Update Model_Result_Overview.py

Model_Result_Overview.py

@@ -1,212 +1,212 @@
-'''
-MMO Build Sprint 3
-additions : contributions calculated using tuned Mixed LM model
-pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model
-
-MMO Build Sprint 4
-additions : response metrics selection
-pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model
-'''
-
-import streamlit as st
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-import pickle
-
-from utilities import load_authenticator
-
-from utilities_with_panel import (set_header,
-                                  overview_test_data_prep_panel,
-                                  overview_test_data_prep_nonpanel,
-                                  initialize_data,
-                                  load_local_css,
-                                  create_channel_summary,
-                                  create_contribution_pie,
-                                  create_contribuion_stacked_plot,
-                                  create_channel_spends_sales_plot,
-                                  format_numbers,
-                                  channel_name_formating)
-
-import plotly.graph_objects as go
-import streamlit_authenticator as stauth
-import yaml
-from yaml import SafeLoader
-import time
-
-st.set_page_config(layout='wide')
-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"])
-
-    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
-
-
-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)
-
-    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
-
-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)
-
-    return X
-
-
-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'
-
-#st.write(media_data)
-
-is_panel = True 
-
-# 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
-
-authenticator = st.session_state.get('authenticator')
-
-if authenticator is None:
-    authenticator = load_authenticator()
-    
-name, authentication_status, username = authenticator.login('Login', 'main')
-auth_status = st.session_state['authentication_status']
-
-if auth_status:
-    authenticator.logout('Logout', 'main')
-    
-    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")
-
-        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
-
-    def rerun():
-        st.rerun()
-
-    metrics_selected='revenue'
-
-    file_selected = (
-            f"metrics_level_data\Overview_data_test_panel@#revenue.xlsx"
-        )
-    panel_list = panel_fetch(file_selected)
-
-    if "selected_markets" not in st.session_state:
-        st.session_state['selected_markets']='DMA1'
-
-
-    st.header('Overview of previous spends')
-
-    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))
-
-    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)))
-
-
-    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)
-            
-    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)
-    #######################################
-
-    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.markdown("<hr>",unsafe_allow_html=True)
-
-# 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)
+'''
+MMO Build Sprint 3
+additions : contributions calculated using tuned Mixed LM model
+pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model
+
+MMO Build Sprint 4
+additions : response metrics selection
+pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model
+'''
+
+import streamlit as st
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+import pickle
+
+from utilities import load_authenticator
+
+from utilities_with_panel import (set_header,
+                                  overview_test_data_prep_panel,
+                                  overview_test_data_prep_nonpanel,
+                                  initialize_data,
+                                  load_local_css,
+                                  create_channel_summary,
+                                  create_contribution_pie,
+                                  create_contribuion_stacked_plot,
+                                  create_channel_spends_sales_plot,
+                                  format_numbers,
+                                  channel_name_formating)
+
+import plotly.graph_objects as go
+import streamlit_authenticator as stauth
+import yaml
+from yaml import SafeLoader
+import time
+
+st.set_page_config(layout='wide')
+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"])
+
+    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
+
+
+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)
+
+    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
+
+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)
+
+    return X
+
+
+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'
+
+#st.write(media_data)
+
+is_panel = True 
+
+# 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
+
+authenticator = st.session_state.get('authenticator')
+
+if authenticator is None:
+    authenticator = load_authenticator()
+    
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state['authentication_status']
+
+if auth_status:
+    authenticator.logout('Logout', 'main')
+    
+    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")
+
+        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
+
+    def rerun():
+        st.rerun()
+
+    metrics_selected='revenue'
+
+    file_selected = (
+            f"Overview_data_test_panel@#revenue.xlsx"
+        )
+    panel_list = panel_fetch(file_selected)
+
+    if "selected_markets" not in st.session_state:
+        st.session_state['selected_markets']='DMA1'
+
+
+    st.header('Overview of previous spends')
+
+    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))
+
+    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)))
+
+
+    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)
+            
+    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)
+    #######################################
+
+    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.markdown("<hr>",unsafe_allow_html=True)
+
+# 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)