary_df['Channel'].apply(channel_name_formating) 
 
-    columns = st.columns((2,1))
-    #with columns[0]:
-    with st.expander('Channel wise overview'):
-        st.markdown(actual_summary_df.style.set_table_styles(
-        [{
-            'selector': 'th',
-            'props': [('background-color', '#FFFFF')]
-        },
-            {
-            'selector' : 'tr:nth-child(even)',
-            'props' : [('background-color', '#FFFFF')]
-            }]).to_html(), unsafe_allow_html=True)
+#     columns = st.columns((2,1))
+#     #with columns[0]:
+#     with st.expander('Channel wise overview'):
+#         st.markdown(actual_summary_df.style.set_table_styles(
+#         [{
+#             'selector': 'th',
+#             'props': [('background-color', '#FFFFF')]
+#         },
+#             {
+#             'selector' : 'tr:nth-child(even)',
+#             'props' : [('background-color', '#FFFFF')]
+#             }]).to_html(), unsafe_allow_html=True)
             
-    st.markdown("<hr>",unsafe_allow_html=True)
-    ##############################
+#     st.markdown("<hr>",unsafe_allow_html=True)
+#     ##############################
 
-    st.plotly_chart(create_contribution_pie(scenario),use_container_width=True)
-    st.markdown("<hr>",unsafe_allow_html=True)
+#     st.plotly_chart(create_contribution_pie(scenario),use_container_width=True)
+#     st.markdown("<hr>",unsafe_allow_html=True)
 
 
-    ################################3
-    st.plotly_chart(create_contribuion_stacked_plot(scenario),use_container_width=True)
-    st.markdown("<hr>",unsafe_allow_html=True)
-    #######################################
+#     ################################3
+#     st.plotly_chart(create_contribuion_stacked_plot(scenario),use_container_width=True)
+#     st.markdown("<hr>",unsafe_allow_html=True)
+#     #######################################
 
-    selected_channel_name = st.selectbox('Channel', st.session_state['channels_list'] + ['non media'], format_func=channel_name_formating)
-    selected_channel = scenario.channels.get(selected_channel_name,None)
+#     selected_channel_name = st.selectbox('Channel', st.session_state['channels_list'] + ['non media'], format_func=channel_name_formating)
+#     selected_channel = scenario.channels.get(selected_channel_name,None)
 
-    st.plotly_chart(create_channel_spends_sales_plot(selected_channel), use_container_width=True)
+#     st.plotly_chart(create_channel_spends_sales_plot(selected_channel), use_container_width=True)
 
-    st.markdown("<hr>",unsafe_allow_html=True)
+#     st.markdown("<hr>",unsafe_allow_html=True)
 
-# elif auth_status == False:
-#     st.error('Username/Password is incorrect')
+# # elif auth_status == False:
+# #     st.error('Username/Password is incorrect')
     
-# if auth_status != True:
-#     try:
-#         username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
-#         if username_forgot_pw:
-#             st.success('New password sent securely')
-#             # Random password to be transferred to user securely
-#         elif username_forgot_pw == False:
-#             st.error('Username not found')
-#     except Exception as e:
-#         st.error(e)
+# # if auth_status != True:
+# #     try:
+# #         username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
+# #         if username_forgot_pw:
+# #             st.success('New password sent securely')
+# #             # Random password to be transferred to user securely
+# #         elif username_forgot_pw == False:
+# #             st.error('Username not found')
+# #     except Exception as e:
+# #         st.error(e)