Update pages/7_Build_Response_Curves.py

pages/7_Build_Response_Curves.py

@@ -1,213 +1,213 @@
-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"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)
-
+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"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)