test5

pages/9_Saved_Scenarios.py

@@ -32,27 +32,31 @@ def create_scenario_summary(scenario_dict):
                              channel_dict.get('modified_total_spends') * channel_dict.get('conversion_rate'),
                              channel_dict.get('actual_total_sales') ,
                              channel_dict.get('modified_total_sales'),
-                             channel_dict.get('actual_total_sales') / (channel_dict.get('actual_total_spends') * channel_dict.get('conversion_rate')), 
-                             channel_dict.get('modified_total_sales') / (channel_dict.get('modified_total_spends') * channel_dict.get('conversion_rate')),
-                             channel_dict.get('actual_mroi'), 
-                             channel_dict.get('modified_mroi'),
-                             channel_dict.get('actual_total_spends') * channel_dict.get('conversion_rate') / channel_dict.get('actual_total_sales'),
-                             channel_dict.get('modified_total_spends') * channel_dict.get('conversion_rate') / channel_dict.get('modified_total_sales')])
+                            #  channel_dict.get('actual_total_sales') / (channel_dict.get('actual_total_spends') * channel_dict.get('conversion_rate')), 
+                            #  channel_dict.get('modified_total_sales') / (channel_dict.get('modified_total_spends') * channel_dict.get('conversion_rate')),
+                            #  channel_dict.get('actual_mroi'), 
+                            #  channel_dict.get('modified_mroi'),
+                            #  channel_dict.get('actual_total_spends') * channel_dict.get('conversion_rate') / channel_dict.get('actual_total_sales'),
+                            #  channel_dict.get('modified_total_spends') * channel_dict.get('conversion_rate') / channel_dict.get('modified_total_sales')
+                            ])
         
     summary_rows.append(['Total',
                          scenario_dict.get('actual_total_spends'), 
                          scenario_dict.get('modified_total_spends'),
                          scenario_dict.get('actual_total_sales'), 
                          scenario_dict.get('modified_total_sales'),
-                         scenario_dict.get('actual_total_sales') / scenario_dict.get('actual_total_spends'),
-                         scenario_dict.get('modified_total_sales') / scenario_dict.get('modified_total_spends'),
-                         '-',
-                         '-',
-                         scenario_dict.get('actual_total_spends') / scenario_dict.get('actual_total_sales'),
-                         scenario_dict.get('modified_total_spends') / scenario_dict.get('modified_total_sales')])
+                        #  scenario_dict.get('actual_total_sales') / scenario_dict.get('actual_total_spends'),
+                        #  scenario_dict.get('modified_total_sales') / scenario_dict.get('modified_total_spends'),
+                        #  '-',
+                        #  '-',
+                        #  scenario_dict.get('actual_total_spends') / scenario_dict.get('actual_total_sales'),
+                        #  scenario_dict.get('modified_total_spends') / scenario_dict.get('modified_total_sales')
+                        ])
     
     columns_index = pd.MultiIndex.from_product([[''],['Channel']], names=["first", "second"])
-    columns_index = columns_index.append(pd.MultiIndex.from_product([['Spends','NRPU','ROI','MROI','Spend per NRPU'],['Actual','Simulated']], names=["first", "second"]))
+    columns_index = columns_index.append(pd.MultiIndex.from_product([['Spends','Prospects'
+                                                                      #,'ROI','MROI','Spend per NRPU'
+                                                                      ],['Actual','Simulated']], names=["first", "second"]))
     return  pd.DataFrame(summary_rows, columns=columns_index)
     
 

response_curves_model_quality.py

@@ -0,0 +1,244 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+from sklearn.preprocessing import MinMaxScaler
+import warnings
+warnings.filterwarnings("ignore")
+import plotly.graph_objects as go
+
+## reading input data
+df= pd.read_csv('response_curves_input_file.csv')
+df.dropna(inplace=True)
+df['Date'] = pd.to_datetime(df['Date'])
+df.reset_index(inplace=True)
+
+channel_cols = [
+    'BroadcastTV',
+    'CableTV',
+    'Connected&OTTTV',
+    'DisplayProspecting',
+    'DisplayRetargeting',
+        'Video',
+    'SocialProspecting',
+    'SocialRetargeting',
+    'SearchBrand',
+    'SearchNon-brand',
+    'DigitalPartners',
+    'Audio',
+    'Email']
+spend_cols = [
+'tv_broadcast_spend', 
+'tv_cable_spend',
+    'stream_video_spend', 
+    'disp_prospect_spend', 
+    'disp_retarget_spend',
+       'olv_spend', 
+        'social_prospect_spend', 
+        'social_retarget_spend',
+       'search_brand_spend', 
+    'search_nonbrand_spend', 
+    'cm_spend',
+       'audio_spend',
+        'email_spend']
+prospect_cols = [
+        'Broadcast TV_Prospects',
+       'Cable TV_Prospects', 
+    'Connected & OTT TV_Prospects', 
+       'Display Prospecting_Prospects', 
+    'Display Retargeting_Prospects',
+    'Video_Prospects',
+       'Social Prospecting_Prospects', 
+    'Social Retargeting_Prospects',
+       'Search Brand_Prospects', 
+    'Search Non-brand_Prospects',
+       'Digital Partners_Prospects',
+    'Audio_Prospects', 
+    'Email_Prospects']
+
+def hill_equation(x, Kd, n):
+    return x**n / (Kd**n + x**n)
+
+
+def hill_func(x_data,y_data,x_minmax,y_minmax):
+    # Fit the Hill equation to the data
+    initial_guess = [1, 1]  # Initial guess for Kd and n
+    params, covariance = curve_fit(hill_equation, x_data, y_data, p0=initial_guess,maxfev = 1000)
+
+    # Extract the fitted parameters
+    Kd_fit, n_fit = params
+    
+
+    # Generate y values using the fitted parameters
+    y_fit = hill_equation(x_data, Kd_fit, n_fit)
+
+    x_data_inv = x_minmax.inverse_transform(np.array(x_data).reshape(-1,1))
+    y_data_inv = y_minmax.inverse_transform(np.array(y_data).reshape(-1,1))
+    y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
+
+#     # Plot the original data and the fitted curve
+#     plt.scatter(x_data_inv, y_data_inv, label='Actual Data')
+#     plt.scatter(x_data_inv, y_fit_inv, label='Fit Data',color='red')
+#     # plt.line(x_data_inv, y_fit_inv, label=f'Fitted Hill Equation (Kd={Kd_fit:.2f}, n={n_fit:.2f})', color='red')
+#     plt.xlabel('Ligand Concentration')
+#     plt.ylabel('Fraction of Binding')
+#     plt.title('Fitting Hill Equation to Data')
+#     plt.legend()
+#     plt.show()
+
+    return y_fit,y_fit_inv,Kd_fit, n_fit
+
+def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext): 
+    fit_col = 'Fit_Data_'+channel
+    plot_df = pd.DataFrame()
+   
+    plot_df[f'{channel}_Spends'] = X
+
+    plot_df['Date'] = df['Date']
+    plot_df['MAT'] = df['MAT']
+    
+    
+
+    y_fit_inv_v2 = []
+    for i in range(len(y_fit_inv)):
+        y_fit_inv_v2.append(y_fit_inv[i][0])
+        
+    plot_df[fit_col] = y_fit_inv_v2
+    
+#     adding extra data
+
+    y_fit_inv_v2_ext = []
+    for i in range(len(y_fit_inv_ext)):
+        y_fit_inv_v2_ext.append(y_fit_inv_ext[i][0])
+    
+#     print(x_ext_data)
+    ext_df = pd.DataFrame()
+    ext_df[f'{channel}_Spends'] = x_ext_data
+    ext_df[fit_col] = y_fit_inv_v2_ext
+    
+    ext_df['Date'] =  [
+                    np.datetime64('1950-01-01'),
+                    np.datetime64('1950-06-15'),
+                    np.datetime64('1950-12-31')
+                ]
+
+    ext_df['MAT'] = ["ext","ext","ext"]
+    
+    print(ext_df)
+    plot_df= plot_df.append(ext_df)
+    return plot_df
+
+def input_data(df,spend_col,prospect_col):  
+    X = np.array(df[spend_col].tolist())
+    y = np.array(df[prospect_col].tolist())
+
+    x_minmax = MinMaxScaler()
+    x_scaled = x_minmax.fit_transform(df[[spend_col]])
+    x_data = []
+    for i in range(len(x_scaled)):
+        x_data.append(x_scaled[i][0])
+
+    y_minmax = MinMaxScaler()
+    y_scaled =  y_minmax.fit_transform(df[[prospect_col]])
+    y_data = []
+    for i in range(len(y_scaled)):
+        y_data.append(y_scaled[i][0])
+
+    return X,y,x_data,y_data,x_minmax,y_minmax
+
+def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
+    print(x_max)
+    x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
+#     x_ext_data = [1500000,2000000,2500000]
+#     x_ext_data = [x_max+100,x_max+200,x_max+5000]
+    x_scaled = x_minmax.transform(pd.DataFrame(x_ext_data))
+    x_data = []
+    for i in range(len(x_scaled)):
+        x_data.append(x_scaled[i][0])
+        
+    print(x_data)
+    y_fit = hill_equation(x_data, Kd_fit, n_fit)
+    y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
+    
+    return x_ext_data,y_fit_inv
+    
+def fit_data(spend_col,prospect_col,channel):
+    ### getting k and n parameters
+    temp_df = df[df[spend_col]>0]
+    temp_df.reset_index(inplace=True)
+
+    X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
+    y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
+    print('k: ',Kd_fit)
+    print('n: ', n_fit)
+    
+    ##### extend_s_curve
+    x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
+    
+    plot_df = data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext)
+    return plot_df
+
+plotly_data = fit_data(spend_cols[0],prospect_cols[0],channel_cols[0])
+plotly_data.tail()
+
+for i in range(1,13):
+    print(i)
+    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', '')
+    ))
+    
+    plotly_data2 = plotly_data.copy()
+    # .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 = 'green'
+        ),
+        name="Current Spends"
+    ))
+
+    fig.add_trace(go.Scatter(
+        x=[x_modified],
+        y=[y_modified],
+        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
+