pages/6_Model_Result_Overview.py

'''
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_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='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
date_col = 'date'

#st.write(media_data)

is_panel = True if len(panel_col)>0 else False

# panel_col='markets'
date_col = 'date'

# Sprint4 - if used_response_metrics is not blank, then select one of the used_response_metrics, else target is revenue by default
if "used_response_metrics" in st.session_state and st.session_state['used_response_metrics']!=[]:
    sel_target_col = st.selectbox("Select the response metric", st.session_state['used_response_metrics'])
    target_col = sel_target_col.lower().replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_")
else :
    sel_target_col = 'Total Approved Accounts - Revenue'
    target_col = 'total_approved_accounts_revenue'

# Sprint4 - Look through all saved tuned models, only show saved models of the sel resp metric (target_col)
# saved_models = st.session_state['saved_model_names']
 # Sprint4 - get the model obj of the selected model
# st.write(sel_model_dict)

# Sprint3 - Contribution
if is_panel:
    # read tuned mixedLM model
    # if st.session_state["tuned_model"] is not None :

    if st.session_state["is_tuned_model"][target_col]==True: #Sprint4
        with open("tuned_model.pkl", 'rb') as file:
            model_dict = pickle.load(file)
        saved_models = list(model_dict.keys())
        required_saved_models = [m.split("__")[0] for m in saved_models if m.split("__")[1] == target_col]
        sel_model = st.selectbox("Select the model to review", required_saved_models)
        sel_model_dict = model_dict[sel_model + "__" + target_col]

        # model=st.session_state["tuned_model"]
        # X_train=st.session_state["X_train_tuned"]
        # X_test=st.session_state["X_test_tuned"]
        # best_feature_set=st.session_state["tuned_model_features"]
        model=sel_model_dict["Model_object"]
        X_train=sel_model_dict["X_train_tuned"]
        X_test=sel_model_dict["X_test_tuned"]
        best_feature_set=sel_model_dict["feature_set"]

        # st.write("features", best_feature_set)
        # st.write(X_test.columns)

    else : # if non tuned model to be used # Pending
        with open("best_models.pkl", 'rb') as file:
            model_dict = pickle.load(file)
        saved_models = list(model_dict.keys())
        required_saved_models = [m.split("__")[0] for m in saved_models if m.split("__")[1] == target_col]
        sel_model = st.selectbox("Select the model to review", required_saved_models)
        sel_model_dict = model_dict[sel_model + "__" + target_col]
        model=st.session_state["base_model"]
        X_train = st.session_state['X_train']
        X_test = st.session_state['X_test']
        # y_train = st.session_state['y_train']
        # y_test = st.session_state['y_test']
        best_feature_set = st.session_state['base_model_feature_set']
        # st.write(best_feature_set)
        # st.write(X_test.columns)

    # Calculate contributions

    with open("data_import.pkl", "rb") as f:
        data = pickle.load(f)

    # Accessing the loaded objects
    st.session_state['orig_media_data'] = data["final_df"]

    st.session_state['orig_media_data'].columns=[col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in st.session_state['orig_media_data'].columns]

    media_data = st.session_state["media_data"]


    # st.session_state['orig_media_data']=st.session_state["media_data"]

    #st.write(media_data)
    
    contri_df = pd.DataFrame()

    y = []
    y_pred = []

    random_eff_df = get_random_effects(media_data, panel_col, model)
    random_eff_df['fixed_effect'] = model.fe_params['Intercept']
    random_eff_df['panel_effect'] = random_eff_df['random_effect'] + random_eff_df['fixed_effect']
    # random_eff_df.to_csv("Test/random_eff_df_contri.csv", index=False)

    coef_df = pd.DataFrame(model.fe_params)
    coef_df.columns = ['coef']

    # coef_df.reset_index().to_csv("Test/coef_df_contri1.csv",index=False)
    # print(model.fe_params)

    x_train_contribution = X_train.copy()
    x_test_contribution = X_test.copy()

    # preprocessing not needed since X_train is already preprocessed
    # X1, X2 = process_train_and_test(x_train_contribution, x_test_contribution, best_feature_set, panel_col, target_col)
    # x_train_contribution[best_feature_set] = X1[best_feature_set]
    # x_test_contribution[best_feature_set] = X2[best_feature_set]

    x_train_contribution = mdf_predict(x_train_contribution, model, random_eff_df)
    x_test_contribution = mdf_predict(x_test_contribution, model, random_eff_df)

    x_train_contribution = pd.merge(x_train_contribution, random_eff_df[[panel_col, 'panel_effect']], on=panel_col,
                                    how='left')
    x_test_contribution = pd.merge(x_test_contribution, random_eff_df[[panel_col, 'panel_effect']], on=panel_col,
                                   how='left')

    inp_coef = coef_df['coef'][1:].tolist() # 0th index is intercept

    for i in range(len(inp_coef)):
        x_train_contribution[str(best_feature_set[i]) + "_contr"] = inp_coef[i] * x_train_contribution[best_feature_set[i]]
        x_test_contribution[str(best_feature_set[i]) + "_contr"] = inp_coef[i] * x_test_contribution[best_feature_set[i]]

    x_train_contribution['sum_contributions'] = x_train_contribution.filter(regex="contr").sum(axis=1)
    x_train_contribution['sum_contributions'] = x_train_contribution['sum_contributions'] + x_train_contribution['panel_effect']

    x_test_contribution['sum_contributions'] = x_test_contribution.filter(regex="contr").sum(axis=1)
    x_test_contribution['sum_contributions'] = x_test_contribution['sum_contributions'] + x_test_contribution['panel_effect']

    # # test
    x_train_contribution.to_csv("Test/x_train_contribution.csv",index=False)
    x_test_contribution.to_csv("Test/x_test_contribution.csv",index=False)
    #
    # st.session_state['orig_media_data'].to_csv("Test/transformed_data.csv",index=False)
    # st.session_state['X_test_spends'].to_csv("Test/test_spends.csv",index=False)
    # # st.write(st.session_state['orig_media_data'].columns)

    st.write(date_col,panel_col)
    # st.write(x_test_contribution)

    overview_test_data_prep_panel(x_test_contribution, st.session_state['orig_media_data'], st.session_state['X_test_spends'],
                        date_col, panel_col, target_col)

else : # NON PANEL
    if st.session_state["is_tuned_model"][target_col]==True: #Sprint4
        with open("tuned_model.pkl", 'rb') as file:
            model_dict = pickle.load(file)
        saved_models = list(model_dict.keys())
        required_saved_models = [m.split("__")[0] for m in saved_models if m.split("__")[1] == target_col]
        sel_model = st.selectbox("Select the model to review", required_saved_models)
        sel_model_dict = model_dict[sel_model + "__" + target_col]

        model=sel_model_dict["Model_object"]
        X_train=sel_model_dict["X_train_tuned"]
        X_test=sel_model_dict["X_test_tuned"]
        best_feature_set=sel_model_dict["feature_set"]

    else : #Sprint4
        with open("best_models.pkl", 'rb') as file:
            model_dict = pickle.load(file)
        saved_models = list(model_dict.keys())
        required_saved_models = [m.split("__")[0] for m in saved_models if m.split("__")[1] == target_col]
        sel_model = st.selectbox("Select the model to review", required_saved_models)
        sel_model_dict = model_dict[sel_model + "__" + target_col]

        model=sel_model_dict["Model_object"]
        X_train=sel_model_dict["X_train"]
        X_test=sel_model_dict["X_test"]
        best_feature_set=sel_model_dict["feature_set"]

    x_train_contribution = X_train.copy()
    x_test_contribution = X_test.copy()

    x_train_contribution['pred'] = model.predict(x_train_contribution[best_feature_set])
    x_test_contribution['pred'] = model.predict(x_test_contribution[best_feature_set])

    for num,i in enumerate(model.params.values):
        col=best_feature_set[num]
        x_train_contribution[col + "_contr"] = X_train[col] * i
        x_test_contribution[col + "_contr"] = X_test[col] * i

    x_test_contribution.to_csv("Test/x_test_contribution_non_panel.csv",index=False)
    overview_test_data_prep_nonpanel(x_test_contribution, st.session_state['orig_media_data'].copy(), st.session_state['X_test_spends'].copy(), date_col, target_col)
# for k, v in st.session_sta
# te.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:

initialize_data(target_col)
scenario = st.session_state['scenario']
raw_df = st.session_state['raw_df']
st.header('Overview of previous spends')

# 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))


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', '#11B6BD')]
        },
            {
            'selector' : 'tr:nth-child(even)',
            'props' : [('background-color', '#11B6BD')]
            }]).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)