Response Curve

pages/2_Scenario_Planner.py

@@ -2028,7 +2028,7 @@ if auth_status == True:
             st.markdown(
                 """<hr class="spends-heading-seperator">""", unsafe_allow_html=True
             )
-            _columns = st.columns((1.5, 2.5, 2, 2, 1))
+            _columns = st.columns((2, 1.5, 3, 3, 1))
             with _columns[0]:
                 generate_spending_header("Channel")
             with _columns[1]:
@@ -2068,7 +2068,7 @@ if auth_status == True:
                 # st.write(st.session_state["scenario"].channels[channel_name].bounds)
                 # st.write(st.session_state["scenario"].channels[channel_name].channel_bounds_min)
 
-                _columns = st.columns((1.5, 2.5, 2, 2, 1))
+                _columns = st.columns((2, 1.5, 3, 3, 1))
                 response_curve_params = pd.read_excel(
                     "response_curves_parameters.xlsx", index_col="channel"
                 )
@@ -2099,7 +2099,7 @@ if auth_status == True:
                     ### # # print(st.session_state[channel_name])
                     # st.write(_channel_class.channel_bounds_min,channel_bounds_min)
                     # st.write(_channel_class.channel_bounds_max,channel_bounds_max)
-                    _columns_min = st.columns(2)
+                    _columns_min = st.columns(1)
                     with _columns_min[0]:
                         spend_input = st.text_input(
                             "Absolute",
@@ -2135,7 +2135,7 @@ if auth_status == True:
                         return float(st.session_state[channel_name_upper_bound])
 
                     channel_name_current = f"{channel_name}_change"
-                    with _columns_min[1]:
+                    with _columns_min[0]:
                         channel_name_upper_bound = f"{channel_name}_upper_bound"
                         if channel_name_upper_bound not in st.session_state:
                             st.session_state[channel_name_upper_bound] = str(100)
@@ -2187,7 +2187,7 @@ if auth_status == True:
                     with _spend_cols[0]:
                         # st.write("Actual")
                         st.markdown(
-                            f"<p> Actual <h5>{format_numbers(actual_channel_spends)}</h5> </p>",
+                            f"<p> Actual Spends<h5>{format_numbers(actual_channel_spends)}</h5> </p>",
                             unsafe_allow_html=True,
                         )
                         # st.metric(
@@ -2199,7 +2199,7 @@ if auth_status == True:
 
                         # st.write("Actual")
                         st.markdown(
-                            f"<p> Change <h5>{format_numbers(spends_delta)}</h5> </p>",
+                            f"<p>Spends Change<h5>{format_numbers(spends_delta)}</h5> </p>",
                             unsafe_allow_html=True,
                         )
                         # st.markdown(f'<h4 class="custom-text1">{format_numbers(spends_delta)}%</h4>', unsafe_allow_html=True)
@@ -2212,11 +2212,11 @@ if auth_status == True:
                         # )
                     with _spend_cols[1]:
                         st.markdown(
-                            f"<p> Simulated <h5>{format_numbers(current_channel_spends)}</h5> </p>",
+                            f"<p>Simulated Spends<h5>{format_numbers(current_channel_spends)}</h5> </p>",
                             unsafe_allow_html=True,
                         )
                         st.markdown(
-                            f'<p>Percent<h5>{numerize(( spends_delta/actual_channel_spends)*100,0) +"%"}</h5> </p>',
+                            f'<p>Spends Percent<h5>{numerize(( spends_delta/actual_channel_spends)*100,0) +"%"}</h5> </p>',
                             unsafe_allow_html=True,
                         )
                         # st.metric(
@@ -2251,11 +2251,11 @@ if auth_status == True:
                     with _prospect_cols[0]:
                         # st.write("Actual")
                         st.markdown(
-                            f"<p> Actual <h5>{format_numbers_f(actual_channel_sales)}</h5> </p>",
+                            f"<p>Actual Revenue<h5>$ {format_numbers_f(actual_channel_sales)}</h5> </p>",
                             unsafe_allow_html=True,
                         )
                         st.markdown(
-                            f"<p> Change <h5>{format_numbers_f(sales_delta)}</h5> </p>",
+                            f"<p>Revenue Change<h5>$ {format_numbers_f(sales_delta)}</h5> </p>",
                             unsafe_allow_html=True,
                         )
 
@@ -2274,11 +2274,11 @@ if auth_status == True:
                     #     )
                     with _prospect_cols[1]:
                         st.markdown(
-                            f"<p> Simulated <h5>{format_numbers_f(current_channel_sales)}</h5> </p>",
+                            f"<p>Simulated Revenue<h5>$ {format_numbers_f(current_channel_sales)}</h5> </p>",
                             unsafe_allow_html=True,
                         )
                         st.markdown(
-                            f'<p>Percent<h5>{numerize(( _channel_class.delta_sales/actual_channel_sales)*100,0) +"%"}</h5> </p>',
+                            f'<p>Revenue Percent<h5>{numerize(( _channel_class.delta_sales/actual_channel_sales)*100,0) +"%"}</h5> </p>',
                             unsafe_allow_html=True,
                         )
 

response_curves_model_quality.py

@@ -187,63 +187,63 @@ for i in range(1,13):
     pdf =  fit_data(spend_cols[i],prospect_cols[i],channel_cols[i])
     plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
     
-def response_curves(channel,x_modified,y_modified):
-
-    # Initialize the Plotly figure
-    fig = go.Figure()
-
-    x_col = (channel+"_Spends").replace('\xa0', '')
-    y_col = ("Fit_Data_"+channel).replace('\xa0', '')
-
-    # fig.add_trace(go.Scatter(
-    #     x=plotly_data[x_col],
-    #     y=plotly_data[y_col],
-    #     mode='markers',
-    #     name=x_col.replace('_Spends', '')
-    # ))
-
-    fig.add_trace(go.Scatter(
-        x=plotly_data.sort_values(by=x_col, ascending=True)[x_col],
-        y=plotly_data.sort_values(by=x_col, ascending=True)[y_col],
-        mode='lines+markers',
-        name=x_col.replace('_Spends', '')
-    ))
+# def response_curves(channel,x_modified,y_modified):
+
+#     # Initialize the Plotly figure
+#     fig = go.Figure()
+
+#     x_col = (channel+"_Spends").replace('\xa0', '')
+#     y_col = ("Fit_Data_"+channel).replace('\xa0', '')
+
+#     # fig.add_trace(go.Scatter(
+#     #     x=plotly_data[x_col],
+#     #     y=plotly_data[y_col],
+#     #     mode='markers',
+#     #     name=x_col.replace('_Spends', '')
+#     # ))
+
+#     fig.add_trace(go.Scatter(
+#         x=plotly_data.sort_values(by=x_col, ascending=True)[x_col],
+#         y=plotly_data.sort_values(by=x_col, ascending=True)[y_col],
+#         mode='lines+markers',
+#         name=x_col.replace('_Spends', '')
+#     ))
     
-    plotly_data2 = plotly_data.copy()
-    plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
-    # # print(plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col])
-    # .dropna(subset=[x_col]).reset_index(inplace = True)
-    fig.add_trace(go.Scatter(
-        x=plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col],
-        y=plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][y_col],
-        mode='markers',
-        marker=dict(
-        size=13  # Adjust the size value to make the markers larger or smaller
-        , color = 'yellow'
-        ),
-        name="Current Spends"
-    ))
-
-    fig.add_trace(go.Scatter(
-        x=[x_modified/104],
-        y=[y_modified/104],
-        mode='markers',
-        marker=dict(
-        size=13  # Adjust the size value to make the markers larger or smaller
-        , color = 'blue'
-        ),
-        name="Optimised Spends"
-    ))
-
-    # Update layout with titles
-    fig.update_layout(
-        title=channel+' Response Curve',
-        xaxis_title='Weekly Spends',
-        yaxis_title='Prospects'
-    )
-
-    # Show the figure
-    return fig
+#     plotly_data2 = plotly_data.copy()
+#     plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
+#     # # print(plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col])
+#     # .dropna(subset=[x_col]).reset_index(inplace = True)
+#     fig.add_trace(go.Scatter(
+#         x=plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col],
+#         y=plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][y_col],
+#         mode='markers',
+#         marker=dict(
+#         size=13  # Adjust the size value to make the markers larger or smaller
+#         , color = 'yellow'
+#         ),
+#         name="Current Spends"
+#     ))
+
+#     fig.add_trace(go.Scatter(
+#         x=[x_modified/104],
+#         y=[y_modified/104],
+#         mode='markers',
+#         marker=dict(
+#         size=13  # Adjust the size value to make the markers larger or smaller
+#         , color = 'blue'
+#         ),
+#         name="Optimised Spends"
+#     ))
+
+#     # Update layout with titles
+#     fig.update_layout(
+#         title=channel+' Response Curve',
+#         xaxis_title='Weekly Spends',
+#         yaxis_title='Prospects'
+#     )
+
+#     # Show the figure
+#     return fig
 
 import pandas as pd
 import numpy as np
@@ -447,24 +447,61 @@ def response_curves(channel,x_modified,y_modified):
     #     mode='markers',
     #     name=x_col.replace('_Spends', '')
     # ))
-    plotly_data1 = plotly_data[plotly_data["MAT"]!="ext"]
+
+    plotly_data2 = plotly_data.copy()
+    plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
+    plotly_data2 = plotly_data2[plotly_data2["MAT"]!="ext"]
+
+    x_actual = np.array(plotly_data2[x_col].mean())
+    y_actual = np.array(plotly_data2[y_col].mean())
+
+    # Filter data within the limits
+    plotly_data1 = plotly_data[(plotly_data["MAT"] != "ext")]
+
+    # Sort values for smooth plotting
+    plotly_data1 = plotly_data1.sort_values(by=x_col, ascending=True)
+    dividing_parameter = len(plotly_data1[plotly_data1[x_col].isnull()==False])
+
+    
+    x_mod = x_modified/dividing_parameter
+    y_mod = y_modified/dividing_parameter
+
+    # Define limits
+    x_limit = 1.2 * max(x_actual, x_mod)
+    # y_limit = 1.5 * y_actual
+
+    plotly_data1 = plotly_data[(plotly_data["MAT"] != "ext") & 
+                            (plotly_data[x_col] <= x_limit)]
+    plotly_data1 = plotly_data1.sort_values(by=x_col, ascending=True)
+
+    # Plot
     fig.add_trace(go.Scatter(
-        x=plotly_data1.sort_values(by=x_col, ascending=True)[x_col],
-        y=plotly_data1.sort_values(by=x_col, ascending=True)[y_col],
+        x=plotly_data1[x_col],
+        y=plotly_data1[y_col],
         mode='lines',
-        marker=dict(color = 'blue'),
+        marker=dict(color='blue'),
         name=x_col.replace('_Spends', '')
     ))
 
-    dividing_parameter = len(plotly_data1[plotly_data1[x_col].isnull()==False])
+
+    # plotly_data1 = plotly_data[plotly_data["MAT"]!="ext"]
+    # fig.add_trace(go.Scatter(
+    #     x=plotly_data1.sort_values(by=x_col, ascending=True)[x_col],
+    #     y=plotly_data1.sort_values(by=x_col, ascending=True)[y_col],
+    #     mode='lines',
+    #     marker=dict(color = 'blue'),
+    #     name=x_col.replace('_Spends', '')
+    # ))
+
+    
     # print(dividing_parameter)
-    plotly_data2 = plotly_data.copy()
-    plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
-    plotly_data2 = plotly_data2[plotly_data2["MAT"]!="ext"]
+    # plotly_data2 = plotly_data.copy()
+    # plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
+    # plotly_data2 = plotly_data2[plotly_data2["MAT"]!="ext"]
     # .dropna(subset=[x_col]).reset_index(inplace = True)
     fig.add_trace(go.Scatter(
-        x=np.array(plotly_data2[x_col].mean()),
-        y=np.array(plotly_data2[y_col].mean()),
+        x=x_actual,
+        y=y_actual,
         mode='markers',
         marker=dict(
         size=13  # Adjust the size value to make the markers larger or smaller
@@ -475,8 +512,8 @@ def response_curves(channel,x_modified,y_modified):
 
     # # print(dividing_parameter)
     fig.add_trace(go.Scatter(
-        x=[x_modified/dividing_parameter],
-        y=[y_modified/dividing_parameter],
+        x=[x_mod],
+        y=[y_mod],
         mode='markers',
         marker=dict(
         size=13  # Adjust the size value to make the markers larger or smaller

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:35f08123f49d9966c361ad8e23d429995e4eac8ac44b70b337e3c2764129ddfc
+oid sha256:b903397f79c77828d7f6b534a45d52f33e534aae4ab41e7352be0809e55a8d64
 size 1756