pages/7_Build_Response_Curves.py

import streamlit as st
import plotly.express as px
import numpy as np
import plotly.graph_objects as go
from utilities_with_panel import channel_name_formating, load_authenticator, initialize_data
from sklearn.metrics import r2_score
from collections import OrderedDict
from classes import class_from_dict,class_to_dict
import pickle
import json
import pandas as pd
from utilities import (
    load_local_css,
    set_header,
    channel_name_formating,
)

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

def s_curve(x,K,b,a,x0):
    return K / (1 + b*np.exp(-a*(x-x0)))

def save_scenario(scenario_name):
    """
    Save the current scenario with the mentioned name in the session state
    
    Parameters
    ----------
    scenario_name
        Name of the scenario to be saved
    """
    if 'saved_scenarios' not in st.session_state:
        st.session_state = OrderedDict()
        
    #st.session_state['saved_scenarios'][scenario_name] = st.session_state['scenario'].save()
    st.session_state['saved_scenarios'][scenario_name] = class_to_dict(st.session_state['scenario'])
    st.session_state['scenario_input'] = ""
    print(type(st.session_state['saved_scenarios']))
    with open('../saved_scenarios.pkl', 'wb') as f:
        pickle.dump(st.session_state['saved_scenarios'],f)


def reset_curve_parameters():
    del st.session_state['K']
    del st.session_state['b']
    del st.session_state['a']
    del st.session_state['x0']
    
def update_response_curve():
    # st.session_state['rcs'][selected_channel_name]['K'] = st.session_state['K']
    # st.session_state['rcs'][selected_channel_name]['b'] = st.session_state['b']
    # st.session_state['rcs'][selected_channel_name]['a'] = st.session_state['a']
    # st.session_state['rcs'][selected_channel_name]['x0'] = st.session_state['x0']
    # rcs = st.session_state['rcs']
    _channel_class = st.session_state['scenario'].channels[selected_channel_name]
    _channel_class.update_response_curves({
                           'K'  : st.session_state['K'], 
                           'b'  : st.session_state['b'], 
                           'a'  : st.session_state['a'],
                           'x0' : st.session_state['x0']})
    

# 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.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")

# Sprint4 - if used_response_metrics is not blank, then select one of the used_response_metrics, else target is revenue by default
st.set_page_config(layout='wide')
load_local_css('styles.css')
set_header()
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

metrics_selected='Revenue'

file_selected = (
        f".\metrics_level_data\Overview_data_test_panel@#{metrics_selected}.xlsx"
    )
panel_list = panel_fetch(file_selected)


    
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'

st.subheader("Build response curves")


st.session_state['selected_markets']= st.selectbox(
        "Select Markets",
        ["Total Market"] + panel_list,
        index=0,
    )
initialize_data(target_col,st.session_state['selected_markets'])


    
channels_list = st.session_state['channels_list']
selected_channel_name = st.selectbox('Channel', st.session_state['channels_list'], format_func=channel_name_formating,on_change=reset_curve_parameters)

rcs = {}
for channel_name in channels_list:
    rcs[channel_name] = st.session_state['scenario'].channels[channel_name].response_curve_params
# rcs = st.session_state['rcs']


if 'K' not in st.session_state:
    st.session_state['K'] = rcs[selected_channel_name]['K']
if 'b' not in st.session_state:
    st.session_state['b'] = rcs[selected_channel_name]['b']
if 'a' not in st.session_state:
    st.session_state['a'] = rcs[selected_channel_name]['a']
if 'x0' not in st.session_state:
    st.session_state['x0'] = rcs[selected_channel_name]['x0']
    
x = st.session_state['actual_input_df'][selected_channel_name].values
y = st.session_state['actual_contribution_df'][selected_channel_name].values

power = (np.ceil(np.log(x.max()) / np.log(10) )- 3)

# fig = px.scatter(x, s_curve(x/10**power,
#                             st.session_state['K'],
#                             st.session_state['b'],
#                             st.session_state['a'],
#                             st.session_state['x0']))

fig = px.scatter(x=x, y=y)
fig.add_trace(go.Scatter(x=sorted(x), y=s_curve(sorted(x)/10**power,st.session_state['K'],
                                    st.session_state['b'],
                                    st.session_state['a'],
                                    st.session_state['x0']),
                        line=dict(color='red')))

fig.update_layout(title_text="Response Curve",showlegend=False)
fig.update_annotations(font_size=10)
fig.update_xaxes(title='Spends')
fig.update_yaxes(title=sel_target_col)

st.plotly_chart(fig,use_container_width=True)

r2 = r2_score(y, s_curve(x / 10**power, 
                        st.session_state['K'],
                        st.session_state['b'],
                        st.session_state['a'],
                        st.session_state['x0']))

st.metric('R2',round(r2,2))
columns = st.columns(4)

with columns[0]:
    st.number_input('K',key='K',format="%0.5f")
with columns[1]:
    st.number_input('b',key='b',format="%0.5f")
with columns[2]:
    st.number_input('a',key='a',step=0.0001,format="%0.5f")
with columns[3]:
    st.number_input('x0',key='x0',format="%0.5f")
    

st.button('Update parameters',on_click=update_response_curve)
st.button('Reset parameters',on_click=reset_curve_parameters)
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)

file_name = st.text_input('rcs download file name', key='file_name_input',placeholder='file name',label_visibility='collapsed')
st.download_button(
                    label="Download response curves",
                    data=json.dumps(rcs),
                    file_name=f"{file_name}.json",
                    mime="application/json",
                    disabled= len(file_name) == 0,
                )


def s_curve_derivative(x, K, b, a, x0):
    # Derivative of the S-curve function
    return a * b * K * np.exp(-a * (x - x0)) / ((1 + b * np.exp(-a * (x - x0))) ** 2)

# Parameters of the S-curve
K = st.session_state['K']
b = st.session_state['b']
a = st.session_state['a']
x0 = st.session_state['x0']

# Optimized spend value obtained from the tool
optimized_spend = st.number_input('value of x')  # Replace this with your optimized spend value

# Calculate the slope at the optimized spend value
slope_at_optimized_spend = s_curve_derivative(optimized_spend, K, b, a, x0)

st.write("Slope ", slope_at_optimized_spend)